Method for user equipment reconfiguration failure with ai / ml

Abstract

Embodiments described herein relate to methods for User Equipment reconfiguration failure with Artificial Intelligence / Machine Learning (AI(ML). A method performed by a UE comprises receiving, from a RAN, node, a RRC configuration message, the RRC configuration message comprises a RRC configuration and one or more Machine Learning, ML, fields relating to at least one ML model; determining whether one or more of the one or more ML fields are applicable if the RRC configuration is applied; and responsive to determining that one or more of the one or more ML fields are not applicable, one or both of: ignoring or not using the one or more ML fields that are not applicable; and storing the one or more ML fields that are not applicable.

Description

[0001] METHOD FOR USER EQUIPMENT RECONFIGURATION FAILURE WITH AI / ML

[0002] TECHNICAL FIELD

[0003] Embodiments described herein relate to methods and apparatus for user equipment reconfiguration failure with AI / ML.

[0004] BACKGROUND

[0005] Artificial Intelligence (AI)ZMachine Learning (ML) for Physical layer (PHY)

[0006] Artificial Intelligence (Al) and Machine Learning (ML) have been investigated, both in academia and industry, as promising tools to optimize the design of the air-interface in wireless communication networks. 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 signalling overhead and beam alignment latency; using deep reinforcement learning to learn an optimal precoding policy for complex Multiple Input Multiple Output (MIMO) precoding problems.

[0007] In 3rdGeneration Partnership Project (3GPP) New Radio (NR) standardization work, a new release 18 study item on AI / ML (also referred to herein as AIML) for the NR air interface started in May 2022. This study item explored 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. The analysis carried out during the Rel.18 is now considered in the context of Rel.19 work item. Additionally, during the rel. 19, a new study item addressing AIML for mobility has been approved. In the context of this new study item, 3GPP will investigate methods for cell-level measurement predictions, and mobility event predictions (e.g. Radio Link Failure (RLF), handover failure, mobility-related events predictions such as A3 / A5).

[0008] Applicability reporting

[0009] The use cases considered in of beam prediction which will be standardized as part of 3GPP Rel.19 work item consists of spatial beam prediction, and temporal beam prediction. The core idea of AIML applied to the Radio Access Network (RAN) is to enable a UE to predict / infer certain performances or certain measurements on a given set A of resources based on experienced performances or performed measurements on a set B of resources, wherein the resources could be for example associated to reference signals (Synchronization Signal Block (SSB) / Channel State Information Reference Signal (CSI-RS) or Positioning-RS (P-RS resources) or frequencies, depending on the specific AIML use case.

[0010] For example, in the case of AIML-based beam management (which is considered by 3GPP in the context of Rel.19), the use case is to predict / infer the “best” beam (or beams) from a Set A of beams (SSB / CSI-RS resources) using measurement results from another Set B of beams (SSB / CSI-RS resources). In particular, according to TR 38.843, the spatial-domain beam prediction for Set A of beams is based on measurement results of Set B of beams, whereas the temporal beam prediction for Set A of beams is based on the historic measurement results of Set B of beams.

[0011] Whether the UE can perform the AI / ML inference given a certain Set A / B of resources, depends on whether the UE has available an AIML model / functionality which can perform the inference / prediction on those Set A of resources given the measurements on the Set B. In other words, on whether the AIML model / functionality is applicable under that Set A / B configuration. For this reason, information related to whether an AIML model / functionality is applicable or not at the UE turns out to be a crucial information that the network needs to know, in order to properly configure the UE with an inference configuration that enables the AIML model / functionality to possibly outperform conventional non-AIML based schemes.

[0012] To this end, applicability reporting has been discussed during the Rel.18 study item, to allow the UE to inform the gNodeB (gNB) about the applicability of an AIML model / functionality while the UE is connected to this gNB. An AIML model / functionality may be applicable or not depending on a number of factors, so called applicability conditions, that are only partly under the control of the gNB. The applicability of a UE-side AIML functionality might be a quite dynamic property, depending for example on whether the UE has an AI / ML model that is applicable given the current location of the UE (e.g. geographical location, or site / cell to which the UE is connected), or given the specific radio condition that the UE is experiencing, or given the current speed of the UE, or given other inputs / measurements performed by device-specific sensors or algorithms. Some of these factors cannot be control led / known by the gNB, because typically it is assumed that the UE-side model is not trained and generated by the gNB (rather, it is typically assumed that the UE-side model is trained and generated by a node outside the RAN, such as an Over-The-Top (OTT) server or Core Network (CN) function controlled by the UE-vendor or by the Mobile Network Operator (MNO)).

[0013] In general, the applicability of an AIML model / functionality to perform inference under certain conditions depends on whether such AIML model / functionality has been trained under such conditions. If this is the case, the performances of an AIML-based inference scheme can outperform conventional methods, otherwise this might not be the case. For example, if network conditions, so-called network (NW)-side additional conditions, (such as transmitting power, antenna configuration, deployment, SSB configuration, etc.) at the time in which the UE is performing the inference do not match the network conditions during the training (e.g. performed by the UE at a previous point in time), then it is likely that the Al ML performances will be worse than the performances achieved via conventional methods, e.g. AIML-based beam management will lead to poor / inaccurate results.

[0014] Two types of applicability reporting were identified during the Rel.18 study item and are currently being discussed in RAN2 for the normative phase, the so-called reactive approach and the proactive approach, further detailed in.

[0015] In the proactive approach, the network enquires the UE capabilities and configures the UE to report the applicability of an AI / ML functionality and, based on the reported information the network configures the UE with an inference configuration, as follows:

[0016] Example of proactive reporting of applicability

[0017] Step 1 : Network sends UECapabilityEnquiry message to initiate the procedure to a UE reporting its AI / ML supported functionalities.

[0018] Step 2: UE sends UECapabilitylnformation message to network, containing supported functionalities at the UE side.

[0019] Step 3: Network configures UE that it is allowed to provide its applicable functionalities.

[0020] Step 4: UE sends applicable functionalities to network upon change of applicable functionality / condition.

[0021] Step 5: Network sends inference configuration for the applicable functionalities to the UE.

[0022] Step 6: Start inference / monitoring based on network / UE activation / deactivation.

[0023] Example of reactive reporting of applicability

[0024] In the reactive approach, the network enquires the UE capabilities and configures the UE with the AI / ML functionality (possibly including the inference configuration) in response to which the UE is able to determine the applicability of the AI / ML functionality and, in case the configured AI / ML functionality is applicable, the functionality could be up and running as soon as possible, without the need to an additional reconfiguration, as follows:

[0025] Step 1 : Network sends UECapabilityEnquiry message to initiate the procedure to a UE reporting its AI / ML supported functionalities.

[0026] Step 2: UE sends UECapabilitylnformation message to network, containing supported functionalities at the UE side.

[0027] Step 3: Network provides network configurations and initiates UE to report its applicable functionalities.

[0028] Step 4: UE sends applicable functionalities to network.

[0029] Step 5: Network sends updated inference configuration for applicable functionalities reported in Step 4 to the UE.

[0030] Step 6: Start inference / monitoring based on network / UE activation / deactivation. NW- / UE-side additional conditions

[0031] The NW- / UE-side additional conditions represent a fundamental concept in the context of AIML for Radio Access Network (RAN). Such conditions represent a set of operating conditions at a network node, and at the UE at the moment of the AIML training and inference. They can be network or UE properties, e.g. transmitting / receiving power, antenna orientation / layout, deployment, etc. or radio configuration properties, such as beam / cell configuration. The importance of this concept is critical because the NW- / UE-side additional conditions experienced / configured at the moment of training should match, or at least be compatible with the NW- / UE-side additional conditions during the inference. This is because the data collected by the training entity, i.e. the so-called training data set, and based on which the AIML model / functionality is generated, should match the environment at the moment of inference. As a simple example, if the UE has collected data considering a certain set A and B during the training, and then during the inference the set A and B is not part of the inference configuration, then the AIML model / functionality would not give optimal results, and likely the AIML performances will be worse than the performances that can be achieved via conventional non- AIML schemes. In general, any NW- / UE-property can influence the final training data set and hence the way the model is generated.

[0032] So for this reason a well-functioning AIML protocol may ensure consistency of the NW- / UE- side additional conditions during inference and training.

