Signaling for user equipment mobility prediction

Abstract

Aspects of the present disclosure generally relate to wireless communications. In some aspects, a first network node in a radio access network (RAN) may send mobility history data for a user equipment (UE) to a second network node in a core network associated with the RAN. The first network node may receive, from the second network node, a UE mobility prediction model based at least in part on the mobility history data. Numerous other aspects are described.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This patent application claims priority to U.S. non-provisional patent application No. 17 / 804,720, entitled “SIGNALING FOR A USER EQUIPMENT MOBILITY PREDICTION,” filed on May 31, 2022, which is hereby expressly incorporated herein by reference. Background Art

[0003] Aspects of the present disclosure relate generally to wireless communications, and to techniques and apparatuses for wireless communications signaling.

[0004] Wireless communication systems are widely deployed to provide a variety of telecommunication services, such as telephone, video, data, messaging, and broadcasting. Typical wireless communication systems may employ multiple access technologies that can support communication with multiple users by sharing available system resources (e.g., bandwidth, transmit power, etc.). Examples of such multiple access technologies include code division multiple access (CDMA) systems, time division multiple access (TDMA) systems, frequency division multiple access (FDMA) systems, orthogonal frequency division multiple access (OFDMA) systems, single carrier frequency division multiple access (SC-FDMA) systems, time division synchronous code division multiple access (TD-SCDMA) systems, and long term evolution (LTE). LTE / LTE-Advanced is a set of enhancements to the Universal Mobile Telecommunications System (UMTS) mobile standard promulgated by the Third Generation Partnership Project (3GPP).

[0005] A wireless network may include one or more network nodes that support communications for one user equipment (UE) or multiple UEs. A UE may communicate with a network node via downlink communications and uplink communications. A "downlink" (or "DL") refers to a communication link from a network node to a UE, and an "uplink" (or "UL") refers to a communication link from a UE to a network node.

[0006] The above-mentioned multiple access technologies have been adopted in various telecommunication standards to provide a common protocol that enables different UEs to communicate at a city, country, region and / or global level. New Radio (NR), which may be referred to as 5G, is a set of enhancements to the LTE mobile standard promulgated by 3GPP. NR is designed to improve spectrum efficiency, reduce costs, improve services, utilize new spectrum, and better integrate with other open standards by using orthogonal frequency division multiplexing (OFDM) (CP-OFDM) with cyclic prefix (CP) on the downlink, using CP-OFDM and / or single carrier frequency division multiplexing (SC-FDM) (also known as discrete Fourier transform spread OFDM (DFT-s-OFDM)) on the uplink, and supporting beamforming, multiple input multiple output (MIMO) antenna technology and carrier aggregation, so as to better support mobile broadband Internet access. As the demand for mobile broadband access continues to increase, further improvements in LTE, NR and other radio access technologies remain useful. Summary of the invention

[0007] Some aspects described herein relate to a method of wireless communication performed by a first network node in a radio access network (RAN). The method may include sending mobility history data of a user equipment (UE) to a second network node in a core network associated with the RAN. The method may include receiving, from the second network node, a UE mobility prediction model based at least in part on the mobility history data.

[0008] Some aspects described herein relate to a method of wireless communication performed by a first network node in a RAN. The method may include sending mobility history data of a UE to a second network node in a core network associated with the RAN. The method may include receiving updated long-term mobility history data based at least in part on the mobility history data from the second network node.

[0009] Some aspects described herein relate to a method of wireless communication performed by a first network node in a decomposed RAN. The method may include sending mobility history data of a UE to a second network node in the decomposed RAN. The method may include receiving, from the second network node, a UE mobility prediction model based at least in part on the mobility history data.

[0010] Some aspects described herein relate to a method of wireless communication performed by a first network node in a decomposed RAN. The method may include sending mobility history data of a UE to a second network node in the decomposed RAN. The method may include receiving updated long-term mobility history data from the second network node based at least in part on the mobility history data.

[0011] Some aspects described herein relate to a method of wireless communication performed by a first network node in a core network. The method may include receiving mobility history data associated with a UE. The method may include sending the mobility history data to a second network node in the core network for use in training a UE mobility prediction model.

[0012] Some aspects described herein relate to a method of wireless communication performed by a first network node in a core network. The method may include receiving a UE mobility prediction model. The method may include sending the UE mobility prediction model to a second network node in the core network for storage at the second network node.

[0013] Some aspects described herein relate to a method of wireless communication performed by a first network node in a core network. The method may include receiving mobility history data associated with a UE from a second network node in the core network. The method may include training a UE mobility prediction model based at least in part on the mobility history data.

[0014] Some aspects described herein relate to a method of wireless communication performed by a first network node in a core network. The method may include receiving mobility history data associated with a UE from a second network node in the core network. The method may include updating long-term mobility history data associated with the UE based at least in part on the mobility history data. The method may include storing the updated long-term mobility history data at the first network node.

[0015] Some aspects described herein relate to a method of wireless communication performed by a first network node in a decomposed RAN. The method may include receiving mobility history data associated with a UE from a second network node in the decomposed RAN. The method may include training a UE mobility prediction model based at least in part on the mobility history data.

[0016] Some aspects described herein relate to a method of wireless communication performed by a first network node in a decomposed RAN. The method may include receiving mobility history data associated with a UE from a second network node in the decomposed RAN. The method may include updating long-term mobility history data associated with the UE based at least in part on the mobility history data. The method may include storing the updated long-term mobility history data at the first network node.

[0017] Some aspects described herein relate to a first network node for wireless communication. The first network node may include a memory and one or more processors coupled to the memory. The one or more processors may be configured to send mobility history data of a UE to a second network node in a core network associated with a RAN, the first network node being configured to operate in the RAN. The one or more processors may be configured to receive a UE mobility prediction model based at least in part on the mobility history data from the second network node.

[0018] Some aspects described herein relate to a first network node for wireless communication. The first network node may include a memory and one or more processors coupled to the memory. The one or more processors may be configured to send mobility history data of a UE to a second network node in a core network associated with a RAN, the first network node being configured to operate in the RAN. The one or more processors may be configured to receive updated long-term mobility history data based at least in part on the mobility history data from the second network node.

[0019] Some aspects described herein relate to a first network node for wireless communication. The first network node may include a memory and one or more processors coupled to the memory. The one or more processors may be configured to send mobility history data of a UE to a second network node in a decomposed RAN, the first network node being configured to operate in the RAN. The one or more processors may be configured to receive a UE mobility prediction model based at least in part on the mobility history data from the second network node.

[0020] Some aspects described herein relate to a first network node for wireless communication. The first network node may include a memory and one or more processors coupled to the memory. The one or more processors may be configured to receive mobility history data associated with a UE. The one or more processors may be configured to send the mobility history data to a second network node in a core network for training a UE mobility prediction model, the first network node being configured to operate in the core network.

[0021] Some aspects described herein relate to a first network node for wireless communication. The first network node may include a memory and one or more processors coupled to the memory. The one or more processors may be configured to receive mobility history data associated with a UE. The one or more processors may be configured to send the mobility history data to a second network node in a core network for training a UE mobility prediction model, the first network node being configured to operate in the core network.

[0022] Some aspects described herein relate to a first network node for wireless communication. The first network node may include a memory and one or more processors coupled to the memory. The one or more processors may be configured to receive mobility history data associated with a UE from a second network node in a core network, the first network node being configured to operate in the core network. The one or more processors may be configured to train a UE mobility prediction model based at least in part on the mobility history data.

[0023] Some aspects described herein relate to a first network node for wireless communication. The first network node may include a memory and one or more processors coupled to the memory. The one or more processors may be configured to receive mobility history data associated with a UE from a second network node in a core network, and the first network node is configured to operate in the core network. The one or more processors may be configured to update long-term mobility history data associated with the UE based at least in part on the mobility history data. The one or more processors may be configured to store the updated long-term mobility history data at the first network node.

[0024] Some aspects described herein relate to a first network node for wireless communication. The first network node may include a memory and one or more processors coupled to the memory. The one or more processors may be configured to receive mobility history data associated with a UE from a second network node in a core network, and the first network node is configured to operate in the core network. The one or more processors may be configured to update long-term mobility history data associated with the UE based at least in part on the mobility history data. The one or more processors may be configured to store the updated long-term mobility history data at the first network node.

[0025] Some aspects described herein relate to a first network node for wireless communication. The first network node may include a memory and one or more processors coupled to the memory. The one or more processors may be configured to receive mobility history data associated with a UE from a second network node in a decomposed RAN, the first network node being configured to operate in the decomposed RAN. The one or more processors may be configured to train a UE mobility prediction model based at least in part on the mobility history data.

[0026] Some aspects described herein relate to a first network node for wireless communication. The first network node may include a memory and one or more processors coupled to the memory. The one or more processors may be configured to receive mobility history data associated with a UE from a second network node in a decomposed RAN, the first network node being configured to operate in the decomposed RAN. The one or more processors may be configured to update long-term mobility history data associated with the UE based at least in part on the mobility history data. The one or more processors may be configured to store the updated long-term mobility history data at the first network node.

[0027] Some aspects described herein relate to a non-transitory computer-readable medium storing an instruction set for wireless communication by a first network node. The instruction set, when executed by one or more processors of the first network node, can cause the first network node to send mobility history data of a UE to a second network node in a core network associated with a RAN, the first network node being configured to operate in the RAN. The instruction set, when executed by one or more processors of the first network node, can cause the first network node to receive a UE mobility prediction model based at least in part on the mobility history data from the second network node.

[0028] Some aspects described herein relate to a non-transitory computer-readable medium storing an instruction set for wireless communication by a first network node. The instruction set, when executed by one or more processors of the first network node, may cause the first network node to send mobility history data of a UE to a second network node in a core network associated with a RAN, the first network node being configured to operate in the RAN. The instruction set, when executed by one or more processors of the first network node, may cause the first network node to receive updated long-term mobility history data from the second network node based at least in part on the mobility history data.

[0029] Some aspects described herein relate to a non-transitory computer-readable medium storing an instruction set for wireless communication by a first network node. The instruction set, when executed by one or more processors of the first network node, may cause the first network node to send mobility history data of a UE to the first network node in a decomposed RAN, the first network node being configured to operate in the decomposed RAN. The instruction set, when executed by one or more processors of the first network node, may cause the first network node to receive a UE mobility prediction model based at least in part on the mobility history data from the second network node.

[0030] Some aspects described herein relate to a non-transitory computer-readable medium storing an instruction set for wireless communication by a first network node. The instruction set, when executed by one or more processors of the first network node, may cause the first network node to send mobility history data of a UE to a second network node in a decomposed RAN, the first network node being configured to operate in the decomposed RAN. The instruction set, when executed by one or more processors of the first network node, may cause the first network node to receive updated long-term mobility history data from the second network node based at least in part on the mobility history data.

[0031] Some aspects described herein relate to a non-transitory computer-readable medium storing an instruction set for wireless communication by a first network node. The instruction set, when executed by one or more processors of the first network node, can cause the first network node to receive mobility history data associated with a UE. The instruction set, when executed by one or more processors of the first network node, can cause the first network node to send the mobility history data to a second network node in a core network for training a UE mobility prediction model, the first network node being configured to operate in the core network.

[0032] Some aspects described herein relate to a non-transitory computer-readable medium storing an instruction set for wireless communication by a first network node. The instruction set, when executed by one or more processors of the first network node, can cause the first network node to receive a UE mobility prediction model. The instruction set, when executed by one or more processors of the first network node, can cause the first network node to send the UE mobility prediction model to a second network node in a core network for storage at the second network node, the first network node being configured to operate in the core network.

[0033] Some aspects described herein relate to a non-transitory computer-readable medium storing an instruction set for wireless communication by a first network node. The instruction set, when executed by one or more processors of the first network node, can cause the first network node to receive mobility history data associated with a UE from a second network node in a core network, the first network node being configured to operate in the core network. The instruction set, when executed by one or more processors of the first network node, can cause the first network node to train a UE mobility prediction model based at least in part on the mobility history data.

[0034] Some aspects described herein relate to a non-transitory computer-readable medium storing an instruction set for wireless communication by a first network node. The instruction set, when executed by one or more processors of the first network node, may cause the first network node to receive mobility history data associated with a UE from a second network node in a core network, the first network node being configured to operate in the core network. The instruction set, when executed by one or more processors of the first network node, may cause the first network node to update long-term mobility history data associated with the UE based at least in part on the mobility history data. The instruction set, when executed by one or more processors of the first network node, may cause the first network node to store the updated long-term mobility history data at the first network node.

[0035] Some aspects described herein relate to a non-transitory computer-readable medium storing an instruction set for wireless communication by a first network node. The instruction set, when executed by one or more processors of the first network node, may cause the first network node to receive mobility history data associated with a UE from a second network node in a decomposed RAN, the first network node being configured to operate in the decomposed RAN. The instruction set, when executed by one or more processors of the first network node, may cause the first network node to train a UE mobility prediction model based at least in part on the mobility history data.

[0036] Some aspects described herein relate to a non-transitory computer-readable medium storing an instruction set for wireless communication by a first network node. The instruction set, when executed by one or more processors of the first network node, may cause the first network node to receive mobility history data associated with a UE from a second network node in a decomposed RAN, the first network node being configured to operate in the decomposed RAN. The instruction set, when executed by one or more processors of the first network node, may cause the first network node to update long-term mobility history data associated with the UE based at least in part on the mobility history data. The instruction set, when executed by one or more processors of the first network node, may cause the first network node to store the updated long-term mobility history data at the first network node.

[0037] Some aspects described herein relate to a first apparatus for wireless communication. The first apparatus includes means for sending mobility history data of a UE to a second apparatus in a core network associated with a RAN, the first apparatus being configured to operate in the RAN. The first apparatus includes means for receiving, from the second apparatus, a UE mobility prediction model based at least in part on the mobility history data.

[0038] Some aspects described herein relate to a first apparatus for wireless communication. The first apparatus includes means for sending mobility history data of a UE to a second apparatus in a core network associated with a RAN, the first apparatus being configured to operate in the RAN. The first apparatus includes means for receiving updated long-term mobility history data based at least in part on the mobility history data from the second apparatus.

[0039] Some aspects described herein relate to a first apparatus for wireless communication. The first apparatus includes means for sending mobility history data of a UE to a second apparatus in a decomposed RAN, the first apparatus being configured to operate in the decomposed RAN. The first apparatus includes means for receiving, from a second network node, a UE mobility prediction model based at least in part on the mobility history data.

[0040] Some aspects described herein relate to a first apparatus for wireless communication. The first apparatus includes means for sending mobility history data of a UE to a second apparatus in a decomposed RAN. The first apparatus includes means for receiving updated long-term mobility history data based at least in part on the mobility history data from a second network node.

[0041] Some aspects described herein relate to a first apparatus for wireless communication. The first apparatus includes means for receiving mobility history data associated with a UE. The first apparatus includes means for sending the mobility history data to a second network node in a core network for training a UE mobility prediction model.

[0042] Some aspects described herein relate to a first apparatus for wireless communication. The first apparatus includes a component for receiving a UE mobility prediction model. The first apparatus includes a component for sending the UE mobility prediction model to a second apparatus in a core network for storage at a second network node, the first apparatus being configured to operate in the core network.

[0043] Some aspects described herein relate to a first apparatus for wireless communication. The first apparatus includes means for receiving mobility history data associated with a UE from a second apparatus in a core network, the first apparatus being configured to operate in the core network. The first apparatus includes means for training a UE mobility prediction model based at least in part on the mobility history data.

[0044] Some aspects described herein relate to a first apparatus for wireless communication. The first apparatus includes means for receiving mobility history data associated with a UE from a second apparatus in a core network, the first apparatus being configured to operate in the core network. The first apparatus includes means for updating long-term mobility history data associated with the UE based at least in part on the mobility history data. The first apparatus includes means for storing the updated long-term mobility history data at the first apparatus.

[0045] Some aspects described herein relate to a first apparatus for wireless communication. The first apparatus includes means for receiving mobility history data associated with a UE from a second apparatus in a decomposed RAN, the first apparatus being configured to operate in the decomposed RAN. The first apparatus includes means for training a UE mobility prediction model based at least in part on the mobility history data.

[0046] Some aspects described herein relate to a first apparatus for wireless communication. The first apparatus includes means for receiving mobility history data associated with a UE from a second apparatus in a decomposed RAN, the first apparatus being configured to operate in the decomposed RAN. The first apparatus includes means for updating long-term mobility history data associated with the UE based at least in part on the mobility history data. The first apparatus includes means for storing the updated long-term mobility history data at a first network node. The first apparatus includes means for sending the updated long-term mobility history data to the second apparatus during a handover procedure of the UE.

[0047] Aspects generally include methods, apparatus, systems, computer program products, non-transitory computer-readable media, user equipment, network nodes, wireless communication devices, and / or processing systems as fully described with reference to and illustrated in the drawings and description.

[0048] The features and technical advantages of the examples according to the present disclosure have been outlined quite extensively above so that the following specific embodiments can be better understood. Additional features and advantages will be described below. The disclosed concepts and specific examples can be easily used as the basis for modifying or designing other structures for the same purpose of achieving the present disclosure. Such equivalent constructions do not depart from the scope of protection of the appended claims. When considered in conjunction with the accompanying drawings, the characteristics of the concepts disclosed herein (both their organization and method of operation) and the associated advantages will be better understood according to the description below. Each of the drawings provided in these drawings is only for illustration and description purposes, and is not used as a definition of the limitations of the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to fully understand the above-mentioned features of the present disclosure, a more specific description of the invention briefly summarized above can be obtained by referring to various aspects (some of which are illustrated in the accompanying drawings). However, it should be noted that the accompanying drawings only illustrate certain typical aspects of the present disclosure and are therefore not to be considered as limiting the scope thereof, as the specification may admit of other equally effective aspects. The same reference numerals in different drawings may identify the same or similar elements.

[0050] Figure 1 is a diagram illustrating an example of a wireless network according to the present disclosure.

[0051] Figure 2 is a diagram illustrating an example of communication between a network node and a user equipment (UE) in a wireless network according to the present disclosure.

[0052] Figure 3 is an example of a disaggregated base station architecture according to the present disclosure.

[0053] Figure 4 is a diagram of an example of a core network configured to provide network slicing according to the present disclosure.

[0054] Figure 5 is a diagram of an example of a UE mobility prediction model according to the present disclosure.

[0055] Figure 6 is a diagram illustrating an example associated with signaling for UE mobility prediction according to the present disclosure.

[0056] Figure 7 is a diagram illustrating an example associated with signaling for UE mobility prediction according to the present disclosure.

[0057] Figure 8 is a diagram illustrating an example associated with signaling for UE mobility prediction according to the present disclosure.

[0058] Fig. 9 is a diagram illustrating an example associated with signaling for UE mobility prediction according to the present disclosure.

[0059] Fig.10 is a diagram illustrating an example associated with signaling for UE mobility prediction according to the present disclosure.

[0060] Fig.11 is a diagram illustrating an example process associated with signaling for UE mobility prediction according to the present disclosure.

[0061] Fig.12 is a diagram illustrating an example process associated with signaling for UE mobility prediction according to the present disclosure.

[0062] Fig.13 is a diagram illustrating an example process associated with signaling for UE mobility prediction according to the present disclosure.

[0063] Fig.14 is a diagram illustrating an example process associated with signaling for UE mobility prediction according to the present disclosure.

[0064] Fig.15 is a diagram illustrating an example process associated with signaling for UE mobility prediction according to the present disclosure.

[0065] Fig.16 is a diagram illustrating an example process associated with signaling for UE mobility prediction according to the present disclosure.

[0066] Fig.17 is a diagram illustrating an example process associated with signaling for UE mobility prediction according to the present disclosure.

[0067] Fig.18 is a diagram illustrating an example process associated with signaling for UE mobility prediction according to the present disclosure.

[0068] Fig.19 is a diagram illustrating an example process associated with signaling for UE mobility prediction according to the present disclosure.

[0069] Fig. 20 is a diagram illustrating an example process associated with signaling for UE mobility prediction according to the present disclosure.

[0070] Fig.21 is a diagram of an example apparatus for wireless communications according to the present disclosure.

[0071] Fig. 22 is a diagram illustrating an example of a hardware implementation for an apparatus employing a processing system according to the present disclosure.

[0072] Fig.23 is a diagram illustrating an example implementation of code and circuits for an apparatus according to the present disclosure.

[0073] Fig.24 is a diagram of an example apparatus for wireless communications according to the present disclosure.

[0074] Fig.25 is a diagram illustrating an example of a hardware implementation for an apparatus employing a processing system according to the present disclosure.

[0075] Fig.26 is a diagram illustrating an example implementation of code and circuits for an apparatus according to the present disclosure.

[0076] Fig. 27is a diagram of an example apparatus for wireless communications according to the present disclosure.

[0077] Fig.28 is a diagram illustrating an example of a hardware implementation for an apparatus employing a processing system according to the present disclosure.

[0078] Fig.29 is a diagram illustrating an example implementation of code and circuits for an apparatus according to the present disclosure.

[0079] Fig.30 is a diagram of an example apparatus for wireless communications according to the present disclosure.

[0080] Fig.31 is a diagram illustrating an example of a hardware implementation for an apparatus employing a processing system according to the present disclosure.

[0081] Fig.32 is a diagram illustrating an example implementation of code and circuits for an apparatus according to the present disclosure. DETAILED DESCRIPTION

[0082] Various aspects of wireless communication between a user equipment (UE) and a wireless network may be affected by the mobility of the UE. In some cases, the mobility of the UE may affect the handover performance of the UE. For example, the serving network node may not know in which direction the UE will travel, may not know which destination the UE can travel to, and / or may not know other mobility-related events associated with the UE. In these cases, the UE may experience mobility-related failures, such as radio link failures due to the UE being switched to a target network node that is far away from the direction in which the UE will eventually travel, or radio link failures due to the UE's handover procedure being initiated too late. In addition, due to the mobility of the UE, the UE may be handed over too early, thereby increasing the number of handover procedures of the UE, which increases the consumption of the UE's processing resources, memory resources, battery resources and / or radio resources. In some cases, the mobility of the UE may affect resource usage, network load and / or energy consumption, etc.

[0083] In some cases, mobility history data may be stored in an operation, maintenance, and management (OAM) system, a minimized drive test (MDT) system, and / or a self-organizing network (SON) system outside the core network to facilitate determining the mobility prediction of the UE. The OAM system may include one or more devices configured to maintain the wireless network, schedule and perform maintenance of the wireless network, and / or manage other offline aspects of the wireless network. The MDT system may include one or more components configured to receive and store drive test data. The storage of mobility history data in these systems may be manual and not continuous. The mobility history data in these systems may not be the latest mobility history data, which may result in slow and inaccurate UE mobility prediction.

[0084] Some aspects described herein provide radio access network (RAN) and core network signaling for supporting UE mobility prediction for a UE. The UE mobility prediction may include an estimate or prediction of a future location of the UE (e.g., a future target cell) and an associated probability that the UE is at the future location at a specific time. According to one or more aspects, a network node may use UE mobility prediction to support various use cases while taking into account key stochastic aspects (e.g., stochastic aspects that may affect service continuity of the UE), such as UE mobility, incoming traffic and / or channel and coverage, etc. For example, a network node may use UE mobility prediction to facilitate intelligent handover of the UE, including candidate target cell identification, conditional handover (CHO) target selection, measurement configuration and / or radio link failure (RLF) recovery, etc. As another example, a network node may use UE mobility prediction to facilitate resource allocation of the UE, load balancing of the UE, and / or energy saving of the UE, etc.

[0085] As described herein, according to one or more examples, signaling between network nodes in a RAN of a wireless network and a core network associated with the wireless network may be used to share and exchange mobility history data, which may be used to generate UE mobility predictions and / or to build, train and / or update a UE mobility prediction model in the core network for generating UE mobility predictions. The mobility history data associated with the UE may include data associated with the movement, location and associated time of the UE. According to one or more aspects, the mobility history data may be transmitted between network nodes in the RAN and network nodes in the core network using RAN-to-core network signaling, and / or transmitted between network nodes in the core network using intra-core network signaling or core network-to-model repository signaling to facilitate storage and aggregation of the mobility history data in the core network.

[0086] A model configured to generate or determine a UE mobility prediction is referred to herein as a UE mobility prediction model. The mobility history data associated with the UE may include recent (or short-term) mobility history data and long-term mobility history data, etc. The recent mobility history data includes mobility history data collected for the UE in a short and recent time period (such as in the past 12 hours or the past 24 hours). The recent mobility history data may include mobility history data indicating the location of the UE during the recent time period, the time and duration of the UE at those locations, the start and end locations of the movement of the UE during the recent time period, the route passed by the UE during the recent time period, and / or other mobility history data. Long-term mobility history data includes mobility history data collected and aggregated for the UE over a long duration such as several months to a year or longer. In general, long-term mobility history includes mobility history data collected over a duration sufficient to identify and / or determine the typical route, destination, movement habits, and / or other mobility events of the UE that are stable and repeatable over a long duration.

[0087] Storing mobility history data in the core network rather than outside the core network (e.g., in an OAM system, in an MDT system, and / or in a SON system) enables UE mobility prediction models used to generate or determine UE mobility predictions to be continuously trained and refined in an automatic manner. As described herein, storing mobility history data in the core network and maintaining UE mobility prediction models ensures that the UE mobility prediction models are up-to-date and accurate, which enables highly accurate UE mobility predictions to be determined and generated. More accurate UE mobility predictions based on these trained and refined UE mobility prediction models (e.g., trained and refined using signaling as described herein) enable more accurate and efficient candidate target cell identification, more accurate and efficient CHO target selection, more accurate and efficient measurement configuration, and / or more accurate and efficient RLF recovery, etc.

[0088] Therefore, the signaling for the transmission of mobility history data described herein can achieve more efficient resource usage of the UE (for example, less time domain and / or frequency domain resources can be wasted). In addition, the signaling for the transmission of mobility history data described herein can achieve more accurate load balancing of the UE by performing load balancing of the UE using a more accurate UE mobility prediction, which is generated using a UE mobility prediction model trained using mobility history data. In addition, the signaling for the transmission of mobility history data described herein can achieve a reduction in the energy consumption of the UE to increase energy saving by configuring the power saving features of the UE using a more accurate UE mobility prediction, which is generated using a UE mobility prediction model trained using mobility history data. In addition, the signaling for the transmission of mobility history data described herein can achieve a reduction in the coverage drop of the UE (for example, during switching) by performing a handover of the UE using a more accurate UE mobility prediction, which is generated using a UE mobility prediction model trained using mobility history data. Furthermore, the signaling for mobility history data transmission described herein may enable reduced RLF recovery time for a UE by maintaining coverage of the UE using a more accurate UE mobility prediction generated using a UE mobility prediction model trained using mobility history data.

[0089] The various aspects of the disclosure are described more fully below with reference to the accompanying drawings. However, the present disclosure can be embodied in many different forms, and should not be interpreted as being limited to any specific structure or function presented throughout the present disclosure. Instead, these aspects are provided so that the present disclosure will be thorough and complete, and the protection scope of the disclosure will be fully conveyed to those skilled in the art. It should be appreciated by those skilled in the art that the scope of the disclosure is intended to cover any aspect of the disclosure disclosed herein, whether it is independently or in combination with any other aspect of the disclosure. For example, any number of aspects set forth herein may be used to implement a device or practice method. In addition, the scope of the present disclosure is intended to cover such a device or method that is practiced using other structures, functionality, or structure and functionality in addition to or different from the various aspects of the disclosure listed herein. It should be understood that any aspect of the disclosure disclosed herein may be embodied by one or more elements of the claims.

[0090] Several aspects of telecommunication systems will now be presented with reference to various devices and techniques. These devices and techniques will be described in the following detailed description and illustrated in the accompanying drawings by various blocks, modules, components, circuits, steps, processes, algorithms, etc. (collectively referred to as "elements"). These elements can be implemented using hardware, software, or a combination thereof. Whether these elements are implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system.

[0091] Although various aspects may be described herein using terminology often associated with 5G or new radio (NR) radio access technology (RAT), various aspects of the present disclosure may be applied to other RATs, such as 3G RAT, 4G RAT, and / or post-5G RATs (e.g., 6G).

