Methods, network nodes, computer programs, computer program products and non-transitory computer-readable media to support differential reporting of predicted positioning information with GNB-CU side model for ai / ML assisted positioning

Abstract

Systems and methods for supporting reporting of predicted, or inferred, positioning information with a Central Unit (CU)-side model for Artificial Intelligence (AI) / Machine Learning (ML) assisted positioning are disclosed. In one embodiment, a method performed by a CU of a Radio Access Network (RAN) node for generating inferred measurements based on a CU-side AI / ML model for AI / ML assisted positioning comprises receiving from a Location Management Function (LMF) a measurement request comprising a request for at least one inferred measurement for AI / ML assisted positioning and transmitting, to the LMF, at least one value associated with the at least one inferred measurement generated using the CU-side AI / ML model for AI / ML assisted positioning. Embodiments of a CU of a RAN node and an LMF, as well a methods of operation thereof, are also disclosed.

Description

[0001] METHODS, NETWORK NODES, COMPUTER PROGRAMS, COMPUTER PROGRAM PRODUCTS

[0002] AND NON-TRANSITORY COMPUTER-READABLE MEDIA TO SUPPORT DIFFERENTIAL REPORTING OF PREDICTED POSITIONING INFORMATION WITH GNB-CU SIDE MODEL FOR AI / ML ASSISTED POSITIONING

[0003] RELATED APPLICATIONS

[0004] This application claims the benefit of provisional patent application serial number 63 / 614,172, filed December 22, 2023, the disclosure of which is hereby incorporated herein by reference in its entirety.

[0005] TECHNICAL FIELD

[0006] The present disclosure relates to Artificial Intelligence (AI) / Machine Learning (ML) assisted positioning in a wireless communications system.

[0007] BACKGROUND

[0008] Third Generation Partnership Project (3GPP) Technical Report (TR) 38.843 V2.0.0 describes the Radio Access Network (RAN) Working Group (WG) 1 (i.e., RANI) study on the analysis of potential enhancements for Release 19 necessary to enable Artificial Intelligence (AI) / Machine Learning (ML) for positioning accuracy enhancements with New Radio (NR) Radio Access Technology (RAT)-dependent positioning methods. Evaluation scenarios and Key Performance Indicators (KPIs) were identified for system level analysis of AI / ML enabled RAT- dependent positioning techniques as described in clause 6.4 of 3GPP TR 38.843. Direct AI / ML positioning and AI / ML assisted positioning were identified and selected as the representative subuse cases. Evaluation results have shown that in considered evaluation scenarios, both direct AI / ML positioning and AI / ML assisted can significantly improve the positioning accuracy compared to existing RAT-dependent positioning methods. Various aspects of AI / ML for positioning accuracy enhancement were investigated and evaluated, as described in clause 6.4 of 3GPP TR 38.843, which provides summary of evaluation results from different sources.

[0009] The necessity, feasibility, and potential enhancements to facilitate the support of AI / ML for positioning accuracy enhancements with NR RAT-dependent positioning methods were studied and the outcome is outlined in clause 7 of 3GPP TR 38.843. Measurements, signaling and procedures were studied to enable AI / ML for positioning accuracy enhancements with NR RAT- dependent positioning methods and is recommended to be further investigated in normative work, and specified if necessary. A variety of enhancements for measurements (e.g., based on extensions to current positioning measurements or with new measurements) were identified as potentially beneficial (e.g., trade-off positioning accuracy requirement and signalling overhead) and are recommended to be investigated further and if needed, specified during normative work.

[0010] Based on conducted analysis, 3 GPP TR 38.843 recommends that 3 GPP proceed with normative work for AI / ML based positioning. It is recommended for 3 GPP to specify necessary measurement, signaling, and procedures to facilitate training, inference, monitoring, and / or other Life Cycle Management (LCM) operations for both direct AI / ML positioning and AI / ML assisted positioning, specifically:

[0011] • specify necessary signalling of data collection; investigate the necessity of other information for supporting data collection, and if needed, specify during normative work

[0012] • investigate on the necessity and signalling details of measurement enhancements, and if needed, specify during normative work

[0013] • investigate on the necessity and signalling details of monitoring method(s), and if needed, specify during normative work

[0014] Clause 5.3 of 3GPP TR 38.843 states the following:

[0015] The following are selected as representative sub-use cases:

[0016] Direct AI / ML positioning:

[0017] AI / ML model output: UE location e.g., fingerprinting based on channel observation as the input of AI / ML model

[0018] AI / ML assisted positioning:

[0019] AI / ML model output: new measurement and / or enhancement of existing measurement e.g., LOS / NLOS identification, timing and / or angle of measurement, likelihood of measurement

[0020] More specifically, the following Cases are considered for the study:

[0021] Case 1 : UE-based positioning with UE-side model, direct AI / ML or AI / ML assisted positioning

[0022] Case 2a: UE-assisted / LMF-based positioning with UE-side model, AI / ML assisted positioning

[0023] Case 2b: UE-assisted / LMF-based positioning with LMF-side model, direct AI / ML positioning

[0024] Case 3a: NG-RAN node assisted positioning with gNB-side model, AI / ML assisted positioning Case 3b: NG-RAN node assisted positioning with LMF-side model, direct AI / ML positioning

[0025] One-sided model whose inference is performed entirely at the UE or at the network is prioritized in Release 18 (Rel-18) System Information (SI).

[0026] For all five positioning cases (Case l / 2a / 2b / 3a / 3b), RANI has not considered prioritization.

[0027] For positioning enhancement use case:

[0028] For model training, training data can be generated by UE / PRU / gNB / LMF.

[0029] For LMF-side model inference (Case 2b, Case 3b), input data can be generated by UE / gNB and terminated at LMF.

[0030] For gNB-side model inference (Case 3a), input data is internally available at gNB.

[0031] For UE-side model inference (Case 1, Case 2a), input data is internally available at UE.

[0032] For performance monitoring at the LMF side, calculated performance metrics (if needed) or data needed for performance metric calculation (if needed) can be generated by UE / gNB and terminated at LMF.

[0033] For performance monitoring at the gNB side, calculated performance metrics (if needed) or data needed for performance metric calculation (if needed) can be generated by at least gNB.

[0034] The Next Generation (NG) RAN (NG-RAN) consists of a set of gNodeBs (gNBs) connected to the 5thGeneration (5G) Core (5GC) through the NG interface. 3 GPP Technical Specification (TS) 38.401 (see, e.g., V17.6.0) describes the overall NG-RAN architecture. A disaggregated gNB may consist of a gNB-Centralized Unit (CU) and one or more gNB-Distributed Unit(s) (DUs). A gNB-CU and a gNB-DU is connected via Fl interface. FIGURE 1 illustrates the Fl interface between the gNB-CU and the gNB-DU. This interface is responsible for possible information and control signaling from the Packet Data Convergence Protocol (PDCP) entity located in CU and Radio Link Control (RLC) entity located in DU.

[0035] FIGURE 2 illustrates the User Equipment (UE) positioning architecture applicable to NG- RAN. The Access and Mobility Management Function (AMF) receives a request for some location service associated with a particular target UE from another entity (e.g., Gateway Mobile Location Centre (GMLC) or UE), or the AMF itself decides to initiate some location service on behalf of a particular target UE (e.g., for an Internet Protocol (IP) Multimedia Subsystem (IMS) emergency call from the UE). The AMF then sends a location services request to a Location Management Function (LMF). The LMF processes the location services request which may include transferring assistance data to the target UE to assist with UE-based and / or UE-assisted positioning and / or may include positioning of the target UE. The LMF then returns the result of the location service back to the AMF (e.g., a position estimate for the UE. In the case of a location service requested by an entity other than the AMF (e.g., a GMLC or UE), the AMF returns the location service result to this entity. An NG-RAN node may control several Transmission / Reception Points / Transmission Points (TRPs / TPs), such as remote radio heads or downlink (DL)-Positioning Reference Signal (PRS)-only TPs for support of PRS-based Terrestrial Beacon System (TBS).

[0036] In NG-RAN architecture, gNB-CU terminates the NR Positioning Protocol A (NRPPa) protocol and the gNB-DU hosts the TRPs.

[0037] SUMMARY

[0038] Systems and methods for supporting reporting of predicted, or inferred, positioning information with a Central Unit (CU)-side model for Artificial Intelligence (Al) / Machine Learning (ML) assisted positioning are disclosed. In one embodiment, a method performed by a CU of a Radio Access Network (RAN) node for generating inferred measurements based on a CU-side AI / ML model for AI / ML assisted positioning comprises receiving from a Location Management Function (LMF) a measurement request comprising a request for at least one inferred measurement for AI / ML assisted positioning and transmitting, to the LMF, at least one value associated with the at least one inferred measurement generated using the CU-side AI / ML model for AI / ML assisted positioning. In this manner, reporting of predicted positioning information with a CU-side model for AI / ML assisted positioning is enabled.

[0039] In one embodiment, the method further comprises using the AI / ML CU-side model to generate the at least one inferred measurement.

[0040] In one embodiment, the method further comprises receiving, from a Distributed Unit (DU) of the RAN node, input data for use in generating, using the CU-side AI / ML model, the at least one value associated with the at least one inferred measurement. In one embodiment, receiving the input data comprises receiving the input data from the DU via an Fl message. In one embodiment, the method further comprises receiving, from the DU, information indicating an age of the input data. In one embodiment, the method further comprises receiving, from the DU, at least one of: at least one UE identifier associated with the input data, at least one UE group identifier associated with the input data, at least one FlaP gNodeB (gNB) DU Identifier (gNB-DU ID) associated with the input data, and at least one Transmission and Reception Point (TRP) ID.

[0041] In one embodiment, a message comprising the input data received from the DU further comprises an indication that the input data is a full report and / or an indication that the message does not comprise fragmented input data.

[0042] In one embodiment, the input data received from the DU comprises fragmented input data. In one embodiment, a message comprising the input data received from the DU further comprises an indication that the input data is a partial report and / or comprises fragmented input data.

[0043] In one embodiment, the method further comprises transmitting, to the DU, second capability information, wherein the second capability information indicates that the CU supports receiving fragmented input data. In one embodiment, the second capability information indicates at least one of: a maximum F1AP message size, and a maximum number of User Equipments (UEs) for which input data should be received in one message.

[0044] In one embodiment, the input data is received in a first message from the DU, and the method further comprises receiving, from the DU, a second message comprising additional input data from the DU and using the input data from the first message and the additional input data from the second message as input to the CU-side AI / ML model when generating the at least one value associated with the at least one predicted measurement. In one embodiment, the input data is associated with a first UE and / or a first group of UEs, and the additional input data is associated with a second UE and / or a second group of UEs.

[0045] In one embodiment, the method further comprises transmitting, to the DU, information comprising at least one of: a request for the input data and / or the additional input data, a time window for receiving the input data and / or the additional input data, at least one UE identifier or UE group identifier for which input data is to be collected and transmitted by the DU, information associated with a UE priority for determining input data to be collected and / or transmitted by the DU, an indication that input data is to be transmitted to the CU as collected, an indication that input data is to be transmitted to the CU within a maximum period of time that begins when measurement data collection is initiated, a maximum threshold value of a measurement volume for a message containing the input data and / or additional input data, at least one time window for collecting the input data and / or additional input data, a volume of input data that may be transmitted in a message to the CU, and an expiration time for the input data. In one embodiment, the information is transmitted in an F1AP message.

[0046] In one embodiment, the method further comprises determining a subset of the received input data from the DU for use in generating the at least one value associated with the at least one inferred measurement.

[0047] In one embodiment, the method further comprises transmitting, to the DU, an indication to stop measuring TRP and / or reporting input data.

[0048] In one embodiment, transmitting the at least one value associated with the at least one predicted measurement comprising transmitting, to the LMF, an NRPPa Measurement Response message comprising the at least one value associated with the at least one inferred measurement. In one embodiment, the NRPPa Measurement Response message comprises at least one additional value associated with a TRP measurement performed by a UE and the at least one value associated with the at least one inferred measurement.

[0049] In one embodiment, the method further comprises transmitting, to the LMF, additional information with the at least one value associated with the at least one inferred measurement, wherein the additional information comprises at least one of: information indicating an accuracy level or uncertainty level of the at least one inferred measurement, information associated with training data used to generate the at least one value associated with the at least one inferred measurement, time information associated with the at least one value associated with the at least one inferred measurement, information indicating how far into the future the at least one inferred measurement is associated, information indicating a quality and / or quality level of the at least one value associated with the at least one inferred measurement.

[0050] In one embodiment, the measurement request comprises an indication of at least one type of predicted measurement that is requested, and wherein the at least one value associated with the at least one predicted measurement is of the at least one type requested.

[0051] In one embodiment, the method further comprises receiving, from the LMF, information that indicates one or more requirements for the requested at least one inferred measurement, wherein the one or more requirements comprise any one or more of the following: a requirement that the at least one value is associated with an uncertainty that is better than or equal to an uncertainty threshold, a requirement that the at least one value is associated with an accuracy level that is equal to or greater than an accuracy threshold, a requirement that the at least one value is calculated for a specific point in time in the future, a requirement that the at least one value is calculated for a time within a defined time window, a requirement that values for the requested at least one inferred measurement be reported periodically at a specific reporting period, a requirement that a value for the requested at least one inferred measurement be reported only once, a requirement that values for the requested at least one inferred measurement be reported periodically at a specific reporting period for a specific time duration, a requirement that the at least one value is associated with a quality level that is equal to or greater than a quality threshold.

[0052] In one embodiment, the method further comprises determining that at least one requirement is fulfilled prior to transmitting the at least one value associated with the at least one inferred measurement, and wherein the at least one requirement comprises any one or more of the following: a requirement that the at least one value is associated with an uncertainty that is better than or equal to an uncertainty threshold, a requirement that the at least one value is associated with an accuracy level that is equal to or greater than an accuracy threshold, a requirement that the at least one value is calculated for a specific point in time in the future, a requirement that the at least one value is calculated for a time within a defined time window, a requirement that values for the requested at least one inferred measurement be reported periodically at a specific reporting period, a requirement that a value for the requested at least one inferred measurement be reported only once, a requirement that values for the requested at least one inferred measurement be reported periodically at a specific reporting period for a specific time duration, a requirement that the at least one value is associated with a quality level that is equal to or greater than a quality threshold. In one embodiment, the method further comprises receiving, from the LMF, the at least one requirement.

[0053] In one embodiment, the method further comprises, in response to receiving the measurement request, transmitting, to the LMF, a response message comprising an indication that one or more of the at least one inferred measurements can be provided.

[0054] In one embodiment, the method further comprises, in response to receiving the measurement request, transmitting, to the LMF, a response message comprising an indication that one or more of the at least one predicted measurements cannot be provided. In one embodiment, the response message comprises at least one of: a cause and / or reason that the one or more of the at least one inferred measurement cannot be provided, an indication that failure to provide the one or more of the at least one inferred measurement is temporary, and an indication that the CU does not support the one or more of the at least one inferred measurement. In one embodiment, the response message indicates at least one of: at least one type of inferred measurement that can be provided and at least one type of inferred measurement that cannot be provided.