[0033] SUMMARY

[0034] Focusing on the UE-side model, it can be assumed that the UE is in control of the UE-side additional conditions, and a good AIML implementation at the UE side can make sure that the UE-additional conditions during the training matches, or at least are compatible, with the conditions during the inference. On the other hand, for the NW-side additional conditions, the network node may assist the UE. For this reason, as part of the applicability evaluation protocol, the network can inform the UE about the NW-side additional conditions that the UE should use to evaluate the applicability of the AIML models / functionalities. Similar assistance is also expected during the training configuration, so that the UE can figure out the NW-side additional conditions at the moment of UE-side model training.

[0035] There currently exist certain challenge(s). In legacy (non-AI / ML) specifications, the Radio Resource Control (RRC) configuration / functionality was described by two aspects at the UE: support (whether the UE is capable at all to support a functionality / parameter / configuration) and activation (whether the UE is told by the NW to apply a configuration that is supported). For AI / ML, a new state of the RRC configuration for inference is introduced, namely “applicable” (i.e. whether the UE has a trained AI / ML model and is ready to start applying a supported functionality / configuration). This new “applicable” state is evaluated by the UE for each of the one or more AIML models / functionalities that the UE may support. In particular, whether an AIML model / functionality is applicable or not may be determined by the UE upon receiving from the gNB the NW-side additional conditions or upon receiving from the gNB an RRC reconfiguration including an AIML inference configuration.

[0036] The current standard does not explicitly specify how the UE should behave if the NW sends an RRC configuration (including for example an inference configuration) that according to the UE radio capabilities is supported by the UE, but that is not applicable at the UE, due to the fact that the AIML model / functionality is not applicable if the said RRC configuration is applied. This may result in ambiguous / unwanted UE behaviour.

[0037] For example, if the UE follows the legacy standardized procedure for RRC reconfiguration failure, the UE may declare itself to not be able to comply with the said RRCReconfiguration. As such, the UE may declare a radio link failure, and initiate the RRC connection re-establishment procedure, which would needlessly cause heavy signalling and interrupt the data transfer.

[0038] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. Some embodiments of this disclosure include a method for the UE to handle AI / ML configurations in the RRCReconfiguration message that are supported according to the UE radio capabilities, but that are not applicable due to the fact that the AIML model / functionality is not applicable if the said RRC configuration is applied.

[0039] In particular, some embodiments of this disclosure include the following two methods in which the UE determines from the AIML related fields included in a reconfiguration message (e.g. RRCReconfiguration) including an AI / ML configuration (for an AI / ML functionality), wherein:

[0040] 1 . If there is an inability to comply with the one or more of the AIML fields included in the reconfiguration message (e.g. RRCReconfiguration), the UE ignores the AI / ML related configuration / fields that are supported but not applicable.

[0041] 2. If there is an inability to comply with the one or more of the AIML fields included in the reconfiguration message (e.g. RRCReconfiguration), the UE stores the AI / ML configuration / fields that are supported but not applicable.

[0042] In one embodiment, when the UE receives the reconfiguration message including an AI / ML configuration and determines that it is unable to comply with the reconfiguration message due to the AI / ML configuration not being applicable (i.e. being non-applicable), the UE does not declare RRC Reconfiguration failure and as such it does not trigger an RRC Re-establishment procedure.

[0043] In one embodiment, when the UE receives the reconfiguration message including an AI / ML configuration and determines that it is unable to comply with the reconfiguration message due to the AI / ML configuration being associated to an AI / ML model which is not available, the UE does not declare RRC Reconfiguration failure and as such it does not trigger an RRC Re-establishment procedure.

[0044] In one embodiment, when the UE receives the reconfiguration message including an AI / ML configuration and determines that it is unable to comply with at least parts of the reconfiguration message due to the UE not being capable of the AI / ML configuration, the UE triggers an RRC Re-establishment procedure.

[0045] In one embodiment, the UE receives the reconfiguration message including an AI / ML configuration and determines that it is unable to comply with the reconfiguration message when the UE is not able to comply at least partially (e.g. with the AIML-related fields) with a configuration within the reconfiguration message, except when the inability to comply is due to the AI / ML configuration not being applicable. In other words, the UE would not really consider that an inability to comply with the reconfiguration message and, consequently, the UE would not trigger the actions that would follow from that: i.e. the UE would not trigger an RRC Re-establishment when the reconfiguration message includes the AI / ML configuration which is not applicable (unless in addition to that, the reconfiguration message also includes other configuration(s) for which the UE is not capable of).

[0046] In some methods disclosed in the IvD, the above methods are not just applied upon receiving the reconfiguration message including an AIML configuration, but also upon determining that an AIML model / functionality becomes applicable or not applicable (after being non-applicable or applicable, respectively) based on the AIML-related fields included in a previously received reconfiguration message and stored by the UE.

[0047] According to some embodiments, there is provided a method performed by a UE. The method comprises receiving, from a Radio Access Network, RAN, node, a Radio Resource Control, RRC, configuration message. The RRC configuration message comprises a RRC configuration and one or more Machine Learning, ML, fields relating to at least one ML model. The method comprises determining whether one or more of the one or more ML fields are applicable if the RRC configuration is applied. The method comprises responsive to determining that one or more of the one or more ML fields are not applicable, one or both of: ignoring or not using the one or more ML fields that are not applicable, and storing the one or more ML fields that are not applicable.

[0048] According to some embodiments, there is provided a method performed by a UE. The method comprises receiving, from a Radio Access Network, RAN, node, a Radio Resource Control, RRC, configuration message. The RRC configuration message comprises a RRC configuration and one or more Machine Learning, ML, fields relating to at least one ML model. The method comprises determining whether the one or more ML fields are applicable if the RRC configuration is applied. The method comprises responsive to determining that the one or more ML fields are not applicable, one or both of: ignoring or not using the one or more ML fields, and storing the one or more ML fields.

[0049] According to some embodiments, there is provided a method performed by a RAN node. The method comprises transmitting, to a User Equipment, UE, a Radio Resource Control, RRC, configuration message. The RRC configuration message comprises a RRC configuration and one or more Machine Learning, ML, fields relating to at least one ML model. The method comprises receiving, from the UE, an applicability report relating to the applicability of the one or more ML fields if the RRC configuration is applied.

[0050] According to some embodiments, there is provided a computer program product. The computer program product comprises a computer readable medium having computer readable code embodied therein. The computer readable code being configured such that, on execution by a suitable computer or processor, the computer or processor is caused to perform the method performed by the UE or by the RAN node as described herein.

[0051] According to some embodiments, there is provided a UE. The UE is configured to receive, from a Radio Access Network, RAN, node, a Radio Resource Control, RRC, configuration message. The RRC configuration message comprises a RRC configuration and one or more Machine Learning, ML, fields relating to at least one ML model. The UE is configured to determine whether one or more of the one or more ML fields are applicable if the RRC configuration is applied. The UE is configured to responsive to determine that one or more of the one or more ML fields are not applicable, one or both of: ignore or not use the one or more ML fields that are not applicable, and store the one or more ML fields that are not applicable.

[0052] According to some embodiments, there is provided a UE. The UE comprises a processor and a memory, said memory containing instructions executable by said processor whereby said UE is operative to perform the method performed by the UE as described herein.

[0053] According to some embodiments, there is provided a RAN node. The RAN node is configured to transmit, to a User Equipment, UE, a Radio Resource Control, RRC, configuration message. The RRC configuration message comprises a RRC configuration and one or more Machine Learning, ML, fields relating to at least one ML model. The RAN node is configured to receive, from the UE, an applicability report relating to the applicability of the one or more ML fields if the RRC configuration is applied.

[0054] According to some embodiments, there is provided a RAN node. The RAN node comprises a processor and a memory, said memory containing instructions executable by said processor whereby said RAN node is operative to perform the method performed by the RAN node as described herein. Certain embodiments may provide one or more of the following technical advantage(s). The proposed techniques avoid the ambiguity at the UE side when the UE is unable to comply with the RRCReconfiguration, due to the RRC reconfiguration not being applicable but supported. Hence, the advantage of some embodiments of this disclosure is twofold:

[0055] 1. The first method avoids the UE declaring a reconfiguration failure which would lead to declaring a radio link failure when it receives an RRC configuration whose AI / ML related configurations / parameters / fields cannot be applied. This avoids unnecessary RRC connection re-establishment procedures which would cause signalling overhead and ultimately affect UE performances.

