Signaling for network slicing

Abstract

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a network node may transmit, to a core network entity, first signaling identifying a network slice prediction or a quality of experience prediction. The network node may receive, from the core network entity and as a response to the first signaling, second signaling identifying a network slice configuration associated with the network slice prediction or information regarding the quality of experience prediction. The network node may communicate with a user equipment using one or more network slices in accordance with the network slice configuration or using a configuration relating to the quality of experience prediction. Numerous other aspects are described.

Description

SIGNALING FOR NETWORK SLICINGCROSS-REFERENCE TO RELATED APPLICATION

[0001] This Patent Application claims priority to India Provisional Patent Application No. 202421000845, filed on January 4, 2024, entitled “SIGNALING FOR NETWORK SLICING,” and to U.S. Provisional Patent Application No. 63 / 617,313, filed on January 3, 2024, entitled “SIGNALING FOR QUALITY OF EXPERIENCE OPTIMIZATION,” which are both assigned to the assignee hereof. The disclosures of the prior Applications are considered part of and are incorporated by reference into this Patent Application.FIELD OF THE DISCLOSURE

[0002] Aspects of the present disclosure generally relate to wireless communication and specifically relate to techniques, apparatuses, and methods for signaling for network slicing and quality of experience optimization.BACKGROUND

[0003] Wireless communication systems are widely deployed to provide various services that may include carrying voice, text, messaging, video, data, and / or other traffic. The services may include unicast, multicast, and / or broadcast services, among other examples. Typical wireless communication systems may employ multiple-access radio access technologies (RATs) capable of supporting communication with multiple users by sharing available system resources (for example, time domain resources, frequency domain resources, spatial domain resources, and / or device transmit power, among other examples). Examples of such multiple-access RATs include code division multiple access (CDMA) systems, time division multiple access (TDMA) systems, frequency division multiple access (FDMA) systems, orthogonal frequency division multiple access (OFDMA) systems, single-carrier frequency division multiple access (SC-FDMA) systems, and time division synchronous code division multiple access (TD-SCDMA) systems.

[0004] The above multiple-access RATs have been adopted in various telecommunication standards to provide common protocols that enable different wireless communication devices to communicate on a municipal, national, regional, or global level. An example telecommunication standard is New Radio (NR). NR, which may also be referred to as 5G, is part of a continuous mobile broadband evolution promulgated by the Third Generation Partnership Project (3GPP). NR (and other mobile broadband evolutions beyond NR) may be designed to better support Internet of things (loT) and reduced capability device deployments, industrial connectivity, millimeter wave (mmWave) expansion, licensed and unlicensed spectrum access, non-terrestrial network (NTN) deployment, sidelink and other device-to-device direct communicationtechnologies (for example, cellular vehicle-to-everything (CV2X) communication), massive multiple -input multiple -output (MIMO), disaggregated network architectures and network topology expansions, multiple-subscriber implementations, high-precision positioning, and / or radio frequency (RF) sensing, among other examples. As the demand for mobile broadband access continues to increase, further improvements in NR may be implemented, and other radio access technologies such as 6G may be introduced, to further advance mobile broadband evolution.SUMMARY

[0005] Some aspects described herein relate to a method of wireless communication performed by a network node. The method may include transmitting, to a core network entity, first signaling identifying a network slice prediction. The method may include receiving, from the core network entity and as a response to the first signaling, second signaling identifying a network slice configuration associated with the network slice prediction. The method may include communicating with a user equipment (UE) using one or more network slices in accordance with the network slice configuration.

[0006] Some aspects described herein relate to a method of wireless communication performed by a core network entity. The method may include receiving, from a network node, first signaling identifying a network slice prediction. The method may include transmitting, to the network node and as a response to the first signaling, second signaling identifying a network slice configuration associated with the network slice prediction. The method may include transmitting, to a UE and as a response to the first signaling, third signaling identifying the network slice configuration to cause the UE to communicate with the network node using one or more network slices in accordance with the network slice configuration.

[0007] Some aspects described herein relate to a method of wireless communication performed by a UE. The method may include receiving, from a core network entity and as a response to a network slice prediction, signaling identifying a network slice configuration associated with the network slice prediction. The method may include communicating with a network node using one or more network slices in accordance with the network slice configuration.

[0008] Some aspects described herein relate to a network node for wireless communication. The network node may include one or more memories and one or more processors coupled to the one or more memories. The one or more processors may be configured to transmit, to a core network entity, first signaling identifying a network slice prediction. The one or more processors may be configured to receive, from the core network entity and as a response to the first signaling, second signaling identifying a network slice configuration associated with the network sliceprediction. The one or more processors may be configured to communicate with a UE using one or more network slices in accordance with the network slice configuration.

[0009] Some aspects described herein relate to a core network entity for wireless communication. The core network entity may include one or more memories and one or more processors coupled to the one or more memories. The one or more processors may be configured to receive, from a network node, first signaling identifying a network slice prediction. The one or more processors may be configured to transmit, to the network node and as a response to the first signaling, second signaling identifying a network slice configuration associated with the network slice prediction. The one or more processors may be configured to transmit, to a UE and as a response to the first signaling, third signaling identifying the network slice configuration to cause the UE to communicate with the network node using one or more network slices in accordance with the network slice configuration.

[0010] Some aspects described herein relate to a UE for wireless communication. The UE may include one or more memories and one or more processors coupled to the one or more memories. The one or more processors may be configured to receive, from a core network entity and as a response to a network slice prediction, signaling identifying a network slice configuration associated with the network slice prediction. The one or more processors may be configured to communicate with a network node using one or more network slices in accordance with the network slice configuration.

[0011] Some aspects described herein relate to a non-transitory computer-readable medium that stores a set of instructions for wireless communication by a network node. The set of instructions, when executed by one or more processors of the network node, may cause the network node to transmit, to a core network entity, first signaling identifying a network slice prediction. The set of instructions, when executed by one or more processors of the network node, may cause the network node to receive, from the core network entity and as a response to the first signaling, second signaling identifying a network slice configuration associated with the network slice prediction. The set of instructions, when executed by one or more processors of the network node, may cause the network node to communicate with a UE using one or more network slices in accordance with the network slice configuration.

[0012] Some aspects described herein relate to a non-transitory computer-readable medium that stores a set of instructions for wireless communication by a core network entity. The set of instructions, when executed by one or more processors of the core network entity, may cause the core network entity to receive, from a network node, first signaling identifying a network slice prediction. The set of instructions, when executed by one or more processors of the core network entity, may cause the core network entity to transmit, to the network node and as a response to the first signaling, second signaling identifying a network slice configuration associated with thenetwork slice prediction. The set of instructions, when executed by one or more processors of the core network entity, may cause the core network entity to transmit, to a UE and as a response to the first signaling, third signaling identifying the network slice configuration to cause the UE to communicate with the network node using one or more network slices in accordance with the network slice configuration.

[0013] Some aspects described herein relate to a non-transitory computer-readable medium that stores a set of instructions for wireless communication by a UE. The set of instructions, when executed by one or more processors of the UE, may cause the UE to receive, from a core network entity and as a response to a network slice prediction, signaling identifying a network slice configuration associated with the network slice prediction. The set of instructions, when executed by one or more processors of the UE, may cause the UE to communicate with a network node using one or more network slices in accordance with the network slice configuration.

[0014] Some aspects described herein relate to an apparatus for wireless communication. The apparatus may include means for transmitting, to a core network entity, first signaling identifying a network slice prediction. The apparatus may include means for receiving, from the core network entity and as a response to the first signaling, second signaling identifying a network slice configuration associated with the network slice prediction. The apparatus may include means for communicating with a UE using one or more network slices in accordance with the network slice configuration.

[0015] Some aspects described herein relate to an apparatus for wireless communication. The apparatus may include means for receiving, from a network node, first signaling identifying a network slice prediction. The apparatus may include means for transmitting, to the network node and as a response to the first signaling, second signaling identifying a network slice configuration associated with the network slice prediction. The apparatus may include means for transmitting, to a UE and as a response to the first signaling, third signaling identifying the network slice configuration to cause the UE to communicate with the network node using one or more network slices in accordance with the network slice configuration.

[0016] Some aspects described herein relate to an apparatus for wireless communication. The apparatus may include means for receiving, from a core network entity and as a response to a network slice prediction, signaling identifying a network slice configuration associated with the network slice prediction. The apparatus may include means for communicating with a network node using one or more network slices in accordance with the network slice configuration.

[0017] In some aspects, a method of wireless communication performed by a first network node includes performing a quality of experience (QoE) prediction based at least in part on an artificial intelligence and machine learning (AI / ML) training and inference operation; and transmitting, to a second network node, information regarding the QoE prediction and at least oneof a protocol data unit (PDU) session identifier (ID), a quality of service flow identifier (QFI), a data radio bearer (DRB) ID, or a slice ID associated with the QoE prediction.

[0018] In some aspects, a method of wireless communication performed by a UE includes receiving, from a network node, assistance information that includes a QoE prediction, wherein the QoE prediction is associated with at least one of a PDU session ID, a QFI, a DRB ID, or a slice ID; and performing a scheduling operation based at least in part on the assistance information.

[0019] In some aspects, an apparatus for wireless communication at a first network node includes one or more memories; and one or more processors coupled to the one or more memories, the one or more processors individually or collectively configured to cause the first network node to: perform a QoE prediction based at least in part on an AI / ML training and inference operation; and transmit, to a second network node, information regarding the QoE prediction and at least one of a PDU session ID, a QFI, a DRB ID, or a slice ID associated with the QoE prediction.

[0020] In some aspects, an apparatus for wireless communication at a UE includes one or more memories; and one or more processors coupled to the one or more memories, the one or more processors individually or collectively configured to cause the UE to: receive, from a network node, assistance information that includes a QoE prediction, wherein the QoE prediction is associated with at least one of a PDU session ID, a QFI, a DRB ID, or a slice ID; and perform a scheduling operation based at least in part on the assistance information.

[0021] In some aspects, a non-transitory computer-readable medium storing a set of instructions for wireless communication includes one or more instructions that, when executed by one or more processors of a first network node, cause the first network node to: perform a QoE prediction based at least in part on an AI / ML training and inference operation; and transmit, to a second network node, information regarding the QoE prediction and at least one of a PDU session ID, a QFI, a DRB ID, or a slice ID associated with the QoE prediction.

[0022] In some aspects, a non-transitory computer-readable medium storing a set of instructions for wireless communication includes one or more instructions that, when executed by one or more processors of a UE, cause the UE to: receive, from a network node, assistance information that includes a QoE prediction, wherein the QoE prediction is associated with at least one of a PDU session ID, a QFI, a DRB ID, or a slice ID; and perform a scheduling operation based at least in part on the assistance information.

[0023] In some aspects, an apparatus for wireless communication includes means for performing a QoE prediction based at least in part on an AI / ML training and inference operation; and means for transmitting, to a second network node, information regarding the QoE predictionand at least one of a PDU session ID, a QFI, a DRB ID, or a slice ID associated with the QoE prediction.

[0024] In some aspects, an apparatus for wireless communication includes means for receiving, from a network node, assistance information that includes a QoE prediction, wherein the QoE prediction is associated with at least one of a PDU session ID, a QFI, a DRB ID, or a slice ID; and means for performing a scheduling operation based at least in part on the assistance information.

[0025] Aspects of the present disclosure may generally be implemented by or as a method, apparatus, system, computer program product, non-transitory computer-readable medium, user equipment, base station, network node, network entity, wireless communication device, and / or processing system as substantially described with reference to, and as illustrated by, the specification and accompanying drawings.

[0026] The foregoing paragraphs of this section have broadly summarized some aspects of the present disclosure. These and additional aspects and associated advantages will be described hereinafter. The disclosed aspects may be used as a basis for modifying or designing other aspects for carrying out the same or similar purposes of the present disclosure. Such equivalent aspects do not depart from the scope of the appended claims. Characteristics of the aspects disclosed herein, both their organization and method of operation, together with associated advantages, will be better understood from the following description when considered in connection with the accompanying drawings.BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The appended drawings illustrate some aspects of the present disclosure, but are not limiting of the scope of the present disclosure because the description may enable other aspects. Each of the drawings is provided for purposes of illustration and description, and not as a definition of the limits of the claims. The same or similar reference numbers in different drawings may identify the same or similar elements.

[0028] Fig. 1 is a diagram illustrating an example of a wireless communication network in accordance with the present disclosure.

[0029] Fig. 2 is a diagram illustrating an example network node in communication with an example UE in a wireless network in accordance with the present disclosure.

[0030] Fig. 3 is a diagram illustrating an example disaggregated base station architecture in accordance with the present disclosure.

[0031] Fig. 4 is a diagram illustrating an example architecture of a functional framework for radio access network (RAN) intelligence enabled by data collection, in accordance with the present disclosure.

[0032] Fig. 5 is a diagram illustrating an example associated with network slice resource prediction at a network node, in accordance with the present disclosure.

[0033] Fig. 6 is a diagram illustrating an example associated with network slice resource prediction at a core network entity, in accordance with the present disclosure.

[0034] Fig. 7 is a diagram illustrating an example associated with network slice based resource prediction at a network node, in accordance with the present disclosure.

[0035] Fig. 8 is a diagram illustrating an example associated with network slice based resource prediction at a network node, in accordance with the present disclosure.

[0036] Fig. 9 is a diagram illustrating an example associated with network slice metric prediction signaling, in accordance with the present disclosure.

[0037] Fig. 10 is a diagram illustrating an example associated with network slice resource prediction at a network node or a UE, in accordance with the present disclosure.

[0038] Fig. 11 is a diagram illustrating an example process performed, for example, at a network node or an apparatus of a network node, in accordance with the present disclosure.

[0039] Fig. 12 is a diagram illustrating an example process performed, for example, at a core network entity or an apparatus of a core network entity, in accordance with the present disclosure.

[0040] Fig. 13 is a diagram illustrating an example process performed, for example, at a UE or an apparatus of a UE, in accordance with the present disclosure.

[0041] Fig. 14 is a diagram of an example apparatus for wireless communication, in accordance with the present disclosure.

[0042] Fig. 15 is a diagram of an example apparatus for wireless communication, in accordance with the present disclosure.

[0043] Fig. 16 is a diagram of an example apparatus for wireless communication, in accordance with the present disclosure.

[0044] Fig. 17 is a diagram illustrating an example of quality of experience (QoE) signaling, in accordance with the present disclosure.

[0045] Figs 18A-18B are diagrams illustrating examples of QoE prediction at a network node using a split network architecture, in accordance with the present disclosure.

[0046] Fig. 19 is a diagram illustrating an example of QoE prediction at a network node, in accordance with the present disclosure.

[0047] Fig. 20 is a diagram illustrating an example of exchanging predicted QoE during handovers, in accordance with the present disclosure.

[0048] Figs. 21A-21B are diagrams illustrating examples of QoE prediction in a dual connectivity environment, in accordance with the present disclosure.

[0049] Fig. 22 is a diagram illustrating an example of QoE prediction at a network node, in accordance with the present disclosure.

[0050] Fig. 23 is a diagram illustrating an example of QoE prediction at a UE, in accordance with the present disclosure.

[0051] Fig. 24 is a diagram illustrating an example of QoE configuration prediction at one or more network nodes, in accordance with the present disclosure.

[0052] Fig. 25 is a diagram illustrating an example of QoE configuration prediction between a master node and a secondary node, in accordance with the present disclosure.

[0053] Fig. 26 is a diagram illustrating an example process performed, for example, at a network node or an apparatus of a network node, in accordance with the present disclosure.

[0054] Fig. 27 is a diagram illustrating an example process performed, for example, at a UE or an apparatus of a UE, in accordance with the present disclosure.

[0055] Fig. 28 is a diagram of an example apparatus for wireless communication, in accordance with the present disclosure.

[0056] Fig. 29 is a diagram of an example apparatus for wireless communication, in accordance with the present disclosure.DETAILED DESCRIPTION

[0057] Various aspects of the present disclosure are described hereinafter with reference to the accompanying drawings. However, aspects of the present disclosure may be embodied in many different forms and is not to be construed as limited to any specific aspect illustrated by or described with reference to an accompanying drawing or otherwise presented in this disclosure. Rather, these aspects are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. One skilled in the art may appreciate that the scope of the disclosure is intended to cover any aspect of the disclosure disclosed herein, whether implemented independently of or in combination with any other aspect of the disclosure. For example, an apparatus may be implemented or a method may be practiced using various combinations or quantities of the aspects set forth herein. In addition, the scope of the disclosure is intended to cover an apparatus having, or a method that is practiced using, other structures and / or functionalities in addition to or other than the structures and / or functionalities with which various aspects of the disclosure set forth herein may be practiced. Any aspect of the disclosure disclosed herein may be embodied by one or more elements of a claim.

[0058] Several aspects of telecommunication systems will now be presented with reference to various methods, operations, apparatuses, and techniques. These methods, operations, apparatuses, and techniques will be described in the following detailed description and illustrated in the accompanying drawings by various blocks, modules, components, circuits, steps, processes, or algorithms (collectively referred to as “elements”). These elements may be implemented using hardware, software, or a combination of hardware and software. Whether such elements areimplemented as hardware or software depends upon the particular application and design constraints imposed on the overall system.

[0059] A network operator, such as a telecommunications provider, may configure network slicing for a wireless network. For example, the network operator may establish a plurality of network slices using a common physical infrastructure. The network slices may represent virtualized or logical networks that are allocated for specific applications, use cases, users, traffic characteristics, or other granularities. For example, a network node may allocate resources for a first network slice for evolved multimedia broadband (eMBB) service, a second network slice for ultra-reliable low -latency communication (URLLC) service, a third network slice for public safety services, a fourth network slice for machine type communication services, a fifth network slice for device-to-device (e.g., vehicle-to-everything (V2X)) service, or a sixth network slice for general purpose services (e.g., services other than the aforementioned services), among other examples.

[0060] In some communications systems, a network node may use artificial intelligence or machine learning (AI / ML) techniques for optimizing network characteristics. For example, a network node (or a UE) may use AI / ML techniques for beam selection, for encoding channel state feedback, or for allocating resources to different devices. However, when establishing and / or managing a set of network slices, a network node, a core network entity, or a UE may lack signaling for exchanging information that can be used as input to an AI / ML model. Accordingly, a predicting node (e.g., the network node, the core network entity, or the UE) may lack external data and may use only internal data (e.g., data collected by the UE when the UE is the predicting node or data collected by the network node when the network node is the predicting node) as an input to an AI / ML model. Use of only internal data for AI / ML model based prediction associated with network slice establishment and / or management may result in non-optimized network slice establishment and / or management. For example, predictions based only on internal data may be less accurate than predictions that include external data (e.g., data received from another device).

[0061] Various aspects relate generally to signaling for network slicing. Some aspects more specifically relate to signaling flows for different devices to exchange data that can be used for AI / ML models associated with generating predictions used for network slice establishment and / or management. In some aspects, a UE, a network node, or a core network entity may use signaling for providing predictions, feedback, or assistance information to other entities associated with handovers, dual -connectivity, network slicing, or network resource allocation. In some aspects, the UE, the network node, or the core network entity may communicate to provide signaling associated with configuring data collection for AI / ML model based network slice establishment and / or management.

[0062] Particular aspects of the subject matter described in this disclosure can be implemented to realize one or more of the following potential advantages. In some examples, by establishingsignaling for network slice establishment and / or management, the described techniques can be used to improve an efficiency of network resource allocations associated with network slices. For example, a UE, a network node, or a core network entity may improve an accuracy of AI / ML model predictions by incorporating data from additional data sources using signaling described herein.

[0063] Quality of experience (QoE) in a wireless communication network refers to an overall level of satisfaction with the wireless communication network. QoE may be based at least in part on a technical performance of the network and / or a subjective satisfaction of the network indicated by a user of the network. Parameters for identifying the QoE of the network may include, for example, perceived signal quality, consistency and reliability, service accessibility and availability, user interface and interaction, and personalization and customization. Radio access network (RAN)-visible quality of experience (RVQoE) may indicate a quality of experience in the wireless communication network that is related to a RAN node. RVQoE may include performance metrics and user experience parameters that are directly influenced by the RAN node, such as signal strength, coverage, and a capacity of the network, among other examples. In some cases, QoE and RVQoE may be optimized using AI / ML.

[0064] RVQoE measurements may be configured by a network node. A subset of QoE metrics may be reported from a UE to the network node as an information element (IE) that is readable by the network node. The RVQoE measurements may be utilized by the network node for network optimization. The RVQoE measurements may be reported in accordance with a reporting periodicity that is different than a reporting periodicity of other QoE measurements. During a RAN overload, the UE may continue to report the configured QoE measurements, even when the corresponding QoE measurement reporting for non-RVQoE metrics are paused. In some cases, the UE and the network node (and / or other network elements described herein) may not be able to communicate information regarding AI / ML-based QoE and RVQoE prediction. This may reduce a capability of the network node and the UE to take proactive actions, such as adjusting resource allocations and scheduling decisions, for achieving an improved quality of experience.

[0065] Various aspects relate generally to wireless communications. Some aspects more specifically relate to signaling for quality of experience optimization. In some aspects, a central unit may provide, to a distributed unit, assistance information for QoE prediction that includes at least one of a protocol data unit (PDU) session identifier (ID), a quality of service flow identifier (QFI), a data radio bearer (DRB) ID, or a slice ID associated with the QoE prediction, and the distributed unit may provide, to the central unit, a QoE prediction that is based at least in part on the assistance information. In some other aspects, the central unit may provide, to the distributed unit, assistance information for QoE prediction that includes at least one of a channel quality indicator (CQI) distribution, a modulation and coding scheme (MCS) distribution, or a radio linkcontrol (RLC) buffer status associated with the QoE prediction, and the central unit may provide, to the distributed unit, a QoE prediction that is based at least in part on the assistance information. In some aspects, a RAN node may perform a QoE prediction based at least in part on an AI / ML training and inference operation. The network node may transmit, to a measurement and collection entity (MCE) or operations and management (OAM) node, the indication of the QoE prediction and at least one of the PDU session ID, the QFI, the DRB ID, or the slice ID associated with the QoE prediction. The MCE or OAM node may transmit, to an application function, the indication of the QoE prediction and at least one of the PDU session ID, the QFI, the DRB ID, or the slice ID associated with the QoE prediction. In some aspects, the RAN node may transmit, to a UE, assistance information that includes the indication of the QoE prediction and that includes a predicted radio resource status, and the UE may transmit, to the RAN node, a QoE report that is based at least in part on the QoE prediction.

[0066] Particular aspects of the subject matter described in this disclosure can be implemented to realize one or more of the following potential advantages. In some examples, by enabling the central unit to provide the assistance information to the distributed unit, and by enabling the distributed unit to provide a predicted QoE to the central unit, the described techniques can be used to enable the central unit to perform mobility optimization and / or load balancing. In some examples, by enabling the distributed unit to provide the assistance information to the central unit, and by enabling the central unit to provide a predicted QoE to the distributed unit, the described techniques can be used to enable the distributed unit to perform scheduling optimizations. In some examples, by enabling the RAN node to transmit the QoE prediction to a network node, the described techniques can be used to enable the network node (for example, an application function associated with the network node) to adjust one or more application buffers and / or to adjust a scheduling based at least in part on the QoE prediction. In some examples, by enabling the RAN node to transmit the QoE prediction to the UE, the described techniques can be used to enable the UE to perform transmission scheduling and / or mobility decisions based at least in part on the QoE predictions. These example advantages, among others, are described in more detail below.