[0055] In one embodiment, the at least one value associated with the at least one inferred measurement is associated with at least one of: Uplink Angle of Arrival (UL-AoA), Uplink Relative Time of Arrival (UL-RTOA), Uplink Sounding Reference Signal (SRS) Reference Signal Received Power (UL-SRS-RSRP), Uplink SRS Reference Signal Received Path Power (UL-SRS-RSRPP), gNB Rx-Tx, and Uplink Reference Signal Carrier Phase (UL-RSCP).

[0056] In one embodiment, transmitting the at least one value associated with the at least one inferred measurement comprises transmitting, to the LMF, the at least one value associated with the at least one inferred measurement via a TRP measurement report Information Element (IE).

[0057] In one embodiment, the method further comprises transmitting to the DU of the RAN node and / or the LMF, capability information indicating a capability of the CU to generate the at least one inferred measurement for AI / ML assisted positioning. In one embodiment, the method further comprises receiving, from the LMF, a request for the capability information, and wherein transmitting the capability information comprises transmitting the capability information to the LMF in response to the request for the capability information. In one embodiment, the request for the capability information is received via NRPPa in a TRP information request message. In one embodiment, the method further comprises receiving, from the DU, a request for the capability information, and wherein transmitting the capability information comprises transmitting the capability information to the DU in response to the request for the capability information received from the DU. In one embodiment, the request for the capability information received from the DU is received via a Fl message. In one embodiment, the capability information is transmitted to the DU in a Fl AP positioning message. In one embodiment, the capability information is transmitted to the LMF in a NRPPa message. In one embodiment, the capability information comprises at least one of: an indication of at least one measurement prediction, inference input, and / or inference output supported by the CU, an indication of at least one type of inferred measurement supported by the CU-side AI / ML model, information associated with an accuracy level and / or uncertainty level of inferred measurements supported by the CU-side AI / ML model, information associated with training data used to train the CU-side AI / ML model, information associated with a time horizon for inferred measurements supported at the CU, information associated with a processing delay for the CU- side AI / ML model, information associated with a maximum measurement resolution the CU-side AI / ML model can provide and / or the CU can provide, information indicating at least one TRP hosted by the CU that supports inferred measurements, and information associated with a maximum update rate for a particular measurement resolution the CU supports.

[0058] Corresponding embodiment of a network node for implementing a CU of a RAN node for generating inferred measurements based on a CU-side AI / ML model for AI / ML assisted positioning are also disclosed. In one embodiment, the network node comprises processing circuitry configured to cause the network node to receive, from an LMF, a measurement request comprising a request for at least one inferred measurement for AI / ML assisted positioning and transmit, to the LMF, at least one value associated with the at least one inferred measurement generated using the CU-side AI / ML model for AI / ML assisted positioning.

[0059] Embodiments of a method performed by an LMF for supporting inferred measurements based on a CU-side AI / ML, model for AI / ML assisted positioning are also disclosed. In one embodiment, the method performed by the LMF comprises transmitting, to a CU of a RAN node, a measurement request comprising a request for at least one inferred measurement for AI / ML assisted positioning and receiving, from the CU, at least one value associated with the at least one inferred measurement generated using the CU-side AI / ML model for AI / ML assisted positioning.

[0060] Corresponding embodiments of a network node for implementing an LMF for supporting inferred measurements based on a CU-side AI / ML model for AI / ML assisted positioning are also disclosed. In one embodiment, the network node comprises processing circuitry configured to cause the network node to transmit, to the CU, a measurement request comprising a request for at least one inferred measurement for AI / ML assisted positioning, and receive, from the CU, at least one value associated with the at least one inferred measurement generated using the CU- side AI / ML model for AI / ML assisted positioning.

[0061] Embodiments of a method performed by a DU of a RAN node for supporting inferred measurements based on a CU-side AI / ML model for AI / ML assisted positioning are also disclosed. In one embodiment, the method performed by the DU of the RAN node comprises receiving, from a CU of the RAN node, a request for input data for use in generating at least one inferred measurement using the CU-side AI / ML model for AI / ML assisted positioning and transmitting, to the CU, input data for use in generating the at least one inferred measurement using the CU-side AI / ML model for AI / ML assisted positioning, in response to the request.

[0062] Corresponding embodiments of a network node for implementing a DU of a RAN node for supporting inferred measurements based on a CU-side AI / ML model for AI / ML assisted positioning are also disclosed. In one embodiment, the network node for implementing the DU of the network node comprises processing circuitry configured to cause the network node to receive, from a CU of the RAN node, a request for input data for use in generating at least one inferred measurement using the CU-side AI / ML model for AI / ML assisted positioning and transmit, to the CU, input data for use in generating the at least one inferred measurement using the CU-side AI / ML model for AI / ML assisted positioning, in response to the request.

[0063] BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the disclosure, and together with the description serve to explain the principles of the disclosure.

[0065] FIGURE 1 illustrates a Next Generation (NG) Radio Access Network (RAN) node as specified by the 3rdGeneration Partnership Project (3 GPP).

[0066] FIGURE 2 illustrates the User Equipment (UE) positioning architecture applicable to NG-RAN.

[0067] FIGURE 3 illustrates a method and signaling diagram for Artificial Intelligence (AI) / Machine Learning (ML) assisted positioning using a Central Unit (CU) side AI / ML model, in accordance with an example embodiment of the present disclosure.

[0068] FIGURE 4 shows an example of a communication system in accordance with some embodiments.

[0069] FIGURE 5 shows a UE, which may be an embodiment of the UE of FIGURE 4, in accordance with some embodiments.

[0070] FIGURE 6 shows a network node, which may be an embodiment of the network node of FIGURE 4, in accordance with some embodiments. FIGURE 7 is a block diagram of a host, which may be an embodiment of the host of FIGURE 4, in accordance with various aspects described herein.

[0071] FIGURE 8 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized.

[0072] FIGURE 9 shows a communication diagram of a host communicating via a network node with a UE over a partially wireless connection in accordance with some embodiments.

[0073] FIGURE 10 illustrates an example method by a CU of a network node for generating predicted measurements based on a CU-side model, according to certain embodiments.

[0074] FIGURE 11 illustrates an example method by a LMF node for supporting predicted measurements based on a CU-side model, according to certain embodiments.

[0075] FIGURE 12 illustrates an example method by a DU for supporting predicted measurements based on a CU-side model, according to certain embodiments.

[0076] DETAILED DESCRIPTION

[0077] Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0078] There currently exist certain challenge(s). For example, until recently, the 3rdGeneration Partnership Project (3 GPP) positioning solutions and methods have relied on measurement reports by the User Equipment (UE) and gNodeB (gNB) to the Location Management Function (LMF). The measured quantities are typically the result of processing at the UE / gNB, e.g. the time of arrival or power from a reference signal. These reports were thus reasonably small in size. In Release 19 (Rel-19), 3 GPP will support so-called “direct AI / ML based positioning” in network nodes (LMF, gNB Central Unit (CU), or gNB Distributed Unit (DU)), where the network collects a much rawer set of measurements which are in fact channel impulse responses, consisting of multipath complex-valued channel response.

[0079] In the case of an inference model for Artificial Intelligence (AI)ZMachine Learning (ML) assisted positioning on the gNB-CU side (case 3a from 3GPP Technical Report (TR) 38.843), with input data generated by the gNB (Transmission / Reception Points (TRPs)) and terminated at the gNB-CU, the issue of signaling overhead may quickly arise on the Fl interface between the TRPs in the gNB-DU and the gNB-CU. In fact, a Next Generation (NG) Radio Access Network (RAN) (i.e., NG-RAN) node can host up to 65,536 TRPs in the gNB-DU and, for each of them, input data needs to be reported from the measuring TRPs to the gNB-CU hosting the inference model for AI / ML assisted positioning. The gNB-CU needs to report the generated predicted positioning measurements to the LMF to calculate the UE location. Due to this centralized model at the gNB- CU network side and the incurring large amount of information for input data that needs to be reported, the overhead will impact the transport network and make the Fl positioning messages very large in size. This creates a decoding problem at the receiver, as decoding capabilities are limited by a maximum message size.

[0080] Another problem is that the current 3 GPP specifications do not allow for the transferring of predicted measurements from the gNB-CU to the LMF

[0081] Additionally, the current 3 GPP specifications do not allow an LMF, requesting for predicted measurements from the NG-RAN, to understand what the AI / ML assisted positioning capabilities of the NG-RAN are and whether the NG-RAN is capable of providing the requested predicted measurements or not.

[0082] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. For example, methods and systems are disclosed that address the message size issue between the different entities involved in NG-RAN node assisted positioning with gNB-CU- side model, AI / ML assisted positioning, by sending input data that have been fragmented by the gNB-DU little by little to the gNB-CU. The fragmentation is either done based on the gNB-DU decision or by recommendation from higher layers (e.g., gNB-CU or LMF).

[0083] Specifically, a first positioning message (e.g., Fl Positioning message from gNB-DU to gNB-CU) will provide a first fragmented input data, then a second message will follow up providing additional fragmented input data, with status indication whether it is still partial reporting or represents the final input data fragment from the gNB-DU. Each fragment will be characterized by an age of location and to which group of UEs it is associated to.

[0084] The partial indication is signaled over Fl interface either in existing positioning procedures, or new ones.

[0085] Certain embodiments may provide one or more of the following technical advantage(s). For example, certain embodiments may provide a technical advantage of reducing Fl positioning protocol message size over the transport network. As another example, certain embodiments may provide a technical advantage of reducing overhead in case of AI / ML inference model at gNB- CU. As another example, certain embodiments may provide a technical advantage of enabling transferring of new inferred measurements from the gNB-CU to the LMF. As another example, certain embodiments may provide a technical advantage of enabling signaling exchange aimed at informing the LMF of the supported capabilities at the gNB-CU for AI / ML assisted positioning. Other advantages may be readily apparent to one having skill in the art. Certain embodiments may have none, some, or all of the recited advantages.

[0086] As used herein, “node” can be a network node or a UE. Examples of network nodes are NodeB, base station (BS), Multi- Standard Radio (MSR) radio node such as MSR BS, eNodeB (eNB), gNodeB (gNB), Master eNB (MeNB), Secondary eNB (SeNB), Integrated Access Backhaul (IAB) node, network controller, Radio Network Controller (RNC), Base Station Controller (BSC), relay, donor node controlling relay, Base Transceiver Station (BTS), Central Unit (e.g. in a gNB), Distributed Unit (e.g. in a gNB), Baseband Unit, Centralized Baseband, Cloud RAN (C-RAN), Access Point (AP), transmission points, transmission nodes, Remote Radio Unit (RRU), Remote Radio Head (RRH), nodes in Distributed Antenna System (DAS), core network node (e.g. Mobile Switching Center (MSC), Mobility Management Entity (MME), etc.), Operations & Maintenance (O&M), Operations Support System (OSS), Self Organizing Network (SON), positioning node (e.g. E-SMLC), etc.

[0087] Another example of a node is User Equipment (UE), which is a non-limiting term and refers to any type of wireless device communicating with a network node and / or with another UE in a cellular or mobile communication system. Examples of UE are target device, device to device (D2D) UE, Vehicular to Vehicular (V2V), machine type UE, Machine Type Communication (MTC) UE or UE capable of Machine To Machine (M2M) communication, Personal Digital Assistant (PDA), Tablet, mobile terminals, smart phone, Laptop Embedded Equipment (LEE), Laptop Mounted Equipment (LME), Universal Serial Bus (USB) dongles, etc.

[0088] In some embodiments, generic terminology, “radio network node” or simply “network node (NW node)”, is used. It can be any kind of network node which may comprise base station, radio base station, base transceiver station, base station controller, network controller, evolved Node B (eNB), Node B, gNodeB (gNB), relay node, access point, radio access point, Remote Radio Unit (RRU) Remote Radio Head (RRH), Central Unit (e.g. in a gNB), Distributed Unit (e.g. in a gNB), Baseband Unit, Centralized Baseband, C-RAN, access point (AP), etc. The term Radio Access Technology (RAT) may refer to any RAT such as, for example, Universal Terrestrial Radio Access Network (UTRA), Evolved Universal Terrestrial Radio Access Network (E-UTRA), Narrow Band Internet of Things (NB-IoT), WiFi, Bluetooth, next generation RAT, New Radio (NR), 4thGeneration (4G), 5thGeneration (5G), etc. Any of the equipment denoted by the terms node, network node or radio network node may be capable of supporting a single or multiple RATs.

[0089] Also note that a radio access network node (i.e., a RAN node) may itself comprise other network nodes. In particular, a RAN node (e.g., a NG-RAN node) may include a CU and one or more DUs, as described herein.

[0090] The term “signal” or “radio signal” used herein can be any physical signal or physical channel. Examples of downlink (DL) physical signals are reference signal (RS) such as Primary Synchronization Signal (PSS), Secondary Synchronization Signal (SSS), Channel State Information (CSI) Reference Signal (CSI-RS), Demodulation Reference Signal (DMRS) signals in Synchronization Signal (SS) / Physical Broadcast Channel (PBCH) Block (SSB), Discovery Reference Signal (DRS), Cell-specific Reference Signal (CRS), Positioning Reference Signal (PRS), etc. A Reference Signal (RS) may be periodic e.g. RS occasion carrying one or more RSs may occur with certain periodicity e.g. 20 milliseconds (ms), 40 ms, etc. The RS may also be aperiodic. Each SSB carries NR-PSS, NR-SSS and NR-PBCH in 4 successive symbols. One or multiple SSBs are transmit in one SSB burst which is repeated with certain periodicity e.g. 5 ms, 10 ms, 20 ms, 40 ms, 80 ms and 160 ms. The UE is configured with information about SSB on cells of certain carrier frequency by one or more SS / PBCH block Measurement Timing Configuration (SMTC) configurations. The SMTC configuration comprising parameters such as SMTC periodicity, SMTC occasion length in time or duration, SMTC time offset with regard to reference time (e.g. serving cell’s System Frame Number (SFN)), etc. Therefore, SMTC occasion may also occur with certain periodicity e.g. 5 ms, 10 ms, 20 ms, 40 ms, 80 ms and 160 ms. Examples of uplink (UL) physical signals are reference signal such as Sounding Reference Signal (SRS), DMRS etc. The term physical channel refers to any channel carrying higher layer information e.g. data, control etc. Examples of physical channels are PBCH, Narrowband PBCH (NPBCH), Physical Downlink Control Channel (PDCCH), Physical Downlink Shared Channel (PDSCH), Physical Uplink Control Channel (PUCCH), Physical Uplink Shared Channel (PUSCH), short PDCCH (sPDCCH), short PDSCH (sPDSCH), short PUCCH (sPUCCH), short PUSCH (sPUSCH), MTC PDCCH (MPDCCH), Narrowband PDCCH (NPDCCH), Narrowband PDSCH (NPDSCH), Enhanced PDCCH (E-PDCCH), Narrowband PUSCH (NPUSCH), etc.