[0056] 2. The second method allows the UE to store a received AI / ML configuration / fields included in an RRC reconfiguration, even if they are not applicable at the moment. In such a way the UE can continue evaluating the applicability of one or more AIML models / functionalities and determine if one or more AIML models / functionalities becomes applicable if the concerned AIML configuration / fields are applied.

[0057] BRIEF DESCRIPTION OF THE DRAWINGS

[0058] For a better understanding of the embodiments of the present disclosure, and to show how it may be put into effect, reference will now be made, by way of example only, to the accompanying drawings, in which:

[0059] Fig. 1 depicts a flow chart illustrating a method in accordance with some embodiments;

[0060] Fig. 2 depicts a flow chart illustrating a method in accordance with some embodiments;

[0061] Fig. 3 shows an example of a communication system in accordance with some embodiments;

[0062] Fig. 4 shows a UE in accordance with some embodiments;

[0063] Fig. 5 shows a network node in accordance with some embodiments; and

[0064] Fig. 6 shows a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized.

[0065] DETAILED DESCRIPTION

[0066] Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0067] In the context of some embodiments of this disclosure, the term “AI / ML functionality” may be called a “supported functionality” the UE can indicate by using UE capability signalling. A supported functionality is one or more functionalities for and / or associated to beam management and / or CSI reporting, or mobility operations, such as the reporting of time domain and / or spatial domain or frequency domain predictions (inference). It may be described as the ability the UE has to produce an output of an inference function. For example, reporting of time-domain prediction(s) of SSB and / or CSI-RS measurement information (e.g. predicted RSRP) may be considered as an AI / ML functionality which is a “supported functionality” by the UE when the UE reports a capability associated to it (via RRC or Long Term Evolution (LTE) Positioning Protocol (LPP) signalling).

[0068] For example, “spatial domain prediction for beam management or mobility procedure e.g., handover or reconfiguration with sync, or Primary cell (PCell) change, or Primary Secondary Cell Group cell (PSCell) change” or a related functionality (e.g. reporting and inference of spatial domain info) may be a supported functionality in which the UE may report that is capable of performing and reporting inference / prediction of a set A of beams or cells (e.g. predicted Layer-1 (L1) Reference Signal Received Power (RSRP) values of one or more beams or one or more SSB indexes of a cell or predicted L1 or Layer-3 (L3) RSRP values of one or more cells) based on measurements performed on a set B of beams (e.g. measured L1 / L3 RSRP values of one or more beams or one or more SSB indexes of a cell), in the case of spatial domain predictions.

[0069] For example, “frequency domain prediction for beam management or mobility procedure e.g., handover” or a related functionality (e.g. reporting and inference of frequency domain info) may be a supported functionality in which the UE may indicate that is capable of performing and reporting inference (e.g., prediction of the radio link quality of a set A of beams or cells (e.g. predicted L1 RSRP values of one or more beams or one or more SSB indexes of a cell or predicted L1 or L3 RSRP values of one or more cells) based on measurements performed on a set B of beams (e.g. measured L1 / L3 RSRP values of one or more beams or one or more SSB indexes of a cell or one or more cells), in the case of frequency domain predictions.

[0070] For example, “time domain prediction for beam management or a mobility procedure e.g., handover or reconfiguration with sync, or Primary cell (PCell) change, or Primary Secondary Cell Group cell (PSCell) change” or a related functionality (e.g. reporting and inference of time domain info) may be a supported functionality in which the UE may report that is capable of performing and reporting inference of a set of A of beams (e.g. predicted L1 / L3 RSRP values of one or more beams or one or more SSB indexes of a cell in future time instances or the L1 / L3 RSRP value of one or more cells in the future time instances) based on measurements performed on a set B of beams or cells (e.g. measured L1 / L3 RSRP values of one or more beams or one or more SSB indexes of a cell and / or L1 / L3 RSRP value of one or more cells), in the case of time domain predictions.

[0071] For example, beam management - Downlink (DL) Transmitted (Tx) beam prediction for both UE-sided model and NW-sided model, including:

[0072] • Spatial-domain DL Tx beam prediction for Set A of beams based on measurement results of Set B of beams (“BM-Case1”).

[0073] • Temporal DL Tx beam prediction for Set A of beams based on the historic measurement results of Set B of beams (“BM-Case2”).

[0074] For example, positioning accuracy enhancements, including:

[0075] • Direct AI / ML positioning, such as:

[0076] ■ UE-based positioning with UE-side model, direct AI / ML positioning.

[0077] ■ UE-assisted / Location Management Function (LMF)-based positioning with LMF-side model, direct AI / ML positioning.

[0078] ■ Next Generation (NG)-RAN node assisted positioning with LMF-side model, direct AI / ML positioning.

[0079] • AI / ML assisted positioning, such as:

[0080] ■ UE-assisted / LMF-based positioning with UE-side model, AI / ML assisted positioning.

[0081] ■ NG-RAN node assisted positioning with gNB-side model, AI / ML assisted positioning.

[0082] For example, CSI compression e.g., considering extending the spatial / frequency compression to spatial / temporal / frequency compression, cell / site specific models, CSI compression plus prediction (compared to Rel-18 non-AI / ML based approach).

[0083] For example, “Radio Link Failure prediction of serving and / or neighbour cells” or a related functionality (e.g. reporting and inference of RLF prediction of serving and / or neighbour cell(s)) may be a supported functionality in which the UE may report that is capable of performing and reporting inference of an RLF in future time instances.

[0084] For example, “Handover Failure (HOF) prediction of a cell” or a related functionality (e.g. reporting and inference of HOF prediction of a cell) may be a supported functionality in which the UE may report that is capable of performing and reporting inference of an HOF in future time instances.

[0085] In the context of some embodiments of this disclosure, the term “inference configuration” or “an inference related configuration” may include one or more Al ML related fields / parameters corresponding to a mobility prediction configuration or Radio resource management (RRM) measurement configuration associated to a mobility procedure to run the inference and report the predictions. That may include one or more of: Measurement configurations (e.g., measConfig) including one or more of the following AIML related fields / parameters.

[0086] • Measurement configurations may comprise a list of one or more measurement objects to add / modify / remove by the UE which may further include any one of the following examples.

[0087] ■ a list of one or more measurement objects may comprise SSB frequency.

[0088] ■ a list of one or more measurement objects may comprise CSI-RS frequency.

[0089] ■ a list of one or more measurement objects may comprise SSB subcarrier spacing.

[0090] ■ a list of one or more measurement objects may comprise measurement timing configuration.

[0091] ■ a list of one or more measurement objects may comprise SSB and or CSI-RS beam consolidation configuration / threshold.

[0092] ■ a list of one or more measurement objects may comprise Number of SSB or CSI-RS measurements to average.

[0093] • Measurement configurations may comprise a list of one or more report configuration to add / modify / remove by the UE.

[0094] • Measurement configurations may comprise SpCell RSRP measurement controlling when the UE is required to perform measurements on non-serving cells.

[0095] • Measurement configurations may comprise Measurement gap configuration e.g., a measurement gap identifier (ID), identifying the measurement gap ID per frequency range (FR).

[0096] • Measurement configurations may comprise Measurement quantity configuration.

[0097] In the context of some embodiments of this disclosure, an inference configuration or an inference related configuration may include one or more AIML related fields / parameters such as the parameters identifying a first set (set A) of measurement resources (e.g. beams, SSB indexes and / or CSI-RS resource identifiers, Mobility Refence Signal identifiers) in which the UE performs radio measurement predictions (inferences, such as predicted RSRP values), and a second set (set B) of radio measurement resources (e.g. beams, SSB indexes and / or CSI-RS resource identifiers, Mobility Refence Signal identifiers) in which the UE can perform radio measurement in order to determine the radio measurement predictions on the first set. That may also include one or more configuration(s) associated to network side (NW-side) additional conditions reflecting the NW operational properties, such as the following AIML related fields / parameters exemplified below.

[0098] - AI / ML related fields / parameters may comprise Set A and / or Set B of resources, represented e.g. by an ID associated to the set of resources or to the resources within the set.

