UE signalling recommended radio measurement configurations based on ai / ML inference configurations

Abstract

In an embodiment, a method performed by a user equipment (UE) is provided for signaling recommended radio measurement configurations based on Artificial Intelligence Machine Learning (AI / ML) inference configurations. The method includes transmitting, to a network node, a set of one or more recommended AI / ML radio measurement configurations for an AI / ML inference or for an AI / ML training for at least one AI / ML functionality / model, wherein the set of one or more recommended AI / ML radio measurement configurations are selected from a set of candidate AI / ML radio measurement configurations included in a first AI / ML inference configuration received from the network node or from a different set of candidate AI / ML radio measurement configurations not included in the first AI / ML inference configuration received from the network node.

Description

UE SIGNALLING RECOMMENDED RADIO MEASUREMENT CONFIGURATIONS BASED ON AI / ML INFERENCE CONFIGURATIONSRELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 667,879, filed July 5, 2024, the disclosure of which is hereby incorporated herein by reference in its entirety.TECHNICAL FIELD

[0002] The present disclosure relates to methods for a User Equipment (UE) to signal recommended radio measurement configurations based on Artificial Intelligence / Machine Learning (AI / ML) inference configurations. A user equipment and network node to perform these methods are also disclosed.BACKGROUND

[0003] 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 signaling overhead and beam alignment latency; using deep reinforcement learning to leam an optimal precoding policy for complex Multiple Input Multiple Output (MIMO) precoding problems.

[0004] In 3rd Generation Partnership Project (3GPP) New Radio (NR) standardization work, a new release 18 study item on AI / ML for 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 airinterface use cases leveraging AI / ML techniques. The analysis carried out during the Rel.18 is now considered in the context of Rel.19. Additionally, during the rel. 19, a new study item addressing AI / ML for mobility has been approved. In the context of this new study item, 3GPPwill 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.

[0005] Building the Al model, or any machine learning model, includes several development steps where the actual training of the Al model is just one step in a training pipeline. An important part in Al developing is the ML model lifecycle management (LCM). This is illustrated in Figure 1, which is an illustration of training and inference pipelines, and their interactions within a model lifecycle management procedure. The model lifecycle management typically consists of:• A training (re-training) pipeline. a. Data Ingestion: Data ingestion refers to gathering raw (training) data from a data storage. After data ingestion, there may also be a step that controls the validity of the gathered data. b. Data Pre-Processing: Data pre-processing refers to some feature engineering applied to the gathered data, e.g., it may include data normalization and possibly a data transformation required for the input data to the Al model. c. Model Training: Model training refers to the actual model training steps as previously outlined. d. Model Evaluation: Model evaluation refers to benchmarking the performance to some model baseline. The iterative steps of model training and model evaluation continue until the acceptable level of performance (as previously exemplified) is achieved. e. Model Registration: Model registration refers to registering the Al model, including any corresponding Al-metadata that provides information on how the Al model was developed, and possibly Al model evaluations performance outcomes.• A deployment stage to make the trained (or re-trained) Al model part of the inference pipeline.• An inference pipeline. a. Data Ingestion: Data ingestion refers to gathering raw (inference) data from a data storage. b. Data Pre-Processing: Data pre-processing stage is typically identical to corresponding processing that occurs in the training pipeline.c. Model Operational: Model operational refers to using the trained and deployed model in an operational mode. d. Data and Model Monitoring: Data & model monitoring refers to validating that the inference data are from a distribution that aligns well with the training data, as well as monitoring model outputs for detecting any performance, or operational, drifts.• A drift detection stage that informs about any drifts in the model operations.

[0006] As an example, Figure 1 illustrates training and inference pipelines and their interactions within a model lifecycle management procedure according to one or more embodiments of the present disclosure. Similarly, Figure 2 illustrates a functional framework that can be used for studying different Network (NW)-UE collaboration levels for the Al for PHY use cases according to one or more embodiments of the present disclosure.

[0007] Among the AI / ML models being discussed in the Rel-18 study item on AI / ML for the NR air interface is the so-called One-sided AI / ML model, which can be a UE-sided AI / ML model whose inference is performed entirely at the UE (which is the focus of the disclosure), or a NW-sided AI / ML model whose inference is performed entirely at the NW.

[0008] The use case of beam prediction which will be standardized as part of 3GPP Rel.19 work item consists of spatial beam prediction, and temporal beam prediction. 3GPP aims to specify predictions of the “best” beam (or beams) from a Set A of beams using measurement results from another Set B of beams.

[0009] 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.

[0010] Figure 3 illustrates Set A and Set B beams according to one or more embodiments of the present disclosure.

[0011] Set A and Set B of beams have not been specified yet, however, the following two examples may be considered as references:• Set B is a subset of a Set A. For example, Set A is a set of 8 Synchronization Signal Block (SSB) / CSI Reference Signal (RS) beams shown in Figure 3 (both light and dark circles). The UE measures Set B (the 4 beams indicated by dark circles). The AI / ML model should predict the best beam (or beams) in Set A using only measurements from Set B.

[0012] Figure 3 is an example where Set B is a subset of Set A. The figure illustrates a grid- of-beam type radiation pattern: Each row (resp. column) depicts a certain zenith (resp. azimuth)angle from the antenna array. Set A has 8 beams and Set B has 4 beams (indicated by dark circles).

[0013] Figure 4 illustrates another example of Set A and Set B beams according to one or more embodiments of the present disclosure. Set A and Set B correspond to two different sets of beams. For example, Set A is a set of 30 narrow CSI-RS beams, and Set B is a set of 8 wide SSB beams. The UE measures beams in Set B and the AI / ML model should predict the best beam(s) from Set A.

[0014] The beam prediction can be performed (e.g., by an AI / ML model) in the gNB (e.g., the radio access network node) and / or in the UE, and the gain is twofold. From the UE point of view, the UE would be able to generate good radio measurement estimations without really measuring certain resources (e.g. actual SSBs, CSI-RS or other RS(s)), thereby saving energy, whereas from the gNB point of view, the gNB can get good radio measurements estimation from the UE without providing the measuring resources, thereby limiting the overhead over the airinterface.