[0092] Figure 1 1 is a diagram illustrating an example of a wireless network 100 according to the present disclosure. The wireless network 100 may be a 5G (e.g., NR) network and / or a 4G (e.g., Long Term Evolution (LTE)) network, or may include elements of a 5G (e.g., NR) network and / or elements of a 4G (e.g., Long Term Evolution (LTE)) network, etc. The wireless network 100 may include one or more network nodes 110 (shown as network node 110a, network node 110b, network node 110c, and network node 110d), one user equipment (UE) 120 or multiple UEs 120 (shown as UE 120a, UE 120b, UE 120c, UE 120d, and UE 120e), and / or other network entities. The base station 110 is an entity that communicates with the UE 120.

[0093] A network node 110 may provide communication coverage for a particular geographic area. In the 3rd Generation Partnership Project (3GPP), the term "cell" may refer to a coverage area of ​​a network node 110 and / or a network node subsystem serving the coverage area, depending on the context in which the term is used.

[0094] In some aspects, the term "network node" or "network entity" may refer to a converged base station, a decomposed base station (e.g., a combined Figure 3110), integrated access and backhaul (IAB) nodes, relay nodes, and / or one or more components thereof. For example, in some aspects, a "base station", "network node", or "network entity" may refer to a central unit (CU), a distributed unit (DU), a radio unit (RU), a near real-time (near RT) RAN intelligent controller (RIC) or a non-real-time (non-RT) RIC, a service management and orchestration (SMO) node, or a combination thereof. In some aspects, the term "base station", "network node", or "network entity" may refer to a device configured to perform one or more functions (such as those described herein in conjunction with network node 110). In some aspects, the term "base station", "network node", or "network entity" may refer to multiple devices configured to perform one or more functions. For example, in some distributed systems, each of a plurality of different devices (which may be located in the same geographic location or in different geographic locations) may be configured to perform at least a portion of a function, or to repeat the execution of at least a portion of the function, and the term "base station", "network node", or "network entity" may refer to any one or more of these different devices. In some aspects, the terms "base station", "network node" or "network entity" may refer to one or more virtual base stations and / or one or more virtual base station functions. For example, in some aspects, two or more base station functions may be instantiated on a single device. In some aspects, the terms "base station", "network node" or "network entity" may refer to one of the base station functions but not the other. In this way, a single device may include more than one base station.

[0095] The network node 110 may provide communication coverage for a macro cell, a pico cell, a femto cell, and / or another type of cell. A macro cell may cover a relatively large geographic area (e.g., a radius of several kilometers) and may allow unrestricted access by a UE 120 with a service subscription. A pico cell may cover a relatively small geographic area and may allow unrestricted access by a UE 120 with a service subscription. A femto cell may cover a relatively small geographic area (e.g., a home) and may allow restricted access by a UE 120 that has an association with the femto cell (e.g., a UE 120 in a closed subscriber group (CSG)). A network node 110 for a macro cell may be referred to as a macro base station. A network node 110 for a pico cell may be referred to as a pico base station. A network node 110 for a femto cell may be referred to as a femto base station or a home base station. In Figure 1 In the example shown, network node 110a may be a macro base station for macro cell 102a, network node 110b may be a pico base station for pico cell 102b, and network node 110c may be a femto base station for femto cell 102c. A network node may support one or more (eg, three) cells.

[0096] In some examples, the cell may not necessarily be stationary, and the geographic area of ​​the cell may move depending on the location of the mobile network node 110 (e.g., a mobile base station). In some examples, the network nodes 110 may be interconnected with each other and / or to one or more other network nodes 110 in the wireless network 100 through various types of backhaul interfaces (such as direct physical connections or virtual networks) using any suitable transport network.

[0097] The wireless network 100 may include one or more relay stations. A relay station is an entity capable of receiving transmissions of data from an upstream station (e.g., a network node 110 or a UE 120) and transmitting transmissions of data to a downstream station (e.g., a UE 120 or a network node 110). A relay station may be a UE 120 that is capable of relaying transmissions for other UEs 120. Figure 1 In the example shown, network node 110d (e.g., a relay base station) may communicate with network node 110a (e.g., a macro base station) and UE 120d to facilitate communications between network node 110a and UE 120d. A network node 110 that relays communications may be referred to as a relay station, relay base station, relay, or the like.

[0098] The wireless network 100 may be a heterogeneous network that includes different types of network nodes 110, such as macro base stations, pico base stations, femto base stations, relay base stations, etc. These different types of network nodes 110 may have different transmit power levels, different coverage areas, and / or different impacts on interference in the wireless network 100. For example, a macro base station may have a high transmit power level (e.g., 5 watts to 40 watts), while a pico base station, a femto base station, and a relay base station may have a lower transmit power level (e.g., 0.1 watt to 2 watts).

[0099] A plurality of network nodes 130a-130n may be included in a core network 140 associated with the wireless network 100. The network node 130 may be coupled to or in communication with a set of network nodes 110 and may provide coordination and control for these network nodes 110. The network node 130 may communicate with the network nodes 110 via a backhaul communication link. The network nodes 110 may also communicate directly with each other or indirectly via a wireless backhaul communication link or a wired backhaul communication link.

[0100] UE 120 can be dispersed throughout the wireless network 100, and each UE 120 can be stationary or mobile. UE 120 can include, for example, access terminals, terminals, mobile stations and / or subscriber units. UE 120 can be a cellular phone (e.g., a smart phone), a personal digital assistant (PDA), a wireless modem, a wireless communication device, a handheld device, a laptop computer, a cordless phone, a wireless local loop (WLL) station, a tablet computer, a camera, a gaming device, a netbook, a smartbook, an ultrabook, a medical device, a biometric device, a wearable device (e.g., a smart watch, smart clothing, smart glasses, a smart wristband, smart jewelry (e.g., a smart ring or a smart bracelet)), an entertainment device (e.g., a music device, a video device and / or a satellite radio, etc.), a vehicle component or sensor, a smart meter / sensor, an industrial manufacturing equipment, a global positioning system device and / or any other suitable device configured to communicate via a wireless or wired medium.

[0101] Some UEs 120 may be considered as machine type communication (MTC) or evolved or enhanced machine type communication (eMTC) UEs. MTC UEs and / or eMTC UEs may include, for example, robots, drones, remote devices, sensors, meters, monitors, and / or location tags that may communicate with a base station, another device (e.g., a remote device), or some other entity. Some UEs 120 may be considered as Internet of Things (IoT) devices and / or may be implemented as NB-IoT (narrowband IoT) devices. Some UEs 120 may be considered as customer premises equipment. UE 120 may be included inside a housing that houses components of UE 120, such as a processor component and / or a memory component. In some examples, the processor component and the memory component may be coupled together. For example, a processor component (e.g., one or more processors) and a memory component (e.g., a memory) may be operationally coupled, communicatively coupled, electronically coupled, and / or electrically coupled.

[0102] In general, any number of wireless networks 100 may be deployed in a given geographic area. Each wireless network 100 may support a specific RAT and may operate on one or more frequencies. RAT may be referred to as a radio technology, air interface, etc. Frequency may be referred to as a carrier, frequency channel, etc. Each frequency may support a single RAT in a given geographic area to avoid interference between wireless networks of different RATs. In some cases, NR or 5G RAT networks may be deployed.

[0103] In some examples, two or more UEs 120 (e.g., shown as UE 120a and UE 120e) may communicate directly using one or more side link channels (e.g., without using network node 110 as an intermediary to communicate with each other). For example, UE 120 may communicate using peer-to-peer (P2P) communication, device-to-device (D2D) communication, vehicle-to-everything (V2X) protocols (e.g., which may include vehicle-to-vehicle (V2V) protocols, vehicle-to-infrastructure (V2I) protocols, or vehicle-to-pedestrian (V2P) protocols), and / or mesh networks. In such examples, UE 120 may perform scheduling operations, resource selection operations, and / or other operations described elsewhere herein as being performed by network node 110.

[0104] The electromagnetic spectrum is typically subdivided by frequency / wavelength into various categories, bands, channels, etc. In 5G NR, two initial operating bands have been identified as frequency range designations FR1 (410 MHz - 7.125 GHz) and FR2 (24.25 GHz - 52.6 GHz). It should be understood that, although portions of FR1 are greater than 6 GHz, FR1 is often (interchangeably) referred to as the "sub-6 GHz" band in various documents and articles. A similar naming issue sometimes occurs with respect to FR2, which is often (interchangeably) referred to as the "millimeter wave" band in documents and articles, although distinct from the extremely high frequency (EHF) band (30 GHz - 300 GHz) identified as the "millimeter wave" band by the International Telecommunication Union (ITU).

[0105] Frequencies between FR1 and FR2 are generally referred to as mid-band frequencies. Recent 5G NR research has identified the operating bands for these mid-band frequencies as frequency range designation FR3 (7.125GHz–24.25GHz). The bands falling within FR3 can inherit FR1 characteristics and / or FR2 characteristics, and therefore the features of FR1 and / or FR2 can be effectively extended to mid-band frequencies. In addition, higher frequency bands are currently being explored to extend 5G NR operations to more than 52.6GHz. For example, three higher operating bands have been identified as frequency range designations FR4a or FR4-1 (52.6GHz-71GHz), FR4 (52.6GHz-114.25GHz) and FR5 (114.25GHz-300GHz). Each of these higher frequency bands falls within the EHF band.

[0106] In view of the above examples, unless otherwise specifically stated, it should be understood that if the term "below 6 GHz" or the like is used herein, the term can be broadly referred to as a frequency that can be lower than 6 GHz, can be within FR1, or can include mid-band frequencies. In addition, unless otherwise specifically stated, it should be understood that if the term "millimeter wave" or the like is used herein, the term can be broadly referred to as a frequency that can include mid-band frequencies, can be within FR2, FR4, FR4-a or FR4-1 and / or FR5, or can be within the EHF band. It is expected that the frequencies included in these operating bands (e.g., FR1, FR2, FR3, FR4, FR4-a, FR4-1, and / or FR5) can be modified, and the techniques described herein are applicable to those modified frequency ranges.

[0107] In a disaggregated RAN deployment in which at least a portion of the wireless network 100 includes a disaggregated network node 110 (or a disaggregated base station), the wireless network 100 may include an additional network node 150 for open RAN functionality. The network node 150 may include, for example, a non-RT RIC, a near-RT RIC, a SMO node, and / or another type of open RAN node.

[0108] In some aspects, the network node 110 may include a communication manager 160. As described in more detail elsewhere herein, the communication manager 160 may send mobility history data of the UE to a second network node in a core network associated with the RAN; and receive a UE mobility prediction model based at least in part on the mobility history data from the second network node. Additionally or alternatively, the communication manager 160 may perform one or more other operations described herein.

[0109] In some aspects, the network node 110 may include a communication manager 160. As described in more detail elsewhere herein, the communication manager 160 may send mobility history data of the UE to a second network node in a core network associated with the RAN; and receive updated long-term mobility history data based at least in part on the mobility history data from the second network node. Additionally or alternatively, the communication manager 160 may perform one or more other operations described herein.

[0110] In some aspects, the network node 110 may include a communication manager 160. As described in more detail elsewhere herein, the communication manager 160 may send mobility history data of the UE to a second network node in the decomposed RAN; and receive a UE mobility prediction model based at least in part on the mobility history data from the second network node. Additionally or alternatively, the communication manager 160 may perform one or more other operations described herein.

[0111] In some aspects, the network node 110 may include a communication manager 160. As described in more detail elsewhere herein, the communication manager 160 may send mobility history data of the UE to a second network node in the decomposed RAN; and receive updated long-term mobility history data based at least in part on the mobility history data from the second network node. Additionally or alternatively, the communication manager 160 may perform one or more other operations described herein.

[0112] In some aspects, the network node 130 may include a communication manager 170. As described in more detail elsewhere herein, the communication manager 170 may receive mobility history data associated with the UE; and send the mobility history data to a second network node in the core network for use in training a UE mobility prediction model. Additionally or alternatively, the communication manager 170 may perform one or more other operations described herein.

[0113] In some aspects, the network node 130 may include a communication manager 170. As described in more detail elsewhere herein, the communication manager 170 may receive a UE mobility prediction model; and send the UE mobility prediction model to a second network node in the core network for storage at the second network node. Additionally or alternatively, the communication manager 170 may perform one or more other operations described herein.

[0114] In some aspects, the network node 130 may include a communication manager 180. As described in more detail elsewhere herein, the communication manager 180 may receive mobility history data associated with the UE from a second network node in the core network; and train a UE mobility prediction model based at least in part on the mobility history data. Additionally or alternatively, the communication manager 180 may perform one or more other operations described herein.

[0115] In some aspects, the network node 130 may include a communication manager 180. As described in more detail elsewhere herein, the communication manager 180 may receive mobility history data associated with the UE from a second network node in the core network; update long-term mobility history data associated with the UE based at least in part on the mobility history data; and store the updated long-term mobility history data at the first network node. Additionally or alternatively, the communication manager 180 may perform one or more other operations described herein.

[0116] In some aspects, the network node 130 may include a communication manager 170. As described in more detail elsewhere herein, the communication manager 190 may receive mobility history data associated with the UE from a second network node in the decomposed RAN; and train a UE mobility prediction model based at least in part on the mobility history data. Additionally or alternatively, the communication manager 190 may perform one or more other operations described herein.

[0117] In some aspects, the network node 10 may include a communication manager 190. As described in more detail elsewhere herein, the communication manager 190 may receive mobility history data associated with a UE from a second network node in a decomposed RAN; update long-term mobility history data associated with the UE based at least in part on the mobility history data; and store the updated long-term mobility history data at the first network node. Additionally or alternatively, the communication manager 190 may perform one or more other operations described herein.

[0118] As indicated above, Figure 1 are provided as examples. Other examples can be found in the Figure 1 The examples described are different.

[0119] Figure 2 2 is a diagram illustrating an example 200 of a network node 110 communicating with a UE 120 in a wireless network 100 according to the present disclosure. The network node 110 may be equipped with a set of antennas 234a to 234t, such as T antennas (T≥1). The UE 120 may be equipped with a set of antennas 252a to 252r, such as R antennas (R≥1).

[0120] At the network node 110, a transmit processor 220 may receive data intended for a UE 120 (or a set of UEs 120) from a data source 212. The transmit processor 220 may select one or more modulation and coding schemes (MCS) for the UE 120 based at least in part on one or more channel quality indicators (CQIs) received from the UE 120. The network node 110 may process (e.g., encode and modulate) the data for the UE 120 based at least in part on the MCS selected for the UE 120, and provide data symbols for the UE 120. The transmit processor 220 may process system information (e.g., for semi-static resource partitioning information (SRPI)) and control information (e.g., CQI requests, grants, and / or upper layer signaling), and provide overhead symbols and control symbols. The transmit processor 220 may generate reference symbols for reference signals (e.g., cell-specific reference signals (CRS) or demodulation reference signals (DMRS)) and synchronization signals (e.g., primary synchronization signals (PSS) or secondary synchronization signals (SSS)). The transmit (TX) multiple-input multiple-output (MIMO) processor 230 may perform spatial processing (e.g., pre-coding) on ​​data symbols, control symbols, overhead symbols, and / or reference symbols, where applicable, and may provide a set of output symbol streams (e.g., T output symbol streams) to a corresponding set of modems 232 (e.g., T modems) (shown as modems 232a to 232t). For example, each output symbol stream may be provided to a modulator component (shown as MOD) of the modem 232. Each modem 232 may process a corresponding output symbol stream (e.g., for OFDM) using a corresponding modulator component to obtain an output sample stream. Each modem 232 may further process (e.g., convert to analog, amplify, filter, and / or upconvert) the output sample stream using a corresponding modulator component to obtain a downlink signal. The modems 232a through 232t may transmit a set of downlink signals (eg, T downlink signals) via a corresponding set of antennas 234 (eg, T antennas) (shown as antennas 234a through 234t).

[0121] At the UE 120, a set of antennas 252 (shown as antennas 252a to 252r) may receive downlink signals from the network node 110 and / or other network nodes 110 and may provide a set of received signals (e.g., R received signals) to a set of modems 254 (e.g., R modems) (shown as modems 254a to 254r). For example, each received signal may be provided to a demodulator component (shown as DEMOD) of the modem 254. Each modem 254 may use a corresponding demodulator component to condition (e.g., filter, amplify, downconvert, and / or digitize) the received signal to obtain input samples. Each modem 254 may use a demodulator component to further process the input samples (e.g., for OFDM) to obtain received symbols. A MIMO detector 256 may obtain received symbols from the modem 254, may perform MIMO detection on the received symbols where applicable, and may provide detected symbols. The receive processor 258 may process (e.g., demodulate and decode) the detected symbols, may provide decoded data for the UE 120 to the data sink 260, and may provide decoded control information and system information to the controller / processor 280. The term "controller / processor" may refer to one or more controllers, one or more processors, or a combination thereof. The channel processor may determine a reference signal received power (RSRP) parameter, a received signal strength indicator (RSSI) parameter, a reference signal received quality (RSRQ) parameter, and / or a CQI parameter, among other things. In some examples, one or more components of the UE 120 may be included in the housing 284.

[0122] The network node 130 and / or the network node 150 may include a communication unit 294, a controller / processor 290, and a memory 292. The network node 130 and / or the network node 150 may include, for example, one or more devices in a core network. The network node 130 and / or the network node 150 130 may communicate with the network node 110 via the communication unit 294.

[0123] One or more antennas (e.g., antennas 234a to 234t and / or antennas 252a to 252r) may include, or may be included within, one or more antenna panels, one or more antenna groups, one or more sets of antenna elements, and / or one or more antenna arrays. Antenna panels, antenna groups, sets of antenna elements, and / or antenna arrays may include one or more antenna elements (in a single housing or multiple housings), sets of coplanar antenna elements, sets of non-coplanar antenna elements, and / or may be coupled to one or more transmit and / or receive components (such as, Figure 2One or more antenna elements of one or more components in.

[0124] On the uplink, at the UE 120, a transmit processor 264 may receive and process data from a data source 262 and control information from a controller / processor 280 (e.g., for reports including RSRP, RSSI, RSRQ, and / or CQI). The transmit processor 264 may generate reference symbols for one or more reference signals. The symbols from the transmit processor 264 may be pre-decoded by the TX MIMO processor 266, where applicable, further processed by the modem 254 (e.g., for DFT-s-OFDM or CP-OFDM), and transmitted to the network node 110. In some examples, the modem 254 of the UE 120 may include a modulator and a demodulator. In some examples, the UE 120 includes a transceiver. The transceiver may include any combination of an antenna 252, a modem 254, a MIMO detector 256, a receive processor 258, a transmit processor 264, and / or a TX MIMO processor 266. The transceiver may be used by a processor (eg, controller / processor 280) and memory 282 to perform aspects of any of the methods described herein.

[0125] At the network node 110, uplink signals from the UE 120 and / or other UEs may be received by the antenna 234, processed by the modem 232 (e.g., a demodulator component of the modem 232 shown as DEMOD), detected by the MIMO detector 236 (where applicable), and further processed by the receive processor 238 to obtain decoded data and control information transmitted by the UE 120. The receive processor 238 may provide the decoded data to the data sink 239 and the decoded control information to the controller / processor 240. The network node 110 may include a communication unit 244 and may communicate with the network 130 and / or the network node 150 via the communication unit 244. The network node 110 may include a scheduler 246 to schedule one or more UEs 120 for downlink and / or uplink communications. In some examples, the modem 232 of the network node 110 may include a modulator and a demodulator. In some examples, the network node 110 includes a transceiver. The transceiver may include any combination of antenna 234, modem 232, MIMO detector 236, receive processor 238, transmit processor 220, and / or TX MIMO processor 230. The transceiver may be used by a processor (e.g., controller / processor 240) and memory 242 to perform aspects of any of the methods described herein.

[0126] The controller / processor 240 of the network node 110, the controller / processor 280 of the UE 120, and / or Figure 2 Any other components of the controller / processor 240 of the network node 110, the controller / processor 280 of the UE 120, and / or Figure 2 Any other component of the can perform or direct e.g. Fig.11 Process 1100, Fig.12 The process 1200 Fig.13 The process of 1300 Fig.14 The process of 1400 Fig.15 The process of 1500 Fig.16 The process of 1600 Fig.17 The process of 1400 Fig.18 The process of 1800 Fig.19 The process of 1900 Fig. 20 2000 and / or operations of other processes as described herein. Memory 242 and memory 282 may store data and program codes for network node 110 and UE 120, respectively. In some examples, memory 242 and / or memory 282 may include a non-transitory computer-readable medium storing one or more instructions (e.g., code and / or program code) for wireless communication. For example, the one or more instructions, when executed (e.g., directly or after compilation, conversion, and / or interpretation) by one or more processors of network node 110 and / or UE 120, may cause the one or more processors, UE 120, and / or network node 110 to perform or direct, for example, Fig.11 Process 1100, Fig.12 The process 1200 Fig.13 The process of 1300 Fig.14 The process of 1400 Fig.15 The process of 1500 Fig.16 The process of 1600 Fig.17 The process of 1400 Fig.18 The process of 1800 Fig.19 The process of 1900 Fig. 20 The process 2000 and / or operations of other processes as described herein. In some examples, executing instructions may include running instructions, converting instructions, compiling instructions, and / or interpreting instructions, etc.

[0127] In some aspects, the network node 110 includes means for sending mobility history data of the UE to a second network node in a core network associated with the RAN; and / or means for receiving a UE mobility prediction model based at least in part on the mobility history data from the second network node. In some aspects, the means for the network node 110 to perform operations described herein may include, for example, one or more of the following: the communication manager 160, the transmit processor 220, the TX MIMO processor 230, the modem 232, the antenna 234, the MIMO detector 236, the receive processor 238, the controller / processor 240, the memory 242, or the scheduler 246.

[0128] In some aspects, the network node 110 includes means for sending mobility history data of the UE to a second network node in a core network associated with the RAN; and / or means for receiving updated long-term mobility history data based at least in part on the mobility history data from the second network node. In some aspects, the means for the network node 110 to perform operations described herein may include, for example, one or more of the following: the communication manager 160, the transmit processor 220, the TX MIMO processor 230, the modem 232, the antenna 234, the MIMO detector 236, the receive processor 238, the controller / processor 240, the memory 242, or the scheduler 246.

[0129] In some aspects, the network node 110 includes means for sending mobility history data of the UE to a second network node in the decomposed RAN; and / or means for receiving a UE mobility prediction model based at least in part on the mobility history data from the second network node. In some aspects, the means for the network node 110 to perform operations described herein may include, for example, one or more of the following: the communication manager 160, the transmit processor 220, the TX MIMO processor 230, the modem 232, the antenna 234, the MIMO detector 236, the receive processor 238, the controller / processor 240, the memory 242, or the scheduler 246.

[0130] In some aspects, the network node 110 includes means for sending mobility history data of the UE to a second network node in the decomposed RAN; and / or means for receiving updated long-term mobility history data based at least in part on the mobility history data from the second network node. In some aspects, the means for the network node 110 to perform operations described herein may include, for example, one or more of the following: the communication manager 160, the transmit processor 220, the TX MIMO processor 230, the modem 232, the antenna 234, the MIMO detector 236, the receive processor 238, the controller / processor 240, the memory 242, or the scheduler 246.

[0131] In some aspects, the network node 130 includes a component for receiving mobility history data associated with the UE; and / or a component for sending the mobility history data to a second network node in the core network for training the UE mobility prediction model. In some aspects, the components for the network node 130 to perform the operations described herein may include, for example, one or more of the communication manager 160170, the controller / processor 290, the memory 292, and the communication unit 294.

[0132] In some aspects, the network node 130 includes means for receiving a UE mobility prediction model; and / or means for sending the UE mobility prediction model to a second network node in the core network for storage at the second network node. In some aspects, the means for the network node 130 to perform the operations described herein may include, for example, one or more of the communication manager 170, the controller / processor 290, the memory 292, and the communication unit 294.

[0133] In some aspects, the network node 130 includes a component for receiving mobility history data associated with the UE from a second network node in the core network; and / or a component for training a UE mobility prediction model based at least in part on the mobility history data. In some aspects, the components for the network node 130 to perform the operations described herein may include, for example, one or more of the communication manager 180, the controller / processor 290, the memory 292, and the communication unit 294.

[0134] In some aspects, the network node 130 includes means for receiving mobility history data associated with the UE from a second network node in the core network; means for updating long-term mobility history data associated with the UE based at least in part on the mobility history data; and / or means for storing the updated long-term mobility history data at the first network node. In some aspects, the means for the network node 130 to perform the operations described herein may include, for example, one or more of the communication manager 180, the controller / processor 290, the memory 292, and the communication unit 294.

[0135] In some aspects, the network node 150 includes means for receiving mobility history data associated with the UE from a second network node in the decomposed RAN; and / or means for training a UE mobility prediction model based at least in part on the mobility history data. In some aspects, the means for the network node 150 to perform the operations described herein may include, for example, one or more of the communication manager 190, the controller / processor 290, the memory 292, and the communication unit 294.

[0136] In some aspects, the network node 150 includes means for receiving mobility history data associated with the UE from a second network node in the decomposed RAN; means for updating long-term mobility history data associated with the UE based at least in part on the mobility history data; and / or means for storing the updated long-term mobility history data at the first network node. In some aspects, the means for the network node 150 to perform the operations described herein may include, for example, one or more of the communication manager 190, the controller / processor 290, the memory 292, and the communication unit 294.

[0137] Although Figure 2 The blocks in the 2000 and 2010 are illustrated as distinct components, but the functionality described above for these blocks may be implemented in a single hardware, software, or combined component or in various combinations of components. For example, the functionality described for the transmit processor 264, the receive processor 258, and / or the TX MIMO processor 266 may be performed by or under the control of the controller / processor 280.

[0138] As mentioned above, Figure 2 are provided as examples. Other examples can be found in the Figure 2 Different than described.

[0139] Figure 3 is an example of a disaggregated base station architecture 300 according to the present disclosure. Figure 3The decomposed base station architecture 300 shown may include one or more central units (CU) 310, which may communicate directly with the core network 140 via a backhaul link, or indirectly with the core network 140 through one or more decomposed base station units or network nodes 150 (such as a near RT RIC 325 via an E2 link, or a non-RT RIC 320 associated with an SMO node 305, or both). The CU 310 may communicate with one or more distributed units (DU) 330 via corresponding midhaul links (such as an F1 interface). The DU 330 may communicate with one or more radio units (RU) 340 via corresponding fronthaul links. The RU 340 may communicate with corresponding UEs 120 via one or more radio frequency (RF) access links. In some implementations, a UE 120 may be served by multiple RUs 340 simultaneously.

[0140] Each of the network nodes (e.g., CU 310, DU 330, RU 340, near-RT RIC 325, non-RT RIC 315, and SMO node 305) may include or be coupled to one or more interfaces configured to receive or send signals, data, or information (collectively, signals) via a wired or wireless transmission medium. Each of the units or an associated processor or controller that provides instructions to the communication interfaces of these units may be configured to communicate with one or more of the other units via a transmission medium. For example, a unit may include a wired interface configured to receive or send signals to one or more of the other units via a wired transmission medium. Additionally, a unit may include a wireless interface that may include a receiver, a transmitter, or a transceiver (such as an RF transceiver) configured to receive or send signals, or both, to one or more of the other units on a wireless transmission medium.

[0141] In some aspects, CU 310 may host one or more higher layer control functions. Such control functions may include radio resource control (RRC), packet data convergence protocol (PDCP), service data adaptation protocol (SDAP), etc. Each control function may be implemented using an interface configured to communicate signals with other control functions hosted by CU 310. CU 310 may be configured to handle user plane functionality (e.g., central unit-user plane (CU-UP)), control plane functionality (e.g., central unit-control plane (CU-CP)), or a combination thereof. In some specific implementations, CU 310 may be logically split into one or more CU-UP units and one or more CU-CP units. When implemented in an O-RAN configuration, the CU-UP unit may communicate bidirectionally with the CU-CP unit via an interface (such as an E1 interface). As needed, CU 310 may be implemented to communicate with DU 330 for network control and signaling.