[0099] - AI / ML related fields / parameters may comprise Mapping relationship of Set A and Set B, including ordering to (a set of ID, or resource).

[0100] - AI / ML related fields / parameters may comprise Consistency of downlink spatial domain transmission filters corresponding to the beams in Set A and Set B.

[0101] - AI / ML related fields / parameters may comprise Quasi co-located (QCL) assumption.

[0102] - AI / ML related fields / parameters may comprise the order of model input and model output between Reference Signal (RS) and Tx beams can be pre-defined.

[0103] - AI / ML related fields / parameters may comprise gNB transmission power.

[0104] - AI / ML related fields / parameters may comprise UE distribution.

[0105] - AI / ML related fields / parameters may comprise gNB antenna height and / or other antenna properties.

[0106] - AI / ML related fields / parameters may comprise network deployment scenarios (e.g., Inter-Site Distance (ISD), Urban Micro (Umi) / Urban Macro (Uma)).

[0107] - AI / ML related fields / parameters may comprise NW-side resource configuration(s) which may be considered as NW implementation-based configurations which may possibly impact the inference performance for a UE sided model. For instance, beam and Tx port mapping relationship in the gNodeB for a given cell, NW antenna shape, Antenna dip angle, height of the tower / gNB, etc.

[0108] - AI / ML related fields / parameters may comprise NW load in terms of connected users, radio resource utilizations (Physical Downlink Control Channel (PDCCH) / Random Access Channel (RACH) / Physical Uplink Control Channel (PUCCH) / Physical Uplink Shared Channel (PUSCH) / Physical Downlink Shared Channel (PDSCH) resource load, number of configured bearers, etc).

[0109] The inference configuration or an inference related configuration which the UE has received in the first cell may include a list of IDs referring to the set A and set B (or to the resources within the set A / B), and referring to one or more NW-side additional conditions.

[0110] In the context of some embodiments of this disclosure, the AI / ML functionality configuration may include one or more of the following below examples.

[0111] - AI / ML functionality configuration may comprise an inference configuration with associated AIML related fields / parameters for a beam management functionality (e.g. time-domain prediction of beam information)

[0112] • In one example, the inference configuration includes the reporting configuration including parameters indicating how the UE is to report time-domain predictions of beam information (e.g. beam indexes and / or SSB indexes and / or time-domain prediction of beam measurements) and / or spatial-domain predictions of beam information.

[0113] • In one example, the inference configuration includes the resource configuration for resources (e.g. SSB indexes and / or CSI-RS resources) which the UE measures and provides as input to an AI / ML model (or inference function) to produce inference outputs e.g. the actual time-domain predictions of beam information (e.g. beam indexes and / or SSB indexes and / or time-domain prediction of beam measurements) and / or spatial-domain predictions of beam information to be included in a report.

[0114] - AI / ML functionality configuration may comprise an inference configuration with associated Al ML related fields / parameters for a L3 Mobility functionality

[0115] • In one example, the inference configuration includes the reporting configuration including parameters indicating how the UE is to report time-domain predictions for neighbour cell(s) which are candidates for a connected mode inter-cell mobility procedure, or for serving cells. The time-domain predictions may be cell identifiers, time-domain predictions of measurements, such as predicted RSRP, predicted RSRQ, as predicted SINR and / or spatial-domain predictions of cell(s).

[0116] • In one example, the inference configuration includes the configuration for the UE to predict the occurrence of a Radio Link failure (RLF) in the second cell.

[0117] • In one example, the inference configuration includes the configuration for the UE to predict the future occurrence of a Handover failure (HOF) when the UE is in second cell and later would move to yet another cell.

[0118] • In one example, the inference configuration includes the configuration for the UE to predict the future occurrence of the fulfilment of a measurement reporting event such as an event A1 , A2, A3, A4, A5, A6, B1 , B2, etc.

[0119] - AI / ML functionality configuration may comprise an inference configuration with associated AIML related fields / parameters for positioning functionality

[0120] - AI / ML functionality configuration may comprise an inference configuration with associated AIML related fields / parameters for CSI reporting functionality

[0121] - AI / ML functionality configuration may comprise a configuration enabling the UE to determine whether the AI / ML functionality is applicable or not. • In one option the UE has reported whether the AI / ML functionality of the second cell (which is the target cell) was applicable or not. However, as that may have changed from the time the UE has transmitted the report (until the time in which the UE is to access the second cell in the handover), the UE can be provided an AI / ML configuration in the handover command, based on which the UE can assess if the AI / ML functionality is applicable or not.

[0122] - AI / ML functionality configuration may comprise network conditions such as Set A / set B configuration(s).

[0123] - AI / ML functionality configuration may comprise a state indication for the AI / ML functionality, e.g., ‘activated’, ‘inactivated’, ‘deactivated’.

[0124] - AI / ML functionality configuration may comprise an indication on whether the UE is allowed to consider the AI / ML functionality as ‘activated’ when the functionality is determined by the UE to be applicable.

[0125] Some embodiments of this disclosure include methods (method 100 and method 200 of Figures 1 and 2 respectively) for a UE (such a UE QQ112A or UE QQ200) to evaluate whether the AIML related fields included in a reconfiguration message (e.g. an RRC reconfiguration, or an RRC Resume) message may be applied by the UE or ignored, wherein the decision to apply or not apply an AIML related field is based on whether the UE is able or unable to comply with such AIML related field, due to one or more AI / ML models / functionalities being applicable or not being applicable.

[0126] The AIML related fields may be included in an inference configuration for the UE to perform the AIML inference according to one or more AIML models / functionalities. The one or more AIML models / functionalities for which the UE may perform the inference may be indicated as part of the inference configuration.

[0127] Upon receiving 102, 202 the said AIML related fields, the UE may evaluate whether the AIML model / functionality, e.g. the one or more AIML models / functionalities indicated in the inference configuration, is applicable or not applicable according to such AIML related fields.

[0128] In a first method 100, the UE determines 104 that the concerned AIML model / functionality is not applicable given such an inference configuration, hence the UE may ignore all the associated AIML related fields included in the configuration (first set of AIML related fields). This can be for example the case in which the gNB includes in the inference configuration a set of radio resources in which the UE may perform radio measurements or radio predictions according to the AIML functionality, but the AIML functionality is not applicable according to such radio resources, e.g. the UE has not trained according to such radio resources, or other UE conditions for the applicability of the AIML functionality are not fulfilled.

[0129] The AIML related fields for which the AIML model / functionality is not applicable may be ignored 104 by the UE, so that the UE does not apply them and it does not declare an RRC Reconfiguration failure. According to this method, the UE applies other parts of the radio configuration for which the UE is able to comply.

[0130] However, the UE may store 106 such ignored AIML related fields. The UE keeps evaluating the applicability conditions of the concerned AIML models / functionalities based on the stored (and previously ignored) AIML related fields of the first set. The UE may then determine that the concerned AIML models / functionalities that were not applicable may become applicable according to the said AIML related fields. This could be for example due to the UE operating conditions being changed (e.g. the UE speed change, the device antenna properties, the radio or other sensors’ measurements change, the UE location, etc), or the results of AIML performance monitoring procedures, according to which the UE assesses from performed radio measurements any change in the applicability conditions of the AIML model / functionality. In such case the stored AIML related fields that were previously ignored may be now applied, and in response of applying such AIML related fields, the UE may also declare as applicable the concerned AIML models / functionalities, and in turn it may transmit to the gNB an applicability report indicating that the concerned AIML models / functionalities are applicable. Further, the UE may move the set of applied AIML related fields from the first set of AIML related fields to the second set of AIML related fields (which includes the set of AIML related fields currently applied by the UE).

[0131] In a second method 200, the UE determines that the concerned AIML model / functionality is applicable given such an inference configuration, hence the UE may apply (and hence store) all the associated AIML related fields included in the configuration (second set of AIML related fields). This can be for example the case in which the gNB includes in the inference configuration a set of radio resources in which the UE may perform radio measurements or radio predictions according to the AIML functionality, and the AIML functionality is applicable according to such radio resources, e.g. the UE has trained according to such radio resources, and any other UE operating condition (e.g. the UE speed change, the device antenna properties, the radio or other sensors’ measurements change, the UE location, etc.) for the applicability of the AIML functionality is fulfilled.

