Applicability report for ai / ml for phy models

Abstract

Systems and methods are disclosed for reporting applicability information of Artificial Intelligence (AI) or Machine Learning (ML) model(s) to a functionality are disclosed. In one embodiment, a method performed by a User Equipment (UE) for reporting applicability information of at least one AI or ML model associated to a functionality comprises sending, to a network node, applicability information for at least one AI or ML model associated to a functionality with which the UE is configured and / or is being configured. In this manner, the UE is able to report, to the network, to report such AI or ML model applicability information in an improved manner as compared to the existing UE capability reporting framework. Corresponding embodiments of a UE are also disclosed. Embodiments of a network node and a method of operation thereof are also disclosed.

Description

APPLICABILITY REPORT FOR AI / ML FOR PHY MODELSRelated Applications

[0001] This application claims the benefit of provisional patent application serial number 63 / 494,670, filed April 6, 2023, the disclosure of which is hereby incorporated herein by reference in its entirety.Technical Field

[0002] The present disclosure relate to a wireless communication system and, more particularly, to User Equipment (UE) reporting of applicability information of at least one Artificial Intelligence (Al) / Machine Learning (ML)-model associated to a functionality.BackgroundAI / ML for PHY Study Item Rel-18

[0003] Artificial Intelligence (Al) and Machine Learning (ML) have been investigated as promising tools to optimize the design of air-interface in wireless communication networks in both academia and industry. Example use cases include using autoencoders for Channel State Information (CSI) compression to reduce the feedback overhead and improve channel prediction accuracy; using deep neural networks for classifying Line-Of-Sight (LOS) and Non-LOS (NLOS) conditions to enhance the positioning accuracy; and using reinforcement learning for beam selection at the network side and / or the User Equipment (UE) side to reduce the signaling overhead and beam alignment latency; using deep reinforcement learning to learn an optimal precoding policy for complex Multiple Input Multiple Output (MIMO) precoding problems.

[0004] In 3rd Generation Partnership Project (3GPP) New Radio (NR) standardization work, a new release 18 study item on AI / ML for NR air interface started in May 2022. This study item will explore the benefits of augmenting the air-interface with features enabling improved support of AI / ML based algorithms for enhanced performance and / or reduced complexity / overhead. Through studying a few selected use cases (CSI feedback, beam management, and positioning), this study item aims at laying the foundation for future air-interface use cases leveraging AI / ML techniques.

[0005] A possible high-level description of AI / ML model life cycle management (LCM) for Al on the Physical layer (PHY) can include the stages and data / signal flows depicted in Figure 1. A detailed description of different LCM stages can be found in R1 -2208908, “Discussion on general aspects of Al ML framework, Ericsson.”

[0006] Data Collection is a stage that collects and provides input data (raw data or pre- processed data) for Model Training, Model inference and Model Monitoring. AI / ML algorithm specific data preparation (e.g., data ingestion and data refinement) is not carried out in the Data Collection stage.

[0007] Model Training is a process that uses featured data in terms of training datasets and validation datasets to train an AI / ML model.

[0008] Model deployment is a process of converting an AI / ML model into an executable form and delivering it to a target UE for inference where model inference is to be performed.

[0009] Model inference is a process of using a deployed AI / ML model to produce a set of outputs based on a set of featured inputs.

[0010] Model monitoring is a process that monitoring drifts in data and model or monitoring performance metrics after the model has been deployed. Based on the monitored performance, decisions like model activation / deactivation / switching / fallback / selection can be taken.

[0011] In 3GPP, the following terminology is also assumed:• Include the following into a working list of terminologies to be used for RANI AI / ML air interface Study Item (SI) discussion.• The description of the terminologies may be further refined as the study progresses.• New terminologies may be added as the study progresses.• It is For Future Study (FFS) which subset of terminologies to capture into the TechnicalReport (TR).Working AssumptionNote: whether and how to indicate Functionality will be discussed separately.Summary

[0012] Systems and methods are disclosed for reporting applicability information of Artificial Intelligence (Al) or Machine Learning (ML) model(s) to a functionality are disclosed. In one embodiment, a method performed by a User Equipment (UE) for reporting applicability information of at least one Al or ML model associated to a functionality comprises sending, to a network node, applicability information for at least one Al or ML model associated to a functionality with which the UE is configured and / or is being configured. In this manner, the UE is able to report, to the network, to report such Al or ML model applicability information in an improved manner as compared to the existing UE capability reporting framework.

[0013] In one embodiment, the applicability information of the at least one Al or ML model associated to the functionality comprises any one or more of the following: an indication that the at least one Al or ML model associated to the functionality is not applicable, an indication that the functionality associated to the at least one Al or ML model is not applicable, a cause value, or a suggestion or recommendation or reconfiguration about how to make the at least one Al or ML model applicable.

[0014] In one embodiment, sending the applicability information comprises sending a complete message comprising the applicability information for the at least one Al or ML model associated to the functionality with which the UE is configured and / or is being configured. In one embodiment, the complete message is a Radio Resource Control (RRC) resume complete message, an RRC setup complete message, or an RRC reconfiguration complete message. In one embodiment, the method further comprises sending a request message to the network node and receiving a response message from the network node responsive to the request message, wherein sending the complete message comprising the applicability information comprises sending the complete message after receiving the response message. In one embodiment, the request message is an RRC resume request, the response message is an RRC resume message, and the complete message is an RRC resume complete message. In another embodiment, the request message is an RRC setup request, the response message is an RRC setup message, and the complete message is an RRC setup complete message. In one embodiment, the response message comprises configuration information for the functionality associated to the at least one Al or ML model or information for activation of the functionality associated to the at least one Al or ML model. In one embodiment, sending the complete message comprises sending the complete message responsive to the configuration information for the functionality associated to the at least one Al or ML model or the information for activation of the functionality associated to the at least one Al or ML model.

[0015] In one embodiment, the method further comprises receiving a reconfiguration message from the network node, wherein sending the complete message comprising the applicability information comprises sending the complete message after receiving the response message. In one embodiment, the reconfiguration message is an RRC reconfiguration message, and the complete message is an RRC reconfiguration complete message. In one embodiment, the reconfiguration message comprises configuration information for the functionality associated to the at least one Al or ML model or information for activation of the functionality associated to the at least one Al or ML model. In one embodiment, sending the complete message comprises sending the complete message responsive to the configuration information for the functionality associated to the at least one Al or ML model or the information for activation of the functionality associated to the at least one Al or ML model.

[0016] In one embodiment, sending the applicability information comprises sending the applicability information in or with a request message. In one embodiment, the request message in an RRC resume request.

[0017] Corresponding embodiments of a UE are also disclosed. In one embodiment, a UE for reporting applicability information of at least one Al or ML model associated to a functionality is adapted to send, to a network node, applicability information for at least one Al or ML model associated to a functionality with which the UE is configured and / or is being configured.

[0018] In one embodiment, a UE for reporting applicability information of at least one Al or ML model associated to a functionality comprises a communication interface comprising a transmitter and a receiver, and processing circuitry associated with the communication interface. The processing circuitry is configured to cause the UE to send, to a network node, applicability information for at least one Al or ML model associated to a functionality with which the UE is configured and / or is being configured.

[0019] Embodiments of a method performed by a network node are also disclosed. In one embodiment, a method performed by a network node for obtaining applicability information of at least one Al or ML model associated to a functionality comprises receiving, from a UE, applicability information for at least one Al or ML model associated to a functionality with which the UE is configured and / or is being configured.

[0020] In one embodiment, the applicability information of the at least one Al or ML model associated to the functionality comprises any one or more of the following: an indication that the at least one Al or ML model associated to the functionality is not applicable, an indication functionality associated to the at least one Al or ML model is not applicable, a cause value, or asuggestion or recommendation or reconfiguration about how to make the at least one Al or ML model applicable.

[0021] In one embodiment, receiving the applicability information comprises receiving a complete message comprising the applicability information for the at least one Al or ML model associated to the functionality with which the UE is configured and / or is being configured. In one embodiment, the complete message is any one or more of an RRC resume complete message, an RRC setup complete message, and an RRC reconfiguration complete message. In one embodiment, the method further comprises receiving) a request message from the UE and sending a response message to the UE responsive to the request message, wherein receiving the complete message comprising the applicability information comprises receiving the complete message after sending the response message. In one embodiment, the request message is an RRC resume request, the response message is an RRC resume message, and the complete message is an RRC resume complete message. In another embodiment, the request message is an RRC setup request, the response message is an RRC setup message, and the complete message is an RRC setup complete message.

[0022] In one embodiment, the response message comprises configuration information for the functionality associated to the at least one Al or ML model or information for activation of the functionality associated to the at least one Al or ML model. In one embodiment, receiving the complete message comprises receiving the complete message responsive to the configuration information for the functionality associated to the at least one Al or ML model or the information for activation of the functionality associated to the at least one Al or ML model.

[0023] In one embodiment, the method further comprises sending a reconfiguration message to the UE, wherein receiving the complete message comprising the applicability information comprises receiving the complete message after sending the response message. In one embodiment, the reconfiguration message is an RRC reconfiguration message, and the complete message is an RRC reconfiguration complete message. In one embodiment, the reconfiguration message comprises configuration information for the functionality associated to the at least one Al or ML model or information for activation of the functionality associated to the at least one Al or ML model. In one embodiment, receiving the complete message comprises receiving the complete message responsive to the configuration information for the functionality associated to the at least one Al or ML model or the information for activation of the functionality associated to the at least one Al or ML model.

[0024] In one embodiment, sending the applicability information comprises sending the applicability information in or with a request message. In one embodiment, the request message in an RRC resume request.

[0025] In one embodiment, the method further comprises performing one or more actions based on the applicability information.

[0026] Corresponding embodiments of a network node are also disclosed. In one embodiment, a network node for obtaining applicability information of at least one Al or ML model associated to a functionality is adapted to receive, from a UE, applicability information for at least one Al or ML model associated to a functionality with which the UE is configured and / or is being configured.

[0027] In one embodiment, a network node for obtaining applicability information of at least one Al or ML model associated to a functionality comprises a communication interface and processing circuitry associated with the communication interface. The processing circuitry is configured to cause the network node to receive, from a UE, applicability information for at least one Al or ML model associated to a functionality with which the UE is configured and / or is being configured.Brief Description of the Drawings

[0028] The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the disclosure, and together with the description serve to explain the principles of the disclosure.

[0029] Figure 1 illustrates a possible high-level description of Artificial Intelligence (Al) / Machine Learning (ML) model Life Cycle Management (LCM) for Al on the Physical layer (PHY);

[0030] Figure 2 illustrates a procedure in which a User Equipment (UE) reports applicability information of at least one AI / ML model associated to a functionality in association with a transition of the UE from inactive state to connected state, in accordance with an embodiment of the present disclosure;

[0031] Figure 3 illustrates a procedure in which a UE reports applicability information of at least one AI / ML model associated to a functionality in association with a transition of the UE from inactive state to connected state, in accordance with another embodiment of the present disclosure;

[0032] Figure 4 illustrates a procedure in which a UE reports applicability information of at least one AI / ML model associated to a functionality in association with a transition of the UE from idle state to connected state, in accordance with an embodiment of the present disclosure;

[0033] Figure 5 illustrates a procedure in which a UE reports applicability information of at least one AI / ML model associated to a functionality in association with a transition of the UE from idle state to connected state, in accordance with another embodiment of the present disclosure;

[0034] Figure 6 shows an example of a communication system in accordance with some embodiments;

[0035] Figure 7 shows a UE in accordance with some embodiments;

[0036] Figure 8 shows a network node in accordance with some embodiments;

[0037] Figure 9 is a block diagram of a host, which may be an embodiment of the host of Figure 6, in accordance with various aspects described herein;

[0038] Figure 10 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized; and

[0039] Figure 11 shows a communication diagram of a host communicating via a network node with a UE over a partially wireless connection in accordance with some embodiments.Detailed Description

[0040] The embodiments set forth below represent information to enable those skilled in the art to practice the embodiments and illustrate the best mode of practicing the embodiments.Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the disclosure and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure.