[0091] The term “time resource” used herein may correspond to any type of physical resource or radio resource expressed in terms of length of time. Examples of time resources are: symbol, time slot, subframe, radio frame, Transmission Time Interval (TH), interleaving time, slot, sub-slot, mini-slot, SFN cycle, hyper-SFN (H-SFN) cycle, etc.

[0092] Certain embodiments described herein can be applicable to, but are not limited to, 3 GPP NR. Thus, a network node can be a NR gNB or 6thGeneration (6G)-RAT base station or any other network node or device with similar functions.

[0093] Embodiments of the present disclosure related to AI / ML positioning capability declaration include the following.

[0094] According to certain embodiments, the network node such as, for example, the gNB central node (e.g., gNB-CU), declares that it supports positioning model for AI / ML assisted positioning by sending an indication in a message to other network nodes. For example, the network node may send the information in the NR Positioning Protocol A (NRPPa) positioning message to LMF, or in in the Fl AP positioning message to gNB-DU, or in Xn message to other gNB-CUs.

[0095] According to certain embodiments, the network nodes declare their capability of supporting the model for AI / ML assisted positioning via 0AM pre-configuration.

[0096] In a particular embodiment, the LMF sends a request indicator via NRPPa to enquire the network nodes to provide information about whether the gNB-CU supports AI / ML assisted positioning and what are its capabilities. For example, the gNB-CU may receive the request via a new codepoint in the NRPPa TRP INFORMAHON REQUEST message to NG-RAN node.

[0097] In a particular embodiment, the gNB-CU signals its capability for supporting the inference model for AI / ML assisted positioning to the LMF. For example, the gNB-CU may signal its capability via a new IE in the NRPPa TRP INFORMATION RESPONSE message to LMF.

[0098] In a particular embodiment, such capability may consist of details about the inference outputs supported and optionally the characteristics of such outputs. Examples of such information may consist of one or more of the following information: i) Information concerning the inferred measurements supported and that can be provided as output of an AI / ML model by the gNB-CU; ii) Information concerning the accuracy or the uncertainty of the predictions the gNB-CU can infer for the requested measurements. Such uncertainty / accuracy information may be provided on a per measurement basis or as an overall parameter for all the measurements that may be inferred; iii) Information concerning the training data used to derive the one or more AI / ML model supported at the gNB-CU and used to derive the inferred measurements that might be requested. Example of such information may be ranges of data used for training, provided for each data type, e.g. for each measurement used as a training data; iv) Information concerning the time horizon for which the inferred measurements that could potentially be requested may be generated. Namely, information about how far into the future and for which points in time into the future the one or more models in use at the gNB-CU may provide measurements predictions; v) Information about the processing delay the one or more gNB-CU side models are affected by when producing inferred measurements. Such delay may be measured from the time inputs are all received to the time inferred measurements are produced; vi) Information concerning the maximum measurement resolution the gNB-CU is able to provide. For example, the maximum number of path in a multiple report; vii) Information concerning the maximum update rate for a given measurement resolution the gNB-CU is able to support. For example, the gNB may be able to provide a measurement every 5ms for a very accurate (many paths complex impulse response), but also a faster update rate (say 1ms) for cruder (less paths in the complex response) measurements.

[0099] In a particular embodiment, the LMF knows the capabilities of a gNB-CU for AI / ML assisted positioning via pre-configuration from an external node / function / system such as the 0 AM, as well as the capabilities of the gNB-DU.

[0100] Embodiments of the present disclosure related to NG-RAN node assisted positioning with gNB-CU-side model, AI / ML assisted positioning include the following.

[0101] In a particular embodiment, the gNB-CU receives a request from the LMF to provide predicted measurements (e.g., to be inferred) for the purpose of AI / ML assisted positioning. As a consequence of receiving such request, the gNB-CU may positively reply confirming that the predicted measurements can be provided. Alternatively, if the gNB-CU is not able to produce some or all of the predicted measurements, the gNB-CU may reply with a message stating which measurements for AI / ML assisted positioning failed to be admitted and cannot be reported and which measurements for AI / ML assisted positioning were successfully admitted and can be reported.

[0102] In a further particular embodiment, if the gNB-CU fails to admit all the predicted measurements requested, the gNB-CU signals back a failure message including information about why the measurements were not admitted.

[0103] In a further particular embodiment, any of the messages above describing that some or all of the requested predicted measurements could not be admitted may contain information concerning whether the measurements could not be derived due to temporary issues, such as shortage of processing power, or whether the measurements are not supported, namely the gNB- CU is not capable of inferring such measurements.

[0104] In a further particular embodiment, any of the messages above describing that some or all of the requested predicted measurements could not be admitted may contain information concerning the capabilities of the gNB-CU, where such capabilities may follow the same description provided in the Section above relating to AI / ML positioning capability declaration.

[0105] In a particular embodiment, the gNB-CU that hosts the model for AI / ML positioning, indicates to the measuring node gNB-DU that it supports receiving fragmented input data over Fl message.

[0106] In a further particular embodiment, the gNB-CU may indicate the maximum F1AP message size according to which input fragmentation has to be applied.

[0107] In another particular embodiment, the gNB-CU includes the maximum number of UEs for which input measurements should be included in one message by the gNB-DU.

[0108] In a particular embodiment, the gNB-DU sends input data to the gNB-CU over Fl message. The gNB-CU uses this input data to (e.g., fingerprinting based on channel observation) to generate predicted positioning measurements.

[0109] In a particular embodiment, the gNB-DU adds an indication in the message containing the input data to the gNB-CU, indicating the status of this input data report, whether it is full or partial report. If the indicator is set to “partial”, the gNB-CU should understand that this represents a fragment of the input data, and that more fragmented input data is to be provided in the next message by gNB-DU. In a further particular embodiment, a fragment of the input data consists of data concerning one or more UEs. Namely the data will be grouped with a per UE granularity and the set of data signaled in a fragment would consist of all the measurements collected for one UE or for a group of UEs.

[0110] In a particular embodiment, the gNB-CU may request the gNB-DU to signal input measurements as soon as they are collected or no later than a given time window starting at measurement collection. In this embodiment the gNB-CU may include in the F1AP message requesting input measurements at the gNB-DU an indication of such maximum time.

[0111] In a further particular embodiment, a maximum “threshold value” of the measurement “volume” (e.g. number of paths, complexity, etc.) is either declared by the gNB-DU to the gNB- CU, or configured between the two nodes, above, or provided by other network entities (e.g., LMF, 0AM, etc.), above which the gNB-DU is not expected to immediately signal measurements. For measurements above the threshold value in volume, a fallback behavior can be to set a new separate window in a separate measurement, or instead do best-effort reporting.

[0112] In a further particular embodiment, the gNB-DU would reply with F1AP messages including measurements collected within time limits indicated by the gNB-CU or within time limits configured at the gNB-DU (if such time limits are not explicitly signaled to the gNB-DU by the gNB-CU). The Fl AP message carrying such input measurements would therefore only contain a subset of the overall set of measurements collected for all the UEs from which input measurements are collected. Such subset of input measurements is calculated by:

[0113] • Taking the maximum message size into account, namely signalling only a number of measurements that can fit into the maximum Fl AP message size.

[0114] • Taking the time limits indicated by the gNB-CU into account, namely every reported measurement shall not be “older” than the maximum time limit indicated by the gNB-CU.

[0115] • Taking into account that the set of measurements provided by the gNB-DU shall consist of all the measurements collected for one UE or for a group of UEs

[0116] As a consequence of this embodiment, the gNB-DU may need to discard some of the measurements collected and not send them to the gNB-CU because e.g. such measurements do not fit into the maximum message size requested by the gNB-CU or pre-configured at the gNB-DU or because the measurements are “older” than the maximum time limit declared by the gNB-CU or preconfigured at the gNB-DU. In a particular generic embodiment, the message from gNB-DU to gNB-CU with the partial (i.e., fragmented) input data contains the age of information of the fragment,

[0117] In further particular embodiment, the gNB-DU may not have received a time limit, measured from the measurement collection and within which the measurements have to be reported. In this case, the gNB-DU is still constrained in sending the input measurements according to the maximum message size requested. Therefore, some of the measurements might have to be signaled in consecutive messages to the gNB-CU. For this reason, the gNB-DU would include in the message the “age” of the reported measurements. Such information may be measured in e.g. ms and it may consist of an average of the age of the reported measurements or in the age of the oldest measurement reported.

[0118] In a particular embodiment, the message from gNB-DU to gNB-CU with the partial (i.e., fragmented) input data contains information concerning the one or more UEs this input data is associated to.

[0119] In a further particular embodiment, input measurements may be grouped on a per UE basis and each of such measurement group may be associated to an identifier for the UE, e.g. the Fl AP gNB-DU ID.

[0120] In another particular embodiment, the gNB-DU may include information about all the UEs for which measurements are reported. As an example, this can be achieved by listing in the message UE identifiers for the UEs of concern, e.g. their Fl AP gNB-DU IDs.

[0121] In a particular embodiment, the partial input data can be sent per TRP ID or per Positioning measurement in the Fl message.

[0122] In a particular embodiment, the gNB-DU may send a second message over Fl AP protocol containing other input data reports. An indication is added to indicate whether this is the last fragmented input data (i.e., “full”). In that case the gNB-CU considers that the partial reporting of input data session has ended by the sender.

[0123] In a particular embodiment, the gNB-CU can decide to use only the first fragment or a limited number of fragments of input data to generate predicted positioning measurement that will be sent to the LMF over the NRPPa message.

[0124] In a particular embodiment, the gNB-CU node once obtaining a first or second fragmented input data, and upon deciding to use only the first received fragment(s), can decide to abort the reporting by the gNB-DU by sending an Abort message over F1AP, or a stop indication to the measuring TRP.

[0125] In a particular embodiment, the gNB-CU includes, in the measurement results provided to the LMF, additional information concerning the predicted measurements. Such additional information enable the LMF to better understand how the predictions were derived and therefore how they can be used for the calculation of UE location. Such additional information may consist of one or more of the following: i) Information concerning the accuracy or the uncertainty of the predicted measurements. Such uncertainty / accuracy information may be provided on a per measurement basis or as an overall parameter for all the measurements inferred; ii) Information concerning the training data used to derive the one or more AI / ML model used at the gNB-CU to derive the inferred measurements that might be requested. Example of such information may be ranges of data used for training, provided for each data type, e.g. for each measurement used as a training data; iii) Information concerning the time horizon for which the inferred measurements were calculated. Namely, information about how far into the future and for which points in time into the future the one or more models in use at the gNB-CU has calculated the measurements predictions; iv) Information about the whether one or more of the predicted measurements were subject to epistemic uncertainty, namely whether the one or more predicted measurements were derived from inputs for which the model in use at the gNB-CU was not trained or it was not sufficiently trained; v) Information concerning the quality of the predicted measurements, in terms of time, angle and phase quality compared to the non-predicted measurements. Such quality score may be provided as a percentage of error , an accuracy or an uncertainty.

[0126] In a particular embodiment, the gNB-CU, once it receives input measurements from the one or more gNB-DUs, provides predicted measurements to the LMF that follow specific requests from the LMF. Such specific requests may be signaled by the LMF as part of a message requesting for predicted measurements for AI / ML assisted positioning. Such message may be e.g. the NRPPa Measurement procedure or new NRPPa message. In various particular embodiments, the requirements from the LMF may consist of one or more of the following: i) The requested predicted measurements shall have an uncertainty or accuracy better or equal than a threshold specified by the LMF; ii) The requested predicted measurements should be calculated for a specific point in time in the future. This point in time may be specified by the LMF as a time window, e.g. in seconds or ms, starting at the time of the reception of the predicted measurements request at the gNB-CU; iii) The requested predicted measurements shall be inferred and reported periodically, at a specific reporting period T specified in e.g. ms; iv) The requested predicted measurements shall be inferred and reported only once, namely not periodically; v) The requested predicted measurements shall be inferred and reported periodically, at a specific reporting period T specified in e.g. ms, and for a specific time duration Td. Namely, the gNB-CU shall generate predicted measurements periodically, with periodicity T, during a time window of duration Td starting at the time the request for predicted measurements is received at the gNB-CU. After the expiration of the time window Td, the gNB-CU shall no longer infer and report predicted measurements; vi) The requested predicted measurements shall be inferred with quality levels equal or better than requested thresholds. Such quality levels may be applied to parameters such as time, angle and phase quality. Quality level as may be calculated with respect to the non-predicted measurements. Such quality thresholds / requirements may be provided as a percentage of error , an accuracy or an uncertainty.

[0127] In a further particular embodiment, the methods described above enabling the gNB-CU to request to the gNB-DU reporting of positioning measurements in a fragmented way may be applied not only in case the AI / ML assisted positioning models are hosted at the gNB-CU but also in cases where the AI / ML assisted positioning models are hosted at the gNB-DU. In this case the gNB-DU provides to the gNB-CU predicted measurements to be used by the LMF to derive the UE position. Such measurements from the gNB-DU may be provided to the gNB-CU in a fragmented way as per the embodiments described above.

[0128] In a particular embodiment, the predicted measurements resulting from AI / ML model inference can cover predicted measurements of existing positioning measurement , e.g., UL- Angle of Arrival (AoA), UL-Relative Time of Arrival (RTOA), UL SRS Reference Signal Received 1

[0129] Power (UL-SRS-RSRP), UL SRS Reference Signal Received Path Power (UL-SRS-RSRPP), gNB Rx-Tx, UL Reference Signal Carrier Phase (UL-RSCP).

[0130] Embodiments of the present disclosure related to higher-layer controlled input data fragmentation are as follows.

[0131] In a particular embodiment, the gNB-CU provides assistance information to assist the gNB- DU performing fragmentation of the input data. For example, the gNB-CU provides a time window for receiving the input data, before which the input data is considered obsolete by the gNB-CU inference model.

[0132] In one embodiment, during the SRS configuration phase, the gNB-CU signals an indication in the Fl POSITIONING INFORMATION REQUEST indicating that input data shall be prioritized by the gNB-DU for specific UEs.

[0133] In a further particular embodiment, the UEs can be identified by means of UE identifiers such as the Fl AP CU APID.

[0134] In a particular embodiment, the gNB-CU signals prioritization for different UEs during the SRS configuration process, indicating whether the input data for some UEs can be grouped by the gNB-DU when providing the partial (i.e., fragmented) reporting.

[0135] FIGURE 3 illustrates a method and signaling diagram for AI / ML assisted positioning using a CU-side AI / ML model, in accordance with an example embodiment of the present disclosure.

[0136] Specifically, FIGURE 3 illustrates the following messages and operations:

[0137] 1. TRP Information capability exchange between gNB-CU, gNB-DU, and / or LMF with gNB-CU indicating AI / ML inference model support.

[0138] 2. The gNB-Cu receives an NRPPa positioning information request from the LMF.