[0142] The DU 330 may correspond to a logical unit that includes one or more base station functions for controlling the operation of one or more RUs 340. In some aspects, the DU 330 may host one or more of the following depending at least in part on a functional split such as that defined by 3GPP: a radio link control (RLC) layer, a medium access control (MAC) layer, and one or more high physical (PHY) layers (such as modules for forward error correction (FEC) encoding and decoding, scrambling, modulation and demodulation, etc.). In some aspects, the DU 330 may also host one or more low PHY layers. Each layer (or module) may be implemented using an interface configured to communicate signals with other layers (and modules) hosted by the DU 330 or with control functions hosted by the CU 310.

[0143] The lower layer functionality may be implemented by one or more RUs 340. In some deployments, a RU 340 controlled by a DU 330 may correspond to a logical node that hosts RF processing functions or low PHY layer functions (such as performing fast Fourier transform (FFT), inverse FFT (iFFT), digital beamforming, physical random access channel (PRACH) extraction and filtering, etc.), or both, based on functional splitting such as lower layer functional splitting. In such an architecture, the RU 340 may be implemented to handle over-the-air (OTA) communications with one or more UEs 120. In some implementations, real-time and non-real-time aspects of control plane and user plane communications with the RU 340 may be controlled by the corresponding DU 330. In some scenarios, this configuration may enable the DU 330 and CU 310 to be implemented in a cloud-based RAN architecture such as a vRAN architecture.

[0144] The SMO node 305 may be configured to support RAN deployment and provisioning of non-virtualized network elements and virtualized network elements. For non-virtualized network elements, the SMO node 305 may be configured to support the deployment of dedicated physical resources for RAN coverage requirements, which may be managed via an operation and maintenance interface (such as an O1 interface). For virtualized network elements, the SMO node 305 may be configured to interact with a cloud computing platform (such as an open cloud (O-Cloud) 390) to perform network element lifecycle management (such as instantiating virtualized network elements) via a cloud computing platform interface (such as an O2 interface). Such virtualized network elements may include, but are not limited to, CU 310, DU 330, RU 340, and near-RT RIC 325. In some specific implementations, the SMO node 305 may communicate with hardware aspects of the 4G RAN such as an open eNB (O-eNB) 315 via the O1 interface. In addition, in some specific implementations, the SMO node 305 may communicate directly with one or more RUs 340 via the O1 interface. The SMO node 305 may also include a non-RT RIC 320 configured to support the functionality of the SMO node 305 .

[0145] The non-RT RIC 320 may be coupled to or in communication with a near-RT RIC 325, such as via an A1 interface. The near-RT RIC 325 may be configured to include logic functions that enable near real-time control and optimization of RAN elements and resources via data collection and actions over an interface connecting one or more CUs 310, one or more DUs 330, or both, and an O-eNB to the near-RT RIC 325, such as via an E2 interface.

[0146] In some aspects, the SMO node 305, the non-RT RIC 320, and / or the near-RT RIC 325 may be included in the core network 140 in a disaggregated base station architecture. The SMO node 305, the non-RT RIC 320, and / or the near-RT RIC 325 may be configured to include logic functions that enable non-real-time control and optimization of RAN elements and resources, artificial intelligence / machine learning (AI / ML) workflows including model training and updating, or policy-based application / feature guidance. In some specific implementations, the SMO node 305, the non-RT RIC 320, and / or the non-RT RIC 320 may receive parameters or external enrichment information from one or more other network nodes included in the core network 140 and / or one or more other network nodes included in the RAN. The information may include mobility history data associated with one or more UEs in the UE 120, among other things. The mobility history data may be used to generate an AI / ML model that the network node 110 may use to generate or determine a UE mobility prediction. The AI / ML model configured to generate or determine UE mobility prediction is referred to herein as a UE mobility prediction model. Figure 5 Examples of UE mobility prediction models are described.

[0147] The mobility history data associated with the UE may include recent (or short-term) mobility history data and long-term mobility history data, etc. The recent mobility history data includes mobility history data collected for the UE within a short and recent time period (such as within the past 12 hours or the past 24 hours). The recent mobility history data may include mobility history data indicating the location of the UE during the recent time period, the time and duration of the UE at those locations, the starting location and the ending location of the movement of the UE during the recent time period, the route traversed by the UE during the recent time period, and / or other mobility history data.

[0148] Long-term mobility history data includes mobility history data collected and aggregated for a UE over a long duration, such as several months to a year or longer. In general, long-term mobility history includes mobility history data collected over a duration sufficient to identify and / or determine a UE's typical routes, destinations, movement habits, and / or other mobility events that are stable and repeatable over a long duration.

[0149] The recent mobility history data may be stored by the SMO node 305, the non-RT RIC 320, and / or the near-RT RIC 325, and aggregated and / or accumulated as part of the long-term mobility history data of the UE. The recent mobility history data and / or the long-term mobility history data may be used by the SMO node 305, the non-RT RIC 320, the near-RT RIC 325, another network node 130 in the core network 140, a network node 110 such as a base station, a CU 310, a DU 330, a RU 340, and / or another network node to generate a UE mobility prediction for a UE in the decomposed base station architecture 300 and / or to generate, build, train, and / or update a UE mobility prediction model.

[0150] As mentioned above, Figure 3 are provided as examples. Other examples can be found in the Figure 3 The examples described are different.

[0151] Figure 4 4 is a diagram of an example 400 of a core network 140 configured to provide network slicing according to the present disclosure. The core network 140 can be communicatively coupled to the wireless network 100. In some aspects, the decomposed base station architecture 300 is implemented in the wireless network 100. The devices and / or networks of the example 400 can be interconnected via wired connections, wireless connections, or a combination thereof.

[0152] The core network 140 may include an example functional architecture in which the systems and / or methods described herein may be implemented. For example, the core network 140 may include an example architecture of a fifth generation (5G) next generation (NG) core network included in a 5G wireless telecommunications system, or included in a subsequent core network architecture such as a 6G core network architecture in a 5G wireless telecommunications system, a 6G wireless telecommunications system, and / or a 6G core network architecture above 6G. Although Figure 4 The example architecture of the core network 140 shown may be an example of a service-based architecture, but in some aspects, the core network 140 may be implemented as a reference point architecture and / or a 4G core network, etc. A service-based architecture includes a core network architecture in which a modular framework is used so that common applications can be deployed using components from different sources. In a service-based architecture, control plane functionality and common data repositories in the core network are delivered through a collection of interconnected network functions, each of which may have authorization to access each other's services over a public service-based interface (SBI). A reference point architecture may include a core network architecture in which network elements use point-to-point interfaces to communicate in the core network, as opposed to using common interfaces such as in a service-based architecture.

[0153] like Figure 4As shown, the core network 140 may include multiple functional elements. The functional element may be implemented as one or more network nodes 130a-130n. The functional element may include, for example, a network slice selection function (NSSF) node 410, a network open function (NEF) node 415, an authentication server function (AUSF) node 420, a unified data management (UDM) / home subscriber service (HSS) node 425, a policy control function (PCF) node 430, an application function (AF) node 435, an access and mobility management function (AMF) node 440, a session management function (SMF) node 445, a user plane function (UPF) node 450, a network data analysis function (NWDAF) node 460 and / or an AI / ML server 470, etc. These functional elements may be communicatively connected via a message bus 455. Figure 4 Each of the functional elements shown may be implemented on one or more devices associated with a wireless telecommunications system. In some implementations, one or more of these functional elements may be implemented on a physical device such as an access point, a base station, and / or a gateway, etc. In some implementations, one or more of these functional elements may be implemented on a computing device in a cloud computing environment.

[0154] NSSF node 410 may include one or more devices that select a network slice instance for UE 120. Network slicing is a network architecture model in which logically different network slices operate using a common network infrastructure. For example, several network slices may operate as isolated end-to-end networks that are customized to meet different target service standards for different types of applications executed at least in part by UE 120 and / or communications to and from UE 120. Network slices can effectively provide communications for different types of services with different service standards.

[0155] NSSF node 410 may determine a set of network slicing policies to be applied at wireless network 100. For example, NSSF node 410 may apply one or more UE routing policy (URSP) rules. In some aspects, NSSF node 410 may select a network slice based on a mapping of a data network name (DNN) field included in a routing description (RSD) to a DNN field included in a service descriptor selected by UE 120. By providing network slicing, NSSF node 410 allows operators to deploy multiple substantially independent end-to-end networks that may have the same infrastructure. In some implementations, each slice may be customized for different services.

[0156] NEF node 415 may include one or more devices that support exposure of capabilities and / or events in a wireless telecommunication system to assist other entities in the wireless telecommunication system in discovering network services. AUSF node 420 may include one or more devices that act as an authentication server and support the process of authenticating UE 120 in a wireless telecommunication system.

[0157] The UDM / HSS node 425 may include one or more devices that store user data and profiles in a wireless telecommunications system. In some aspects, the UDM / HSS node 425 may be used for fixed access and / or mobile access in the core network 140, etc. In some aspects, the UDM / HSS node 425 includes a 5G UDM node. In some aspects, the UDM / HSS node 425 includes a 4G / LTE HSS node. In some aspects, the UDM / HSS node 425 may be deployed with an internal database. In some aspects, the UDM / HSS node 425 may be deployed with an external database called a unified data repository (UDR) node 425a.

[0158] In some aspects, the UDM / HSS node 425 is configured to store mobility history data associated with one or more UEs 120. In some aspects, the UDM / HSS node 425 is configured to receive the mobility history data from the AMF node 440 and / or another network node 130 in the core network 140. In some aspects, the UDM / HSS node 425 is configured to generate a UE mobility prediction model based on the mobility history data stored by the UDM / HSS node 425. In some aspects, the UDM / HSS node 425 is configured to send the UE mobility prediction to the AMF node 440 to send to the network node 110 in the wireless network 100. In some aspects, the UDM / HSS node 425 is configured to send the mobility history data (e.g., recent mobility history data, long-term mobility history data) to the AMF node 440 to send to the network node 110 in the wireless network 100 so that the network node 110 can use the mobility history data to generate a UE mobility prediction model.

[0159] The PCF node 430 may include one or more devices that provide a policy framework that incorporates network slicing, roaming, packet processing, and / or mobility management, among other things. In some aspects, the PCF node 430 may include one or more URSP rules used by the NSSF node 410 to select a network slice instance for the UE 120. The AF node 435 may include one or more devices that support application impact on traffic routing, access to the NEF node 415, and / or policy control, among other things. The AMF node 440 may include one or more devices that act as a termination point for non-access stratum (NAS) signaling and / or mobility management, among other things. In some aspects, the AMF node 440 may request the NSSF node 410 to select a network slice instance for the UE 120, for example, at least in part in response to a request for data services from the UE 120.

[0160] In some aspects, the AMF node 440 is configured to receive mobility history data from a network node 110 in the wireless network 100. In some aspects, the AMF node 440 is configured to send the mobility history data to a UDM / HSS node 425 and / or another network node 130 in the core network 140. In some aspects, the AMF node 440 is configured to send the mobility history data to a SMO node 305, a non-RT RIC 320, and / or a near-RT RIC 325 included in the RAN of the wireless network 100 or in the core network 140. In some aspects, the AMF node 440 is configured to receive a UE mobility prediction based on the stored mobility history data from the UDM / HSS node 425. In some aspects, the AMF node 440 is configured to receive a UE mobility prediction based on the stored mobility history data from a SMO node 305, a non-RT RIC 320, and / or a near-RT RIC 325 included in the RAN of the wireless network 100 or in the core network 140. In some aspects, the AMF node 440 is configured to send the UE mobility prediction to the network node 110 in the wireless network 100.

[0161] In some aspects, the AMF node 440 is configured to receive mobility history data (e.g., recent mobility history data, long-term mobility history data) from the UDM / HSS node 425 and / or another network node 130 included in the core network 140. In some aspects, the AMF node 440 is configured to receive mobility history data (e.g., recent mobility history data, long-term mobility history data) from the SMO node 305, the non-RT RIC 320, and / or the near-RT RIC 325 included in the RAN of the wireless network 100 or in the core network 140. In some aspects, the AMF node 440 is configured to send the mobility history data (e.g., recent mobility history data, long-term mobility history data) to the network node 110 in the wireless network 100 so that the network node 110 can use the mobility history data to generate a UE mobility prediction model.

[0162] The SMF node 445 may include one or more devices that support the establishment, modification, and release of communication sessions in the wireless telecommunications system. For example, the SMF node 445 may configure traffic steering policies and / or implement user equipment IP address allocation and policies at the UPF node 450, etc. In some aspects, the SMF node 445 may provision the network slice instance selected by the NSSF node 410 for the UE 120.

[0163] The UPF node 450 may include one or more devices that act as an anchor point for intra-RAT and / or inter-RAT mobility. In some aspects, the UPF node 450 may apply rules to packets, such as rules related to packet routing, service reporting, and / or handling user plane QoS, etc. The UPF node 450 may communicate with network nodes in the wireless network 100 over a backhaul interface, such as an N3 interface. For example, the UPF node 450 may communicate with a CU 310 in the wireless network 100.

[0164] The message bus 455 can be a logical and / or physical communication structure for communicating between functional elements. Thus, the message bus 455 can allow communication between two or more functional elements, whether logically (e.g., using one or more application programming interfaces (APIs), etc.) and / or physically (e.g., using one or more wired and / or wireless connections).

[0165] The NWDAF node 460 may be a network node 130 configured to store network data associated with the wireless network 100 and / or the UE 120 in the wireless network 100. In addition, the NWDAF node 460 may be configured to generate various analyses based on the network data. In some aspects, the NWDAF node 460 is configured to store mobility history data associated with the UE 120 in the wireless network 100. In some aspects, the NWDAF node 460 is configured to generate a UE mobility prediction model based on the mobility history data.

[0166] The AI / ML server 470 may be a network node 130 configured to generate, train, and / or update various AI / ML models for the wireless network 100 and / or for the UEs 120 in the wireless network 100. In some aspects, the AI / ML server 470 is configured to store mobility history data associated with the UEs 120 in the wireless network 100. In some aspects, the AI / ML server 470 is configured to generate a UE mobility prediction model based on the mobility history data.

[0167] Figure 4 The number and arrangement of devices and networks shown are provided as examples. In practice, there may be Figure 4 The devices and / or networks shown may include additional devices and / or networks, fewer devices and / or networks, different devices and / or networks, or devices and / or networks arranged in a different manner than those shown. Figure 4 Two or more of the devices shown may be implemented in a single device, or Figure 4 The single device shown may be implemented as multiple distributed devices. Additionally or alternatively, a collection of devices (eg, one or more devices) of example environment 400 may perform one or more functions described as being performed by another collection of devices of example environment 400.

[0168] Figure 5 5 is a diagram of an example 500 of a UE mobility prediction model 505 according to the present disclosure. The UE mobility prediction model 505 may include an AI / ML model used by a network node (e.g., network node 110, network node 130, network node 150, SMO node 305, non-RTRIC 320, near-RT RIC 325, UDM / HSS node 425, AI / ML server 470, and / or another type of network node) to estimate or predict the future location of UE 120 at a particular time.

[0169] In some aspects, the UE mobility prediction model 505 may include a UE-specific mobility prediction model, which is a UE mobility prediction model that is generated, trained, and used to generate UE mobility predictions for a specific UE 120. Data and parameters specific to a UE 120 may be used to generate and train a UE-specific mobility prediction model associated with a UE 120. These data and parameters may include, for example, a recent mobility history of the UE 120, a long-term mobility history of the UE 120, and / or other data and parameters.

[0170] In some aspects, the UE mobility prediction model 505 may include a common UE mobility prediction model, which is a UE mobility prediction model that is generated, trained, and used to generate UE mobility predictions for multiple UEs 120. The common UE mobility prediction model may be used to generate UE mobility predictions in a particular cell, in a particular geographic area, at or near a particular point of interest, and / or another location. The data and parameters used to train the common UE mobility prediction model may include, for example, recent mobility history associated with the multiple UEs 120, long-term mobility history associated with the multiple UEs 120, and / or other data and parameters.

[0171] like Figure 5 As further shown, input parameters 510 may be provided to the UE mobility prediction model 505 as a set of constraints and / or values ​​for determining an output 515 using the UE mobility prediction model 505. The input parameters 510 may include a long-term mobility history associated with the UE 120 (and in some cases, one or more other UEs 120), a recent mobility history associated with the UE 120 (and in some cases, one or more other UEs 120), a current location of the UE 120, one or more radio measurements generated by the UE 120 (e.g., RSRP measurements, RSSI measurements, RSRQ measurements, CQI measurements), sensor information provided by the UE 120 (e.g., accelerometer information, gyroscope information), date and time information associated with the UE 120, and / or other parameters.

[0172] Output 515 may include a UE mobility prediction for UE 120. The UE mobility prediction may include a data set including a predicted or estimated target cell for UE 120, a predicted or estimated time at which UE 120 will enter the target cell, and a predicted or estimated probability that the predicted or estimated target cell and the predicted or estimated time are correct.

[0173] As mentioned above, Figure 5 are provided as examples. Other examples can be found in the Figure 5 The examples described are different.

[0174] Figure 66 is a diagram of an example 600 of signaling for UE mobility prediction according to the present disclosure. Figure 6 As shown, example 600 may include communications between UE 120 and multiple network nodes (including network node 110, AMF node 440, and network node 130). UE 120 and network node 110 may be included in a RAN of wireless network 100. AMF node 440 and network node 130 may be included in a core network 140 associated with the RAN. Network node 110 may include a base station, CU 310, DU 330, RU 340, and / or another RAN node. Network node 130 may include UDM / HSS node 425 (with or without associated UDR node 425a), NWDAF node 460, AI / ML server 470, and / or another core network node.

[0175] The signaling described in example 600 may be implemented to support a UE mobility prediction model training process 605. In addition, the signaling described in example 600 may be implemented to support a UE mobility prediction model inference process 610. The UE mobility prediction model 505 may be trained based at least in part on mobility history data associated with the UE 120 and, in some cases, on mobility history data associated with one or more other UEs as part of the UE mobility prediction model training process 605. The trained UE mobility prediction model 505 may be provided to the network node 110 as part of the UE mobility prediction model inference process 610, so that the network node 110 may use the trained UE mobility prediction model 505 to generate or determine a UE mobility prediction for the UE 120.

[0176] In the UE mobility prediction model training process 605, the network node 110 may send (and the AMF node 440 may receive) a UE context release request at 615. The network node 110 may send (and the AMF node 440 may receive) the UE context release request on the backhaul link between the RAN and the core network 140. The network node 110 may send the UE context release request as part of an RRC release operation associated with the UE 120. In some aspects, the RRC release operation may be performed to transition the UE 120 to an RRC idle mode, an RRC inactive mode, and / or another mode in which the RRC context of the UE 120 is released. In some aspects, the RRC release operation may be performed in conjunction with a path switch or handover of the UE 120. The UE context release request may be a message including a request to release the RRC context of the UE 120.

[0177] At 620, AMF node 440 may send (and network node 110 may receive) a UE context release command. AMF node 440 may send a UE context release command in response to the UE context release request. The UE context release command may be a message indicating that network node 110 will release the RRC context of UE 120.

[0178] At 625, network node 110 may send (and UE 120 may receive) an RRC release message. The RRC release message may indicate to UE 120 that UE 120 is allowed to transition to RRC idle mode or RRC inactive mode, or to switch a communication path of UE 120 as part of a handover.

[0179] At 630, the network node 110 sends a UE context release complete message to the AMF node 440, and the AMF node 440 may receive the UE context release complete message from the network node 110. The UE context release complete message may include an indication that the RRC release of the UE 120 has been successfully completed.

[0180] like Figure 6 As further shown, the network node 110 may send mobility history data (associated with the UE 120) to the AMF node 440, and the AMF node 440 may receive the mobility history data from the network node 110. The mobility history data may include recent mobility history data associated with the UE 120. The UE history information upload may be from the RAN 100 to the core network 140. The network node 110 may send (and the UE 120 may receive) the mobility history data in a UE context release complete message, in a path switch request, and / or in another message. The UE 120 may generate the mobility history data and provide it to the network node 110, the network node 110 may generate the mobility history data for the UE 120, or a combination thereof.

[0181] At 635, the AMF node 440 may send the mobility history data to the network node 130, and the network node 130 may receive the mobility history data from the AMF node 440. The network node 130 may store the mobility history data, and may generate long-term mobility history data of the UE 120 by accumulating the mobility history data of the UE 120. Here, the UE history data upload may be from the core network 140 (e.g., from the AMF node 440 of the core network 140) to the model repository. In some aspects, the model repository (e.g., the network node 130) is a UDR node 425a associated with the UDM / HSS node 425, and the UE history data upload includes AMF-UDM signaling. In some aspects, the model repository (e.g., the network node 130) is a NWDAF node 460, and the UE history information upload includes AMF-NWDAF signaling. In some aspects, the model repository (eg, the network node 130) is the AI / ML server 470, and the UE history information upload comprises AMF / NWDAF-AI / ML server signaling.

[0182] At 640, the network node 130 may train a UE mobility prediction model (e.g., UE mobility prediction model 505). The network node 130 may train the UE mobility prediction model based at least in part on the mobility history data received from the AMF node 440. Training the UE mobility prediction model may include updating the decision-making aspects of the UE mobility prediction model based on the mobility history data, so that the UE mobility prediction model can be used for more accurate and up-to-date UE mobility predictions. In some aspects, the UE mobility prediction model may include a UE-specific mobility prediction model associated with the UE 120. Here, the network node 130 may train the UE-specific mobility prediction model based at least in part on the long-term mobility history data associated with the UE 120. As an example, the model is trained by long-term (e.g., several months) history data of the target UE. The long-term mobility history of the target UE is implicitly included in the trained model.

[0183] As part of the UE mobility prediction model inference process 610, the UE 120 may send (and the network node 110 may receive) an RRC setup request message at 645. In some aspects, the UE 120 may send the RRC setup request message to the network node 110 to initiate an RRC connection establishment process with the network node 110. In some aspects, the UE 120 may send the RRC setup request message to the network node 110 as part of a handover or path switch to the network node 110.

[0184] At 650, network node 110 may send (and UE 120 may receive) an RRC setup message. Network node 110 may send the RRC setup message as part of an RRC connection establishment or handover for UE 120 based on receiving the RRC setup request.

[0185] At 655, UE 120 may send (and network node 110 may receive) an RRC setup complete message. UE 120 may send the RRC setup complete message as part of an RRC connection establishment or handover for UE 120 based on receiving the RRC setup message.

[0186] At 660, the network node 110 may send (and the AMF node 440 may receive) an initial UE message. The initial UE message may be an initial message from the UE 120 to the AMF node 440 as part of an RRC connection establishment or handover of the UE 120.

[0187] At 665, AMF node 440 may send (and network node 130 may receive) a UDM request message. The UDM request message may include a request for subscription data associated with UE 120. AMF node 440 may send the UDM request message to network node 130 based on receiving the initial UE message.

[0188] At 670, the network node 130 may send (and the AMF node 440 may receive) a UDM response message. The UDM response message may include subscription data associated with the UE 120. The network node 130 may send the UDM response message to the AMF node 440 based on receiving the UDM request message from the AMF node 440.

[0189] In addition, the network node 130 may send a UE mobility prediction model to the AMF node 440, and the AMF node 440 may receive the UE mobility prediction model from the network node 130. This may be performed as part of a model transmission to the RAN (specifically, the model repository to the AMF). Here, the UE mobility prediction model may be transmitted from the model repository to the core network 140 (e.g., transmitted to the AMF node 440 of the core network 140). In some aspects, the model repository (e.g., the network node 130) is a UDR node 425a associated with the UDM / HSS node 425, and the UE mobility prediction model transmission includes AMF-UDM signaling. In some aspects, the model repository (e.g., the network node 130) is a NWDAF node 460, and the UE mobility prediction model transmission includes AMF-NWDAF signaling. In some aspects, the model repository (e.g., the network node 130) is an AI / ML server 470, and the UE mobility prediction model transmission includes AMF / NWDAF-AI / ML server signaling. The network node 130 may send (and the AMF node 440 may receive) the UE mobility prediction model in the UDM response message and / or in another message.

[0190] At 675, AMF node 440 may send (and network node 110 may receive) a UE context setup request message. The UE context setup request message may include subscription data associated with UE 120. AMF node 440 may send the UE context setup request message to network node 130 based on receiving the subscription data from network node 110.

[0191] In addition, the AMF node 440 may send a UE mobility prediction model to the AMF node 440, and the AMF node 440 may receive the UE mobility prediction model from the network node 130. This may be performed as part of a model transmission to the RAN (specifically, AMF to RAN). In some aspects, the AMF node 440 sends the UE mobility prediction model to the network node 110 in a UE context establishment request (e.g., an initial UE context establishment request), and the network node 110 receives the UE mobility prediction model from the AMF node 440 in the UE context establishment request. In some aspects, the AMF node 440 sends the UE mobility prediction model to the network node 110 in a UE context modification request message, and the network node 110 receives the UE mobility prediction model from the AMF node 440 in a UE context modification request message. In some aspects, the AMF node 440 sends the UE mobility prediction model to the network node 110 in a path switch request confirmation message, and the network node 110 receives the UE mobility prediction model from the AMF node 440 in a path switch request confirmation message.

[0192] Additionally and / or alternatively, the network node 110 may send the UE mobility prediction model to another network node 110 in the RAN as part of the handover of the UE 120. Here, the source network node 110 transmits the UE mobility prediction model to the target network node 110. In an Xn handover, the UE mobility prediction model may be transmitted in a handover request. In an NG handover, the UE mobility prediction model may be transmitted in a source-to-target transparent container.

[0193] The UE mobility prediction model may be delivered to the network node 110 during RRC connection establishment, during handover to the network node 110, and / or during another process. The network node 110 may use the UE mobility prediction model to generate or determine a UE mobility prediction for the UE 120. For example, the network node 110 may use the above in conjunction with Figure 5 One or more techniques are described to generate or determine a UE mobility prediction for UE 120 based on a UE mobility prediction model.

[0194] As mentioned above, Figure 6 are provided as examples. Other examples can be found in the Figure 6 The examples described are different.

[0195] Figure 7 is a diagram of an example 700 of signaling for UE mobility prediction according to the present disclosure. Example 700 is similar to Figure 6 Example 600 of RAN, except that the network node 110 in the RAN trains the UE mobility prediction model based on the mobility history data associated with the UE 120. Therefore, the updated UE mobility prediction model is sent from the network node 110 to the AMF node 440, and from the AMF node 440 to the network node 130, which is opposite to (and / or in addition to) the mobility history data.

[0196] like Figure 7 As shown, example 700 may include communications between a UE 120 and a plurality of network nodes, including a network node 110, an AMF node 440, and a network node 130. The UE 120 and the network node 110 may be included in a RAN of the wireless network 100. The AMF node 440 and the network node 130 may be included in a core network 140 associated with the RAN. The network node 110 may include a base station, a CU 310, a DU 330, a RU 340, and / or another RAN node. The network node 130 may include a UDM / HSS node 425 (with or without an associated UDR node 425a), a NWDAF node 460, an AI / ML server 470, and / or another core network node.

[0197] The signaling described in the example 700 may be implemented to support a UE mobility prediction model training process 705. In addition, the signaling described in the example 700 may be implemented to support a UE mobility prediction model inference process 710. The UE mobility prediction model 505 may be trained as part of the UE mobility prediction model training process 705 based at least in part on mobility history data associated with the UE 120, and in some cases, based on mobility history data associated with one or more other UEs. The trained UE mobility prediction model 505 may be provided to the network node 110 as part of the UE mobility prediction model inference process 710, so that the network node 110 may use the trained UE mobility prediction model 505 to generate or determine a UE mobility prediction for the UE 120.