[0041] There currently exist certain challenge(s). The 3rdGeneration Partnership Project (3GPP) has identified the different phases of Life Cycle Management (LCM) for Artificial Intelligence (Al) / Machine Learning (ML) for the physical layer (PHY), as shown in Rl- 2208908. However, a new issue has been identified in in 3GPP TSG-RAN WG1 Meeting #112, and the following has been proposed in Rl- 2301868, “Final Summary of General Aspects of AIML Framework, Moderator (Qualcomm)”:To enable the development of models applicable to specific conditions (e.g., scenario, configuration, site, device type, etc.), study ways to associate a dataset with a specific applicability condition:Assistance signaling of NW-side applicability information for UE-side data collectionAssistance signaling of UE-side applicability information for NW-side data collectionNote: The study should consider the feasibility of disclosure of propriety information andAt least for UE-side models and UE-part of two-sided models, RANI to study- How to define and study a (set of) applicable conditions for functionalities / models. o Note: Applicable conditions may be used to enable development of scenario / configuration / [site]-specific models and, if needed, report the models’ applicability to the Network.- Whether and how UE reports a (set of) applicable conditions for supported functionalities (and if needed, for supported models) and / or supported set of functionalities.- Whether and how to define performance requirements (possibly as a part of applicable conditions) for functionality / models- Potential enhancement of legacy UE feature for reporting

[0042] In addition, RAN2 has also agreed on the following in RAN2 120 (Nov, Toulouse), for CSI:• RAN2 scope includes procedures, protocols, and signaling for two-sided CSI use case(s), e.g.1. Ensuring UE and gNB side models are configured / applied based on their applicable configurations / scenarios.2. Ensuring that models are matched properly at both UE and gNB sides, i.e., when a CSI encoder is used at the UE corresponding CSI decoder is used at the gNB3. Achieving simultaneous (de)activation and switching of the two-sided model

[0043] The problem to be solved by embodiments of the solution(s) described herein is that an AI / ML model for a given functionality (e.g., Beam Management, Channel State Information (CSI), positioning) may be applicable or not under certain conditions, but not all conditions. This is particularly true for User Equipment (UE)-sided AI / ML models.

[0044] For example, a UE capable of performing AI / ML for PHY, for a Beam Management functionality, may be equipped with an AI / ML model which has not been trained with certain data sets associated to one or more beam configuration(s) and / or one or more network areas (wide coverage, lower frequency layers). Thus, the accuracy in some of these scenarios may not be suitable so the model may be considered not applicable, or it may not even be possible to use the AI / ML model in certain conditions.

[0045] Even if the UE would report that in its UE capabilities, together with the fact that the UE is equipped with an AI / ML model for a given functionality / feature (e.g., CSI, Beam Management, positioning), there would still be various problems such as, e.g. :• AI / ML model applicability is dynamic and may depend on where the UE is. An AI / ML model may not work in one cell, but as the UE moves, it may work in another cell to which the UE moves.• AI / ML model applicability is dynamic and may change after the UE performs AI / ML model training for new conditions.• AI / ML model applicability may work only under certain configurations with which the network configures the UE.Such a dynamicity of the AI / ML model applicability does not qualify it to consider as a typical UE capability.

[0046] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. Embodiments of a method performed by a UE for reporting applicability information of at least one AI / ML-model associated to a functionality are disclosed. In one embodiment, the UE reports (e.g., in a complete message) applicability information of at least one AI / ML-model associated to a functionality with which the UE is configured and / or is being configured. This complements the UE capability at the network side with accurate information regarding whether a UE-sided AI / ML model functionality is applicable or not, i.e., whether an AI / ML model functionality of which the UE is capable may be used under certain conditions (e.g., in a given cell or set of serving cells, under a given UE current configuration, etc.).

[0047] Some example embodiments are as follows:• AL A method at a User Equipment (UE) for reporting applicability information of at least one AI / ML-model associated to a functionality the method comprising: o Reporting (e.g., in a complete message) applicability information of at least one AI / ML-model associated to a functionality with which the UE is configured and / or is being configured.• A2. A method according to Al, wherein the reporting is in a complete message, and the complete message comprises any one or more of: o An RRC Setup Complete; o An RRC Resume Complete; o An RRC Reconfiguration Complete.• A3. A method according to Al, wherein the applicability information of at least one AI / ML-model associated to a functionality comprises one or more of: o An indication that the AI / ML-model associated to a functionality is not applicable;o A cause value;• A4. A method according to Al, wherein the UE reports the applicability information of at least one AI / ML-model associated to a functionality after it transmits a request message, wherein the request message comprises any one or more of: o An RRC Resume Request message; o An RRC Setup message;• A5. A method according to Al, wherein the UE reports the applicability information of at least one AI / ML-model associated to a functionality in response to the reception of a message configuring the at least one AI / ML-model associated to a functionality, wherein the message comprises one or more of: o An RRC Resume message; o An RRC Setup message; o An RRC Reconfiguration message;• A6. A method at a User Equipment (UE) for reporting applicability information of at least one AI / ML-model associated to a functionality, the method comprising: o Reporting in or with a request message applicability information of at least one AI / ML-model associated to a functionality with which the UE is configured and / or is being configured.• A7. A method according to A6, wherein the UE request message comprises an RRC Resume Request.• A8. A method according to A7, wherein the UE includes the applicability information of at least one AI / ML-model associated to a functionality in a message multiplexed with the RRC Resume Request message.

[0048] Certain embodiments may provide one or more of the following technical advantage(s). One of the advantages of reporting the AI / ML-model applicability indication in a complete message to the network, e.g., during a state transition to CONNECTED, is that the applicability is something more dynamic than the UE capability and may vary depending on, e.g., scenarios, location, and UE configuration, which typically change during state transitions and / or mobility. Another advantage of such reporting is that it allows the UE to report its model needs / conditions with greater granularity compared to what eventually could be conveyed using the existing UE capability reporting framework. Additionally, this way of reporting eventually reduces indicating unnecessary “applicability-related” information as it is linked to the current scenario and / or UE configuration.1 Initial Considerations

[0049] In the present disclosure, the terms “ML-model”, “Al-model” or “AI / ML model” are interchangeable. An AI / ML model can be defined as a functionality or be part of a functionality that is deployed / implemented in a first node, e.g. a UE, in the case of a UE-sided model. An AI / ML model can be defined as a feature or part of a feature that is implemented / supported in a first node. This first node can indicate the feature version to a second node. If the ML-model is updated, the feature version may be changed by the first node.

[0050] An AI / ML-model may correspond to a function which receives one or more inputs (e.g., measurements, configured on(s)) and provide as outcome one or more predict! on(s) / estimates of a certain type (e.g., time-domain and / or spatial domain predictions of beam measurements). In one example, an ML-model may correspond to a function receiving as input the measurement of a reference signal at time instance tO (e.g., transmitted in beam-X) and provide as outcome the prediction of the reference signal in timer tO+T. In another example, an ML-model may correspond to a function receiving as input the measurement of a reference signal X (e.g., transmitted in beam-x), such as a Synchronization Signal Block (SSB) whose index is ‘x’, and provide as outcome the prediction of other reference signals transmitted in different beams, e.g. reference signal Y (e.g., transmitted in beam-x), such as an SSB whose index is ‘x’ . Another example is a ML model for aid in CSI estimation, in such a setup the ML-model will be specific ML-model with a UE and an ML-model within the network (NW) side. Jointly both ML- models provide joint network. The function of the ML-model at the UE would be to compress a channel input and the function of the ML-model at the NW side would be to decompress the received output from the UE. It is further possible to apply something similar for positioning wherein the input may be a channel impulse in some form related to a certain reference point (typically a TP (transmit point)) in time. The purpose on the NW side would be to detect different peaks within the impulse response, that reflects the multipath experienced by the radio signals arriving at the UE side. For positioning another way is to input multiple sets of measurements into an ML network and based on that derive an estimated position of the UE. Another ML- model would be an ML-model to be able to aid the UE in channel estimation or interference estimation for channel estimation. The channel estimation could for example be for the Physical Downlink Shared Channel (PDSCH) and be associated with specific set of reference signals patterns that are transmitted from the NW to the UE. The ML-model will then be part of the receiver chain within the UE and may not be directly visible within the reference signal pattern as such that is configured / scheduled to be used between the NW and UE. Another example of an ML-model for CSI estimation is to predict a suitable Channel Quality Indicator (CQI), PrecodingMatrix Indicator (PMI), Rank Indicator (RI), CSI-RS Resource Indicator (CRI), or similar value into the future. The future may be a certain number of slots after the UE has performed the last measurement or targeting a specific slot in time within the future.

[0051] In the present disclosure, the term “beam” may correspond to a spatial direction in which a signal is transmitted (e.g., by a network node) or received (e.g., by the UE), or a spatial filter applied to a signal which is transmitted or received. Thus, transmitting signals different beams could correspond to transmitting signals in different spatial directions. When the text refers to a “beam which is selected” it may refer to a beam index and / or a Reference Signal (RS) index or identifier, such as a SSB index, or a CSI Reference Signal (CSI-RS) resource identifier. Thus, selecting a beam may correspond to selecting an SSB, associated to an SSB index, or selecting a beam may correspond to selecting a CSI-RS, associated to a CSI-RS resource identifier.

[0052] The network (NW) in the present disclosure can be one of a generic NW node, gNodeB (gNB), base station, unit within the base station to handle at least some ML operation, relay node, core network node, a core network node that handle at least some ML operations, a device supporting Device-to-Device (D2D) communication, a Location Management Function (LMF) or other types of location server.

[0053] Another way to describe an AI / ML model is as follows:• In terms of the time / frequency / spatial domain, the output of the AI / ML model may be in a different time instance, or at a different frequency location, or at a different spatial direction, or a combination of time / frequency / space, than those of the model input. In one example (time domain), an ML-model may correspond to a function receiving as input the measurement of a reference signal at time instance tO (e.g., transmitted in beam-X) and provide as outcome the prediction of the reference signal in time instance tO+T. In another example (spatial domain), an ML-model may correspond to a function receiving as input the measurement of a reference signal X (e.g., transmitted in beam-x), such as an SSB whose index is ‘x’ , and provide as outcome the estimation / prediction of the link quality of other reference signals transmitted in different beams, e.g. reference signal Y (e.g., transmitted in beam-y).• In terms of model structure, the ML model may be fully contained within the UE, or split between the UE and network.• One example of split structure is a ML model for aid in CSI estimation, where a possible setup of the ML-model is a split model, which comprise a specific sub- ML-model within a UE and a sub- ML-model within the NW side which collaborate to generate a desiredoutcome for the overall ML model. The function of the sub- ML-model at the UE would be to compress a channel input and the function of the sub- ML-model at the NW side would be to decompress the received output from the UE. It is further possible to apply something similar for positioning wherein the input may be a channel impulse in some form related to a certain reference point in time. The purpose on the NW side would be to detect different peaks within the impulse response, that corresponds to different reception directions of radio signals at the UE side.• One example of ML contained within the UE is ML enhanced positioning, e.g., an ML model implemented in the UE takes as input multiple sets of measurements (each corresponding to a downlink (DL) signal from a different network node), and based on that derive an estimated position of the UE.• In terms of utility for physical layer, the ML model can be used for many functions, including: channel estimation, Line Of Sight (LOS) / Non-Line Of Sight (NLOS) classification, beam selection, position estimation of the UE, link adaption, etc. For example, an ML-model that is able to aid the UE in channel estimation which may or may not incorporate interference estimation. The channel estimation could for example be for the Physical Downlink Shared Channel (PDSCH) and be associated with specific set of reference signals patterns that are transmitted from the NW to the UE. The ML-model will then be part of the receiver chain within the UE and may not be directly visible within the reference signal pattern as such that is configured / scheduled to be used between the NW and UE. Another example of an ML-model for CSI estimation is to predict a suitable CQI, PMI, RI, or similar value into the future. The future may be a certain number of slots after the UE has performed the last measurement or targeting a specific slot in time within the future.