[0015] Whether the UE can perform the beam prediction on a certain set of resources with a certain accuracy, depends on so called applicability conditions of an AI / ML model / function. In particular, an AI / ML model / function may be trained to perform the beam prediction under certain applicability conditions e.g. speed, location, beam configuration(s), deployment, etc.Such applicability conditions need to be fulfilled in order for the AI / ML model / function to generate the expected output, i.e. beam prediction for this use case, with enough accuracy. The applicability conditions may include a set of parameters / variables under which the AI / ML model / function was trained e.g. a given Set B for the inference of a Set A. Such set may include for example UE-specific conditions under which the model was trained, as the UE speed, the UE antenna shape, UE sensors information such as UE orientation, motion sensors etc.; whereas some other parameters / variables may depend on the specific network configuration under which the model was trained, e.g. the deployment scenario (e.g. indoor / outdoor), the carrier frequency, the gNB TX port number, the gNB Transmit (TX) power, etc.

[0016] In order to determine whether an AI / ML model / function is applicable or not, the UE needs to assess the applicability conditions of such AI / ML model / function with respect to the output (beam prediction) that needs the generated and received input (e.g. radio measurement resources configured by the gNB).

[0017] Related to the discussion above, the applicability reporting has been discussed during the Rel.18 study item. The applicability reporting allows the UE to inform the gNB about the applicability of an AI / ML model / functionality while the UE is connected to this gNB. An AI / MLmodel / 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. For example, whether the UE has an AI / ML model that is applicable given the current location of the UE, or given the current speed of the UE, is not something that the network can control or it can know, 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).

[0018] Two types of applicability reporting were identified during the Rel.18 study item, i.e. the proactive reporting and the reactive reporting. The reactive reporting implies the gNB inquiring the UE about the applicability of AI / ML model / functionality, and the UE responding with the AI / ML models / functionalities that are applicable, whereas with the proactive reporting UE signals to the network autonomously, i.e. without any inquiry, about the AI / ML model / functionalities that are applicable.

[0019] The former, i.e. reactive reporting can be used for example in response to a network configuration, e.g. inference related configuration, including for example beam resource configuration of Set A and / or Set B. The UE will then respond indicating if the AI / ML model / functionality is applicable based on this inference configuration.

[0020] The latter, i.e. the proactive reporting can be configured to the UE to allow the UE to report at any point in time a change in the applicability of an AI / ML model / functionality, i.e. an AI / ML model / functionality that was not applicable becomes applicable or vice versa

[0021] From signaling procedure point of view, a summary is provided in Figure 5 which illustrates a flowchart of a method for reactive reporting according to one or more embodiments of the present disclosure.

[0022] Step 1: Network sends UECapabilityEnquiry message to initiate the procedure to a UE reporting its AI / ML supported functionalities.

[0023] Step 2: UE sends UECapablitylnformation message to network, containing supported functionalities at the UE side.

[0024] Step 3: Network provides network configurations and initiates UE to report its applicable functionalities.

[0025] Step 4: UE sends applicable functionalities to network.

[0026] Step 5: Network sends updated inference configuration for applicable functionalities reported in Step 4 to the UE. (see Q2-6)

[0027] Step 6: Start inference / monitoring based on network / UE activation / deactivation.

[0028] As shown in step 6, a “supported functionality” (e.g., beam management, or reporting of spatial domain and / or time domain prediction(s) or inference(s)) is activated, to refer to functionalities which are activated and for which a UE is performing inference [1], in response to a network configuration in Step 3

[0029] Figure 6 illustrates a flowchart of a method for proactive reporting according to one or more embodiments of the present disclosure.

[0030] Step 1: Network sends UECapabilityEnqiry message to initiate the procedure to a UE reporting its AI / ML supported functionalities

[0031] Step 2: UE sends UECapablitylnformation message to network, containing supported functionalities at the UE side

[0032] Step 3: Network configures UE that it is allowed to provide its applicable functionalities

[0033] Step 4: UE sends applicable functionalities to network upon change of applicable functi onality / condition

[0034] Step 5: Network sends inference configuration for the applicable functionalities to the UE

[0035] Step 6: Start inference / monitoring based on network / UE activation / deactivation.SUMMARY

[0036] Various embodiments described herein provide for a method for signaling recommended radio measurement configurations based on AI / ML inference configurations to a network node. The method includes transmitting, to a network node, a set of one or more recommended AI / ML radio measurement configurations for an AI / ML inference for at least one AI / ML functi onality / model, wherein the set of one or more recommended AI / ML radio measurement configurations are selected from a set of candidate AI / ML radio measurement configurations included in a first AI / ML inference configuration received from the network node or from a different set of candidate AI / ML radio measurement configurations not included in the first AI / ML inference configuration received from the network node.

[0037] In an embodiment, a method performed by a user equipment (UE) is provided for signaling recommended radio measurement configurations based on Artificial Intelligence Machine Learning (AI / ML) inference configurations. The method includes transmitting, to a network node, a set of one or more recommended AI / ML radio measurement configurations for an AI / ML inference or for an AI / ML training for at least one AI / ML functionality / model, wherein the set of one or more recommended AI / ML radio measurement configurations areselected from a set of candidate AI / ML radio measurement configurations included in a first AI / ML inference configuration received from the network node or from a different set of candidate AI / ML radio measurement configurations not included in the first AI / ML inference configuration received from the network node.

[0038] In an embodiment, the first AI / ML inference configuration comprises a configuration flag that indicates whether the UE is allowed to transmit the set of recommended AI / ML radio measurement configurations different than the candidate AI / ML radio measurement configurations.

[0039] In an embodiment, the configuration flag is signaled by the network node explicitly.

[0040] In an embodiment, in response to transmitting the set of one or more recommended AI / ML radio measurement configurations to the network node, the method further comprises receiving, from the network node, a second AI / ML inference configuration to perform AI / ML inference comprising the indicated recommended AI / ML radio measurement configurations or a subset thereof.

[0041] In an embodiment, the method further comprises activating the AI / ML functionality / model with one of the recommended AI / ML radio measurement configurations of the set of recommended AI / ML radio measurement configurations, if the recommended AI / ML radio measurement configurations is selected from the one or more candidate radio configurations.

[0042] In an embodiment, in response to transmitting the set of one or more recommended AI / ML radio measurement configurations to the network node, the method further comprises activating (710) the AI / ML functionality / model in response to receiving (708) an activation command from the network node.

[0043] In an embodiment, the UE does not signal the set of recommended AI / ML radio measurement configurations to the network node, if no AI / ML model is available for a concerned AI / ML functionality indicated in the first AI / ML inference configuration.