[0198] In the UE mobility prediction model training process 705, at 715, the network node 110 may send (and the AMF node 440 may receive) a UE context release request. The network node 110 may send (and the AMF node 440 may receive) the UE context release request on the backhaul link between the RAN and the core network 140. The network node 110 may send the UE context release request as part of an RRC release operation associated with the UE 120. In some aspects, the RRC release operation may be performed to transition the UE 120 to an RRC idle mode, an RRC inactive mode, and / or another mode in which the RRC context of the UE 120 is released. In some aspects, the RRC release operation may be performed in conjunction with a path switch or handover of the UE 120. The UE context release request may be a message including a request to release the RRC context of the UE 120.

[0199] At 720, AMF node 440 may send (and network node 110 may receive) a UE context release command. AMF node 440 may send the UE context release command in response to the UE context release request. The UE context release command may be a message indicating that network node 110 will release the RRC context of UE 120.

[0200] At 725, network node 110 may send (and UE 120 may receive) an RRC release message. The RRC release message may indicate to UE 120 that UE 120 is allowed to transition to RRC idle mode or RRC inactive mode, or to switch a communication path of UE 120 as part of a handover.

[0201] At 730, the network node 110 may train a UE mobility prediction model (e.g., UE mobility prediction model 505). The network node 110 may train the UE mobility prediction model based at least in part on mobility history data. The mobility history data may be generated by the network node 110 and / or received from the UE 120. Training the UE mobility prediction model may include updating the decision-making aspects of the UE mobility prediction model based on the mobility history data, so that the UE mobility prediction model can be used for more accurate and up-to-date UE mobility predictions. In some aspects, the UE mobility prediction model may include a UE-specific mobility prediction model associated with the UE 120. Here, the network node 110 may train the UE-specific mobility prediction model based at least in part on long-term mobility history data associated with the UE 120. As an example, the model is trained by long-term (e.g., several months) history data of the target UE. The long-term mobility history of the target UE is implicitly included in the trained model.

[0202] At 735, the network node 110 sends a UE context release complete message to the AMF node 440, and the AMF node 440 may receive the UE context release complete message from the network node 110. The UE context release complete message may include an indication that the RRC release of the UE 120 has been successfully completed.

[0203] like Figure 7 As further shown, the network node 110 may send a UE mobility prediction model (associated with the UE 120) to the AMF node 440, and the AMF node 440 may receive the UE mobility prediction model from the network node 110. The UE mobility prediction model upload may be from the RAN 100 to the core network 140. The network node 110 may send (and the UE 120 may receive) the UE mobility prediction model in a UE context release complete message, in a path switch request, and / or in another message.

[0204] At 735, the AMF node 440 may send the UE mobility prediction model to the network node 130, and the network node 130 may receive the UE mobility prediction model from the AMF node 440. The network node 130 may store the UE mobility prediction model. Here, the UE mobility prediction model upload may be from the core network 140 (e.g., from the AMF node 440 of the core network 140) to the model repository. In some aspects, the model repository (e.g., the network node 130) is a UDR node 425a associated with the UDM / HSS node 425, and the UE mobility prediction model upload includes AMF-UDM signaling. In some aspects, the model repository (e.g., the network node 130) is a NWDAF node 460, and the UE mobility prediction model includes AMF-NWDAF signaling. In some aspects, the model repository (e.g., the network node 130) is an AI / ML server 470, and the UE mobility prediction model includes AMF / NWDAF-AI / ML server signaling.

[0205] As part of the UE mobility prediction model inference process 710, the UE 120 may send (and the network node 110 may receive) an RRC setup request message at 745. In some aspects, the UE 120 may send the RRC setup request message to the network node 110 to initiate an RRC connection establishment process with the network node 110. In some aspects, the UE 120 may send the RRC setup request message to the network node 110 as part of a handover or path switch to the network node 110.

[0206] At 750, network node 110 may send (and UE 120 may receive) an RRC setup message. Network node 110 may send the RRC setup message as part of an RRC connection establishment or handover for UE 120 based on receiving the RRC setup request.

[0207] At 755, UE 120 may send (and network node 110 may receive) an RRC setup complete message. UE 120 may send the RRC setup complete message as part of an RRC connection establishment or handover for UE 120 based on receiving the RRC setup message.

[0208] At 760, the network node 110 may send (and the AMF node 440 may receive) an initial UE message. The initial UE message may be an initial message from the UE 120 to the AMF node 440 as part of an RRC connection establishment or handover of the UE 120.

[0209] At 765, AMF node 440 may send (and network node 130 may receive) a UDM request message. The UDM request message may include a request for subscription data associated with UE 120. AMF node 440 may send the UDM request message to network node 130 based on receiving the initial UE message.

[0210] At 770, the network node 130 may send (and the AMF node 440 may receive) a UDM response message. The UDM response message may include subscription data associated with the UE 120. The network node 130 may send the UDM response message to the AMF node 440 based on receiving the UDM request message from the AMF node 440.

[0211] In addition, the network node 130 may send a UE mobility prediction model to the AMF node 440, and the AMF node 440 may receive the UE mobility prediction model from the network node 130. This may be performed as part of a model transmission to the RAN (specifically, the model repository to the AMF). Here, the UE mobility prediction model may be transmitted from the model repository to the core network 140 (e.g., transmitted to the AMF node 440 of the core network 140). In some aspects, the model repository (e.g., the network node 130) is a UDR node 425a associated with the UDM / HSS node 425, and the UE mobility prediction model transmission includes AMF-UDM signaling. In some aspects, the model repository (e.g., the network node 130) is a NWDAF node 460, and the UE mobility prediction model transmission includes AMF-NWDAF signaling. In some aspects, the model repository (e.g., the network node 130) is an AI / ML server 470, and the UE mobility prediction model transmission includes AMF / NWDAF-AI / ML server signaling. The network node 130 may send (and the AMF node 440 may receive) the UE mobility prediction model in the UDM response message and / or in another message.

[0212] At 775, AMF node 440 may send (and network node 110 may receive) a UE context setup request message. The UE context setup request message may include subscription data associated with UE 120. AMF node 440 may send the UE context setup request message to network node 130 based on receiving the subscription data from network node 110.

[0213] In addition, the AMF node 440 may send a UE mobility prediction model to the AMF node 440, and the AMF node 440 may receive the UE mobility prediction model from the network node 130. This may be performed as part of a model transmission to the RAN (specifically, AMF to RAN). In some aspects, the AMF node 440 sends the UE mobility prediction model to the network node 110 in a UE context establishment request (e.g., an initial UE context establishment request), and the network node 110 receives the UE mobility prediction model from the AMF node 440 in the UE context establishment request. In some aspects, the AMF node 440 sends the UE mobility prediction model to the network node 110 in a UE context modification request message, and the network node 110 receives the UE mobility prediction model from the AMF node 440 in a UE context modification request message. In some aspects, the AMF node 440 sends the UE mobility prediction model to the network node 110 in a path switch request confirmation message, and the network node 110 receives the UE mobility prediction model from the AMF node 440 in a path switch request confirmation message.

[0214] Additionally and / or alternatively, the network node 110 may send the UE mobility prediction model to another network node 110 in the RAN as part of the handover of the UE 120. Here, the source network node 110 transmits the UE mobility prediction model to the target network node 110. In an Xn handover, the UE mobility prediction model may be transmitted in a handover request. In an NG handover, the UE mobility prediction model may be transmitted in a source-to-target transparent container.

[0215] The UE mobility prediction model may be delivered to the network node 110 during RRC connection establishment, during handover to the network node 110, and / or during another process. The network node 110 may use the UE mobility prediction model to generate or determine a UE mobility prediction for the UE 120. For example, the network node 110 may use the above in conjunction with Figure 5 One or more techniques are described to generate or determine a UE mobility prediction for UE 120 based on a UE mobility prediction model.

[0216] As mentioned above, Figure 7 are provided as examples. Other examples can be found in the Figure 7 The examples described are different.

[0217] Figure 8 8 is a diagram of an example 800 of signaling for UE mobility prediction according to the present disclosure. Figure 8As shown, example 800 may include communications between UE 120 and multiple network nodes, including network node 110, AMF node 440, and network node 130. UE 120 and network node 110 may be included in a RAN of wireless network 100. AMF node 440 and network node 130 may be included in a core network 140 associated with the RAN. Network node 110 may include a base station, CU 310, DU 330, RU 340, and / or another RAN node. Network node 130 may include UDM / HSS node 425 (with or without an associated UDR node 425a), NWDAF node 460, AI / ML server 470, and / or another core network node.

[0218] The signaling described in example 800 may be implemented to support a long-term mobility history update procedure 805 associated with the UE 120. Additionally, the signaling described in example 800 may be implemented to support a UE mobility prediction model inference procedure 810. The long-term mobility history may be provided to the network node 110 as part of the UE mobility prediction model inference procedure 810. The UE mobility prediction model 505 may be trained based at least in part on the long-term mobility history data associated with the UE 120 and used as part of the UE mobility prediction model inference procedure 810. In some cases, mobility history data associated with one or more other UEs may also be used to train the UE mobility prediction model 505.

[0219] In the long-term mobility history update procedure 805, at 815, the network node 110 may send (and the AMF node 440 may receive) a UE context release request. The network node 110 may send (and the AMF node 440 may receive) the UE context release request on a backhaul link between the RAN and the core network 140. The network node 110 may send the UE context release request as part of an RRC release operation associated with the UE 120. In some aspects, the RRC release operation may be performed to transition the UE 120 to an RRC idle mode, an RRC inactive mode, and / or another mode in which the RRC context of the UE 120 is released. In some aspects, the RRC release operation may be performed in conjunction with a path switch or handover of the UE 120. The UE context release request may be a message that includes a request to release the RRC context of the UE 120.

[0220] At 820, AMF node 440 may send (and network node 110 may receive) a UE context release command. AMF node 440 may send a UE context release command in response to the UE context release request. The UE context release command may be a message indicating that network node 110 will release the RRC context of UE 120.

[0221] At 825, network node 110 may send (and UE 120 may receive) an RRC release message. The RRC release message may indicate to UE 120 that UE 120 is allowed to transition to RRC idle mode or RRC inactive mode, or to switch a communication path of UE 120 as part of a handover.

[0222] At 830, the network node 110 sends a UE context release complete message to the AMF node 440, and the AMF node 440 may receive the UE context release complete message from the network node 110. The UE context release complete message may include an indication that the RRC release of the UE 120 has been successfully completed.

[0223] like Figure 8 As further shown, the network node 110 may send mobility history data (associated with the UE 120) to the AMF node 440, and the AMF node 440 may receive the mobility history data from the network node 110. The mobility history data may include recent mobility history data associated with the UE 120. The UE history information upload may be from the RAN 100 to the core network 140. The network node 110 may send (and the UE 120 may receive) the mobility history data in a UE context release complete message, in a path switch request, and / or in another message. The UE 120 may generate the mobility history data and provide it to the network node 110, the network node 110 may generate the mobility history data for the UE 120, or a combination thereof.

[0224] At 835, the AMF node 440 may send the mobility history data to the network node 130, and the network node 130 may receive the mobility history data from the AMF node 440. The network node 130 may store the mobility history data, and may generate long-term mobility history data of the UE 120 by accumulating the mobility history data of the UE 120. Here, the UE history data upload may be from the core network 140 (e.g., from the AMF node 440 of the core network 140) to the model repository. In some aspects, the model repository (e.g., the network node 130) is a UDR node 425a associated with the UDM / HSS node 425, and the UE history data upload includes AMF-UDM signaling. In some aspects, the model repository (e.g., the network node 130) is a NWDAF node 460, and the UE history information upload includes AMF-NWDAF signaling. In some aspects, the model repository (eg, the network node 130) is the AI / ML server 470, and the UE history information upload comprises AMF / NWDAF-AI / ML server signaling.

[0225] At 840, the network node 130 may update and store the long-term mobility history data associated with the UE 120. The network node 130 may update and store the long-term mobility history data associated with the UE 120 by updating the typical routes, destinations, movement habits, and / or any of other mobility events that are stable and repeatable over a long duration of time of the UE 120 based at least in part on the mobility history data received from the AMF node 440. The long-term mobility history data associated with the UE 120 may include statistics of the mobility history of the UE 120 and / or other mobility history data.

[0226] As part of the UE mobility prediction model inference process 810, the UE 120 may send (and the network node 110 may receive) an RRC setup request message at 845. In some aspects, the UE 120 may send the RRC setup request message to the network node 110 to initiate an RRC connection establishment process with the network node 110. In some aspects, the UE 120 may send the RRC setup request message to the network node 110 as part of a handover or path switch to the network node 110.

[0227] At 850, network node 110 may send (and UE 120 may receive) an RRC setup message. Network node 110 may send the RRC setup message as part of an RRC connection establishment or handover for UE 120 based on receiving the RRC setup request.

[0228] At 855, UE 120 may send (and network node 110 may receive) an RRC setup complete message. UE 120 may send the RRC setup complete message as part of an RRC connection establishment or handover for UE 120 based on receiving the RRC setup message.

[0229] At 860, the network node 110 may send (and the AMF node 440 may receive) an initial UE message. The initial UE message may be an initial message from the UE 120 to the AMF node 440 as part of an RRC connection establishment or handover of the UE 120.

[0230] At 865, AMF node 440 may send (and network node 130 may receive) a UDM request message. The UDM request message may include a request for subscription data associated with UE 120. AMF node 440 may send the UDM request message to network node 130 based on receiving the initial UE message.

[0231] At 870, the network node 130 may send (and the AMF node 440 may receive) a UDM response message. The UDM response message may include subscription data associated with the UE 120. The network node 130 may send the UDM response message to the AMF node 440 based on receiving the UDM request message from the AMF node 440.

[0232] In addition, the network node 130 may send long-term mobility history data associated with the UE 120 to the AMF node 440, and the AMF node 440 may receive long-term mobility history data associated with the UE 120 from the network node 130. In some aspects, the model repository (e.g., the network node 130) is a UDR node 425a associated with the UDM / HSS node 425, and the long-term mobility history data transmission includes AMF-UDM signaling. In some aspects, the model repository (e.g., the network node 130) is a NWDAF node 460, and the long-term mobility history data transmission includes AMF-NWDAF signaling. In some aspects, the model repository (e.g., the network node 130) is an AI / ML server 470, and the long-term mobility history data transmission includes AMF / NWDAF-AI / ML server signaling. The network node 130 may send (and the AMF node 440 may receive) the long-term mobility history data associated with the UE 120 in a UDM response message and / or in another message.

[0233] At 875, AMF node 440 may send (and network node 110 may receive) a UE context setup request message. The UE context setup request message may include subscription data associated with UE 120. AMF node 440 may send the UE context setup request message to network node 130 based on receiving the subscription data from network node 110.

[0234] In addition, the AMF node 440 may send long-term mobility history data associated with the UE 120 to the AMF node 440, and the AMF node 440 may receive the long-term mobility history data associated with the UE 120 from the network node 130. This may be performed as part of a model transmission to the RAN (specifically, AMF to RAN). In some aspects, the AMF node 440 sends the long-term mobility history data associated with the UE 120 to the network node 110 in a UE context establishment request (e.g., an initial UE context establishment request), and the network node 110 receives the long-term mobility history data associated with the UE 120 from the AMF node 440 in the UE context establishment request. In some aspects, the AMF node 440 sends the long-term mobility history data associated with the UE 120 to the network node 110 in a UE context modification request message, and the network node 110 receives the long-term mobility history data associated with the UE 120 from the AMF node 440 in a UE context modification request message. In some aspects, the AMF node 440 sends the long-term mobility history data associated with the UE 120 to the network node 110 in a path switch request confirm message, and the network node 110 receives the long-term mobility history data associated with the UE 120 from the AMF node 440 in a path switch request confirm message.

[0235] Additionally and / or alternatively, the network node 110 may send the long-term mobility history data associated with the UE 120 to another network node 110 in the RAN as part of the handover of the UE 120. Here, the source network node 110 transmits the long-term mobility history data associated with the UE 120 to the target network node 110. In an Xn handover, the long-term mobility history data associated with the UE 120 may be transmitted in a handover request. In an NG handover, the long-term mobility history data associated with the UE 120 may be transmitted in a source-to-target transparent container.

[0236] The long-term mobility history data associated with the UE 120 may be delivered to the network node 110 during RRC connection establishment, during handover to the network node 110, and / or during another process. The network node 110 may train a UE mobility prediction model using the long-term mobility history data associated with the UE 120, and use the UE mobility prediction model to generate or determine a UE mobility prediction for the UE 120. The UE mobility prediction model may include a UE-specific mobility prediction model associated with the UE 120 or a common UE mobility prediction model that may be used for multiple UEs 120.

[0237] A "public" UE mobility prediction model may refer to a UE mobility prediction model trained by historical data of a large number of UEs (e.g., mobility history data associated with hundreds or thousands of UEs 120). A public UE mobility prediction model may be used for UEs 120 in a specific cell or for predicting a typical mobility next hop in a specific area. Long-term mobility history data may be used as an optional input parameter of a public UE mobility prediction model.

[0238] As mentioned above, Figure 8 are provided as examples. Other examples can be found in the Figure 8 The examples described are different.

[0239] Fig. 9 9 is a diagram of an example 900 of signaling for UE mobility prediction according to the present disclosure. Fig. 9 As shown, example 900 may include communications between a UE 120 and a plurality of network nodes, including a network node 110, an AMF node 440, and a network node 150. The UE 120 and the network node 110 may be included in a decomposed RAN of a wireless network 100. The decomposed RAN may include a decomposed base station architecture 300 of the wireless network 100. The AMF node 440 may be included in a core network 140 associated with the decomposed RAN. The network node 150 may also be included in the decomposed RAN and may include a SMO node 305, a non-RTRIC 320, a near-RT RIC 325, and / or another type of RIC.

[0240] The signaling described in example 900 may be implemented to support a UE mobility prediction model training process 905. In addition, the signaling described in example 900 may be implemented to support a UE mobility prediction model inference process 910. The UE mobility prediction model 505 may be trained as part of the UE mobility prediction model training process 905 based at least in part on mobility history data associated with the UE 120, and in some cases, based on mobility history data associated with one or more other UEs. The trained UE mobility prediction model 505 may be provided to the network node 110 as part of the UE mobility prediction model inference process 910, so that the network node 110 may use the trained UE mobility prediction model 505 to generate or determine a UE mobility prediction for the UE 120. In this way, in conjunction with a decomposed RAN deployment, the UE mobility prediction model 505 may be updated more quickly and maintained more easily.

[0241] In the UE mobility prediction model training process 905, at 915, the network node 110 may send (and the AMF node 440 may receive) a UE context release request. The network node 110 may send (and the AMF node 440 may receive) the UE context release request on the backhaul link between the RAN and the core network 140. The network node 110 may send the UE context release request as part of an RRC release operation associated with the UE 120. In some aspects, the RRC release operation may be performed to transition the UE 120 to an RRC idle mode, an RRC inactive mode, and / or another mode in which the RRC context of the UE 120 is released. In some aspects, the RRC release operation may be performed in conjunction with a path switch or handover of the UE 120. The UE context release request may be a message including a request to release the RRC context of the UE 120.

[0242] At 920, AMF node 440 may send (and network node 110 may receive) a UE context release command. AMF node 440 may send a UE context release command in response to the UE context release request. The UE context release command may be a message indicating that network node 110 will release the RRC context of UE 120.

[0243] At 925, network node 110 may send (and UE 120 may receive) an RRC release message. The RRC release message may indicate to UE 120 that UE 120 is allowed to transition to RRC idle mode or RRC inactive mode, or to switch a communication path of UE 120 as part of a handover.

[0244] At 930, the network node 110 sends a UE context release complete message to the AMF node 440, and the AMF node 440 may receive the UE context release complete message from the network node 110. The UE context release complete message may include an indication that the RRC release of the UE 120 has been successfully completed.

[0245] At 935, network node 110 may send (and network node 150 may receive) an event report message. The event report message may include an indication of an RRC release.

[0246] like Fig. 9 As further shown, the network node 110 may send the mobility history data to the network node 150, and the network node 150 may receive the mobility history data from the network node 110. The mobility history data may include recent mobility history data associated with the UE 120. The UE 120 may generate the mobility history data and provide it to the network node 110, the network node 110 may generate the mobility history data for the UE 120, or a combination thereof.

[0247] The network node 150 may store the mobility history data, and may generate the long-term mobility history data of the UE 120 by accumulating the mobility history data of the UE 120. Here, the UE history information upload may occur within the decomposed RAN. For example, in a specific implementation where the network node 150 is the SMO node 305 or the non-RT RIC 320, the network node 110 may send the mobility history data to the network node 150 over the O1 interface, and the network node 150 may receive the mobility history data from the network node 110 over the O1 interface. As another example, in a specific implementation where the network node 150 is the near-RT RIC 325, the network node 110 may send the mobility history data to the network node 150 over the E2 interface, and the network node 150 may receive the mobility history data from the network node 110 over the E2 interface. In some aspects, the mobility history data may be transmitted between the near-RT RIC 325 and the SMO node 305 (or the non-RT RIC 320) over the A1 interface.

[0248] At 940, the network node 150 may train a UE mobility prediction model (e.g., UE mobility prediction model 505). The network node 150 may train the UE mobility prediction model based at least in part on the mobility history data received from the network node 110. Training the UE mobility prediction model may include updating the decision-making aspects of the UE mobility prediction model based on the mobility history data, so that the UE mobility prediction model can be used for more accurate and up-to-date UE mobility predictions. In some aspects, the UE mobility prediction model may include a UE-specific mobility prediction model associated with the UE 120. Here, the network node 150 may train the UE-specific mobility prediction model based at least in part on the long-term mobility history data associated with the UE 120. As an example, the model is trained by long-term (e.g., several months) history data of the target UE. The long-term mobility history of the target UE is implicitly included in the trained model.

[0249] As part of the UE mobility prediction model inference process 910, the UE 120 may send (and the network node 110 may receive) an RRC setup request message at 945. In some aspects, the UE 120 may send the RRC setup request message to the network node 110 to initiate an RRC connection establishment process with the network node 110. In some aspects, the UE 120 may send the RRC setup request message to the network node 110 as part of a handover or path switch to the network node 110.

[0250] At 950, network node 110 may send (and UE 120 may receive) an RRC setup message. Network node 110 may send the RRC setup message as part of an RRC connection establishment or handover for UE 120 based on receiving the RRC setup request.

[0251] At 955, UE 120 may send (and network node 110 may receive) an RRC setup complete message. UE 120 may send the RRC setup complete message as part of an RRC connection establishment or handover for UE 120 based on receiving the RRC setup message.

[0252] At 960, the network node 110 may send (and the AMF node 440 may receive) an initial UE message. The initial UE message may be an initial message from the UE 120 to the AMF node 440 as part of an RRC connection establishment or handover of the UE 120.

[0253] At 965, the AMF node 440 may send (and the network node 110 may receive) a UE context setup request message. The UE context setup request message may include subscription data associated with the UE 120. The AMF node 440 may send the UE context setup request message to the network node 110 based on receiving the initial UE message from the network node 110.

[0254] At 970, the network node 110 may send (and the network node 150 may receive) an event report message. The event report message may include an indication of an initial UE context establishment associated with the UE 120. The network node 110 may send the event report message to the network node 150 based on receiving the initial UE context establishment request message from the AMF node 440.

[0255] At 975, the network node 150 may send (and the network node 110 may receive) a UE context setup message. The network node 150 may send the UE context setup message to the network node 110 based on receiving the event report message from the network node 110.

[0256] In addition, the network node 150 may send the UE mobility prediction model to the network node 110, and the network node 110 may receive the UE mobility prediction model from the network node 150. This may be performed as part of the model transmission within the decomposed RAN. For example, in a specific implementation where the network node 150 is the SMO node 305 or the non-RT RIC 320, the network node 150 may send the UE mobility prediction model to the network node 110 over the O1 interface, and the network node 110 may receive the UE mobility prediction model from the network node 150 over the O1 interface. As another example, in a specific implementation where the network node 150 is the near-RT RIC 325, the network node 150 may send the UE mobility prediction model to the network node 110 over the E2 interface, and the network node 110 may receive the UE mobility prediction model from the network node 150 over the E2 interface. In some aspects, the UE mobility prediction model may be transmitted between the near-RT RIC 325 and the SMO node 305 (or the non-RT RIC 320) over the A1 interface.

[0257] Additionally and / or alternatively, the network node 110 may send the UE mobility prediction model to another network node 110 in the RAN as part of the handover of the UE 120. Here, the source network node 110 transmits the UE mobility prediction model to the target network node 110. In an Xn handover, the UE mobility prediction model may be transmitted in a handover request. In an NG handover, the UE mobility prediction model may be transmitted in a source-to-target transparent container.

[0258] The UE mobility prediction model may be delivered to the network node 110 during RRC connection establishment, during handover to the network node 110, and / or during another process. The network node 110 may use the UE mobility prediction model to generate or determine a UE mobility prediction for the UE 120. For example, the network node 110 may use the above in conjunction with Figure 5 One or more techniques are described to generate or determine a UE mobility prediction for UE 120 based on a UE mobility prediction model.

[0259] As mentioned above, Fig. 9 are provided as examples. Other examples can be found in the Fig. 9 The examples described are different.

[0260] Fig.10 is a diagram of an example 1000 of signaling for UE mobility prediction according to the present disclosure. Example 1000 is similar to Fig. 9Example 900 of the present invention is different in that, instead of training the UE mobility prediction model, the network node 150 updates the long-term mobility history data associated with the UE 120. Instead, the network node 150 provides the updated long-term mobility history to the network node 110, which can train the UE mobility prediction model based on the updated long-term mobility history.

[0261] like Fig.10 As shown, example 1000 may include communications between a UE 120 and a plurality of network nodes, including a network node 110, an AMF node 440, and a network node 150. The UE 120 and the network node 110 may be included in a decomposed RAN of a wireless network 100. The decomposed RAN may include a decomposed base station architecture 300 of the wireless network 100. The AMF node 440 may be included in a core network 140 associated with the decomposed RAN. The network node 150 may also be included in the decomposed RAN and may include a SMO node 305, a non-RT RIC 320, a near-RT RIC 325, and / or another type of RIC.

[0262] The signaling described in example 1000 may be implemented to support a long-term mobility history update procedure 1005. In addition, the signaling described in example 1000 may be implemented to support a UE mobility prediction model inference procedure 1010. The long-term mobility history may be provided to the network node 110 as part of the UE mobility prediction model inference procedure 1010. The UE mobility prediction model 505 may be trained based at least in part on long-term mobility history data associated with the UE 120 and used as part of the UE mobility prediction model inference procedure 1010. In some cases, mobility history data associated with one or more other UEs may also be used to train the UE mobility prediction model 505. In this way, in conjunction with a decomposed RAN deployment, the UE mobility prediction model 505 may be updated more quickly and maintained more easily.

[0263] In the long-term mobility history update training procedure 1005, at 1015, the network node 110 may send (and the AMF node 440 may receive) a UE context release request. The network node 110 may send (and the AMF node 440 may receive) the UE context release request on a backhaul link between the RAN and the core network 140. The network node 110 may send the UE context release request as part of an RRC release operation associated with the UE 120. In some aspects, the RRC release operation may be performed to transition the UE 120 to an RRC idle mode, an RRC inactive mode, and / or another mode in which the RRC context of the UE 120 is released. In some aspects, the RRC release operation may be performed in conjunction with a path switch or handover of the UE 120. The UE context release request may be a message that includes a request to release the RRC context of the UE 120.

[0264] At 1020, the AMF node 440 may send (and the network node 110 may receive) a UE context release command. The AMF node 440 may send the UE context release command in response to the UE context release request. The UE context release command may be a message indicating that the network node 110 will release the RRC context of the UE 120.

[0265] At 1025, network node 110 may send (and UE 120 may receive) an RRC release message. The RRC release message may indicate to UE 120 that UE 120 is allowed to transition to RRC idle mode or RRC inactive mode, or to switch a communication path of UE 120 as part of a handover.

[0266] At 1030, the network node 110 sends a UE context release complete message to the AMF node 440, and the AMF node 440 may receive the UE context release complete message from the network node 110. The UE context release complete message may include an indication that the RRC release of the UE 120 has been successfully completed.

[0267] At 1035, network node 110 may send (and network node 150 may receive) an event report message. The event report message may include an indication of an RRC release.