[0132] The UE keeps evaluating the applicability conditions of the concerned AIML models / functionalities based on the applied AIML related fields of the second set. The UE may then determine 204 that the concerned AIML models / functionalities that were applicable may become not-applicable according to the said AIML related fields. This could be for example due to the UE operating conditions being changed (e.g. the UE speed change, the device antenna properties, the radio or other sensors’ measurements change, the UE location, etc), or the results of AIM L performance monitoring procedures, according to which the UE assesses from performed radio measurements any change in the applicability conditions of the AIML model / functionality. In such case the applied AIML related fields may be now ignored, and in response of ignoring such AIML related fields, the UE may also declare as non-applicable the concerned AIML models / functionalities, and in turn it may transmit to the gNB an applicability report indicating that the concerned AIML models / functionalities is no longer applicable. Further the UE may move the set of ignored AIML related fields from the second set of AIML related fields to the first set of AIML related fields (which includes the set of AIML related fields currently ignored by the UE).

[0133] As in the first method 100, the ignored AIML related fields may be kept stored 106 by the UE, and used to evaluate whether the concerned AIML models / functionalities become applicable again according to the said ignored AIML related fields.

[0134] In a method dependent on the first and second method above, the UE receives multiple inference configurations associated to one or more AIML models / functionalities. According to the methods above, the UE may apply the AIML related fields associated to those inference configurations for which the one or more AIML models / functionalities are applicable, and it may ignore and store the AIML related fields associated to those inference configurations for which the one or more AIML models / functionalities are not applicable.

[0135] In another method dependent on the first and second method above, for a given AIML model / functionality the UE may receive multiple candidate inference configurations, each including a set of AIML related fields.

[0136] In one example, if the said AIML model / functionality is applicable only according to one of the multiple inference configurations, the UE only applies the AIML related fields associated to such inference configuration, and it ignores and stores the AIML related fields associated to the other inference configurations.

[0137] In another example, if the said AIML model / functionality is applicable according to more than one inference configurations, the UE selects one inference configuration (e.g. the preferred once) and it only applies the AIML related fields associated to such inference configuration, and it ignores and stores the AIML related fields associated to the other inference configurations. In such an example, the ignored AIML related fields do not correspond to inference configurations for which the AIML models / functionalities are not applicable, rather to inference configurations not preferred by the UE. In response of determining its preference, the UE indicates as part of the applicability report the preferred inference configuration, and it may further indicate whether there are other inference configurations for which there are AIML models / functionalities applicable. The UE may successively apply the AIML related fields of the non- preferred / recommended inference configurations, if its preference / recommendation changes.

[0138] In another method dependent on the first and second method above, the UE ignores and does not store the AIML fields of the inference configuration and it may or may not store and apply (if supported) any other AIML fields associated to AIML performance monitoring procedures (e.g. the AIML fields related to the resources the UE may monitor to determine the applicability / non- applicability of the AIML model / functionality).

[0139] In another method dependent on the first and second method above, the UE ignores and does not store the AIML fields of the inference configuration and it stores and applies (if supported) any other AIML fields associated to AIML training procedures (e.g. the AIML fields related to the resources in which the UE performs measurements for the purpose of UE-side model training).

[0140] In one embodiment, the UE ignores, i.e. does not take an action on and does not store, the fields that it does not support or does not comprehend or fields of an AI / ML configuration which is determined to be not applicable.

[0141] In one embodiment, when the UE is unable to comply with (part of) the configuration included in a reconfiguration message (e.g. the RRCReconfiguration message) received over the SRB1 , or when the upper layers indicate that the nas-Container is invalid, and the inability to comply is not due to an Al / ML functionality which is not applicable, the UE performs a recovery failure e.g. RRC Re-establishment if security was activated or Non-Access Stratum (NAS) recovery via IDLE (if security was not activated).

[0142] In one embodiment, when the UE is unable to comply with (part of) the configuration included in a reconfiguration message (e.g. the RRCReconfiguration message) received over the SRB1 , and the inability to comply is due to an Al / ML functionality which is not applicable, apply remaining parameters of the RRCReconfiguration except the AI / ML configuration.

[0143] Below is an example of changes to 3GPP 38.331 v18.3.0 to implement aspects and embodiments of the techniques described herein. The changes are shown using underline. According to these aspects and embodiments, the UE does not take any action on, and does not store fields of the AI / ML CSI inference and performance monitoring configuration (that may be associated with the inference configuration), if the fields of the AIML functionality are not applicable but the UE supports them.

[0144] Below is another example of changes to 3GPP TS 38.331 v18.3.0 to implement aspects and embodiments of the techniques described herein. The changes are shown using underline. According to these aspects and embodiments, the UE does not take any action on, and does not store fields of the AI / ML CSI inference, if the fields of the inference configuration are not applicable but the UE supports them. However, the UE stores the performance monitoring configuration.

[0145] Below is another example of changes to 3GPP TS 38.331 v18.3.0 to implement aspects and embodiments of the techniques described herein. The changes are shown using underline. According to these aspects and embodiments, the UE does not take any action, except for storing fields of the CSI inference configuration, if the UE supports them, but does not apply them.

[0146] Fig. 3 shows an example of a communication system QQ100 in accordance with some embodiments.

[0147] In the example, the communication system QQ100 includes a telecommunication network QQ102 that includes an access network QQ104, such as a radio access network (RAN), and a core network QQ106, which includes one or more core network nodes QQ108. The access network QQ104 includes one or more access network nodes, such as access network nodes QQ110a and QQ110b (one or more of which are also referred to as RAN network nodes or RAN nodes QQ110 herein), or any other similar 3rdGeneration Partnership Project (3GPP) access nodes or non-3GPP access points (AP). 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 QQ102 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network QQ102 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 QQ102, including one or more network nodes QQ110 and / or core network nodes QQ108.

[0148] Examples of an ORAN network node include an open radio unit (0-Rll), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O-CU-CP) or an O- Cll user plane (O-CU-UP), a RAN intelligent controller (RIC) (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 A1 , F1 , W1 , E1 , 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 (SMO) Framework via an 0-2 interface defined by the O-RAN Alliance or comparable technologies.

[0149] The network nodes QQ110 facilitate direct or indirect connection of wireless devices (also referred to interchangeably herein as user equipment (UE)), such as by connecting UEs QQ112a, QQ112b, QQ112c, and QQ112d (one or more of which may be generally referred to as UEs QQ112) to the core network QQ106 over one or more wireless connections. The access network nodes QQ110 may be, for example, access points (APs) (e.g. radio access points), base stations (BSs) (e.g. radio base stations, Node Bs, evolved Node Bs (eNBs) and New Radio (NR) NodeBs (gNBs)).

[0150] 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 QQ100 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system QQ100 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0151] The wireless devices / UEs QQ112 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 QQ110 and other communication devices. Similarly, the access network nodes QQ110 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs QQ112 and / or with other network nodes or equipment in the telecommunication network QQ102 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 QQ102.

[0152] In the depicted example, the core network QQ106 connects the access network nodes QQ110 to one or more host computing systems, such as host QQ116. 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 QQ106 includes one more core network nodes (e.g. core network node QQ108) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the wireless devices / UEs, access network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node QQ108. 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).

[0153] The host QQ116 may be under the ownership or control of a service provider other than an operator or provider of the access network QQ104 and / or the telecommunication network QQ102. The host QQ116 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.

[0154] As a whole, the communication system QQ100 of Figure 3 enables connectivity between the wireless devices / UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2ndGeneration (2G), 3rdGeneration (3G), 4thGeneration (4G), 5thGeneration (5G) standards, or any applicable future generation standard (e.g. 6thGeneration (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.

[0155] In some examples, the telecommunication network QQ102 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network QQ102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network QQ102. For example, the telecommunications network QQ102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC) / Massive loT services to yet further UEs.

[0156] In some examples, the UEs QQ112 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 QQ104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network QQ104. Additionally, a UE may be configured for operating in single- or multi-Radio Access Technology (RAT) or multistandard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E- UTRAN (Evolved-UTRA (UMTS Terrestrial Radio Access) Network) New Radio - Dual Connectivity (EN-DC).