[0067] Multiple-access radio access technologies (RATs) have been adopted in various telecommunication standards to provide common protocols that enable wireless communication devices to communicate on a municipal, enterprise, national, regional, or global level. For example, 5G New Radio (NR) is part of a continuous mobile broadband evolution promulgated by the Third Generation Partnership Project (3GPP). 5G NR supports various technologies and use cases including enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), massive machine-type communication (mMTC), millimeter wave (mmWave) technology, beamforming, network slicing, edge computing, Internet of Things (loT) connectivity and management, and network function virtualization (NFV).

[0068] As the demand for broadband access increases and as technologies supported by wireless communication networks evolve, further technological improvements may be adopted in or implemented for 5G NR or future RATs, such as 6G, to further advance the evolution of wireless communication for a wide variety of existing and new use cases and applications. Such technological improvements may be associated with new frequency band expansion, licensed and unlicensed spectrum access, overlapping spectrum use, small cell deployments, non-terrestrial network (NTN) deployments, disaggregated network architectures and network topology expansion, device aggregation, advanced duplex communication, sidelink and other device-to- device direct communication, loT (including passive or ambient loT) networks, reduced capability (RedCap) UE functionality, industrial connectivity, multiple-subscriber implementations, high-precision positioning, radio frequency (RF) sensing, and / or artificial intelligence or machine learning (AI / ML), among other examples. These technological improvements may support use cases such as wireless backhauls, wireless data centers, extended reality (XR) and metaverse applications, meta services for supporting vehicle connectivity, holographic and mixed reality communication, autonomous and collaborative robots, vehicle platooning and cooperative maneuvering, sensing networks, gesture monitoring, human-brain interfacing, digital twin applications, asset management, and universal coverage applications using non-terrestrial and / or aerial platforms, among other examples. The methods, operations, apparatuses, and techniques described herein may enable one or more of the foregoing technologies and / or support one or more of the foregoing use cases.

[0069] Fig. 1 is a diagram illustrating an example of a wireless communication network 100 in accordance with the present disclosure. The wireless communication network 100 may be or may include elements of a 5G (or NR) network or a 6G network, among other examples. The wireless communication network 100 may include multiple network nodes 110, shown as a network node (NN) 110a, a network node 110b, a network node 110c, and a network node 1 lOd. The network nodes 110 may support communications with multiple UEs 120, shown as a UE 120a, a UE 120b, a UE 120c, a UE 120d, and a UE 120e.

[0070] The network nodes 110 and the UEs 120 of the wireless communication network 100 may communicate using the electromagnetic spectrum, which may be subdivided by frequency or wavelength into various classes, bands, carriers, and / or channels. For example, devices of the wireless communication network 100 may communicate using one or more operating bands. In some aspects, multiple wireless communication networks 100 may be deployed in a given geographic area. Each wireless communication network 100 may support a particular RAT (which may also be referred to as an air interface) and may operate on one or more carrier frequencies in one or more frequency ranges. Examples of RATs include a 4G RAT, a 5G / NR RAT, and / or a 6G RAT, among other examples. In some examples, when multiple RATs aredeployed in a given geographic area, each RAT in the geographic area may operate on different frequencies to avoid interference with one another.

[0071] Various operating bands have been defined as frequency range designations FR1 (410 MHz through 7.125 GHz), FR2 (24.25 GHz through 52.6 GHz), FR3 (7.125 GHz through 24.25 GHz), FR4a or FR4-1 (52.6 GHz through 71 GHz), FR4 (52.6 GHz through 114.25 GHz), and FR5 (114.25 GHz through 300 GHz). Although a portion of FR1 is greater than 6 GHz, FR1 is often referred to (interchangeably) as a “Sub-6 GHz” band in some documents and articles. Similarly, FR2 is often referred to (interchangeably) as a “millimeter wave” band in some documents and articles, despite being different than the extremely high frequency (EHF) band (30 GHz through 300 GHz), which is identified by the International Telecommunications Union (ITU) as a “millimeter wave” band. The frequencies between FR1 and FR2 are often referred to as mid-band frequencies, which include FR3. Frequency bands falling within FR3 may inherit FR1 characteristics or FR2 characteristics, and thus may effectively extend features of FR1 or FR2 into mid-band frequencies. Thus, “sub-6 GHz,” if used herein, may broadly refer to frequencies that are less than 6 GHz, that are within FR1, and / or that are included in mid -band frequencies. Similarly, the term “millimeter wave,” if used herein, may broadly refer to frequencies that are included in mid-band frequencies, that are within FR2, FR4, FR4-a or FR4-1, or FR5, and / or that are within the EHF band. Higher frequency bands may extend 5G NR operation, 6G operation, and / or other RATs beyond 52.6 GHz. For example, each of FR4a, FR4- 1, FR4, and FR5 falls within the EHF band. In some examples, the wireless communication network 100 may implement dynamic spectrum sharing (DSS), in which multiple RATs (for example, 4G / LTE and 5G / NR) are implemented with dynamic bandwidth allocation (for example, based on user demand) in a single frequency band. It is contemplated that the frequencies included in these operating bands (for example, FR1, FR2, FR3, FR4, FR4-a, FR4-1, and / or FR5) may be modified, and techniques described herein may be applicable to those modified frequency ranges.

[0072] A network node 110 may include one or more devices, components, or systems that enable communication between a UE 120 and one or more devices, components, or systems of the wireless communication network 100. A network node 110 may be, may include, or may also be referred to as an NR network node, a 5G network node, a 6G network node, a Node B, an eNB, a gNB, an access point (AP), a transmission reception point (TRP), a mobility element, a core, a network entity, a network element, a network equipment, and / or another type of device, component, or system included in a radio access network (RAN).

[0073] A network node 110 may be implemented as a single physical node (for example, a single physical structure) or may be implemented as two or more physical nodes (for example, two or more distinct physical structures). For example, a network node 110 may be a device orsystem that implements part of a radio protocol stack, a device or system that implements a full radio protocol stack (such as a full gNB protocol stack), or a collection of devices or systems that collectively implement the full radio protocol stack. For example, and as shown, a network node 110 may be an aggregated network node (having an aggregated architecture), meaning that the network node 110 may implement a full radio protocol stack that is physically and logically integrated within a single node (for example, a single physical structure) in the wireless communication network 100. For example, an aggregated network node 110 may consist of a single standalone base station or a single TRP that uses a full radio protocol stack to enable or facilitate communication between a UE 120 and a core network of the wireless communication network 100.

[0074] Alternatively, and as also shown, a network node 110 may be a disaggregated network node (sometimes referred to as a disaggregated base station), meaning that the network node 110 may implement a radio protocol stack that is physically distributed and / or logically distributed among two or more nodes in the same geographic location or in different geographic locations. For example, a disaggregated network node may have a disaggregated architecture. In some deployments, disaggregated network nodes 110 may be used in an integrated access and backhaul (IAB) network, in an open radio access network (O-RAN) (such as a network configuration in compliance with the O-RAN Alliance), or in a virtualized radio access network (vRAN), also known as a cloud radio access network (C-RAN), to facilitate scaling by separating base station functionality into multiple units that can be individually deployed.

[0075] The network nodes 110 of the wireless communication network 100 may include one or more central units (CUs), one or more distributed units (DUs), and / or one or more radio units (RUs). A CU may host one or more higher layer control functions, such as radio resource control (RRC) functions, packet data convergence protocol (PDCP) functions, and / or service data adaptation protocol (SDAP) functions, among other examples. A DU may host one or more of a radio link control (RLC) layer, a medium access control (MAC) layer, and / or one or more higher physical (PHY) layers depending, at least in part, on a functional split, such as a functional split defined by the 3GPP. In some examples, a DU also may host one or more lower PHY layer functions, such as a fast Fourier transform (FFT), an inverse FFT (iFFT), beamforming, physical random access channel (PRACH) extraction and filtering, and / or scheduling of resources for one or more UEs 120, among other examples. An RU may host RF processing functions or lower PHY layer functions, such as an FFT, an iFFT, beamforming, or PRACH extraction and filtering, among other examples, according to a functional split, such as a lower layer functional split. In such an architecture, each RU can be operated to handle over the air (OTA) communication with one or more UEs 120.

[0076] In some aspects, a single network node 110 may include a combination of one or more CUs, one or more DUs, and / or one or more RUs. Additionally or alternatively, a network node 110 may include one or more Near-Real Time (Near-RT) RAN Intelligent Controllers (RICs) and / or one or more Non-Real Time (Non-RT) RICs. In some examples, a CU, a DU, and / or an RU may be implemented as a virtual unit, such as a virtual central unit (VCU), a virtual distributed unit (VDU), or a virtual radio unit (VRU), among other examples. A virtual unit may be implemented as a virtual network function, such as associated with a cloud deployment.

[0077] Some network nodes 110 (for example, a base station, an RU, or a TRP) may provide communication coverage for a particular geographic area. In the 3GPP, the term “cell” can refer to a coverage area of a network node 110 or to a network node 110 itself, depending on the context in which the term is used. A network node 110 may support one or multiple (for example, three) cells. In some examples, a network node 110 may provide communication coverage for a macro cell, a pico cell, a femto cell, or another type of cell. A macro cell may cover a relatively large geographic area (for example, several kilometers in radius) and may allow unrestricted access by UEs 120 with service subscriptions. A pico cell may cover a relatively small geographic area and may allow unrestricted access by UEs 120 with service subscriptions. A femto cell may cover a relatively small geographic area (for example, a home) and may allow restricted access by UEs 120 having association with the femto cell (for example, UEs 120 in a closed subscriber group (CSG)). A network node 110 for a macro cell may be referred to as a macro network node. A network node 110 for a pico cell may be referred to as a pico network node. A network node 110 for a femto cell may be referred to as a femto network node or an in- home network node. In some examples, a cell may not necessarily be stationary. For example, the geographic area of the cell may move according to the location of an associated mobile network node 110 (for example, a train, a satellite base station, an unmanned aerial vehicle, or an NTN network node).

[0078] The wireless communication network 100 may be a heterogeneous network that includes network nodes 110 of different types, such as macro network nodes, pico network nodes, femto network nodes, relay network nodes, aggregated network nodes, and / or disaggregated network nodes, among other examples. In the example shown in Fig. 1, the network node 110a may be a macro network node for a macro cell 130a, the network node 110b may be a pico network node for a pico cell 130b, and the network node 110c may be a femto network node for a femto cell 130c.Various different types of network nodes 110 may generally transmit at different power levels, serve different coverage areas, and / or have different impacts on interference in the wireless communication network 100 than other types of network nodes 110. For example, macro network nodes may have a high transmit power level (for example, 5 to 40 watts), whereas piconetwork nodes, femto network nodes, and relay network nodes may have lower transmit power levels (for example, 0.1 to 2 watts).

[0079] In some examples, a network node 110 may be, may include, or may operate as an RU, a TRP, or a base station that communicates with one or more UEs 120 via a radio access link (which may be referred to as a “Uu” link). The radio access link may include a downlink and an uplink. “Downlink” (or “DL”) refers to a communication direction from a network node 110 to a UE 120, and “uplink” (or “UL”) refers to a communication direction from a UE 120 to a network node 110. Downlink channels may include one or more control channels and one or more data channels. A downlink control channel may be used to transmit downlink control information (DCI) (for example, scheduling information, reference signals, and / or configuration information) from a network node 110 to a UE 120. A downlink data channel may be used to transmit downlink data (for example, user data associated with a UE 120) from a network node 110 to a UE 120. Downlink control channels may include one or more physical downlink control channels (PDCCHs), and downlink data channels may include one or more physical downlink shared channels (PDSCHs). Uplink channels may similarly include one or more control channels and one or more data channels. An uplink control channel may be used to transmit uplink control information (UCI) (for example, reference signals and / or feedback corresponding to one or more downlink transmissions) from a UE 120 to a network node 110. An uplink data channel may be used to transmit uplink data (for example, user data associated with a UE 120) from a UE 120 to a network node 110. Uplink control channels may include one or more physical uplink control channels (PUCCHs), and uplink data channels may include one or more physical uplink shared channels (PUSCHs). The downlink and the uplink may each include a set of resources on which the network node 110 and the UE 120 may communicate.

[0080] Downlink and uplink resources may include time domain resources (frames, subframes, slots, and / or symbols), frequency domain resources (frequency bands, component carriers, subcarriers, resource blocks, and / or resource elements), and / or spatial domain resources (particular transmit directions and / or beam parameters). Frequency domain resources of some bands may be subdivided into bandwidth parts (BWPs). A BWP may be a continuous block of frequency domain resources (for example, a continuous block of resource blocks) that are allocated for one or more UEs 120. A UE 120 may be configured with both an uplink BWP and a downlink BWP (where the uplink BWP and the downlink BWP may be the same BWP or different BWPs). A BWP may be dynamically configured (for example, by a network node 110 transmitting a DCI configuration to the one or more UEs 120) and / or reconfigured, which means that a BWP can be adjusted in real-time (or near-real-time) based on changing network conditions in the wireless communication network 100 and / or based on the specific requirements of the one or more UEs 120. This enables more efficient use of the available frequency domain resources in the wireless communication network 100 because fewer frequency domain resources may beallocated to a BWP for a UE 120 (which may reduce the quantity of frequency domain resources that a UE 120 is required to monitor), leaving more frequency domain resources to be spread across multiple UEs 120. Thus, BWPs may also assist in the implementation of lower-capability UEs 120 by facilitating the configuration of smaller bandwidths for communication by such UEs 120.

[0081] As described above, in some aspects, the wireless communication network 100 may be, may include, or may be included in, an IAB network. In an IAB network, at least one network node 110 is an anchor network node that communicates with a core network. An anchor network node 110 may also be referred to as an IAB donor (or “lAB-donor”). The anchor network node 110 may connect to the core network via a wired backhaul link. For example, an Ng interface of the anchor network node 110 may terminate at the core network. Additionally or alternatively, an anchor network node 110 may connect to one or more devices of the core network that provide a core access and mobility management function (AMF). An IAB network also generally includes multiple non-anchor network nodes 110, which may also be referred to as relay network nodes or simply as IAB nodes (or “lAB-nodes”). Each non-anchor network node 110 may communicate directly with the anchor network node 110 via a wireless backhaul link to access the core network, or may communicate indirectly with the anchor network node 110 via one or more other non-anchor network nodes 110 and associated wireless backhaul links that form a backhaul path to the core network. Some anchor network node 110 or other non-anchor network node 110 may also communicate directly with one or more UEs 120 via wireless access links that carry access traffic. In some examples, network resources for wireless communication (such as time resources, frequency resources, and / or spatial resources) may be shared between access links and backhaul links.

[0082] In some examples, any network node 110 that relays communications may be referred to as a relay network node, a relay station, or simply as a relay. A relay may receive a transmission of a communication from an upstream station (for example, another network node 110 or a UE 120) and transmit the communication to a downstream station (for example, a UE 120 or another network node 110). In this case, the wireless communication network 100 may include or be referred to as a “multi-hop network.” In the example shown in Fig. 1, the network node 1 lOd (for example, a relay network node) may communicate with the network node 110a (for example, a macro network node) and the UE 120d in order to facilitate communication between the network node 110a and the UE 120d. Additionally or alternatively, a UE 120 may be or may operate as a relay station that can relay transmissions to or from other UEs 120. A UE 120 that relays communications may be referred to as a UE relay or a relay UE, among other examples.

[0083] The UEs 120 may be physically dispersed throughout the wireless communication network 100, and each UE 120 may be stationary or mobile. A UE 120 may be, may include, or may be included in an access terminal, another terminal, a mobile station, or a subscriber unit. A UE 120 may be, include, or be coupled with a cellular phone (for example, a smart phone), a personal digital assistant (PDA), a wireless modem, a wireless communication device, a handheld device, a laptop computer, a cordless phone, a wireless local loop (WLL) station, a tablet, a camera, a gaming device, a netbook, a smartbook, an ultrabook, a medical device, a biometric device, a wearable device (for example, a smart watch, smart clothing, smart glasses, a smart wristband, and / or smart jewelry, such as a smart ring or a smart bracelet), an entertainment device (for example, a music device, a video device, and / or a satellite radio), an XR device, a vehicular component or sensor, a smart meter or sensor, industrial manufacturing equipment, a Global Navigation Satellite System (GNSS) device (such as a Global Positioning System device or another type of positioning device), a UE function of a network node, and / or any other suitable device or function that may communicate via a wireless medium.

[0084] A UE 120 and / or a network node 110 may include one or more chips, system-on-chips (SoCs), chipsets, packages, or devices that individually or collectively constitute or comprise a processing system. The processing system includes processor (or “processing”) circuitry in the form of one or multiple processors, microprocessors, processing units (such as central processing units (CPUs), graphics processing units (GPUs), neural processing units (NPUs) and / or digital signal processors (DSPs)), processing blocks, application-specific integrated circuits (ASIC), programmable logic devices (PLDs) (such as field programmable gate arrays (FPGAs)), or other discrete gate or transistor logic or circuitry (all of which may be generally referred to herein individually as “processors” or collectively as “the processor” or “the processor circuitry”). One or more of the processors may be individually or collectively configurable or configured to perform various functions or operations described herein. A group of processors collectively configurable or configured to perform a set of functions may include a first processor configurable or configured to perform a first function of the set and a second processor configurable or configured to perform a second function of the set, or may include the group of processors all being configured or configurable to perform the set of functions.

[0085] The processing system may further include memory circuitry in the form of one or more memory devices, memory blocks, memory elements or other discrete gate or transistor logic or circuitry, each of which may include tangible storage media such as random-access memory (RAM) or read-only memory (ROM), or combinations thereof (all of which may be generally referred to herein individually as “memories” or collectively as “the memory” or “the memory circuitry”). One or more of the memories may be coupled (for example, operatively coupled, communicatively coupled, electronically coupled, or electrically coupled) with one or more of theprocessors and may individually or collectively store processor-executable code (such as software) that, when executed by one or more of the processors, may configure one or more of the processors to perform various functions or operations described herein. Additionally or alternatively, in some examples, one or more of the processors may be preconfigured to perform various functions or operations described herein without requiring configuration by software. The processing system may further include or be coupled with one or more modems (such as a Wi-Fi (for example, IEEE compliant) modem or a cellular (for example, 3 GPP 4G LTE, 5G, or 6G compliant) modem). In some implementations, one or more processors of the processing system include or implement one or more of the modems. The processing system may further include or be coupled with multiple radios (collectively “the radio”), multiple RF chains, or multiple transceivers, each of which may in turn be coupled with one or more of multiple antennas. In some implementations, one or more processors of the processing system include or implement one or more of the radios, RF chains or transceivers. The UE 120 may include or may be included in a housing that houses components associated with the UE 120 including the processing system.

[0086] Some UEs 120 may be considered machine-type communication (MTC) UEs, evolved or enhanced machine-type communication (eMTC), UEs, further enhanced eMTC (feMTC) UEs, or enhanced feMTC (efeMTC) UEs, or further evolutions thereof, all of which may be simply referred to as “MTC UEs”). An MTC UE may be, may include, or may be included in or coupled with a robot, an uncrewed aerial vehicle, a remote device, a sensor, a meter, a monitor, and / or a location tag. Some UEs 120 may be considered loT devices and / or may be implemented as NB- loT (narrowband loT) devices. An loT UE or NB-IoT device may be, may include, or may be included in or coupled with an industrial machine, an appliance, a refrigerator, a doorbell camera device, a home automation device, and / or a light fixture, among other examples. Some UEs 120 may be considered Customer Premises Equipment, which may include telecommunications devices that are installed at a customer location (such as a home or office) to enable access to a service provider's network (such as included in or in communication with the wireless communication network 100).

[0087] Some UEs 120 may be classified according to different categories in association with different complexities and / or different capabilities. UEs 120 in a first category may facilitate massive loT in the wireless communication network 100, and may offer low complexity and / or cost relative to UEs 120 in a second category. UEs 120 in a second category may include mission-critical loT devices, legacy UEs, baseline UEs, high-tier UEs, advanced UEs, fullcapability UEs, and / or premium UEs that are capable of URLLC, enhanced mobile broadband (eMBB), and / or precise positioning in the wireless communication network 100, among other examples. A third category of UEs 120 may have mid-tier complexity and / or capability (for example, a capability between UEs 120 of the first category and UEs 120 of the secondcapability). A UE 120 of the third category may be referred to as a reduced capacity UE (“RedCap UE”), a mid-tier UE, an NR-Light UE, and / or an NR-Lite UE, among other examples. RedCap UEs may bridge a gap between the capability and complexity of NB-IoT devices and / or eMTC UEs, and mission-critical loT devices and / or premium UEs. RedCap UEs may include, for example, wearable devices, loT devices, industrial sensors, and / or cameras that are associated with a limited bandwidth, power capacity, and / or transmission range, among other examples. RedCap UEs may support healthcare environments, building automation, electrical distribution, process automation, transport and logistics, and / or smart city deployments, among other examples.

[0088] In some examples, two or more UEs 120 (for example, shown as UE 120a and UE 120e) may communicate directly with one another using sidelink communications (for example, without communicating by way of a network node 110 as an intermediary). As an example, the UE 120a may directly transmit data, control information, or other signaling as a side link communication to the UE 120e. This is in contrast to, for example, the UE 120a first transmitting data in an UL communication to a network node 110, which then transmits the data to the UE 120e in a DL communication. In various examples, the UEs 120 may transmit and receive sidelink communications using peer-to-peer (P2P) communication protocols, device-to-device (D2D) communication protocols, vehicle-to-everything (V2X) communication protocols (which may include vehicle-to-vehicle (V2V) protocols, vehicle-to-infrastructure (V2I) protocols, and / or vehicle-to-pedestrian (V2P) protocols), and / or mesh network communication protocols. In some deployments and configurations, a network node 110 may schedule and / or allocate resources for sidelink communications between UEs 120 in the wireless communication network 100. In some other deployments and configurations, a UE 120 (instead of a network node 110) may perform, or collaborate or negotiate with one or more other UEs to perform, scheduling operations, resource selection operations, and / or other operations for side link communications.

[0089] In various examples, some of the network nodes 110 and the UEs 120 of the wireless communication network 100 may be configured for full -duplex operation in addition to halfduplex operation. A network node 110 or a UE 120 operating in a half-duplex mode may perform only one of transmission or reception during particular time resources, such as during particular slots, symbols, or other time periods. Half-duplex operation may involve time-division duplexing (TDD), in which DL transmissions of the network node 110 and UL transmissions of the UE 120 do not occur in the same time resources (that is, the transmissions do not overlap in time). In contrast, a network node 110 or a UE 120 operating in a full-duplex mode can transmit and receive communications concurrently (for example, in the same time resources). By operating in a full-duplex mode, network nodes 110 and / or UEs 120 may generally increase the capacity of the network and the radio access link. In some examples, full-duplex operation may involve frequency-division duplexing (FDD), in which DL transmissions of the network node 110 areperformed in a first frequency band or on a first component carrier and transmissions of the UE 120 are performed in a second frequency band or on a second component carrier different than the first frequency band or the first component carrier, respectively. In some examples, full -duplex operation may be enabled for a UE 120 but not for a network node 110. For example, a UE 120 may simultaneously transmit an UL transmission to a first network node 110 and receive a DL transmission from a second network node 110 in the same time resources. In some other examples, full-duplex operation may be enabled for a network node 110 but not for a UE 120. For example, a network node 110 may simultaneously transmit a DL transmission to a first UE 120 and receive an UL transmission from a second UE 120 in the same time resources. In some other examples, full-duplex operation may be enabled for both a network node 110 and a UE 120.