[0268] like Fig.10 As further shown, the network node 110 may send the mobility history data to the network node 150, and the network node 150 may receive the mobility history data from the network node 110. The mobility history data may include recent mobility history data associated with the UE 120. The UE 120 may generate the mobility history data and provide it to the network node 110, the network node 110 may generate the mobility history data for the UE 120, or a combination thereof.

[0269] The network node 150 may store the mobility history data, and may generate the long-term mobility history data of the UE 120 by accumulating the mobility history data of the UE 120. Here, the UE history information upload may occur within the decomposed RAN. For example, in a specific implementation where the network node 150 is the SMO node 305 or the non-RT RIC 320, the network node 110 may send the mobility history data to the network node 150 over the O1 interface, and the network node 150 may receive the mobility history data from the network node 110 over the O1 interface. As another example, in a specific implementation where the network node 150 is the near-RT RIC 325, the network node 110 may send the mobility history data to the network node 150 over the E2 interface, and the network node 150 may receive the mobility history data from the network node 110 over the E2 interface. In some aspects, the mobility history data may be transmitted between the near-RT RIC 325 and the SMO node 305 (or the non-RT RIC 320) over the A1 interface.

[0270] At 1040, the network node 150 may update and store the long-term mobility history data associated with the UE 120. The network node 150 may update and store the long-term mobility history data associated with the UE 120 by updating the typical routes, destinations, movement habits, and / or any of other movement events that are stable and repeatable over a long duration of time of the UE 120 based at least in part on the mobility history data received from the network node 110. The long-term mobility history data associated with the UE 120 may include statistics of the mobility history of the UE 120 and / or other mobility history data.

[0271] As part of the UE mobility prediction model inference process 1010, the UE 120 may send (and the network node 110 may receive) an RRC setup request message at 1045. In some aspects, the UE 120 may send the RRC setup request message to the network node 110 to initiate an RRC connection establishment process with the network node 110. In some aspects, the UE 120 may send the RRC setup request message to the network node 110 as part of a handover or path switch to the network node 110.

[0272] At 1050, network node 110 may send (and UE 120 may receive) an RRC setup message. Network node 110 may send the RRC setup message as part of an RRC connection establishment or handover for UE 120 based on receiving the RRC setup request.

[0273] At 1055, UE 120 may send (and network node 110 may receive) an RRC setup complete message. UE 120 may send the RRC setup complete message as part of an RRC connection establishment or handover for UE 120 based on receiving the RRC setup message.

[0274] At 1060, the network node 110 may send (and the AMF node 440 may receive) an initial UE message. The initial UE message may be an initial message from the UE 120 to the AMF node 440 as part of an RRC connection establishment or handover of the UE 120.

[0275] At 1065, the AMF node 440 may send (and the network node 110 may receive) a UE context setup request message. The UE context setup request message may include subscription data associated with the UE 120. The AMF node 440 may send the UE context setup request message to the network node 110 based on receiving the initial UE message from the network node 110.

[0276] At 1070, the network node 110 may send (and the network node 150 may receive) an event report message. The event report message may include an indication of an initial UE context establishment associated with the UE 120. The network node 110 may send the event report message to the network node 150 based on receiving the initial UE context establishment request message from the AMF node 440.

[0277] At 1075, the network node 150 may send (and the network node 110 may receive) a UE context setup message. The network node 150 may send the UE context setup message to the network node 110 based on receiving the event report message from the network node 110.

[0278] In addition, the network node 150 may send the long-term mobility history data associated with the UE 120 to the network node 110, and the network node 110 may receive the long-term mobility history data associated with the UE 120 from the network node 150. This may be performed as part of the mobility history transmission within the decomposed RAN. For example, in a specific implementation where the network node 150 is a SMO node 305 or a non-RT RIC 320, the network node 150 may send the long-term mobility history data associated with the UE 120 to the network node 110 over the O1 interface, and the network node 110 may receive the long-term mobility history data associated with the UE 120 from the network node 150 over the O1 interface. As another example, in an implementation where the network node 150 is a near-RT RIC 325, the network node 150 may send the long-term mobility history data associated with the UE 120 to the network node 110 over the E2 interface, and the network node 110 may receive the long-term mobility history data associated with the UE 120 from the network node 150 over the E2 interface. In some aspects, the long-term mobility history data associated with the UE 120 may be transmitted between the near-RT RIC 325 and the SMO node 305 (or non-RT RIC 320) over the A1 interface.

[0279] Additionally and / or alternatively, the network node 110 may send the UE mobility prediction model to another network node 110 in the RAN as part of the handover of the UE 120. Here, the source network node 110 transmits the UE mobility prediction model to the target network node 110. In an Xn handover, the UE mobility prediction model may be transmitted in a handover request. In an NG handover, the UE mobility prediction model may be transmitted in a source-to-target transparent container.

[0280] The long-term mobility history data associated with the UE 120 may be delivered to the network node 110 during RRC connection establishment, during handover to the network node 110, and / or during another process. The network node 110 may train a UE mobility prediction model using the long-term mobility history data associated with the UE 120, and use the UE mobility prediction model to generate or determine a UE mobility prediction for the UE 120. The UE mobility prediction model may include a UE-specific mobility prediction model associated with the UE 120 or a common UE mobility prediction model that may be used for multiple UEs 120.

[0281] The public UE mobility prediction model may refer to a UE mobility prediction model trained by historical data of a large number of UEs (e.g., mobility history data associated with hundreds or thousands of UEs 120). The public UE mobility prediction model may be used for UEs 120 in a specific cell or for predicting a typical mobility next hop in a specific area. Long-term mobility history data may be used as an optional input parameter of the public UE mobility prediction model.

[0282] As mentioned above, Fig.10 are provided as examples. Other examples can be found in the Fig.10 The examples described are different.

[0283] Fig.11 1 is a diagram illustrating an example process 1100 performed, for example, by a first network node according to the present disclosure. Example process 1100 is an example of a first network node (eg, network node 110) performing operations associated with signaling for UE mobility prediction. The first network node may be included in a RAN.

[0284] like Fig.11 As shown, in some aspects, process 1100 may include sending mobility history data of the UE to a second network node in a core network associated with the RAN (block 1110). Fig.21 The communication manager 160 and / or the sending component 2104 depicted in FIG. 2 may send the mobility history data of the UE to a second network node in a core network associated with the RAN, as described above.

[0285] like Fig.11 As further shown, in some aspects, process 1100 may include receiving a UE mobility prediction model based at least in part on mobility history data from a second network node (block 1120). Fig.21 The communication manager 160 and / or receiving component 2102 depicted in FIG. 2 may receive a UE mobility prediction model based at least in part on mobility history data from a second network node, as described above.

[0286] Process 1100 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in conjunction with one or more other processes described elsewhere herein.

[0287] In the first aspect, the second network node is an AMF node.

[0288] In a second aspect, alone or in combination with the first aspect, sending the mobility history data to the second network node includes sending the mobility history data to the second network node in a UE context release complete message.

[0289] In a third aspect, alone or in combination with one or more of the first and second aspects, sending the mobility history data to the second network node includes sending the mobility history data to the second network node in a path switch request message.

[0290] In a fourth aspect, alone or in combination with one or more of the first to third aspects, receiving the UE mobility prediction model from the second network node includes receiving the UE mobility prediction model from the second network node during an RRC connection establishment procedure of the UE.

[0291] In a fifth aspect, alone or in combination with one or more of the first to fourth aspects, receiving the UE mobility prediction model from the second network node includes receiving the UE mobility prediction model from the second network node during a handover procedure of the UE.

[0292] In a sixth aspect, alone or in combination with one or more of the first to fifth aspects, receiving the UE mobility prediction model from the second network node includes receiving the UE mobility prediction model from the second network node in an initial UE context setup request message.

[0293] In a seventh aspect, alone or in combination with one or more of the first to sixth aspects, receiving the UE mobility prediction model from the second network node includes receiving the UE mobility prediction model from the second network node in a UE context modification request message.

[0294] In an eighth aspect, alone or in combination with one or more of the first to seventh aspects, receiving the UE mobility prediction model from the second network node includes receiving the UE mobility prediction model from the second network node in a path switch request confirm message.

[0295] In a ninth aspect, alone or in combination with one or more of the first to eighth aspects, the UE mobility prediction model is trained based at least in part on recent mobility history data associated with the UE and long-term mobility history data collected for the UE.

[0296] In a tenth aspect, alone or in combination with one or more of the first to ninth aspects, the UE mobility prediction model comprises a UE-specific UE mobility prediction model of the UE.

[0297] although Fig.11 An example block diagram of process 1100 is shown, but in some aspects, process 1100 may include Fig.11 The blocks depicted in the process 1100 may include additional blocks, fewer blocks, different blocks, or blocks arranged in a different manner than those depicted in the process 1100. Additionally or alternatively, two or more blocks in the blocks of the process 1100 may be executed in parallel.

[0298] Fig.121 is a diagram illustrating an example process 1200 performed, for example, by a first network node according to the present disclosure. Example process 1200 is an example of a first network node (eg, network node 110) performing operations associated with signaling for UE mobility prediction. The first network node may be included in a RAN.

[0299] like Fig.12 As shown, in some aspects, process 1200 may include sending mobility history data of the UE to a second network node in a core network associated with the RAN (block 1210). Fig.21 The communication manager 160 and / or the sending component 2104 depicted in FIG. 2 may send the mobility history data of the UE to a second network node in a core network associated with the RAN, as described above.

[0300] like Fig.12 As further shown, in some aspects, process 1200 may include receiving updated long-term mobility history data based at least in part on the mobility history data from a second network node (block 1220). Fig.21 The communications manager 160 and / or receiving component 2102 depicted in FIG. 2 may receive updated long-term mobility history data based at least in part on the mobility history data from the second network node, as described above.

[0301] Process 1200 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in conjunction with one or more other processes described elsewhere herein.

[0302] In the first aspect, the second network node is an AMF node.

[0303] In a second aspect, alone or in combination with the first aspect, sending the mobility history data to the second network node includes sending the mobility history data to the second network node in a UE context release complete message.

[0304] In a third aspect, alone or in combination with one or more of the first and second aspects, sending the mobility history data to the second network node includes sending the mobility history data to the second network node in a path switch request message.

[0305] In a fourth aspect, alone or in combination with one or more of the first to third aspects, receiving updated long-term mobility history data from the second network node comprises receiving updated long-term mobility history data from the second network node during an RRC connection establishment procedure of the UE.

[0306] In a fifth aspect, alone or in combination with one or more of the first to fourth aspects, receiving updated long-term mobility history data from the second network node includes receiving updated long-term mobility history data from the second network node during a handover procedure of the UE.

[0307] In a sixth aspect, alone or in combination with one or more of the first to fifth aspects, receiving updated long-term mobility history data from the second network node includes receiving updated long-term mobility history data from the second network node in an initial UE context setup request message.

[0308] In a seventh aspect, alone or in combination with one or more of the first to sixth aspects, receiving updated long-term mobility history data from the second network node includes receiving updated long-term mobility history data from the second network node in a UE context modification request message.

[0309] In an eighth aspect, alone or in combination with one or more of the first to seventh aspects, receiving updated long-term mobility history data from the second network node includes receiving updated long-term mobility history data from the second network node in a path switch request confirm message.

[0310] although Fig.12 An example block diagram of process 1200 is shown, but in some aspects, process 1200 may include Fig.12 The blocks depicted in the process 1200 may include additional blocks, fewer blocks, different blocks, or blocks arranged in a different manner than those depicted in the process 1200. Additionally or alternatively, two or more blocks in the blocks of the process 1200 may be executed in parallel.

[0311] Fig.13 1 is a diagram illustrating an example process 1300 performed, for example, by a first network node according to the present disclosure. Example process 1300 is an example of a first network node (eg, network node 110) performing operations associated with signaling for UE mobility prediction. The first network node may be included in a decomposed RAN.

[0312] like Fig.13 As shown, in some aspects, process 1300 may include sending mobility history data of the UE to a second network node in the decomposed RAN (block 1310). Fig.21 The communication manager 160 and / or the sending component 2104 depicted in FIG. 2 may send the mobility history data of the UE to a second network node in the decomposed RAN, as described above.

[0313] like Fig.13As further shown, in some aspects, process 1300 may include receiving a UE mobility prediction model based at least in part on mobility history data from a second network node (block 1320). Fig.21 The communication manager 160 and / or receiving component 2102 depicted in FIG. 2 may receive a UE mobility prediction model based at least in part on mobility history data from a second network node, as described above.

[0314] Process 1300 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in conjunction with one or more other processes described elsewhere herein.

[0315] In a first aspect, the first network node is a base station.

[0316] In a second aspect, alone or in combination with the first aspect, the first network node is a central unit.

[0317] In a third aspect, alone or in combination with one or more of the first and second aspects, the first network node is a distributed unit.

[0318] In a fourth aspect, alone or in combination with one or more of the first to third aspects, the first network node is a radio unit.

[0319] In a fifth aspect, alone or in combination with one or more of the first to sixth aspects, the second network node is a SMO node.

[0320] In a sixth aspect, alone or in combination with one or more of the first to fifth aspects, the second network node is a RIC node.

[0321] In a seventh aspect, alone or in combination with one or more of the first to sixth aspects, sending the mobility history data to the second network node includes sending the mobility history data to the second network node in an event report.

[0322] In an eighth aspect, alone or in combination with one or more of the first to seventh aspects, receiving the UE mobility prediction model from the second network node includes receiving the UE mobility prediction model from the second network node during an RRC connection establishment procedure of the UE.

[0323] In a ninth aspect, alone or in combination with one or more of the first to eighth aspects, receiving the UE mobility prediction model from the second network node includes receiving the UE mobility prediction model from the second network node during a handover procedure of the UE.

[0324] In a tenth aspect, alone or in combination with one or more of the first to ninth aspects, receiving the UE mobility prediction model from the second network node includes receiving the UE mobility prediction model from the second network node in a UE context setup message.

[0325] In an eleventh aspect, alone or in combination with one or more of the first to tenth aspects, the UE mobility prediction model is trained based at least in part on recent mobility history data associated with the UE and long-term mobility history data collected for the UE.

[0326] In a twelfth aspect, alone or in combination with one or more of the first to eleventh aspects, the UE mobility prediction model comprises a UE-specific UE mobility prediction model of the UE.

[0327] although Fig.13 Example blocks of process 1300 are shown, but in some aspects, process 1300 may include Fig.13 The blocks depicted in the process 1300 may include additional blocks, fewer blocks, different blocks, or blocks arranged in a different manner than those depicted in the process 1300. Additionally or alternatively, two or more blocks in the blocks of the process 1300 may be executed in parallel.

[0328] Fig.14 1 is a diagram illustrating an example process 1400 performed, for example, by a first network node according to the present disclosure. Example process 1400 is an example of a first network node (eg, network node 110) performing operations associated with signaling for UE mobility prediction. The first network node may be included in a decomposed RAN.

[0329] like Fig.14 As shown, in some aspects, process 1400 may include sending mobility history data of the UE to a second network node in the decomposed RAN (block 1410). Fig.21 The communication manager 160 and / or the sending component 2104 depicted in FIG. 2 may send the mobility history data of the UE to a second network node in the decomposed RAN, as described above.

[0330] like Fig.14 As further shown, in some aspects, process 1400 may include receiving updated long-term mobility history data based at least in part on the mobility history data from a second network node (block 1420). Fig.21 The communications manager 160 and / or receiving component 2102 depicted in FIG. 2 may receive updated long-term mobility history data based at least in part on the mobility history data from the second network node, as described above.

[0331] Process 1400 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in conjunction with one or more other processes described elsewhere herein.

[0332] In a first aspect, the first network node is a base station.

[0333] In a second aspect, alone or in combination with the first aspect, the first network node is a central unit.

[0334] In a third aspect, alone or in combination with one or more of the first and second aspects, the first network node is a distributed unit.

[0335] In a fourth aspect, alone or in combination with one or more of the first to third aspects, the first network node is a radio unit.

[0336] In a fifth aspect, alone or in combination with one or more of the first to sixth aspects, the second network node is a SMO node.

[0337] In a sixth aspect, alone or in combination with one or more of the first to fifth aspects, the second network node is a RIC node.

[0338] In a seventh aspect, alone or in combination with one or more of the first to sixth aspects, sending the mobility history data to the second network node includes sending the mobility history data to the second network node in an event report.

[0339] In an eighth aspect, alone or in combination with one or more of the first to seventh aspects, receiving updated long-term mobility history data from the second network node includes receiving updated long-term mobility history data from the second network node during an RRC connection establishment procedure of the UE.

[0340] In a ninth aspect, alone or in combination with one or more of the first to eighth aspects, receiving updated long-term mobility history data from the second network node comprises receiving updated long-term mobility history data from the second network node during a handover procedure of the UE.

[0341] In a tenth aspect, alone or in combination with one or more of the first to ninth aspects, receiving updated long-term mobility history data from the second network node comprises receiving updated long-term mobility history data from the second network node in a UE context setup message.

[0342] although Fig.14 Example blocks of process 1400 are shown, but in some aspects, process 1400 may include Fig.14The blocks depicted in the process 1400 may include additional blocks, fewer blocks, different blocks, or blocks arranged in a different manner than those depicted in the process 1400. Additionally or alternatively, two or more blocks in the blocks of the process 1400 may be executed in parallel.

[0343] Fig.15 is a diagram illustrating an example process 1500 performed, for example, by a first network node according to the present disclosure. Example process 1500 is an example of a first network node (eg, network node 130) performing operations associated with signaling for UE mobility prediction. The network node may be included in a core network.

[0344] like Fig.15 As shown, in some aspects, process 1500 may include receiving mobility history data associated with a UE (block 1510). For example, a first network node (e.g., using Fig.24 The communications manager 170 and / or receiving component 2402 depicted in FIG. 24 may receive mobility history data associated with the UE, as described above.

[0345] like Fig.15 As further shown, in some aspects, process 1500 may include sending mobility history data to a second network node in the core network for use in training a UE mobility prediction model (block 1520). Fig.24 The communication manager 170 and / or the sending component 2404 depicted in the core network may send the mobility history data to a second network node in the core network for use in training the UE mobility prediction model, as described above.

[0346] Process 1500 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in conjunction with one or more other processes described elsewhere herein.

[0347] In a first aspect, receiving the mobility history data comprises receiving the mobility history data from a third network node included in a RAN associated with the core network.

[0348] In a second aspect, alone or in combination with the first aspect, receiving the mobility history data from the third network node includes receiving the mobility history data from the third network node in a UE context release complete message.

[0349] In a third aspect, alone or in combination with one or more of the first and second aspects, receiving the mobility history data from the third network node includes receiving the mobility history data from the third network node in a path switch request message.

[0350] In a fourth aspect, alone or in combination with one or more of the first to third aspects, the first network node is an AMF node, and the third network node is a base station.

[0351] In a fifth aspect, alone or in combination with one or more of the first to fourth aspects, the second network node is at least one of a UDM node, a UDR node, or a HSS node.

[0352] In a sixth aspect, alone or in combination with one or more of the first to fifth aspects, the second network node is a network data analysis function (NWDAF) node.

[0353] In a seventh aspect, either alone or in combination with one or more of the first to sixth aspects, the second network node is a machine learning model server node.

[0354] In an eighth aspect, alone or in combination with one or more of the first to seventh aspects, sending the mobility history data to the second network node includes sending the mobility history data to the second network node for training a UE mobility prediction model.

[0355] In a ninth aspect, alone or in combination with one or more of the first to eighth aspects, process 1500 includes receiving a UE mobility prediction model from a second network node, and sending the UE mobility prediction model to a third network node included in a RAN associated with a core network.

[0356] In a tenth aspect, alone or in combination with one or more of the first to ninth aspects, sending the UE mobility prediction model to the third network node includes sending the UE mobility prediction model to the third network node during an RRC connection establishment procedure of the UE.

[0357] In an eleventh aspect, alone or in combination with one or more of the first to tenth aspects, sending the UE mobility prediction model to the third network node includes sending the UE mobility prediction model to the third network node during a handover procedure of the UE.

[0358] In a twelfth aspect, alone or in combination with one or more of the first to eleventh aspects, sending the UE mobility prediction model to the third network node includes sending the UE mobility prediction model to the third network node in an initial UE context setup request message.

[0359] In a thirteenth aspect, alone or in combination with one or more of the first to twelfth aspects, sending the UE mobility prediction model to the third network node includes sending the UE mobility prediction model to the third network node in a UE context modification request message.

[0360] In a fourteenth aspect, alone or in combination with one or more of the first to thirteenth aspects, sending the UE mobility prediction model to the third network node includes sending the UE mobility prediction model to the third network node in a path switch request confirm message.

[0361] In a fifteenth aspect, alone or in combination with one or more of the first to fourteenth aspects, the UE mobility prediction model is trained based at least in part on mobility history data and long-term mobility history data collected for the UE.

[0362] In a sixteenth aspect, alone or in combination with one or more of the first to fifteenth aspects, sending the mobility history data to the second network node includes sending the mobility history data to the second network node for storage at the second network node.

[0363] In a seventeenth aspect, either alone or in combination with one or more of aspects one to sixteen, process 1500 comprises receiving long-term mobility history data from a second network node, wherein the long-term mobility history data is at least partially based on the mobility history data, and sending the long-term mobility history data to a third network node included in a RAN associated with the core network.

[0364] In an eighteenth aspect, alone or in combination with one or more of the first to seventeenth aspects, sending the long-term mobility history data to the third network node includes sending the long-term mobility history data to the third network node during an RRC connection establishment procedure of the UE.

[0365] In a nineteenth aspect, alone or in combination with one or more of the first to eighteenth aspects, sending the long term mobility history data to the third network node includes sending the long term mobility history data to the third network node during a handover procedure of the UE.

[0366] In a twentieth aspect, alone or in combination with one or more of the first to nineteenth aspects, sending the long-term mobility history data to the third network node includes sending the long-term mobility history data to the third network node in an initial UE context setup request message.

[0367] In a twenty-first aspect, alone or in combination with one or more of the first to twentieth aspects, sending the long-term mobility history data to the third network node includes sending the long-term mobility history data to the third network node in a UE context modification request message.

[0368] In a twenty-second aspect, alone or in combination with one or more of the first to twenty-first aspects, sending the long-term mobility history data to the third network node includes sending the long-term mobility history data to the third network node in a path switch request confirm message.

[0369] although Fig.15 Example blocks of process 1500 are shown, but in some aspects, process 1500 may include Fig.15 The blocks depicted in the process 1500 may include additional blocks, fewer blocks, different blocks, or blocks arranged in a different manner than those depicted in the process 1500. Additionally or alternatively, two or more blocks in the blocks of the process 1500 may be executed in parallel.

[0370] Fig.16 1 is a diagram illustrating an example process 1600 performed, for example, by a first network node according to the present disclosure. Example process 1600 is an example of a first network node (eg, network node 130) performing operations associated with signaling for UE mobility prediction. The first network node may be included in a core network.

[0371] like Fig.16 As shown, in some aspects, process 1600 may include receiving a UE mobility prediction model (block 1610). For example, a first network node (e.g., using Fig.24 The communication manager 170 and / or receiving component 2402 depicted in FIG. 24 may receive a UE mobility prediction model, as described above.

[0372] like Fig.16 As further shown, in some aspects, process 1600 may include sending the UE mobility prediction model to a second network node in the core network for storage at the second network node (block 1620). Fig.24 The communication manager 170 and / or the sending component 2404 depicted in the core network may send the UE mobility prediction model to a second network node in the core network for storage at the second network node, as described above.

[0373] Process 1600 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in conjunction with one or more other processes described elsewhere herein.

[0374] In a first aspect, receiving the UE mobility prediction model includes receiving the UE mobility prediction model from a third network node included in a RAN associated with the core network.

[0375] In a second aspect, alone or in combination with the first aspect, receiving the UE mobility prediction model from the third network node includes receiving the UE mobility prediction model from the third network node in a UE context release complete message.

[0376] In a third aspect, alone or in combination with one or more of the first and second aspects, receiving the UE mobility prediction model from the third network node includes receiving the UE mobility prediction model from the third network node in a path switch request message.

[0377] In a fourth aspect, alone or in combination with one or more of the first to third aspects, the first network node is an AMF node, and the third network node is a base station.

[0378] In a fifth aspect, alone or in combination with one or more of the first to sixth aspects, the second network node is a UDM node.

[0379] In a sixth aspect, alone or in combination with one or more of the first to fifth aspects, the second network node is a UDR node.

[0380] In a seventh aspect, alone or in combination with one or more of the first to sixth aspects, the second network node is a HSS node.

[0381] In an eighth aspect, alone or in combination with one or more of the first to seventh aspects, the second network node is a NWDAF node.

[0382] In a ninth aspect, either alone or in combination with one or more of the first to eighth aspects, the second network node is a machine learning model server node.

[0383] In a tenth aspect, alone or in combination with one or more of the first to ninth aspects, process 1600 includes receiving a UE mobility prediction model from a second network node, and sending the UE mobility prediction model to a third network node included in a RAN associated with a core network.

[0384] In an eleventh aspect, alone or in combination with one or more of the first to tenth aspects, sending the UE mobility prediction model to the third network node includes sending the UE mobility prediction model to the third network node during an RRC connection establishment procedure of the UE.

[0385] In a twelfth aspect, alone or in combination with one or more of the first to eleventh aspects, sending the UE mobility prediction model to the third network node includes sending the UE mobility prediction model to the third network node during a handover procedure of the UE.

[0386] In a thirteenth aspect, alone or in combination with one or more of the first to twelfth aspects, sending the UE mobility prediction model to the third network node includes sending the UE mobility prediction model to the third network node in an initial UE context setup request message.

[0387] In a fourteenth aspect, alone or in combination with one or more of the first to thirteenth aspects, sending the UE mobility prediction model to the third network node includes sending the UE mobility prediction model to the third network node in a UE context modification request message.

[0388] In a fifteenth aspect, alone or in combination with one or more of the first to fourteenth aspects, sending the UE mobility prediction model to the third network node includes sending the UE mobility prediction model to the third network node in a path switch request confirm message.

[0389] In a sixteenth aspect, alone or in combination with one or more of the first to fifteenth aspects, the UE mobility prediction model is trained based at least in part on recent mobility history data associated with the UE and long-term mobility history data collected for the UE.

[0390] In a seventeenth aspect, alone or in combination with one or more of the first to sixteenth aspects, the UE mobility prediction model comprises a UE-specific UE mobility prediction model of the UE.

[0391] although Fig.16 An example block diagram of process 1600 is shown, but in some aspects, process 1600 may include Fig.16 The blocks depicted in the process 1600 may include additional blocks, fewer blocks, different blocks, or blocks arranged in a different manner than those depicted in the process 1600. Additionally or alternatively, two or more blocks in the blocks of the process 1600 may be executed in parallel.

[0392] Fig.17 17 is a diagram illustrating an example process 1700 performed, for example, by a first network node according to the present disclosure. Example process 1700 is an example of a first network node (eg, network node 130) performing operations associated with signaling for UE mobility prediction. The first network node may be included in a core network.

[0393] like Fig.17 As shown, in some aspects, process 1700 may include receiving mobility history data associated with the UE from a second network node in the core network (block 1710). Fig. 27 The communication manager 180 and / or receiving component 2702 depicted in FIG. 2 may receive mobility history data associated with the UE from a second network node in the core network, as described above.

[0394] like Fig.17 As further shown, in some aspects, process 1700 may include training a UE mobility prediction model based at least in part on mobility history data (block 1720). Fig. 27 The communication manager 180 and / or training component 2708 depicted in FIG. 2 may train a UE mobility prediction model based at least in part on mobility history data, as described above.

[0395] Process 1700 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in conjunction with one or more other processes described elsewhere herein.

[0396] In the first aspect, the second network node is an AMF node.

[0397] In a second aspect, alone or in combination with the first aspect, the first network node is a UDM node.

[0398] In a third aspect, alone or in combination with one or more of the first and second aspects, the first network node is a UDR node.

[0399] In a fourth aspect, alone or in combination with one or more of the first to third aspects, the first network node is a HSS node.