[0054] According to embodiments of the present disclosure, the UE is connected to the network (e.g., it may receive and transmit data and / or control information), i.e. in RRC CONNECTED state, and, being configured to perform a specific function by using an AI / ML-model (which may be referred as an AI / ML-model functionality, e.g. beam measurement predictions in time-domain). The specific functionality or function of an AI / ML model can for example be for one of the following examples, which could also be grouped to as a functionality area (one or more AI / ML-model functionality per area), as follows:• CSI reporting• Beam Management (BM)o In one option , there may be a BM functionality of an AI / ML-model(s) wherein an AI / ML model (e.g., at the UE) is capable of performing the inference of one or more time-domain predictions related to beam management. For example, the UE may be configured by the network to report (e.g., on Physical Uplink Control Channel (PUCCH) and / or Physical Uplink Shared Channel (PUSCH)) one or more time-domain predictions of SSB and / or CSI-RS and / or Phase Tracking Reference Signal (PTRS) measurements), e.g., by receiving a reporting configuration for AI / ML. o In one option, there may be a BM functionality of an AI / ML-model(s) wherein an AI / ML model (e.g., at the UE) is capable of performing the inference of one or more spatial-domain predictions related to beam management. o In one option, there may be a BM functionality of an AI / ML-model(s) wherein an AI / ML model (e.g., at the UE) is capable of performing the inference of both time and spatial-domain predictions related to beam management. o The UE is considered to be configured with an AI / ML functionality when at least one action related to that functionality is configured, e.g. the UE is configured to report predictions of beam measurement to one of its configured serving cell(s) and / or CSI(s) and / or SSB(s) of a serving cell.• Radio Resource Management (RRM) Measurement o Such as mobility measurement, i.e., Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Received Strength of Signal Indicator (RSSI), but also aspects related to radio link failure, e.g. Radio Link Failure (RLF) predictions. Further, Radio Link Monitoring (RLM) related timers (T310) and counters (N310 and N311) related predictions could also be considered here. o Such as the measurement framework defined in §5.5., 3GPP Technical Specification (TS) 38.331 (see, e.g., V16.12.0 and V17.4.0) comprising how the UE perform measurements (e.g., measurement configuration), what triggers measurement reports (e.g., event-triggered reports, periodic reports), and content to be included in measurement reports).• Link adaptation• Hybrid Automatic Repeat Request (HARQ) transmission• Data transmission• Data reception• Power control• Positioning of the UE• Random access transmission• Energy efficiency (e.g., Discontinuous Reception (DRX) settings)

[0055] The present disclosure refers to an AI / ML-model applicability for a functionality to refer to the property of an AI / ML-model of the functionality which may be configured and used by the UE (e.g., by a UE, in the case of UE-sided models) under certain conditions. An AI / ML- model for a functionality is considered applicable when the AI / ML model is able to:- Produce outputs, e.g. time-domain predictions of beam measurements and / or when the AI / ML model is able to produce outputs (e.g., time-domain predictions of beam measurements) with a suitable (e.g., good enough) accuracy. Accuracy can for example for BM comprise of: o Average Layer 1 (Ll)-Reference Signal Received Power (RSRP) difference of Top-1 predicted beam in comparison to the ideal beam (sweep all beams) o Cumulative Distribution Function (CDF)-percentile of Ll-RSRP difference for Top-1 predicted beam o Beam prediction accuracy (%) with 1 decibel (dB) margin for Top-1 beam. The UE may further receive from the NW a certain threshold to compare with the accuracy Key Performance Indicators (KPIs) to understand if the model is “applicable”.- Produce outputs that can include a certain confidence value of the prediction. If UE is able to estimate how confident each prediction is, the NW can on a per-input sample basis decide whether to use the prediction. That is, the model is “applicable” for some of its experienced data. For example, the UE can report o Predicted confidence interval (DL and / or uplink (UL)) where the predicted Ll- RSRP / Signal to Interference plus Noise Ratio (SINR) of a beam with a x probability resides. For example, the Ll-RSRP is with 95 % probability in SINR range of [8 dB,10 dB] o A predicted value can be reported as probability density function, using, e.g., Gaussian mixtures, the prediction is then reported using the parameters describing the mixed gaussian components. Its mean, variation, and component weight for each of the components.- Have collected and trained a model in the current UE configuration / scenario, where the model is not older than a certain threshold value T. The NW can determine T based on, e.g., deployment changes (new beam pattern, new cells etc.).

[0056] An AI / ML-model for a functionality is considered not applicable when the AI / ML model is NOT able to produce outputs, e.g. time-domain predictions of beam measurements and / or when the AI / ML model is able to produce outputs (e.g., time-domain predictions of beam measurements) BUT not with a suitable (e.g., good enough) accuracy. In case the UE has multiple AI / ML-model(s) for the same functionality, where different models are applicable for different scenarios, one may say that the AI / ML-model is not applicable for a functionality when none of the AI / ML-models for that functionality are applicable (otherwise, instead of reporting that the model is not applicable the UE switches to another model for that functionality which is applicable). Similarly, one may say that the AI / ML-model is applicable for a functionality when at least one of the AI / ML-models for that functionality is applicable when the UE is configured.

[0057] According to the present disclosure, there may be different reasons for an AI / ML- model not being applicable such as:- Location / geographical area o An AI / ML-model for a functionality may be applicable in a first area of a network the UE is registered to, but the same AI / ML-model for the functionality might NOT be applicable in a second area of a network the UE is registered to. For example, when the UE camps in a cell A while in IDLE and transitions to CONNECTED, the AI / ML-model for the functionality is applicable for cell A. However, if the UE in IDLE performs cell reselection and moves to a cell C, the AI / ML model for the functionality may not be applicable. o One reason for that could be the training data set used for training the AI / ML model, which could be representative of an area, but not another area. o Another reason could be due to regulatory restrictions, e.g., AI / ML-models for a functionality could only be used in certain locations. o A “location” in this context may comprises one or more of the following:■ One or more cell(s), e.g., defined by one or more cell identified s) such as Global Cell Identifiers.■ One or more Tracking Area(s)■ One or more Tracking Area code(s)■ One or more Registration Area(s)■ One or more Radio Access Network (RAN)-based Notification Area■ GPS location and / or delimited area■ Within the coverage area of a list of Wireless Local Area Network (WLAN) Access Points (APs)■ Within the coverage area of a list of Bluetooth beacons■ Deployment type, e.g. small cells, large cells, indoor, outdoor■ A list of Public Land Mobile Networks (PLMNs) and / or Non-Public Network (NPN) Identifiers (for example, a NPN Identifier could indicate a specific factory setting and any training data used in the factory could be applicable only inside such a factory and not elsewhere)- UE configuration o An AI / ML-model for a functionality may be applicable when the UE is configured with a first configuration, e.g. represented by an RRCReconfiguration(l), including lower layers, bearer configuration, measurement configuration(s), etc. However, the same AI / ML-model for the functionality might NOT be applicable when the UE is configured with a second configuration, e.g. represented by an RRCReconfiguration(2), including lower layers, bearer configuration, measurement configuration(s), etc. For example, when the UE transitions to CONNECTED and receives a configuration equivalent to RRCReconfiguration(l), the AI / ML-model for the functionality is applicable. However, if the UE transitions to CONNECTED and receives a configuration equivalent to RRCReconfiguration(2), the AI / ML- model for the functionality is considered NOT applicable. o One reason for that could be the training data set used for training the AI / ML model, for a given UE configuration, may lead to a model which does not produce accurate outputs (in inference) for some UE configuration(s); for example, the AI / ML-model for the functionality is applicable for predictions of measurements in a first set of frequencies (e.g. Frequency Range 1 (FR1) and / or fO, fl, f2), but not for predictions of measurements in a second set of frequencies (e.g. FR2 and / or f7, f8, f9).Another reason could be that the AI / ML model is trained using a certain CSL RS periodicity, for example the UE expects 20ms periodicity to be able to perform a forecast of the channel for the next 10ms (in-betweenmeasurements), however, the UE maybe is configured with aperiodic CSI-RS or 40ms periodicity making the model inaccurate. o Another reason could be that a new configuration might not be applicable to existing configuration parameters already set in the UE, e.g., conflict with non- AI / ML related features, or be related to hardware / software limitations.- Network configuration o Single beam vs multi-beam o Configuration information broadcasted by the network o Beamforming pattern o For example, the NW indicates that itself performs beam predictions, CSI predictions or positioning using AI / ML, hence the UE should not activate such features- Mobility characteristics o An AI / ML-model for a functionality may be applicable when the UE’s mobility characteristics is of one category, but the same AI / ML-model for the functionality might NOT be applicable in a second category of the mobility characteristics. For example, a UE might be slow speed (as classified by the UE based on its sensor based measurements and / or a network defined criterion like speedStateReselectionPars defined in RRC specification, TS 38.331 vl7.3) and the training data used to train an AI-ML model in exclusively in this mobility class. However, at the time of transitioning from IDLE / Inactive if the UE is in a different mobility class, then the AI / ML model for the functionality may not be applicable.■ For example, temporal beam predictions are only applicable based on the UE mobility, for example when UE is moving at constant / near- constant speed. Or based on the type of mobility, e.g., in vehicle or train which can provide a more predictable trajectory. Or based on whether the UE is rotating or not.

[0058] How to determine whether an AI / ML model is or is not applicable?

[0059] The determination of whether a model is applicable could be done using the following approaches.UE-based method: For example, the UE determines if a model is applicable by comparing the experienced configuration, location, and / or mobility with the training data used by the model.• NW-assisted method: For example, the NW always activates the UE-sided models, then, based on model monitoring, the NW can check the KPIs for the model and if the performance is inadequate. The NW signals to the UE that the model is not applicable for the current configuration, and the UE updates the model applicability. The NW can also, in another embodiment, indicate to the UE certain requirements for the model such as, for example, the NW requires a model that can provide reliable confidence measures for its predictions or, as another example, what accuracy the model needs to fulfil.

[0060] Inactive state

[0061] In one option, an Inactive state corresponds to a protocol state in which the UE stores a UE Context and considers the connection (e.g., one or more bearers) as suspended. The UE restores the UE context when it attempts to resume the connection and transitions to a Connected state. When the UE determines to resume the connection the UE transmits a Resume Request message (e.g., RRCResumeRequest, or RRCResumeRequestl) and may receive in response an RRC Resume message, based on which the UE enters a CONNECTED state. In this context, the UE Inactive AS Context may correspond to the UE Context which is stored when the connection is suspended and restored when the connection is resumed.2 Embodiments related to Transition from Inactive state

[0062] As illustrated in the example of Figure 2, in a set of embodiments, a UE in an Inactive state (e.g. RRC INACTIVE) transmits to the network (e.g. a gNodeB) a request message (e.g. RRC Resume Request message, like RRCResumeRequest or RRCResumeRequestl as defined in 3GPP Technical Specification (TS) 38.331) (step 200) and receives a response message (e.g.RRC Resume, like an RRCResume message as defined in 3GPP TS 38.331) (step 208) based on which the UE enters a Connected state (RRC CONNECTED), wherein the response message (e.g. RRCResume) configures (and / or activates) or tries to configure the UE with one or more AI / ML-model functionality(ies).- For example, one AI / ML functionality may correspond to the UE being configured to report and / or perform (e.g., to one of its configured serving cell(s)) one or more timedomain predictions of SSB and / or CSI-RS measurements (e.g., of resources associated to one of its configured serving cell(s)).- For example, one AI / ML functionality may correspond to the UE being configured to report and / or perform (e.g., to one of its configured serving cell(s)) one or more spatial-domain predictions of SSB and / or CSI-RS measurements (e.g., of resources associated to one of its configured serving cell(s)).- For example, one AI / ML functionality may correspond to the UE being configured to report and / or perform CSI predictions;- For example, one AI / ML functionality may correspond to the UE being configured to report and / or perform positioning predictions.

[0063] According to the method, one alternative would be for the NW to include in its response message (e.g., in step 208) an explicit configuration which further allows the UE to report the applicability -related information of the AI / ML-model functionality(ties) in the complete message.

[0064] While in another alternative, the UE is allowed to proactively report the applicability- related information of the AI / ML-model functionality(ties) in the complete message (step 212).

[0065] In response to being configured with one or more AI / ML-model functionality(ies) (e.g., in step 208), the UE transmits a complete message to the network (e.g. an RRC Resume Complete message) including at least one indication indicating that_at least one of the configured (or to be configured / to be activated) AI / ML-model functionality(ies) which is being configured (and / or activated) is not applicable (when the UE determines that the AI / ML-model is not applicable) (step 212). According to the method, in one option, before the UE transmits the complete message to the network (e.g. an RRC Resume Complete message) including at least one indication indicating that at least one of the configured (or to be configured / to be activated) AI / ML-model functionality(ies) which is being configured (and / or activated) is not applicable (when the UE determines that the AI / ML-model is not applicable) (e.g., in step 212), the UE determines whether or not the AI / ML-model functionality(ies) is applicable or not (step 210).