[0090] In some examples, the UEs 120 and the network nodes 110 may perform MIMO communication. “MIMO” generally refers to transmitting or receiving multiple signals (such as multiple layers or multiple data streams) simultaneously over the same time and frequency resources. MIMO techniques generally exploit multipath propagation. MIMO may be implemented using various spatial processing or spatial multiplexing operations. In some examples, MIMO may support simultaneous transmission to multiple receivers, referred to as multi-user MIMO (MU-MIMO). Some RATs may employ advanced MIMO techniques, such as mTRP operation (including redundant transmission or reception on multiple TRPs), reciprocity in the time domain or the frequency domain, single-frequency-network (SFN) transmission, or noncoherent joint transmission (NC-JT). In some examples, a network node 110 may communicate with a core network 170, which includes one or more core network entities 172. For example, the network node 110 may communicate with an access mobility and management function (AMF) or a network slice selection function (NSSF), among other examples.

[0091] In some aspects, the network node 110 may include a communication manager 150. As described in more detail elsewhere herein, the communication manager 150 may transmit, to a core network entity, first signaling identifying a network slice prediction; receive, from the core network entity and as a response to the first signaling, second signaling identifying a network slice configuration associated with the network slice prediction; and communicate with a UE using one or more network slices in accordance with the network slice configuration. In some aspects, the communication manager 150 may perform a QoE prediction based at least in part on an AI / ML training and inference operation; and transmit, to a another network node, information regarding the QoE prediction and at least one of a PDU session ID, a QFI, a DRB ID, or a slice ID associated with the QoE prediction. Additionally, or alternatively, the communication manager 150 may perform one or more other operations described herein.

[0092] In some aspects, the core network entity 172 may include a communication manager 174. As described in more detail elsewhere herein, the communication manager 174 may receive,from a network node, first signaling identifying a network slice prediction; transmit, to the network node and as a response to the first signaling, second signaling identifying a network slice configuration associated with the network slice prediction; and transmit, to a UE and as a response to the first signaling, third signaling identifying the network slice configuration to cause the UE to communicate with the network node using one or more network slices in accordance with the network slice configuration. Additionally, or alternatively, the communication manager 174 may perform one or more other operations described herein.

[0093] In some aspects, the UE 120 may include a communication manager 140. As described in more detail elsewhere herein, the communication manager 140 may receive, from a core network entity and as a response to a network slice prediction, signaling identifying a network slice configuration associated with the network slice prediction; and communicate with a network node using one or more network slices in accordance with the network slice configuration. In some aspects, the communication manager 140 may receive, from a network node, assistance information that includes a QoE prediction, wherein the QoE prediction is associated with at least one of a PDU session ID, a QFI, a DRB ID, or a slice ID; and perform a scheduling operation based at least in part on the assistance information. Additionally, or alternatively, the communication manager 140 may perform one or more other operations described herein.

[0094] As indicated above, Fig. 1 is provided as an example. Other examples may differ from what is described with regard to Fig. 1.

[0095] Fig. 2 is a diagram illustrating an example network node 110 in communication with an example UE 120 in a wireless network in accordance with the present disclosure.

[0096] As shown in Fig. 2, the network node 110 may include a data source 212, a transmit processor 214, a transmit (TX) MIMO processor 216, a set of modems 232 (shown as 232a through 232t, where t > 1), a set of antennas 234 (shown as 234a through 234v, where v > 1), a MIMO detector 236, a receive processor 238, a data sink 239, a controller / processor 240, a memory 242, a communication unit 244, a scheduler 246, and / or a communication manager 150, among other examples. In some configurations, one or a combination of the antenna(s) 234, the modem(s) 232, the MIMO detector 236, the receive processor 238, the transmit processor 214, and / or the TX MIMO processor 216 may be included in a transceiver of the network node 110. The transceiver may be under control of and used by one or more processors, such as the controller / processor 240, and in some aspects in conjunction with processor-readable code stored in the memory 242, to perform aspects of the methods, processes, and / or operations described herein. In some aspects, the network node 110 may include one or more interfaces, communication components, and / or other components that facilitate communication with the UE 120 or another network node. Although some components are described herein in terms of a network node 110 and a UE 120, it is contemplated that a core network entity 172 may includeone or more components described herein, such as a data source, a controller / processor, a memory, a communication unit, a scheduler, or a communication manager, among other examples.

[0097] The terms “processor,” “controller,” or “controller / processor” may refer to one or more controllers and / or one or more processors. For example, reference to “a / the processor,” “a / the controller / processor,” or the like (in the singular) should be understood to refer to any one or more of the processors described in connection with Fig. 2, such as a single processor or a combination of multiple different processors. Reference to “one or more processors” should be understood to refer to any one or more of the processors described in connection with Fig. 2. For example, one or more processors of the network node 110 may include transmit processor 214, TX MIMO processor 216, MIMO detector 236, receive processor 238, and / or controller / processor 240. Similarly, one or more processors of the UE 120 may include MIMO detector 256, receive processor 258, transmit processor 264, TX MIMO processor 266, and / or controller / processor 280.

[0098] In some aspects, a single processor may perform all of the operations described as being performed by the one or more processors. In some aspects, a first set of (one or more) processors of the one or more processors may perform a first operation described as being performed by the one or more processors, and a second set of (one or more) processors of the one or more processors may perform a second operation described as being performed by the one or more processors. The first set of processors and the second set of processors may be the same set of processors or may be different sets of processors. Reference to “one or more memories” should be understood to refer to any one or more memories of a corresponding device, such as the memory described in connection with Fig. 2. For example, operation described as being performed by one or more memories can be performed by the same subset of the one or more memories or different subsets of the one or more memories.

[0099] For downlink communication from the network node 110 to the UE 120, the transmit processor 214 may receive data (“downlink data”) intended for the UE 120 (or a set of UEs that includes the UE 120) from the data source 212 (such as a data pipeline or a data queue). In some examples, the transmit processor 214 may select one or more MCSs for the UE 120 in accordance with one or more channel quality indicators (CQIs) received from the UE 120. The network node 110 may process the data (for example, including encoding the data) for transmission to the UE 120 on a downlink in accordance with the MCS(s) selected for the UE 120 to generate data symbols. The transmit processor 214 may process system information (for example, semi-static resource partitioning information (SRPI)) and / or control information (for example, CQI requests, grants, and / or upper layer signaling) and provide overhead symbols and / or control symbols. The transmit processor 214 may generate reference symbols for reference signals (for example, a cellspecific reference signal (CRS), a demodulation reference signal (DMRS), or a channel stateinformation (CSI) reference signal (CSI-RS)) and / or synchronization signals (for example, a primary synchronization signal (PSS) or a secondary synchronization signals (SSS)).

[0100] The TX MIMO processor 216 may perform spatial processing (for example, precoding) on the data symbols, the control symbols, the overhead symbols, and / or the reference symbols, if applicable, and may provide a set of output symbol streams (for example, T output symbol streams) to the set of modems 232. For example, each output symbol stream may be provided to a respective modulator component (shown as MOD) of a modem 232. Each modem 232 may use the respective modulator component to process (for example, to modulate) a respective output symbol stream (for example, for orthogonal frequency division multiplexing (OFDM)) to obtain an output sample stream. Each modem 232 may further use the respective modulator component to process (for example, convert to analog, amplify, fdter, and / or upconvert) the output sample stream to obtain a time domain downlink signal. The modems 232a through 232t may together transmit a set of downlink signals (for example, T downlink signals) via the corresponding set of antennas 234.

[0101] A downlink signal may include a DCI communication, a MAC control element (MAC- CE) communication, an RRC communication, a downlink reference signal, or another type of downlink communication. Downlink signals may be transmitted on a PDCCH, a PDSCH, and / or on another downlink channel. A downlink signal may carry one or more transport blocks (TBs) of data. A TB may be a unit of data that is transmitted over an air interface in the wireless communication network 100. A data stream (for example, from the data source 212) may be encoded into multiple TBs for transmission over the air interface . The quantity of TBs used to carry the data associated with a particular data stream may be associated with a TB size common to the multiple TBs. The TB size may be based on or otherwise associated with radio channel conditions of the air interface, the MCS used for encoding the data, the downlink resources allocated for transmitting the data, and / or another parameter. In general, the larger the TB size, the greater the amount of data that can be transmitted in a single transmission, which reduces signaling overhead. However, larger TB sizes may be more prone to transmission and / or reception errors than smaller TB sizes, but such errors may be mitigated by more robust error correction techniques.

[0102] For uplink communication from the UE 120 to the network node 110, uplink signals from the UE 120 may be received by an antenna 234, may be processed by a modem 232 (for example, a demodulator component, shown as DEMOD, of a modem 232), may be detected by the MIMO detector 236 (for example, a receive (Rx) MIMO processor) if applicable, and / or may be further processed by the receive processor 238 to obtain decoded data and / or control information. The receive processor 238 may provide the decoded data to a data sink 239 (whichmay be a data pipeline, a data queue, and / or another type of data sink) and provide the decoded control information to a processor, such as the controller / processor 240.

[0103] The network node 110 may use the scheduler 246 to schedule one or more UEs 120 for downlink or uplink communications. In some aspects, the scheduler 246 may use DCI to dynamically schedule DL transmissions to the UE 120 and / or UL transmissions from the UE 120. In some examples, the scheduler 246 may allocate recurring time domain resources and / or frequency domain resources that the UE 120 may use to transmit and / or receive communications using an RRC configuration (for example, a semi-static configuration), for example, to perform semi-persistent scheduling (SPS) or to configure a configured grant (CG) for the UE 120.

[0104] One or more of the transmit processor 214, the TX MIMO processor 216, the modem 232, the antenna 234, the MIMO detector 236, the receive processor 238, and / or the controller / processor 240 may be included in an RF chain of the network node 110. An RF chain may include one or more filters, mixers, oscillators, amplifiers, analog-to-digital converters (ADCs), and / or other devices that convert between an analog signal (such as for transmission or reception via an air interface) and a digital signal (such as for processing by one or more processors of the network node 110). In some aspects, the RF chain may be or may be included in a transceiver of the network node 110.

[0105] In some examples, the network node 110 may use the communication unit 244 to communicate with a core network and / or with other network nodes. The communication unit 244 may support wired and / or wireless communication protocols and / or connections, such as Ethernet, optical fiber, common public radio interface (CPRI), and / or a wired or wireless backhaul, among other examples. The network node 110 may use the communication unit 244 to transmit and / or receive data associated with the UE 120 or to perform network control signaling, among other examples. The communication unit 244 may include a transceiver and / or an interface, such as a network interface.

[0106] The UE 120 may include a set of antennas 252 (shown as antennas 252a through 252r, where r > 1), a set of modems 254 (shown as modems 254a through 254u, where u > 1), a MIMO detector 256, a receive processor 258, a data sink 260, a data source 262, a transmit processor 264, a TX MIMO processor 266, a controller / processor 280, a memory 282, and / or a communication manager 140, among other examples. One or more of the components of the UE 120 may be included in a housing 284. In some aspects, one or a combination of the antenna(s) 252, the modem(s) 254, the MIMO detector 256, the receive processor 258, the transmit processor 264, or the TX MIMO processor 266 may be included in a transceiver that is included in the UE 120. The transceiver may be under control of and used by one or more processors, such as the controller / processor 280, and in some aspects in conjunction with processor-readable code stored in the memory 282, to perform aspects of the methods, processes, or operations described herein.In some aspects, the UE 120 may include another interface, another communication component, and / or another component that facilitates communication with the network node 110 and / or another UE 120.

[0107] For downlink communication from the network node 110 to the UE 120, the set of antennas 252 may receive the downlink communications or signals from the network node 110 and may provide a set of received downlink signals (for example, R received signals) to the set of modems 254. For example, each received signal may be provided to a respective demodulator component (shown as DEMOD) of a modem 254. Each modem 254 may use the respective demodulator component to condition (for example, fdter, amplify, downconvert, and / or digitize) a received signal to obtain input samples. Each modem 254 may use the respective demodulator component to further demodulate or process the input samples (for example, for OFDM) to obtain received symbols. The MIMO detector 256 may obtain received symbols from the set of modems 254, may perform MIMO detection on the received symbols if applicable, and may provide detected symbols. The receive processor 258 may process (for example, decode) the detected symbols, may provide decoded data for the UE 120 to the data sink 260 (which may include a data pipeline, a data queue, and / or an application executed on the UE 120), and may provide decoded control information and system information to the controller / processor 280.

[0108] For uplink communication from the UE 120 to the network node 110, the transmit processor 264 may receive and process data (“uplink data”) from a data source 262 (such as a data pipeline, a data queue, and / or an application executed on the UE 120) and control information from the controller / processor 280. The control information may include one or more parameters, feedback, one or more signal measurements, and / or other types of control information. In some aspects, the receive processor 258 and / or the controller / processor 280 may determine, for a received signal (such as received from the network node 110 or another UE), one or more parameters relating to transmission of the uplink communication. The one or more parameters may include a reference signal received power (RSRP) parameter, a received signal strength indicator (RS SI) parameter, a reference signal received quality (RSRQ) parameter, a CQI parameter, or a transmit power control (TPC) parameter, among other examples. The control information may include an indication of the RSRP parameter, the RS SI parameter, the RSRQ parameter, the CQI parameter, the TPC parameter, and / or another parameter. The control information may facilitate parameter selection and / or scheduling for the UE 120 by the network node 110.

[0109] The transmit processor 264 may generate reference symbols for one or more reference signals, such as an uplink DMRS, an uplink sounding reference signal (SRS), and / or another type of reference signal. The symbols from the transmit processor 264 may be precoded by the TX MIMO processor 266, if applicable, and further processed by the set of modems 254 (forexample, for DFT-s-OFDM or CP-OFDM). The TX MIMO processor 266 may perform spatial processing (for example, precoding) on the data symbols, the control symbols, the overhead symbols, and / or the reference symbols, if applicable, and may provide a set of output symbol streams (for example, U output symbol streams) to the set of modems 254. For example, each output symbol stream may be provided to a respective modulator component (shown as MOD) of a modem 254. Each modem 254 may use the respective modulator component to process (for example, to modulate) a respective output symbol stream (for example, for OFDM) to obtain an output sample stream. Each modem 254 may further use the respective modulator component to process (for example, convert to analog, amplify, filter, and / or upconvert) the output sample stream to obtain an uplink signal.

[0110] The modems 254a through 254u may transmit a set of uplink signals (for example, R uplink signals or U uplink symbols) via the corresponding set of antennas 252. An uplink signal may include a UCI communication, a MAC-CE communication, an RRC communication, or another type of uplink communication. Uplink signals may be transmitted on a PUSCH, a PUCCH, and / or another type of uplink channel. An uplink signal may carry one or more TBs of data. Sidelink data and control transmissions (that is, transmissions directly between two or more UEs 120) may generally use similar techniques as were described for uplink data and control transmission, and may use sidelink-specific channels such as a physical sidelink shared channel (PSSCH), a physical sidelink control channel (PSCCH), and / or a physical sidelink feedback channel (PSFCH).[OHl] One or more antennas of the set of antennas 252 or the set of antennas 234 may include, or may be included within, one or more antenna panels, one or more antenna groups, one or more sets of antenna elements, or one or more antenna arrays, among other examples. An antenna panel, an antenna group, a set of antenna elements, or an antenna array may include one or more antenna elements (within a single housing or multiple housings), a set of coplanar antenna elements, a set of non-coplanar antenna elements, or one or more antenna elements coupled with one or more transmission or reception components, such as one or more components of Fig. 2. As used herein, “antenna” can refer to one or more antennas, one or more antenna panels, one or more antenna groups, one or more sets of antenna elements, or one or more antenna arrays. “Antenna panel” can refer to a group of antennas (such as antenna elements) arranged in an array or panel, which may facilitate beamforming by manipulating parameters of the group of antennas. “Antenna module” may refer to circuitry including one or more antennas, which may also include one or more other components (such as fdters, amplifiers, or processors) associated with integrating the antenna module into a wireless communication device.

[0112] In some examples, each of the antenna elements of an antenna 234 or an antenna 252 may include one or more sub-elements for radiating or receiving radio frequency signals. Forexample, a single antenna element may include a first sub-element cross-polarized with a second sub-element that can be used to independently transmit cross-polarized signals. The antenna elements may include patch antennas, dipole antennas, and / or other types of antennas arranged in a linear pattern, a two-dimensional pattern, or another pattern. A spacing between antenna elements may be such that signals with a desired wavelength transmitted separately by the antenna elements may interact or interfere constructively and destructively along various directions (such as to form a desired beam). For example, given an expected range of wavelengths or frequencies, the spacing may provide a quarter wavelength, a half wavelength, or another fraction of a wavelength of spacing between neighboring antenna elements to allow for the desired constructive and destructive interference patterns of signals transmitted by the separate antenna elements within that expected range.

[0113] The amplitudes and / or phases of signals transmitted via antenna elements and / or subelements may be modulated and shifted relative to each other (such as by manipulating phase shift, phase offset, and / or amplitude) to generate one or more beams, which is referred to as beamforming. The term “beam” may refer to a directional transmission of a wireless signal toward a receiving device or otherwise in a desired direction. “Beam” may also generally refer to a direction associated with such a directional signal transmission, a set of directional resources associated with the signal transmission (for example, an angle of arrival, a horizontal direction, and / or a vertical direction), and / or a set of parameters that indicate one or more aspects of a directional signal, a direction associated with the signal, and / or a set of directional resources associated with the signal. In some implementations, antenna elements may be individually selected or deselected for directional transmission of a signal (or signals) by controlling amplitudes of one or more corresponding amplifiers and / or phases of the signal(s) to form one or more beams. The shape of a beam (such as the amplitude, width, and / or presence of side lobes) and / or the direction of a beam (such as an angle of the beam relative to a surface of an antenna array) can be dynamically controlled by modifying the phase shifts, phase offsets, and / or amplitudes of the multiple signals relative to each other.

[0114] Different UEs 120 or network nodes 110 may include different numbers of antenna elements. For example, a UE 120 may include a single antenna element, two antenna elements, four antenna elements, eight antenna elements, or a different number of antenna elements. As another example, a network node 110 may include eight antenna elements, 24 antenna elements, 64 antenna elements, 128 antenna elements, or a different number of antenna elements. Generally, a larger number of antenna elements may provide increased control over parameters for beam generation relative to a smaller number of antenna elements, whereas a smaller number of antenna elements may be less complex to implement and may use less power than a larger number of antenna elements. Multiple antenna elements may support multiple -layer transmission, in which a first layer of a communication (which may include a first data stream) and a secondlayer of a communication (which may include a second data stream) are transmitted using the same time and frequency resources with spatial multiplexing.

[0115] While blocks in Fig. 2 are illustrated as distinct components, the functions described above with respect to the blocks may be implemented in a single hardware, software, or combination component or in various combinations of components. For example, the functions described with respect to the transmit processor 264, the receive processor 258, and / or the TX MIMO processor 266 may be performed by or under the control of the controller / processor 280.

[0116] In some aspects, the network node 110 includes means for transmitting, to a core network entity, first signaling identifying a network slice prediction; means for receiving, from the core network entity and as a response to the first signaling, second signaling identifying a network slice configuration associated with the network slice prediction; and / or means for communicating with a UE using one or more network slices in accordance with the network slice configuration. In some aspects, the network node 110 includes means for performing a QoE prediction based at least in part on an AI / ML training and inference operation; and / or means for transmitting, to another network node, information regarding the QoE prediction and at least one of a PDU session ID, a QFI, a DRB ID, or a slice ID associated with the QoE prediction. The means for the network node 110 to perform operations described herein may include, for example, one or more of communication manager 150, transmit processor 214, TX MIMO processor 216, modem 232, antenna 234, MIMO detector 236, receive processor 238, controller / processor 240, memory 242, or scheduler 246.

[0117] In some aspects, a core network entity (e.g., the core network entity 172) includes means for receiving, from a network node, first signaling identifying a network slice prediction; means for transmitting, to the network node and as a response to the first signaling, second signaling identifying a network slice configuration associated with the network slice prediction; and / or means for transmitting, to a UE and as a response to the first signaling, third signaling identifying the network slice configuration to cause the UE to communicate with the network node using one or more network slices in accordance with the network slice configuration. In some aspects, the means for the core network entity to perform operations described herein may include, for example, one or more of components described herein in terms of a UE 120 or a network node 110, such as a communication manager, a transmit processor, a TX MIMO processor, a modem, an antenna, a MIMO detector, a receive processor, a controller / processor, a memory, or a scheduler. In other words, in some examples, a core network entity may be a network node 110 that is deployed in a core network rather than an access network.

[0118] In some aspects, the UE 120 includes means for receiving, from a core network entity and as a response to a network slice prediction, signaling identifying a network slice configuration associated with the network slice prediction; and / or means for communicating with a networknode using one or more network slices in accordance with the network slice configuration. In some aspects, the UE 120 includes means for receiving, from a network node, assistance information that includes a QoE prediction, wherein the QoE prediction is associated with at least one of a PDU session ID, a QFI, a DRB ID, or a slice ID; and / or means for performing a scheduling operation based at least in part on the assistance information. The means for the UE 120 to perform operations described herein may include, for example, one or more of communication manager 140, antenna 252, modem 254, MIMO detector 256, receive processor 258, transmit processor 264, TX MIMO processor 266, controller / processor 280, or memory 282.

[0119] As indicated above, Fig. 2 is provided as an example. Other examples may differ from what is described with regard to Fig. 2.

[0120] Fig. 3 is a diagram illustrating an example disaggregated base station architecture 300 in accordance with the present disclosure. One or more components of the example disaggregated base station architecture 300 may be, may include, or may be included in one or more network nodes (such one or more network nodes 110). The disaggregated base station architecture 300 may include a CU 310 that can communicate directly with a core network 320 via a backhaul link, or that can communicate indirectly with the core network 320 via one or more disaggregated control units, such as a Non-RT RIC 350 associated with a Service Management and Orchestration (SMO) Framework 360 and / or a Near-RT RIC 370 (for example, via an E2 link). The CU 310 may communicate with one or more DUs 330 via respective midhaul links, such as via Fl interfaces. Each of the DUs 330 may communicate with one or more RUs 340 via respective fronthaul links. Each of the RUs 340 may communicate with one or more UEs 120 via respective RF access links. In some deployments, a UE 120 may be simultaneously served by multiple RUs 340.

[0121] Each of the components of the disaggregated base station architecture 300, including the CUs 310, the DUs 330, the RUs 340, the Near-RT RICs 370, the Non-RT RICs 350, and the SMO Framework 360, may include one or more interfaces or may be coupled with one or more interfaces for receiving or transmitting signals, such as data or information, via a wired or wireless transmission medium.

[0122] In some aspects, the CU 310 may be logically split into one or more CU user plane (CU-UP) units and one or more CU control plane (CU-CP) units. A CU-UP unit may communicate bidirectionally with a CU-CP unit via an interface, such as the El interface when implemented in an O-RAN configuration. The CU 310 may be deployed to communicate with one or more DUs 330, as necessary, for network control and signaling. Each DU 330 may correspond to a logical unit that includes one or more base station functions to control the operation of one or more RUs 340. For example, a DU 330 may host various layers, such as an RLC layer, a MAC layer, or one or more PHY layers, such as one or more high PHY layers orone or more low PHY layers. Each layer (which also may be referred to as a module) may be implemented with an interface for communicating signals with other layers (and modules) hosted by the DU 330, or for communicating signals with the control functions hosted by the CU 310. Each RU 340 may implement lower layer functionality. In some aspects, real-time and non-real- time aspects of control and user plane communication with the RU(s) 340 may be controlled by the corresponding DU 330.