[0139] 3. The gNB-CU sends a F 1 Ap positioning information request to the gNB-DU, which includes an indication of UE priority for input data reporting.

[0140] 4. Configures the UE SRS transmission

[0141] 5. The gNB-CU sends a NRPPa positioning information response to the LMF.

[0142] 6. The LMF signals the NRPPa Measurement Request to the gNB-CU. Such request may include an indication of which measurement(s) to provided predicted measurement and / or an indication of input data time window. 7. The gNB-CU signals a F1AP Measurement Request to the gNB-DU. The request includes an indication of which measurement(s) to provide predicted measurement and / or an indication of input data time window.

[0143] 8. The gNB-DU receives input data and applies fragmentation, if needed.

[0144] 9. The gNB-DU transmits, to the gNB-CU, a F1AP Measurement Response that reports the TRP Measurement result with input data. The message may include partial indicator if fragmented and / or age of information of the input data, in particular embodiments.

[0145] 10. The gNB-CU applies inference model training and generates predictive positioning measurements.

[0146] 11. The gNB-DU sends an F1AP Measurement Report, which reports the TRP Measurement results with further input data. The message may include partial indicator if the further input data is fragmented and / or the age of information of the further input data, in particular embodiments.

[0147] 12. The gNB-CU applies inference model training and generates predictive positioning measurements.

[0148] 13. The gNB-CU transmits, to the LMF, a NRPPa Measurement Response that reports the TRP Measurement result with predicted measurements.

[0149] The below changes can be applicable to Fl AP specifications. The below changes represent one example implementation of at least some of the embodiments described above.

[0150] Changes to TS 38.473 (option 1 - predicted measurement per TRP )

[0151] 9.3.1.166 Positioning Measurement Result

[0152] The purpose of this information element is to provide the measurement result(s).

[0153] Changes to 38.473 ( option 2 - input data list per TRP)

[0154] 9.2.12.4POSITIONING MEASUREMENT RESPONSE

[0155] This message is sent by the gNB-DU to report positioning measurements for the target UE.

[0156] Direction: gNB-DU -> gNB-CU.

[0157] 9.2.12.6POSITIONING MEASUREMENT REPORT

[0158] This message is sent by the gNB-DU to report positioning measurements for the target UE.

[0159] Direction: gNB-DU -> gNB-CU.

[0160] 9.2.12.9POSITIONING MEASUREMENT UPDATE

[0161] This message is sent by the gNB-CU to update a previously configured measurement.

[0162] Direction: gNB-CU -> gNB-DU.

[0163] Changes to TS 38.473 (request from sender for partial input data support)

[0164] 9.2.12.4POSITIONING MEASUREMENT RESPONSE

[0165] This message is sent by the gNB-DU to report positioning measurements for the target UE.

[0166] Direction: gNB-DU -> gNB-CU.

[0167] 9.2.12.6POSITIONING MEASUREMENT REPORT

[0168] This message is sent by the gNB-DU to report positioning measurements for the target UE.

[0169] Direction: gNB-DU -> gNB-CU.

[0170] 9.2.12.9POSITIONING MEASUREMENT UPDATE

[0171] This message is sent by the gNB-CU to update a previously configured measurement.

[0172] Direction: gNB-CU -> gNB-DU.

[0173] FIGURE 4 shows an example of a communication system 400 in accordance with some embodiments.

[0174] In the example, the communication system 400 includes a telecommunication network 402 that includes an access network 404, such as a radio access network (RAN), and a core network 406, which includes one or more core network nodes 408. The access network 404 includes one or more access network nodes, such as network nodes 410a and 410b (one or more of which may be generally referred to as network nodes 410), or any other similar 3rd Generation Partnership Project (3 GPP) access node or non-3GPP access point. The network nodes 410 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 412a, 412b, 412c, and 412d (one or more of which may be generally referred to as UEs 412) to the core network 406 over one or more wireless connections.

[0175] Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 400 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system 400 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0176] The UEs 412 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes 410 and other communication devices. Similarly, the network nodes 410 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 412 and / or with other network nodes or equipment in the telecommunication network 402 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network 402.

[0177] In the depicted example, the core network 406 connects the network nodes 410 to one or more hosts, such as host 416. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 406 includes one more core network nodes (e.g., core network node 408) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 408. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0178] The host 416 may be under the ownership or control of a service provider other than an operator or provider of the access network 404 and / or the telecommunication network 402, and may be operated by the service provider or on behalf of the service provider. The host 416 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0179] As a whole, the communication system 400 of FIGURE 4 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox.

[0180] In some examples, the telecommunication network 402 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 402 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 402. For example, the telecommunications network 402 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC) / Massive loT services to yet further UEs.

[0181] In some examples, the UEs 412 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 404 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 404. Additionally, a UE may be configured for operating in single- or multi-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LIE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0182] In the example, the hub 414 communicates with the access network 404 to facilitate indirect communication between one or more UEs (e.g., UE 412c and / or 412d) and network nodes (e.g., network node 410b). In some examples, the hub 414 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 414 may be a broadband router enabling access to the core network 406 for the UEs. As another example, the hub 414 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 410, or by executable code, script, process, or other instructions in the hub 414. As another example, the hub 414 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 414 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 414 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 414 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 414 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy loT devices.

[0183] The hub 414 may have a constant / persistent or intermittent connection to the network node 410b. The hub 414 may also allow for a different communication scheme and / or schedule between the hub 414 and UEs (e.g., UE 412c and / or 412d), and between the hub 414 and the core network 406. In other examples, the hub 414 is connected to the core network 406 and / or one or more UEs via a wired connection. Moreover, the hub 414 may be configured to connect to an M2M service provider over the access network 404 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 410 while still connected via the hub 414 via a wired or wireless connection. In some embodiments, the hub 414 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 410b. In other embodiments, the hub 414 may be a nondedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 410b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0184] FIGURE 5 shows a UE 500, which may be an embodiment of the UE 112 of FIGURE 4, in accordance with some embodiments.

[0185] As used herein, a UE refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0186] A UE may support device-to-device (D2D) communication, for example by implementing a 3 GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to-everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0187] The UE 500 includes processing circuitry 502 that is operatively coupled via a bus 504 to an input / output interface 506, a power source 508, a memory 510, a communication interface 512, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in FIGURE 5. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0188] The processing circuitry 502 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine- readable computer programs in the memory 510. The processing circuitry 502 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field- programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 502 may include multiple central processing units (CPUs).

[0189] In the example, the input / output interface 506 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE 500. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0190] In some embodiments, the power source 508 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source 508 may further include power circuitry for delivering power from the power source 508 itself, and / or an external power source, to the various parts of the UE 500 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 508. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 508 to make the power suitable for the respective components of the UE 500 to which power is supplied.

[0191] The memory 510 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 510 includes one or more application programs 514, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 516. The memory 510 may store, for use by the UE 500, any of a variety of various operating systems or combinations of operating systems.

[0192] The memory 510 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 510 may allow the UE 500 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 510, which may be or comprise a device-readable storage medium.

[0193] The processing circuitry 502 may be configured to communicate with an access network or other network using the communication interface 512. The communication interface 512 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 522. The communication interface 512 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter 518 and / or a receiver 520 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 518 and receiver 520 may be coupled to one or more antennas (e.g., antenna 522) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0194] In the illustrated embodiment, communication functions of the communication interface 512 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0195] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 512, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected, an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0196] As another example, a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

[0197] A UE, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or itemtracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the UE 500 shown in FIGURE 5.

[0198] As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements, and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UE may in this case be an M2M device, which may in a 3 GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3 GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0199] In practice, any number of UEs may be used together with respect to a single use case. For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed. The first and / or the second UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0200] FIGURE 6 shows a network node 600, which may be an embodiment of the network node 110 of FIGURE 4, in accordance with some embodiments.

[0201] As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)).

[0202] Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0203] Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0204] The network node 600 includes a processing circuitry 602, a memory 604, a communication interface 606, and a power source 608. The network node 600 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 600 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 600 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 604 for different RATs) and some components may be reused (e.g., a same antenna 610 may be shared by different RATs). The network node 600 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 600, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 600.

[0205] The processing circuitry 602 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other network node 600 components, such as the memory 604, to provide network node 600 functionality.

[0206] In some embodiments, the processing circuitry 602 includes a system on a chip (SOC). In some embodiments, the processing circuitry 602 includes one or more of radio frequency (RF) transceiver circuitry 612 and baseband processing circuitry 614. In some embodiments, the radio frequency (RF) transceiver circuitry 612 and the baseband processing circuitry 614 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 612 and baseband processing circuitry 614 may be on the same chip or set of chips, boards, or units.

[0207] The memory 604 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 602. The memory 604 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 602 and utilized by the network node 600. The memory 604 may be used to store any calculations made by the processing circuitry 602 and / or any data received via the communication interface 606. In some embodiments, the processing circuitry 602 and memory 604 is integrated.

[0208] The communication interface 606 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, the communication interface 606 comprises port(s) / terminal(s) 616 to send and receive data, for example to and from a network over a wired connection. The communication interface 606 also includes radio frontend circuitry 618 that may be coupled to, or in certain embodiments a part of, the antenna 610. Radio front-end circuitry 618 comprises filters 620 and amplifiers 622. The radio front-end circuitry 618 may be connected to an antenna 610 and processing circuitry 602. The radio frontend circuitry may be configured to condition signals communicated between antenna 610 and processing circuitry 602. The radio front-end circuitry 618 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 618 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 620 and / or amplifiers 622. The radio signal may then be transmitted via the antenna 610. Similarly, when receiving data, the antenna 610 may collect radio signals which are then converted into digital data by the radio front-end circuitry 618. The digital data may be passed to the processing circuitry 602. In other embodiments, the communication interface may comprise different components and / or different combinations of components. In certain alternative embodiments, the network node 600 does not include separate radio front-end circuitry 618, instead, the processing circuitry 602 includes radio front-end circuitry and is connected to the antenna 610. Similarly, in some embodiments, all or some of the RF transceiver circuitry 612 is part of the communication interface 606. In still other embodiments, the communication interface 606 includes one or more ports or terminals 616, the radio front-end circuitry 618, and the RF transceiver circuitry 612, as part of a radio unit (not shown), and the communication interface 606 communicates with the baseband processing circuitry 614, which is part of a digital unit (not shown).

[0209] The antenna 610 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 610 may be coupled to the radio front-end circuitry 618 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 610 is separate from the network node 600 and connectable to the network node 600 through an interface or port.

[0210] The antenna 610, communication interface 606, and / or the processing circuitry 602 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna 610, the communication interface 606, and / or the processing circuitry 602 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.

[0211] The power source 608 provides power to the various components of network node 600 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 608 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 600 with power for performing the functionality described herein. For example, the network node 600 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 608. As a further example, the power source 608 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0212] Embodiments of the network node 600 may include additional components beyond those shown in FIGURE 6 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node 600 may include user interface equipment to allow input of information into the network node 600 and to allow output of information from the network node 600. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 600.

[0213] FIGURE 7 is a block diagram of a host 700, which may be an embodiment of the host 416 of FIGURE 4, in accordance with various aspects described herein.

[0214] As used herein, the host 700 may be or comprise various combinations hardware and / or software, including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm. The host 700 may provide one or more services to one or more UEs.

[0215] The host 700 includes processing circuitry 702 that is operatively coupled via a bus 704 to an input / output interface 706, a network interface 708, a power source 710, and a memory 712. Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices of previous figures, such as FIGURES 5 and 6, such that the descriptions thereof are generally applicable to the corresponding components of host 700.

[0216] The memory 712 may include one or more computer programs including one or more host application programs 714 and data 716, which may include user data, e.g., data generated by a UE for the host 700 or data generated by the host 700 for a UE. Embodiments of the host 700 may utilize only a subset or all of the components shown. The host application programs 714 may be implemented in a container- based architecture and may provide support for video codecs (e.g., Versatile Video Coding (WC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), MPEG, VP9) and audio codecs (e.g., FLAC, Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems). The host application programs 714 may also provide for user authentication and licensing checks and may periodically report health, routes, and content availability to a central node, such as a device in or on the edge of a core network. Accordingly, the host 700 may select and / or indicate a different host for over-the-top services for a UE. The host application programs 714 may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real-Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), Dynamic Adaptive Streaming over HTTP (MPEG-DASH), etc.

[0217] FIGURE 8 is a block diagram illustrating a virtualization environment 800 in which functions implemented by some embodiments may be virtualized.

[0218] In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 800 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized.

[0219] Applications 802 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0220] Hardware 804 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 806 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 808a and 808b (one or more of which may be generally referred to as VMs 808), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 806 may present a virtual operating platform that appears like networking hardware to the VMs 808. The VMs 808 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 806. Different embodiments of the instance of a virtual appliance 802 may be implemented on one or more of VMs 808, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

[0221] In the context of NFV, a VM 808 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 808, and that part of hardware 804 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs 808 on top of the hardware 804 and corresponds to the application 802.

[0222] Hardware 804 may be implemented in a standalone network node with generic or specific components. Hardware 804 may implement some functions via virtualization. Alternatively, hardware 804 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 810, which, among others, oversees lifecycle management of applications 802. In some embodiments, hardware 804 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 812 which may alternatively be used for communication between hardware nodes and radio units.

[0223] FIGURE 9 shows a communication diagram of a host 902 communicating via a network node 904 with a UE 906 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as a UE 412a of FIGURE 4 and / or UE 500 of FIGURE 5), network node (such as network node 410a of FIGURE 4 and / or network node 600 of FIGURE 6), and host (such as host 416 of FIGURE 4 and / or host 700 of FIGURE 7) discussed in the preceding paragraphs will now be described with reference to FIGURE 9.

[0224] Like host 700, embodiments of host 902 include hardware, such as a communication interface, processing circuitry, and memory. The host 902 also includes software, which is stored in or accessible by the host 902 and executable by the processing circuitry. The software includes a host application that may be operable to provide a service to a remote user, such as the UE 906 connecting via an over-the-top (OTT) connection 950 extending between the UE 906 and host 902. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection 950.

[0225] The network node 904 includes hardware enabling it to communicate with the host 902 and UE 906. The connection 960 may be direct or pass through a core network (like core network 406 of FIGURE 4) and / or one or more other intermediate networks, such as one or more public, private, or hosted networks. For example, an intermediate network may be a backbone network or the Internet.

[0226] The UE 906 includes hardware and software, which is stored in or accessible by UE 906 and executable by the UE’s processing circuitry. The software includes a client application, such as a web browser or operator-specific “app” that may be operable to provide a service to a human or non-human user via UE 906 with the support of the host 902. In the host 902, an executing host application may communicate with the executing client application via the OTT connection 950 terminating at the UE 906 and host 902. In providing the service to the user, the UE's client application may receive request data from the host's host application and provide user data in response to the request data. The OTT connection 950 may transfer both the request data and the user data. The UE's client application may interact with the user to generate the user data that it provides to the host application through the OTT connection 950.