[0066] According to the method, in one option, when the UE transmits the RRC Resume Request message to the target network node (e.g., target gNodeB) (e.g., in step 200), the UE is not configured with the AI / ML-model functionality: i.e., the UE does not have in its stored UE Context (UE Access Stratum Inactive context) the configuration of the AI / ML-model functionality. In other words, the AI / ML-model functionality configuration (e.g., reporting configuration for the UE to report one or more time-domain predictions of beam measurements such as predictions of Synchronization Signal (SS)-Reference Signal Received Power (RSRP) for a serving cell) is explicitly included in the Radio Resource Control (RRC) Resume message (e.g., in step 208). In response to that configuration, the UE determines whether the AI / ML-model functionality is applicable or not (e.g., in step 210).

[0067] According to the method, in another option, when the UE transmits the RRC Resume Request message to the target network node (e.g., target gNodeB)(e.g., in step 200), the UE is configured with the AI / ML-model functionality: i.e., the UE has in its stored UE Context (UEAccess Stratum Inactive context) the configuration of the AI / ML-model functionality. That AI / ML model functionality is restored when the UE receives the RRC Resume message (e.g., in step 208), and the UE determines whether the restored AI / ML-model functionality, under the configuration resulting from the UE applying the RRC Resume message, is applicable or not (e.g., in step 210).

[0068] According to the method, in one option, when the UE determines that the AI / ML- model functionality is NOT applicable (e.g., in step 210), the UE includes the indication in the RRC Resume Complete message and transmits the RRC Resume Complete message to the target network node (e.g., in step 212), wherein the indication is indicating that the configured (or to be configured / to be activated) AI / ML-model functionality(ies), whose configuration is included in the RRC Resume, is not applicable (e.g. under current scenario(s) and / or configuration).

[0069] According to the method, in one option, when the UE determines that the AI / ML- model functionality is applicable (e.g., in step 210), the UE does not include the indication in the RRC Resume Complete message and transmits the RRC Resume Complete message to the target network node (e.g., in step 212). When the target network node receives the RRC Resume Complete message with the indication absent, the target network node understands that the AI / ML model functionality which has been configured is applicable.

[0070] As illustrated in the example of Figure 3, in a set of embodiments, a UE is in an Inactive state (e.g. RRC INACTIVE), and, when the UE transmits the RRC Resume Request message to the target network node (e.g. target gNodeB) (e.g., in step 304), the UE is configured with at least one AI / ML-model functionality: i.e., the UE has in its stored UE Context (UE Access Stratum Inactive context) the configuration of at least one AI / ML-model functionality. The UE restores that AI / ML model functionality when the UE needs to transmit the RRC Resume Request (or during resume preparation) (step 300), and the UE determines whether the restored AI / ML-model functionality is applicable or not, under the current UE configuration (restored) and existing condition(s), e.g. target cell in which the UE is trying to resume (step 302).In one option, when the UE determines that the AI / ML-model functionality is NOT applicable in step 302, the UE includes the indication in the RRC Resume Request message and transmits the RRC Resume Request message to the target network node in step 304, wherein the indication is indicating that a restored AI / ML-model functionality(ies) in the UE context is not applicable (e.g., under current scenario(s) and / or configuration). The RRC Resume Request enables the target network to retrieve the UE context and, after receiving the UE context, and having previously received theindication in the RRC Resume Request, the target network node has the possibility to release or deactivate the AI / ML functionality which has been reported as not applicable in the RRC Resume Request.In another option, when the UE determines that the AI / ML-model functionality is NOT applicable in step 302, the UE includes the indication in a message to be multiplexed with the RRC Resume Request message and transmits the RRC Resume Request message to the target network node in step 304, wherein the indication is indicating that a restored AI / ML-model functionality(ies) in the UE context is not applicable (e.g. under current scenario(s) and / or configuration). The RRC Resume Request enables the target network to retrieve the UE context and, after receiving the UE context, and having previously received the indication in the RRC Resume Request, the target network node has the possibility to release or deactivate the AI / ML functionality which has been reported as not applicable in the RRC Resume Request. In another option, when the UE determines that the AI / ML-model functionality is NOT applicable in step 302, the UE deactivates the AI / ML-model functionality when it restores it. That action may be combined with the reporting of the indication of the non-applicability in the RRC Resume Request in step 304. The benefit here is that if the target network node does not want to bother about the non-applicable AI / ML- model(s), it would not have to, as they would anyways be deactivated by the UE. In another option, when the UE determines that the AI / ML-model functionality is NOT applicable in step 302, the UE releases the configuration(s) of the AI / ML-model functionality. That action may be combined with the reporting of the indication of the non-applicability in the RRC Resume Request in step 304. The benefit here is that if the target network node does not want to bother about the non-applicable AI / ML- model(s) it would not have to, as they would be released by the UE.According to the method, in one option, when the UE determines that the AI / ML- model functionality is applicable in step 302, the UE does not include the indication in nor with the RRC Resume Request message in step 304. The UE transmits the RRC Resume Request message to the target network node. When the target network node receives the RRC Resume Request message with the indication absent, the target network node understands that the AI / ML model functionality which has been restored is applicable.3 Embodiments related to Transition from Idle state

[0071] Figure 4 illustrates an example for a set of embodiments in which a UE in an Idle state (e.g. RRC IDLE) transmits to the network (e.g. a gNodeB) a request message (e.g. RRC Setup Request message, like RRCSetupRequest as defined in 3GPP TS 38.331) (step 400) and receives a response message (e.g. RRC Setup, like an RRCSetup message as defined in TS 38.331) (step 404) based on which the UE enters a Connected state (RRC CONNECTED), wherein the response message (e.g. RRCSetup) configures (and / or activates) or tries to configure the UE with one or more AI / ML-model functionality(ies).In one option, the one or more AI / ML-model functionality(ies) may be configured to the UE before security is activated.In one option, the target network node (e.g., target gNodeB in which the UE is trying to transition to CONNECTED) is able to retrieve the UE capabilities regarding AI / ML-model(s) functionalities based on information about the UE within the RRC Setup Request, such as a UE identifier or a part of it. For example, bits of the UE’s identifier, such as bits of the 5G S-TMSI allocated when the UE is registered, in case the UE is registered.- For example, one AI / ML functionality may correspond to the UE being configured to report and / or perform (e.g., to one of its configured serving cell(s)) one or more timedomain predictions of SSB and / or CSLRS measurements (e.g., of resources associated to one of its configured serving cell(s)).- For example, one AI / ML functionality may correspond to the UE being configured to report and / or perform (e.g., to one of its configured serving cell(s)) one or more spatial-domain predictions of SSB and / or CSLRS measurements (e.g., of resources associated to one of its configured serving cell(s)).- For example, one AI / ML functionality may correspond to the UE being configured to report and / or perform CSI predictions;- For example, one AI / ML functionality may correspond to the UE being configured to report and / or perform positioning predictions.

[0072] According to the method, one alternative would be for the NW to include in its response message (e.g., in step 404) an explicit configuration which further allows the UE to report the applicability -related information of the AI / ML-model functionality(ties) in the complete message in step 408.

[0073] While in another alternative, the UE is allowed to proactively report the applicability- related information of the AI / ML-model functionality(ties) in the complete message.

[0074] In response to being configured with one or more AI / ML-model functionality(ies), in the RRC Setup message, the UE transmits a complete message to the network, such as an RRC Setup Complete message, including at least one indication indicating that at least one of the configured (or to be configured / to be activated) AI / ML-model functionality(ies) which is being configured (and / or activated) is not applicable (when the UE determines that the AI / ML-model is not applicable) (step 408).

[0075] According to the method, in one option, before the UE transmits the complete message to the network, e.g. an RRC Setup Complete message, including at least one indication indicating that at least one of the configured (or to be configured / to be activated) AI / ML-model functionality(ies) which is being configured (and / or activated) is not applicable (when the UE determines that the AI / ML-model is not applicable) in step 408, the UE determines whether or not the AI / ML-model functionality(ies) is applicable or not (step 406).

[0076] According to the method, in one option, when the target network node receives the RRC Setup Complete message in step 408, the target network node becomes aware of the UE capabilities in terms of AI / ML-model functionality (as capabilities may be retrieved from the Core network) and its applicability status (e.g. based on the indication or the absence of the indication) for the UE’s current configuration (e.g. according to the RRC Setup) and conditions (e.g. radio environment, target cell and / or area the UE is connected). Thus, the target network node is able to determine whether it configures (or activates) or not the AI / ML-model functionality in the RRC Reconfiguration which also configures the Data Radio Bearers for the UE which has just transitioned to RRC CONNECTED.500.

[0077] As illustrated in the example of Figure 5, in a set of embodiments, a UE in an Idle state (e.g. RRC IDLE) transmits to the network (e.g. a gNodeB) a request message (e.g. RRC Setup Request message, like RRCSetupRequest as defined in TS 38.331) (step 500), receives a response message (e.g. RRC Setup, like an RRCSetup message as defined in TS 38.331) based on which the UE enters a Connected state (RRC CONNECTED) (step 504), activates the Access Stratum (AS) security (see, e.g., 506-522), and receives an RRC Reconfiguration message (e.g. RRCReconfiguration) which configures (and / or activates) or tries to configure the UE with one or more AI / ML-model functionality(ies) (step 524).In one option, the one or more AI / ML-model functionality(ies) may be configured to the UE after security is activated.In one option, the target network node (e.g., target gNodeB in which the UE is trying to transition to CONNECTED) is able to retrieve the UE capabilities regardingAI / ML-model(s) functionalities when the UE context is setup at the target network node, e.g. when the target gNodeB receives the Initial UE Context Setup Request.- For example, one AI / ML functionality may correspond to the UE being configured to report and / or perform (e.g., to one of its configured serving cell(s)) one or more timedomain predictions of SSB and / or CSI-RS measurements (e.g., of resources associated to one of its configured serving cell(s)).- For example, one AI / ML functionality may correspond to the UE being configured to report and / or perform (e.g., to one of its configured serving cell(s)) one or more spatial-domain predictions of SSB and / or CSI-RS measurements (e.g., of resources associated to one of its configured serving cell(s)).- For example, one AI / ML functionality may correspond to the UE being configured to report and / or perform CSI predictions;- For example, one AI / ML functionality may correspond to the UE being configured to report and / or perform positioning predictions.

[0078] In response to being configured with one or more AI / ML-model functionality(ies), in the first RRC Reconfiguration message when the UE transitions to CONNECTED (see, e.g., step 524), the UE transmits a complete message to the network, such as an RRC Reconfiguration Complete message, including at least one indication indicating that at least one of the configured (or to be configured / to be activated) AI / ML-model functionality(ies) which is being configured (and / or activated) is not applicable (when the UE determines that the AI / ML-model is not applicable) (step 528).

[0079] According to the method, in one option, before the UE transmits the complete message to the network, e.g. an RRC Reconfiguration Complete message, including at least one indication indicating that at least one of the configured (or to be configured / to be activated) AI / ML-model functionality(ies) which is being configured (and / or activated) is not applicable (when the UE determines that the AI / ML-model is not applicable) in step 528, the UE determines whether or not the AI / ML-model functionality(ies) is applicable or not (step 526).

[0080] The set of embodiments in Section 3 describe the scenario in which the UE is transitioning to IDLE and receives the first RRC Reconfiguration which includes the configuration(s) of at least one AI / ML-model functionality, e.g. UE configured to report and / or perform one or more time / spatial-domain predict! on(s) of beam measurements. However, the methods are also applicable for at least the case in which the UE is in RRC CONNECTED, and receives any RRC Reconfiguration which configures (e.g., add and / or modify) an AI / ML-model functionality, so that the indication regarding the applicability of the AI / ML-model functionalitybeing configured and / or added and / or modified, may be included in the RRC Reconfiguration complete, which is not necessarily the first after the UE transitions from IDLE.4 Indication that the AI / ML-Model Functionality is not Applicable

[0081] According to one embodiment, the UE reports applicability information of at least one AI / ML-model associated to a functionality. According to one embodiment, the UE reports in a complete message applicability information of at least one AI / ML-model associated to a functionality the UE is configured with and / or is being configured with.

[0082] In Sections 2 and 3 above, the applicability information of at least one AI / ML-model associated to a functionality is presented as an indication that the AI / ML-model associated to a functionality is not applicable. However, that should not be limited to that and, in this section, other examples of the applicability information of the AI / ML-model associated to a functionality which may be reported are presented, in addition to the indication in Sections 2 and 3 and / or instead of the indication in Sections 2 and 3. Herein, the term “indication” or applicability information are used interchangeably.