[0123] The SMO Framework 360 may support RAN deployment and provisioning of nonvirtualized and virtualized network elements. For non-virtualized network elements, the SMO Framework 360 may support the deployment of dedicated physical resources for RAN coverage requirements, which may be managed via an operations and maintenance interface, such as an 01 interface. For virtualized network elements, the SMO Framework 360 may interact with a cloud computing platform (such as an open cloud (O-Cloud) platform 390) to perform network element life cycle management (such as to instantiate virtualized network elements) via a cloud computing platform interface, such as an 02 interface. A virtualized network element may include, but is not limited to, a CU 310, a DU 330, an RU 340, a non-RT RIC 350, and / or a Near-RT RIC 370. In some aspects, the SMO Framework 360 may communicate with a hardware aspect of a 4G RAN, a 5G NR RAN, and / or a 6G RAN, such as an open eNB (O-eNB) 380, via an 01 interface. Additionally or alternatively, the SMO Framework 360 may communicate directly with each of one or more RUs 340 via a respective 01 interface. In some deployments, this configuration can enable each DU 330 and the CU 310 to be implemented in a cloud -based RAN architecture, such as a vRAN architecture.

[0124] The Non-RT RIC 350 may include or may implement a logical function that enables non-real-time control and optimization of RAN elements and resources, AI / ML workflows including model training and updates, and / or policy-based guidance of applications and / or features in the Near-RT RIC 370. The Non-RT RIC 350 may be coupled to or may communicate with (such as via an Al interface) the Near-RT RIC 370. The Near-RT RIC 370 may include or may implement a logical function that enables near-real-time control and optimization of RAN elements and resources via data collection and actions via an interface (such as via an E2 interface) connecting one or more CUs 310, one or more DUs 330, and / or an O-eNB with the Near-RT RIC 370.

[0125] In some aspects, to generate AI / ML models to be deployed in the Near-RT RIC 370, the Non-RT RIC 350 may receive parameters or external enrichment information from external servers. Such information may be utilized by the Near-RT RIC 370 and may be received at the SMO Framework 360 or the Non-RT RIC 350 from non-network data sources or from network functions. In some examples, the Non-RT RIC 350 or the Near-RT RIC 370 may tune RAN behavior or performance. For example, the Non-RT RIC 350 may monitor long-term trends andpaterns for performance and may employ AI / ML models to perform corrective actions via the SMO Framework 360 (such as reconfiguration via an 01 interface) or via creation of RAN management policies (such as Al interface policies).

[0126] As indicated above, Fig. 3 is provided as an example. Other examples may differ from what is described with regard to Fig. 3.

[0127] The network node 110, the controller / processor 240 of the network node 110, the UE 120, the controller / processor 280 of the UE 120, the CU 310, the DU 330, the RU 340, or any other componcnt(s) of Figs. 1, 2, or 3 may implement one or more techniques or perform one or more operations associated with signaling for network slicing, as described in more detail elsewhere herein. For example, the controller / processor 240 of the network node 110, the controller / processor 280 of the UE 120, any other component s) of Fig. 2, the CU 310, the DU 330, or the RU 340 may perform or direct operations of, for example, process 1100 of Fig. 11, process 1200 of Fig. 12, process 1300 of Fig. 13, process 2600 of Fig. 26, process 2700 of Fig. 27, or other processes as described herein (alone or in conjunction with one or more other processors). The memory 242 may store data and program codes for the network node 110, the network node 110, the CU 310, the DU 330, or the RU 340. The memory 282 may store data and program codes for the UE 120. In some examples, the memory 242 or the memory 282 may include a non-transitory computer-readable medium storing a set of instructions (for example, code or program code) for wireless communication. The memory 242 may include one or more memories, such as a single memory or multiple different memories (of the same type or of different types). The memory 282 may include one or more memories, such as a single memory or multiple different memories (of the same type or of different types). For example, the set of instructions, when executed (for example, directly, or after compiling, converting, or interpreting) by one or more processors of the network node 110, the UE 120, the CU 310, the DU 330, or the RU 340, may cause the one or more processors to perform process 1100 of Fig. 11, process 1200 of Fig. 12, process 1300 of Fig. 13, process 2600 of Fig. 26, process 2700 of Fig. 27, or other processes as described herein. In some examples, executing instructions may include running the instructions, converting the instructions, compiling the instructions, and / or interpreting the instructions, among other examples.

[0128] As indicated above, Fig. 3 is provided as an example. Other examples may differ from what is described with regard to Fig. 3.

[0129] Fig. 4 is a diagram illustrating an example architecture 400 of a functional framework for radio access network (RAN) intelligence enabled by data collection, in accordance with the present disclosure. In some scenarios, the functional framework for RAN intelligence may be enabled by further enhancement of data collection through use cases and / or examples. For example, principles or algorithms for RAN intelligence enabled by AI / ML and the associatedfunctional framework (e.g., the Al functionality and / or the input / output of the component for Al enabled optimization) have been utilized or studied to identify the benefits of Al enabled RAN through possible use cases (e.g., beam management, energy saving, load balancing, mobility management, and / or coverage optimization, among other examples). In one example, as shown by the architecture 400, a functional framework for RAN intelligence may include multiple logical entities, such as a model training host 402, a model inference host 404, data sources 406, and an actor 408.

[0130] The model inference host 404 may be configured to run an AI / ML model based on inference data provided by the data sources 406, and the model inference host 404 may produce an output (e.g., a prediction) with the inference data input to the actor 408. The actor 408 may be an element or an entity of a core network or a RAN. For example, the actor 408 may be a UE, a network node, a core network entity, a base station (e.g., a gNB), a CU, a DU, and / or an RU, among other examples. In addition, the actor 408 may also depend on the type of tasks performed by the model inference host 404, type of inference data provided to the model inference host 404, and / or type of output produced by the model inference host 404. For example, if the output from the model inference host 404 is associated with position determination, the actor 408 may be a UE, a DU or an RU. In some examples, the model inference host 404 may be instantiated on the actor 408. For example, a UE may be the actor 408 and may host the model inference host 404. In some aspects, a UE (e.g., the actor 408) may be a data source 406. For example, the UE may perform a measurement (e.g., an NR measurement), may input the measurement to the AI / ML model at the model inference host 404 (or may provide the measurement to the model inference host 404), and may act based on an output of the AI / ML model (e.g., identify a set of configurations associated with a handover, a dual-connectivity operation, a network slice, or a resource allocation).

[0131] After the actor 408 receives an output from the model inference host 404, the actor 408 may determine whether to act based on the output. For example, if the actor 408 is a UE and the output from the model inference host 404 is associated with position information, the actor 408 may determine whether to report the position information, or reconfigure a beam, among other examples. If the actor 408 determines to act based on the output, in some examples, the actor 408 may indicate the action to at least one subject of action 410.

[0132] The data sources 406 may also be configured for collecting data that is used as training data for training an ML model or as inference data for feeding an ML model inference operation. For example, the data sources 406 may collect data from one or more core network and / or RAN entities, which may include the actor 408 or the subject of action 410, and provide the collected data to the model training host 402 for ML model training. In some aspects, the model training host 402 may be co-located with the model inference host 404 and / or the actor 408. For example,the actor 408 or the subject of action 410 may provide performance feedback associated with the beam configuration to the data sources 406, where the performance feedback may be used by the model training host 402 for monitoring or evaluating the ML model performance, such as by evaluating whether the output (e.g., prediction) provided to the actor 408 is accurate. In some examples, the model training host 402 may monitor or evaluate ML model performance using a training position value, which may be provided by a node (e.g., a UE 120 or a network node 110), as described elsewhere herein. In some examples, if the output provided by the actor 408 is inaccurate (or the accuracy is below an accuracy threshold), then the model training host 402 may determine to modify or retrain the ML model used by the model inference host, such as via an ML model deployment / update.

[0133] As indicated above, Fig. 4 is provided as an example. Other examples may differ from what is described with regard to Fig. 4.

[0134] Network slices may represent virtualized or logical networks that are allocated for specific applications, use cases, users, traffic characteristics, or other granularities. For example, a network node may allocate resources for evolved multimedia broadband (eMBB) service, ultrareliable low-latency communication (URLLC) service, public safety services, machine type communication (MTC) services, or vehicle-to-everything (V2X), among other examples.

[0135] As described above, a network node may use AI / ML techniques for optimizing (or selecting) network characteristics. For example, a network node, a UE, or a core network entity may use AI / ML techniques for beam selection, for encoding channel state feedback, or for allocating resources to different devices. However, when establishing and / or managing a set of network slices, a network node, core network entity, or UE may lack signaling for exchanging information that can be used as input to an AI / ML model. Accordingly, a predicting node (e.g., the network node, the core network entity, or the UE) may lack external data and may use only internal data (e.g., data collected by the UE when the UE is the predicting node, or data collected by the network node when the network node is the predicting node) as an input to an AI / ML model. Use of only internal data for AI / ML model based prediction associated with network slice establishment and / or management may result in non-optimized network slice establishment and / or management. For example, predictions based only on internal data may be less accurate than predictions that include external data (e.g., data received from another device).

[0136] Various aspects relate generally to signaling for network slicing. Some aspects more specifically relate to signaling flows for different devices to exchange data that can be used for AI / ML models associated with generating predictions used for network slice establishment and / or management. In some aspects, a UE, a network node, or a core network entity may use signaling for providing predictions, feedback, or assistance information to other entities associated with handovers, dual -connectivity, network slicing, or network resource allocation. In some aspects,the UE, the network node, or the core network entity may communicate to provide signaling associated with configuring data collection for AI / ML model based network slice establishment and / or management.

[0137] Particular aspects of the subject matter described in this disclosure can be implemented to realize one or more of the following potential advantages. In some examples, by establishing signaling for network slice establishment and / or management, the described techniques can be used to improve an efficiency of network resource allocations associated with network slices. For example, a UE, a network node, or a core network entity may improve an accuracy of AI / ML model predictions by incorporating data from additional data sources using signaling described herein.

[0138] Fig. 5 is a diagram illustrating an example 500 associated with network slice resource prediction at a network node, in accordance with the present disclosure. As shown in Fig. 5, example 500 includes communication between a network node 110, a UE 120, and a core network entity 510 (e.g., an access and mobility management function (AMF) 510a or a network slice selection function (NSSF) 510b). In some aspects, the core network entity 510 (e.g., the AMF 510a or NSSF 510b) or another core network entity described herein may correspond to a network node 110, a component thereof, or a device in communication therewith, as described in more detail herein.

[0139] As further shown in Fig. 5, and by reference number 515, a network node 110 may train and utilize an AI / ML model. For example, the network node 110 may use an AI / ML training procedure to train an AI / ML model and may use the trained AI / ML model for an AI / ML inference procedure (e.g., to generate a prediction). In this case, the network node 110 may generate a prediction relating to a network slice. In some aspects, the network node 110 may generate the prediction for a configured time interval, for a configured area (e.g., for a tracking area (TA), a random access (RA) area, or a cell), for a configured group of UEs, or for the UE 120, among other examples. In some aspects, the network node 110 may predict a set of network slice related metrics. For example, the network node 110 may determine a predicted network slice RAN resource status, such as a predicted network slice availability status, a predicted network slice status (e.g., whether a network slice will be unavailable or overloaded), or a predicted network slice usage (e.g., a physical resource block (PRB) usage, a transport network layer (TNL) capacity, a quantity of active UEs, a call admission control (CAC) parameter, or a quantity of radio resource control (RRC) connections), among other examples. Additionally, or alternatively, the network node 110 may predict one or more network slice key performance indicators (KPIs). For example, the network node 110 may determine a predicted network slice accessibility metric (e.g., a quantity of registered subscribers or a registration success rate), a predicted network slice integrity metric (e.g., a downlink or uplink throughput, a UE throughput, or a latency), or apredicted network slice utilization metric (e.g., a quantity of protocol data unit (PDU) sessions or a virtual resource utilization), among other examples. Additional details of network slice metrics are described in 3GPP Technical Specification 28.554, Release 18, version 18.4.0.

[0140] As further shown in Fig. 5, and by reference numbers 520 and 530, the network node 110 may transmit information identifying the network slice prediction to the core network entity 510. For example, the network node 110 may transmit the information identifying the network slice prediction to the AMF 510a, which may provide the information identifying the network slice prediction to the NSSF 510b. In this case, the network node 110 transmits information directly to a core network entity 510. In another example, the network node 110 may transmit information indirectly to a core network entity 510, such as by transmitting information identifying the network slice prediction to an operations and management (0AM) node, which may direct the information identifying the network slice prediction onward to the NSSF 510b. In some aspects, the network node 110 may transmit information identifying a particular network type of network slice prediction, such as a prediction of a network slice that is associated with the predicted network slice RAN resource status. Additionally, or alternatively, the network node 110 may transmit information identifying a network slice KPI.

[0141] As further shown in Fig. 5, and by reference numbers 540 and 550, the core network entity 510 may determine a network slice resource allocation. For example, the NSSF 510b may transmit a resource allocation indication identifying an allocation of network resources associated with the network slice prediction, and the AMF 510a may determine whether to accept the resource allocation indication (or modify or reject the resource allocation indication). In some aspects, the core network entity 510 may use the network slice prediction to perform a service level agreement (SLA) assurance procedure for a network slice. For example, the core network entity 510 may use a prediction related to a network slice to proactively adapt network slice subnet management parameters, confirm network slice SLA assurance, allocate network slices to UEs 120, perform dynamic network slice re-allocation among UEs 120, or perform another network slice management procedure. In some aspects, the NSSF 510b may transmit a request to perform a dynamic slice resource re-allocation based at least in part on a network slice prediction, and the AMF 510a may determine whether to confirm, modify, or deny the dynamic slice resource re -allocation.

[0142] As further shown in Fig. 5, and by reference numbers 560 and 570, the core network entity 510 may transmit a set of network slice indications. For example, based at least in part on determining the network slice resource allocation, the AMF 510a may transmit a network slice indication to the network node 110 and / or the UE 120. In this case, the core network entity 510 may transmit indications to the network node 110 of resources allocated to network slices and / or to UEs 120 thereof. Additionally, or alternatively, the core network entity 510 may transmitindications to one or more UEs 120 of network slices for which the one or more UEs 120 have allocated resources.

[0143] As indicated above, Fig. 5 is provided as an example. Other examples may differ from what is described with respect to Fig. 5.

[0144] Fig. 6 is a diagram illustrating an example 600 associated with network slice resource prediction at a core network entity, in accordance with the present disclosure. As shown in Fig. 6, example 600 includes communication between a network node 110, a UE 120, and a core network entity 610 (e.g., an AMF 610a or an NSSF 610b).

[0145] As further shown in Fig. 6, and by reference number 615, the core network entity 610 may train and utilize an AI / ML model. For example, the core network entity 610 (e.g., the AMF 610a) may use an AI / ML training procedure to train an AI / ML model and may use the trained AI / ML model for an AI / ML inference procedure (e.g., to generate a prediction). In this case, the core network entity 610 may generate a prediction relating to a network slice. In some aspects, the core network entity 610 may generate the prediction relating to the network slice based at least in part on received measurement data. For example, the core network entity 610 may receive data from the network node 110, as shown by reference number 620, and may generate a prediction using the data received from the network node 110. In this case, the AMF 610a may report a network slice prediction to the network node 110, as shown by reference number 630, which may use the network slice prediction to perform one or more mobility actions, as shown by reference number 640.

[0146] In some aspects, the core network entity 610 may predict a particular type of network slice related metric. For example, the network node 110 may determine a predicted network slice RAN resource status or a network slice KPI. In some aspects, the core network entity 610 may report the particular type of network slice related metric to optimize a mobility or radio resource management (RRM) action. For example, the network node 110 may receive information identifying a predicted slice available capacity, a predicted slice status, a PRB usage, a predicted network slice support list, or a predicted target network slice identifier (e.g., a predicted allowed, partially allowed, or target network slice selection assistance identifier (NSSAI)), among other examples. In this case, the network node 110 may optimize mobility of one or more UEs 120 using the network slice prediction.

[0147] Additionally, or alternatively, the AMF 610a may report a network slice prediction to the NSSF 610b, as shown by reference number 650. The NSSF 610b may receive information identifying a predicted network slice resource status and may use the predicted network slice resource status to determine and indicate a network slice resource allocation, as shown by reference number 655. For example, the NSSF 610b may proactively adapt a network slice subnet management parameter or perform network slice SLA assurance. As shown by referencenumbers 660, 670, and 680, based at least in part on receiving the indication of the network slice resource allocation, the AMF 610a may confirm (or reject or modify) the network slice resource allocation and indicate the network slice resource allocation to the network node 110 and / or the UE 120.

[0148] As indicated above, Fig. 6 is provided as an example. Other examples may differ from what is described with respect to Fig. 6.

[0149] Fig. 7 is a diagram illustrating an example 700 associated with network slice based resource prediction at a network node, in accordance with the present disclosure. As shown in Fig. 7, example 700 includes communication between a CU 310 (e.g., a gNB CU) and a DU 330 (e.g., a gNB DU) of a network node 110 (e.g., a gNB).

[0150] As further shown in Fig. 7, and by reference number 710, the network node 110 may train and utilize an AI / ME model. For example, the network node 110 (e.g., the CU 310 or the DU 330) may use an AI / ME training procedure to train an AI / ML model and may use the trained AI / ML model for an AI / ML inference procedure (e.g., to generate a prediction). In this case, the network node 110 may generate a prediction relating to a network slice. For example, the CU 310 may transmit a data collection request to the DU 330, as shown by reference number 720. In this case, the CU 310 may request a prediction of a network resource status or an indication of an actual network resource status at a network slice level or at a network slice group (e.g., a network slice aggregation group (NSAG) level). For example, the CU 310 may transmit a data collection request to the DU 330, which may transmit a data collection response, as shown by reference number 730, or a data collection update, as shown by reference number 740. The data collection response or the data collection update may include information identifying a predicted network slice available capacity, a predicted network slice status, a PRB usage, a TNL capacity, a quantity of active UEs, a CAC parameter, a quantity of RRC connections, a predicted network slice support list, or a KPI, among other examples. In some aspects, the network node 110 (e.g., the CU 310 or the DU 330) may obtain measurement data from one or more other network nodes.

[0151] As further shown in Fig. 7, and by reference numbers 750 and 760, the network node 110 (e.g., the CU 310) may generate a network slice prediction based at least in part on the prediction of the network resource status or the indication of the actual network resource status. For example, the CU 310 may determine a prediction of an overall network slice availability status and / or a network slice based resource prediction. Based at least in part on generating the network slice prediction, the network node 110 (e .g . , the CU 310) may perform a set of RRM actions (e.g., a mobility action or an admission control action) proactively using the network slice prediction. In some aspects, the network node 110 may transmit information identifying the network slice prediction to one or more other network nodes (e.g., of a disaggregated gNB architecture) to enable the one or more network nodes to perform RRM actions.

[0152] As indicated above, Fig. 7 is provided as an example. Other examples may differ from what is described with respect to Fig. 7.

[0153] Fig. 8 is a diagram illustrating an example 800 associated with network slice based resource prediction at a network node, in accordance with the present disclosure. As shown in Fig. 8, example 800 includes communication between a CU 310 (e.g., a gNB CU) and a DU 330 (e.g., a gNB DU) of a network node 110 (e.g., a gNB).

[0154] As further shown in Fig. 8, and by reference number 810, the network node 110 may train and utilize an AI / MU model. For example, the network node 110 (e.g., the CU 310 or the DU 330) may use an AI / MU training procedure to train an AI / MU model and may use the trained AI / MU model for an AI / MU inference procedure (e.g., to generate a prediction). In this case, the network node 110 may generate a prediction relating to a network slice. For example, the DU 330 may transmit a data collection request to the CU 310, as shown by reference number 820. In this case, the DU 330 may request a prediction of a network resource status or an indication of an actual network resource status at a network slice level or at a network slice group level (e.g., an NSAG level). For example, the DU 330 may transmit a data collection request to the CU 310, which may transmit a data collection response, as shown by reference number 830, or a data collection update, as shown by reference number 840. The data collection response or the data collection update may include information identifying a predicted network slice available capacity, a predicted network slice status, a PRB usage, a TNU capacity, a quantity of active UEs, a CAC parameter, a quantity of RRC connections, a predicted network slice support list, or a KPI, among other examples. In some aspects, the network node 110 (e.g., the CU 310 or the DU 330) may obtain measurement data from one or more other network nodes.

[0155] As further shown in Fig. 8, and by reference numbers 850 and 860, the network node 110 (e.g., the DU 330) may generate a network slice prediction based at least in part on the prediction of the network resource status or the indication of the actual network resource status. For example, the DU 330 may determine a prediction of an overall network slice availability status and / or a network slice based resource prediction. Based at least in part on generating the network slice prediction, the network node 110 (e.g., the DU 330) may perform a set of RRM actions (e.g., a mobility action or an admission control action) proactively using the network slice prediction.

[0156] In some aspects, the network node 110 may transmit information identifying the network slice prediction to one or more other network nodes (e.g., of a disaggregated gNB architecture) to enable the one or more other network nodes to perform RRM actions. In some aspects, the network node 110 may collect data from or provide predictions to the one or more other network nodes in connection with or based at least in part on a request. In some aspects, the network node 110 may transmit one or more signals associated with a network slice prediction.For example, the DU 330 may predict a set of network slice metrics (e.g., a predicted cell reselection priority or a predicted random access channel (RACH) access prioritization for network slicing, each of which may be predicted at a network slice level or a network slice group level) and provide the set of network slice metrics to the CU 310 for including in a system information block (SIB) broadcast. In this case, the CU 310 may cause the SIB broadcast to be transmitted to a set of UEs 120 to enable the set of UEs 120 to use the predicted set of network slice metrics for, for example, mobility actions (e.g., cell reselection or RACH procedures). Additionally, or alternatively, the CU 310 may use the set of network slice metrics for resource allocation (e.g., for idle mode or connected mode UEs 120).

[0157] As indicated above, Fig. 8 is provided as an example. Other examples may differ from what is described with respect to Fig. 8.

[0158] Fig. 9 is a diagram illustrating an example 900 associated with network slice metric prediction signaling, in accordance with the present disclosure. As shown in Fig. 9, example 900 includes communication between a source network node 110, a target network node 110, and a UE 120.

[0159] As further shown in Fig. 9, and by reference number 910, the source network node 110 may train and utilize an AI / ML model. For example, the source network node 110 may use an AI / ML training procedure to train an AI / ML model and may use the trained AI / ML model for an AI / ML inference procedure (e.g., to generate a prediction). In this case, the source network node 110 may generate a prediction relating to a network slice. For example, the source network node 110 may generate a predicted network slice RAN resource status, which may include a predicted slice available capacity, a predicted network slice status, a PRB usage, a TNL capacity, a quantity of active UEs, a quantity of RRC connections, or a predicted network slice support list. In some aspects, the source network node 110 may generate the prediction of the network slice resource status based at least in part on receiving a request (e.g., the target network node 110 or another network node). Additionally, or alternatively, the source network node 110 may generate a prediction of a network slice metric, such as a UE slice bit rate (e.g., a downlink bit rate, an uplink bit rate, a total bit rate, a guaranteed bit rate (GBR), a non-guaranteed bit rate (non-GBR), an average bit rate, a minimum bit rate, a maximum bit rate, or an instantaneous bit rate).Additionally, or alternatively, the source network node 110 may generate a prediction of another network slice metric, such as a predicted UE slice average bit rate (e.g., a downlink bit rate, uplink bit rate, total bit rate, GBR, or non-GBR) or a predicted network slice identifier (e.g., an allowed, partially allowed, or target NSSAI, which may be received from an AMF).