[0227] The OTT connection 950 may extend via a connection 960 between the host 902 and the network node 904 and via a wireless connection 970 between the network node 904 and the UE 906 to provide the connection between the host 902 and the UE 906. The connection 960 and wireless connection 970, over which the OTT connection 950 may be provided, have been drawn abstractly to illustrate the communication between the host 902 and the UE 906 via the network node 904, without explicit reference to any intermediary devices and the precise routing of messages via these devices.

[0228] As an example of transmitting data via the OTT connection 950, in step 908, the host 902 provides user data, which may be performed by executing a host application. In some embodiments, the user data is associated with a particular human user interacting with the UE 906. In other embodiments, the user data is associated with a UE 906 that shares data with the host 902 without explicit human interaction. In step 910, the host 902 initiates a transmission carrying the user data towards the UE 906. The host 902 may initiate the transmission responsive to a request transmitted by the UE 906. The request may be caused by human interaction with the UE 906 or by operation of the client application executing on the UE 906. The transmission may pass via the network node 904, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step 912, the network node 904 transmits to the UE 906 the user data that was carried in the transmission that the host 902 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 914, the UE 906 receives the user data carried in the transmission, which may be performed by a client application executed on the UE 906 associated with the host application executed by the host 902.

[0229] In some examples, the UE 906 executes a client application which provides user data to the host 902. The user data may be provided in reaction or response to the data received from the host 902. Accordingly, in step 916, the UE 906 may provide user data, which may be performed by executing the client application. In providing the user data, the client application may further consider user input received from the user via an input / output interface of the UE 906. Regardless of the specific manner in which the user data was provided, the UE 906 initiates, in step 918, transmission of the user data towards the host 902 via the network node 904. In step 920, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 904 receives user data from the UE 906 and initiates transmission of the received user data towards the host 902. In step 922, the host 902 receives the user data carried in the transmission initiated by the UE 906.

[0230] One or more of the various embodiments improve the performance of OTT services provided to the UE 906 using the OTT connection 950, in which the wireless connection 970 forms the last segment. More precisely, the teachings of these embodiments may improve one or more of, for example, data rate, latency, and / or power consumption and, thereby, provide benefits such as, for example, reduced user waiting time, relaxed restriction on file size, improved content resolution, better responsiveness, and / or extended battery lifetime.

[0231] In an example scenario, factory status information may be collected and analyzed by the host 902. As another example, the host 902 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host 902 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host 902 may store surveillance video uploaded by a UE. As another example, the host 902 may store or control access to media content such as video, audio, VR or AR which it can broadcast, multicast or unicast to UEs. As other examples, the host 902 may be used for energy pricing, remote control of non-time critical electrical load to balance power generation needs, location services, presentation services (such as compiling diagrams etc. from data collected from remote devices), or any other function of collecting, retrieving, storing, analyzing and / or transmitting data.

[0232] In some examples, a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring the OTT connection 950 between the host 902 and UE 906, in response to variations in the measurement results. The measurement procedure and / or the network functionality for reconfiguring the OTT connection may be implemented in software and hardware of the host 902 and / or UE 906. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection 950 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software may compute or estimate the monitored quantities. The reconfiguring of the OTT connection 950 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of the network node 904. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signaling that facilitates measurements of throughput, propagation times, latency and the like, by the host 902. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 950 while monitoring propagation times, errors, etc. FIGURE 10 illustrates an example method by a CU of a network node for generating predicted measurements based on a CU-side model, according to certain embodiments. In the illustrated embodiment, the method includes at least one of a transmitting step at 1002, a receiving step at 1004, and a transmitting step at 1006. For example, at step 1002, the CU may transmit, to a DU of the network node and / or an EMF, capability information indicating a capability of the CU to generate at least one predicted measurement based on the CU-side model. At step 1004, for example, the DU may receive, from the LMF, a measurement request comprising a request for at least one predicted measurement generated using the CU-side model. At step 1006, for example, the DU may transmit, to the LMF of the network node, at least one value associated with the at least one predicted measurement generated using the CU-side model.

[0233] FIGURE 11 illustrates an example method by a LMF node for supporting predicted measurements based on a CU-side model, according to certain embodiments. In the illustrated embodiment, the method includes at least one of a receiving step at 1102, a transmitting step at 1104, and a receiving step at 1106. For example, at step 1102, the LMF may receive, from the CU of a network node, capability information indicating a capability of the CU to generate at least one predicted measurement based on the CU-side model. At step 1104, for example, the LMF may transmit, to the CU, a measurement request comprising a request for at least one predicted measurement generated using the CU-side model. At step 1106, the LMF may receive, from the CU, at least one value associated with the at least one predicted measurement generated using the CU-side model.

[0234] FIGURE 12 illustrates an example method by a DU for supporting predicted measurements based on a CU-side model, according to certain embodiments. In the illustrated embodiment, the method includes at least one of a receiving step at 1202, a transmitting step at 1204, and a receiving step at 1206. For example, at step 1202, the DU may receive, from the CU of the network node, capability information indicating a capability of the CU to generate at least one predicted measurement based on the CU-side model. At step 1104, for example, the DU may transmit, to the CU, input data for use in generating at least one predicted measurement using the CU-side model. At step 1106, the DU may receive, from the CU, at least one value associated with the at least one predicted measurement generated using the CU-side model.

[0235] Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

[0236] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer-readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer-readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and / or by end users and a wireless network generally.

[0237] Some example embodiments of the present disclosure are as follows: Group A Example Embodiments

[0238] Example Embodiment Al . A method performed by a user equipment comprising:

[0239] - any of the user equipment steps, features, or functions described above, either alone or in combination with other steps, features, or functions described above.

[0240] Example Embodiment A2. The method of the previous embodiment, further comprising one or more additional user equipment steps, features or functions described above.

[0241] Example Embodiment A3. The method of any of the previous embodiments, further comprising:

[0242] - providing user data; and

[0243] - forwarding the user data to a host computer via the transmission to the network node.

[0244] Group B Example Embodiments

[0245] Example Embodiment Bl. A method performed by a network node for predicted measurements based on a gNB-CU side model, the method comprising:

[0246] - any of the network node steps, features, or functions described above, either alone or in combination with other steps, features, or functions described above.

[0247] Example Embodiment B2. The method of the previous embodiment, further comprising one or more additional network node steps, features or functions described above.

[0248] Example Embodiment B3. The method of any of the previous embodiments, further comprising:

[0249] - obtaining user data; and

[0250] - forwarding the user data to a host or a user equipment.

[0251] Group C Example Embodiments

[0252] Example Embodiment Cl . A method performed by a Centralized Unit (CU) of a network node for generating predicted measurements based on a CU-side model, the method comprising at least one of: transmitting, to a Distributed Unit (DU) of the network node and / or a Location Management Function (LMF), capability information indicating a capability of the CU to generate at least one predicted measurement based on the CU-side model; and / or receiving, from the LMF, a measurement request comprising a request for at least one predicted measurement generated using the CU-side model; and / or transmitting, to the LMF of the network node, at least one value associated with the at least one predicted measurement generated using the CU-side model.

[0253] Example Embodiment C2. The method of Example Embodiment Cl, wherein the CU-side model comprises an Al and / or ML model at the CU.

[0254] Example Embodiment C3. The method of any one of Example Embodiments Cl to C2, comprising using the CU-side model to generate the at least one predicted measurement.

[0255] Example Embodiment C4. The method of any one of Example Embodiments Cl to C3, wherein the at least one predicted measurement is generated for Al and / or ML assisted positioning.

[0256] Example Embodiment C5. The method of any one of Example Embodiments Cl to C4, wherein the at least one value associated with the at least one predicted measurement is transmitted to the LMF in a NRPPa Measurement Response message.

[0257] Example Embodiment C6. The method of Example Embodiment C5, wherein the NRPPa Measurement Response message comprises at least one additional value associated with a TRP Measurement performed by a UE and the at least one value associated with the at least one predicted measurement.

[0258] Example Embodiment C7. The method of any one of Example Embodiments Cl to C6, comprising transmitting, to the LMF, additional information with the at least one value associated with the at least one predicted measurement, wherein the additional information comprises at least one of: information indicating an accuracy level or uncertainty level of the at least one predicted measurement, information associated with training data used to generate the at least one value associated with the at least one predicted measurement, time information associated with the at least one value associated with the at least one predicted measurement, information indicating how far into the future the at least one predicted measurement is associated, information indicating a quality and / or quality level of the at least one value associated with the at least one predicted measurement.

[0259] Example Embodiment C8. The method of any one of Example Embodiments Cl to C7, wherein the measurement request comprises an indication of at least one type of predicted measurement that is requested, and wherein the at least one value associated with the at least one predicted measurement is of the at least one type requested.

[0260] Example Embodiment C9. The method of any one of Example Embodiments Cl to C8, comprising determining that at least one condition is fulfilled prior to transmitting the at least one value associated with the at least one predicted measurement, and wherein the at least one condition comprises: the at least one value is associated with an uncertainty or accuracy level that is equal to or greater than a threshold; the at least one value is calculated for a specific point in time in the future or within a time window; the DU is configured to report the predicted measurements periodically and a time duration since a last reported predicted measurement is greater than a threshold; and / or the at least one value is associated with a quality level that is equal to or greater than a threshold.

[0261] Example Embodiment D9b. The method of Example Embodiment D9, comprising receiving, from the LMF, the at least one condition.

[0262] Example Embodiment CIO. The method of any one of Example Embodiments Cl to C9b, comprising: in response to receiving the measurement request, transmitting a response message comprising an indication that one or more of the at least one predicted measurements can / will / may / is to be provided. Example Embodiment Cl 1. The method of any one of Example Embodiments Cl to CIO, comprising: in response to receiving the measurement request, transmitting a response message comprising an indication that one or more of the at least one predicted measurements cannot / will not / may not / is not to be provided.

[0263] Example Embodiment Cl 2. The method of any one of Example Embodiments CIO to Cl l, wherein the response message indicates at least one of: at least one type of predicted measurement that can / will / may / is to be provided, and at least one type of predicted measurement that cannot / will not / may not / is not to be provided.

[0264] Example Embodiment Cl 3. The method of any one of Example Embodiments Cl l to C12, wherein the response message comprises at least one of: a cause and / or reason that the one or more predicted measurements cannot / will not / may not / is not to be provided, an indication that the failure to provide the one or more predicted measurements is temporary, and / or an indication that the CU does not support the one or more predicted measurements.

[0265] Example Embodiment C14. The method of any one of Example Embodiments Cl to Cl 3, wherein the at least one value associated with the at least one predicted measurement is associated with at least one of: UL-AoA, UL-RTOA, UL-SRS-RSRP, UL-SRS-RSRPP, gNB Rx-Tx, and UL-RSCP.

[0266] Example Embodiment Cl 5. The method of any one of Example Embodiments Cl to Cl 4, wherein the at least one value associated with the at least one predicted measurement is transmitted to the LMF via a TRP measurement report IE.

[0267] Example Embodiment Cl 6. The method of any one of Example Embodiments Cl to Cl 5, wherein the at least one value associated with the at least one predicted measurement is transmitted to the LMF via a prediction report.

[0268] Example Embodiment Cl 7. The method of any one of Example Embodiments Cl to Cl 6, comprising receiving input data from the DU for use in generating, using the CU-side model, the at least one value associated with the at least one predicted measurement. Example Embodiment Cl 8. The method of Example Embodiment Cl 7, wherein the input data is received via an Fl message.

[0269] Example Embodiment Cl 9. The method of anyone of Example Embodiments Cl 7 to Cl 8, comprising receiving, from the DU, information indicating an age of the input data.

[0270] Example Embodiment C20. The method of Example Embodiment Cl 7 to Cl 9, comprising receiving, from the DU, at least one of: at least one UE identifier associated with the input data, at least one UE group identifier associated with the input data, at least one FlaP gNB-DU ID associated with the input data, and at least one TRP ID.

[0271] Example Embodiment C21. The method of Example Embodiment Cl 7 to C20, wherein a message comprising the input data received from the DU further comprises an indication that the input data is a full report and / or an indication that the message does not comprise fragmented input data.

[0272] Example Embodiment C22. The method of any one of Example Embodiments Cl 7 to C21, wherein the input data received from the DU comprises fragmented input data (i.e., a partial report).

[0273] Example Embodiment C23. The method of Example Embodiment C22, wherein a message comprising the input data received from the DU further comprises an indication that the input data is a partial report and / or comprises fragmented input data.

[0274] Example Embodiment C24. The method of any one of Example Embodiments C22 to C23, comprising transmitting, to the DU, second capability information, wherein the second capability information indicates that the CU supports receiving fragmented input data.

[0275] Example Embodiment C25. The method of Example Embodiment C24, wherein the second capability information indicates at least one of: a maximum Fl AP message size, and a maximum number of UEs for which input data should be received.

[0276] Example Embodiment C26. The method of any one of Example Embodiments C22 to C25, wherein the input data is received in a first message from the DU and the method further comprises: receiving, from the DU, a second message comprising additional input data from the DU; and using the input data from the first message and the additional input data from the second message as input to the model when generating the at least one value associated with the at least one predicted measurement.

[0277] Example Embodiment C27. The method of Example Embodiment C26, wherein the input data is associated with a first UE and / or a first group of UEs, and wherein the additional input data is associated with a second UE and / or a second group of UEs.

[0278] Example Embodiment C28. The method of any one of Example Embodiments C17 to C27, comprising transmitting, to the DU, information comprising at least one of: a request for the input data and / or the additional input data, a time window for receiving the input data and / or the additional input data, at least one UE identifier or UE group identifier for which input data is to be collected and transmitted by the DU, information associated with a UE priority for determining input data to be collected and / or transmitted by the DU, an indication that input data is to be transmitted to the CU as collected, an indication that input data is to be transmitted to the CU within a maximum period of time that begins when measurement data collection is initiated, a maximum threshold value of a measurement volume for a message containing the input data and / or additional input data, at least one time window for collecting the input data and / or additional input data, a volume of input data that may be transmitted in a message to the CU, and an expiration time for the input data.

[0279] Example Embodiment C29. The method of Example Embodiment C28, wherein the information is transmitted in an Fl AP message.

[0280] Example Embodiment C30. The method of any one of Example Embodiments C17 to C29, comprising determining a subset of the received input data from the DU for use in generating the at least one value associated with the at least one predicted measurement. Example Embodiment C31. The method of any one of Example Embodiments Cl 7 to C30, comprising transmitting, to the DU, an indication to stop measuring TRP and / or reporting input data.

[0281] Example Embodiment C32. The method of any one of Example Embodiments Cl to C31, comprising receiving, from the LMF, a request for the capability information, and wherein the capability information is transmitted in response to the request.

[0282] Example Embodiment C33. The method of Example Embodiment C32, wherein the requests is received via NRPPa in a TRP information request message.

[0283] Example Embodiment C34. The method of any one of Example Embodiments Cl to C33, comprising receiving, from the DU, a request for the capability information, and wherein the capability information is transmitted in response to the request.