[0400] In a fifth aspect, alone or in combination with one or more of the first to sixth aspects, the first network node is a NWDAF node.

[0401] In a sixth aspect, either alone or in combination with one or more of the first to fifth aspects, the first network node is a machine learning model server node.

[0402] In a seventh aspect, alone or in combination with one or more of the first to sixth aspects, training the UE mobility prediction model includes training a UE-specific UE mobility prediction model of the UE.

[0403] In an eighth aspect, alone or in combination with one or more of the first to seventh aspects, the process 1700 includes sending a UE mobility prediction model to a second network node.

[0404] In a ninth aspect, alone or in combination with one or more of the first to eighth aspects, sending the UE mobility prediction model to the second network node includes sending the UE mobility prediction model to the second network node during an RRC connection establishment procedure of the UE.

[0405] In a tenth aspect, alone or in combination with one or more of the first to ninth aspects, sending the UE mobility prediction model to the second network node includes sending the UE mobility prediction model to the second network node during a handover procedure of the UE.

[0406] In an eleventh aspect, alone or in combination with one or more of the first to tenth aspects, sending the UE mobility prediction model to the second network node includes sending the UE mobility prediction model to the second network node in a UDM response message.

[0407] In a twelfth aspect, alone or in combination with one or more of the first to eleventh aspects, training the UE mobility prediction model includes training the UE mobility prediction model based at least in part on long-term mobility history data collected for the UE.

[0408] although Fig.17 An example block diagram of process 1700 is shown, but in some aspects, process 1700 may include Fig.17 The blocks depicted in the process 1700 may include additional blocks, fewer blocks, different blocks, or blocks arranged in a different manner than those depicted in the process 1700. Additionally or alternatively, two or more blocks in the blocks of the process 1700 may be executed in parallel.

[0409] Fig.18 18 is a diagram illustrating an example process 1800 performed, for example, by a first network node according to the present disclosure. Example process 1800 is an example of a first network node (eg, network node 130) performing operations associated with signaling for UE mobility prediction. The first network node may be included in a core network.

[0410] like Fig.18 As shown, in some aspects, process 1800 may include receiving mobility history data associated with the UE from a second network node in the core network (block 1810). Fig. 27 The communication manager 180 and / or receiving component 2702 depicted in FIG. 2 may receive mobility history data associated with the UE from a second network node in the core network, as described above.

[0411] like Fig.18 As further shown, in some aspects, process 1800 may include updating long-term mobility history data associated with the UE based at least in part on the mobility history data (block 1820). Fig. 27 The communication manager 180 and / or updating component 2710 depicted in FIG. 2 may update long-term mobility history data associated with the UE based at least in part on the mobility history data, as described above.

[0412] like Fig.18 As further shown, in some aspects, process 1800 may include storing updated long-term mobility history data at the first network node (block 1830). For example, the first network node (e.g., using Fig. 27 The communication manager 180 and / or storage component 2712 depicted in FIG. 2 may store updated long-term mobility history data at the first network node, as described above.

[0413] Process 1800 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in conjunction with one or more other processes described elsewhere herein.

[0414] In the first aspect, the second network node is an AMF node.

[0415] In a second aspect, alone or in combination with the first aspect, the first network node is a UDM node.

[0416] In a third aspect, alone or in combination with one or more of the first and second aspects, the first network node is a UDR node.

[0417] In a fourth aspect, alone or in combination with one or more of the first to third aspects, the first network node is a HSS node.

[0418] In a fifth aspect, alone or in combination with one or more of the first to sixth aspects, the first network node is a NWDAF node.

[0419] In a sixth aspect, either alone or in combination with one or more of the first to fifth aspects, the first network node is a machine learning model server node.

[0420] In a seventh aspect, alone or in combination with one or more of the first to sixth aspects, the process 1800 includes sending updated long-term mobility history data to the second network node.

[0421] In an eighth aspect, alone or in combination with one or more of the first to seventh aspects, sending the updated long-term mobility history data to the second network node includes sending the updated long-term mobility history data to the second network node during an RRC connection establishment procedure of the UE.

[0422] In a ninth aspect, alone or in combination with one or more of the first to eighth aspects, sending updated long-term mobility history data to the second network node includes sending updated long-term mobility history data to the second network node during a handover procedure of the UE.

[0423] In a tenth aspect, alone or in combination with one or more of the first to ninth aspects, sending the updated long-term mobility history data to the second network node includes sending the updated long-term mobility history data to the second network node in a UDM response message.

[0424] although Fig.18 An example block diagram of process 1800 is shown, but in some aspects, process 1800 may include Fig.18The blocks depicted in the process 1800 may include additional blocks, fewer blocks, different blocks, or blocks arranged in a different manner than those depicted in the process 1800. Additionally or alternatively, two or more blocks in the blocks of the process 1800 may be executed in parallel.

[0425] Fig.19 1 is a diagram illustrating an example process 1900 performed, for example, by a first network node according to the present disclosure. Example process 1900 is an example of a first network node (eg, network node 150) performing operations associated with signaling for UE mobility prediction. The first network node may be included in a decomposed RAN.

[0426] like Fig.19 As shown, in some aspects, process 1900 may include receiving mobility history data associated with a UE from a second network node in a decomposed RAN (block 1910). Fig.30 The communication manager 190 and / or receiving component 3002 depicted in FIG. 3 may receive mobility history data associated with the UE from a second network node in the decomposed RAN, as described above.

[0427] like Fig.19 As further shown, in some aspects, process 1900 may include training a UE mobility prediction model based at least in part on mobility history data (block 1920). Fig.30 The communication manager 190 and / or training component 3008 depicted in FIG. 10 may train a UE mobility prediction model based at least in part on mobility history data, as described above.

[0428] Process 1900 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in conjunction with one or more other processes described elsewhere herein.

[0429] In a first aspect, the second network node is a base station.

[0430] In a second aspect, alone or in combination with the first aspect, the second network node is a central unit.

[0431] In a third aspect, alone or in combination with one or more of the first and second aspects, the second network node is a distributed unit.

[0432] In a fourth aspect, alone or in combination with one or more of the first to third aspects, the second network node is a radio unit.

[0433] In a fifth aspect, alone or in combination with one or more of the first to fourth aspects, the first network node is a SMO node.

[0434] In a sixth aspect, alone or in combination with one or more of the first to fifth aspects, the first network node is a RIC node.

[0435] In a seventh aspect, alone or in combination with one or more of the first to sixth aspects, training the UE mobility prediction model includes training a UE-specific UE mobility prediction model of the UE.

[0436] In an eighth aspect, alone or in combination with one or more of the first to seventh aspects, the process 1900 includes sending a UE mobility prediction model to a second network node.

[0437] In a ninth aspect, alone or in combination with one or more of the first to eighth aspects, receiving the mobility history data from the second network node includes receiving the mobility history data from the second network node in an event report.

[0438] In a tenth aspect, alone or in combination with one or more of the first to ninth aspects, sending the UE mobility prediction model to the second network node includes sending the UE mobility prediction model to the second network node during an RRC connection establishment procedure of the UE.

[0439] In an eleventh aspect, alone or in combination with one or more of the first to tenth aspects, sending the UE mobility prediction model to the second network node includes sending the UE mobility prediction model to the second network node during a handover procedure of the UE.

[0440] In a twelfth aspect, alone or in combination with one or more of the first to eleventh aspects, sending the UE mobility prediction model to the second network node includes sending the UE mobility prediction model to the second network node in a UE context setup message.

[0441] In a thirteenth aspect, alone or in combination with one or more of the first to twelfth aspects, training the UE mobility prediction model includes training the UE mobility prediction model based at least in part on long-term mobility history data collected for the UE.

[0442] although Fig.19 An example block diagram of process 1900 is shown, but in some aspects, process 1900 may include Fig.19 The blocks depicted in the process 1900 may include additional blocks, fewer blocks, different blocks, or blocks arranged in a different manner than those depicted in the process 1900. Additionally or alternatively, two or more blocks in the blocks of the process 1900 may be executed in parallel.

[0443] Fig. 202 is a diagram illustrating an example process 2000 performed, for example, by a first network node according to the present disclosure. Example process 2000 is an example of a first network node (e.g., network node 150) performing operations associated with signaling for UE mobility prediction. First network node 150 may be included in a decomposed RAN.

[0444] like Fig. 20 As shown, in some aspects, process 2000 may include receiving mobility history data associated with a UE from a second network node in a decomposed RAN (block 2010). For example, a first network node (e.g., using Fig.30 The communication manager 190 and / or receiving component 3002 depicted in FIG. 3 may receive mobility history data associated with the UE from a second network node in the decomposed RAN, as described above.

[0445] like Fig. 20 As further shown, in some aspects, process 2000 may include updating long-term mobility history data associated with the UE based at least in part on the mobility history data (block 2020). For example, the first network node (e.g., using Fig.30 The communication manager 190 and / or updating component 3010 depicted in FIG. 1 may update long-term mobility history data associated with the UE based at least in part on the mobility history data, as described above.

[0446] like Fig. 20 As further shown, in some aspects, process 2000 may include storing updated long-term mobility history data at the first network node (block 2030). For example, the first network node (e.g., using Fig.30 The communication manager 190 and / or storage component 3012 depicted in FIG. 3 may store updated long-term mobility history data at the first network node, as described above.

[0447] Process 2000 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in conjunction with one or more other processes described elsewhere herein.

[0448] In a first aspect, the second network node is a base station.

[0449] In a second aspect, alone or in combination with the first aspect, the second network node is a central unit.

[0450] In a third aspect, alone or in combination with one or more of the first and second aspects, the second network node is a distributed unit.

[0451] In a fourth aspect, alone or in combination with one or more of the first to third aspects, the second network node is a radio unit.

[0452] In a fifth aspect, alone or in combination with one or more of the first to fourth aspects, the first network node is a SMO node.

[0453] In a sixth aspect, alone or in combination with one or more of the first to fifth aspects, the first network node is a RIC node.

[0454] In a seventh aspect, alone or in combination with one or more of the first to sixth aspects, the process 2000 includes sending updated long-term mobility history data to the second network node.

[0455] In an eighth aspect, alone or in combination with one or more of the first to seventh aspects, sending the updated long-term mobility history data to the second network node includes sending the updated long-term mobility history data to the second network node during an RRC connection establishment procedure of the UE.

[0456] In a ninth aspect, alone or in combination with one or more of the first to eighth aspects, sending updated long-term mobility history data to the second network node includes sending updated long-term mobility history data to the second network node during a handover procedure of the UE.

[0457] In a tenth aspect, alone or in combination with one or more of the first to ninth aspects, sending the updated long-term mobility history data to the second network node includes sending the updated long-term mobility history data to the second network node in a UE context setup message.

[0458] although Fig. 20 An example block diagram of process 2000 is shown, but in some aspects, process 2000 may include Fig. 20 The blocks depicted in the process 2000 may include additional blocks, fewer blocks, different blocks, or blocks arranged in a different manner than those depicted in the process 2000. Additionally or alternatively, two or more blocks in the blocks of the process 2000 may be executed in parallel.

[0459] Fig.21 2 is a diagram of an example apparatus 2100 for wireless communication. Apparatus 2100 may be a network node (e.g., network node 110), or a network node may include apparatus 2100. In some aspects, apparatus 2100 includes a receiving component 2102 and a sending component 2104 that may communicate with each other (e.g., via one or more buses and / or one or more other components). As shown, apparatus 2100 may communicate with another apparatus 2106 (such as a UE, a base station, or another wireless communication device) using receiving component 2102 and sending component 2104. As further shown, apparatus 2100 may include communication manager 160.

[0460] In some aspects, the apparatus 2100 may be configured to perform Figures 5 to 10 Additionally or alternatively, the apparatus 2100 may be configured to perform one or more processes described herein, such as Fig.11 Process 1100, Fig.12 The process 1200 Fig.13 The process of 1300 Fig.14 In some aspects, Fig.21 The device 2100 and / or one or more components shown may include a combination of Figure 2 Additionally or alternatively, Fig.21 One or more of the components shown may be combined with Figure 2 Additionally or alternatively, one or more components in the set of components may be implemented at least in part as software stored in a memory. For example, a component (or part of a component) may be implemented as instructions or codes that are stored in a non-transitory computer-readable medium and can be executed by a controller or processor to perform the function or operation of the component.

[0461] The receiving component 2102 may receive communications, such as reference signals, control information, data communications, or combinations thereof, from the device 2106. The receiving component 2102 may provide the received communications to one or more other components of the device 2100. In some aspects, the receiving component 2102 may perform signal processing (such as filtering, amplification, demodulation, analog-to-digital conversion, demultiplexing, deinterleaving, demapping, equalization, interference cancellation, or decoding) on ​​the received communications and may provide the processed signals to one or more other components of the device 2100. In some aspects, the receiving component 2102 may include combining Figure 2 One or more antennas, modems, demodulators, MIMO detectors, receive processors, controllers / processors, memories, or combinations thereof, of the described network nodes.

[0462] The transmitting component 2104 may transmit communications, such as reference signals, control information, data communications, or combinations thereof, to the device 2106. In some aspects, one or more other components of the device 2100 may generate communications and may provide the generated communications to the transmitting component 2104 for transmission to the device 2106. In some aspects, the transmitting component 2104 may perform signal processing (such as filtering, amplification, modulation, digital-to-analog conversion, multiplexing, interleaving, mapping, or encoding, etc.) on the generated communications and may transmit the processed signals to the device 2106. In some aspects, the transmitting component 2104 may include combining Figure 2One or more antennas, modems, modulators, transmit MIMO processors, transmit processors, controllers / processors, memories, or combinations thereof of the described network nodes. In some aspects, the transmit component 2104 can be co-located with the receive component 2102 in a transceiver.

[0463] The sending component 2104 can send the mobility history data of the UE to an apparatus 2106 in a core network associated with the RAN. The receiving component 2102 can receive from the apparatus 2106 a UE mobility prediction model based at least in part on the mobility history data.

[0464] The sending component 2104 can send the mobility history data of the UE to an apparatus 2106 in a core network associated with the RAN. The receiving component 2102 can receive, from the apparatus 2106, updated long-term mobility history data based at least in part on the mobility history data.

[0465] The sending component 2104 can send the mobility history data of the UE to the device 2106 in the disaggregated RAN. The receiving component 2102 can receive from the device 2106 a UE mobility prediction model based at least in part on the mobility history data.

[0466] The sending component 2104 can send the mobility history data of the UE to the apparatus 2106 in the disaggregated RAN. The receiving component 2102 can receive from the apparatus 2106 updated long-term mobility history data based at least in part on the mobility history data.

[0467] Fig.21 The number and arrangement of components shown are provided as examples. In practice, there may be Fig.21 Additional components, fewer components, different components, or components arranged differently than those shown. Fig.21 Two or more components shown may be implemented in a single component, or Fig.21 The single component shown may be implemented as multiple distributed components. Additionally or alternatively, Fig.21 The assembly of (one or more) components shown may perform the operations described by Fig.21 Another collection of components shown performs one or more functions.

[0468] Fig. 22 is a diagram illustrating an example 2200 of a hardware implementation of an apparatus 2205 employing a processing system 2210. The apparatus 2205 may be a network node.

[0469] The processing system 2210 may be implemented with a bus architecture, generally represented by bus 2215. Bus 2215 may include any number of interconnecting buses and bridges, depending on the specific application of the processing system 2210 and the overall design constraints. Bus 2215 links together various circuits including one or more processors and / or hardware components (represented by processor 2220, components shown, and computer readable media / memory 2225). Bus 2215 may also link various other circuits, such as timing sources, peripherals, voltage regulators, and / or power management circuits.

[0470] The processing system 2210 may be coupled to a transceiver 2230. The transceiver 2230 is coupled to one or more antennas 2235. The transceiver 2230 provides a means for communicating with various other devices via a transmission medium. The transceiver 2230 receives signals from one or more antennas 2235, extracts information from the received signals, and provides the extracted information to the processing system 2210 (specifically, the receiving component 2102). In addition, the transceiver 2230 receives information from the processing system 2210 (specifically, the transmitting component 2104) and generates a signal to be applied to the one or more antennas 2235 based at least in part on the received information.

[0471] The processing system 2210 includes a processor 2220 coupled to a computer-readable medium / memory 2225. The processor 2220 is responsible for general processing, including executing software stored on the computer-readable medium / memory 2225. The software, when executed by the processor 2220, causes the processing system 2210 to perform various functions described herein for any particular device. The computer-readable medium / memory 2225 can also be used to store data manipulated by the processor 2220 when executing the software. The processing system also includes at least one of the illustrated components. A component can be a software module running in the processor 2220, resident / stored in the computer-readable medium / memory 2225, one or more hardware modules coupled to the processor 2220, or some combination thereof.

[0472] As described elsewhere herein, the processing system 2210 may include the TX MIMO processor 230, the receive processor 238, and / or the controller / processor 240. In one configuration, the aforementioned components may be the TX MIMO processor 230, the receive processor 238, and / or the controller / processor 240 configured to perform the functions and / or operations stated herein.

[0473] In some aspects, the apparatus 2205 for wireless communication includes a component for sending mobility history data of the UE to an apparatus 2106 in a core network associated with the RAN; and a component for receiving a UE mobility prediction model based at least in part on the mobility history data from the apparatus 2106.

[0474] In some aspects, the apparatus 2205 for wireless communication includes means for sending mobility history data of the UE to an apparatus 2106 in a core network associated with the RAN; and means for receiving updated long-term mobility history data from the apparatus 2106 based at least in part on the mobility history data.

[0475] In some aspects, the apparatus 2205 for wireless communication includes means for sending mobility history data of the UE to the apparatus 2106 in the decomposed RAN; and means for receiving, from the apparatus 2106, a UE mobility prediction model based at least in part on the mobility history data.

[0476] In some aspects, the apparatus 2205 for wireless communication includes means for sending mobility history data of the UE to the apparatus 2106 in the decomposed RAN; and means for receiving updated long-term mobility history data from the apparatus 2106 based at least in part on the mobility history data.

[0477] The aforementioned means may be one or more of the aforementioned components of the processing system 2210 of the device 2100 and / or the device 2205 configured to perform the functions recited by the aforementioned means.

[0478] Fig. 22 Provided as an example. Other examples can be combined with Fig. 22 The examples described are different.

[0479] Fig.23 is a diagram of an example 2300 illustrating a specific implementation of code and circuits for an apparatus 2305 according to the present disclosure. The apparatus 2305 may be a network node, or a network node may include the apparatus 2305.

[0480] like Fig.23 As shown, the device 2305 may include a circuit (circuit 2310) for sending the mobility history data of the UE to a second network node in a core network associated with the RAN. For example, the circuit 2310 may enable the device 2305 to send the mobility history data of the UE to the second network node in the core network associated with the RAN.

[0481] like Fig.23 As shown, the device 2305 may include a code (code 2315) stored in the computer-readable medium 2225 for sending the mobility history data of the UE to the second network node in the core network associated with the RAN. For example, when the code 2315 is executed by the processor 2220, the processor 2220 may cause the transceiver 2230 to send the mobility history data of the UE to the second network node in the core network associated with the RAN.

[0482] like Fig.23 As shown, the device 2305 may include a circuit (circuit 2320) for receiving a UE mobility prediction model based at least in part on mobility history data from a second network node. For example, the circuit 2320 may enable the device 2305 to receive a UE mobility prediction model based at least in part on mobility history data from a second network node.

[0483] like Fig.23 As shown, the device 2305 may include a code (code 2325) for receiving a UE mobility prediction model based at least in part on mobility history data from a second network node stored in a computer-readable medium 2225. For example, when the code 2325 is executed by the processor 2220, the processor 2220 may cause the transceiver 2230 to receive a UE mobility prediction model based at least in part on mobility history data from the second network node.

[0484] like Fig.23 As shown, the device 2305 may include a circuit (circuit 2310) for sending the mobility history data of the UE to a second network node in a core network associated with the RAN. For example, the circuit 2310 may enable the device 2305 to send the mobility history data of the UE to the second network node in the core network associated with the RAN.

[0485] like Fig.23 As shown, the device 2305 may include a code (code 2315) stored in the computer-readable medium 2225 for sending the mobility history data of the UE to the second network node in the core network associated with the RAN. For example, when the code 2315 is executed by the processor 2220, the processor 2220 may cause the transceiver 2230 to send the mobility history data of the UE to the second network node in the core network associated with the RAN.

[0486] like Fig.23 As shown, the device 2305 may include a circuit (circuit 2330) for receiving updated long-term mobility history data based at least in part on the mobility history data from the second network node. For example, the circuit 2330 may enable the device 2305 to receive updated long-term mobility history data based at least in part on the mobility history data from the second network node.

[0487] like Fig.23As shown, the apparatus 2305 may include code (code 2335) for receiving updated long-term mobility history data based at least in part on the mobility history data from the second network node stored in the computer-readable medium 2225. For example, the code 2335, when executed by the processor 2220, may cause the processor 2220 to cause the transceiver 2230 to receive updated long-term mobility history data based at least in part on the mobility history data from the second network node.

[0488] like Fig.23 As shown, the device 2305 may include a circuit (circuit 2310) for sending the mobility history data of the UE to the second network node in the decomposed RAN. For example, the circuit 2310 may enable the device 2305 to send the mobility history data of the UE to the second network node in the decomposed RAN.

[0489] like Fig.23 As shown, the device 2305 may include a code (code 2315) for sending the mobility history data of the UE to the second network node in the decomposed RAN stored in the computer-readable medium 2225. For example, when the code 2315 is executed by the processor 2220, the processor 2220 may cause the transceiver 2230 to send the mobility history data of the UE to the second network node in the decomposed RAN.

[0490] like Fig.23 As shown, the device 2305 may include a circuit (circuit 2320) for receiving a UE mobility prediction model based at least in part on mobility history data from a second network node. For example, the circuit 2320 may enable the device 2305 to receive a UE mobility prediction model based at least in part on mobility history data from a second network node.

[0491] like Fig.23 As shown, the device 2305 may include a code (code 2325) for receiving a UE mobility prediction model based at least in part on mobility history data from a second network node stored in a computer-readable medium 2225. For example, when the code 2325 is executed by the processor 2220, the processor 2220 may cause the transceiver 2230 to receive a UE mobility prediction model based at least in part on mobility history data from the second network node.

[0492] like Fig.23 As shown, the device 2305 may include a circuit (circuit 2310) for sending the mobility history data of the UE to the second network node in the decomposed RAN. For example, the circuit 2310 may enable the device 2305 to send the mobility history data of the UE to the second network node in the decomposed RAN.

[0493] like Fig.23As shown, the device 2305 may include a code (code 2315) for sending the mobility history data of the UE to the second network node in the decomposed RAN stored in the computer-readable medium 2225. For example, when the code 2315 is executed by the processor 2220, the processor 2220 may cause the transceiver 2230 to send the mobility history data of the UE to the second network node in the decomposed RAN.

[0494] like Fig.23 As shown, the device 2305 may include a circuit (circuit 2320) for receiving updated long-term mobility history data based at least in part on the mobility history data from the second network node. For example, the circuit 2320 may enable the device 2305 to receive updated long-term mobility history data based at least in part on the mobility history data from the second network node.

[0495] like Fig.23 As shown, the apparatus 2305 may include code (code 2325) for receiving updated long-term mobility history data based at least in part on the mobility history data from the second network node stored in the computer-readable medium 2225. For example, the code 2325, when executed by the processor 2220, may cause the processor 2220 to cause the transceiver 2230 to receive updated long-term mobility history data based at least in part on the mobility history data from the second network node.

[0496] Fig.23 Provided as an example. Other examples can be combined with Fig.23 The examples described are different.

[0497] Fig.24 2400 for wireless communication. The apparatus 2400 may be a network node (e.g., the network node 130), or the network node may include the apparatus 2400. In some aspects, the apparatus 2400 includes a receiving component 2402 and a sending component 2404 that may communicate with each other (e.g., via one or more buses and / or one or more other components). As shown, the apparatus 2400 may communicate with another apparatus 2406 (such as a UE, a base station, or another wireless communication device) using the receiving component 2402 and the sending component 2404. As further shown, the apparatus 2400 may include the communication manager 170.

[0498] In some aspects, the apparatus 2400 may be configured to perform Figures 5 to 10 Additionally or alternatively, the apparatus 2400 may be configured to perform one or more processes described herein, such as Fig.15 The process of 1500 Fig.16 In some aspects, Fig.24The device 2400 and / or one or more components shown may include a combination of Figure 2 Additionally or alternatively, Fig.24 One or more of the components shown may be combined with Figure 2 Additionally or alternatively, one or more components in the set of components may be implemented at least in part as software stored in a memory. For example, a component (or part of a component) may be implemented as instructions or codes that are stored in a non-transitory computer-readable medium and can be executed by a controller or processor to perform the function or operation of the component.

[0499] The receiving component 2402 may receive communications, such as reference signals, control information, data communications, or combinations thereof, from the device 2406. The receiving component 2402 may provide the received communications to one or more other components of the device 2400. In some aspects, the receiving component 2402 may perform signal processing (such as filtering, amplification, demodulation, analog-to-digital conversion, demultiplexing, deinterleaving, demapping, equalization, interference cancellation, or decoding) on ​​the received communications and may provide the processed signals to one or more other components of the device 2400. In some aspects, the receiving component 2402 may include combining Figure 2 One or more antennas, modems, demodulators, MIMO detectors, receive processors, controllers / processors, memories, or combinations thereof, of the described network nodes.

[0500] Transmit component 2404 may transmit communications, such as reference signals, control information, data communications, or combinations thereof, to device 2406. In some aspects, one or more other components of device 2400 may generate communications and may provide the generated communications to transmit component 2404 for transmission to device 2406. In some aspects, transmit component 2404 may perform signal processing (such as filtering, amplification, modulation, digital-to-analog conversion, multiplexing, interleaving, mapping, or encoding, etc.) on the generated communications and may transmit the processed signals to device 2406. In some aspects, transmit component 2404 may include combining Figure 2 One or more antennas, modems, modulators, transmit MIMO processors, transmit processors, controllers / processors, memories, or combinations thereof of the described network nodes. In some aspects, the transmit component 2404 can be co-located with the receive component 2402 in a transceiver.

[0501] Receiving component 2402 can receive mobility history data associated with the UE. Sending component 2404 can send the mobility history data to an apparatus 2406 in a core network for use in training a UE mobility prediction model.

[0502] Receiving component 2402 can receive a UE mobility prediction model from apparatus 2406 .

[0503] The sending component 2404 may send the UE mobility prediction model to another device 2406 included in a RAN associated with a core network.

[0504] Receiving component 2402 can receive long-term mobility history data from device 2406, wherein the long-term mobility history data is based at least in part on the mobility history data.

[0505] The transmitting component 2404 can transmit the long-term mobility history data to another device 2406 included in a RAN associated with the core network.

[0506] Receiving component 2402 can receive the UE mobility prediction model. Sending component 2404 can send the UE mobility prediction model to device 2406 in the core network for storage at device 2406.

[0507] Receiving component 2402 can receive a UE mobility prediction model from apparatus 2406 .

[0508] The sending component 2404 may send the UE mobility prediction model to another device 2406 included in a RAN associated with a core network.

[0509] Fig.24 The number and arrangement of components shown are provided as examples. In practice, there may be Fig.24 Additional components, fewer components, different components, or components arranged differently than those shown. Fig.24 Two or more components shown may be implemented in a single component, or Fig.24 The single component shown may be implemented as multiple distributed components. Additionally or alternatively, Fig.24 The assembly of (one or more) components shown may perform the operations described by Fig.24 Another collection of components shown performs one or more functions.

[0510] Fig.25 is a diagram illustrating an example 2500 of a hardware implementation of an apparatus 2505 employing a processing system 2510. The apparatus 2505 may be a network node.

[0511] The processing system 2510 may be implemented with a bus architecture, generally represented by bus 2515. Bus 2515 may include any number of interconnecting buses and bridges, depending on the specific application of the processing system 2510 and the overall design constraints. Bus 2515 links together various circuits including one or more processors and / or hardware components (represented by processor 2520, components shown, and computer readable media / memory 2525). Bus 2515 may also link various other circuits, such as timing sources, peripherals, voltage regulators, and / or power management circuits.