[0160] In some aspects, the source network node 110 may report an output of an AI / ML model (e.g., a prediction). For example, the source network node 110 may transmit information identifying a network slice prediction in connection with a UE context retrieval in an RRCinactive mode (e.g., when the source network node 110 is an anchor cell and the target network node 110 is a target cell). Additionally, or alternatively, the source network node 110 may transmit information identifying the network slice prediction in connection with a secondary node (SN) addition or modification (e.g., when the source network node 110 is a main node (MN) and the target network node 110 is an SN).

[0161] In some aspects, as shown by reference numbers 920 and 930, the source network node 110 may transmit information identifying a network slice prediction in connection with a handover request. For example, the source network node 110 may transmit information identifying a UE slice average bit rate and / or a predicted UE slice average bit rate to the target node 110 to initiate a handover procedure for the UE 120. As shown by reference number 940, based on receiving the handover request (e.g., with the network slice prediction) and transmitting a handover request acknowledgment, the target network node 110 may perform a network slice optimization action. For example, the target network node 110 may pre-allocate network resources for the UE 120 based at least in part on predicted network slice resources.

[0162] In some aspects, a receiving node (e.g., the target network node 110) may reject a prediction of one or more network slice metrics or a resource allocation associated therewith. For example, when the receiving node receives a prediction of one or more network slice metrics, the receiving node may determine to accept, reject, or partially accept a resource allocation associated with the prediction of the one or more network slice metrics. In this case, when the receiving node rejects or partially accepts a resource allocation, the receiving node may provide a cause indication identifying the rejection or partial acceptance (e.g., a cause indication that indicates that a network slice is not supported).

[0163] In some aspects, a receiving node (e.g., the target node 110) may provide a slice or slice group specific performance metric (e.g., generated at a cell, a UE, or a public land mobile network (PLMN) level) as feedback to a prediction of a set of network slice metrics. For example, as shown by reference numbers 950 and 960, the target network node 110 may receive an RRC connected message from the UE 120 and provide feedback to the source network node 110. For example, the target network node 110 may receive an indication, from the UE 120, of a set of network slice metrics measured at the UE 120 and may provide the indication to the source network node 110. In this case, the source network node 110 may use the indication of the set of network slice metrics as feedback (e.g., as a correction) for re-training the AI / ML model (e.g., as feedback for a predicted set of network slice metrics).

[0164] As indicated above, Fig. 9 is provided as an example. Other examples may differ from what is described with respect to Fig. 9.

[0165] Fig. 10 is a diagram illustrating an example 1000 associated with network slice resource prediction at a network node or a UE, in accordance with the present disclosure. As shown in Fig. 10, example 1000 includes communication between a network node 110 and a UE 120.

[0166] As further shown in Fig. 10, and by reference number 1010, the network node 110 may train and utilize an AI / ML model. For example, the network node 110 may use an AI / ML training procedure to train an AI / ML model and may use the trained AI / ML model for an AI / ML inference procedure (e.g., to generate a prediction). In this case, the network node 110 may generate a prediction relating to a network slice. For example, the source network node 110 may generate a predicted network slice RAN resource status (e.g., at a network slice or network slice group level), which may include a predicted slice available capacity, a predicted network slice status, a PRB usage, a TNL capacity, a quantity of active UEs, a quantity of RRC connections, a predicted network slice support list, a predicted cell reselection priority, or a predicted RACH access prioritization for network slicing. Although some aspects are described in terms of a network node 110 training and using an AI / ML model, in other aspects the UE 120 may train and use an AI / ML model (e.g., to generate and provide a prediction relating a predicted resource status, a predicted UE throughput, or a predicted UE latency). For example, the UE 120 may transmit information identifying the prediction via an RRC message (e.g., an RRC response message), as shown by reference number 1020.

[0167] As further shown in Fig. 10, and by reference number 1030, the UE 120 may use the network slice prediction for generating assistance information. For example, the UE 120 may generate assistance information for idle mode cell reselection. In this case, the UE 120 may predict which cell to attempt to reselect to during a cell reselection procedure. As shown by reference number 1040, the UE 120 may transmit information identifying a measured network slice utilization as feedback information, as shown by reference number 1050, which may enable the network node 110 to re-train or update an AI / ML model.

[0168] As indicated above, Fig. 10 is provided as an example. Other examples may differ from what is described with respect to Fig. 10.

[0169] Fig. 11 is a diagram illustrating an example process 1100 performed, for example, at a network node or an apparatus of a network node, in accordance with the present disclosure. Example process 1100 is an example where the apparatus or the network node (e.g., network node 110) performs operations associated with signaling for network slicing.

[0170] As shown in Fig. 11, in some aspects, process 1100 may include transmitting, to a core network entity, first signaling identifying a network slice prediction (block 1110). For example, the network node (e.g., using transmission component 1404 and / or communication manager 1406, depicted in Fig. 14) may transmit, to a core network entity, first signaling identifying a network slice prediction, as described above.

[0171] As further shown in Fig. 11, in some aspects, process 1100 may include receiving, from the core network entity and as a response to the first signaling, second signaling identifying a network slice configuration associated with the network slice prediction (block 1120). For example, the network node (e.g., using reception component 1402 and / or communication manager 1406, depicted in Fig. 14) may receive, from the core network entity and as a response to the first signaling, second signaling identifying a network slice configuration associated with the network slice prediction, as described above.

[0172] As further shown in Fig. 11, in some aspects, process 1100 may include communicating with a UE using one or more network slices in accordance with the network slice configuration (block 1130). For example, the network node (e.g., using reception component 1402, transmission component 1404, and / or communication manager 1406, depicted in Fig. 14) may communicate with a UE using one or more network slices in accordance with the network slice configuration, as described above.

[0173] Process 1100 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in connection with one or more other processes described elsewhere herein.

[0174] In a first aspect, the first signaling identifying the network slice prediction includes information identifying at least one of a predicted network slice capacity, a predicted network slice availability status, or a predicted network slice resource utilization parameter.

[0175] In a second aspect, alone or in combination with the first aspect, transmitting the first signaling comprises transmitting information associated with a determination of a mobility action or radio resource management action associated with the network slice configuration, the information including information associated with at least one of a predicted network slice resource, a predicted network slice support list, or a predicted network slice allowability status.

[0176] In a third aspect, alone or in combination with one or more of the first and second aspects, process 1100 includes transmitting, to another network node, a data collection request, and receiving, from the other network node, a response message including network data, and communicating with the UE using one or more network slices comprises communicating with the UE using one or more network slices based at least in part on the network data.

[0177] In a fourth aspect, alone or in combination with one or more of the first through third aspects, the response message includes a predicted resource status or a measured resource status, process 1100 includes generating a prediction relating to the one or more network slices using the predicted resource status or the measured resource status, and communicating using the one or more network slices comprises communicating using a network slice, of the one or more network slices, based at least in part on the prediction.

[0178] In a fifth aspect, alone or in combination with one or more of the first through fourth aspects, the predicted resource status or the measured resource status is reported at a network slice level or a network slice group level.

[0179] In a sixth aspect, alone or in combination with one or more of the first through fifth aspects, process 1100 includes receiving, from another network node, a data collection request, and transmitting, to the other network node, a response message including a predicted resource status or a measured resource status.

[0180] In a seventh aspect, alone or in combination with one or more of the first through sixth aspects, process 1100 includes receiving resource status information from a plurality of other network nodes, generating a prediction of a network slice metric using the resource status information, and transmitting response information identifying the prediction of the network slice metric.

[0181] In an eighth aspect, alone or in combination with one or more of the first through seventh aspects, process 1100 includes transmitting a request for the resource status information, and receiving the resource status information comprises receiving the resource status information as a response to transmitting the request for the resource status information.

[0182] In a ninth aspect, alone or in combination with one or more of the first through eighth aspects, the resource status information includes one or more predicted network slice metrics.

[0183] In a tenth aspect, alone or in combination with one or more of the first through ninth aspects, the resource status information is received in connection with a mobility event, a device context event, or a network configuration event.

[0184] In an eleventh aspect, alone or in combination with one or more of the first through tenth aspects, the network node is configured to generate a prediction of a network slice metric using an AI / ML model.

[0185] In a twelfth aspect, alone or in combination with one or more of the first through eleventh aspects, process 1100 includes using a received network slice metric prediction for a handover or mobility procedure.

[0186] Although Fig. i l shows example blocks of process 1100, in some aspects, process 1100 may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in Fig. 11. Additionally, or alternatively, two or more of the blocks of process 1100 may be performed in parallel.

[0187] Fig. 12 is a diagram illustrating an example process 1200 performed, for example, at a core network entity or an apparatus of a core network entity, in accordance with the present disclosure. Example process 1200 is an example where the apparatus or the core network entity (e.g., core network entity 172, 510, or 610) performs operations associated with signaling for network slicing.

[0188] As shown in Fig. 12, in some aspects, process 1200 may include receiving, from a network node, first signaling identifying a network slice prediction (block 1210). For example, the core network entity (e.g., using reception component 1502 and / or communication manager 1506, depicted in Fig. 15) may receive, from a network node, first signaling identifying a network slice prediction, as described above.

[0189] As further shown in Fig. 12, in some aspects, process 1200 may include transmitting, to the network node and as a response to the first signaling, second signaling identifying a network slice configuration associated with the network slice prediction (block 1220). For example, the core network entity (e.g., using transmission component 1504 and / or communication manager 1506, depicted in Fig. 15) may transmit, to the network node and as a response to the first signaling, second signaling identifying a network slice configuration associated with the network slice prediction, as described above.

[0190] As further shown in Fig. 12, in some aspects, process 1200 may include transmitting, to a UE and as a response to the first signaling, third signaling identifying the network slice configuration to cause the UE to communicate with the network node using one or more network slices in accordance with the network slice configuration (block 1230). For example, the core network entity (e.g., using transmission component 1504 and / or communication manager 1506, depicted in Fig. 15) may transmit, to a UE and as a response to the first signaling, third signaling identifying the network slice configuration to cause the UE to communicate with the network node using one or more network slices in accordance with the network slice configuration, as described above.

[0191] Process 1200 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in connection with one or more other processes described elsewhere herein.

[0192] In a first aspect, the first signaling identifying the network slice prediction includes information identifying at least one of a predicted network slice capacity, a predicted network slice availability status, or a predicted network slice resource utilization parameter.

[0193] In a second aspect, alone or in combination with the first aspect, process 1200 includes predicting one or more network slice metrics, and determining the network slice configuration based at least in part on the one or more network slice metrics, the one or more network slice metrics including at least one of a predicted network accessibility metric, a predicted network integrity metric, or a predicted network slice resource utilization metric.

[0194] In a third aspect, alone or in combination with one or more of the first and second aspects, process 1200 includes predicting one or more network slice metrics, and determining a mobility action or radio resource management action associated with the network slice configuration based at least in part on the one or more network slice metrics, the one or morenetwork slice metrics including at least one of a predicted network slice resource, a predicted network slice support list, or a predicted network slice allowability status.

[0195] In a fourth aspect, alone or in combination with one or more of the first through third aspects, process 1200 includes adapting a subnet management parameter or a slice assurance in connection with the mobility action or the radio resource management action.

[0196] Although Fig. 12 shows example blocks of process 1200, in some aspects, process 1200 may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in Fig. 12. Additionally, or alternatively, two or more of the blocks of process 1200 may be performed in parallel.

[0197] Fig. 13 is a diagram illustrating an example process 1300 performed, for example, at a UE or an apparatus of a UE, in accordance with the present disclosure. Example process 1300 is an example where the apparatus or the UE (e.g., UE 120) performs operations associated with signaling for network slicing.

[0198] As shown in Fig. 13, in some aspects, process 1300 may include receiving, from a core network entity and as a response to a network slice prediction, signaling identifying a network slice configuration associated with the network slice prediction (block 1310). For example, the UE (e.g., using reception component 1602 and / or communication manager 1606, depicted in Fig. 16) may receive, from a core network entity and as a response to a network slice prediction, signaling identifying a network slice configuration associated with the network slice prediction, as described above.

[0199] As further shown in Fig. 13, in some aspects, process 1300 may include communicating with a network node using one or more network slices in accordance with the network slice configuration (block 1320). For example, the UE (e.g., using reception component 1602, transmission component 1604, and / or communication manager 1606, depicted in Fig. 16) may communicate with a network node using one or more network slices in accordance with the network slice configuration, as described above.

[0200] Process 1300 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in connection with one or more other processes described elsewhere herein.

[0201] In a first aspect, process 1300 includes receiving, from the network node, a network slice prediction, and performing a mobility action, cell selection action, or initial access action in accordance with the network slice prediction.

[0202] In a second aspect, alone or in combination with the first aspect, the network slice prediction includes at least one of a predicted resource status, a predicted slice capacity, a predicted slice availability status, a predicted slice support list, a predicted cell reselection priority, or a predicted random access channel access priority.

[0203] In a third aspect, alone or in combination with one or more of the first and second aspects, process 1300 includes generating a network slice prediction, and transmitting information identifying the network slice prediction to the network node, wherein the one or more network slices are based at least in part on the information identifying the network slice prediction.

[0204] In a fourth aspect, alone or in combination with one or more of the first through third aspects, the network slice prediction is related to at least one of a predicted resource status, a predicted UE throughput, or a predicted UE latency.

[0205] Although Fig. 13 shows example blocks of process 1300, in some aspects, process 1300 may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in Fig. 13. Additionally, or alternatively, two or more of the blocks of process 1300 may be performed in parallel.

[0206] Fig. 14 is a diagram of an example apparatus 1400 for wireless communication, in accordance with the present disclosure. The apparatus 1400 may be a network node, or a network node may include the apparatus 1400. In some aspects, the apparatus 1400 includes a reception component 1402, a transmission component 1404, and / or a communication manager 1406, which may be in communication with one another (for example, via one or more buses and / or one or more other components). In some aspects, the communication manager 1406 is the communication manager 150 described in connection with Fig. 1. As shown, the apparatus 1400 may communicate with another apparatus 1408, such as a UE or a network node (such as a CU, a DU, an RU, or a base station), using the reception component 1402 and the transmission component 1404.

[0207] In some aspects, the apparatus 1400 may be configured to perform one or more operations described herein in connection with Figs. 5-10. Additionally, or alternatively, the apparatus 1400 may be configured to perform one or more processes described herein, such as process 1100 of Fig. 11. In some aspects, the apparatus 1400 and / or one or more components shown in Fig. 14 may include one or more components of the network node described in connection with Fig. 2. Additionally, or alternatively, one or more components shown in Fig. 14 may be implemented within one or more components described in connection with Fig. 2. Additionally, or alternatively, one or more components of the set of components may be implemented at least in part as software stored in one or more memories. For example, a component (or a portion of a component) may be implemented as instructions or code stored in a non-transitory computer-readable medium and executable by one or more controllers or one or more processors to perform the functions or operations of the component.

[0208] The reception component 1402 may receive communications, such as reference signals, control information, data communications, or a combination thereof, from the apparatus 1408. The reception component 1402 may provide received communications to one or more othercomponents of the apparatus 1400. In some aspects, the reception component 1402 may perform signal processing on the received communications (such as filtering, amplification, demodulation, analog-to-digital conversion, demultiplexing, deinterleaving, de-mapping, equalization, interference cancellation, or decoding, among other examples), and may provide the processed signals to the one or more other components of the apparatus 1400. In some aspects, the reception component 1402 may include one or more antennas, one or more modems, one or more demodulators, one or more MIMO detectors, one or more receive processors, one or more controllers / processors, one or more memories, or a combination thereof, of the network node described in connection with Fig. 2. In some aspects, the reception component 1402 and / or the transmission component 1404 may include or may be included in a network interface. The network interface may be configured to obtain and / or output signals for the apparatus 1400 via one or more communications links, such as a backhaul link, a midhaul link, and / or a fronthaul link.

[0209] The transmission component 1404 may transmit communications, such as reference signals, control information, data communications, or a combination thereof, to the apparatus 1408. In some aspects, one or more other components of the apparatus 1400 may generate communications and may provide the generated communications to the transmission component 1404 for transmission to the apparatus 1408. In some aspects, the transmission component 1404 may perform signal processing on the generated communications (such as filtering, amplification, modulation, digital-to-analog conversion, multiplexing, interleaving, mapping, or encoding, among other examples), and may transmit the processed signals to the apparatus 1408. In some aspects, the transmission component 1404 may include one or more antennas, one or more modems, one or more modulators, one or more transmit MIMO processors, one or more transmit processors, one or more controllers / processors, one or more memories, or a combination thereof, of the network node described in connection with Fig. 2. In some aspects, the transmission component 1404 may be co-located with the reception component 1402 in one or more transceivers.

[0210] The communication manager 1406 may support operations of the reception component 1402 and / or the transmission component 1404. For example, the communication manager 1406 may receive information associated with configuring reception of communications by the reception component 1402 and / or transmission of communications by the transmission component 1404. Additionally, or alternatively, the communication manager 1406 may generate and / or provide control information to the reception component 1402 and / or the transmission component 1404 to control reception and / or transmission of communications.

[0211] The transmission component 1404 may transmit, to a core network entity, first signaling identifying a network slice prediction. The reception component 1402 may receive,from the core network entity and as a response to the first signaling, second signaling identifying a network slice configuration associated with the network slice prediction. The reception component 1402 and / or the transmission component 1404 may communicate with a UE using one or more network slices in accordance with the network slice configuration.

[0212] The transmission component 1404 may transmit, to another network node, a data collection request. The reception component 1402 may receive, from the other network node, a response message including network data. The reception component 1402 may receive, from another network node, a data collection request. The transmission component 1404 may transmit, to the other network node, a response message including a predicted resource status or a measured resource status. The reception component 1402 may receive resource status information from a plurality of other network nodes. The communication manager 1406 may generate a prediction of a network slice metric using the resource status information. The transmission component 1404 may transmit response information identifying the prediction of the network slice metric. The transmission component 1404 may transmit a request for the resource status information. The communication manager 1406 may use a received network slice metric prediction for a handover or mobility procedure.

[0213] The number and arrangement of components shown in Fig. 14 are provided as an example. In practice, there may be additional components, fewer components, different components, or differently arranged components than those shown in Fig. 14. Furthermore, two or more components shown in Fig. 14 may be implemented within a single component, or a single component shown in Fig. 14 may be implemented as multiple, distributed components. Additionally, or alternatively, a set of (one or more) components shown in Fig. 14 may perform one or more functions described as being performed by another set of components shown in Fig. 14.

[0214] Fig. 15 is a diagram of an example apparatus 1500 for wireless communication, in accordance with the present disclosure. The apparatus 1500 may be a core network entity 172, or a core network entity 172 may include the apparatus 1500. In some aspects, the apparatus 1500 includes a reception component 1502, a transmission component 1504, and / or a communication manager 1506, which may be in communication with one another (for example, via one or more buses and / or one or more other components). In some aspects, the communication manager 1506 is the communication manager 174 described in connection with Fig. 1. As shown, the apparatus 1500 may communicate with another apparatus 1508, such as a UE or a network node (such as a CU, a DU, an RU, or a base station), using the reception component 1502 and the transmission component 1504.

[0215] In some aspects, the apparatus 1500 may be configured to perform one or more operations described herein in connection with Figs. 5-10. Additionally, or alternatively, theapparatus 1500 may be configured to perform one or more processes described herein, such as process 1200 of Fig. 12. In some aspects, the apparatus 1500 and / or one or more components shown in Fig. 15 may include one or more components of the core network entity described in connection with Fig. 2. Additionally, or alternatively, one or more components shown in Fig. 15 may be implemented within one or more components described in connection with Fig. 2. Additionally, or alternatively, one or more components of the set of components may be implemented at least in part as software stored in one or more memories. For example, a component (or a portion of a component) may be implemented as instructions or code stored in a non-transitory computer-readable medium and executable by one or more controllers or one or more processors to perform the functions or operations of the component.

[0216] The reception component 1502 may receive communications, such as reference signals, control information, data communications, or a combination thereof, from the apparatus 1508. The reception component 1502 may provide received communications to one or more other components of the apparatus 1500. In some aspects, the reception component 1502 may perform signal processing on the received communications (such as filtering, amplification, demodulation, analog-to-digital conversion, demultiplexing, deinterleaving, de-mapping, equalization, interference cancellation, or decoding, among other examples), and may provide the processed signals to the one or more other components of the apparatus 1500. In some aspects, the reception component 1502 may include one or more antennas, one or more modems, one or more demodulators, one or more MIMO detectors, one or more receive processors, one or more controllers / processors, one or more memories, or a combination thereof, of the core network entity described in connection with Fig. 2.

[0217] The transmission component 1504 may transmit communications, such as reference signals, control information, data communications, or a combination thereof, to the apparatus 1508. In some aspects, one or more other components of the apparatus 1500 may generate communications and may provide the generated communications to the transmission component 1504 for transmission to the apparatus 1508. In some aspects, the transmission component 1504 may perform signal processing on the generated communications (such as filtering, amplification, modulation, digital-to-analog conversion, multiplexing, interleaving, mapping, or encoding, among other examples), and may transmit the processed signals to the apparatus 1508. In some aspects, the transmission component 1504 may include one or more antennas, one or more modems, one or more modulators, one or more transmit MIMO processors, one or more transmit processors, one or more controllers / processors, one or more memories, or a combination thereof, of the core network entity described in connection with Fig. 2. In some aspects, the transmission component 1504 may be co-located with the reception component 1502 in one or more transceivers.

[0218] The communication manager 1506 may support operations of the reception component 1502 and / or the transmission component 1504. For example, the communication manager 1506 may receive information associated with configuring reception of communications by the reception component 1502 and / or transmission of communications by the transmission component 1504. Additionally, or alternatively, the communication manager 1506 may generate and / or provide control information to the reception component 1502 and / or the transmission component 1504 to control reception and / or transmission of communications.

[0219] The reception component 1502 may receive, from a network node, first signaling identifying a network slice prediction. The transmission component 1504 may transmit, to the network node and as a response to the first signaling, second signaling identifying a network slice configuration associated with the network slice prediction. The transmission component 1504 may transmit, to a UE and as a response to the first signaling, third signaling identifying the network slice configuration to cause the UE to communicate with the network node using one or more network slices in accordance with the network slice configuration.

[0220] The communication manager 1506 may predict one or more network slice metrics. The communication manager 1506 may determine the network slice configuration based at least in part on the one or more network slice metrics, the one or more network slice metrics including at least one of a predicted network accessibility metric, a predicted network integrity metric, or a predicted network slice resource utilization metric. The communication manager 1506 may predict one or more network slice metrics. The communication manager 1506 may determine a mobility action or radio resource management action associated with the network slice configuration based at least in part on the one or more network slice metrics, the one or more network slice metrics including at least one of a predicted network slice resource, a predicted network slice support list, or a predicted network slice allowability status. The communication manager 1506 may adapt a subnet management parameter or a slice assurance in connection with the mobility action or the radio resource management action.

[0221] The number and arrangement of components shown in Fig. 15 are provided as an example. In practice, there may be additional components, fewer components, different components, or differently arranged components than those shown in Fig. 15. Furthermore, two or more components shown in Fig. 15 may be implemented within a single component, or a single component shown in Fig. 15 may be implemented as multiple, distributed components. Additionally, or alternatively, a set of (one or more) components shown in Fig. 15 may perform one or more functions described as being performed by another set of components shown in Fig. 15.