[0284] Example Embodiment C35. The method of Example Embodiment C36, wherein the requests is received via a Fl message.

[0285] Example Embodiment C36. The method of any one of Example Embodiments Cl to C35, wherein the capability information is transmitted to the DU in a Fl AP positioning message.

[0286] Example Embodiment C37. The method of any one of Example Embodiments Cl to C36, wherein the capability information is transmitted to the LMF in a NRPPa message.

[0287] Example Embodiment C38. The method of any one of Example Embodiments Cl to C37, wherein the capability information is transmitted via an 0AM.

[0288] Example Embodiment C39. The method of any one of Example Embodiments Cl to C38, wherein the capability information comprises at least one of: an indication of at least one measurement prediction, inference input, and / or inference output supported by the CU, an indication of at least one type of inferred measurement supported by the model, information associated with an accuracy level and / or uncertainty level of measurement predictions supported by the model at the CU, information associated with training data used to train the model at the CU, information associated with a time horizon for prediction measurements supported at the

[0289] CU, information associated with a processing delay for the model at the CU, information associated with a maximum measurement resolution the model can provide and / or the CU can provide, information indicating at least one TRP hosted by the CU that support measurement prediction, and information associated with a maximum update rate for a particular measurement resolution the CU supports.

[0290] Example Embodiment C40. The method of any of the previous Example Embodiments, further comprising: obtaining user data; and forwarding the user data to a host or a user equipment.

[0291] Example Embodiment C41. A network node comprising processing circuitry configured to perform any of the methods of Example Embodiments Cl to C40.

[0292] Example Embodiment C42. A network node configured and / or adapted to perform any of the methods of Example Embodiments Cl to C40.

[0293] Example Embodiment C43. A computer program comprising instructions which when executed on a computer perform any of the methods of Example Embodiments Cl to C40.

[0294] Example Embodiment C44 A computer program product comprising computer program, the computer program comprising instructions which when executed on a computer perform any of the methods of Example Embodiments Cl to C40.

[0295] Example Embodiment C45. A non-transitory computer readable medium storing instructions which when executed by a computer perform any of the methods of Example Embodiments Cl to C40.

[0296] Group D Example Embodiments

[0297] Example Embodiment DI. A method performed by a Location Management Function (LMF) for supporting predicted measurements based on a Centralized Unit (CU)-side model, the method comprising at least one of: receiving, from the CU of a network node, capability information indicating a capability of the CU to generate at least one predicted measurement based on the CU-side model; and / or transmitting, to the CU, a measurement request comprising a request for at least one predicted measurement generated using the CU-side model; and / or receiving, from the CU, at least one value associated with the at least one predicted measurement generated using the CU-side model.

[0298] Example Embodiment D2. The method of Example Embodiment DI, wherein the CU-side model comprises an Al and / or ML model at the CU.

[0299] Example Embodiment D3. The method of any one of Example Embodiments D 1 to D2, wherein the at least one predicted measurement is generated for Al and / or ML assisted positioning.

[0300] Example D4. The method of any one of Example Embodiments DI to D3, comprising performing at least one positioning operation for at least one UE based on the at least one value associated with at least one predicted measurement.

[0301] Example Embodiment D5. The method of any one of Example Embodiments DI to D4, wherein the at least one value associated with the at least one predicted measurement is received from the CU in a NRPPa Measurement Response message.

[0302] Example Embodiment D6. The method of Example Embodiment D5, wherein the NRPPa Measurement Response message comprises at least one additional value associated with a TRP Measurement performed by a UE and the at least one value associated with the at least one predicted measurement.

[0303] Example Embodiment D7. The method of any one of Example Embodiments DI to D6, comprising receiving, from the CU, additional information with the at least one value associated with the at least one predicted measurement, wherein the additional information comprises at least one of: information indicating an accuracy level or uncertainty level of the at least one predicted measurement, information associated with training data used to generate the at least one value associated with the at least one predicted measurement, time information associated with the at least one value associated with the at least one predicted measurement, information indicating how far into the future the at least one predicted measurement is associated, information indicating a quality and / or quality level of the at least one value associated with the at least one predicted measurement.

[0304] Example Embodiment D8. The method of any one of Example Embodiments DI to D7, wherein the measurement request transmitted to the CU comprises an indication of at least one type of predicted measurement that is requested from the CU, and wherein the at least one value associated with the at least one predicted measurement received from the CU is of the at least one type requested.

[0305] Example Embodiment D9. The method of any one of Example Embodiments DI to D8, wherein a characteristic of the at least one value received from the CU fulfills at least one condition, and wherein the at least one condition comprises: the at least one value is associated with an uncertainty or accuracy level that is equal to or greater than a threshold; the at least one value is calculated for a specific point in time in the future or within a time window; the DU is configured to report the predicted measurements periodically and a time duration since a last reported predicted measurement is greater than a threshold; and / or the at least one value is associated with a quality level that is equal to or greater than a threshold.

[0306] Example Embodiment DIO. The method of Example Embodiment D9, comprising transmitting, to the CU, the at least one condition.

[0307] Example Embodiment Dl l. The method of any one of Example Embodiments DI to DIO, comprising: receiving, from the CU, a response message comprising an indication that one or more of the at least one predicted measurements can / will / may / is to be provided by the CU. Example Embodiment DI 2. The method of any one of Example Embodiments DI to Dl l, comprising: receiving, from the CU, a response message comprising an indication that one or more of the at least one predicted measurements cannot / will not / may not / is not to be provided.

[0308] Example Embodiment DI 3. The method of any one of Example Embodiments Dl l to DI 2, wherein the response message indicates at least one of: at least one type of predicted measurement that can / will / may / is to be provided, and at least one type of predicted measurement that cannot / will not / may not / is not to be provided.

[0309] Example Embodiment DI 4. The method of any one of Example Embodiments D12 to D13, wherein the response message comprises at least one of: a cause and / or reason that the one or more predicted measurements cannot / will not / may not / is not to be provided, an indication that the failure to provide the one or more predicted measurements is temporary, and / or an indication that the CU does not support the one or more predicted measurements.

[0310] Example Embodiment DI 5. The method of any one of Example Embodiments DI to D14, wherein the at least one value associated with the at least one predicted measurement is associated with at least one of: UL-AoA, UL-RTOA, UL-SRS-RSRP, UL-SRS-RSRPP, gNB Rx-Tx, and UL-RSCP.

[0311] Example Embodiment DI 6. The method of any one of Example Embodiments DI to DI 5, wherein the at least one value associated with the at least one predicted measurement is received from the CU via a TRP measurement report IE.

[0312] Example Embodiment DI 7. The method of any one of Example Embodiments DI to DI 6, wherein the at least one value associated with the at least one predicted measurement is received from the CU via a prediction report.

[0313] Example Embodiment DI 8. The method of any one of Example Embodiments DI to DI 7, comprising transmitting, to the CU, a request for the capability information, and wherein the capability information is received in response to the request. Example Embodiment Cl 9. The method of Example Embodiment Cl 8, wherein the requests is transmitted to the CU via NRPPa in a TRP information request message.

[0314] Example Embodiment D20. The method of any one of Example Embodiments DI to DI 9, wherein the capability information is received from the CU in a NRPPa message .

[0315] Example Embodiment D21. The method of any one of Example Embodiments DI to D20, wherein the capability information comprises at least one of: an indication of at least one measurement prediction, inference input, and / or inference output supported by the CU, an indication of at least one type of inferred measurement supported by the model, information associated with an accuracy level and / or uncertainty level of measurement predictions supported by the model at the CU, information associated with training data used to train the model at the CU, information associated with a time horizon for prediction measurements supported at the CU, information associated with a processing delay for the model at the CU, information associated with a maximum measurement resolution the model can provide and / or the CU can provide, information indicating at least one TRP hosted by the CU that support measurement prediction, and information associated with a maximum update rate for a particular measurement resolution the CU supports.

[0316] Example Embodiment D22. The method of any of the previous Example Embodiments, further comprising: obtaining user data; and forwarding the user data to a host or a user equipment.

[0317] Example Embodiment D23. A network node comprising processing circuitry configured to perform any of the methods of Example Embodiments DI to D22. Example Embodiment D24. A network node configured to and / or adapted to perform any of the methods of Example Embodiments DI to D22.

[0318] Example Embodiment D25. A computer program comprising instructions which when executed on a computer perform any of the methods of Example Embodiments DI to D22.

[0319] Example Embodiment D26. A computer program product comprising computer program, the computer program comprising instructions which when executed on a computer perform any of the methods of Example Embodiments DI to D22.

[0320] Example Embodiment D27. A non-transitory computer readable medium storing instructions which when executed by a computer perform any of the methods of Example Embodiments DI to D22.

[0321] Group E Example Embodiments

[0322] Example Embodiment El . A method performed by a Distributed Unit (DU) of a network node for supporting predicted measurements based on a Centralized Unit (CU)-side model, the method comprising at least one of: receiving, from the CU of the network node, capability information indicating a capability of the CU to generate at least one predicted measurement based on the CU-side model; and / or transmitting, to the CU, input data for use in generating at least one predicted measurement using the CU-side model; and / or receiving, from the CU, at least one value associated with the at least one predicted measurement generated using the CU-side model.

[0323] Example Embodiment E2. The method of Example Embodiment El, wherein the CU-side model comprises an Al and / or ML model at the CU.

[0324] Example Embodiment E3. The method of any one of Example Embodiments El to E2, wherein the at least one predicted measurement and / or the at least one additional predicted measurement is generated for Al and / or ML assisted positioning.

[0325] Example Embodiment E4. The method of any one of Example Embodiments El to E3, wherein the input data is transmitted via a Fl message. Example Embodiment E5. The method of anyone of Example Embodiments El to E4, comprising transmitting, to the CU, information indicating an age of the input data.

[0326] Example Embodiment E6. The method of Example Embodiment El to E5, comprising transmitting, to the CU, at least one of: at least one UE identifier associated with the input data, at least one UE group identifier associated with the input data, at least one FlaP gNB-DU ID associated with the input data, and at least one TRP ID.

[0327] Example Embodiment E7. The method of Example Embodiment El to E6, comprising transmitting, to the CU, an indication that the input data is a full report and / or an indication that the message does not comprise fragmented input data.

[0328] Example Embodiment E8. The method of any one of Example Embodiments El to E6, wherein the input data received from the DU comprises fragmented input data (i.e., a partial report).

[0329] Example Embodiment E9. The method of Example Embodiment E8, comprising transmitting, to the CU, an indication that the input data is a partial report and / or comprises fragmented input data.

[0330] Example Embodiment El 0. The method of any one of Example Embodiments E8 to E8, comprising receiving, from the CU, second capability information, wherein the second capability information indicates that the CU supports receiving fragmented input data.

[0331] Example Embodiment El l. The method of Example Embodiment E10, wherein the second capability information indicates at least one of: a maximum Fl AP message size, and a maximum number of UEs for which input data should be received.

[0332] Example Embodiment El 2. The method of any one of Example Embodiments El to El 1, wherein the input data is transmitted in a first message to the CU and the method further comprises: transmitting, to the CU, a second message comprising additional input data for use when generating the at least one value associated with the at least one predicted measurement. Example Embodiment El 3. The method of Example Embodiment El 2, wherein the input data is associated with a first UE and / or a first group of UEs, and wherein the additional input data is associated with a second UE and / or a second group of UEs.

[0333] Example Embodiment El 4. The method of any one of Example Embodiments El to El 3, comprising receiving, from the CU, information comprising at least one of: a request for the input data and / or the additional input data, a time window for receiving the input data and / or the additional input data, at least one UE identifier or UE group identifier for which input data is to be collected and transmitted by the DU, information associated with a UE priority for determining input data to be collected and / or transmitted by the DU, an indication that input data is to be transmitted to the CU as collected, an indication that input data is to be transmitted to the CU within a maximum period of time that begins when measurement data collection is initiated, a maximum threshold value of a measurement volume for a message containing the input data and / or additional input data, at least one time window for collecting the input data and / or additional input data, a volume of input data that may be transmitted in a message to the CU, and an expiration time for the input data.

[0334] Example Embodiment El 5. The method of Example Embodiment El 4, wherein the information is transmitted in an Fl AP message.

[0335] Example Embodiment El 6. The method of any one of Example Embodiments El to El 5, wherein the input data comprise a subset of a larger amount of data collected by the DU, and wherein the method comprises determining the input data to be transmitted to the CU based on the input data fulfilling at least one condition.

[0336] Example Embodiment El 7. The method of Example Embodiment El 6, comprising receiving the at least one condition from the CU or another network node. Example Embodiment El 8. The method of any one of Example Embodiments El to El 7, comprising receiving, from the CU, an indication to stop measuring TRP and / or reporting input data.

[0337] Example Embodiment El 9. The method of any one of Example Embodiments El to El 8, comprising transmitting, to the CU, a request for the capability information, and wherein the capability information is received based on the request.

[0338] Example Embodiment e20. The method of Example Embodiment El 9, wherein the requests is transmitted via an Fl message.

[0339] Example Embodiment E21. The method of any one of Example Embodiments El to E20, wherein the capability information is received in a Fl AP positioning message.

[0340] Example Embodiment E22. The method of any one of Example Embodiments El to E21, wherein the capability information comprises at least one of: an indication of at least one measurement prediction, inference input, and / or inference output supported by the CU, an indication of at least one type of inferred measurement supported by the model, information associated with an accuracy level and / or uncertainty level of measurement predictions supported by the model at the CU, information associated with training data used to train the model at the CU, information associated with a time horizon for prediction measurements supported at the

[0341] CU, information associated with a processing delay for the model at the CU, information associated with a maximum measurement resolution the model can provide and / or the CU can provide, information indicating at least one TRP hosted by the CU that support measurement prediction, and information associated with a maximum update rate for a particular measurement resolution the CU supports.

[0342] Example Embodiment E23. The method of any of the previous Example Embodiments, further comprising: obtaining user data; and forwarding the user data to a host or a user equipment.

[0343] Example Embodiment E24. A network node comprising processing circuitry configured to perform any of the methods of Example Embodiments El to E23.

[0344] Example Embodiment E25. A network node configured to perform any of the methods of Example Embodiments El to E23.

[0345] Example Embodiment E26. A computer program comprising instructions which when executed on a computer perform any of the methods of Example Embodiments El to E23.

[0346] Example Embodiment E27. A computer program product comprising computer program, the computer program comprising instructions which when executed on a computer perform any of the methods of Example Embodiments El to E23.

[0347] Example Embodiment E28. A non-transitory computer readable medium storing instructions which when executed by a computer perform any of the methods of Example Embodiments El to E23.

[0348] Group F Example Embodiments

[0349] Example Embodiment Fl . A user equipment comprising: processing circuitry configured to perform any of the steps of any of the Group A Example Embodiments; and power supply circuitry configured to supply power to the processing circuitry.

[0350] Example Embodiment F2. A network node comprising: processing circuitry configured to perform any of the steps of any of the Group B, C, D, and E Example Embodiments; power supply circuitry configured to supply power to the processing circuitry.