[0083] Thus, according to one embodiment, the applicability information the UE reports (or the indication that an AI / ML model functionality is not applicable) comprises any one or more of the following:1) An indication that an AI / ML-model functionality the UE is configured with or is being configured with is not applicable. o Alternatively, an indication that an AI / ML-model functionality the UE is configured with or is being configured with is applicable. The absence of this indication indicates that the AI / ML-model functionality is not applicable.2) Multiple indication(s) that multiple AI / ML-model functionality(ies) the UE is configured with or is being configured with is not applicable. o In this case, the UE may be capable of a plurality of AI / ML-model functionalities, such as■ AI / ML model functionality 1) a BM functionality of an AI / ML- model(s) wherein an AI / ML model (e.g., at the UE) is capable of performing the inference of one or more time-domain predictions related to beam management. For example, the UE may be configured by the network to report (e.g., on PUSCH and / or Physical Uplink Control Channel (PUCCH)) one or more time-domain predictions of SSB and / or CSLRS measurements), e.g., by receiving a reporting configuration for AI / ML.■ AI / ML model functionality 2) a BM functionality of an AI / ML- model(s) wherein an AI / ML model (e.g., at the UE) is capable of performing the inference of one or more spatial-domain predictions related to beam management.■ AI / ML model functionality 3) a BM functionality of an AI / ML- model(s) wherein an AI / ML model (e.g., at the UE) is capable of performing the inference of both time and spatial-domain predictions related to beam management. Then, the UE may be configured with these multiple AI / ML-model functionalities a), b), c), e.g. all these different reports described in a), b), c). Then, the UE determines for each whether the functionality is applicable or not, and includes the indication(s) for the one(s) which are not applicable. In one option, each AI / ML-model functionality configuration is associated to at least one identifier, e.g. a configuration ID, such as reporting configuration ID. When the UE indicates the AI / ML-model functionality which is NOT applicable the UE includes that identifier. For example, the UE may have been configured with reporting configuration(s) or prediction reporting configuration(s) as follows:■ For a)• Prediction reporting configuration ID=1• Prediction reporting configuration ID=2■ For b)• Prediction reporting configuration ID=3• Prediction reporting configuration ID=4■ For c)• Prediction reporting configuration ID=5• Prediction reporting configuration ID=6 As c) is not applicable, the UE includes in the complete message an indication as follows:■ Prediction reporting configuration ID=5not applicable;■ Prediction reporting configuration ID=6not applicable; Alternatively, multiple indication(s) that AI / ML-model functionality(ies) the UE is configured with or is being configured with are applicable. The absenceof these indications indicate that the AI / ML-model functionality(ies) are not applicable. ) A cause value or further applicability information o The UE may indicate one or more cause value(s) associated to an AI / ML- model functionality reported as non-applicable. o Examples of cause value could be:■ Non-applicable network location, e.g. UE connected to a cell for which the AI / ML-model functionality does not provide outputs and / or provide outputs with low or non suitable accuracy and / or high errors and / or uncertainties;■ Non-applicable AI / ML-model output. For example, the model cannot provide a confidence measure of its prediction. Needed by the NW to utilize the predictions. For example, the confidence of the prediction can be used to set how many beams to transmit.■ AI / ML-model is too old. The NW can determine that models older than a certain time is invalid (e.g., due to NW changes).■ Non-applicable UE configuration, e.g. UE has been configured to report on frequency layers for which the AI / ML-model functionality is not trained for;■ Non-applicable network configuration, e.g. UE is connected to a network with a configuration for which the AI / ML-model functionality is not trained for;■ Non-applicable UE mobility criterion e.g., UE has trained the model using measurements while it is a slow speed UE (as classified via UE’s internal sensor based measurements and / or via the speedStateReselectionPars as broadcasted by the serving cell) whereas the UE’s current mobility class is high speed.■ Non-applicable CSLRS configuration, e.g. the network is providing CSLRS resources for channel estimation with a configuration (e.g., periodicity or aperiodic configuration) for which the AI / ML-model functionality is not trained for;■ Non-applicable computational resource availability, e.g. due to limited computational resources at the UE (which may be impacted by UEconfigurations and traffic patterns) the predictions cannot be enough accurate■ Non-applicable UE mobility criterion e.g., UE has trained the model using measurements while it is a slow speed UE (as classified via UE’s internal sensor based measurements and / or via the speedStateReselectionPars as broadcasted by the serving cell) whereas the UE’s current mobility class is high speed.Suggestion / recommendation or a reconfiguration; o The UE may indicate one or more recommendations or suggestions of reconfigurations to make the AI / ML-model functionality applicable. For example:■ New frequency layers;■ New reference signal configuration for reporting and / or performing inference and / or predictions, e.g. the network may have configured the UE to perform time-domain predictions of CSI-RS measurements, while for that cell the AI / ML-model functionality is NOT applicable for CSI-RS but it is applicable for SSB(s), so that the UE indicates that to the network. The UE can for example suggest a new CSI-RS periodicity.■ New cell?■ New MIMO configuration? CSLMeasConfig?■ What else?■ Be configured with data collection enabling configuration (e.g., NW sweeps all beams in the BM use case). This can enable the model to be updated and applicable in the current scenario.■ An indication indicating when in time the AI / ML-model functionality is expected to be applicable, which can depend for example on temporary shortage of computational resources, or a new received configuration. In another example, the UE may indicate that the AI / ML-model functionality is applicable when the UE enters a low mobility state

[0084] The above methods wherein the applicability information the UE reports (or the indication that an AI / ML model functionality is not applicable) indicates that at least one AI / ML- model functionality the UE is configured with or is being configured with is not applicable. For example, upon determining that none of the AI / ML-models associated to a functionality, forwhich the UE is trained for, is applicable, the UE reports in a message (as disclosed in the previous embodiments) the applicability information (as disclosed in the previous embodiments) of the associated AI / ML-model functionality, e.g. by indicating that the AI / ML model functionality is not applicable. In response to it, the target network node receiving the applicability information may further request information, e.g. in a UE Information Request message regarding the AI / ML-model functionality. In response to it, the UE transmits further applicability information (e.g., in a UE Information Response message) such cause value(s) for the non-applicability and / or one or more suggested configuration(s). In the said applicability information message, the UE may include one or more recommendations or suggestions of reconfigurations such that at least one of the multiple AI / ML-models associated to the said AI / ML model functionality can be applicable.5 Other Solutions

[0085] In a set of embodiments, the UE is configured by the network to report the applicability information of at least one associated AI / ML-model. When the UE is not configured to report the applicability information of at least one associated AI / ML-model and the UE determines that a given AI / ML-model with which the UE is configured and / or is being configured is not applicable, the UE performs any one or more of the following actions:• When the UE is in CONNECTED state, the UE initiates an RRC Reestablishment procedure;• When the UE is in CONNECTED state, the UE declares a reconfiguration failure, and triggers an RRC Re-establishment procedure.• When the UE is transitioning to CONNECTED state from IDLE or INACTIVE, the UE declares an RRC Re-establishment procedure if security had been activated, otherwise the UE goes back to IDLE and indicates a failure to upper layers.6 Further Description

[0086] Figure 6 shows an example of a communication system 600 in accordance with some embodiments.

[0087] In the example, the communication system 600 includes a telecommunication network 602 that includes an access network 604, such as a Radio Access Network (RAN), and a core network 606, which includes one or more core network nodes 608. The access network 604 includes one or more access network nodes, such as network nodes 610A and 610B (one or more of which may be generally referred to as network nodes 610), or any other similar ThirdGeneration Partnership Project (3GPP) access nodes or non-3GPP Access Points (APs). Moreover, as will be appreciated by those of skill in the art, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunication network 602 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network 602 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other nodes to implement one or more functionalities of any node in the telecommunication network 602, including one or more network nodes 610 and / or core network nodes 608.

[0088] Examples of an ORAN network node include an Open Radio Unit (O-RU), an Open Distributed Unit (O-DU), an Open Central Unit (O-CU), including an O-CU Control Plane (O- CU-CP) or an O-CU User Plane (O-CU-UP), a RAN intelligent controller (near-real time or non- real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an Al, Fl, Wl, El, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN access node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an O-2 interface defined by the O-RAN Alliance or comparable technologies. The network nodes 610 facilitate direct or indirect connection of User Equipment (UE), such as by connecting UEs 612A, 612B, 612C, and 612D (one or more of which may be generally referred to as UEs 612) to the core network 606 over one or more wireless connections.

[0089] Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 600 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of dataand / or signals whether via wired or wireless connections. The communication system 600 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0090] The UEs 612 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes 610 and other communication devices. Similarly, the network nodes 610 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 612 and / or with other network nodes or equipment in the telecommunication network 602 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network 602.

[0091] In the depicted example, the core network 606 connects the network nodes 610 to one or more hosts, such as host 616. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 606 includes one more core network nodes (e.g., core network node 608) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 608. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-Concealing Function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0092] The host 616 may be under the ownership or control of a service provider other than an operator or provider of the access network 604 and / or the telecommunication network 602, and may be operated by the service provider or on behalf of the service provider. The host 616 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0093] As a whole, the communication system 600 of Figure 6 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system 600 may beconfigured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable Second, Third, Fourth, or Fifth Generation (2G, 3G, 4G, or 5G) standards, or any applicable future generation standard (e.g., Sixth Generation (6G)); Wireless Local Area Network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and / or any Low Power Wide Area Network (LPWAN) standards such as LoRa and Sigfox.

[0094] In some examples, the telecommunication network 602 is a cellular network that implements 3 GPP standardized features. Accordingly, the telecommunication network 602 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 602. For example, the telecommunication network 602 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing enhanced Mobile Broadband (eMBB) services to other UEs, and / or massive Machine Type Communication (mMTC) / massive Internet of Things (loT) services to yet further UEs.

[0095] In some examples, the UEs 612 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 604 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 604. Additionally, a UE may be configured for operating in single- or multi -Radio Access Technology (RAT) or multi-standard mode. For example, a UE may operate with any one or combination of WiFi, New Radio (NR), and LTE, i.e. being configured for Multi -Radio Dual Connectivity (MR-DC), such as Evolved UMTS Terrestrial RAN (E-UTRAN) NR - Dual Connectivity (EN-DC).

[0096] In the example, a hub 614 communicates with the access network 604 to facilitate indirect communication between one or more UEs (e.g., UE 612C and / or 612D) and network nodes (e.g., network node 610B). In some examples, the hub 614 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 614 may be a broadband router enabling access to the core network 606 for the UEs. As another example, the hub 614 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 610, or by executable code, script, process, or otherinstructions in the hub 614. As another example, the hub 614 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 614 may be a content source. For example, for a UE that is a Virtual Reality (VR) headset, display, loudspeaker or other media delivery device, the hub 614 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 614 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 614 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0097] The hub 614 may have a constant / persistent or intermittent connection to the network node 610B. The hub 614 may also allow for a different communication scheme and / or schedule between the hub 614 and UEs (e.g., UE 612C and / or 612D), and between the hub 614 and the core network 606. In other examples, the hub 614 is connected to the core network 606 and / or one or more UEs via a wired connection. Moreover, the hub 614 may be configured to connect to a Machine-to-Machine (M2M) service provider over the access network 604 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 610 while still connected via the hub 614 via a wired or wireless connection. In some embodiments, the hub 614 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 610B. In other embodiments, the hub 614 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and the network node 610B, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0098] Figure 7 shows a UE 700 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged, and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, Voice over Internet Protocol (VoIP) phone, wireless local loop phone, desktop computer, Personal Digital Assistant (PDA), wireless camera, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, Laptop Embedded Equipment (LEE), Laptop Mounted Equipment (LME), smart device, wireless Customer Premise Equipment (CPE), vehicle, vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3GPP, including a Narrowband Internet of Things (NB-IoT) UE, a Machine Type Communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0099] A UE may support Device-to-Device (D2D) communication, for example by implementing a 3 GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), Vehi cl e-to- Vehicle (V2V), Vehicle-to-Infrastructure (V2I), or Vehicle- to-Everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller).Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0100] The UE 700 includes processing circuitry 702 that is operatively coupled via a bus 704 to an input / output interface 706, a power source 708, memory 710, a communication interface 712, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 7. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0101] The processing circuitry 702 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 710. The processing circuitry 702 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general purpose processors, such as a microprocessor or Digital Signal Processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 702 may include multiple Central Processing Units (CPUs).