[0044] In an embodiment, the set of recommended AI / ML radio measurement configurations are indicated by the UE only if none of the candidate radio configurations can make the AI / ML functionality / model indicated in the inference configuration applicable.

[0045] In an embodiment, the different set of candidate AI / ML radio measurement configurations not included in the first AI / ML inference configuration are also received from the network node.

[0046] In an embodiment, the set of recommended AI / ML radio measurement configurations or candidate AI / ML radio measurement configurations comprise one or more of a set A and / or setB of radio resources where the radio resources comprise real radio resources, a network-side operation property, and a UE-side operation property.

[0047] In an embodiment, the set A and / or set B of radio resources comprise one or more of a set of Synchronization Signal Blocks (SSB) for a cell; a set of Channel State Information Reference Signal (CSI-RS) resources for a cell; a set of Synchronization Signal (SS) Physical Broadcast Channel (PBCH) block resource sets for a cell; a set of cells; a set of frequencies; a set of SSB for a list of cells; a set of CSI-RS resources for a list of cells; and a set of SS / PBCH block resource sets for a list of cells.

[0048] In an embodiment, the network-side / UE-side operational properties comprises one or more of a mapping relationship of Set A and Set B; an indication of consistency of downlink spatial domain transmission filters corresponding to the beams in Set A and Set B; a quasi co-location (QCL) assumption; an order of model input and model output; transmission power; a UE distribution; antenna height; a deployment scenario information; and a UE speed.

[0049] In an embodiment, the set of recommended AI / ML radio measurement configurations comprise a list of identifiers each referring to a specific configuration.

[0050] In an embodiment, the set of recommended AI / ML radio measurement configurations comprises one or more of a measurement to prediction pattern; a measurement periodicity configuration or the time interval between measurement samples; and a configuration of observation window.

[0051] In an embodiment, the method further comprises providing to the network node a request to perform data collection for training an AI / ML functionality / model.

[0052] In an embodiment, a UE is provided for signaling recommended radio measurement configurations based on AI / ML inference configurations. The UE comprises processing circuitry configured to transmit, to a network node, a set of one or more recommended AI / ML radio measurement configurations for an AI / ML inference or for an AI / ML training for at least one AI / ML functionality / model, wherein the set of one or more recommended AI / ML radio measurement configurations are selected from a set of candidate AI / ML radio measurement configurations included in a first AI / ML inference configuration received from the network node or from a different set of candidate AI / ML radio measurement configurations not included in the first AI / ML inference configuration received from the network node. The processing circuitry can also perform any of the embodiments described above.

[0053] In an embodiment, a method performed by a network node is provided for facilitating signaling recommended radio measurement configurations based on AI / ML inference configurations. The method includes receiving, from a UE a set of one or more recommendedAI / ML radio measurement configurations for an AI / ML inference or for an AI / ML training for at least one AI / ML functionality / model, wherein the set of one or more recommended AI / ML radio measurement configurations are selected from a set of candidate AI / ML radio measurement configurations included in a first AI / ML inference configuration provided to the UE or from a different set of candidate AI / ML radio measurement configurations not included in the first AI / ML inference configuration. In an embodiment, a network node is provided that includes processing circuitry configured to perform the method performed by the network node described above.BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the disclosure, and together with the description serve to explain the principles of the disclosure.

[0055] Figure 1 illustrates training and inference pipelines and their interactions within a model lifecycle management procedure according to one or more embodiments of the present disclosure;

[0056] Figure 2 illustrates a functional framework that can be used for studying different Network / User Equipment collaboration levels for Artificial Intelligence (Al) for Physical Layer (PHY) use cases according to one or more embodiments of the present disclosure;

[0057] Figure 3 illustrates Set A and Set B beams according to one or more embodiments of the present disclosure;

[0058] Figure 4 illustrates another example of Set A and Set B beams according to one or more embodiments of the present disclosure;

[0059] Figure 5 illustrates a flowchart of a method for reactive reporting according to one or more embodiments of the present disclosure;

[0060] Figure 6 illustrates a flowchart of a method for proactive reporting according to one or more embodiments of the present disclosure;

[0061] Figure 7 illustrates a flowchart of a method for signaling recommended radio measurement configurations based on Artificial Intelligence Machine Learning (AI / ML) inference configurations according to one or more embodiments of the present disclosure;

[0062] Figure 8 illustrates an example of a measurement to prediction pattern according to one or more embodiments of the present disclosure;

[0063] Figure 9 shows an example of a communication system in accordance with some embodiments of the present disclosure;

[0064] Figure 10 shows a User Equipment device (UE) in accordance with some embodiments of the present disclosure;

[0065] Figure 11 shows a network node in accordance with some embodiments of the present disclosure; and

[0066] Figure 12 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized in accordance with some embodiments of the present disclosure.DETAILED DESCRIPTION

[0067] The embodiments set forth below represent information to enable those skilled in the art to practice the embodiments and illustrate the best mode of practicing the embodiments. Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the disclosure and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure.

[0068] 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.

[0069] There currently exist certain challenge(s). In order for a User Equipment (UE)-side model to generate as output the predictions in a set of resources (so called set A of resources, like beams, frequencies, cells, etc.), the UE needs to perform measurements on a set of other resources (so called set B of resources). However, the gNB or (e.g., a radio access network node or just “network node” as used herein) might not know beforehand which are the set of resources in which the UE can generate accurate enough predictions, and also the resources that the UE needs to measure in order to generate this prediction. Especially if the UE-side training entity is outside the gNB-vendor control, this information will not immediately be available at the gNB. Hence, the gNB might not have enough information available to determine the radio configuration that the UE really needs in order to perform the Artificial Intelligence Machine Learning (AI / ML) inference with enough accuracy.

[0070] However, at the same time the radio configurations that the UE needs for the inference may not fit the network preference. For example, the UE may recommend a certain set of resources that the gNB may not provide, because they are congested, or because they are already configured to other UEs, or because the gNB prefers to assign to the UE another set ofresources based on gNB-side models, or based on current radio conditions / measurements, gNB scheduling policies, etc.

[0071] Hence it is a problem how to make sure that the radio configurations for the inference fits at the same time the UE and the gNB preference.

[0072] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges.