[0157] In the example, the hub QQ114 communicates with the access network QQ104 to facilitate indirect communication between one or more UEs (e.g., UE QQ112c and / or QQ112d) and network nodes (e.g., network node QQ110b). In some examples, the hub QQ114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub QQ114 may be a broadband router enabling access to the core network QQ106 for the UEs. As another example, the hub QQ114 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 QQ110, or by executable code, script, process, or other instructions in the hub QQ114. As another example, the hub QQ114 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 QQ114 may be a content source. For example, for a UE that is a VR device, display, loudspeaker, or other media delivery device, the hub QQ114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub QQ114 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub QQ114 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy Internet of Things (loT) devices.

[0158] The hub QQ114 may have a constant / persistent or intermittent connection to the network node QQ110b. The hub QQ114 may also allow for a different communication scheme and / or schedule between the hub QQ114 and UEs (e.g., UE QQ112c and / or QQ112d), and between the hub QQ114 and the core network QQ106. In other examples, the hub QQ114 is connected to the core network QQ106 and / or one or more UEs via a wired connection. Moreover, the hub QQ114 may be configured to connect to a machine-to-machine (M2M) service provider over the access network QQ104 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes QQ110 while still connected via the hub QQ114 via a wired or wireless connection. In some embodiments, the hub QQ114 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 QQ110b. In other embodiments, the hub QQ114 may be a nondedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node QQ110b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0159] Fig. 4 shows a wireless device or UE QQ200 in accordance with some embodiments. The UE QQ200 presents additional details of some embodiments of the UE QQ112 of Fig. 3. As used herein, a wireless device / UE refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a wireless device / UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage / playback device, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptopmounted equipment (LME), an Augmented Reality (AR) or Virtual Reality (VR) device, wireless customer-premise equipment (CPE), vehicle, vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-loT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0160] A wireless device / UE may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to- everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0161] The UE QQ200 includes processing circuitry QQ202 that is operatively coupled via a bus QQ204 to an input / output interface QQ206, a power source QQ208, a memory QQ210, a communication interface QQ212, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Fig. QQ2. 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.

[0162] The processing circuitry QQ202 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 QQ210. The processing circuitry QQ202 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 QQ202 may include multiple central processing units (CPUs). The processing circuitry QQ202 may be configured to cause the UE QQ202 to perform the methods as described herein.

[0163] In the example, the input / output interface QQ206 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 QQ200. 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 presencesensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0164] In some embodiments, the power source QQ208 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 QQ208 may further include power circuitry for delivering power from the power source QQ208 itself, and / or an external power source, to the various parts of the UE QQ200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source QQ208. Power circuitry may perform any formatting, converting, or other modification to the power from the power source QQ208 to make the power suitable for the respective components of the UE QQ200 to which power is supplied.

[0165] The memory QQ210 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory QQ210 includes one or more application programs QQ214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data QQ216. The memory QQ210 may store, for use by the UE QQ200, any of a variety of various operating systems or combinations of operating systems.

[0166] The memory QQ210 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a Universal SIM (USIM) and / or Integrated SIM (ISIM), other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUlCC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card’. The memory QQ210 may allow the UE QQ200 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory QQ210, which may be or comprise a device-readable storage medium.

[0167] The processing circuitry QQ202 may be configured to communicate with an access network or other network using the communication interface QQ212. The communication interface QQ212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna QQ222. The communication interface QQ212 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 QQ218 and / or a receiver QQ220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter QQ218 and receiver QQ220 may be coupled to one or more antennas (e.g., antenna QQ222) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0168] In the illustrated embodiment, communication functions of the communication interface QQ212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) or other Global Navigation Satellite System (GNSS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and / or standards, such as IEEE 802.11 , Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0169] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface QQ212, 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).

[0170] 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.

[0171] A UE, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking 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 QQ200 shown in Figure 4.

[0172] 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 3GPP NB-loT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0173] In practice, any number of UEs may be used together with respect to a single use case. For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed. The first and / or the second UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0174] Fig. 5 shows a network node, access network node or RAN node QQ300 in accordance with some embodiments.

[0175] As used herein, access network node or RAN network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other RAN network nodes or equipment, or core network nodes, in a telecommunication network. Examples of access network nodes include, but are not limited to, access points (APs) (e.g. radio access points), base stations (BSs) (e.g. radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)), Open-RAN (O-RAN) nodes or components of an O-RAN node (e.g., O- RU, O-DU, O-CU).

[0176] 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 O-RAN access node) and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0177] Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g. Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0178] The RAN network node QQ300 includes a processing circuitry QQ302, a memory QQ304, a communication interface QQ306, and a power source QQ308. The RAN network node QQ300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the RAN network node QQ300 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the RAN network node QQ300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory QQ304 for different RATs) and some components may be reused (e.g., a same antenna QQ310 may be shared by different RATs). The RAN network node QQ300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node QQ300, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within RAN network node QQ300.

[0179] The processing circuitry QQ302 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other network node QQ300 components, such as the memory QQ304, to provide RAN network node QQ300 functionality. For example, the processing circuitry QQ302 may be configured to cause the RAN network node to perform the methods as described herein.

[0180] In some embodiments, the processing circuitry QQ302 includes a system on a chip (SOC). In some embodiments, the processing circuitry QQ302 includes one or more of radio frequency (RF) transceiver circuitry QQ312 and baseband processing circuitry QQ314. In some embodiments, the radio frequency (RF) transceiver circuitry QQ312 and the baseband processing circuitry QQ314 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry QQ312 and baseband processing circuitry QQ314 may be on the same chip or set of chips, boards, or units.

[0181] The memory QQ304 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry QQ302. The memory QQ304 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 QQ302 and utilized by the RAN node QQ300. The memory QQ304 may be used to store any calculations made by the processing circuitry QQ302 and / or any data received via the communication interface QQ306. In some embodiments, the processing circuitry QQ302 and memory QQ304 is integrated.

[0182] The communication interface QQ306 is used in wired or wireless communication of signalling and / or data between network nodes, the access network, the core network, and / or UE. As illustrated, the communication interface QQ306 comprises port(s) / terminal(s) QQ316 to send and receive data, for example to and from a network over a wired connection. The communication interface QQ306 also includes radio front-end circuitry QQ318 that may be coupled to, or in certain embodiments a part of, the antenna QQ310. Radio front-end circuitry QQ318 comprises filters QQ320 and amplifiers QQ322. The radio front-end circuitry QQ318 may be connected to an antenna QQ310 and processing circuitry QQ302. The radio front-end circuitry may be configured to condition signals communicated between antenna QQ310 and processing circuitry QQ302. The radio front-end circuitry QQ318 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 QQ318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters QQ320 and / or amplifiers QQ322. The radio signal may then be transmitted via the antenna QQ310. Similarly, when receiving data, the antenna QQ310 may collect radio signals which are then converted into digital data by the radio front-end circuitry QQ318. The digital data may be passed to the processing circuitry QQ302. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0183] In certain alternative embodiments, the RAN node QQ300 does not include separate radio front-end circuitry QQ318, instead, the processing circuitry QQ302 includes radio front-end circuitry and is connected to the antenna QQ310. Similarly, in some embodiments, all or some of the RF transceiver circuitry QQ312 is part of the communication interface QQ306. In still other embodiments, the communication interface QQ306 includes one or more ports or terminals QQ316, the radio front-end circuitry QQ318, and the RF transceiver circuitry QQ312, as part of a radio unit (not shown), and the communication interface QQ306 communicates with the baseband processing circuitry QQ314, which is part of a digital unit (not shown). The antenna QQ310 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna QQ310 may be coupled to the radio front-end circuitry QQ318 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna QQ310 is separate from the network node QQ300 and connectable to the network node QQ300 through an interface or port.

[0184] The antenna QQ310, communication interface QQ306, and / or the processing circuitry QQ302 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna QQ310, the communication interface QQ306, and / or the processing circuitry QQ302 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.

[0185] The power source QQ308 provides power to the various components of network node QQ300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source QQ308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node QQ300 with power for performing the functionality described herein. For example, the network node QQ300 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source QQ308. As a further example, the power source QQ308 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.