[0222] Fig. 16 is a diagram of an example apparatus 1600 for wireless communication, in accordance with the present disclosure. The apparatus 1600 may be a UE, or a UE may includethe apparatus 1600. In some aspects, the apparatus 1600 includes a reception component 1602, a transmission component 1604, and / or a communication manager 1606, which may be in communication with one another (for example, via one or more buses and / or one or more other components). In some aspects, the communication manager 1606 is the communication manager 140 described in connection with Fig. 1. As shown, the apparatus 1600 may communicate with another apparatus 1608, such as a UE or a network node (such as a CU, a DU, an RU, or a base station), using the reception component 1602 and the transmission component 1604.

[0223] In some aspects, the apparatus 1600 may be configured to perform one or more operations described herein in connection with Figs. 5-10. Additionally, or alternatively, the apparatus 1600 may be configured to perform one or more processes described herein, such as process 1300 of Fig. 13. In some aspects, the apparatus 1600 and / or one or more components shown in Fig. 16 may include one or more components of the UE described in connection with Fig. 2. Additionally, or alternatively, one or more components shown in Fig. 16 may be implemented within one or more components described in connection with Fig. 2. Additionally, or alternatively, one or more components of the set of components may be implemented at least in part as software stored in one or more memories. For example, a component (or a portion of a component) may be implemented as instructions or code stored in a non-transitory computer- readable medium and executable by one or more controllers or one or more processors to perform the functions or operations of the component.

[0224] The reception component 1602 may receive communications, such as reference signals, control information, data communications, or a combination thereof, from the apparatus 1608. The reception component 1602 may provide received communications to one or more other components of the apparatus 1600. In some aspects, the reception component 1602 may perform signal processing on the received communications (such as filtering, amplification, demodulation, analog-to-digital conversion, demultiplexing, deinterleaving, de-mapping, equalization, interference cancellation, or decoding, among other examples), and may provide the processed signals to the one or more other components of the apparatus 1600. In some aspects, the reception component 1602 may include one or more antennas, one or more modems, one or more demodulators, one or more MIMO detectors, one or more receive processors, one or more controllers / processors, one or more memories, or a combination thereof, of the UE described in connection with Fig. 2.

[0225] The transmission component 1604 may transmit communications, such as reference signals, control information, data communications, or a combination thereof, to the apparatus 1608. In some aspects, one or more other components of the apparatus 1600 may generate communications and may provide the generated communications to the transmission component 1604 for transmission to the apparatus 1608. In some aspects, the transmission component 1604may perform signal processing on the generated communications (such as filtering, amplification, modulation, digital-to-analog conversion, multiplexing, interleaving, mapping, or encoding, among other examples), and may transmit the processed signals to the apparatus 1608. In some aspects, the transmission component 1604 may include one or more antennas, one or more modems, one or more modulators, one or more transmit MIMO processors, one or more transmit processors, one or more controllers / processors, one or more memories, or a combination thereof, of the UE described in connection with Fig. 2. In some aspects, the transmission component 1604 may be co-located with the reception component 1602 in one or more transceivers.

[0226] The communication manager 1606 may support operations of the reception component 1602 and / or the transmission component 1604. For example, the communication manager 1606 may receive information associated with configuring reception of communications by the reception component 1602 and / or transmission of communications by the transmission component 1604. Additionally, or alternatively, the communication manager 1606 may generate and / or provide control information to the reception component 1602 and / or the transmission component 1604 to control reception and / or transmission of communications.

[0227] The reception component 1602 may receive, from a core network entity and as a response to a network slice prediction, signaling identifying a network slice configuration associated with the network slice prediction. The reception component 1602 and / or the transmission component 1604 may communicate with a network node using one or more network slices in accordance with the network slice configuration.

[0228] The reception component 1602 may receive, from the network node, a network slice prediction. The communication manager 1606 may perform a mobility action, cell selection action, or initial access action in accordance with the network slice prediction. The communication manager 1606 may generate a network slice prediction. The transmission component 1604 may transmit information identifying the network slice prediction to the network node, wherein the one or more network slices are based at least in part on the information identifying the network slice prediction.

[0229] The number and arrangement of components shown in Fig. 16 are provided as an example. In practice, there may be additional components, fewer components, different components, or differently arranged components than those shown in Fig. 16. Furthermore, two or more components shown in Fig. 16 may be implemented within a single component, or a single component shown in Fig. 16 may be implemented as multiple, distributed components.Additionally, or alternatively, a set of (one or more) components shown in Fig. 16 may perform one or more functions described as being performed by another set of components shown in Fig. 16.

[0230] Fig. 17 is a diagram illustrating an example 1700 of quality of experience signaling, in accordance with the present disclosure.

[0231] QoE may indicate an overall level of satisfaction with a wireless communication network and may be based at least in part on a technical performance of the network and / or a subjective satisfaction with the network indicated by a user of the network. Parameters for identifying the QoE of the network may include, for example, perceived signal quality, consistency and reliability, service accessibility and availability, user interface and interaction, and personalization and customization. RVQoE may indicate a quality of experience in the wireless communication network that is related to a RAN node. RVQoE may include performance metrics and user experience parameters that are directly influenced by the RAN node, such as signal strength, coverage, and a capacity of the network, among other examples. QoE and RVQoE may be optimized using AI / ML.

[0232] As shown in example 1700, a communication network may include a trace collection entity (TCE) and MCE (TCE / MCE) 1705, an operation and maintenance (0AM) node 1710, a central node (CN) 1715, a RAN node 1720 (for example, a next generation (NG) RAN node), a UE access stratum (UE-AS) 1725, and a UE application (UE App) 1730. As shown by reference number 1735, the UE-AS 1725 may transmit, and the RAN node 1720 may receive, capability information. The 0AM node 1710 may initiate a QoE measurement activation for the UE via the CN 1715. For example, as shown by reference number 1740, the 0AM node 1710 may configure a QoE measurement at the CN 1715. Configuring the QoE measurement at the CN 1715 may include providing a QoE measurement configuration to the CN 1715. The CN node 1715 may initiate a QoE measurement activation for the UE via the RAN node 1720. For example, as shown by reference number 1745, the CN 1715 may activate a QoE measurement at the RAN node 1720. Activating the QoE measurement at the RAN node 1720 may include providing the QoE measurement configuration to the RAN node 1720. The operations described in connection with reference number 1740 and reference number 1745 may be associated with a signaling -based QoE measurement activation operation. As shown by reference number 1750, the 0AM node 1710 may activate a management-based QoE measurement at the RAN node 1720. For example, the 0AM node 1710 may send one or more QoE measurement configurations to the RAN node 1720. The RAN node 1720 may receive one or more QoE measurement configurations from the 0AM node 1710 and / or the CN 1715, where each QoE measurement configuration includes one or more of a QoE configuration container (such as an extensible markup language (XML) file), a QoE reference, a service type, an MCE Internet Protocol (IP) address, an area scope, a slice scope, minimization of drive test (MDT) alignment information, and / or an available RAN -visible QoE metric. As shown by reference number 1755, the RAN node 1720 may transmit, and the UE-AS 1725 may receive, a radio resource control (RRC) reconfiguration indication. The RRCreconfiguration indication may include a QoE configuration container (such as an XML file), a service type, and / or a measurement configuration application layer ID (measConfigAppLayerlD), as described, for example, in Technical Specification (TS) 38.331 of the 3GPP Specification. A mapping between the measurement configuration application layer ID and the QoE reference may be maintained at the RAN node 1720. As shown by reference number 1760, the UE-AS 1725 may transmit, and the UE App may receive, an attention (AT) command. The AT command may include the configuration container, the service type, and / or the measurement configuration application layer ID.

[0233] RVQoE measurements may be configured by a network node. A subset of the QoE metrics may be reported from the UE as an IE that is readable by the network node. The RVQoE measurement may be utilized by the network node for network optimization. In some examples, RVQoE measurements, such as buffer level measurements and play out delays for media start-up (as described, for example, in TS 26.247 of the 3GPP Specifications), may be supported for dynamic adaptive streaming over hypertext transfer protocol (HTTP) (DASH) streaming and virtual reality (VR) services. In some cases, PDU session IDs corresponding to a service that is subject to the QoE measurements may be reported by the UE with the RVQoE measurement results. The RVQoE measurements may be reported in accordance with a reporting periodicity that is different than a reporting periodicity of other QoE measurements. During a RAN overload, the UE may continue to report the configured QoE measurements, even when the corresponding QoE measurement reporting for non-RVQoE metrics is paused. In some cases, the UE and the network node (and / or other network elements described herein) may not be able to communicate information regarding AI / ML-based QoE and RVQoE prediction. In some cases, the UE and the network node (and / or other network elements described herein) may not be able to communicate information regarding AI / ML-based QoE and RVQoE prediction. This may reduce a capability of the network node and the UE to take proactive actions, such as adjusting resource allocations and scheduling decisions, for achieving an improved quality of experience.

[0234] As indicated above, Fig. 17 is provided as an example. Other examples may differ from what is described with regard to Fig. 17.

[0235] Figs 18A-18B are diagrams illustrating examples of QoE prediction at a network node using a split network architecture, in accordance with the present disclosure. As shown in Fig. 18A and example 1800, a CU 1805 may communicate with a DU 1810 for performing QoE predictions (for example, RVQoE predictions) at the DU 1810. The CU 1805 may include one or more features of the CU 310. The DU 1810 may include one or more features of the DU 330. As shown by reference number 1815, the DU 1810 may send, to the CU 1805, a data collection request. The data collection request may include a request for assistance information for performing QoE prediction. As shown by reference number 1820, the CU 1805 may send, to theDU 1810, a data collection response. As shown by reference number 1825, the CU 1805 may send, to the DU 1810, a data collection update. The data collection update may include assistance information for QoE prediction. The assistance information may include a DRB indicator, a PDU session ID, a QFI, a slice ID, and / or a call admission control (CAC) indication. The assistance information may be sent by the CU 1805 to the DU 1810 (for example, along with a QoE report) to enable the DU 1810 to perform QoE prediction. As shown by reference number 1830, the DU 1810 may perform AI / ML-based QoE prediction. As shown by reference number 1835, the DU 1810 may provide, to the CU 1805, an indication of an action. The indication of the action may include an indication of the predicted QoE. As shown by reference number 1840, the CU 1805 may perform a radio resource management (RRM) action based at least in part on the QoE prediction. For example, the CU 1805 may perform a mobility optimization operation and / or a load balancing operation based at least in part on the QoE prediction.

[0236] As shown in Fig. 18B and example 1845, the CU 1805 may communicate with the DU 1810 for performing QoE predictions at the CU 1805. As shown by reference number 1850, the CU 1805 may send, to the DU 1810, a data collection request. The data collection request may include a request for assistance information for performing QoE prediction. As shown by reference number 1855, the DU 1810 may send, to the CU 1805, a data collection response. As shown by reference number 1860, the DU 1810 may send, to the CU 1805, a data collection update. The data collection update may include assistance information for QoE prediction. The assistance information may include a channel quality indicator (CQI) distribution, a modulation and coding scheme (MCS) distribution, and / or a radio link control (RLC) buffer status. The assistance information may be sent by the DU 1810 to the CU 1805 (for example, along with a QoE report) to enable the CU 1805 to perform QoE prediction. As shown by reference number 1865, the CU 1805 may perform AI / ML-based QoE prediction. As shown by reference number 1870, the CU 1805 may provide, to the DU 1810, an indication of an action. The indication of the action may include an indication of the predicted QoE. As shown by reference number 1875, the DU 1810 may perform an RRM action based at least in part on the QoE prediction. For example, the DU 1810 may perform a scheduling optimization operation based at least in part on the QoE prediction.

[0237] As indicated above, Figs. 18A-18B are provided as examples. Other examples may differ from what is described with regard to Figs. 18A-18B.

[0238] Fig. 19 is a diagram illustrating an example 1900 of QoE prediction at a network node, in accordance with the present disclosure. A UE 120, a RAN node 1905, an MCE / OAM 1910, and an application function 1915 may communicate within a wireless communication network. As shown by reference number 1920, the RAN node 1905 may perform AI / ML training and inference. As shown by reference number 1925, the RAN node 1905 may send, to theMCE / OAM 1910, a predicted QoE (for example, a predicted RVQoE) and at least one of a PDU session ID, a QFI, a DRB ID, or a slice ID. As shown by reference number 1930, the MCE / OAM 1910 may send, to the application function 1915, the predicted QoE and at least one of the PDU session ID, the QFI, the DRB ID, or the slice ID. As shown by reference number 1935, the application function 1915 may perform an action based at least in part on the predicted QoE. For example, the application function 1915 may adjust one or more application buffers and / or may perform a scheduling operation based at least in part on the predicted QoE. As shown by reference number 1940, the application function 1915 may send, to the UE 120, an indication of a DASH or VR streaming adaptation that is based at least in part on the QoE prediction. As shown by reference number 1945, the RAN node 1905 may transmit, and the UE 120 may receive, assistance information. The assistance information may include, for example, the predicted QoE and / or may include a predicted radio resource status. The RAN node 1905 may transmit the assistance information to the UE 120 in accordance with an uplink throughput adaptation, a downlink throughput adaptation, a delay tolerance, and / or a latency tolerance. As shown by reference number 1950, the UE 120 may perform one or more actions based at least in part on the predicted QoE. For example, the UE 120 may perform a mobility action based at least in part on the predicted QoE.

[0239] As indicated above, Fig. 19 is provided as an example. Other examples may differ from what is described with regard to Fig. 19.

[0240] Fig. 20 is a diagram illustrating an example 2000 of exchanging predicted QoE during handovers, in accordance with the present disclosure. A source network node 2005, a target network node 2010, and a UE 120 may communicate in a wireless communication network. As shown by reference number 2015, the source network node 2005 may perform AI / ML training and inference. As shown by reference number 2020, the source network node 2005 may transmit, and the target network node 2010 may receive, a handover request. The handover request may include a predicted QoE (for example, a predicted RVQoE) and may include at least one of a PDU session ID, a QFI, a DRB ID, or a slice ID. The handover request may be associated with an NG-based handover or an Xn-based handover. In some examples, the handover may be a Layer 3 (L3) handover that includes a conditional handover (CHO) or a lower-layer triggered mobility (LTM). As shown by reference number 2025, the target network node 2010 may transmit, and the source network node 2005 may receive, a handover request acknowledgement. As shown by reference number 2030, the target network node 2010 may perform an action. For example, the target network node 2010 may pre-allocate resources for the predicted PDU session, DRB, quality of service (QoS) flow, or slice based at least in part on the predicted QoE. As shown by reference number 2035, the target network node 2010 may set up a signaling radio bearer (SRB) (such as an SRB4) for collecting QoE reports. The target network node 2010 may configure the UE 120 to provide QoE reports. As shown by reference number 2040, the UE 120 may transmit, and thetarget network node 2010 may receive, a QoE or QoE report. As shown by reference number 2045, the target network node 2010 may transmit, and the source network node 2005 may receive, feedback. The feedback may include an indication of the QoE or the QoE report. In some aspects, the source network node 2005 may request the target network node 2010 to provide feedback. In some other aspects, the target network node 2010 may provide the feedback autonomously. In some aspects, the feedback (which may include the actual QoE metrics experienced by the UE 120 in the target network node 2010) can be used as ground truth for AI / ML model retraining or performance monitoring at the source network node 2005.

[0241] As indicated above, Fig. 20 is provided as an example. Other examples may differ from what is described with regard to Fig. 20.

[0242] Figs. 21A-21B are diagrams illustrating examples of QoE prediction in a dual connectivity environment, in accordance with the present disclosure. As shown in Fig. 21A and example 2100, a master node (MN) 2105 may communicate with a secondary node (SN) 2110 for performing QoE predictions (for example, RVQoE predictions) at the SN 2110. As shown by reference number 2115, the MN 2105 may perform AI / ML training and inference. As shown by reference number 2120, the MN 2105 may transmit, and the SN 2110 may receive, an SN addition or modification request. The SN addition or modification request may include a predicted time during which the application is to use secondary cell group (SCG) bearers, a predicted QoE, and / or a predicted radio resource status. As shown by reference number 2125, the SN 2110 may transmit, and the MN 2105 may receive, an SN addition or modification response. As shown by reference number 2130, the SN 2110 may perform an action. For example, the SN 2110 may prepare or pre-allocate resources for the SCG bearers for a future time that is indicated in the SN addition or modification request. As shown by reference number 2135, the SN 2110 may transmit, and the MN 2105 may receive, feedback. The feedback may include an actual QoE metric using the SCG bearer. In this example, if the MN 2105 predicts that an application will be using an SCG bearer in the future, the MN 2105 can inform the SN 2110 about this prediction and may send the predicted QoE to the SN 2110 during SN addition or modification. The SN 2110 can use this prediction to proactively take scheduling actions, for example, to prepare or pre- allocate resources for the SCG bearers for a future time indicated in the predictions.

[0243] As shown in Fig. 21B and example 2140, the MN 2105 may communicate with the SN 2110 for performing QoE predictions at the MN 2105. As shown by reference number 2145, the SN 2110 may perform AI / ML training and inference. As shown by reference number 2150, the SN 2110 may transmit, and the MN 2105 may receive, an indication that SN modification is required. The indication that the SN modification is required may include a predicted time during which the application is to use master cell group (MCG) bearers or split bearers, a predicted QoE, and / or a predicted radio resource status. As shown by reference number 2155, the MN 2105 maytransmit, and the SN 2110 may receive, an SN modification notification. As shown by reference number 2160, the MN 2105 may perform an action. For example, the MN 2105 may prepare or pre-allocate resources for the MCG bearers or the split bearers for a future time that is indicated in the SN modification notification. As shown by reference number 2165, the MN 2105 may transmit, and the SN 2110 may receive, feedback. The feedback may include an actual QoE metric using the MCG bearer or the split bearer. In this example, if the SN 2110 predicts that an application will be using an MCG bearer or a split bearer in the future, the SN 2110 can inform the MN 2105 about this prediction and may send the predicted QoE to the MN 2105 during SN modification. The MN 2105 can use this prediction to proactively take scheduling actions, for example, to prepare or pre-allocate resources for the MCG bearers or the split bearers for a future time indicated in the predictions.

[0244] As indicated above, Figs. 21A-21B are provided as examples. Other examples may differ from what is described with regard to Figs. 21A-21B.

[0245] Fig. 22 is a diagram illustrating an example 2200 of QoE prediction at a network node, in accordance with the present disclosure. A RAN node 2205 may communicate with an MCE 2210 in a wireless communication network. The MCE 2210 may be associated with an 0AM node. The MCE 2210 may be configured to predict a QoE (for example, an RVQoE) based at least in part on a QoE report received from a UE, and may be configured to send the predicted QoE to the RAN node 2205 as assistance information. As shown by reference number 2215, the MCE 2210 may perform AI / ML training and inference. As shown by reference number 2220, the MCE 2210 may transmit, and the RAN node 2205 may receive, a predicted QoE and at least one of a PDU session ID, a QFI, a DRB ID, or a slice ID. As shown by reference number 2225, the RAN node 2205 may perform an action. The action may include, for example, adapting radio resource allocations for achieving a between QoE.

[0246] As indicated above, Fig. 22 is provided as an example. Other examples may differ from what is described with regard to Fig. 22.

[0247] Fig. 23 is a diagram illustrating an example 2300 of QoE prediction at a UE, in accordance with the present disclosure. The UE 120 may communicate with a source network node 2305 in a wireless communication network. In some aspects, the UE 120 may be configured to predict the QoE (for example, the RVQoE) and to provide the predicted QoE and / or a predicted uplink or downlink data volume to the source network node 2305 as assistance information. The source network node 110 may use the information for performing radio resource allocations and mobility decisions. As shown by reference number 2310, the UE 120 may perform AI / ML training and inference. As shown by reference number 2315, the UE 120 may transmit, and the source network node 2305 may receive, a predicted QoE and / or a predicted uplink or downlink data volume. As shown by reference number 2320, the source node 2305 may adapt a radioresource allocation based at least in part on the predicted QoE and / or the predicted uplink or downlink data volume.

[0248] As indicated above, Fig. 23 is provided as an example. Other examples may differ from what is described with regard to Fig. 23.

[0249] Fig. 24 is a diagram illustrating an example 2400 of QoE configuration prediction at one or more network nodes, in accordance with the present disclosure. An MCE / OAM 2405 may communicate with the network node 110. In some aspects, as shown by reference number 2415, the network node 110 may perform AI / ML training and inference. Additionally, or alternatively, as shown by reference number 2410, the MCE / OAM 2405 may perform AI / ML training and inference. In some aspects, the MCE / OAM 2405 may predict one or more QoE configuration parameters and may send the QoE configuration parameters to the network node 110 to enable the network node 110 to achieve an optimal QoE configuration. Additionally, or alternatively, the network node 110 may predict one or more QoE configuration parameters based at least in part on an input from a UE. As shown by reference number 2420, the MCE / OAM 2405 and the network node 110 may communicate QoE configuration parameters. For example, the MCE / OAM 2405 may transmit QoE configuration parameters to the network node 110, or the network node 110 may transmit QoE configuration parameters to the MCE / OAM 2405. In some aspects, predicting the QoE configuration parameters may include predicting a time at which the QoE (for example, the RVQoE) will be triggered (e.g., in case of event triggered QoE) so that the MCE / OAM 2405 or network node 110 can prepare resources for reception of QoE reports.

[0250] In some aspects, predicting the QoE configuration parameters may include predicting an MDT or trace session that will be aligned with QoE. In some aspects, predicting the QoE configuration parameters may include predicting one or more priorities for a QoE measurement configuration. In some aspects, predicting the QoE configuration parameters may include predicting a session status (e.g., predicting whether the session status is ongoing, completed, or hasn’t started) in order to enable the network node 110 to determine whether or not to keep the QoE configuration or release the QoE configuration. In some aspects, predicting the QoE configuration parameters may include configuring an area scope for the QoE, for example, based at least in part on a UE trajectory prediction, to enable the UE to predict a QoE score. As shown by reference number 2425, the network node 110 may adapt a radio resource allocation based at least in part on the configuration parameters.

[0251] As indicated above, Fig. 24 is provided as an example. Other examples may differ from what is described with regard to Fig. 24.

[0252] Fig. 25 is a diagram illustrating an example 2500 of QoE configuration prediction between a master node and a secondary node, in accordance with the present disclosure. An MN 2505 may communicate with an SN 2510 in a wireless communication network. As shown byreference number 2515, the MN 2505 may perform AI / ML training and inference. Additionally, or alternatively, as shown by reference number 2520, the SN 2510 may perform AI / ML training and inference. As shown by reference number 2525, the MN 2505 and the SN 2510 may communicate at least one of a predicted SRB or a predicted QoE configuration. In some aspects, the predicted SRB may include a predicted SRB (such as SRB4 or SRB4) to be used for collecting QoE reports. In some aspects, the MN 2505 and / or the SN 2510 may predict whether a peer node is interested in receiving QoE reports. In some aspects, the predicted QoE configuration may be a QoE configuration that is desired by the peer node. The SN 2510 may predict that the MN 2505 is to receive a same QoE configuration in the future and, therefore, does not need to coordinate with the MN 2505 regarding the QoE configuration. Additionally, or alternatively, MN 2505 may predict that the SN 2510 is to receive a same QoE configuration in the future and, therefore, does not need to coordinate with the SN 2510 regarding the QoE configuration. As shown by reference number 2530, the SN 2510 may adapt a radio resource allocation based at least in part on the predicted SRB or the predicted QoE configuration.

[0253] As indicated above, Fig. 25 is provided as an example. Other examples may differ from what is described with regard to Fig. 25.

[0254] Fig. 26 is a diagram illustrating an example process 2600 performed, for example, at a network node or an apparatus of a network node, in accordance with the present disclosure. Example process 2600 is an example where the apparatus or the network node (e.g., network node 110) performs operations associated with signaling for quality of experience optimization.