[0073] Various embodiments described herein provide for a method for signaling recommended radio measurement configurations based on AI / ML inference configurations to a network node. The method includes transmitting, to a network node, a set of one or more recommended AI / ML radio measurement configurations for an AI / ML inference for at least one AI / ML functionality / model, wherein the set of one or more recommended AI / ML radio measurement configurations are selected from a set of candidate AI / ML radio measurement configurations included in a first AI / ML inference configuration received from the network node or from a different set of candidate AI / ML radio measurement configurations not included in the first AI / ML inference configuration received from the network node.

[0074] The present disclosure provides for a method in which a gNB configures the UE with an AI / ML first inference configuration, wherein the AI / ML inference can include configurations for one or more AI / ML models / functionalities. This configuration may convey one or more AI / ML radio measurement configurations associated to one AI / ML model / functionality. Such AI / ML radio measurement configurations may be candidate AI / ML radio measurement configurations that the UE may apply for the AI / ML inference of the said AI / ML model / functionality .

[0075] In one method, the gNB may signal as part of this inference configuration whether the UE can report to the gNB other AI / ML radio measurement configurations, say recommended radio configurations. In this case the recommended radio configurations can be different from any of the candidate radio configurations included in the inference configuration. For example, this can be the AI / ML radio configuration that the UE stored at the time of training, and as such, this would be the AI / ML radio configuration that would fit the AI / ML inference. Whether the UE is allowed to transmit its recommended radio configurations when they are different from any of the candidate radio configurations, it is controlled by specific configuration flag implicitly or explicitly included in the inference configuration.

[0076] In another method, the UE just signals to the gNB, as part of the recommended radio configurations, which of the one or more candidate AI / ML radio configurations the UE can apply for the AI / ML inference.

[0077] In an additional method, the first inference configuration may be followed by a second inference configuration. This can be the case, for example, in which the UE indicates one or more recommended radio configurations that are different from any of the candidate radio configurations included in the first AI / ML inference configuration. Or if the UE indicates multiple recommended radio configurations selected by the UE from the candidate radio configurations included in the first AI / ML inference configuration. In both such cases, the gNB may configure the UE in a second AI / ML inference configuration to perform AI / ML inference for the concerned AI / ML model / functionality according to one of the radio configurations included in the recommended radio configurations by the UE.

[0078] Certain embodiments may provide one or more of the following technical advantage(s). The proposed solutions allows the gNB to control the radio configurations that the UE can use for the AI / ML inference of an AI / ML model / functionality. The resources can be selected from a set of candidate radio resources indicated by the gNB, or the network can allow the UE to indicate its recommendation of radio resources to use for the inference, and the gNB can select which of such recommended radio resource the UE should apply for the AI / ML inference of such AI / ML model / functionality.

[0079] Figure 7 illustrates a flowchart of a method for signaling recommended radio measurement configurations based on Artificial Intelligence Machine Learning, AI / ML, inference configurations according to one or more embodiments of the present disclosure.

[0080] The present disclosure discloses a method in which a UE 912 may optionally send to a network node 910 a request for data collection to perform training for the concerned AI / ML model / functionality. At step 702, theUE 912 receives a first AI / ML inference configuration in step 702 from a network node 910 that includes either candidate data collection configurations or candidate radio measurement configurations. At 704, the UE 912 transmits to the network node a set of one or more recommended AI / ML radio measurement configurations or a set of recommended data collection configurations for an AI / ML inference for at least one AI / ML functionality / model, wherein the set of one or more recommended AI / ML radio measurement configurations are selected from a set of candidate AI / ML radio measurement configurations included in a first AI / ML inference configuration received from the network node 910 or from a different set of candidate AI / ML radio measurement configurations not included in the first AI / ML inference configuration received from the network node 910.

[0081] At 706, the UE 912 receives from the network node a second AI / ML inference configuration to perform AI / ML inference comprising the indicated recommended AI / MLradio measurement configurations or a subset thereof or indicated recommended data collection configurations.

[0082] At 710, the UE can activate the AI / ML functionality / model with one of the recommended AI / ML radio measurement configurations of the set of recommended AI / ML radio measurement configurations, if the recommended AI / ML radio measurement configurations is selected from the one or more candidate radio configurations.

[0083] In an embodiment, the activation in step 710, can be in response to a receiving an activation command at 708.

[0084] In one method the UE transmits to the gNB one or more recommended radio configurations for the AI / ML inference for at least one AI / ML functionality / model, wherein the recommended radio configurations are those configurations that makes the concerned AI / ML functionality / model applicable. In another embodiment the recommended configurations are the configurations needed to “activate” an applicable model / functionality /

[0085] In one option, when the UE transmits to the network a recommended radio configuration for making the supported functionality as ‘applicable’, the UE considers the functionality initially deactivated. Then, the UE further receives an RRC Reconfiguration with the recommended configuration which has been reported, so the UE determines that for such a configuration the supported functionality becomes applicable, so that the UE autonomously activate the supported functionality.

[0086] In another option, when the UE transmits to the network a recommended radio configuration for making the supported functionality as ‘applicable’, the UE considers the functionality initially deactivated. Then, the UE further receives an RRC Reconfiguration with the recommended configuration which has been reported, so the UE determines that for such a configuration the supported functionality becomes applicable, and the UE further receives a command to activate the supported functionality, based on which the UE activates the supported functionality.

[0087] In another option, when the UE transmits to the network a recommended radio configuration for making the supported functionality as ‘applicable’, the UE considers the functionality initially deactivated. Then, the UE further receives an RRC Reconfiguration with the recommended configuration which has been reported, so the UE determines that for such a configuration the supported functionality becomes applicable, and the UE determines to activate the supported functionality when the RRC reconfiguration includes an indication indicating an initial applicability status set to ‘activate’, so that the UE activate the functionality when the configuration leads to an applicable functionality.

[0088] One radio measurement configuration may consist for example a first set (set A) of measurement resources in which the UE should perform radio measurement predictions, and a second set (set B) of radio measurement resources in which the UE can perform radio measurement in order to determine the radio measurement predictions on the first set. The radio measurement configuration may also consist of NW-side additional conditions reflecting the NW operational properties, such as• Mapping relationship of Set A and Set B, including ordering to (a set of IDs, or resources);• Consistency of downlink spatial domain transmission filters corresponding to the beams in Set A and Set B;• Quasi Co-location (QCL) assumption;• The order of model input and model output;• between Reference Signals (RS) and Transmit (Tx) beams can be pre-defined;• Transmission power;• UE distribution;• antenna height;• Deployment scenarios (e.g., Intersite Distance (ISD), Umi / Uma / rural / indoor / indoor office / indoor factory, specific area(s)); and• UE speed.