[0351] Example Embodiment F3. A user equipment (UE) comprising: an antenna configured to send and receive wireless signals; radio front-end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry; the processing circuitry being configured to perform any of the steps of any of the Group A Example Embodiments; an input interface connected to the processing circuitry and configured to allow input of information into the UE to be processed by the processing circuitry; an output interface connected to the processing circuitry and configured to output information from the UE that has been processed by the processing circuitry; and a battery connected to the processing circuitry and configured to supply power to the UE. Example Embodiment E4. A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a cellular network for transmission to a user equipment (UE), wherein the UE comprises a communication interface and processing circuitry, the communication interface and processing circuitry of the UE being configured to perform any of the steps of any of the Group A Example Embodiments to receive the user data from the host.

[0352] Example Embodiment F5. The host of the previous Example Embodiment, wherein the cellular network further includes a network node configured to communicate with the UE to transmit the user data to the UE from the host.

[0353] Example Embodiment F6. The host of the previous 2 Example Embodiments, wherein: the processing circuitry of the host is configured to execute a host application, thereby providing the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application.

[0354] Example Embodiment F7. A method implemented by a host operating in a communication system that further includes a network node and a user equipment (UE), the method comprising: providing user data for the UE; and initiating a transmission carrying the user data to the UE via a cellular network comprising the network node, wherein the UE performs any of the operations of any of the Group A embodiments to receive the user data from the host. Example Embodiment F8. The method of the previous Example Embodiment, further comprising: at the host, executing a host application associated with a client application executing on the UE to receive the user data from the UE.

[0355] Example Embodiment F9. The method of the previous Example Embodiment, further comprising: at the host, transmitting input data to the client application executing on the UE, the input data being provided by executing the host application, wherein the user data is provided by the client application in response to the input data from the host application.

[0356] Example Embodiment F10. A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a cellular network for transmission to a user equipment (UE), wherein the UE comprises a communication interface and processing circuitry, the communication interface and processing circuitry of the UE being configured to perform any of the steps of any of the Group A Example Embodiments to transmit the user data to the host.

[0357] Example Embodiment Fl 1. The host of the previous Example Embodiment, wherein the cellular network further includes a network node configured to communicate with the UE to transmit the user data from the UE to the host.

[0358] Example Embodiment Fl 2. The host of the previous 2 Example Embodiments, wherein: the processing circuitry of the host is configured to execute a host application, thereby providing the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application.

[0359] Example Embodiment Fl 3. A method implemented by a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: at the host, receiving user data transmitted to the host via the network node by the UE, wherein the UE performs any of the steps of any of the Group A Example Embodiments to transmit the user data to the host.

[0360] Example Embodiment Fl 4. The method of the previous Example Embodiment, further comprising: at the host, executing a host application associated with a client application executing on the UE to receive the user data from the UE.

[0361] Example Embodiment Fl 5. The method of the previous Example Embodiment, further comprising: at the host, transmitting input data to the client application executing on the UE, the input data being provided by executing the host application, wherein the user data is provided by the client application in response to the input data from the host application.

[0362] Example Embodiment Fl 6. A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a network node in a cellular network for transmission to a user equipment (UE), the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B, C, D, and E Example Embodiments to transmit the user data from the host to the UE.

[0363] Example Embodiment Fl 7. The host of the previous Example Embodiment, wherein: the processing circuitry of the host is configured to execute a host application that provides the user data; and the UE comprises processing circuitry configured to execute a client application associated with the host application to receive the transmission of user data from the host.

[0364] Example Embodiment Fl 8. A method implemented in a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: providing user data for the UE; and initiating a transmission carrying the user data to the UE via a cellular network comprising the network node, wherein the network node performs any of the operations of any of the Group B, C, D, and E Example Embodiments to transmit the user data from the host to the UE.

[0365] Example Embodiment Fl 9. The method of the previous Example Embodiment, further comprising, at the network node, transmitting the user data provided by the host for the UE.

[0366] Example Embodiment F20. The method of any of the previous 2 Example Embodiments, wherein the user data is provided at the host by executing a host application that interacts with a client application executing on the UE, the client application being associated with the host application.

[0367] Example Embodiment F21. A communication system configured to provide an over-the-top service, the communication system comprising: a host comprising: processing circuitry configured to provide user data for a user equipment (UE), the user data being associated with the over-the-top service; and a network interface configured to initiate transmission of the user data toward a cellular network node for transmission to the UE, the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B, C, D, and E Example Embodiments to transmit the user data from the host to the UE.

[0368] Example Embodiment F22. The communication system of the previous Example Embodiment, further comprising: the network node; and / or the user equipment.

[0369] Example Embodiment F23. A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to initiate receipt of user data; and a network interface configured to receive the user data from a network node in a cellular network, the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B, C, D, and E Example Embodiments to receive the user data from a user equipment (UE) for the host.

[0370] Example Embodiment F24. The host of the previous 2 Example Embodiments, wherein: the processing circuitry of the host is configured to execute a host application, thereby providing the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application.

[0371] Example Embodiment F25. The host of the any of the previous 2 Example Embodiments, wherein the initiating receipt of the user data comprises requesting the user data. Example Embodiment F26. A method implemented by a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: at the host, initiating receipt of user data from the UE, the user data originating from a transmission which the network node has received from the UE, wherein the network node performs any of the steps of any of the Group B, C, D, and E Example Embodiments to receive the user data from the UE for the host.

[0372] Example Embodiment F27. The method of the previous Example Embodiment, further comprising at the network node, transmitting the received user data to the host.

Claims

CLAIMS1. A method performed by a Centralized Unit, CU, of a Radio Access Network, RAN, node for generating inferred measurements based on a CU-side Artificial Intelligence, Al, / Machine Learning, ML, model for AI / ML assisted positioning, the method comprising: receiving (Fig. 3, step 6) from a Location Management Function, LMF , a measurement request comprising a request for at least one inferred measurement for AI / ML assisted positioning; and transmitting (Fig. 3, step 13), to the LMF, at least one value associated with the at least one inferred measurement generated using the CU-side AI / ML model for AI / ML assisted positioning.

2. The method of any one of claim 1, further comprising using (Fig. 3, steps 10 and 12) the AI / ML CU-side model to generate the at least one inferred measurement.

3. The method of any one of claims 1 to 2, further comprising receiving (Fig. 3, step 9), from a Distributed Unit, DU, of the RAN node, input data for use in generating, using the CU-side AI / ML model, the at least one value associated with the at least one inferred measurement.

4. The method of claim 3, wherein receiving the input data comprises receiving (Fig. 3, step 9) the input data from the DU via an Fl message.

5. The method of any one of claims 3 to 4, further comprising receiving (Fig. 3, step 9), from the DU, information indicating an age of the input data.

6. The method of any of claims 3 to 5, further comprising receiving, from the DU, at least one of: at least one UE identifier associated with the input data, at least one UE group identifier associated with the input data, at least one FlaP gNB-DU ID associated with the input data, and at least one TRP ID.

7. The method of any one of claims 3 to 6, wherein a message comprising the input data received from the DU further comprises an indication that the input data is a full report and / or an indication that the message does not comprise fragmented input data.

8. The method of any one of claims 3 to 6, wherein the input data received from the DU comprises fragmented input data.

9. The method of claim 8, wherein a message comprising the input data received from the DU further comprises an indication that the input data is a partial report and / or comprises fragmented input data.

10. The method of any one of claims 8 to 9, further comprising transmitting (Fig. 3, step 7), to the DU, second capability information, wherein the second capability information indicates that the CU supports receiving fragmented input data.

11. The method of claim 10, wherein the second capability information indicates at least one of: a maximum Fl AP message size, and a maximum number of User Equipments, UEs, for which input data should be received in one message.

12. The method of any one of claims 8 to 11, wherein the input data is received in a first message from the DU, and the method further comprises: receiving (Fig. 3, step 11), from the DU, a second message comprising additional input data from the DU; and using (Fig. 3, step 12) the input data from the first message and the additional input data from the second message as input to the CU-side AI / ML model when generating the at least one value associated with the at least one predicted measurement.

13. The method of claim 12, wherein the input data is associated with a first UE and / or a first group of UEs, and the additional input data is associated with a second UE and / or a second group ofUEs.

14. The method of any one of claims 3 to 13, further comprising transmitting (Fig. 3, step 7), to the DU, information comprising at least one of: a request for the input data and / or the additional input data, a time window for receiving the input data and / or the additional input data, at least one UE identifier or UE group identifier for which input data is to be collected and transmitted by the DU, information associated with a UE priority for determining input data to be collected and / or transmitted by the DU, an indication that input data is to be transmitted to the CU as collected, an indication that input data is to be transmitted to the CU within a maximum period of time that begins when measurement data collection is initiated, a maximum threshold value of a measurement volume for a message containing the input data and / or additional input data, at least one time window for collecting the input data and / or additional input data, a volume of input data that may be transmitted in a message to the CU, and an expiration time for the input data.

15. The method of claim 14, wherein the information is transmitted in an F1AP message.

16. The method of any one of claims 3 to 15, further comprising determining a subset of the received input data from the DU for use in generating the at least one value associated with the at least one inferred measurement.

17. The method of any one of claims 3 to 16, further comprising transmitting, to the DU, an indication to stop measuring TRP and / or reporting input data.

18. The method of any one of claims 1 to 17, wherein transmitting (Fig. 3, step 13) the at least one value associated with the at least one predicted measurement comprising transmitting (Fig. 3, step 13), to the LMF, an NRPPa Measurement Response message comprising the at least one value associated with the at least one inferred measurement.

19. The method of claim 18, wherein the NRPPa Measurement Response message comprises at least one additional value associated with a Transmission and Reception Point, TRP, measurement performed by a UE and the at least one value associated with the at least one inferred measurement.

20. The method of any one of claims 1 to 19, further comprising transmitting (Fig. 3, step 13), to the LMF, additional information with the at least one value associated with the at least one inferred measurement, wherein the additional information comprises at least one of: information indicating an accuracy level or uncertainty level of the at least one inferred measurement, information associated with training data used to generate the at least one value associated with the at least one inferred measurement, time information associated with the at least one value associated with the at least one inferred measurement, information indicating how far into the future the at least one inferred measurement is associated, information indicating a quality and / or quality level of the at least one value associated with the at least one inferred measurement.

21. The method of any one of claims 1 to 20, wherein the measurement request comprises an indication of at least one type of predicted measurement that is requested, and wherein the at least one value associated with the at least one predicted measurement is of the at least one type requested.

22. The method of any one of claims 1 to 21, further comprising receiving (Fig. 3, step 6), from the LMF, information that indicates one or more requirements for the requested at least oneinferred measurement, wherein the one or more requirements comprise any one or more of the following: a requirement that the at least one value is associated with an uncertainty that is better than or equal to an uncertainty threshold; a requirement that the at least one value is associated with an accuracy level that is equal to or greater than an accuracy threshold; a requirement that the at least one value is calculated for a specific point in time in the future; a requirement that the at least one value is calculated for a time within a defined time window; a requirement that values for the requested at least one inferred measurement be reported periodically at a specific reporting period; a requirement that a value for the requested at least one inferred measurement be reported only once; a requirement that values for the requested at least one inferred measurement be reported periodically at a specific reporting period for a specific time duration; a requirement that the at least one value is associated with a quality level that is equal to or greater than a quality threshold.

23. The method of any one of claims 1 to 21, further comprising determining that at least one requirement is fulfilled prior to transmitting (Fig. 3, step 13) the at least one value associated with the at least one inferred measurement, and wherein the at least one requirement comprises any one or more of the following: a requirement that the at least one value is associated with an uncertainty that is better than or equal to an uncertainty threshold; a requirement that the at least one value is associated with an accuracy level that is equal to or greater than an accuracy threshold; a requirement that the at least one value is calculated for a specific point in time in the future; a requirement that the at least one value is calculated for a time within a defined time window;a requirement that values for the requested at least one inferred measurement be reported periodically at a specific reporting period; a requirement that a value for the requested at least one inferred measurement be reported only once; a requirement that values for the requested at least one inferred measurement be reported periodically at a specific reporting period for a specific time duration; a requirement that the at least one value is associated with a quality level that is equal to or greater than a quality threshold.

24. The method of claim 23, further comprising receiving (Fig. 3, step 6), from the LMF, the at least one requirement.

25. The method of any one of claims 1 to 24, further comprising, in response to receiving the measurement request, transmitting, to the LMF, a response message comprising an indication that one or more of the at least one inferred measurements can be provided.

26. The method of any one of claims 1 to 25, further comprising, in response to receiving the measurement request, transmitting, to the LMF, a response message comprising an indication that one or more of the at least one predicted measurements cannot be provided.

27. The method of claim 26, wherein the response message comprises at least one of: a cause and / or reason that the one or more of the at least one inferred measurement cannot be provided, an indication that failure to provide the one or more of the at least one inferred measurement is temporary, an indication that the CU does not support the one or more of the at least one inferred measurement.

28. The method of any one of claims 25 to 27, wherein the response message indicates at least one of: at least one type of inferred measurement that can be provided,at least one type of inferred measurement that cannot be provided.

29. The method of any one of claims 1 to 28, wherein the at least one value associated with the at least one inferred measurement is associated with at least one of: Uplink Angle of Arrival (UL- AoA), Uplink Relative Time of Arrival (UL-RTOA), Uplink Sounding Reference Signal (SRS) Reference Signal Received Power (UL-SRS-RSRP), Uplink SRS Reference Signal Received Path Power (UL-SRS-RSRPP), gNB Rx-Tx, and Uplink Reference Signal Carrier Phase (UL-RSCP).

30. The method of any one of claims 1 to 29, wherein transmitting (Fig. 3, step 13) the at least one value associated with the at least one inferred measurement comprises transmitting (Fig. 7, step 13), to the LMF, the at least one value associated with the at least one inferred measurement via a Transmission and Reception Point, TRP, measurement report Information Element, IE.

31. The method of any one of claims 1 to 30, further comprising transmitting (Fig. 3, step 1) to the DU of the RAN node and / or the LMF, capability information indicating a capability of the CU to generate the at least one inferred measurement for AI / ML assisted positioning.

32. The method of claim 31, further comprising receiving (Fig. 3, step 1), from the LMF, a request for the capability information, and wherein transmitting (Fig. 3, step 1) the capability information comprises transmitting (Fig. 3, step 1) the capability information to the LMF in response to the request for the capability information.

33. The method of claim 32, wherein the request for the capability information is received via NRPPa in a Transmission and Reception Point, TRP, information request message.

34. The method of any one of claims 31 to 33, further comprising receiving (Fig. 3, step 1), from the DU, a request for the capability information, and wherein transmitting (Fig. 3, step 1) the capability information comprises transmitting (Fig. 3, step 1) the capability information to the DU in response to the request for the capability information received from the DU.

35. The method of claim 34, wherein the request for the capability information received from the DU is received via a Fl message.

36. The method of any one of claims 31 to 35, wherein the capability information is transmitted to the DU in a Fl AP positioning message.