[0255] As shown in Fig. 26, in some aspects, process 2600 may include performing a QoE prediction based at least in part on an AI / ML training and inference operation (block 2610). For example, the network node (e.g., using communication manager 2806, depicted in Fig. 28) may perform a QoE prediction based at least in part on an AI / ML training and inference operation, as described above.

[0256] As further shown in Fig. 26, in some aspects, process 2600 may include transmitting, to a second network node, information regarding the QoE prediction and at least one of a PDU session ID, a QFI, a DRB ID, or a slice ID associated with the QoE prediction (block 2620). For example, the network node (e.g., using transmission component 2804 and / or communication manager 2806, depicted in Fig. 28) may transmit, to a second network node, information regarding the QoE prediction and at least one of a PDU session ID, a QFI, a DRB ID, or a slice ID associated with the QoE prediction, as described above.

[0257] Process 2600 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in connection with one or more other processes described elsewhere herein.

[0258] In a first aspect, the information regarding the QoE prediction is configured to be provided to an application function for adapting an application buffer associated with an application.

[0259] In a second aspect, alone or in combination with the first aspect, the QoE prediction is an RVQoE prediction.

[0260] In a third aspect, alone or in combination with one or more of the first and second aspects, process 2600 includes providing, by a central unit of the first network node to a distributed unit of the first network node, assistance information for performing the QoE prediction.

[0261] In a fourth aspect, alone or in combination with one or more of the first through third aspects, the assistance information includes at least one of the PDU session ID, the QFI, the DRB ID, the slice ID, or a load metric.

[0262] In a fifth aspect, alone or in combination with one or more of the first through fourth aspects, performing the QoE prediction comprises performing, at the distributed unit of the first network node, the QoE prediction based at least in part on the assistance information and the AI / ML training and inference operation, and providing the information regarding the QoE prediction to the central unit of the first network node.

[0263] In a sixth aspect, alone or in combination with one or more of the first through fifth aspects, process 2600 includes providing, by a distributed unit of the first network node to a central unit of the first network node, assistance information for performing the QoE prediction.

[0264] In a seventh aspect, alone or in combination with one or more of the first through sixth aspects, the assistance information includes at least one of a channel quality indicator, a buffer status, or a lower layer measurement performed at the distributed unit of the first network node.

[0265] In an eighth aspect, alone or in combination with one or more of the first through seventh aspects, performing the QoE prediction comprises performing, at the central unit of the first network node, the QoE prediction based at least in part on the assistance information and the AI / ML training and inference operation, and providing the information regarding the QoE prediction to the distributed unit of the first network node.

[0266] In a ninth aspect, alone or in combination with one or more of the first through eighth aspects, process 2600 includes transmitting, to a user equipment, assistance information that includes the information regarding the QoE prediction and that includes at least one of a predicted radio resource status, a protocol data unit session identifier, or a quality of service flow identifier.

[0267] In a tenth aspect, alone or in combination with one or more of the first through ninth aspects, the assistance information is associated with at least one of an uplink throughput adaptation, a downlink throughput adaptation, a delay tolerance, a latency tolerance, or a mobility action.

[0268] In an eleventh aspect, alone or in combination with one or more of the first through tenth aspects, process 2600 includes transmitting, to a third network node, a handover request that includes the information regarding the QoE prediction and at least one of the PDU session ID, the QFI, the DRB ID, or the slice ID associated with the QoE prediction.

[0269] In a twelfth aspect, alone or in combination with one or more of the first through eleventh aspects, process 2600 includes providing, by a master node associated with the first network node to a secondary node associated with the first network node, an addition request or a modification request that includes the information regarding the QoE prediction and at least one of a predicted time during which a SCG bearer is to be used, a QoE associated with the SCG, or a radio resource usage associated with the SCG.

[0270] In a thirteenth aspect, alone or in combination with one or more of the first through twelfth aspects, providing the addition request or the modification request comprises providing the addition request or the modification request based at least in part on a prediction by the master node that an application is to use the SCG bearer.

[0271] In a fourteenth aspect, alone or in combination with one or more of the first through thirteenth aspects, process 2600 includes receiving feedback that indicates one or more actual QoE metrics associated with the SCG bearer.

[0272] In a fifteenth aspect, alone or in combination with one or more of the first through fourteenth aspects, process 2600 includes providing, by a secondary node associated with the first network node to a master node associated with the first network node, a modification request that includes the information regarding the QoE prediction and at least one of a predicted time during which a SCG bearer is to be used, a QoE associated with the SCG, or a radio resource usage associated with the SCG.

[0273] In a sixteenth aspect, alone or in combination with one or more of the first through fifteenth aspects, providing the modification request comprises providing the modification request based at least in part on a prediction by the secondary node that an application is to use an MCG bearer or a split bearer.

[0274] In a seventeenth aspect, alone or in combination with one or more of the first through sixteenth aspects, process 2600 includes receiving feedback that indicates one or more actual QoE metrics associated with the SCG bearer.

[0275] In an eighteenth aspect, alone or in combination with one or more of the first through seventeenth aspects, process 2600 includes receiving, from a third network node, assistance information that includes the information regarding the predicted QoE.

[0276] In a nineteenth aspect, alone or in combination with one or more of the first through eighteenth aspects, process 2600 includes adapting, at a radio access network associated with thefirst network node, one or more radio resource allocations based at least in part on the predicted QoE.

[0277] In a twentieth aspect, alone or in combination with one or more of the first through nineteenth aspects, process 2600 includes receiving, from a user equipment, assistance information that includes the information regarding the predicted QoE.

[0278] In a twenty-first aspect, alone or in combination with one or more of the first through twentieth aspects, the assistance information further includes at least one of a predicted uplink data volume or a predicted downlink data volume.

[0279] In a twenty-second aspect, alone or in combination with one or more of the first through twenty -first aspects, process 2600 includes performing, at a radio access network associated with the first network node, a radio resource allocation or a mobility decision based at least in part on the predicted QoE.

[0280] In a twenty-third aspect, alone or in combination with one or more of the first through twenty-second aspects, process 2600 includes receiving, from a third network node, one or more QoE parameters associated with a QoE configuration.

[0281] In a twenty-fourth aspect, alone or in combination with one or more of the first through twenty -third aspects, the one or more parameters include a prediction of a time at which the QoE is to be triggered, a prediction of a MDT trace session that is to be aligned with the QoE, a prediction of a priority for a QoE measurement configuration, a prediction of a session status, or a prediction of an area of scope associated with the QoE.

[0282] In a twenty-fifth aspect, alone or in combination with one or more of the first through twenty -fourth aspects, process 2600 includes transmitting, to a third network node, based at least in part on information received from a user equipment, one or more QoE parameters associated with a QoE configuration.

[0283] In a twenty-sixth aspect, alone or in combination with one or more of the first through twenty -fifth aspects, the one or more parameters include a prediction of a time at which the QoE is to be triggered, a prediction of a MDT trace session that is to be aligned with the QoE, a prediction of a priority for a QoE measurement configuration, a prediction of a session status, or a prediction of an area of scope associated with the QoE.

[0284] In a twenty-seventh aspect, alone or in combination with one or more of the first through twenty-sixth aspects, process 2600 includes predicting, by a secondary node associated with the first network node, that a master node associated with the first network node is to receive a same QoE configuration as the secondary node.

[0285] In a twenty-eighth aspect, alone or in combination with one or more of the first through twenty-seventh aspects, process 2600 includes predicting, by the secondary node, at least one of asignaling radio bearer to be used for collecting QoE reports, whether the master node is to receive QoE reports, or an QoE configuration that is to be transmitted to the master node.

[0286] In a twenty-ninth aspect, alone or in combination with one or more of the first through twenty -eighth aspects, process 2600 includes predicting, by a master node associated with the first network node, that a secondary node associated with the first network node is to receive a same QoE configuration as the master node.

[0287] In a thirtieth aspect, alone or in combination with one or more of the first through twenty-ninth aspects, process 2600 includes predicting, by the master node, at least one of a signaling radio bearer to be used for collecting QoE reports, whether the secondary node is to receive QoE reports, or an QoE configuration that is to be transmitted to the secondary node.

[0288] Although Fig. 26 shows example blocks of process 2600, in some aspects, process 2600 may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in Fig. 26. Additionally, or alternatively, two or more of the blocks of process 2600 may be performed in parallel.

[0289] Fig. 27 is a diagram illustrating an example process 2700 performed, for example, at a UE or an apparatus of a UE, in accordance with the present disclosure. Example process 2700 is an example where the apparatus or the UE (e.g., UE 120) performs operations associated with signaling for quality of experience optimization.

[0290] As shown in Fig. 27, in some aspects, process 2700 may include receiving, from a network node, assistance information that includes a QoE prediction, wherein the QoE prediction is associated with at least one of a PDU session ID, a QFI, a DRB ID, or a slice ID (block 2710). For example, the UE (e.g., using reception component 2902 and / or communication manager 2906, depicted in Fig. 29) may receive, from a network node, assistance information that includes a QoE prediction, wherein the QoE prediction is associated with at least one of a PDU session ID, a QFI, a DRB ID, or a slice ID, as described above.

[0291] As further shown in Fig. 27, in some aspects, process 2700 may include performing a scheduling operation based at least in part on the assistance information (block 2720). For example, the UE (e.g., using communication manager 2906, depicted in Fig. 29) may perform a scheduling operation based at least in part on the assistance information, as described above.

[0292] Process 2700 may include additional aspects, such as any single aspect or any combination of aspects described below and / or in connection with one or more other processes described elsewhere herein.

[0293] In a first aspect, the QoE prediction is RAN-visible QoE prediction.

[0294] In a second aspect, alone or in combination with the first aspect, the assistance information further includes a predicted radio resource status.

[0295] In a third aspect, alone or in combination with one or more of the first and second aspects, process 2700 includes identifying at least one of an uplink throughput adaptation, a downlink throughput adaptation, a delay tolerance, a latency tolerance, or a mobility action based at least in part on the assistance information.

[0296] In a fourth aspect, alone or in combination with one or more of the first through third aspects, performing the scheduling operation comprises scheduling an uplink transmission based at least in part on the assistance information.

[0297] In a fifth aspect, alone or in combination with one or more of the first through fourth aspects, performing the scheduling operation comprises scheduling a movement or a handover based at least in part on the assistance information.

[0298] In a sixth aspect, alone or in combination with one or more of the first through fifth aspects, the assistance information is based at least in part on an AI / ML training and inference operation.

[0299] Although Fig. 27 shows example blocks of process 2700, in some aspects, process 2700 may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in Fig. 27. Additionally, or alternatively, two or more of the blocks of process 2700 may be performed in parallel.

[0300] Fig. 28 is a diagram of an example apparatus 2800 for wireless communication, in accordance with the present disclosure. The apparatus 2800 may be a network node, or a network node may include the apparatus 2800. In some aspects, the apparatus 2800 includes a reception component 2802, a transmission component 2804, and / or a communication manager 2806, which may be in communication with one another (for example, via one or more buses and / or one or more other components). In some aspects, the communication manager 2806 is the communication manager 150 described in connection with Fig. 1. As shown, the apparatus 2800 may communicate with another apparatus 2808, such as a UE or a network node (such as a CU, a DU, an RU, or a base station), using the reception component 2802 and the transmission component 2804.

[0301] In some aspects, the apparatus 2800 may be configured to perform one or more operations described herein in connection with Figs. 18A-25. Additionally, or alternatively, the apparatus 2800 may be configured to perform one or more processes described herein, such as process 2600 of Fig. 26. In some aspects, the apparatus 2800 and / or one or more components shown in Fig. 28 may include one or more components of the network node described in connection with Fig. 2. Additionally, or alternatively, one or more components shown in Fig. 28 may be implemented within one or more components described in connection with Fig. 2. Additionally, or alternatively, one or more components of the set of components may be implemented at least in part as software stored in one or more memories. For example, acomponent (or a portion of a component) may be implemented as instructions or code stored in a non-transitory computer-readable medium and executable by one or more controllers or one or more processors to perform the functions or operations of the component. In some aspects, the apparatus 2800 may correspond to the apparatus 1400 of Fig. 14 or another apparatus described herein.

[0302] The reception component 2802 may receive communications, such as reference signals, control information, data communications, or a combination thereof, from the apparatus 2808. The reception component 2802 may provide received communications to one or more other components of the apparatus 2800. In some aspects, the reception component 2802 may perform signal processing on the received communications (such as filtering, amplification, demodulation, analog-to-digital conversion, demultiplexing, deinterleaving, de-mapping, equalization, interference cancellation, or decoding, among other examples), and may provide the processed signals to the one or more other components of the apparatus 2800. In some aspects, the reception component 2802 may include one or more antennas, one or more modems, one or more demodulators, one or more MIMO detectors, one or more receive processors, one or more controllers / processors, one or more memories, or a combination thereof, of the network node described in connection with Fig. 2. In some aspects, the reception component 2802 and / or the transmission component 2804 may include or may be included in a network interface. The network interface may be configured to obtain and / or output signals for the apparatus 2800 via one or more communications links, such as a backhaul link, a midhaul link, and / or a fronthaul link.

[0303] The transmission component 2804 may transmit communications, such as reference signals, control information, data communications, or a combination thereof, to the apparatus 2808. In some aspects, one or more other components of the apparatus 2800 may generate communications and may provide the generated communications to the transmission component 2804 for transmission to the apparatus 2808. In some aspects, the transmission component 2804 may perform signal processing on the generated communications (such as filtering, amplification, modulation, digital-to-analog conversion, multiplexing, interleaving, mapping, or encoding, among other examples), and may transmit the processed signals to the apparatus 2808. In some aspects, the transmission component 2804 may include one or more antennas, one or more modems, one or more modulators, one or more transmit MIMO processors, one or more transmit processors, one or more controllers / processors, one or more memories, or a combination thereof, of the network node described in connection with Fig. 2. In some aspects, the transmission component 2804 may be co-located with the reception component 2802 in one or more transceivers.

[0304] The communication manager 2806 may support operations of the reception component 2802 and / or the transmission component 2804. For example, the communication manager 2806 may receive information associated with configuring reception of communications by the reception component 2802 and / or transmission of communications by the transmission component 2804. Additionally, or alternatively, the communication manager 2806 may generate and / or provide control information to the reception component 2802 and / or the transmission component 2804 to control reception and / or transmission of communications.

[0305] The communication manager 2806 may perform a QoE prediction based at least in part on an AI / ML training and inference operation. The transmission component 2804 may transmit, to a second network node, information regarding the QoE prediction and at least one of a PDU session ID, a QFI, a DRB ID, or a slice ID associated with the QoE prediction.

[0306] The communication manager 2806 may provide assistance information for performing the QoE prediction. The communication manager 2806 may provide assistance information for performing the QoE prediction. The transmission component 2804 may transmit, to a user equipment, assistance information that includes the information regarding the QoE prediction and that includes at least one of a predicted radio resource status, a protocol data unit session identifier, or a quality of service flow identifier. The transmission component 2804 may transmit, to a third network node, a handover request that includes the information regarding the QoE prediction and at least one of the PDU session ID, the QFI, the DRB ID, or the slice ID associated with the QoE prediction. The communication manager 2806 may provide an addition request or a modification request that includes the information regarding the QoE prediction and at least one of a predicted time during which a SCG bearer is to be used, a QoE associated with the SCG, or a radio resource usage associated with the SCG. The reception component 2802 may receive feedback that indicates one or more actual QoE metrics associated with the SCG bearer. The communication manager 2806 may provide a modification request that includes the information regarding the QoE prediction and at least one of a predicted time during which a SCG bearer is to be used, a QoE associated with the SCG, or a radio resource usage associated with the SCG. The reception component 2802 may receive feedback that indicates one or more actual QoE metrics associated with the SCG bearer.

[0307] The reception component 2802 may receive, from a third network node, assistance information that includes the information regarding the predicted QoE. The communication manager 2806 may adapt, at a radio access network associated with the first network node, one or more radio resource allocations based at least in part on the predicted QoE. The reception component 2802 may receive, from a user equipment, assistance information that includes the information regarding the predicted QoE. The communication manager 2806 may perform, at a radio access network associated with the first network node, a radio resource allocation or amobility decision based at least in part on the predicted QoE. The reception component 2802 may receive, from a third network node, one or more QoE parameters associated with a QoE configuration. The transmission component 2804 may transmit, to a third network node, based at least in part on information received from a user equipment, one or more QoE parameters associated with a QoE configuration. The communication manager 2806 may predict that a master node associated with the first network node is to receive a same QoE configuration as the secondary node. The communication manager 2806 may predict at least one of a signaling radio bearer to be used for collecting QoE reports, whether the master node is to receive QoE reports, or an QoE configuration that is to be transmitted to the master node. The communication manager 2806 may predict that a secondary node associated with the first network node is to receive a same QoE configuration as the master node. The communication manager 2806 may predict at least one of a signaling radio bearer to be used for collecting QoE reports, whether the secondary node is to receive QoE reports, or an QoE configuration that is to be transmitted to the secondary node.

[0308] The number and arrangement of components shown in Fig. 28 are provided as an example. In practice, there may be additional components, fewer components, different components, or differently arranged components than those shown in Fig. 28. Furthermore, two or more components shown in Fig. 28 may be implemented within a single component, or a single component shown in Fig. 28 may be implemented as multiple, distributed components. Additionally, or alternatively, a set of (one or more) components shown in Fig. 28 may perform one or more functions described as being performed by another set of components shown in Fig. 28.

[0309] Fig. 29 is a diagram of an example apparatus 2900 for wireless communication, in accordance with the present disclosure. The apparatus 2900 may be a UE, or a UE may include the apparatus 2900. In some aspects, the apparatus 2900 includes a reception component 2902, a transmission component 2904, and / or a communication manager 2906, which may be in communication with one another (for example, via one or more buses and / or one or more other components). In some aspects, the communication manager 2906 is the communication manager 140 described in connection with Fig. 1. As shown, the apparatus 2900 may communicate with another apparatus 2908, such as a UE or a network node (such as a CU, a DU, an RU, or a base station), using the reception component 2902 and the transmission component 2904.

[0310] In some aspects, the apparatus 2900 may be configured to perform one or more operations described herein in connection with Figs. 18A-25. Additionally, or alternatively, the apparatus 2900 may be configured to perform one or more processes described herein, such as process 2700 of Fig. 27. In some aspects, the apparatus 2900 and / or one or more components shown in Fig. 29 may include one or more components of the UE described in connection withFig. 2. Additionally, or alternatively, one or more components shown in Fig. 29 may be implemented within one or more components described in connection with Fig. 2. Additionally, or alternatively, one or more components of the set of components may be implemented at least in part as software stored in one or more memories. For example, a component (or a portion of a component) may be implemented as instructions or code stored in a non-transitory computer- readable medium and executable by one or more controllers or one or more processors to perform the functions or operations of the component. In some aspects, the apparatus 2900 may correspond to the apparatus 1600 of Fig. 16 or another apparatus described herein.

[0311] The reception component 2902 may receive communications, such as reference signals, control information, data communications, or a combination thereof, from the apparatus 2908. The reception component 2902 may provide received communications to one or more other components of the apparatus 2900. In some aspects, the reception component 2902 may perform signal processing on the received communications (such as filtering, amplification, demodulation, analog-to-digital conversion, demultiplexing, deinterleaving, de-mapping, equalization, interference cancellation, or decoding, among other examples), and may provide the processed signals to the one or more other components of the apparatus 2900. In some aspects, the reception component 2902 may include one or more antennas, one or more modems, one or more demodulators, one or more MIMO detectors, one or more receive processors, one or more controllers / processors, one or more memories, or a combination thereof, of the UE described in connection with Fig. 2.

[0312] The transmission component 2904 may transmit communications, such as reference signals, control information, data communications, or a combination thereof, to the apparatus 2908. In some aspects, one or more other components of the apparatus 2900 may generate communications and may provide the generated communications to the transmission component 2904 for transmission to the apparatus 2908. In some aspects, the transmission component 2904 may perform signal processing on the generated communications (such as filtering, amplification, modulation, digital-to-analog conversion, multiplexing, interleaving, mapping, or encoding, among other examples), and may transmit the processed signals to the apparatus 2908. In some aspects, the transmission component 2904 may include one or more antennas, one or more modems, one or more modulators, one or more transmit MIMO processors, one or more transmit processors, one or more controllers / processors, one or more memories, or a combination thereof, of the UE described in connection with Fig. 2. In some aspects, the transmission component 2904 may be co-located with the reception component 2902 in one or more transceivers.

[0313] The communication manager 2906 may support operations of the reception component 2902 and / or the transmission component 2904. For example, the communication manager 2906 may receive information associated with configuring reception of communications by thereception component 2902 and / or transmission of communications by the transmission component 2904. Additionally, or alternatively, the communication manager 2906 may generate and / or provide control information to the reception component 2902 and / or the transmission component 2904 to control reception and / or transmission of communications.

[0314] The reception component 2902 may receive, from a network node, assistance information that includes a QoE prediction, wherein the QoE prediction is associated with at least one of a PDU session ID, a QFI, a DRB ID, or a slice ID. The communication manager 2906 may perform a scheduling operation based at least in part on the assistance information. The communication manager 2906 may identify at least one of an uplink throughput adaptation, a downlink throughput adaptation, a delay tolerance, a latency tolerance, or a mobility action based at least in part on the assistance information.

[0315] The number and arrangement of components shown in Fig. 29 are provided as an example. In practice, there may be additional components, fewer components, different components, or differently arranged components than those shown in Fig. 29. Furthermore, two or more components shown in Fig. 29 may be implemented within a single component, or a single component shown in Fig. 29 may be implemented as multiple, distributed components.Additionally, or alternatively, a set of (one or more) components shown in Fig. 29 may perform one or more functions described as being performed by another set of components shown in Fig. 29.

[0316] The following provides an overview of some Aspects of the present disclosure:

[0317] Aspect 1 : A method of wireless communication performed by a network node, comprising: transmitting, to a core network entity, first signaling identifying a network slice prediction; receiving, from the core network entity and as a response to the first signaling, second signaling identifying a network slice configuration associated with the network slice prediction; and communicating with a user equipment (UE) using one or more network slices in accordance with the network slice configuration.

[0318] Aspect 2: The method of Aspect 1, wherein the first signaling identifying the network slice prediction includes information identifying at least one of: a predicted network slice capacity, a predicted network slice availability status, or a predicted network slice resource utilization parameter.

[0319] Aspect 3: The method of any of Aspects 1-2, wherein transmitting the first signaling comprises: transmitting information associated with a determination of a mobility action or radio resource management action associated with the network slice configuration, the information including information associated with at least one of: a predicted network slice resource, a predicted network slice support list, or a predicted network slice allowability status.

[0320] Aspect 4: The method of any of Aspects 1-3, further comprising: transmitting, to another network node, a data collection request; and receiving, from the other network node, a response message including network data; and wherein communicating with the UE using one or more network slices comprises: communicating with the UE using one or more network slices based at least in part on the network data.

[0321] Aspect 5: The method of Aspect 4, wherein the response message includes a predicted resource status or a measured resource status; and further comprising: generating a prediction relating to the one or more network slices using the predicted resource status or the measured resource status; and wherein communicating using the one or more network slices comprises: communicating using a network slice, of the one or more network slices, based at least in part on the prediction.

[0322] Aspect 6: The method of Aspect 5, wherein the predicted resource status or the measured resource status is reported at a network slice level or a network slice group level.

[0323] Aspect 7: The method of any of Aspects 1-6, further comprising: receiving, from another network node, a data collection request; and transmitting, to the other network node, a response message including a predicted resource status or a measured resource status.