[0186] Embodiments of the network node QQ300 may include additional components beyond those shown in Fig. 5 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 QQ300 may include user interface equipment to allow input of information into the network node QQ300 and to allow output of information from the network node QQ300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node QQ300. In some embodiments providing a core network node, such as core network node 108 of Fig. 3, some components, such as the radio front-end circuitry QQ318 and the RF transceiver circuitry QQ312 may be omitted. Fig. 6 is a block diagram illustrating a virtualization environment QQ400 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 QQ400 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, access network node, RAN 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 QQ400 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an 0-2 interface. Virtualization may facilitate distributed implementations of an access network node, network node, RAN node, UE, core network node, or host.

[0187] Applications QQ402 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0188] Hardware QQ404 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 QQ406 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs QQ408a and QQ408b (one or more of which may be generally referred to as VMs QQ408), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer QQ406 may present a virtual operating platform that appears like networking hardware to the VMs QQ408.

[0189] The VMs QQ408 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer QQ406. Different embodiments of the instance of a virtual appliance QQ402 may be implemented on one or more of VMs QQ408, 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.

[0190] In the context of NFV, a VM QQ408 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 QQ408, and that part of hardware QQ404 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs QQ408 on top of the hardware QQ404 and corresponds to the application QQ402.

[0191] Hardware QQ404 may be implemented in a standalone network node with generic or specific components. Hardware QQ404 may implement some functions via virtualization. Alternatively, hardware QQ404 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 QQ410, which, among others, oversees lifecycle management of applications QQ402. In some embodiments, hardware QQ404 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signalling can be provided with the use of a control system QQ412 which may alternatively be used for communication between hardware nodes and radio units.

[0192] Although the computing devices described herein (e.g., UEs, network nodes) 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.

[0193] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer-readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device- readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer-readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and / or by end users and a wireless network generally.

[0194] The foregoing merely illustrates the principles of the disclosure. Various modifications and alterations to the described embodiments will be apparent to those skilled in the art in view of the teachings herein. It will thus be appreciated that those skilled in the art will be able to devise numerous systems, arrangements, and procedures that, although not explicitly shown or described herein, embody the principles of the disclosure and can be thus within the scope of the disclosure. Various exemplary embodiments can be used together with one another, as well as interchangeably therewith, as should be understood by those having ordinary skill in the art.

[0195] The following statements relate to exemplary methods of operating a UE according to the techniques described herein:

[0196] A1 . A method through which the UE determines, from a configuration including one or more AIML related fields (e.g. parameters, information elements, configurations) within a reconfiguration message (e.g. an RRCReconfiguration), at least one of the following:

[0197] • A first set consisting of one or more AIML related fields that the UE is unable to comply with, due to one or more AI / ML models / functionalities not being applicable according to the said AIML related fields.

[0198] • A second set consisting of one or more AIML related fields that the UE is able to comply due to one or more AI / ML models / functionalities being applicable according to the said AIML related fields.

[0199] The method further comprising one or more of:

[0200] • The UE ignoring the one or more AIML related fields included in the first set.

[0201] • The UE storing the one or more AIML related fields included in the first set.

[0202] • The UE applying and storing the one or more AIML related fields included in the second set.

[0203] • The UE not declaring a reconfiguration failure if it determined at least one AIML related fields that the UE is unable to comply with.

[0204] A2. The method of A1 , wherein the UE evaluates the applicability conditions of the one or more AIML models / functionalities and:

[0205] • In response of determining that at least one AIML model / functionality becomes applicable by applying at least one of the one or more stored AIML related fields previously ignored and included in the first set, the UE applies the said at least one of the one or more stored AIML related fields for which the AIML model / functionality becomes applicable.

[0206] • In response of determining that at least one AIML model / functionality becomes not applicable given the one or more applied AIML related fields included in the second set, the UE ignores and keeps stored the said one or more stored AIML related fields included in the second set.

[0207] A3. The method of A2, wherein in response of determining that one AIML related field is applied, moving the said AIML related field to the second set, and in response of determining that one AIML related field is ignored moving the said AIML related field to the first set.

[0208] A4. The method of A1 wherein the AIML related fields are associated to an AIML inference configuration for the UE to operate according to one or more AIML models / functionalities.

[0209] A5. The method of A1 , wherein the AIML related fields are supported by the UE according to the UE radio access capabilities.

[0210] A6. The method of any of the previous methods, wherein in response of ignoring or applying one or more AIML related fields, the UE transmits to the gNB an applicability report indicating respectively the non-applicability or applicability of one or more AIML models / functionalities. EMBODIMENTS

[0211] A Embodiments

[0212] 1. A method performed by a User Equipment, UE, the method comprising: receiving, from a Radio Access Network, RAN, node, a Radio Resource Control, RRC, configuration message, wherein the RRC configuration message comprises a RRC configuration and one or more Machine Learning, ML, fields relating to at least one ML model; determining whether one or more of the ML fields are applicable if the RRC configuration is applied; and responsive to determining that one or more of the ML fields are not applicable, one or both of: ignoring or not using the one or more ML fields that are not applicable; and storing the one or more ML fields that are not applicable.

[0213] 2. A method performed by a User Equipment, UE, the method comprising: receiving, from a Radio Access Network, RAN, node, a Radio Resource Control, RRC, configuration message, wherein the RRC configuration message comprises a RRC configuration and one or more Machine Learning, ML, fields relating to at least one ML model: determining whether the one or more ML fields are applicable if the RRC configuration is applied; and responsive to determining that the one or more ML fields are not applicable, one or both of: ignoring or not using the one or more ML fields; and storing the one or more ML fields.

[0214] 3. The method of embodiment 1 or 2, wherein determining comprises determining whether one or more applicability conditions for an inference phase of the at least one ML model are consistent with one or more applicability conditions for a training phase for the at least one ML model.

[0215] 4. The method of embodiment 3, further comprising, responsive to determining that at least one of the one more applicability conditions for the inference phase are not consistent with at least one of the one or more applicability conditions for the training phase, determining that the ML field(s) is not applicable. 5. The method of any of embodiments 3 to 4, wherein the one or more applicability conditions comprise, or relate to, any one or more of: a speed of the UE; antenna properties of the UE; a location of the UE; a geographical location of the UE; a cell to which the UE is connected; a radio condition of the UE; radio conditions at the UE; network conditions; radio resources used by the UE; transmission power; receiving power; antenna configuration; deployment; Synchronisation Signal Block, SSB, configuration.

[0216] 6. The method of any of embodiments 1 to 5, wherein, responsive to determining that the ML field(s) is not applicable, the UE does not transmit an RRC Reconfiguration failure message to the RAN node.

[0217] 7. The method of any of embodiments 1 to 6, further comprising, responsive to determining that the ML field(s) is not applicable, determining whether the ML field(s) subsequently becomes applicable.

[0218] 8. The method of any of embodiments 1 to 7, further comprising, responsive to determining that the ML field(s) is applicable, applying the ML field(s).

[0219] 9. The method of any of embodiments 1 to 8, further comprising, responsive to determining that the ML field(s) is applicable, applying the RRC configuration.

[0220] 10. The method of any of embodiments 1 to 9, further comprising transmitting, to the RAN node, an applicability report relating to the applicability of the ML field(s).

[0221] 11 . The method of embodiment 10, wherein the applicability report comprises: a non-applicability indication responsive to determining that the ML field(s) is not applicable, wherein the non-applicability indication indicates to the RAN node that the ML field(s) is not applicable; and / or an applicability indication responsive to determining that the ML field(s) is applicable, wherein the applicability indication indicates to the RAN node that the ML field(s) is applicable.

[0222] Group B Embodiments

[0223] 12. A method performed by a Radio Access Network, RAN, node, the method comprising: transmitting, to a User Equipment, UE, a Radio Resource Control, RRC, configuration message, wherein the RRC configuration message comprises a RRC configuration and one or more Machine Learning, ML, fields relating to at least one ML model; and receiving, from the UE, an applicability report relating to the applicability of the one or more ML fields if the RRC configuration is applied.

[0224] 13. The method of embodiment 12, wherein the applicability report comprises: a non-applicability indication that indicates that the one or more ML field(s) is not applicable; and / or an applicability indication that indicates that the one or more ML field(s) is applicable.

[0225] Group C Embodiments

[0226] 14. A computer program product comprising a computer readable medium having computer readable code embodied therein, the computer readable code being configured such that, on execution by a suitable computer or processor, the computer or processor is caused to perform the method of any of the Group A embodiments and the Group B embodiments.