[0102] In the example, the input / output interface 706 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE 700. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitivedisplay may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0103] In some embodiments, the power source 708 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source 708 may further include power circuitry for delivering power from the power source 708 itself, and / or an external power source, to the various parts of the UE 700 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 708.Power circuitry may perform any formatting, converting, or other modification to the power from the power source 708 to make the power suitable for the respective components of the UE 700 to which power is supplied.

[0104] The memory 710 may be or be configured to include memory such as Random Access Memory (RAM), Read Only Memory (ROM), Programmable ROM (PROM), Erasable PROM (EPROM), Electrically EPROM (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 710 includes one or more application programs 714, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 716. The memory 710 may store, for use by the UE 700, any of a variety of various operating systems or combinations of operating systems.

[0105] The memory 710 may be configured to include a number of physical drive units, such as Redundant Array of Independent Disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, High Density Digital Versatile Disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, Holographic Digital Data Storage (HDDS) optical disc drive, external mini Dual In-line Memory Module (DIMM), Synchronous Dynamic RAM (SDRAM), external micro-DIMM SDRAM, smartcard memory such as a tamper resistant module in the form of a Universal Integrated Circuit Card (UICC) including one or more Subscriber Identity Modules (SIMs), such as a Universal SIM (USIM) and / or Internet Protocol Multimedia Services Identity Module (ISIM), other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as a ‘SIM card.’ The memory 710 may allow the UE 700 to access instructions, application programs, and the like stored on transitory ornon-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system, may be tangibly embodied as or in the memory 710, which may be or comprise a device-readable storage medium.

[0106] The processing circuitry 702 may be configured to communicate with an access network or other network using the communication interface 712. The communication interface 712 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 722. The communication interface 712 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter 718 and / or a receiver 720 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 718 and receiver 720 may be coupled to one or more antennas (e.g., the antenna 722) and may share circuit components, software, or firmware, or alternatively be implemented separately.

[0107] In the illustrated embodiment, communication functions of the communication interface 712 may include cellular communication, WiFi communication, LPWAN communication, data communication, voice communication, multimedia communication, short- range communications such as Bluetooth, NFC, location-based communication such as the use of the Global Positioning System (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband CDMA (WCDMA), GSM, LTE, NR, UMTS, WiMax, Ethernet, Transmission Control Protocol / Internet Protocol (TCP / IP), Synchronous Optical Networking (SONET), Asynchronous Transfer Mode (ATM), Quick User Datagram Protocol Internet Connection (QUIC), Hypertext Transfer Protocol (HTTP), and so forth.

[0108] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 712, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0109] As another example, a UE comprises an actuator, a motor, or a switch related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

[0110] A UE, when in the form of an loT device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application, and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a television, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or VR, a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or itemtracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the UE 700 shown in Figure 7.[OHl] As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3 GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship, an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0112] In practice, any number of UEs may be used together with respect to a single use case.For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE mayadjust the throttle on the drone (e.g., by controlling an actuator) to increase or decrease the drone’s speed. The first and / or the second UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the actuator and handle communication of data for both the speed sensor and the actuators.

[0113] Figure 8 shows a network node 800 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged, and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment in a telecommunication network. Examples of network nodes include, but are not limited to, APs (e.g., radio APs), Base Stations (BSs) (e.g., radio BSs, Node Bs, evolved Node Bs (eNBs), NR Node Bs (gNBs)), and 0-RAN nodes or components of an 0-RAN node (e.g., O-RU, O-DU, O- CU).

[0114] Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an 0-RAN access node), and / or Remote Radio Units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such RRUs may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a Distributed Antenna System (DAS).

[0115] Other examples of network nodes include multiple Transmission Point (multi-TRP) 5G access nodes, Multi -Standard Radio (MSR) equipment such as MSR BSs, network controllers such as Radio Network Controllers (RNCs) or BS Controllers (BSCs), Base Transceiver Stations (BTSs), transmission points, transmission nodes, Multi-Cell / Multicast Coordination Entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0116] The network node 800 includes processing circuitry 802, memory 804, a communication interface 806, and a power source 808. The network node 800 may be composed of multiple physically separate components (e.g., a NodeB component and an RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 800 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may beshared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair may in some instances be considered a single separate network node. In some embodiments, the network node 800 may be configured to support multiple RATs. In such embodiments, some components may be duplicated (e.g., separate memory 804 for different RATs) and some components may be reused (e.g., a same antenna 810 may be shared by different RATs). The network node 800 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 800, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, Long Range Wide Area Network (LoRaWAN), Radio Frequency Identification (RFID), or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within the network node 800.

[0117] The processing circuitry 802 may comprise a combination of one or more of a microprocessor, controller, microcontroller, CPU, DSP, ASIC, FPGA, or any other suitable computing device, resource, or combination of hardware, software, and / or encoded logic operable to provide, either alone or in conjunction with other network node 800 components, such as the memory 804, to provide network node 800 functionality.

[0118] In some embodiments, the processing circuitry 802 includes a System on a Chip (SOC). In some embodiments, the processing circuitry 802 includes one or more of Radio Frequency (RF) transceiver circuitry 812 and baseband processing circuitry 814. In some embodiments, the RF transceiver circuitry 812 and the baseband processing circuitry 814 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of the RF transceiver circuitry 812 and the baseband processing circuitry 814 may be on the same chip or set of chips, boards, or units.

[0119] The memory 804 may comprise any form of volatile or non-volatile computer- readable memory including, without limitation, persistent storage, solid state memory, remotely mounted memory, magnetic media, optical media, RAM, ROM, mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD), or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable, and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 802. The memory 804 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 802 and utilized by the network node 800. The memory 804 may be used to store any calculations made by the processing circuitry 802 and / orany data received via the communication interface 806. In some embodiments, the processing circuitry 802 and the memory 804 are integrated.

[0120] The communication interface 806 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, the communication interface 806 comprises port(s) / terminal(s) 816 to send and receive data, for example to and from a network over a wired connection. The communication interface 806 also includes radio front-end circuitry 818 that may be coupled to, or in certain embodiments a part of, the antenna 810. The radio front-end circuitry 818 comprises filters 820 and amplifiers 822. The radio front-end circuitry 818 may be connected to the antenna 810 and the processing circuitry 802. The radio front-end circuitry 818 may be configured to condition signals communicated between the antenna 810 and the processing circuitry 802. The radio front-end circuitry 818 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 818 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of the filters 820 and / or the amplifiers 822. The radio signal may then be transmitted via the antenna 810. Similarly, when receiving data, the antenna 810 may collect radio signals which are then converted into digital data by the radio front-end circuitry 818. The digital data may be passed to the processing circuitry 802. In other embodiments, the communication interface 806 may comprise different components and / or different combinations of components.

[0121] In certain alternative embodiments, the network node 800 does not include separate radio front-end circuitry 818; instead, the processing circuitry 802 includes radio front-end circuitry and is connected to the antenna 810. Similarly, in some embodiments, all or some of the RF transceiver circuitry 812 is part of the communication interface 806. In still other embodiments, the communication interface 806 includes the one or more ports or terminals 816, the radio front-end circuitry 818, and the RF transceiver circuitry 812 as part of a radio unit (not shown), and the communication interface 806 communicates with the baseband processing circuitry 814, which is part of a digital unit (not shown).

[0122] The antenna 810 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 810 may be coupled to the radio front-end circuitry 818 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 810 is separate from the network node 800 and connectable to the network node 800 through an interface or port.

[0123] The antenna 810, the communication interface 806, and / or the processing circuitry 802 may be configured to perform any receiving operations and / or certain obtaining operationsdescribed herein as being performed by the network node 800. Any information, data, and / or signals may be received from a UE, another network node, and / or any other network equipment. Similarly, the antenna 810, the communication interface 806, and / or the processing circuitry 802 may be configured to perform any transmitting operations described herein as being performed by the network node 800. Any information, data, and / or signals may be transmitted to a UE, another network node, and / or any other network equipment.

[0124] The power source 808 provides power to the various components of the network node 800 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 808 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 800 with power for performing the functionality described herein. For example, the network node 800 may be connectable to an external power source (e.g., the power grid or an electricity outlet) via input circuitry or an interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 808. As a further example, the power source 808 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0125] Embodiments of the network node 800 may include additional components beyond those shown in Figure 8 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node 800 may include user interface equipment to allow input of information into the network node 800 and to allow output of information from the network node 800. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 800.

[0126] Figure 9 is a block diagram of a host 900, which may be an embodiment of the host 616 of Figure 6, in accordance with various aspects described herein. As used herein, the host 900 may be or comprise various combinations of hardware and / or software including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm. The host 900 may provide one or more services to one or more UEs.

[0127] The host 900 includes processing circuitry 902 that is operatively coupled via a bus 904 to an input / output interface 906, a network interface 908, a power source 910, and memory 912. Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices of previous figures,such as Figures 7 and 8, such that the descriptions thereof are generally applicable to the corresponding components of the host 900.

[0128] The memory 912 may include one or more computer programs including one or more host application programs 914 and data 916, which may include user data, e.g. data generated by a UE for the host 900 or data generated by the host 900 for a UE. Embodiments of the host 900 may utilize only a subset or all of the components shown. The host application programs 914 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (VVC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), Moving Picture Experts Group (MPEG), VP9) and audio codecs (e.g., Free Lossless Audio Codec (FL AC), Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, and heads-up display systems). The host application programs 914 may also provide for user authentication and licensing checks and may periodically report health, routes, and content availability to a central node, such as a device in or on the edge of a core network. Accordingly, the host 900 may select and / or indicate a different host for Over-The-Top (OTT) services for a UE. The host application programs 914 may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real-Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), Dynamic Adaptive Streaming over HTTP (DASH or MPEG-DASH), etc.

[0129] Figure 10 is a block diagram illustrating a virtualization environment 1000 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices, and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more Virtual Machines (VMs) implemented in one or more virtual environments 1000 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment 1000 includes components defined by the 0-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an O-2 interface.

[0130] Applications 1002 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 1000 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0131] Hardware 1004 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 1006 (also referred to as hypervisors or VM Monitors (VMMs)), provide VMs 1008 A and 1008B (one or more of which may be generally referred to as VMs 1008), and / or perform any of the functions, features, and / or benefits described in relation with some embodiments described herein. The virtualization layer 1006 may present a virtual operating platform that appears like networking hardware to the VMs 1008.

[0132] The VMs 1008 comprise virtual processing, virtual memory, virtual networking, or interface and virtual storage, and may be run by a corresponding virtualization layer 1006. Different embodiments of the instance of a virtual appliance 1002 may be implemented on one or more of the VMs 1008, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as Network Function Virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers and customer premise equipment.

[0133] In the context of NFV, a VM 1008 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 1008, and that part of the hardware 1004 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs 1008, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs 1008 on top of the hardware 1004 and corresponds to the application 1002.

[0134] The hardware 1004 may be implemented in a standalone network node with generic or specific components. The hardware 1004 may implement some functions via virtualization. Alternatively, the hardware 1004 may be part of a larger cluster of hardware (e.g., such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 1010, which, among others, oversees lifecycle management of the applications 1002. In some embodiments, the hardware 1004 is coupled to one or more radiounits that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a RAN or a base station. In some embodiments, some signaling can be provided with the use of a control system 1012 which may alternatively be used for communication between hardware nodes and radio units.

[0135] Figure 11 shows a communication diagram of a host 1102 communicating via a network node 1104 with a UE 1106 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as the UE 612A of Figure 6 and / or the UE 700 of Figure 7), the network node (such as the network node 610A of Figure 6 and / or the network node 800 of Figure 8), and the host (such as the host 616 of Figure 6 and / or the host 900 of Figure 9) discussed in the preceding paragraphs will now be described with reference to Figure 11.

[0136] Like the host 900, embodiments of the host 1102 include hardware, such as a communication interface, processing circuitry, and memory. The host 1102 also includes software, which is stored in or is accessible by the host 1102 and executable by the processing circuitry. The software includes a host application that may be operable to provide a service to a remote user, such as the UE 1106 connecting via an OTT connection 1150 extending between the UE 1106 and the host 1102. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection 1150.

[0137] The network node 1104 includes hardware enabling it to communicate with the host 1102 and the UE 1106. The connection 1160 may be direct or pass through a core network (like the core network 606 of Figure 6) and / or one or more other intermediate networks, such as one or more public, private, or hosted networks. For example, an intermediate network may be a backbone network or the Internet.