[0089] The recommended radio measurement configurations may also include• Measurement to prediction pattern (M to P pattern) e.g., how many measurement samples (say M measurement sample) in time domain or frequency domain or space domain is needed to perform one or more prediction (say P prediction). In a non-limiting example M can be 3 measurements and N can be 1 prediction e.g., 3 consecutives measurements are to be performed to provide the input to the inference engine and come up with a prediction in a future time instances.• Measurement periodicity configuration or the time interval between measurement samples• Configuration of observation window (the time needed to collect the measurements) and the prediction window (the time period in which the UE performs inference instead of performing measurements)• Additional information on whether the recommended configuration is targeting the measurement overhead reduction purpose or the future prediction e.g., to enhance the mobility reliability.

[0090] In an embodiment, the set A and / or Set B radio resources comprise one or more of:• a set of Synchronization Signal Blocks (SSB) for a cell;• a set of Channel State Information Reference Signal (CSI-RS) resources for a cell;• a set of Synchronization Signal, SS, Physical Broadcast Channel (PBCH) block resource sets for a cell;• a set of cells;• a set of frequencies;• a set of SSB for a list of cells;• a set of CSI-RS resources for a list of cells; and• a set of SS / PBCH block resource sets for a list of cells.

[0091] Figure 8 illustrates an example of a measurement to prediction pattern according to one or more embodiments of the present disclosure. In Figure 8, depicts recommended / available measurement to prediction pattern (so-called M-to-P pattern). For example, in a) (M-to-P) pattern is (1-tol), in b) (M-to-P) pattern is (2-tol) and in c) (M-to-P) pattern is (3-tol). The UE as part of recommended configurations include the applicable M-to-P pattern which might be dependent to environmental parameters and scenarios such as UE speed and location or dependent to the accuracy or confidence of the prediction that might be requested by the network node.

[0092] The said recommended radio configurations may be selected from a set of one or more candidate radio configurations included in a first AI / ML inference configuration message by the gNB, e.g. the recommended radio configurations may be a subset of the candidate radio configurations. The candidate radio configurations are the radio configurations that the gNB may configure to the UE, and that the UE can apply for the AI / ML inference. Such candidate configurations may be sent to the UE via dedicated signals or being broadcasted in serving cell via system information block message (SIB message).

[0093] In another embodiment the said recommended radio configurations may be selected based on the UE mobility characteristics such as actual or predicted UE speed, and / or location and / or UE trajectory or instructed by the over-the-top server. In yet another embodiment, the recommended configuration might be selected based on the coverage quality i.e., in poor coverage area the UE may recommend to the network a different set of configurations and in high quality coverage area the UE may recommend a different set of recommended configurations.

[0094] In yet another embodiment the UE may select the recommended radio configurations based on the expected / required accuracy / confidence of the predictions i.e., the network may configure a prediction accuracy threshold (possibly per AI / ML functionality or per use case) andupon receiving this accuracy threshold UE recommends one or more radio configurations with which the UE’s prediction can fulfil the expected / requested accuracy. The UE may later update (via an uplink signal e.g., using UE assistance information signal) the recommended configuration upon changing the prediction accuracy / confidence or upon changing some environmental parameters such as UE speed, location etc., or upon changing the network configuration or the network coverage quality. Network node upon receiving the updated recommended configuration may reconfigure the UE based on the received recommended configuration.

[0095] In another method, the UE may signal as recommended radio configurations, a set of one or more radio configurations different from the set of candidate radio configurations received from the gNB. In one method, this can happen only if the gNB configures explicitly the UE in the inference configuration message with an indication, e.g. a configuration / request flag, that allows / requests the UE to transmit this recommended set of radio configurations different from the set of candidate radio configurations. In another method, the provisioning of this indication can be implicit, e.g. the inference configuration message may not contain any candidate radio configuration, e.g. it may just contain an indication of the AI / ML model / functionality that the gNB configures / requests. In this case, the UE signals its recommendation for the indicated AI / ML model / functionality. In another solution for the implicit flag in the inference configuration message is that it is allowed for the UE to response with the recommended radio configurations to the gNB if the UE receives the candidate AI / ML radio configurations included in AI / ML inference configuration (e.g., the first AI / ML inference configuration).

[0096] In one method, the UE may not signal any recommendation for the AI / ML model / functionality requested by the gNB. This can be the case for example, when the UE does not have any available model for the concerned AI / ML model / functionality. Instead, the UE may request, at step 701 from Figure 7, the gNB to perform training for the concerned AI / ML model / functionality, i.e. the UE may signal that via a flag or it may indicate the resources in which the UE needs to perform the 702 for the purpose of training the concerned AI / ML model / functionality. In another method, the UE does not signal any request, instead it may notify the training entity, e.g. OTT server, that no (applicable) AI / ML model / functionality is available, and the UE may download from the said entity at least an AI / ML model for the concerned AI / ML functionality. In another method, the UE does not signal any recommendation for the AI / ML model / functionality requested by the gNB; instead the UE sends the information to the gNB via for example a flag, where the information indicates that the status of the requestedmodel / functionality (e.g. download status, update status) and the UE may notify the training entity, e.g. Over The Top (OTT) server, that no (applicable) AI / ML model / functionality is available and may download from the said entity at least an AI / ML model for the concerned AI / ML functionality. In one embodiment, the UE may notify the reason / cause for not having any recommendation for the gNB, wherein the reason / cause could be ‘no model available’, or a request such as “training needed”, or “model to be downloaded”.

[0097] In one method, the UE signals the recommended AI / ML radio configurations, only if none of the candidate AI / ML radio configuration can be applied by the UE, e.g. the UE does not consider any of the candidate AI / ML radio configurations as suitable to make the concerned AI / ML model / functionality applicable. For example, in this method, the said configuration flag is only to allow the UE to transmit the recommended AI / ML radio configurations, only if none of the candidate AI / ML radio configuration can be applied by the UE for the concerned AI / ML model / functionality. Alternatively, even if the configuration flag is absent / not configured, the UE is always allowed to transmit the recommended AI / ML radio configurations, if none of the candidate AI / ML radio configuration can be applied by the UE for the concerned AI / ML model / functionality .