[0227] 15. A user equipment, UE, configured to perform the method of any of the Group A embodiments.

[0228] 16. A user equipment, UE, comprising a processor and a memory, said memory containing instructions executable by said processor whereby said UE is operative to perform the method of any of the Group A embodiments.

[0229] 17. A radio access network, RAN, node, configured to perform the method of any of the Group B embodiments.

[0230] 18. A radio access network, RAN, node comprising a processor and a memory, said memory containing instructions executable by said processor whereby said RAN node is operative to perform the method of any of the Group B embodiments.

[0231] 19. A radio access network, RAN, node, comprising: processing circuitry configured to cause the RAN node to perform any of the steps of any of the Group B embodiments; power supply circuitry configured to supply power to the processing circuitry. 20. A user equipment, UE, comprising: an antenna configured to send and receive wireless signals; radio front-end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry; the processing circuitry being configured to perform any of the steps of any of the Group A 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.

Claims

CLAIMS1. A method performed by a User Equipment, UE, the method comprising: receiving, from a Radio Access Network, RAN, node, a Radio Resource Control, RRC, configuration message, wherein the RRC configuration message comprises a RRC configuration and one or more Machine Learning, ML, fields relating to at least one ML model; determining whether one or more of the one or more ML fields are applicable if the RRC configuration is applied; and responsive to determining that one or more of the one or more ML fields are not applicable, one or both of: ignoring or not using the one or more ML fields that are not applicable; and storing the one or more ML fields that are not applicable.

2. The method of claim 1 , wherein determining comprises determining whether one or more applicability conditions for an inference phase of the at least one ML model are consistent with one or more applicability conditions for a training phase for the at least one ML model.

3. The method of claim 2, further comprising, responsive to determining that at least one of the one more applicability conditions for the inference phase are not consistent with at least one of the one or more applicability conditions for the training phase, determining that the ML field(s) is not applicable.

4. The method of any of claim 2 to 3, wherein the one or more applicability conditions comprise, or relate to, any one or more of: a speed of the UE; antenna properties of the UE; a location of the UE; a geographical location of the UE; a cell to which the UE is connected; a radio condition of the UE; radio conditions at the UE; network conditions; radio resources used by the UE; transmission power; receiving power; antenna configuration; deployment; Synchronisation Signal Block, SSB, configuration.

5. The method of any of claims 1 to 4, wherein, responsive to determining that the ML field(s) is not applicable, the UE does not transmit an RRC Reconfiguration failure message to the RAN node.

6. The method of any of claims 1 to 5, further comprising, responsive to determining that the ML field(s) is not applicable, determining whether the ML field(s) subsequently becomes applicable.

7. The method of any of claims 1 to 6, further comprising, responsive to determining that the ML field(s) is applicable, applying the ML field(s).

8. The method of any of claims 1 to 7, further comprising, responsive to determining that the ML field(s) is applicable, applying the RRC configuration.

9. The method of any of claims 1 to 8, further comprising transmitting, to the RAN node, an applicability report relating to the applicability of the ML field(s).

10. The method of claims 9, wherein the applicability report comprises: a non-applicability indication responsive to determining that the ML field(s) is not applicable, wherein the non-applicability indication indicates to the RAN node that the ML field(s) is not applicable; and / or an applicability indication responsive to determining that the ML field(s) is applicable, wherein the applicability indication indicates to the RAN node that the ML field(s) is applicable.

11. A method performed by a User Equipment, UE, the method comprising: receiving, from a Radio Access Network, RAN, node, a Radio Resource Control, RRC, configuration message, wherein the RRC configuration message comprises a RRC configuration and one or more Machine Learning, ML, fields relating to at least one ML model: determining whether the one or more ML fields are applicable if the RRC configuration is applied; and responsive to determining that the one or more ML fields are not applicable, one or both of: ignoring or not using the one or more ML fields; and storing the one or more ML fields.

12. The method of claim 11 , wherein determining comprises determining whether one or more applicability conditions for an inference phase of the at least one ML model are consistent with one or more applicability conditions for a training phase for the at least one ML model.

13. The method of claim 12 further comprising, responsive to determining that at least one of the one more applicability conditions for the inference phase are not consistent with at least one of the one or more applicability conditions for the training phase, determining that the ML field(s) is not applicable.

14. The method of any of claim 12 to 13, wherein the one or more applicability conditions comprise, or relate to, any one or more of: a speed of the UE; antenna properties of the UE; a location of the UE; a geographical location of the UE; a cell to which the UE is connected; a radio condition of the UE; radio conditions at the UE; network conditions; radio resources used by the UE; transmission power; receiving power; antenna configuration; deployment; Synchronisation Signal Block, SSB, configuration.

15. The method of any of claims 11 to 14, wherein, responsive to determining that the ML field(s) is not applicable, the UE does not transmit an RRC Reconfiguration failure message to the RAN node.

16. The method of any of claims 11 to 15, further comprising, responsive to determining that the ML field(s) is not applicable, determining whether the ML field(s) subsequently becomes applicable.

17. The method of any of claims 11 to 16, further comprising, responsive to determining that the ML field(s) is applicable, applying the ML field(s).

18. The method of any of claims 11 to 17, further comprising, responsive to determining that the ML field(s) is applicable, applying the RRC configuration.

19. The method of any of claims 11 to 18, further comprising transmitting, to the RAN node, an applicability report relating to the applicability of the ML field(s).

20. The method of claims 19, wherein the applicability report comprises: a non-applicability indication responsive to determining that the ML field(s) is not applicable, wherein the non-applicability indication indicates to the RAN node that the ML field(s) is not applicable; and / or an applicability indication responsive to determining that the ML field(s) is applicable, wherein the applicability indication indicates to the RAN node that the ML field(s) is applicable.

21. A method performed by a Radio Access Network, RAN, node, the method comprising: transmitting, to a User Equipment, UE, a Radio Resource Control, RRC, configuration message, wherein the RRC configuration message comprises a RRC configuration and one or more Machine Learning, ML, fields relating to at least one ML model; and receiving, from the UE, an applicability report relating to the applicability of the one or more ML fields if the RRC configuration is applied.

22. The method of embodiment 121 , wherein the applicability report comprises: a non-applicability indication that indicates that the one or more ML field(s) is not applicable; and / or an applicability indication that indicates that the one or more ML field(s) is applicable.

23. A computer program product comprising a computer readable medium having computer readable code embodied therein, the computer readable code being configured such that, on execution by a suitable computer or processor, the computer or processor is caused to perform the method of any of the claims 1 to 10 or 11 to 20 and the claims 21 to 22.

24. A user equipment, UE, configured to: receive, from a Radio Access Network, RAN, node, a Radio Resource Control, RRC, configuration message, wherein the RRC configuration message comprises a RRC configuration and one or more Machine Learning, ML, fields relating to at least one ML model; determine whether one or more of the one or more ML fields are applicable if the RRC configuration is applied; and responsive to determine that one or more of the one or more ML fields are not applicable, one or both of: ignore or not use the one or more ML fields that are not applicable; and store the one or more ML fields that are not applicable.

25. The UE of claim 24, further configured to perform the method of any of claims 2 to 10.

26. A user equipment, UE, comprising a processor and a memory, said memory containing instructions executable by said processor whereby said UE is operative to perform the method of any of the claims 1 to 10 or claims 11 to 20.

27. A radio access network, RAN, node, configured:transmit, to a User Equipment, UE, a Radio Resource Control, RRC, configuration message, wherein the RRC configuration message comprises a RRC configuration and one or more Machine Learning, ML, fields relating to at least one ML model; and receive, from the UE, an applicability report relating to the applicability of the one or more ML fields if the RRC configuration is applied.

28. The RAN node of claim 27, further configured to perform the method of claim 22.

29. A radio access network, RAN, node comprising a processor and a memory, said memory containing instructions executable by said processor whereby said RAN node is operative to perform the method of any of the claims 21 to 22.

30. A radio access network, RAN, node, comprising: processing circuitry configured to cause the RAN node to perform any of the steps of any of the claims 21 to 22; power supply circuitry configured to supply power to the processing circuitry.31 . A user equipment, UE, comprising: an antenna configured to send and receive wireless signals; radio front-end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry; the processing circuitry being configured to perform any of the steps of any of the Group A 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.

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