[0512] The processing system 2510 may be coupled to a transceiver 2530. The transceiver 2530 is coupled to one or more antennas 2535. The transceiver 2530 provides a means for communicating with various other devices via a transmission medium. The transceiver 2530 receives signals from the one or more antennas 2535, extracts information from the received signals, and provides the extracted information to the processing system 2510 (specifically, the receiving component 2402). In addition, the transceiver 2530 receives information from the processing system 2510 (specifically, the transmitting component 2404) and generates a signal to be applied to the one or more antennas 2535 based at least in part on the received information.

[0513] The processing system 2510 includes a processor 2520 coupled to a computer-readable medium / memory 2525. The processor 2520 is responsible for general processing, including executing software stored on the computer-readable medium / memory 2525. The software, when executed by the processor 2520, causes the processing system 2510 to perform various functions described herein for any particular device. The computer-readable medium / memory 2525 can also be used to store data manipulated by the processor 2520 when executing the software. The processing system also includes at least one of the illustrated components. A component can be a software module running in the processor 2520, resident / stored in the computer-readable medium / memory 2525, one or more hardware modules coupled to the processor 2520, or some combination thereof.

[0514] In some aspects, the processing system 2510 may be a component of the network node 130 and may include the memory 292 , the controller / processor 290 , and / or the communication unit 294 .

[0515] In some aspects, the apparatus 2505 for wireless communication includes means for receiving mobility history data associated with the UE; and means for sending the mobility history data to the apparatus 2406 in the core network for use in training the UE mobility prediction model.

[0516] In some aspects, the apparatus 2505 for wireless communication includes means for receiving a UE mobility prediction model from the apparatus 2406 .

[0517] In some aspects, the apparatus 2505 for wireless communication includes means for sending a UE mobility prediction model to another apparatus 2406 included in a RAN associated with a core network.

[0518] In some aspects, the apparatus 2505 for wireless communication includes means for receiving long-term mobility history data from the apparatus 2406, wherein the long-term mobility history data is based at least in part on the mobility history data.

[0519] In some aspects, the apparatus 2505 for wireless communication includes means for sending the long-term mobility history data to another apparatus 2406 included in a RAN associated with a core network.

[0520] In some aspects, the apparatus 2505 for wireless communication includes means for receiving a UE mobility prediction model. The transmitting component 2404 may transmit the UE mobility prediction model to the apparatus 2406 in the core network for storage at the apparatus 2406.

[0521] In some aspects, the apparatus 2505 for wireless communication includes means for receiving a UE mobility prediction model from the apparatus 2406 .

[0522] In some aspects, the apparatus 2505 for wireless communication includes means for sending a UE mobility prediction model to another apparatus 2406 included in a RAN associated with a core network.

[0523] The aforementioned means may be one or more of the aforementioned components of the processing system 2510 of the device 2400 and / or the device 2505 configured to perform the functions recited by the aforementioned means. As described elsewhere herein, the processing system 2510 may include a memory 292, a controller / processor 290, and / or a communication unit 294 configured to perform the functions and / or operations described herein.

[0524] Fig.25 Provided as an example. Other examples can be combined with Fig.25 The examples described are different.

[0525] Fig.26 2600 is a diagram illustrating an example implementation of code and circuits for an apparatus 2605 according to the present disclosure. The apparatus 2605 may be a network node (eg, the network node 130), or a network node may include the apparatus 2605.

[0526] like Fig.26As shown, the device 2605 may include a circuit for receiving mobility history data associated with the UE (circuitry 2610). For example, the circuitry 2610 may enable the device 2605 to receive mobility history data associated with the UE.

[0527] like Fig.26 As shown, the apparatus 2605 may include code (code 2615) for receiving mobility history data associated with the UE stored in the computer-readable medium 2525. For example, the code 2615, when executed by the processor 2520, may cause the processor 2520 to cause the transceiver 2530 to receive mobility history data associated with the UE.

[0528] like Fig.26 As shown, the device 2605 may include a circuit (circuit 2620) for sending mobility history data to a second network node in the core network for training the UE mobility prediction model. For example, the circuit 2620 may enable the device 2605 to send mobility history data to a second network node in the core network for training the UE mobility prediction model.

[0529] like Fig.26 As shown, the device 2605 may include a code (code 2625) stored in the computer-readable medium 2525 for sending mobility history data to a second network node in the core network for training the UE mobility prediction model. For example, when the code 2625 is executed by the processor 2520, the processor 2520 may cause the transceiver 2530 to send mobility history data to the second network node in the core network for training the UE mobility prediction model.

[0530] like Fig.26 As shown, the device 2605 may include a circuit (circuit 2630) for receiving a UE mobility prediction model. For example, the circuit 2630 may enable the device 2605 to receive a UE mobility prediction model.

[0531] like Fig.26 As shown, the apparatus 2605 may include code (code 2635) for receiving a UE mobility prediction model stored in the computer-readable medium 2525. For example, the code 2635, when executed by the processor 2520, may cause the processor 2520 to cause the transceiver 2530 to receive the UE mobility prediction model.

[0532] like Fig.26 As shown, the device 2605 may include a circuit (circuit 2640) for sending the UE mobility prediction model to the second network node in the core network for storage at the second network node. For example, the circuit 2640 may enable the device 2605 to send the UE mobility prediction model to the second network node in the core network for storage at the second network node.

[0533] like Fig.26 As shown, the device 2605 may include a code (code 2645) stored in the computer-readable medium 2525 for sending the UE mobility prediction model to the second network node in the core network for storage at the second network node. For example, when the code 2645 is executed by the processor 2520, the processor 2520 may cause the transceiver 2530 to send the UE mobility prediction model to the second network node in the core network for storage at the second network node.

[0534] Fig.26 Provided as an example. Other examples can be combined with Fig.26 The examples described are different.

[0535] Fig. 27 2700 for wireless communication. The apparatus 2700 may be a network node (e.g., the network node 130), or the network node may include the apparatus 2700. In some aspects, the apparatus 2700 includes a receiving component 2702 and a sending component 2704 that may communicate with each other (e.g., via one or more buses and / or one or more other components). As shown, the apparatus 2700 may communicate with another apparatus 2706 (such as a UE, a base station, or another wireless communication device) using the receiving component 2702 and the sending component 2704. As further shown, the apparatus 2700 may include a communication manager 180. The communication manager 180 may include one or more of a training component 2708, an update component 2710, or a storage component 2712, etc.

[0536] In some aspects, the apparatus 2700 may be configured to perform Figures 5 to 10 Additionally or alternatively, the apparatus 2700 may be configured to perform one or more processes described herein, such as Fig.17 Process 1700, Fig.18 In some aspects, Fig. 27 The device 2700 and / or one or more components shown may include a combination of Figure 2 Additionally or alternatively, Fig. 27 One or more of the components shown may be combined with Figure 2 Additionally or alternatively, one or more components in the set of components may be implemented at least in part as software stored in a memory. For example, a component (or part of a component) may be implemented as instructions or codes that are stored in a non-transitory computer-readable medium and can be executed by a controller or processor to perform the function or operation of the component.

[0537] The receiving component 2702 may receive communications, such as reference signals, control information, data communications, or combinations thereof, from the device 2706. The receiving component 2702 may provide the received communications to one or more other components of the device 2700. In some aspects, the receiving component 2702 may perform signal processing (such as filtering, amplification, demodulation, analog-to-digital conversion, demultiplexing, deinterleaving, demapping, equalization, interference cancellation, or decoding) on ​​the received communications and may provide the processed signals to one or more other components of the device 2700. In some aspects, the receiving component 2702 may include combining Figure 2 One or more antennas, modems, demodulators, MIMO detectors, receive processors, controllers / processors, memories, or combinations thereof, of the described network nodes.

[0538] The transmitting component 2704 may transmit communications, such as reference signals, control information, data communications, or combinations thereof, to the device 2706. In some aspects, one or more other components of the device 2700 may generate communications and may provide the generated communications to the transmitting component 2704 for transmission to the device 2706. In some aspects, the transmitting component 2704 may perform signal processing (such as filtering, amplification, modulation, digital-to-analog conversion, multiplexing, interleaving, mapping, or encoding, etc.) on the generated communications and may transmit the processed signals to the device 2706. In some aspects, the transmitting component 2704 may include combining Figure 2 One or more antennas, modems, modulators, transmit MIMO processors, transmit processors, controllers / processors, memories, or combinations thereof of the described network nodes. In some aspects, the transmit component 2704 can be co-located with the receive component 2702 in a transceiver.

[0539] Receiving component 2702 can receive mobility history data associated with the UE from apparatus 2706 in the core network. Training component 2708 can train a UE mobility prediction model based at least in part on the mobility history data.

[0540] The sending component 2704 can send the UE mobility prediction model to the device 2706.

[0541] Receiving component 2702 can receive mobility history data associated with the UE from device 2706 in the core network. Updating component 2710 can update long-term mobility history data associated with the UE based at least in part on the mobility history data. Storing component 2712 can store the updated long-term mobility history data at the first network node.

[0542] Transmitting component 2704 can transmit the updated long-term mobility history data to the second network node.

[0543] Fig. 27The number and arrangement of components shown are provided as examples. In practice, there may be Fig. 27 Additional components, fewer components, different components, or components arranged differently than those shown. Fig. 27 Two or more components shown may be implemented in a single component, or Fig. 27 The single component shown may be implemented as multiple distributed components. Additionally or alternatively, Fig. 27 The assembly of (one or more) components shown may perform the operations described by Fig. 27 Another collection of components shown performs one or more functions.

[0544] Fig.28 is a diagram illustrating an example 2800 of a hardware implementation of an apparatus 2805 employing a processing system 2810. The apparatus 2805 may be a network node.

[0545] The processing system 2810 may be implemented with a bus architecture, generally represented by bus 2815. Bus 2815 may include any number of interconnecting buses and bridges, depending on the specific application of the processing system 2810 and the overall design constraints. Bus 2815 links together various circuits including one or more processors and / or hardware components (represented by processor 2820, components shown, and computer readable media / memory 2825). Bus 2815 may also link various other circuits, such as timing sources, peripherals, voltage regulators, and / or power management circuits.

[0546] The processing system 2810 may be coupled to a transceiver 2830. The transceiver 2830 is coupled to one or more antennas 2835. The transceiver 2830 provides a means for communicating with various other devices via a transmission medium. The transceiver 2830 receives signals from one or more antennas 2835, extracts information from the received signals, and provides the extracted information to the processing system 2810 (specifically, the receiving component 2702). In addition, the transceiver 2830 receives information from the processing system 2810 (specifically, the transmitting component 2704) and generates a signal to be applied to the one or more antennas 2835 based at least in part on the received information.

[0547] The processing system 2810 includes a processor 2820 coupled to a computer-readable medium / memory 2825. The processor 2820 is responsible for general processing, including executing software stored on the computer-readable medium / memory 2825. The software, when executed by the processor 2820, causes the processing system 2810 to perform various functions described herein for any particular device. The computer-readable medium / memory 2825 can also be used to store data manipulated by the processor 2820 when executing the software. The processing system also includes at least one of the illustrated components. A component can be a software module running in the processor 2820, resident / stored in the computer-readable medium / memory 2825, one or more hardware modules coupled to the processor 2820, or some combination thereof.

[0548] In some aspects, the processing system 2810 may be a component of the network node 130 and may include the memory 292 , the controller / processor 290 , and / or the communication unit 294 .

[0549] In some aspects, the apparatus 2805 for wireless communication includes means for receiving mobility history data associated with the UE from the apparatus 2706 in the core network; and means for training a UE mobility prediction model based at least in part on the mobility history data.

[0550] In some aspects, the apparatus 2805 for wireless communication includes means for sending the UE mobility prediction model to the apparatus 2706 .

[0551] In some aspects, the apparatus 2805 for wireless communication includes a component for receiving mobility history data associated with the UE from the apparatus 2706 in the core network; a component for updating long-term mobility history data associated with the UE based at least in part on the mobility history data; and a component for storing the updated long-term mobility history data at the first network node.

[0552] In some aspects, the apparatus 2805 for wireless communication includes means for sending updated long-term mobility history data to the second network node.

[0553] The aforementioned means may be one or more of the aforementioned components of the processing system 2810 of the device 2700 and / or the device 2805 configured to perform the functions recited by the aforementioned means. As described elsewhere herein, the processing system 2810 may include a memory 292, a controller / processor 290, and / or a communication unit 294 configured to perform the functions and / or operations described herein.

[0554] Fig.28 Provided as an example. Other examples can be combined with Fig.28 The examples described are different.

[0555] Fig.29 is a diagram of an example 2900 illustrating a specific implementation of code and circuits for an apparatus 2905 according to the present disclosure. The apparatus 2905 may be a network node, or a network node may include the apparatus 2905.

[0556] like Fig.29 As shown, the device 2905 may include a circuit (circuit 2910) for receiving mobility history data associated with the UE from a second network node in the core network. For example, the circuit 2910 may enable the device 2905 to receive mobility history data associated with the UE from the second network node in the core network.

[0557] like Fig.29 As shown, the device 2905 may include a code (code 2915) for receiving mobility history data associated with the UE from a second network node in the core network stored in the computer-readable medium 2825. For example, when the code 2915 is executed by the processor 2820, the processor 2820 may cause the transceiver 2830 to receive mobility history data associated with the UE from the second network node in the core network.

[0558] like Fig.29 As shown, the device 2905 may include a circuit (circuit 2920) for training a UE mobility prediction model based at least in part on mobility history data. For example, the circuit 2920 may enable the device 2905 to train a UE mobility prediction model based at least in part on mobility history data.

[0559] like Fig.29 As shown, the device 2905 may include code (code 2925) stored in the computer-readable medium 2825 for training the UE mobility prediction model based at least in part on the mobility history data. For example, when the code 2925 is executed by the processor 2820, the processor 2820 may train the UE mobility prediction model based at least in part on the mobility history data.

[0560] like Fig.29 As shown, the device 2905 may include a circuit (circuit 2910) for receiving mobility history data associated with the UE from a second network node in the core network. For example, the circuit 2910 may enable the device 2905 to receive mobility history data associated with the UE from the second network node in the core network.

[0561] like Fig.29As shown, the device 2905 may include a code (code 2915) for receiving mobility history data associated with the UE from a second network node in the core network stored in the computer-readable medium 2825. For example, when the code 2915 is executed by the processor 2820, the processor 2820 may cause the transceiver 2830 to receive mobility history data associated with the UE from the second network node in the core network.

[0562] like Fig.29 As shown, the device 2905 may include a circuit (circuit 2930) for updating long-term mobility history data associated with the UE based at least in part on the mobility history data. For example, the circuit 2930 may enable the device 2905 to update the long-term mobility history data associated with the UE based at least in part on the mobility history data.

[0563] like Fig.29 As shown, the device 2905 may include code (code 2935) stored in the computer-readable medium 2825 for updating long-term mobility history data associated with the UE based at least in part on the mobility history data. For example, the code 2935, when executed by the processor 2820, may cause the processor 2820 to update the long-term mobility history data associated with the UE based at least in part on the mobility history data.

[0564] like Fig.29 As shown, the device 2905 may include a circuit (circuit 2940) for storing updated long-term mobility history data at the first network node. For example, the circuit 2940 may enable the device 2905 to store updated long-term mobility history data at the first network node.

[0565] like Fig.29 As shown, the device 2905 may include code (code 2945) for storing updated long-term mobility history data at the first network node stored in the computer-readable medium 2825. For example, the code 2945, when executed by the processor 2820, may cause the processor 2820 to store updated long-term mobility history data at the first network node.

[0566] Fig.29 Provided as an example. Other examples can be combined with Fig.29 The examples described are different.

[0567] Fig.303000 for wireless communication. The apparatus 3000 may be a network node (e.g., the network node 150), or the network node may include the apparatus 3000. In some aspects, the apparatus 3000 includes a receiving component 3002 and a sending component 3004 that may communicate with each other (e.g., via one or more buses and / or one or more other components). As shown, the apparatus 3000 may communicate with another apparatus 3006 (such as a UE, a base station, or another wireless communication device) using the receiving component 3002 and the sending component 3004. As further shown, the apparatus 3000 may include a communication manager 190. The communication manager 190 may include one or more of a training component 3008, an update component 3010, or a storage component 3012, etc.

[0568] In some aspects, the apparatus 3000 may be configured to perform Figures 5 to 10 Additionally or alternatively, the apparatus 3000 may be configured to perform one or more processes described herein, such as Fig.19 The process of 1900 Fig. 20 In some aspects, Fig.30 The device 3000 and / or one or more components shown may include a combination of Figure 2 Additionally or alternatively, Fig.30 One or more of the components shown may be combined with Figure 2 Additionally or alternatively, one or more components in the set of components may be implemented at least in part as software stored in a memory. For example, a component (or part of a component) may be implemented as instructions or codes that are stored in a non-transitory computer-readable medium and can be executed by a controller or processor to perform the function or operation of the component.

[0569] The receiving component 3002 may receive communications, such as reference signals, control information, data communications, or combinations thereof, from the device 3006. The receiving component 3002 may provide the received communications to one or more other components of the device 3000. In some aspects, the receiving component 3002 may perform signal processing (such as filtering, amplification, demodulation, analog-to-digital conversion, demultiplexing, deinterleaving, demapping, equalization, interference cancellation, or decoding) on ​​the received communications and may provide the processed signals to one or more other components of the device 3000. In some aspects, the receiving component 3002 may include combining Figure 2 One or more antennas, modems, demodulators, MIMO detectors, receive processors, controllers / processors, memories, or combinations thereof, of the described network nodes.

[0570] The transmitting component 3004 may transmit communications, such as reference signals, control information, data communications, or combinations thereof, to the device 3006. In some aspects, one or more other components of the device 3000 may generate communications and may provide the generated communications to the transmitting component 3004 for transmission to the device 3006. In some aspects, the transmitting component 3004 may perform signal processing (such as filtering, amplification, modulation, digital-to-analog conversion, multiplexing, interleaving, mapping, or encoding, etc.) on the generated communications and may transmit the processed signals to the device 3006. In some aspects, the transmitting component 3004 may include combining Figure 2 One or more antennas, modems, modulators, transmit MIMO processors, transmit processors, controllers / processors, memories, or combinations thereof of the described network nodes. In some aspects, the transmit component 3004 can be co-located with the receive component 3002 in a transceiver.

[0571] Receiving component 3002 can receive mobility history data associated with the UE from device 3006 in the disaggregated RAN. Training component 3008 can train a UE mobility prediction model based at least in part on the mobility history data.

[0572] The sending component 3004 can send the UE mobility prediction model to the device 3006.

[0573] Receiving component 3002 can receive mobility history data associated with the UE from device 3006 in the disaggregated RAN. Updating component 2710 can update long-term mobility history data associated with the UE based at least in part on the mobility history data. Storing component 2712 can store the updated long-term mobility history data.

[0574] The sending component 3004 can send the updated long-term mobility history data to the device 3006.

[0575] Fig.30 The number and arrangement of components shown are provided as examples. In practice, there may be Fig.30 Additional components, fewer components, different components, or components arranged differently than those shown. Fig.30 Two or more components shown may be implemented in a single component, or Fig.30 The single component shown may be implemented as multiple distributed components. Additionally or alternatively, Fig.30 The assembly of (one or more) components shown may perform the operations described by Fig.30 Another collection of components shown performs one or more functions.

[0576] Fig.31is a diagram illustrating an example 3100 of a hardware implementation of an apparatus 3105 employing a processing system 3110. The apparatus 3105 may be a network node (eg, network node 150).

[0577] The processing system 3110 may be implemented with a bus architecture, generally represented by bus 3115. Bus 3115 may include any number of interconnecting buses and bridges, depending on the specific application of the processing system 3110 and the overall design constraints. Bus 3115 links together various circuits including one or more processors and / or hardware components (represented by processor 3120, components shown, and computer readable media / memory 3125). Bus 3115 may also link various other circuits, such as timing sources, peripherals, voltage regulators, and / or power management circuits.

[0578] The processing system 3110 may be coupled to a transceiver 3130. The transceiver 3130 is coupled to one or more antennas 3135. The transceiver 3130 provides a means for communicating with various other devices via a transmission medium. The transceiver 3130 receives signals from one or more antennas 3135, extracts information from the received signals, and provides the extracted information to the processing system 3110 (specifically, the receiving component 3002). In addition, the transceiver 3130 receives information from the processing system 3110 (specifically, the transmitting component 3004) and generates a signal to be applied to the one or more antennas 3135 based at least in part on the received information.

[0579] The processing system 3110 includes a processor 3120 coupled to a computer readable medium / memory 3125. The processor 3120 is responsible for general processing, including executing software stored on the computer readable medium / memory 3125. The software, when executed by the processor 3120, causes the processing system 3110 to perform various functions described herein for any particular device. The computer readable medium / memory 3125 can also be used to store data manipulated by the processor 3120 when executing the software. The processing system also includes at least one of the illustrated components. A component can be a software module running in the processor 3120, resident / stored in the computer readable medium / memory 3125, one or more hardware modules coupled to the processor 3120, or some combination thereof.

[0580] In some aspects, the processing system 3110 may be a component of the network node 150 and may include the memory 292 , the controller / processor 290 , and / or the communication unit 294 .

[0581] In some aspects, the apparatus 3105 for wireless communication includes means for receiving mobility history data associated with the UE from the apparatus 3006 in the decomposed RAN; and means for training a UE mobility prediction model based at least in part on the mobility history data.

[0582] In some aspects, the apparatus 3105 for wireless communication includes means for sending the UE mobility prediction model to the apparatus 3006 .

[0583] In some aspects, the apparatus 3105 for wireless communication includes a component for receiving mobility history data associated with a UE from an apparatus 3006 in a decomposed RAN; a component for updating long-term mobility history data associated with the UE based at least in part on the mobility history data; and a component for storing the updated long-term mobility history data.

[0584] In some aspects, the means 3105 for wireless communication includes means for sending updated long-term mobility history data to the means 3006 .

[0585] The aforementioned means may be one or more of the aforementioned components of the processing system 3110 of the device 3000 and / or device 3105 configured to perform the functions recited by the aforementioned means. As described elsewhere herein, the processing system 3110 may include a memory 292, a controller / processor 290, and / or a communication unit 294 configured to perform the functions and / or operations described herein.

[0586] Fig.31 Provided as an example. Other examples can be combined with Fig.31 The examples described are different.

[0587] Fig.32 is a diagram of an example 3200 illustrating a specific implementation of code and circuits for an apparatus 3205 according to the present disclosure. The apparatus 3205 may be a network node, or a network node may include the apparatus 3205.

[0588] like Fig.32 As shown, the device 3205 may include a circuit (circuit 3210) for receiving mobility history data associated with the UE from a second network node in the decomposed RAN. For example, the circuit 3210 may enable the device 3205 to receive mobility history data associated with the UE from the second network node in the decomposed RAN.

[0589] like Fig.32As shown, the apparatus 3205 may include code (code 3215) stored in the computer-readable medium 3125 for receiving mobility history data associated with the UE from a second network node in the decomposed RAN. For example, the code 3215, when executed by the processor 3120, may cause the processor 3120 to cause the transceiver 3130 to receive mobility history data associated with the user equipment (UE) from the second network node in the decomposed RAN.

[0590] like Fig.32 As shown, the device 3205 may include a circuit (circuit 3220) for training a UE mobility prediction model based at least in part on mobility history data. For example, the circuit 3220 may enable the device 3205 to train a UE mobility prediction model based at least in part on mobility history data.

[0591] like Fig.32 As shown, the device 3205 may include code (code 3225) stored in the computer-readable medium 3125 for training the UE mobility prediction model based at least in part on the mobility history data. For example, when the code 3225 is executed by the processor 3120, the processor 3120 may train the UE mobility prediction model based at least in part on the mobility history data.

[0592] like Fig.32 As shown, the device 3205 may include a circuit (circuit 3210) for receiving mobility history data associated with the UE from a second network node in the decomposed RAN. For example, the circuit 3210 may enable the device 3205 to receive mobility history data associated with the UE from the second network node in the decomposed RAN.

[0593] like Fig.32 As shown, the apparatus 3205 may include code (code 3215) stored in the computer-readable medium 3125 for receiving mobility history data associated with the UE from a second network node in the decomposed RAN. For example, the code 3215, when executed by the processor 3120, may cause the processor 3120 to cause the transceiver 3130 to receive mobility history data associated with the user equipment (UE) from the second network node in the decomposed RAN.

[0594] like Fig.32 As shown, the device 3205 may include a circuit (circuit 3230) for updating long-term mobility history data associated with the UE based at least in part on the mobility history data. For example, the circuit 3230 may enable the device 3205 to update the long-term mobility history data associated with the UE based at least in part on the mobility history data.

[0595] like Fig.32As shown, the device 3205 may include code (code 3235) stored in the computer-readable medium 3125 for updating long-term mobility history data associated with the UE based at least in part on the mobility history data. For example, the code 3235, when executed by the processor 3120, may cause the processor 3120 to update the long-term mobility history data associated with the UE based at least in part on the mobility history data.

[0596] like Fig.32 As shown, the device 3205 may include a circuit (circuit 3240) for storing updated long-term mobility history data at the first network node. For example, the circuit 3240 may enable the device 3205 to store updated long-term mobility history data at the first network node.

[0597] like Fig.32 As shown, the device 3205 may include code (code 3245) for storing updated long-term mobility history data at the first network node stored in the computer-readable medium 3125. For example, the code 3245, when executed by the processor 3120, may cause the processor 3120 to store updated long-term mobility history data at the first network node.

[0598] Fig.32 Provided as an example. Other examples can be combined with Fig.32 The examples described are different.

[0599] The following provides an overview of some aspects of the disclosure:

[0600] Aspect 1: A method of wireless communication performed by a first network node in a radio access network (RAN), comprising: sending mobility history data of a user equipment (UE) to a second network node in a core network associated with the RAN; and receiving a UE mobility prediction model based at least in part on the mobility history data from the second network node.

[0601] Aspect 2: The method according to aspect 1, wherein the second network node is an access management function (AMF) node.

[0602] Aspect 3: The method according to aspect 1 or 2, wherein sending the mobility history data to the second network node includes: sending the mobility history data to the second network node in a UE context release complete message.

[0603] Aspect 4: The method according to one or more of aspects 1 to 3, wherein sending the mobility history data to the second network node comprises: sending the mobility history data to the second network node in a path switch request message.

[0604] Aspect 5: A method according to one or more of Aspects 1 to 4, wherein receiving the UE mobility prediction model from the second network node includes: receiving the UE mobility prediction model from the second network node during a radio resource control (RRC) connection establishment procedure of the UE.

[0605] Aspect 6: The method according to one or more of aspects 1 to 5, wherein receiving the UE mobility prediction model from the second network node includes: receiving the UE mobility prediction model from the second network node during a handover procedure of the UE.

[0606] Aspect 7: The method according to one or more of aspects 1 to 6, wherein receiving the UE mobility prediction model from the second network node includes: receiving the UE mobility prediction model from the second network node in an initial UE context establishment request message.

[0607] Aspect 8: The method according to one or more of Aspects 17, wherein receiving the UE mobility prediction model from the second network node comprises: receiving the UE mobility prediction model from the second network node in a UE context modification request message.

[0608] Aspect 9: The method according to one or more of aspects 1 to 8, wherein receiving the UE mobility prediction model from the second network node includes: receiving the UE mobility prediction model from the second network node in a path switch request confirmation message.

[0609] Aspect 10: The method according to one or more of aspects 1 to 9, wherein the UE mobility prediction model is trained at least in part based on: recent mobility history data associated with the UE and long-term mobility history data collected for the UE.

[0610] Aspect 11: The method according to one or more of aspects 1 to 10, wherein the UE mobility prediction model comprises: a UE-specific UE mobility prediction model of the UE.

[0611] Aspect 12: A method of wireless communication performed by a first network node in a radio access network (RAN), comprising: sending mobility history data of a user equipment (UE) to a second network node in a core network associated with the RAN; and receiving updated long-term mobility history data based at least in part on the mobility history data from the second network node.