[0098] The recommendation may consist of one radio measurement configuration. If this recommended radio measurement configuration is selected from the candidate radio measurement configuration included in the first AI / ML inference configuration, the UE may consider itself to be configured with such radio measurement configuration and it may activate the concerned AI / ML model / functionality.

[0099] In another method, the UE before activating the AI / ML model / functionality may wait for an activation command (e.g., via Medium Access Control (MAC) Control Element (CE), via Downlink Control Information (DCI) CE) from the gNB. For example, the activation command from the gNB may also indicate which of the multiple recommended radio configurations, the UE should consider itself to be configured, and the UE may then activate the AI / ML model / functionality and apply such indicated configuration.In another method, in case the UE transmitted to the gNB a recommendation of one or more radio configurations different from the set of candidate radio configurations (included in the first AI / ML inference configuration), the gNB may transmit a second AI / ML inference configuration including for example a configuration which is selected by the gNB from the recommended radio configurations signalled by the UE. Upon receiving this second AI / ML inference configuration, the UE may consider itself to be configured with the radio configuration therein included, and it may then activate the concerned AI / ML model / functionality and apply the saidconfiguration. The same can happen in case the UE transmitted to the gNB a recommendation of multiple radio configurations selected by the UE from the set of candidate radio configurations. Also in this latter case, the gNB may transmit a second AI / ML inference configuration configuring the UE with one of such multiple recommended radio configurations.

[0100] The inference configuration may include the list of AI / ML models / functionalities for which the gNB requests the UE to transmit its recommendation. The UE may transmit the response to the gNB with a list of AI / ML models / functionalities and corresponding radio configurations, if any.

[0101] The inference configuration message could be for example an RRC message transmitted by the UE, such as an RRCReconfiguration message. The UE may signal the recommendation in an RRCReconfigurationComplete message, or in UEAssistancelnformation message, or in a MAC CE, and it may contain a reference, e.g. an ID, to one or more of the candidate radio configurations included in the first AI / ML inference configuration.The second AI / ML inference configuration message could also be an RRC message, such as an RRCReconfiguration message, or a MAC CE. The second AI / ML inference configuration transmitted by the gNB, may contain a reference, e.g. an identifier (ID), to one of the candidate radio configurations included in the first AI / ML inference configuration, or to one of the recommended radio configurations signalled by the UE.

[0102] Figure 9 shows an example of a communication system 900 in accordance with some embodiments.

[0103] In the example, the communication system 900 includes a telecommunication network 902 that includes an access network 904, such as a Radio Access Network (RAN), and a core network 906, which includes one or more core network nodes 908. The access network 904 includes one or more access network nodes, such as network nodes 910A and 910B (one or more of which may be generally referred to as network nodes 910), or any other similar Third Generation Partnership Project (3GPP) access nodes or non-3GPP Access Points (APs).Moreover, as will be appreciated by those of skill in the art, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunication network 902 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network 902 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 morefunctionalities of any node in the telecommunication network 902, including one or more network nodes 910 and / or core network nodes 908. The network nodes 910, in various embodiments, perform the functionality as described herein in Fig. 7.

[0104] Examples of an ORAN network node include an Open Radio Unit (O-RU), an Open Distributed Unit (O-DU), an Open Central Unit (O-CU), including an O-CU Control Plane (O- CU-CP) or an O-CU User Plane (O-CU-UP), a RAN intelligent controller (near-real time or non- real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or anon-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an Al, Fl, Wl, El, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN access node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an O-2 interface defined by the O-RAN Alliance or comparable technologies. The network nodes 910 facilitate direct or indirect connection of User Equipment (UE), such as by connecting UEs 912A, 912B, 912C, and 912D (one or more of which may be generally referred to as UEs 912) to the core network 906 over one or more wireless connections.

[0105] 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 900 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 900 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0106] The UEs 912 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 910 and other communication devices. Similarly, the network nodes 910 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 912 and / or with other network nodes or equipment in the telecommunication network 902to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network 902. The UEs 912 in various embodiments perform the functionality as described in Fig. 7.

[0107] In the depicted example, the core network 906 connects the network nodes 910 to one or more hosts, such as host 916. 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 906 includes one more core network nodes (e.g., core network node 908) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 908. 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).

[0108] The host 916 may be under the ownership or control of a service provider other than an operator or provider of the access network 904 and / or the telecommunication network 902, and may be operated by the service provider or on behalf of the service provider. The host 916 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.

[0109] As a whole, the communication system 900 of Figure 9 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system 900 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 Second, Third, Fourth, or Fifth Generation (2G, 3G, 4G, or 5G) standards, or any applicable future generation standard (e.g., Sixth Generation (6G)); Wireless Local Area Network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE)802.11 standards (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and / or any Low Power Wide Area Network (LPWAN) standards such as LoRa and Sigfox.

[0110] In some examples, the telecommunication network 902 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunication network 902 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 902. For example, the telecommunication network 902 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing enhanced Mobile Broadband (eMBB) services to other UEs, and / or massive Machine Type Communication (mMTC) / massive Internet of Things (loT) services to yet further UEs.[oni] In some examples, the UEs 912 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 904 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 904. Additionally, a UE may be configured for operating in single- or multi-Radio Access Technology (RAT) or multi-standard mode. For example, a UE may operate with any one or combination of WiFi, New Radio (NR), and LTE, i.e. being configured for Multi -Radio Dual Connectivity (MR-DC), such as Evolved UMTS Terrestrial RAN (E-UTRAN) NR - Dual Connectivity (EN-DC).