[0138] The UE 1106 includes hardware and software, which is stored in or accessible by the UE 1106 and executable by the UE’s processing circuitry. The software includes a client application, such as a web browser or operator-specific “app” that may be operable to provide a service to a human or non-human user via the UE 1106 with the support of the host 1102. In the host 1102, an executing host application may communicate with the executing client application via the OTT connection 1150 terminating at the UE 1106 and the host 1102. In providing the service to the user, the UE's client application may receive request data from the host's host application and provide user data in response to the request data. The OTT connection 1150 may transfer both the request data and the user data. The UE's client application may interact with theuser to generate the user data that it provides to the host application through the OTT connection 1150.

[0139] The OTT connection 1150 may extend via the connection 1160 between the host 1102 and the network node 1104 and via a wireless connection 1170 between the network node 1104 and the UE 1106 to provide the connection between the host 1102 and the UE 1106. The connection 1160 and the wireless connection 1170, over which the OTT connection 1150 may be provided, have been drawn abstractly to illustrate the communication between the host 1102 and the UE 1106 via the network node 1104, without explicit reference to any intermediary devices and the precise routing of messages via these devices.

[0140] As an example of transmitting data via the OTT connection 1150, in step 1108, the host 1102 provides user data, which may be performed by executing a host application. In some embodiments, the user data is associated with a particular human user interacting with the UE 1106. In other embodiments, the user data is associated with a UE 1106 that shares data with the host 1102 without explicit human interaction. In step 1110, the host 1102 initiates a transmission carrying the user data towards the UE 1106. The host 1102 may initiate the transmission responsive to a request transmitted by the UE 1106. The request may be caused by human interaction with the UE 1106 or by operation of the client application executing on the UE 1106. The transmission may pass via the network node 1104 in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step 1112, the network node 1104 transmits to the UE 1106 the user data that was carried in the transmission that the host 1102 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 1114, the UE 1106 receives the user data carried in the transmission, which may be performed by a client application executed on the UE 1106 associated with the host application executed by the host 1102.

[0141] In some examples, the UE 1106 executes a client application which provides user data to the host 1102. The user data may be provided in reaction or response to the data received from the host 1102. Accordingly, in step 1116, the UE 1106 may provide user data, which may be performed by executing the client application. In providing the user data, the client application may further consider user input received from the user via an input / output interface of the UE 1106. Regardless of the specific manner in which the user data was provided, the UE 1106 initiates, in step 1118, transmission of the user data towards the host 1102 via the network node 1104. In step 1120, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 1104 receives user data from the UE 1106 and initiatestransmission of the received user data towards the host 1102. In step 1122, the host 1102 receives the user data carried in the transmission initiated by the UE 1106.

[0142] One or more of the various embodiments improve the performance of OTT services provided to the UE 1106 using the OTT connection 1150, in which the wireless connection 1170 forms the last segment.

[0143] In an example scenario, factory status information may be collected and analyzed by the host 1102. As another example, the host 1102 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host 1102 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host 1102 may store surveillance video uploaded by a UE. As another example, the host 1102 may store or control access to media content such as video, audio, VR, or AR which it can broadcast, multicast, or unicast to UEs. As other examples, the host 1102 may be used for energy pricing, remote control of non-time critical electrical load to balance power generation needs, location services, presentation services (such as compiling diagrams etc. from data collected from remote devices), or any other function of collecting, retrieving, storing, analyzing, and / or transmitting data.

[0144] In some examples, a measurement procedure may be provided for the purpose of monitoring data rate, latency, and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring the OTT connection 1150 between the host 1102 and the UE 1106 in response to variations in the measurement results. The measurement procedure and / or the network functionality for reconfiguring the OTT connection 1150 may be implemented in software and hardware of the host 1102 and / or the UE 1106. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection 1150 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or by supplying values of other physical quantities from which software may compute or estimate the monitored quantities. The reconfiguring of the OTT connection 1150 may include message format, retransmission settings, preferred routing, etc.; the reconfiguring need not directly alter the operation of the network node 1104. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signaling that facilitates measurements of throughput, propagation times, latency, and the like by the host 1102. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 1150 while monitoring propagation times, errors, etc.

[0145] Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions, and methods disclosed herein. Determining, calculating, obtaining, or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box or nested within multiple boxes, in practice computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

[0146] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer- readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hardwired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer- readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole and / or by end users and a wireless network generally.

[0147] Example embodiments of the present disclosure are as follows:Group A Embodiments

[0148] Embodiment 1 : A method performed by a User Equipment, UE, for reporting applicability information of at least one Artificial Intelligence, Al, or Machine Learning, ML, model associated to a functionality, the method comprising: sending (212; 304; 408; 528), to a network node, applicability information for at least one Al or ML model associated to a functionality with which the UE is configured and / or is being configured.

[0149] Embodiment 2: The method of embodiment 1, wherein the applicability information of the at least one Al or ML model associated to the functionality comprises any one or more of the following: an indication that the at least one Al or ML model associated to the functionality is not applicable; an indication functionality associated to the at least one Al or ML model is not applicable; a cause value; a suggestion or recommendation or reconfiguration about how to make the at least one Al or ML model applicable.

[0150] Embodiment 3: The method of embodiment 1 or 2, wherein sending (212; 408; 528) the applicability information comprises sending (212; 408; 528) a complete message comprising the applicability information for the at least one Al or ML model associated to the functionality with which the UE is configured and / or is being configured.

[0151] Embodiment 4: The method of embodiment 3, wherein the complete message is any one or more of: an RRC resume complete message, an RRC setup complete message, and an RRC reconfiguration complete message.

[0152] Embodiment 5: The method of embodiment 3 or 4, further comprising: sending (200; 400) a request message to the network node; receiving (208; 404) a response message from the network node responsive to the request message; wherein sending (212; 408) the complete message comprising the applicability information comprises sending (212; 408) the complete message after receiving the response message.

[0153] Embodiment 6: The method of embodiment 5 wherein the request message is an RRC resume request, the response message is an RRC resume message, and the complete message is an RRC resume complete message.

[0154] Embodiment 7: The method of embodiment 5 wherein the request message is an RRC setup request, the response message is an RRC setup message, and the complete message is an RRC setup complete message.

[0155] Embodiment 8: The method of any of embodiments 5 to 7, wherein the response message comprises configuration information for the functionality associated to the at least one Al or ML model or information for activation of the functionality associated to the at least one Al or ML model.

[0156] Embodiment 9: The method of embodiment 8, wherein sending (212; 408) the complete message comprises sending (212; 408) the complete message responsive to the configuration information for the functionality associated to the at least one Al or ML model or the information for activation of the functionality associated to the at least one Al or ML model.

[0157] Embodiment 10: The method of embodiment 3 or 4, further comprising: receiving (524) a reconfiguration message from the network node; wherein sending (528) the complete message comprising the applicability information comprises sending (528) the complete message after receiving the response message.

[0158] Embodiment 11 : The method of embodiment 10 wherein the reconfiguration message is an RRC reconfiguration message, and the complete message is an RRC reconfiguration complete message.

[0159] Embodiment 12: The method of embodiment 10 or 11, wherein the reconfiguration message comprises configuration information for the functionality associated to the at least one Al or ML model or information for activation of the functionality associated to the at least one Al or ML model.

[0160] Embodiment 13 : The method of embodiment 12, wherein sending (528) the complete message comprises sending (528) the complete message responsive to the configuration information for the functionality associated to the at least one Al or ML model or the information for activation of the functionality associated to the at least one Al or ML model.

[0161] Embodiment 14: The method of embodiment 1 or 2, wherein sending the applicability information comprises sending the applicability information in or with a request message.

[0162] Embodiment 15: The method of embodiment 14 wherein the request message in an RRC resume request.

[0163] Embodiment 16: The method of any of the previous embodiments, further comprising: providing user data; and forwarding the user data to a host via the transmission to the network node.Group B Embodiments

[0164] Embodiment 17: A method performed by a network node for obtaining applicability information of at least one Artificial Intelligence, Al, or Machine Learning, ML, model associated to a functionality, the method comprising: receiving (212; 304; 408; 528), from a User Equipment (UE), applicability information for at least one Al or ML model associated to a functionality with which the UE is configured and / or is being configured.

[0165] Embodiment 18: The method of embodiment 17, wherein the applicability information of the at least one Al or ML model associated to the functionality comprises any one or more of the following: an indication that the at least one Al or ML model associated to the functionality is not applicable; an indication functionality associated to the at least one Al or ML model is not applicable; a cause value; a suggestion or recommendation or reconfiguration about how to make the at least one Al or ML model applicable.

[0166] Embodiment 19: The method of embodiment 17 or 18, wherein receiving (212; 408; 528) the applicability information comprises receiving (212; 408; 528) a complete message comprising the applicability information for the at least one Al or ML model associated to the functionality with which the UE is configured and / or is being configured.

[0167] Embodiment 20: The method of embodiment 19, wherein the complete message is any one or more of: an RRC resume complete message, an RRC setup complete message, and an RRC reconfiguration complete message.

[0168] Embodiment 21 : The method of embodiment 19 or 20, further comprising: receiving (200; 400) a request message from the UE; sending (208; 404) a response message to the UE responsive to the request message; wherein receiving (212; 408) the complete message comprising the applicability information comprises receiving (212; 408) the complete message after sending the response message.

[0169] Embodiment 22: The method of embodiment 21 wherein the request message is an RRC resume request, the response message is an RRC resume message, and the complete message is an RRC resume complete message.

[0170] Embodiment 23 : The method of embodiment 21 wherein the request message is an RRC setup request, the response message is an RRC setup message, and the complete message is an RRC setup complete message.

[0171] Embodiment 24: The method of any of embodiments 21 to 23, wherein the response message comprises configuration information for the functionality associated to the at least one Al or ML model or information for activation of the functionality associated to the at least one Al or ML model.

[0172] Embodiment 25: The method of embodiment 24, wherein receiving (212; 408) the complete message comprises receiving (212; 408) the complete message responsive to the configuration information for the functionality associated to the at least one Al or ML model or the information for activation of the functionality associated to the at least one Al or ML model.

[0173] Embodiment 26: The method of embodiment 19 or 20, further comprising: sending (524) a reconfiguration message to the UE; wherein receiving (528) the complete messagecomprising the applicability information comprises receiving (528) the complete message after sending the response message.

[0174] Embodiment 27 : The method of embodiment 26 wherein the reconfiguration message is an RRC reconfiguration message, and the complete message is an RRC reconfiguration complete message.

[0175] Embodiment 28: The method of embodiment 26 or 27, wherein the reconfiguration message comprises configuration information for the functionality associated to the at least one Al or ML model or information for activation of the functionality associated to the at least one Al or ML model.

[0176] Embodiment 29: The method of embodiment 28, wherein receiving (528) the complete message comprises receiving (528) the complete message responsive to the configuration information for the functionality associated to the at least one Al or ML model or the information for activation of the functionality associated to the at least one Al or ML model.

[0177] Embodiment 30: The method of embodiment 17 or 18, wherein sending the applicability information comprises sending the applicability information in or with a request message.

[0178] Embodiment 31 : The method of embodiment 30 wherein the request message in an RRC resume request.

[0179] Embodiment 32: The method of any of embodiments 17 to 31, further comprising performing one or more actions based on the applicability information.

[0180] Embodiment 33 : The method of any of the previous embodiments, further comprising: obtaining user data; and forwarding the user data to a host or a user equipment.Group C Embodiments

[0181] Embodiment 34: A user equipment comprising: processing circuitry configured to perform any of the steps of any of the Group A embodiments; and power supply circuitry configured to supply power to the processing circuitry.

[0182] Embodiment 35: A network node comprising: processing circuitry configured to perform any of the steps of any of the Group B embodiments; and power supply circuitry configured to supply power to the processing circuitry.

[0183] Embodiment 36: A user equipment (UE) comprising: an antenna configured to send and receive wireless signals; radio front-end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry; the processing circuitry being configured to perform any of the steps of anyof the Group A embodiments; an input interface connected to the processing circuitry and configured to allow input of information into the UE to be processed by the processing circuitry; an output interface connected to the processing circuitry and configured to output information from the UE that has been processed by the processing circuitry; and a battery connected to the processing circuitry and configured to supply power to the UE.

[0184] Embodiment 37: A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a network node in a cellular network for transmission to a user equipment (UE), the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B embodiments to transmit the user data from the host to the UE.

[0185] Embodiment 38: The host of the previous embodiment, wherein: the processing circuitry of the host is configured to execute a host application that provides the user data; and the UE comprises processing circuitry configured to execute a client application associated with the host application to receive the transmission of user data from the host.