37. The method of any one of claims 31 to 36, wherein the capability information is transmitted to the LMF in a NRPPa message .

38. The method of any one of claims 31 to 37, wherein the capability information comprises at least one of: an indication of at least one measurement prediction, inference input, and / or inference output supported by the CU, an indication of at least one type of inferred measurement supported by the CU-side AI / ML model, information associated with an accuracy level and / or uncertainty level of inferred measurements supported by the CU-side AI / ML model, information associated with training data used to train the CU-side AI / ML model, information associated with a time horizon for inferred measurements supported at the CU, information associated with a processing delay for the CU-side AI / ML model, information associated with a maximum measurement resolution the CU-side AI / ML model can provide and / or the CU can provide, information indicating at least one TRP hosted by the CU that supports inferred measurements, and information associated with a maximum update rate for a particular measurement resolution the CU supports.

39. A network node for implementing a Centralized Unit, CU, of a Radio Access Network, RAN, node for generating inferred measurements based on a CU-side Artificial Intelligence, Al, / Machine Learning, ML, model for AI / ML assisted positioning, the network node comprising processing circuitry configured to cause the network node to:receive (Fig. 3, step 6) from a Location Management Function, LMF, a measurement request comprising a request for at least one inferred measurement for AI / ML assisted positioning; and transmit (Fig. 3, step 13), to the LMF, at least one value associated with the at least one inferred measurement generated using the CU-side AI / ML model for AI / ML assisted positioning.

40. The network node of claim 39, wherein the processing circuitry is further configured to cause the network node to perform the method of any of claims 2 to 38.

41. A network node adapted to perform the method of any of claims 1 to 38.

42. A computer program comprising instructions which when executed on a computer perform the method of any of claims 1 to 38.

43. A computer program product comprising computer program, the computer program comprising instructions which when executed on a computer perform the method of any of claims 1 to 38.

44. A non-transitory computer readable medium storing instructions which when executed by a computer perform the method of any of claims 1 to 38.

45. A method performed by a Location Management Function, LMF, for supporting inferred measurements based on a Centralized Unit, CU,-side Artificial Intelligence, Al, / Machine Learning, ML, model, for AI / ML assisted positioning, the method comprising: transmitting (Fig. 3, step 6), to a CU of a Radio Access Network, RAN, node, a measurement request comprising a request for at least one inferred measurement for AI / ML assisted positioning; and receiving (Fig. 3, step 13), from the CU, at least one value associated with the at least one inferred measurement generated using the CU-side AI / ML model for AI / ML assisted positioning.

46. The method of claim 45, further comprising performing at least one positioning operation for at least one User Equipment, UE, based on the at least one value associated with at least one inferred measurement.

47. The method of any one of claims 45 to 46, wherein receiving (Fig. 3, step 13) the at least one value associated with the at least one inferred measurement comprising receiving (Fig. 3, step 13), from the CU, an NRPPa Measurement Response message comprising the at least one value associated with the at least one inferred measurement.

48. The method of claim 47, wherein the NRPPa Measurement Response message comprises at least one additional value associated with a Transmission and Reception Point, TRP, measurement performed by a UE and the at least one value associated with the at least one inferred measurement.

49. The method of any one of claims 45 to 48, further comprising receiving (Fig. 13, step 13), from the CU, additional information with the at least one value associated with the at least one inferred measurement, wherein the additional information comprises at least one of: information indicating an accuracy level or uncertainty level of the at least one inferred measurement, information associated with training data used to generate the at least one value associated with the at least one inferred measurement, time information associated with the at least one value associated with the at least one inferred measurement, information indicating how far into the future the at least one inferred measurement is associated, information indicating a quality and / or quality level of the at least one value associated with the at least one inferred measurement.

50. The method of any one of claims 45 to 49, wherein the measurement request transmitted to the CU comprises an indication of at least one type of inferred measurement that is requestedfrom the CU, and wherein the at least one value associated with the at least one inferred measurement received from the CU is of the at least one type requested.

51. The method of any one of claims 45 to 50, wherein a characteristic of the at least one value received from the CU fulfills at least one requirement, and wherein the at least one requirement comprises any one or more of the following: a requirement that the at least one value is associated with an uncertainty that is better than or equal to an uncertainty threshold; a requirement that the at least one value is associated with an accuracy level that is equal to or greater than an accuracy threshold; a requirement that the at least one value is calculated for a specific point in time in the future; a requirement that the at least one value is calculated for a time within a defined time window; a requirement that values for the requested at least one inferred measurement be reported periodically at a specific reporting period; a requirement that a value for the requested at least one inferred measurement be reported only once; a requirement that values for the requested at least one inferred measurement be reported periodically at a specific reporting period for a specific time duration; a requirement that the at least one value is associated with a quality level that is equal to or greater than a quality threshold.

52. The method of claim 51, further comprising transmitting (Fig. 3, step 6), to the CU, the at least one requirement.

53. The method of any one of claims 45 to 52, further comprising receiving, from the CU, a response message comprising an indication that one or more of the at least one inferred measurements can to be provided by the CU.

54. The method of any one of claims 45 to 53, further comprising receiving, from the CU, a response message comprising an indication that one or more of the at least one inferred measurements cannot be provided.

55. The method of claim 54, wherein the response message comprises at least one of: a cause and / or reason that the one or more of the at least one inferred measurement cannot be provided, and / or an indication that failure to provide the one or more of the at least one inferred measurement is temporary, and / or an indication that the CU does not support the one or more of the at least one inferred measurement.

56. The method of any one of claims 53 to 55, wherein the response message indicates at least one of: at least one type of inferred measurement that can be provided, and at least one type of inferred measurement that cannot be provided.

57. The method of any one of claims 45 to 56, wherein the at least one value associated with the at least one inferred measurement is associated with at least one of: Uplink Angle of Arrival (UL-AoA), Uplink Relative Time of Arrival (UL-RTOA), Uplink Sounding Reference Signal (SRS) Reference Signal Received Power (UL-SRS-RSRP), Uplink SRS Reference Signal Received Power Path (UL-SRS-RSRPP), gNB Rx-Tx, and Uplink Received Signal Code Power (UL-RSCP).

58. The method of any one of claims 45 to 57, wherein receiving (Fig. 3, step 13) the at least one value associated with the at least one inferred measurement comprises receiving (Fig. 3, step 13), from the UE, the at least one value associated with the at least one inferred measurement is received from the CU via a Transmission and Reception Point, TRP, measurement report Information Element, IE.

59. The method of any one of claims 45 to 58, further comprising receiving (Fig. 3, step 1), from the CU, capability information indicating a capability of the CU to generate the at least one inferred measurement based on the CU-side AI / ML model for AI / ML assisted positioning.

60. The method of claim 59, further comprising transmitting (Fig. 3, step 1), to the CU, a request for the capability information, and wherein receiving (Fig. 3, step 1) the capability information comprises receiving (Fig. 3, step 1) the capability information from the UE in response to the request.

61. The method of claim 60, wherein transmitting (Fig. 3, step 1) the request comprising transmitting (Fig. 3, step 1) the request to the CU via NRPPa in a TRP information request message.

62. The method of any one of claims 59 to 61, wherein receiving (Fig. 3, step 1) the capability information comprises receiving (Fig. 3, step 1) the capability information from the CU in a NRPPa message .

63. The method of any one of claims 59 to 62, wherein the capability information comprises at least one of: an indication of at least one inferred measurement, inference input, and / or inference output supported by the CU, an indication of at least one type of inferred measurement supported by the CU-side AI / ML model for AI / ML assisted positioning, information associated with an accuracy level and / or uncertainty level of inferred measurements supported by the CU-side AI / ML model for AI / ML assisted positioning , information associated with training data used to train the CU-side AI / ML model for AI / ML assisted positioning, information associated with a time horizon for inferred measurements supported at the CU, information associated with a processing delay for the CU-side AI / ML model for AI / ML assisted positioning,information associated with a maximum measurement resolution the CU-side AI / ML model for AI / ML assisted positioning can provide and / or the CU can provide, information indicating at least one TRP hosted by the CU that supports inferred measurements, and information associated with a maximum update rate for a particular measurement resolution the CU supports.

64. A network node for implementing a Location Management Function, LMF, for supporting inferred measurements based on a Centralized Unit, CU,-side Artificial Intelligence, Al, / Machine Learning, ML, model, for AI / ML assisted positioning, the network node comprising processing circuitry configured to cause the network node to: transmit (Fig. 3, step 6), to the CU, a measurement request comprising a request for at least one inferred measurement for AI / ML assisted positioning; and receive (Fig. 3, step 13), from the CU, at least one value associated with the at least one inferred measurement generated using the CU-side AI / ML model for AI / ML assisted positioning.

65. The network node of claim 64, wherein the processing circuitry is further configured to cause the network node to perform any of the methods of claims 46 to 63.

66. A network node adapted to perform the method of any of claims 45 to 63.

67. A computer program comprising instructions which when executed on a computer perform the method of any of claims 45 to 63.

68. A computer program product comprising computer program, the computer program comprising instructions which when executed on a computer perform the method of any of claims 45 to 63.

69. A non-transitory computer readable medium storing instructions which when executed by a computer perform the method of any of claims 45 to 63.

70. A method performed by a Distributed Unit, DU, of a Radio Access Network, RAN, node for supporting inferred measurements based on a Centralized Unit, CU,-side Artificial Intelligence, Al, / Machine Learning, ML, model for AI / ML assisted positioning, the method comprising: receiving (Fig. 3, step 7), from a CU of the RAN node, a request for input data for use in generating at least one inferred measurement using the CU-side AI / ML model for AI / ML assisted positioning; and transmitting (Fig. 3, step 9), to the CU, input data for use in generating the at least one inferred measurement using the CU-side AI / ML model for AI / ML assisted positioning, in response to the request.

71. The method of claim 70, wherein transmitting (Fig. 3, step 9) the input data comprises transmitting (Fig. 3, step 9) the input data to the CU via an Fl message.

72. The method of any one of claims 70 to 71, further comprising transmitting (Fig. 3, step 9), to the CU, information indicating an age of the input data.

73. The method of any one of claims 70 to 72, further comprising transmitting (Fig. 3, step 9), to the CU, at least one of: at least one UE identifier associated with the input data, at least one UE group identifier associated with the input data, at least one FlaP gNB-DU ID associated with the input data, and at least one TRP ID.

74. The method of any one of claims 70 to 73, further comprising transmitting (Fig. 3, step 9), to the CU, an indication that the input data is a full report and / or an indication that the message does not comprise fragmented input data.

75. The method of any one of claims 70 to 73, wherein the input data received from the DU comprises fragmented input data.

76. The method of claim 75, further comprising transmitting (Fig. 3, step 9), to the CU, an indication that the input data is a partial report and / or comprises fragmented input data.

77. The method of any one of claims 75 to 76, further comprising receiving (Fig. 3, step 7), from the CU, second capability information, wherein the second capability information indicates that the CU supports receiving fragmented input data.

78. The method of claim 77, wherein the second capability information indicates at least one of: a maximum Fl AP message size, and a maximum number of UEs for which input data should be received.

79. The method of any one of claims 70 to 78, wherein the input data is transmitted in a first message to the CU, and the method further comprises: transmitting (Fig. 3, step 11), to the CU, a second message comprising additional input data for use when generating the at least one value associated with the at least one inferred measurement.

80. The method of claim 79, wherein the input data is associated with a first UE and / or a first group of UEs, and the additional input data is associated with a second UE and / or a second group of UEs.

81. The method of any one of claims 70 to 80, further comprising receiving (Fig. 3, step 3 and / or step 7), from the CU, information comprising at least one of: a time window for receiving the input data and / or the additional input data, at least one UE identifier or UE group identifier for which input data is to be collected and transmitted by the DU, information associated with a UE priority for determining input data to be collected and / or transmitted by the DU, an indication that input data is to be transmitted to the CU as collected,an indication that input data is to be transmitted to the CU within a maximum period of time that begins when measurement data collection is initiated, a maximum threshold value of a measurement volume for a message containing the input data and / or additional input data, at least one time window for collecting the input data and / or additional input data, a volume of input data that may be transmitted in a message to the CU, and an expiration time for the input data.

82. The method of claim 81, wherein the information is received in an F1AP message.

83. The method of any one of claims 70 to 82, wherein the input data comprises a subset of a larger amount of data collected by the DU, and the method further comprises determining the input data to be transmitted to the CU based on the input data fulfilling at least one condition.

84. The method of any one of claims 70 to 83, further comprising receiving (Fig. 3, step 1), from the CU, capability information indicating a capability of the CU to generate at least one inferred measurement based on the CU-side AI / ML model for AI / ML assisted positioning.

85. The method of claim 84, further comprising transmitting (Fig. 3, step 1), to the CU, a request for the capability information, and wherein the capability information is received based on the request.

86. The method of claim 85, wherein the requests is transmitted via a Fl message.

87. The method of any one of claims 84 to 86, wherein the capability information is received in a Fl AP positioning message.

88. The method of any one of claims 70 to 87, wherein the capability information comprises at least one of: an indication of at least one inferred measurement, inference input, and / or inference output supported by the CU,an indication of at least one type of inferred measurement supported by the CU-side AI / ML model for AI / ML assisted positioning, information associated with an accuracy level and / or uncertainty level of inferred measurements supported by the CU-side AI / ML model for AI / ML assisted positioning, information associated with training data used to train the CU-side AI / ML model for AI / ML assisted positioning, information associated with a time horizon for prediction measurements supported at the CU, information associated with a processing delay for the CU-side AI / ML model for AI / ML assisted positioning, information associated with a maximum measurement resolution the CU-side AI / ML model for AI / ML assisted positioning can provide and / or the CU can provide, information indicating at least one TRP hosted by the CU that supports inferred measurements, and information associated with a maximum update rate for a particular measurement resolution the CU supports.

89. A network node for implementing a Distributed Unit, DU, of a Radio Access Network, RAN, node for supporting inferred measurements based on a Centralized Unit, CU,-side Artificial Intelligence, Al, / Machine Learning, ML, model for AI / ML assisted positioning, the network node comprising processing circuitry configured to cause the network node to: receive (Fig. 3, step 7), from a CU of the RAN node, a request for input data for use in generating at least one inferred measurement using the CU-side AI / ML model for AI / ML assisted positioning; and transmit (Fig. 3, step 9), to the CU, input data for use in generating the at least one inferred measurement using the CU-side AI / ML model for AI / ML assisted positioning, in response to the request.

90. The network node of claim 89, wherein the processing circuitry is further configured to cause the network node to perform the method of any of claims 71to 88.

91. A network node configured to perform the method of any of claims 70 to 88.

92. A computer program comprising instructions which when executed on a computer perform the method of any of claims 70 to 88.

93. A computer program product comprising computer program, the computer program comprising instructions which when executed on a computer perform any of the method of any of claims 70 to 88.

94. A non-transitory computer readable medium storing instructions which when executed by a computer perform any of the method of any of claims 70 to 88.

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