[0112] In the example, a hub 914 communicates with the access network 904 to facilitate indirect communication between one or more UEs (e.g., UE 912C and / or 912D) and network nodes (e.g., network node 910B). In some examples, the hub 914 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 914 may be a broadband router enabling access to the core network 906 for the UEs. As another example, the hub 914 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 910, or by executable code, script, process, or other instructions in the hub 914. As another example, the hub 914 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 914 may be a content source. For example, for a UE that is a Virtual Reality (VR) headset, display, loudspeaker or other media delivery device, the hub 914 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 914 then provides to the UE eitherdirectly, after performing local processing, and / or after adding additional local content. In still another example, the hub 914 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0113] The hub 914 may have a constant / persistent or intermittent connection to the network node 910B. The hub 914 may also allow for a different communication scheme and / or schedule between the hub 914 and UEs (e.g., UE 912C and / or 912D), and between the hub 914 and the core network 906. In other examples, the hub 914 is connected to the core network 906 and / or one or more UEs via a wired connection. Moreover, the hub 914 may be configured to connect to a Machine-to-Machine (M2M) service provider over the access network 904 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 910 while still connected via the hub 914 via a wired or wireless connection. In some embodiments, the hub 914 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 910B. In other embodiments, the hub 914 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and the network node 910B, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0114] Figure 10 shows a UE 1000 in accordance with some embodiments. The UE 1000 is an example of the UE 912 as described herein in Figs. 7 and 9. As used herein, a UE refers to a device capable, configured, arranged, and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, Voice over Internet Protocol (VoIP) phone, wireless local loop phone, desktop computer, Personal Digital Assistant (PDA), wireless camera, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, Laptop Embedded Equipment (LEE), Laptop Mounted Equipment (LME), smart device, wireless Customer Premise Equipment (CPE), vehicle, vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3GPP, including a Narrowband Internet of Things (NB-IoT) UE, a Machine Type Communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0115] A 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 devicethat 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).

[0116] The UE 1000 includes processing circuitry 1002 that is operatively coupled via a bus 1004 to an input / output interface 1006, a power source 1008, memory 1010, a communication interface 1012, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 10. 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.

[0117] The processing circuitry 1002 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 1010. The processing circuitry 1002 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 1002 may include multiple Central Processing Units (CPUs).

[0118] In the example, the input / output interface 1006 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 1000. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an inputdevice. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0119] In some embodiments, the power source 1008 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 1008 may further include power circuitry for delivering power from the power source 1008 itself, and / or an external power source, to the various parts of the UE 1000 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 1008. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 1008 to make the power suitable for the respective components of the UE 1000 to which power is supplied.

[0120] The memory 1010 may be or be configured to include memory such as Random Access Memory (RAM), Read Only Memory (ROM), Programmable ROM (PROM), Erasable PROM (EPROM), Electrically EPROM (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 1010 includes one or more application programs 1014, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 1016. The memory 1010 may store, for use by the UE 1000, any of a variety of various operating systems or combinations of operating systems.

[0121] The memory 1010 may be configured to include a number of physical drive units, such as Redundant Array of Independent Disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, High Density Digital Versatile Disc (HD- DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, Holographic Digital Data Storage (HDDS) optical disc drive, external mini Dual In-line Memory Module (DIMM), Synchronous Dynamic RAM (SDRAM), external micro-DIMM SDRAM, smartcard memory such as a tamper resistant module in the form of a Universal Integrated Circuit Card (UICC) including one or more Subscriber Identity Modules (SIMs), such as a Universal SIM (USIM) and / or Internet Protocol Multimedia Services Identity Module (ISIM), other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as a ‘SIM card.’ The memory 1010 may allow the UE 1000 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 1010, which may be or comprise a device-readable storage medium.

[0122] The processing circuitry 1002 may be configured to communicate with an access network or other network using the communication interface 1012. The communication interface 1012 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 1022. The communication interface 1012 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 1018 and / or a receiver 1020 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 1018 and receiver 1020 may be coupled to one or more antennas (e.g., the antenna 1022) and may share circuit components, software, or firmware, or alternatively be implemented separately.

[0123] In the illustrated embodiment, communication functions of the communication interface 1012 may include cellular communication, WiFi communication, LPWAN communication, data communication, voice communication, multimedia communication, short- range communications such as Bluetooth, NFC, location-based communication such as the use of the Global Positioning System (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband CDMA (WCDMA), GSM, LTE, NR, UMTS, WiMax, Ethernet, Transmission Control Protocol / Intemet Protocol (TCP / IP), Synchronous Optical Networking (SONET), Asynchronous Transfer Mode (ATM), Quick User Datagram Protocol Internet Connection (QUIC), Hypertext Transfer Protocol (HTTP), and so forth.

[0124] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 1012, 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).

[0125] 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 surfacesor 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.

[0126] A UE, when in the form of an loT device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application, and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a television, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or VR, a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or 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 1000 shown in Figure 10.

[0127] 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-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship, an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0128] 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.

[0129] Figure 11 shows a network node 1100 in accordance with some embodiments. The network node 1100 is an example of the network node 910 as described herein in Figs. 7 and 9. As used herein, network node refers to equipment capable, configured, arranged, and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment in a telecommunication network. Examples of network nodes include, but are not limited to, APs (e.g., radio APs), Base Stations (BSs) (e.g., radio BSs, Node Bs, evolved Node Bs (eNBs), NR Node Bs (gNBs)), and O-RAN nodes or components of an O-RAN node (e.g., O-RU, O-DU, O-CU).

[0130] 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 RRUs may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a Distributed Antenna System (DAS).

[0131] Other examples of network nodes include multiple Transmission Point (multi-TRP) 5G access nodes, Multi-Standard Radio (MSR) equipment such as MSR BSs, network controllers such as Radio Network Controllers (RNCs) or BS Controllers (BSCs), Base Transceiver Stations (BTSs), transmission points, transmission nodes, Multi-Cell / Multicast Coordination Entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0132] The network node 1100 includes processing circuitry 1102, memory 1104, a communication interface 1106, and a power source 1108. The network node 1100 may be composed of multiple physically separate components (e.g., aNodeB component and an RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 1100 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair may in some instances be considered a single separate network node. In some embodiments, the network node 1100 maybe configured to support multiple RATs. In such embodiments, some components may be duplicated (e.g., separate memory 1104 for different RATs) and some components may be reused (e.g., a same antenna 1110 may be shared by different RATs). The network node 1100 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1100, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, Long Range Wide Area Network (LoRaWAN), Radio Frequency Identification (RFID), or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within the network node 1100.

[0133] The processing circuitry 1102 may comprise a combination of one or more of a microprocessor, controller, microcontroller, CPU, DSP, ASIC, FPGA, or any other suitable computing device, resource, or combination of hardware, software, and / or encoded logic operable to provide, either alone or in conjunction with other network node 1100 components, such as the memory 1104, to provide network node 1100 functionality.