[0612] Aspect 13: The method according to aspect 12, wherein the second network node is an access management function (AMF) node.

[0613] Aspect 14: The method according to aspect 12 or 13, wherein sending the mobility history data to the second network node includes: sending the mobility history data to the second network node in a UE context release complete message.

[0614] Aspect 15: The method according to one or more of aspects 12 to 14, wherein sending the mobility history data to the second network node comprises: sending the mobility history data to the second network node in a path switch request message.

[0615] Aspect 16: A method according to one or more of Aspects 12 to 15, wherein receiving the updated long-term mobility history data from the second network node includes: receiving the updated long-term mobility history data from the second network node during a radio resource control (RRC) connection establishment procedure of the UE.

[0616] Aspect 17: The method according to one or more of aspects 12 to 16, wherein receiving the updated long-term mobility history data from the second network node comprises: receiving the updated long-term mobility history data from the second network node during a handover procedure of the UE.

[0617] Aspect 18: A method according to one or more of aspects 12 to 17, wherein receiving the updated long-term mobility history data from the second network node comprises: receiving the updated long-term mobility history data from the second network node in an initial UE context establishment request message.

[0618] Aspect 19: A method according to one or more of aspects 12 to 18, wherein receiving the updated long-term mobility history data from the second network node comprises: receiving the updated long-term mobility history data from the second network node in a UE context modification request message.

[0619] Aspect 20: The method according to one or more of aspects 12 to 19, wherein receiving the updated long-term mobility history data from the second network node comprises: receiving the updated long-term mobility history data from the second network node in a path switch request confirm message.

[0620] Aspect 21: A method of wireless communication performed by a first network node in a decomposed radio access network (RAN), comprising: sending mobility history data of a user equipment (UE) to a second network node in the decomposed RAN; and receiving a UE mobility prediction model based at least in part on the mobility history data from the second network node.

[0621] Aspect 22: The method according to aspect 21, wherein the first network node is a base station.

[0622] Aspect 23: The method according to aspect 21 or 22, wherein the first network node is a central unit.

[0623] Aspect 24: The method according to one or more of aspects 21 to 23, wherein the first network node is a distributed unit.

[0624] Aspect 25: The method according to one or more of aspects 21 to 24, wherein the first network node is a radio unit.

[0625] Aspect 26: The method according to one or more of aspects 21 to 25, wherein the second network node is a Service Management and Orchestration (SMO) node.

[0626] Aspect 27: A method according to one or more of aspects 21 to 26, wherein the second network node is a RAN Intelligent Controller (RIC) node.

[0627] Aspect 28: The method according to one or more of aspects 21 to 27, wherein sending the mobility history data to the second network node comprises sending the mobility history data to the second network node in an event report.

[0628] Aspect 29: A method according to one or more of Aspects 21 to 28, wherein receiving the UE mobility prediction model from the second network node includes: receiving the UE mobility prediction model from the second network node during a radio resource control (RRC) connection establishment procedure of the UE.

[0629] Aspect 30: The method according to one or more of aspects 21 to 29, wherein receiving the UE mobility prediction model from the second network node includes: receiving the UE mobility prediction model from the second network node during a handover procedure of the UE.

[0630] Aspect 31: A method according to one or more of aspects 21 to 30, wherein receiving the UE mobility prediction model from the second network node includes: receiving the UE mobility prediction model from the second network node in a UE context establishment message.

[0631] Aspect 32: A method according to one or more of aspects 21 to 31, wherein the UE mobility prediction model is trained at least in part based on: recent mobility history data associated with the UE and long-term mobility history data collected for the UE.

[0632] Aspect 33: A method according to one or more of aspects 21 to 32, wherein the UE mobility prediction model comprises: a UE-specific UE mobility prediction model of the UE.

[0633] Aspect 34: A method of wireless communication performed by a first network node in a decomposed radio access network (RAN), comprising: sending mobility history data of a user equipment (UE) to a second network node in the decomposed RAN; and receiving updated long-term mobility history data based at least in part on the mobility history data from the second network node.

[0634] Aspect 35: The method according to aspect 34, wherein the first network node is a base station.

[0635] Aspect 36: The method according to aspect 34 or 35, wherein the first network node is a central unit.

[0636] Aspect 37: A method according to one or more of aspects 34 to 36, wherein the first network node is a distributed unit.

[0637] Aspect 38: A method according to one or more of aspects 34 to 37, wherein the first network node is a radio unit.

[0638] Aspect 39: A method according to one or more of aspects 34 to 38, wherein the second network node is a Service Management and Orchestration (SMO) node.

[0639] Aspect 40: A method according to one or more of aspects 34 to 39, wherein the second network node is a RAN Intelligent Controller (RIC) node.

[0640] Aspect 41: The method according to one or more of aspects 34 to 40, wherein sending the mobility history data to the second network node comprises sending the mobility history data to the second network node in an event report.

[0641] Aspect 42: A method according to one or more of Aspects 34 to 41, wherein receiving the updated long-term mobility history data from the second network node includes: receiving the updated long-term mobility history data from the second network node during a radio resource control (RRC) connection establishment procedure of the UE.

[0642] Aspect 43: A method according to one or more of aspects 34 to 42, wherein receiving the updated long-term mobility history data from the second network node comprises: receiving the updated long-term mobility history data from the second network node during a handover procedure of the UE.

[0643] Aspect 44: A method according to one or more of aspects 34 to 43, wherein receiving the updated long-term mobility history data from the second network node comprises: receiving the updated long-term mobility history data from the second network node in a UE context establishment message.

[0644] Aspect 45: A method of wireless communication performed by a first network node in a core network, comprising: receiving mobility history data associated with a user equipment (UE); and sending the mobility history data to a second network node in the core network for training a UE mobility prediction model.

[0645] Aspect 46: The method according to aspect 45, wherein receiving the mobility history data comprises: receiving the mobility history data from a third network node included in a radio access network (RAN) associated with the core network.

[0646] Aspect 47: The method according to aspect 46, wherein receiving the mobility history data from the third network node includes: receiving the mobility history data from the third network node in a UE context release complete message.

[0647] Aspect 48: The method according to aspect 46 or 47, wherein receiving the mobility history data from the third network node comprises: receiving the mobility history data from the third network node in a path switch request message.

[0648] Aspect 49: A method according to one or more of aspects 46 to 48, wherein the first network node is an access management function (AMF) node; and wherein the third network node is a base station.

[0649] Aspect 50: The method according to aspect 49, wherein the second network node is at least one of the following: a unified data management (UDM) node, a unified data repository (UDR) node, or a home subscriber service (HSS) node.

[0650] Aspect 51: The method according to aspect 49 or 50, wherein the second network node is a network data analysis function (NWDAF) node.

[0651] Aspect 52: A method according to one or more of Aspects 49 to 51, wherein the second network node is a machine learning model server node.

[0652] Aspect 53: A method according to one or more of aspects 45 to 52, wherein sending the mobility history data to the second network node comprises: sending the mobility history data to the second network node for training a UE mobility prediction model.

[0653] Aspect 54: The method according to Aspect 53 further includes: receiving the UE mobility prediction model from the second network node; and sending the UE mobility history data to a third network node included in a radio access network (RAN) associated with the core network.

[0654] Aspect 55: The method according to aspect 54, wherein sending the UE mobility prediction model to the third network node includes: sending the UE mobility prediction model to the third network node during a radio resource control (RRC) connection establishment procedure of the UE.

[0655] Aspect 56: The method according to aspect 54 or 55, wherein sending the UE mobility prediction model to the third network node includes: sending the UE mobility prediction model to the third network node during a handover procedure of the UE.

[0656] Aspect 57: A method according to one or more of aspects 54 to 56, wherein sending the UE mobility prediction model to the third network node includes: sending the UE mobility prediction model to the third network node in an initial UE context establishment request message.

[0657] Aspect 58: A method according to one or more of aspects 54 to 57, wherein sending the UE mobility prediction model to the third network node includes: sending the UE mobility prediction model to the third network node in a UE context modification request message.

[0658] Aspect 59: A method according to one or more of aspects 54 to 58, wherein sending the UE mobility prediction model to the third network node includes: sending the UE mobility prediction model to the third network node in a path switch request confirmation message.

[0659] Aspect 60: A method according to one or more of aspects 53 to 59, wherein the UE mobility prediction model is trained at least in part based on: the mobility history data and long-term mobility history data collected for the UE.

[0660] Aspect 61: A method according to one or more of aspects 45 to 60, wherein sending the mobility history data to the second network node comprises sending the mobility history data to the second network node for storage at the second network node.

[0661] Aspect 62: The method according to Aspect 61 further includes: receiving long-term mobility history data from the second network node, wherein the long-term mobility history data is at least partially based on the mobility history data; and sending the long-term mobility history data to a third network node included in a radio access network (RAN) associated with the core network.

[0662] Aspect 63: The method according to aspect 62, wherein sending the long-term mobility history data to the third network node comprises: sending the long-term mobility history data to the third network node during a radio resource control (RRC) connection establishment procedure of the UE.

[0663] Aspect 64: The method according to aspect 62 or 63, wherein sending the long-term mobility history data to the third network node comprises: sending the long-term mobility history data to the third network node during a handover procedure of the UE.

[0664] Aspect 65: A method according to one or more of aspects 62 to 64, wherein sending the long-term mobility history data to the third network node comprises: sending the long-term mobility history data to the third network node in an initial UE context establishment request message.

[0665] Aspect 66: A method according to one or more of aspects 62 to 65, wherein sending the long-term mobility history data to the third network node comprises: sending the long-term mobility history data to the third network node in a UE context modification request message.

[0666] Aspect 67: A method according to one or more of aspects 62 to 66, wherein sending the long-term mobility history data to the third network node comprises: sending the long-term mobility history data to the third network node in a path switch request confirm message.

[0667] Aspect 68: A method of wireless communication performed by a first network node in a core network, comprising: receiving a user equipment (UE) mobility prediction model; and sending the UE mobility prediction model to a second network node in the core network for storage at the second network node.

[0668] Aspect 69: The method according to aspect 68, wherein receiving the UE mobility prediction model includes: receiving the UE mobility prediction model from a third network node included in a radio access network (RAN) associated with the core network.

[0669] Aspect 70: The method according to aspect 69, wherein receiving the UE mobility prediction model from the third network node includes: receiving the UE mobility prediction model from the third network node in a UE context release complete message.

[0670] Aspect 71: The method according to aspect 69 or 70, wherein receiving the UE mobility prediction model from the third network node includes: receiving the UE mobility prediction model from the third network node in a path switch request message.

[0671] Aspect 72: A method according to one or more of aspects 69 to 71, wherein the first network node is an access management function (AMF) node; and wherein the third network node is a base station.

[0672] Aspect 73: The method according to aspect 72, wherein the second network node is a unified data management (UDM) node.

[0673] Aspect 74: The method according to aspect 72 or 73, wherein the second network node is a unified data repository (UDR) node.

[0674] Aspect 75: The method according to one or more of aspects 72 to 74, wherein the second network node is a Home Subscriber Service (HSS) node.

[0675] Aspect 76: A method according to one or more of aspects 72 to 75, wherein the second network node is a network data analysis function (NWDAF) node.

[0676] Aspect 77: A method according to one or more of Aspects 72 to 76, wherein the second network node is a machine learning model server node.

[0677] Aspect 78: The method according to one or more of Aspects 68 to 77 also includes: receiving the UE mobility prediction model from the second network node; and sending the UE mobility history data to a third network node included in a radio access network (RAN) associated with the core network.

[0678] Aspect 79: The method according to aspect 78, wherein sending the UE mobility prediction model to the third network node includes: sending the UE mobility prediction model to the third network node during a radio resource control (RRC) connection establishment procedure of the UE.

[0679] Aspect 80: The method according to aspect 78 or 79, wherein sending the UE mobility prediction model to the third network node includes: sending the UE mobility prediction model to the third network node during a handover procedure of the UE.

[0680] Aspect 81: A method according to one or more of aspects 78 to 80, wherein sending the UE mobility prediction model to the third network node includes: sending the UE mobility prediction model to the third network node in an initial UE context establishment request message.

[0681] Aspect 82: A method according to one or more of aspects 78 to 81, wherein sending the UE mobility prediction model to the third network node includes: sending the UE mobility prediction model to the third network node in a UE context modification request message.

[0682] Aspect 83: A method according to one or more of aspects 78 to 82, wherein sending the UE mobility prediction model to the third network node includes: sending the UE mobility prediction model to the third network node in a path switch request confirmation message.

[0683] Aspect 84: A method according to one or more of aspects 78 to 83, wherein the UE mobility prediction model is trained at least in part based on: recent mobility history data associated with the UE and long-term mobility history data collected for the UE.

[0684] Aspect 85: The method according to aspect 84, wherein the UE mobility prediction model comprises: a UE-specific UE mobility prediction model of the UE.

[0685] Aspect 86: A method of wireless communication performed by a first network node in a core network, comprising: receiving mobility history data associated with a user equipment (UE) from a second network node in the core network; and training a UE mobility prediction model based at least in part on the mobility history data.

[0686] Aspect 87: The method according to aspect 86, wherein the second network node is an access management function (AMF) node.

[0687] Aspect 88: The method according to aspect 86 or 87, wherein the first network node is a unified data management (UDM) node.

[0688] Aspect 89: A method according to one or more of aspects 86 to 88, wherein the first network node is a unified data repository (UDR) node.

[0689] Aspect 90: The method according to one or more of aspects 86 to 89, wherein the first network node is a Home Subscriber Service (HSS) node.

[0690] Aspect 91: A method according to one or more of aspects 86 to 98, wherein the first network node is a network data analysis function (NWDAF) node.

[0691] Aspect 92: A method according to one or more of Aspects 86 to 91, wherein the first network node is a machine learning model server node.

[0692] Aspect 93: A method according to one or more of aspects 86 to 92, wherein training the UE mobility prediction model comprises: training a UE-specific UE mobility prediction model of the UE.

[0693] Aspect 94: The method according to one or more of aspects 86 to 93 further includes: sending the UE mobility prediction model to the second network node.

[0694] Aspect 95: A method according to aspect 94, wherein sending the UE mobility prediction model to the second network node includes: sending the UE mobility prediction model to the second network node during a radio resource control (RRC) connection establishment procedure of the UE.

[0695] Aspect 96: The method according to aspect 94 or 95, wherein sending the UE mobility prediction model to the second network node includes: sending the UE mobility prediction model to the second network node during a handover procedure of the UE.

[0696] Aspect 97: A method according to one or more of aspects 94 to 96, wherein sending the UE mobility prediction model to the second network node includes: sending the UE mobility prediction model to the second network node in a unified data management (UDM) response message.

[0697] Aspect 98: A method according to one or more of aspects 86 to 97, wherein training the UE mobility prediction model includes: training the UE mobility prediction model based at least in part on long-term mobility history data collected for the UE.

[0698] Aspect 99: A method of wireless communication performed by a first network node in a core network, comprising: receiving mobility history data associated with a user equipment (UE) from a second network node in the core network; updating long-term mobility history data associated with the UE based at least in part on the mobility history data; and storing the updated long-term mobility history data at the first network node.

[0699] Aspect 100: The method according to aspect 99, wherein the second network node is an access management function (AMF) node.

[0700] Aspect 101: The method according to aspect 99 or 100, wherein the first network node is a unified data management (UDM) node.

[0701] Aspect 102: The method according to one or more of aspects 99 to 101, wherein the first network node is a unified data repository (UDR) node.

[0702] Aspect 103: The method according to one or more of aspects 99 to 102, wherein the first network node is a Home Subscriber Service (HSS) node.

[0703] Aspect 104: A method according to one or more of aspects 99 to 103, wherein the first network node is a network data analysis function (NWDAF) node.

[0704] Aspect 105: A method according to one or more of Aspects 99 to 104, wherein the first network node is a machine learning model server node.

[0705] Aspect 106: The method according to one or more of aspects 99 to 105, further comprising: sending the updated long-term mobility history data to the second network node.

[0706] Aspect 107: The method according to aspect 106, wherein sending the updated long-term mobility history data to the second network node comprises: sending the updated long-term mobility history data to the second network node during a radio resource control (RRC) connection establishment procedure of the UE.

[0707] Aspect 108: The method according to aspect 106 or 107, wherein sending the updated long term mobility history data to the second network node comprises sending the updated long term mobility history data to the second network node during a handover procedure of the UE.

[0708] Aspect 109: A method according to one or more of aspects 106 to 109, wherein sending the updated long-term mobility history data to the second network node comprises: sending the updated long-term mobility history data to the second network node in a unified data management (UDM) response message.

[0709] Aspect 110: A method of wireless communication performed by a first network node in a decomposed radio access network (RAN), comprising: receiving mobility history data associated with a user equipment (UE) from a second network node in the decomposed RAN; and training a UE mobility prediction model based at least in part on the mobility history data.

[0710] Aspect 111: The method according to aspect 110, wherein the second network node is a base station.

[0711] Aspect 112: The method according to aspect 110 or 111, wherein the second network node is a central unit.

[0712] Aspect 113: The method according to one or more of aspects 110 to 112, wherein the second network node is a distributed unit.

[0713] Aspect 114: The method according to one or more of aspects 110 to 113, wherein the second network node is a radio unit.

[0714] Aspect 115: The method according to one or more of aspects 110 to 114, wherein the first network node is a Service Management and Orchestration (SMO) node.

[0715] Aspect 116: A method according to one or more of aspects 110 to 115, wherein the first network node is a RAN Intelligent Controller (RIC) node.

[0716] Aspect 117: The method according to one or more of aspects 110 to 115, wherein training the UE mobility prediction model comprises: training a UE-specific UE mobility prediction model of the UE.

[0717] Aspect 118: The method according to one or more of aspects 110 to 116 further comprises: sending the UE mobility prediction model to the second network node.

[0718] Aspect 119: The method according to one or more of aspects 110 to 116, wherein receiving the mobility history data from the second network node comprises: receiving the mobility history data in an event report from the second network node.

[0719] Aspect 120: The method according to aspect 119, wherein sending the UE mobility prediction model to the second network node includes: sending the UE mobility prediction model to the second network node during a radio resource control (RRC) connection establishment procedure of the UE.

[0720] Aspect 121: The method according to aspect 119 or 120, wherein sending the UE mobility prediction model to the second network node includes: sending the UE mobility prediction model to the second network node during a handover procedure of the UE.

[0721] Aspect 122: A method according to one or more of aspects 119 to 121, wherein sending the UE mobility prediction model to the second network node includes: sending the UE mobility prediction model to the second network node in a UE context establishment message.

[0722] Aspect 123: A method according to one or more of aspects 110 to 122, wherein training the UE mobility prediction model comprises: training the UE mobility prediction model based at least in part on long-term mobility history data collected for the UE.

[0723] Aspect 124: A method of wireless communication performed by a first network node in a decomposed radio access network (RAN), comprising: receiving mobility history data associated with a user equipment (UE) from a second network node in the decomposed RAN; updating long-term mobility history data associated with the UE based at least in part on the mobility history data; and storing the updated long-term mobility history data at the first network node.

[0724] Aspect 125: The method according to aspect 124, wherein the second network node is a base station.

[0725] Aspect 126: The method according to aspect 124 or 125, wherein the second network node is a central unit.

[0726] Aspect 127: A method according to one or more of aspects 124 to 126, wherein the second network node is a distributed unit.

[0727] Aspect 128: The method according to one or more of aspects 124 to 127, wherein the second network node is a radio unit.

[0728] Aspect 129: The method according to one or more of aspects 124 to 128, wherein the first network node is a Service Management and Orchestration (SMO) node.

[0729] Aspect 130: A method according to one or more of aspects 124 to 129, wherein the first network node is a RAN Intelligent Controller (RIC) node.

[0730] Aspect 131: The method according to one or more of aspects 124 to 130, further comprising: sending the updated long-term mobility history data to the second network node.

[0731] Aspect 132: A method according to aspect 131, wherein sending the updated long-term mobility history data to the second network node comprises: sending the updated long-term mobility history data to the second network node during a radio resource control (RRC) connection establishment procedure of the UE.

[0732] Aspect 133: The method according to aspect 131 or 132, wherein sending the updated long-term mobility history data to the second network node comprises: sending the updated long-term mobility history data to the second network node during a handover procedure of the UE.

[0733] Aspect 134: A method according to one or more of aspects 131 to 133, wherein sending the updated long-term mobility history data to the second network node comprises: sending the updated long-term mobility history data to the second network node in a UE context setup message.

[0734] Aspect 135: An apparatus for wireless communication at a device, comprising: a processor; a memory coupled to the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to perform one or more of the methods described in Aspects 1 to 11.

[0735] Aspect 136: An apparatus for wireless communication, comprising: a memory; and one or more processors, the one or more processors coupled to the memory and configured to perform the method according to one or more of aspects 1 to 11.

[0736] Aspect 137: An apparatus for wireless communication, comprising at least one means for performing the method according to one or more of aspects 1 to 11.

[0737] Aspect 138: A non-transitory computer-readable medium storing code for wireless communication, the code comprising instructions executable by a processor to perform the method according to one or more of aspects 1 to 11.

[0738] Aspect 139: A non-transitory computer-readable medium storing an instruction set for wireless communication, the instruction set comprising one or more instructions that, when executed by one or more processors of a device, cause the device to perform one or more of the methods described in aspects 1 to 11.

[0739] Aspect 140: An apparatus for wireless communication at a device, comprising: a processor; a memory coupled to the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to perform one or more of the methods described in Aspects 12 to 20.

[0740] Aspect 141: An apparatus for wireless communication, comprising: a memory; and one or more processors, the one or more processors coupled to the memory and configured to perform the method according to one or more of aspects 12 to 20.

[0741] Aspect 142: An apparatus for wireless communication, comprising at least one means for performing the method according to one or more of aspects 12 to 20.

[0742] Aspect 143: A non-transitory computer-readable medium storing code for wireless communication, the code comprising instructions executable by a processor to perform the method according to one or more of aspects 12 to 20.

[0743] Aspect 144: A non-transitory computer-readable medium storing an instruction set for wireless communication, the instruction set comprising one or more instructions that, when executed by one or more processors of a device, cause the device to perform one or more of the methods described in aspects 12 to 20.

[0744] Aspect 145: An apparatus for wireless communication at a device, comprising: a processor; a memory coupled to the processor; and instructions stored in the memory and executable by the processor to cause the apparatus to perform one or more of the methods described in Aspects 21 to 33.

[0745] Aspect 146: An apparatus for wireless communication, comprising: a memory; and one or more processors, the one or more processors coupled to the memory and configured to perform the method according to one or more of aspects 21 to 33.

[0746] Aspect 147: An apparatus f...

Claims

1. A first network node for wireless communication, comprising: Memory; and one or more processors coupled to the memory and configured to: sending mobility history data of a user equipment (UE) to a second network node in a core network associated with a radio access network (RAN), the first network node being configured to operate in the RAN; as well as A UE mobility prediction model based at least in part on the mobility history data is received from the second network node.

2. The first network node according to claim 1, wherein the second network node is an access management function (AMF) node.

3. The first network node of claim 1 , wherein to send the mobility history data to the second network node, the one or more processors are configured to: The mobility history data is sent to the second network node in a UE context release complete message.

4. The first network node of claim 1 , wherein to send the mobility history data to the second network node, the one or more processors are configured to: The mobility history data is sent to the second network node in a path switch request message.

5. The first network node of claim 1 , wherein in order to receive the UE mobility prediction model from the second network node, the one or more processors are configured to: The UE mobility prediction model is received from the second network node during a radio resource control (RRC) connection establishment procedure of the UE.

6. The first network node of claim 1 , wherein in order to receive the UE mobility prediction model from the second network node, the one or more processors are configured to: The UE mobility prediction model is received from the second network node during a handover procedure of the UE.

7. The first network node of claim 1 , wherein in order to receive the UE mobility prediction model from the second network node, the one or more processors are configured to: The UE mobility prediction model is received from the second network node in an initial UE context setup request message.

8. A first network node for wireless communication, comprising: Memory; and one or more processors coupled to the memory and configured to: sending mobility history data of a user equipment (UE) to a second network node in a core network associated with a radio access network (RAN), the first network node being configured to operate in the RAN; as well as Updated long-term mobility history data based at least in part on the mobility history data is received from the second network node.

9. The first network node according to claim 8, wherein the second network node is an Access Management Function (AMF) node.

10. The first network node of claim 8, wherein to send the mobility history data to the second network node, the one or more processors are configured to: The mobility history data is sent to the second network node in a UE context release complete message.

11. The first network node of claim 8, wherein to send the mobility history data to the second network node, the one or more processors are configured to: The mobility history data is sent to the second network node in a path switch request message.

12. The first network node of claim 8, wherein to receive the updated long-term mobility history data from the second network node, the one or more processors are configured to: The updated long-term mobility history data is received from the second network node during a radio resource control (RRC) connection establishment procedure of the UE.

13. The first network node of claim 8, wherein to receive the updated long-term mobility history data from the second network node, the one or more processors are configured to: The updated long-term mobility history data is received from the second network node during a handover procedure of the UE.

14. The first network node of claim 8, wherein to receive the updated long-term mobility history data from the second network node, the one or more processors are configured to: The updated long-term mobility history data is received from the second network node in an initial UE context setup request message.

15. The first network node of claim 8, wherein to receive the updated long-term mobility history data from the second network node, the one or more processors are configured to: The updated long-term mobility history data is received from the second network node in a UE context modification request message.

16. The first network node of claim 8, wherein to receive the updated long-term mobility history data from the second network node, the one or more processors are configured to: The updated long-term mobility history data is received from the second network node in a path switch request confirm message.

17. A first network node for wireless communication, comprising: Memory; and one or more processors coupled to the memory and configured to: sending mobility history data of a user equipment (UE) to a second network node in a decomposed radio access network (RAN), the first network node being configured to operate in the RAN; as well as A UE mobility prediction model based at least in part on the mobility history data is received from the second network node.

18. The first network node of claim 17, wherein to send the mobility history data to the second network node, the one or more processors are configured to: The mobility history data is sent to the second network node in an event report.

19. The first network node of claim 17, wherein to receive the UE mobility prediction model from the second network node, the one or more processors are configured to: The UE mobility prediction model is received from the second network node during a radio resource control (RRC) connection establishment procedure of the UE.

20. The first network node of claim 17, wherein to receive the UE mobility prediction model from the second network node, the one or more processors are configured to: The UE mobility prediction model is received from the second network node during a handover procedure of the UE.

21. The first network node of claim 17, wherein to receive the UE mobility prediction model from the second network node, the one or more processors are configured to: The UE mobility prediction model is received from the second network node in a UE context setup message.

22. The first network node of claim 17, wherein the UE mobility prediction model is trained at least in part based on: recent mobility history data associated with the UE, and Long-term mobility history data collected for the UE.

23. The first network node according to claim 17, wherein the UE mobility prediction model comprises: A UE-specific UE mobility prediction model of the UE.

24. A first network node for wireless communication, comprising: Memory; and one or more processors coupled to the memory and configured to: sending mobility history data of a user equipment (UE) to a second network node in a decomposed radio access network (RAN), the first network node being configured to operate in the RAN; as well as Updated long-term mobility history data based at least in part on the mobility history data is received from the second network node.

25. The first network node of claim 24, wherein the second network node is a Service Management and Orchestration (SMO) node.

26. The first network node of claim 24, wherein the second network node is a RAN Intelligent Controller (RIC) node.

27. The first network node of claim 24, wherein to send the mobility history data to the second network node, the one or more processors are configured to: The mobility history data is sent to the second network node in an event report.

28. The first network node of claim 24, wherein to receive the updated long-term mobility history data from the second network node, the one or more processors are configured to: The updated long-term mobility history data is received from the second network node during a radio resource control (RRC) connection establishment procedure of the UE.

29. The first network node of claim 24, wherein to receive the updated long-term mobility history data from the second network node, the one or more processors are configured to: The updated long-term mobility history data is received from the second network node during a handover procedure of the UE.

30. The first network node of claim 24, wherein to receive the updated long-term mobility history data from the second network node, the one or more processors are configured to: The updated long-term mobility history data is received from the second network node in a UE context setup message.

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