[0186] Embodiment 39: A method implemented in a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: providing user data for the UE; and initiating a transmission carrying the user data to the UE via a cellular network comprising the network node, wherein the network node performs any of the operations of any of the Group B embodiments to transmit the user data from the host to the UE.

[0187] Embodiment 40: The method of the previous embodiment, further comprising, at the network node, transmitting the user data provided by the host for the UE.

[0188] Embodiment 41 : The method of any of the previous 2 embodiments, wherein the user data is provided at the host by executing a host application that interacts with a client application executing on the UE, the client application being associated with the host application.

[0189] Embodiment 42: A communication system configured to provide an over-the-top (OTT) service, the communication system comprising a host comprising: processing circuitry configured to provide user data for a user equipment (UE), the user data being associated with the over-the-top service; and a network interface configured to initiate transmission of the user data toward a cellular network node for transmission to the UE, the network node having a communication interface and processing circuitry, the processing circuitry of the network nodeconfigured to perform any of the operations of any of the Group B embodiments to transmit the user data from the host to the UE.

[0190] Embodiment 43 : The communication system of the previous embodiment, further comprising: the network node; and / or the UE.

[0191] Embodiment 44: A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to initiate receipt of user data; and a network interface configured to receive the user data from a network node in a cellular network, the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B embodiments to receive the user data from a user equipment (UE) for the host.

[0192] Embodiment 45: The host of the previous 2 embodiments, wherein: the processing circuitry of the host is configured to execute a host application that receives the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application.

[0193] Embodiment 46: The host of the any of the previous 2 embodiments, wherein the initiating receipt of the user data comprises requesting the user data.

[0194] Embodiment 47 : A method implemented by a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: at the host, initiating receipt of user data from the UE, the user data originating from a transmission which the network node has received from the UE, wherein the network node performs any of the steps of any of the Group B embodiments to receive the user data from the UE for the host.

[0195] Embodiment 48: The method of the previous embodiment, further comprising at the network node, transmitting the received user data to the host.

[0196] Embodiment 49: A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a cellular network for transmission to a user equipment (UE), wherein the UE comprises a communication interface and processing circuitry, the communication interface and processing circuitry of the UE being configured to perform any of the operations of any of the Group A embodiments to receive the user data from the host.

[0197] Embodiment 50: the host of the previous embodiment, wherein the cellular network further includes a network node configured to communicate with the UE to transmit the user data to the UE from the host.

[0198] Embodiment 51 : The host of the previous 2 embodiments, wherein: the processing circuitry of the host is configured to execute a host application, thereby providing the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application.

[0199] Embodiment 52: A method implemented by a host operating in a communication system that further includes a network node and a user equipment (UE), the method comprising: providing user data for the UE; and initiating a transmission carrying the user data to the UE via a cellular network comprising the network node, wherein the UE performs any of the operations of any of the Group A embodiments to receive the user data from the host.

[0200] Embodiment 53 : The method of the previous embodiment, further comprising: at the host, executing a host application associated with a client application executing on the UE to receive the user data from the host application.

[0201] Embodiment 54: The method of the previous embodiment, further comprising: at the host, transmitting input data to the client application executing on the UE, the input data being provided by executing the host application, wherein the user data is provided by the client application in response to the input data from the host application.

[0202] Embodiment 55: A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a cellular network for transmission to a user equipment (UE), wherein the UE comprises a communication interface and processing circuitry, the communication interface and processing circuitry of the UE being configured to perform any of the steps of any of the Group A embodiments to transmit the user data to the host.

[0203] Embodiment 56: The host of the previous embodiment, wherein the cellular network further includes a network node configured to communicate with the UE to transmit the user data from the UE to the host.

[0204] Embodiment 57: The host of the previous 2 embodiments, wherein: the processing circuitry of the host is configured to execute a host application, thereby providing the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application.

[0205] Embodiment 58: A method implemented by a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: at the host, receiving user data transmitted to the host via the network node by the UE, wherein the UE performs any of the steps of any of the Group A embodiments to transmit the user data to the host.

[0206] Embodiment 59: The method of the previous embodiment, further comprising: at the host, executing a host application associated with a client application executing on the UE to receive the user data from the UE.

[0207] Embodiment 60: The method of the previous 2 embodiments, further comprising: at the host, transmitting input data to the client application executing on the UE, the input data being provided by executing the host application, wherein the user data is provided by the client application in response to the input data from the host application.

[0208] Those skilled in the art will recognize improvements and modifications to the embodiments of the present disclosure. All such improvements and modifications are considered within the scope of the concepts disclosed herein.

Claims

Claims1. A method performed by a User Equipment, UE, for reporting applicability information of at least one Artificial Intelligence, Al, or Machine Learning, ML, model associated to a functionality, the method comprising: sending (212; 304; 408; 528), to a network node, applicability information for at least one Al or ML model associated to a functionality with which the UE is configured and / or is being configured.

2. The method of claim 1, wherein the applicability information of the at least one Al or ML model associated to the functionality comprises any one or more of the following: an indication that the at least one Al or ML model associated to the functionality is not applicable; an indication that the functionality associated to the at least one Al or ML model is not applicable; a cause value; a suggestion or recommendation or reconfiguration about how to make the at least one Al or ML model applicable.

3. The method of claim 1 or 2, wherein sending (212; 408; 528) the applicability information comprises sending (212; 408; 528) a complete message comprising the applicability information for the at least one Al or ML model associated to the functionality with which the UE is configured and / or is being configured.

4. The method of claim 3, wherein the complete message is a Radio Resource Control, RRC, resume complete message, an RRC setup complete message, or an RRC reconfiguration complete message.

5. The method of claim 3 or 4, further comprising: sending (200; 400) a request message to the network node; receiving (208; 404) a response message from the network node responsive to the request message; wherein sending (212; 408) the complete message comprising the applicability information comprises sending (212; 408) the complete message after receiving the response message.

6. The method of claim 5 wherein the request message is an RRC resume request, the response message is an RRC resume message, and the complete message is an RRC resume complete message.

7. The method of claim 5 wherein the request message is an RRC setup request, the response message is an RRC setup message, and the complete message is an RRC setup complete message.

8. The method of any of claims 5 to 7, wherein the response message comprises configuration information for the functionality associated to the at least one Al or ML model or information for activation of the functionality associated to the at least one Al or ML model.

9. The method of claim 8, wherein sending (212; 408) the complete message comprises sending (212; 408) the complete message responsive to the configuration information for the functionality associated to the at least one Al or ML model or the information for activation of the functionality associated to the at least one Al or ML model.

10. The method of claim 3 or 4, further comprising: receiving (524) a reconfiguration message from the network node; wherein sending (528) the complete message comprising the applicability information comprises sending (528) the complete message after receiving the response message.

11. The method of claim 10 wherein the reconfiguration message is an RRC reconfiguration message, and the complete message is an RRC reconfiguration complete message.

12. The method of claim 10 or 11, wherein the reconfiguration message comprises configuration information for the functionality associated to the at least one Al or ML model or information for activation of the functionality associated to the at least one Al or ML model.

13. The method of claim 12, wherein sending (528) the complete message comprises sending (528) the complete message responsive to the configuration information for the functionality associated to the at least one Al or ML model or the information for activation of the functionality associated to the at least one Al or ML model.

14. The method of claim 1 or 2, wherein sending the applicability information comprises sending the applicability information in or with a request message.

15. The method of claim 14 wherein the request message in an RRC resume request.

16. A User Equipment, UE, 612; 700) for reporting applicability information of at least one Artificial Intelligence, Al, or Machine Learning, ML, model associated to a functionality, the UE adapted to: send (212; 304; 408; 528), to a network node, applicability information for at least one Al or ML model associated to a functionality with which the UE is configured and / or is being configured.

17. The UE of claim 16, further adapted to perform the method any of claims 2 to 15.

18. A User Equipment, UE, (612; 700) for reporting applicability information of at least one Artificial Intelligence, Al, or Machine Learning, ML, model associated to a functionality, the UE (612; 700) comprising: a communication interface (712) comprising a transmitter (718) and a receiver (720); and processing circuitry (702) associated with the communication interface (712), the processing circuitry (702) configured to cause the UE (612; 700) to send (212; 304; 408; 528), to a network node, applicability information for at least one Al or ML model associated to a functionality with which the UE is configured and / or is being configured.

19. The UE of claim 18, wherein the processing circuitry (702) is further configured to cause the UE (612; 700) to perform the method any of claims 2 to 15.

20. A method performed by a network node for obtaining applicability information of at least one Artificial Intelligence, Al, or Machine Learning, ML, model associated to a functionality, the method comprising: receiving (212; 304; 408; 528), from a User Equipment (UE), applicability information for at least one Al or ML model associated to a functionality with which the UE is configured and / or is being configured.

21. The method of claim 20, wherein the applicability information of the at least one Al or ML model associated to the functionality comprises any one or more of the following: an indication that the at least one Al or ML model associated to the functionality is not applicable; an indication functionality associated to the at least one Al or ML model is not applicable; a cause value; a suggestion or recommendation or reconfiguration about how to make the at least one Al or ML model applicable.

22. The method of claim 20 or 21, wherein receiving (212; 408; 528) the applicability information comprises receiving (212; 408; 528) a complete message comprising the applicability information for the at least one Al or ML model associated to the functionality with which the UE is configured and / or is being configured.

23. The method of claim 22, wherein the complete message is any one or more of: an RRC resume complete message, an RRC setup complete message, and an RRC reconfiguration complete message.

24. The method of claim 22 or 23, further comprising: receiving (200; 400) a request message from the UE; sending (208; 404) a response message to the UE responsive to the request message; wherein receiving (212; 408) the complete message comprising the applicability information comprises receiving (212; 408) the complete message after sending the response message.

25. The method of claim 24 wherein the request message is an RRC resume request, the response message is an RRC resume message, and the complete message is an RRC resume complete message.

26. The method of claim 24 wherein the request message is an RRC setup request, the response message is an RRC setup message, and the complete message is an RRC setup complete message.

27. The method of any of claims 24 to 26, wherein the response message comprises configuration information for the functionality associated to the at least one Al or ML model or information for activation of the functionality associated to the at least one Al or ML model.

28. The method of claim 27, wherein receiving (212; 408) the complete message comprises receiving (212; 408) the complete message responsive to the configuration information for the functionality associated to the at least one Al or ML model or the information for activation of the functionality associated to the at least one Al or ML model.

29. The method of claim 22 or 23, further comprising: sending (524) a reconfiguration message to the UE; wherein receiving (528) the complete message comprising the applicability information comprises receiving (528) the complete message after sending the response message.

30. The method of claim 29 wherein the reconfiguration message is an RRC reconfiguration message, and the complete message is an RRC reconfiguration complete message.

31. The method of claim 29 or 30, wherein the reconfiguration message comprises configuration information for the functionality associated to the at least one Al or ML model or information for activation of the functionality associated to the at least one Al or ML model.

32. The method of claim 31, wherein receiving (528) the complete message comprises receiving (528) the complete message responsive to the configuration information for the functionality associated to the at least one Al or ML model or the information for activation of the functionality associated to the at least one Al or ML model.

33. The method of claim 20 or 21, wherein sending the applicability information comprises sending the applicability information in or with a request message.

34. The method of claim 33 wherein the request message in an RRC resume request.

35. The method of any of claims 20 to 34, further comprising performing one or more actions based on the applicability information.

36. A network node (610; 800) for obtaining applicability information of at least one Artificial Intelligence, Al, or Machine Learning, ML, model associated to a functionality, the network node (610; 800) adapted to: receive (212; 304; 408; 528), from a User Equipment (UE), applicability information for at least one Al or ML model associated to a functionality with which the UE is configured and / or is being configured.

37. The network node (610; 800) of claim 36, further adapted to perform the method of any of claims 21 to 35.

38. A network node (610; 800) for obtaining applicability information of at least one Artificial Intelligence, Al, or Machine Learning, ML, model associated to a functionality, the network node (610; 800) comprising: a communication interface (806); and processing circuitry (802) associated with the communication interface (806), the processing circuitry (802) configured to cause the network node (610; 800) to receive (212; 304; 408; 528), from a User Equipment (UE), applicability information for at least one Al or ML model associated to a functionality with which the UE is configured and / or is being configured.

39. The network node (610; 800) of claim 38, further adapted to perform the method of any of claims 21 to 35.

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