[0324] Aspect 8: The method of any of Aspects 1-7, further comprising: receiving resource status information from a plurality of other network nodes; generating a prediction of a network slice metric using the resource status information; and transmitting response information identifying the prediction of the network slice metric.

[0325] Aspect 9: The method of Aspect 8, further comprising: transmitting a request for the resource status information; and wherein receiving the resource status information comprises: receiving the resource status information as a response to transmitting the request for the resource status information.

[0326] Aspect 10: The method of Aspect 9, wherein the resource status information includes one or more predicted network slice metrics.

[0327] Aspect 11 : The method of Aspect 9, wherein the resource status information is received in connection with a mobility event, a device context event, or a network configuration event.

[0328] Aspect 12: The method of Aspect 9, wherein the resource status information includes information identifying at least one of: a UE slice bit rate, a predicted UE slice bit rate, a predicted network slice allowability status, a predicted cell reselection priority,

[0329] a predicted RACH access prioritization, a predicted network slice resource status, a predicted network slice availability status, a predicted network slice support list, a predicted cell reselection priority, a predicted resource status, a predicted UE throughput, or a predicted UE latency.

[0330] Aspect 13: The method of any of Aspects 1-12, wherein the network node is configured to generate a prediction of a network slice metric using an artificial intelligence or machine learning (AI / ML) model.

[0331] Aspect 14: The method of any of Aspects 1-13, further comprising: using a received network slice metric prediction for a handover or mobility procedure.

[0332] Aspect 15: The method of any of Aspects 1-14, further comprising transmitting feedback information identifying an actual value for a predicted metric.

[0333] Aspect 16: The method of any of Aspects 1-15, wherein the network slice prediction is associated with at least one of: a configured time interval, a configured area,

[0334] a configured group of UEs, or the UE.

[0335] Aspect 17: The method of any of Aspects 1-16, further comprising transmitting the first signaling at least one of: directly to the core network entity or indirectly to the core network entity via another node.

[0336] Aspect 18: A method of wireless communication performed by a core network entity, comprising: receiving, from a network node, first signaling identifying a network slice prediction; transmitting, to the network node and as a response to the first signaling, second signaling identifying a network slice configuration associated with the network slice prediction; and transmitting, to a user equipment (UE) and as a response to the first signaling, third signaling identifying the network slice configuration to cause the UE to communicate with the network node using one or more network slices in accordance with the network slice configuration.

[0337] Aspect 19: The method of Aspect 18, wherein the first signaling identifying the network slice prediction includes information identifying at least one of: a predicted network slice capacity, a predicted network slice availability status, or a predicted network slice resource utilization parameter.

[0338] Aspect 20: The method of any of Aspects 18-19, further comprising: predicting one or more network slice metrics; and determining the network slice configuration based at least in part on the one or more network slice metrics, the one or more network slice metrics including at least one of: a predicted network accessibility metric, a predicted network integrity metric, or a predicted network slice resource utilization metric.

[0339] Aspect 21: The method of any of Aspects 18-20, further comprising: predicting one or more network slice metrics; and determining a mobility action or radio resource management action associated with the network slice configuration based at least in part on the one or more network slice metrics, the one or more network slice metrics including at least one of: a predicted network slice resource, a predicted network slice support list, or a predicted network slice allowability status.

[0340] Aspect 22: The method of Aspect 21, further comprising: adapting a subnet management parameter or a slice assurance in connection with the mobility action or the radio resource management action.

[0341] Aspect 23: A method of wireless communication performed by a user equipment (UE), comprising: receiving, from a core network entity and as a response to a network slice prediction, signaling identifying a network slice configuration associated with the network slice prediction; and communicating with a network node using one or more network slices in accordance with the network slice configuration.

[0342] Aspect 24: The method of Aspect 23, further comprising: receiving, from the network node, a network slice prediction; and performing a mobility action, cell selection action, or initial access action in accordance with the network slice prediction.

[0343] Aspect 25: The method of Aspect 24, wherein the network slice prediction includes at least one of: a predicted resource status, a predicted slice capacity, a predicted slice availability status, a predicted slice support list, a predicted cell reselection priority, or a predicted random access channel access priority.

[0344] Aspect 26: The method of any of Aspects 23-25, further comprising: generating a network slice prediction; and transmitting information identifying the network slice prediction to the network node, wherein the one or more network slices are based at least in part on the information identifying the network slice prediction.

[0345] Aspect 27 : The method of Aspect 26, wherein the network slice prediction is related to at least one of: a predicted resource status, a predicted UE throughput, or a predicted UE latency.

[0346] Aspect 28: A method of wireless communication performed by a first network node, comprising: performing a quality of experience (QoE) prediction based at least in part on an artificial intelligence and machine learning (AI / ML) training and inference operation; and transmitting, to a second network node, information regarding the QoE prediction and at least one of a protocol data unit (PDU) session identifier (ID), a quality of service flow identifier (QFI), a data radio bearer (DRB) ID, or a slice ID associated with the QoE prediction.

[0347] Aspect 29: The method of Aspect 28, wherein the information regarding the QoE prediction is configured to be provided to an application function for adapting an application buffer associated with an application.

[0348] Aspect 30: The method of any of Aspects 28-29, wherein the QoE prediction is radio access network (RAN)-visible QoE prediction.

[0349] Aspect 31 : The method of any of Aspects 28-30, further comprising providing, by a central unit of the first network node to a distributed unit of the first network node, assistance information for performing the QoE prediction.

[0350] Aspect 32: The method of Aspect 31, wherein the assistance information includes at least one of the PDU session ID, the QFI, the DRB ID, the slice ID, or a load metric.

[0351] Aspect 33: The method of Aspect 31, wherein performing the QoE prediction comprises: performing, at the distributed unit of the first network node, the QoE prediction based at least in part on the assistance information and the AI / ML training and inference operation; and providing the information regarding the QoE prediction to the central unit of the first network node.

[0352] Aspect 34: The method of any of Aspects 28-33, further comprising providing, by a distributed unit of the first network node to a central unit of the first network node, assistance information for performing the QoE prediction.

[0353] Aspect 35: The method of Aspect 34, wherein the assistance information includes at least one of a channel quality indicator, a buffer status, or a lower layer measurement performed at the distributed unit of the first network node.

[0354] Aspect 36: The method of Aspect 34, wherein performing the QoE prediction comprises: performing, at the central unit of the first network node, the QoE prediction based at least in part on the assistance information and the AEML training and inference operation; and providing the information regarding the QoE prediction to the distributed unit of the first network node.

[0355] Aspect 37: The method of any of Aspects 28-36, further comprising transmitting, to a user equipment, assistance information that includes the information regarding the QoE prediction and that includes at least one of a predicted radio resource status, a protocol data unit session identifier, or a quality of service flow identifier.

[0356] Aspect 38: The method of Aspect 37, wherein the assistance information is associated with at least one of an uplink throughput adaptation, a downlink throughput adaptation, a delay tolerance, a latency tolerance, or a mobility action.

[0357] Aspect 39: The method of any of Aspects 28-38, further comprising transmitting, to a third network node, a handover request that includes the information regarding the QoE prediction and at least one of the PDU session ID, the QFI, the DRB ID, or the slice ID associated with the QoE prediction.

[0358] Aspect 40: The method of any of Aspects 27-39, further comprising providing, by a master node associated with the first network node to a secondary node associated with the first network node, an addition request or a modification request that includes the information regarding the QoE prediction and at least one of a predicted time during which a secondary cell group (SCG) bearer is to be used, a QoE associated with the SCG, or a radio resource usage associated with the SCG.

[0359] Aspect 41 : The method of Aspect 40, wherein providing the addition request or the modification request comprises providing the addition request or the modification request based at least in part on a prediction by the master node that an application is to use the SCG bearer.

[0360] Aspect 42: The method of Aspect 40, further comprising receiving feedback that indicates one or more actual QoE metrics associated with the SCG bearer.

[0361] Aspect 43: The method of any of Aspects 28-42, further comprising providing, by a secondary node associated with the first network node to a master node associated with the first network node, a modification request that includes the information regarding the QoE prediction and at least one of a predicted time during which a secondary cell group (SCG) bearer is to be used, a QoE associated with the SCG, or a radio resource usage associated with the SCG.

[0362] Aspect 44: The method of Aspect 43, wherein providing the modification request comprises providing the modification request based at least in part on a prediction by the secondary node that an application is to use a master cell group bearer or a split bearer.

[0363] Aspect 45: The method of Aspect 43, further comprising receiving feedback that indicates one or more actual QoE metrics associated with the SCG bearer.

[0364] Aspect 46: The method of any of Aspects 28-45, further comprising receiving, from a third network node, assistance information that includes the information regarding the predicted QoE.

[0365] Aspect 47: The method of Aspect 19, further comprising adapting, at a radio access network associated with the first network node, one or more radio resource allocations based at least in part on the predicted QoE.

[0366] Aspect 48: The method of any of Aspects 27-20, further comprising receiving, from a user equipment, assistance information that includes the information regarding the predicted QoE.

[0367] Aspect 49: The method of Aspect 48, wherein the assistance information further includes at least one of a predicted uplink data volume or a predicted downlink data volume.

[0368] Aspect 50: The method of Aspect 48, further comprising performing, at a radio access network associated with the first network node, a radio resource allocation or a mobility decision based at least in part on the predicted QoE.

[0369] Aspect 51 : The method of any of Aspects 28-50, further comprising receiving, from a third network node, one or more QoE parameters associated with a QoE configuration.

[0370] Aspect 52: The method of Aspect 51, wherein the one or more parameters include a prediction of a time at which the QoE is to be triggered, a prediction of a minimization of drive test (MDT) trace session that is to be aligned with the QoE, a prediction of a priority for a QoE measurement configuration, a prediction of a session status, or a prediction of an area of scope associated with the QoE.

[0371] Aspect 53: The method of any of Aspects 28-52, further comprising transmitting, to a third network node, based at least in part on information received from a user equipment, one or more QoE parameters associated with a QoE configuration.

[0372] Aspect 54: The method of Aspect 53, wherein the one or more parameters include a prediction of a time at which the QoE is to be triggered, a prediction of a minimization of drive test (MDT) trace session that is to be aligned with the QoE, a prediction of a priority for a QoE measurement configuration, a prediction of a session status, or a prediction of an area of scope associated with the QoE.

[0373] Aspect 55: The method of any of Aspects 28-54, further comprising predicting, by a secondary node associated with the first network node, that a master node associated with the first network node is to receive a same QoE configuration as the secondary node.

[0374] Aspect 56: The method of Aspect 55, further comprising predicting, by the secondary node, at least one of a signaling radio bearer to be used for collecting QoE reports, whether the master node is to receive QoE reports, or an QoE configuration that is to be transmitted to the master node.

[0375] Aspect 57: The method of any of Aspects 28-56, further comprising predicting, by a master node associated with the first network node, that a secondary node associated with the first network node is to receive a same QoE configuration as the master node.

[0376] Aspect 58: The method of Aspect 57, further comprising predicting, by the master node, at least one of a signaling radio bearer to be used for collecting QoE reports, whether the secondary node is to receive QoE reports, or an QoE configuration that is to be transmitted to the secondary node.

[0377] Aspect 59: A method of wireless communication performed by a user equipment (UE), comprising: receiving, from a network node, assistance information that includes a quality of experience (QoE) prediction, wherein the QoE prediction is associated with at least one of a protocol data unit (PDU) session identifier (ID), a quality of service flow identifier (QFI), a data radio bearer (DRB) ID, or a slice ID; and performing a scheduling operation based at least in part on the assistance information.

[0378] Aspect 60: The method of Aspect 59, wherein the QoE prediction is radio access network (RAN)-visible QoE prediction.

[0379] Aspect 61: The method of any of Aspects 59-60, wherein the assistance information further includes a predicted radio resource status.

[0380] Aspect 62: The method of any of Aspects 59-61, further comprising identifying at least one of an uplink throughput adaptation, a downlink throughput adaptation, a delay tolerance, a latency tolerance, or a mobility action based at least in part on the assistance information.

[0381] Aspect 63: The method of any of Aspects 59-62, wherein performing the scheduling operation comprises scheduling an uplink transmission based at least in part on the assistance information.

[0382] Aspect 64: The method of any of Aspects 59-63, wherein performing the scheduling operation comprises scheduling a movement or a handover based at least in part on the assistance information.

[0383] Aspect 65: The method of any of Aspects 59-64, wherein the assistance information is based at least in part on an artificial intelligence and machine learning (AI / ML) training and inference operation.

[0384] Aspect 66: An apparatus for wireless communication at a device, the apparatus comprising one or more processors; one or more memories coupled with the one or more processors; and instructions stored in the one or more memories and executable by the one or more processors to cause the apparatus to perform the method of one or more of Aspects 1 -65.

[0385] Aspect 67: An apparatus for wireless communication at a device, the apparatus comprising one or more memories and one or more processors coupled to the one or more memories, the one or more processors configured to cause the device to perform the method of one or more of Aspects 1 -65.

[0386] Aspect 68: An apparatus for wireless communication, the apparatus comprising at least one means for performing the method of one or more of Aspects 1 -65.

[0387] Aspect 69: A non-transitory computer-readable medium storing code for wireless communication, the code comprising instructions executable by one or more processors to perform the method of one or more of Aspects 1-65.

[0388] Aspect 70: A non-transitory computer-readable medium storing a set of instructions for wireless communication, the set of instructions comprising one or more instructions that, when executed by one or more processors of a device, cause the device to perform the method of one or more of Aspects 1 -65.

[0389] Aspect 71: A device for wireless communication, the device comprising a processing system that includes one or more processors and one or more memories coupled with the one or more processors, the processing system configured to cause the device to perform the method of one or more of Aspects 1 -65.

[0390] Aspect 72: An apparatus for wireless communication at a device, the apparatus comprising one or more memories and one or more processors coupled to the one or more memories, the one or more processors individually or collectively configured to cause the device to perform the method of one or more of Aspects 1 -65.

[0391] The foregoing disclosure provides illustration and description but is not intended to be exhaustive or to limit the aspects to the precise forms disclosed. Modifications and variations may be made in light of the above disclosure or may be acquired from practice of the aspects.

[0392] As used herein, the term “component” is intended to be broadly construed as hardware or a combination of hardware and at least one of software or firmware. “Software” shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executables, threads of execution, procedures, or functions, among other examples, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise. As used herein, a “processor” is implemented in hardware or a combination of hardware and software. It will be apparent that systems or methods described herein may be implemented in different forms of hardware or a combination of hardware and software. The actual specialized control hardware or software code used to implement these systems or methods is not limiting of the aspects. Thus, the operation and behavior of the systems or methods are described herein without reference to specific software code, because those skilled in the art will understand that software and hardware can be designed to implement the systems or methods based, at least in part, on the description herein. A component being configured to perform a function means that the component has a capability to perform the function, and does not require the function to be actually performed by the component, unless noted otherwise.

[0393] As used herein, “satisfying a threshold” may, depending on the context, refer to a value being greater than the threshold, greater than or equal to the threshold, less than the threshold, less than or equal to the threshold, equal to the threshold, or not equal to the threshold, among other examples.

[0394] As used herein, a phrase referring to “at least one of’ a list of items refers to any combination of those items, including single members. As an example, “at least one of: a, b, or c” is intended to cover a, b, c, a + b, a + c, b + c, and a + b + c, as well as any combination with multiples of the same element (for example, a + a, a + a + a, a + a + b, a + a + c, a + b + b, a + c + c, b + b, b + b + b, b + b + c, c + c, and c + c + c, or any other ordering of a, b, and c).

[0395] No element, act, or instruction used herein should be construed as critical or essential unless explicitly described as such. Also, as used herein, the articles “a” and “an” are intended to include one or more items and may be used interchangeably with “one or more.” Further, as used herein, the article “the” is intended to include one or more items referenced in connection with the article “the” and may be used interchangeably with “the one or more.” Furthermore, as used herein, the terms “set” and “group” are intended to include one or more items and may be used interchangeably with “one or more.” Where only one item is intended, the phrase “only one” orsimilar language is used. Also, as used herein, the terms “has,” “have,” “having,” and similar terms are intended to be open-ended terms that do not limit an element that they modify (for example, an element “having” A may also have B). Further, the phrase “based on” is intended to mean “based on or otherwise in association with” unless explicitly stated otherwise. Also, as used herein, the term “or” is intended to be inclusive when used in a series and may be used interchangeably with “and / or,” unless explicitly stated otherwise (for example, if used in combination with “either” or “only one of’). It should be understood that “one or more” is equivalent to “at least one.”

[0396] Even though particular combinations of features are recited in the claims or disclosed in the specification, these combinations are not intended to limit the disclosure of various aspects. Many of these features may be combined in ways not specifically recited in the claims or disclosed in the specification. The disclosure of various aspects includes each dependent claim in combination with every other claim in the claim set.

Claims

WHAT IS CLAIMED IS:

1. A network node for wireless communication, comprising: one or more memories; and one or more processors coupled to the one or more memories, the one or more processors individually or collectively configured to: transmit, to a core network entity, first signaling identifying a network slice prediction; receive, from the core network entity and as a response to the first signaling, second signaling identifying a network slice configuration associated with the network slice prediction; and communicate with a user equipment (UE) using one or more network slices in accordance with the network slice configuration.

2. The network node of claim 1, wherein the one or more processors, to transmit the first signaling identifying the network slice prediction, are configured to: transmit the first signaling at least one of: directly to the core network entity, indirectly to the core network entity via another node.

3. The network node of claim 1, wherein the first signaling identifying the network slice prediction includes information identifying at least one of: a predicted network slice capacity, a predicted network slice availability status, or a predicted network slice resource utilization parameter.

4. The network node of claim 1, wherein the network slice prediction is associated with at least one of: a configured time interval, a configured area, a configured group of UEs, or the UE.

5. The network node of claim 1, wherein the one or more processors, to transmit the first signaling, are configured to:transmit information associated with a determination of a mobility action or radio resource management action associated with the network slice configuration, the information including information associated with at least one of: a predicted network slice resource, a predicted network slice support list, or a predicted network slice allowability status.

6. The network node of claim 1, wherein the one or more processors are further configured to: transmit, to another network node, a data collection request; and receive, from the other network node, a response message including network data; and wherein the one or more processors, to communicate with the UE using one or more network slices, are configured to: communicate with the UE using one or more network slices based at least in part on the network data.

7. The network node of claim 6, wherein the response message includes a predicted resource status or a measured resource status; and wherein the one or more processors are further configured to: generate a prediction relating to the one or more network slices using the predicted resource status or the measured resource status; and wherein the one or more processors, to communicate using the one or more network slices, are configured to: communicate using a network slice, of the one or more network slices, based at least in part on the prediction.

8. The network node of claim 7, wherein the predicted resource status or the measured resource status is reported at a network slice level or a network slice group level.

9. The network node of claim 1, wherein the one or more processors are further configured to: receive, from another network node, a data collection request; and transmit, to the other network node, a response message including a predicted resource status or a measured resource status.

10. The network node of claim 1, wherein the one or more processors are further configured to:receive resource status information from a plurality of other network nodes; generate a prediction of a network slice metric using the resource status information; and transmit response information identifying the prediction of the network slice metric.

11. The network node of claim 10, wherein the one or more processors are further configured to: transmit a request for the resource status information; and wherein the one or more processors, to receive the resource status information, are configured to: receive the resource status information as a response to transmitting the request for the resource status information.

12. The network node of claim 11, wherein the resource status information includes one or more predicted network slice metrics.

13. The network node of claim 11, wherein the resource status information is received in connection with a mobility event, a device context event, or a network configuration event.

14. The network node of claim 11, wherein the resource status information includes information identifying at least one of: a UE slice bit rate, a predicted UE slice bit rate, a predicted network slice allowability status, a predicted cell reselection priority, a predicted RACH access prioritization, a predicted network slice resource status, a predicted network slice availability status, a predicted network slice support list, a predicted cell reselection priority, a predicted resource status, a predicted UE throughput, or a predicted UE latency.

15. The network node of claim 1, wherein the network node is configured to generate a prediction of a network slice metric using an artificial intelligence or machine learning (AI / ML) model.

16. The network node of claim 1, wherein the one or more processors are further configured to: use a received network slice metric prediction for a handover or mobility procedure.

17. The network node of claim 1, wherein the one or more processors are further configured to: transmit feedback information identifying an actual value for a predicted metric.

18. A core network entity for wireless communication, comprising: one or more memories; and one or more processors coupled to the one or more memories, the one or more processors individually or collectively configured to: receive, from a network node, first signaling identifying a network slice prediction; transmit, to the network node and as a response to the first signaling, second signaling identifying a network slice configuration associated with the network slice prediction; and transmit, to a user equipment (UE) and as a response to the first signaling, third signaling identifying the network slice configuration to cause the UE to communicate with the network node using one or more network slices in accordance with the network slice configuration.

19. The core network entity of claim 18, wherein the first signaling identifying the network slice prediction includes information identifying at least one of: a predicted network slice capacity, a predicted network slice availability status, or a predicted network slice resource utilization parameter.

20. The core network entity of claim 18, wherein the one or more processors are further configured to: predict one or more network slice metrics; and determine the network slice configuration based at least in part on the one or more network slice metrics, the one or more network slice metrics including at least one of: a predicted network accessibility metric, a predicted network integrity metric, or a predicted network slice resource utilization metric.

21. The core network entity of claim 18, wherein the one or more processors are further configured to: predict one or more network slice metrics; and determine a mobility action or radio resource management action associated with the network slice configuration based at least in part on the one or more network slice metrics, the one or more network slice metrics including at least one of: a predicted network slice resource, a predicted network slice support list, or a predicted network slice allowability status.

22. The core network entity of claim 21, wherein the one or more processors are further configured to: adapt a subnet management parameter or a slice assurance in connection with the mobility action or the radio resource management action.

23. A user equipment (UE) for wireless communication, comprising: one or more memories; and one or more processors coupled to the one or more memories, the one or more processors individually or collectively configured to: receive, from a core network entity and as a response to a network slice prediction, signaling identifying a network slice configuration associated with the network slice prediction; and communicate with a network node using one or more network slices in accordance with the network slice configuration.

24. The UE of claim 23, wherein the one or more processors are further configured to: receive, from the network node, a network slice prediction; and perform a mobility action, cell selection action, or initial access action in accordance with the network slice prediction.

25. The UE of claim 24, wherein the network slice prediction includes at least one of: a predicted resource status, a predicted slice capacity, a predicted slice availability status, a predicted slice support list, a predicted cell reselection priority, or a predicted random access channel access priority.

26. The UE of claim 23, wherein the one or more processors are further configured to: generate a network slice prediction; and transmit information identifying the network slice prediction to the network node, wherein the one or more network slices are based at least in part on the information identifying the network slice prediction.

27. The UE of claim 26, wherein the network slice prediction is related to at least one of: a predicted resource status, a predicted UE throughput, or a predicted UE latency.

28. A first network node for wireless communication, comprising: one or more memories; and one or more processors coupled to the one or more memories, the one or more processors individually or collectively configured to cause the first network node to: perform a quality of experience (QoE) prediction based at least in part on an artificial intelligence and machine learning (AI / ML) training and inference operation; and transmit, to a second network node, information regarding the QoE prediction and at least one of a protocol data unit (PDU) session identifier (ID), a quality of service flow identifier (QFI), a data radio bearer (DRB) ID, or a slice ID associated with the QoE prediction.

29. The first network node of claim 28, wherein the information regarding the QoE prediction is configured to be provided to an application function for adapting an application buffer associated with an application.

30. The first network node of claim 28, wherein the QoE prediction is radio access network (RAN)-visible QoE prediction.

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