[0134] In some embodiments, the processing circuitry 1102 includes a System on a Chip (SOC). In some embodiments, the processing circuitry 1102 includes one or more of Radio Frequency (RF) transceiver circuitry 1112 and baseband processing circuitry 1114. In some embodiments, the RF transceiver circuitry 1112 and the baseband processing circuitry 1114 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of the RF transceiver circuitry 1112 and the baseband processing circuitry 1114 may be on the same chip or set of chips, boards, or units.

[0135] The memory 1104 may comprise any form of volatile or non-volatile computer- readable memory including, without limitation, persistent storage, solid state memory, remotely mounted memory, magnetic media, optical media, RAM, ROM, mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD), or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable, and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 1102. The memory 1104 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 1102 and utilized by the network node 1100. The memory 1104 may be used to store any calculations made by the processing circuitry 1102 and / or any data received via the communication interface 1106. In some embodiments, the processing circuitry 1102 and the memory 1104 are integrated.

[0136] The communication interface 1106 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, the communication interface 1106 comprises port(s) / terminal(s) 1116 to send and receive data, for example to and from a network over a wired connection. The communication interface 1106 also includes radio front-end circuitry 1118 that may be coupled to, or in certain embodiments a part of, the antenna 1110. The radio front-end circuitry 1118 comprises filters 1120 and amplifiers 1122. The radio front-end circuitry 1118 may be connected to the antenna 1110 and the processing circuitry 1102. The radio front-end circuitry 1118 may be configured to condition signals communicated between the antenna 1110 and the processing circuitry 1102. The radio front-end circuitry 1118 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 1118 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of the filters 1120 and / or the amplifiers 1122. The radio signal may then be transmitted via the antenna 1110. Similarly, when receiving data, the antenna 1110 may collect radio signals which are then converted into digital data by the radio front-end circuitry 1118. The digital data may be passed to the processing circuitry 1102. In other embodiments, the communication interface 1106 may comprise different components and / or different combinations of components.

[0137] In certain alternative embodiments, the network node 1100 does not include separate radio front-end circuitry 1118; instead, the processing circuitry 1102 includes radio front-end circuitry and is connected to the antenna 1110. Similarly, in some embodiments, all or some of the RF transceiver circuitry 1112 is part of the communication interface 1106. In still other embodiments, the communication interface 1106 includes the one or more ports or terminals 1116, the radio front-end circuitry 1118, and the RF transceiver circuitry 1112 as part of a radio unit (not shown), and the communication interface 1106 communicates with the baseband processing circuitry 1114, which is part of a digital unit (not shown).

[0138] The antenna 1110 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 1110 may be coupled to the radio front-end circuitry 1118 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 1110 is separate from the network node 1100 and connectable to the network node 1100 through an interface or port.

[0139] The antenna 1110, the communication interface 1106, and / or the processing circuitry 1102 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node 1100. Any information, data, and / or signals may be received from a UE, another network node, and / or any other network equipment.Similarly, the antenna 1110, the communication interface 1106, and / or the processing circuitry 1102 may be configured to perform any transmitting operations described herein as being performed by the network node 1100. Any information, data, and / or signals may be transmitted to a UE, another network node, and / or any other network equipment.

[0140] The power source 1108 provides power to the various components of the network node 1100 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 1108 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 1100 with power for performing the functionality described herein. For example, the network node 1100 may be connectable to an external power source (e.g., the power grid or an electricity outlet) via input circuitry or an interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 1108. As a further example, the power source 1108 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.

[0141] Embodiments of the network node 1100 may include additional components beyond those shown in Figure 11 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 1100 may include user interface equipment to allow input of information into the network node 1100 and to allow output of information from the network node 1100. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 1100. In some embodiments, some components, such as the radio front-end circuitry 1118 and the RF transceiver circuitry 1112 may be omitted.

[0142] Figure 12 is a block diagram illustrating a virtualization environment 1200 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 virtualization environments 1200 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, a UE, a core network node, or ahost. 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 1200 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 a network node, a UE, a core network node, or a host.

[0143] Applications 1202 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 1200 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0144] Hardware 1204 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, an input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 1206 (also referred to as hypervisors or Virtual Machine Monitors (VMMs)), provide VMs 1208A and 1208B (one or more of which may be generally referred to as VMs 1208), and / or perform any of the functions, features, and / or benefits described in relation with some embodiments described herein. The virtualization layer 1206 may present a virtual operating platform that appears like networking hardware to the VMs 1208.

[0145] The VMs 1208 comprise virtual processing, virtual memory, virtual networking, or interface and virtual storage, and may be run by a corresponding virtualization layer 1206. Different embodiments of the instance of a virtual appliance 1202 may be implemented on one or more of VMs 1208, 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.

[0146] In the context of NFV, a VM 1208 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 1208, and that part of the hardware 1204 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 1208 on top of the hardware 1204 and corresponds to the application 1202.

[0147] The hardware 1204 may be implemented in a standalone network node with generic or specific components. The hardware 1204 may implement some functions via virtualization. Alternatively, the hardware 1204 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 1210, which, among others, oversees lifecycle management of the applications 1202. In some embodiments, the hardware 1204 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 1212 which may alternatively be used for communication between hardware nodes and radio units.

[0148] 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.

[0149] 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 certainembodiments 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.

[0150] Those skilled in the art will recognize improvements and modifications to the embodiments of the present disclosure. All such improvements and modifications are considered within the scope of the concepts disclosed herein.

[0151] Some of the embodiments of the disclosure include

[0152] Embodiment 1 : A method performed by a user equipment, UE, (912) for signaling recommended radio measurement configurations based on Artificial Intelligence Machine Learning, AI / ML, inference configurations, the method comprising: transmitting (704), to a network node (910), a set of one or more recommended AI / ML radio measurement configurations for an AI / ML inference for at least one AI / ML functionality / model, wherein the set of one or more recommended AI / ML radio measurement configurations are selected from a set of candidate AI / ML radio measurement configurations included in a first AI / ML inference configuration received (702) from the network node (910) or from a different set of candidate AI / ML radio measurement configurations not included in the first AI / ML inference configuration received from the network node (910).

[0153] Embodiment 2: The method of embodiment 1, wherein the first AI / ML inference configuration comprises a configuration flag that indicates whether the UE (912) is allowed to transmit the set of recommended AI / ML radio measurement configurations different than the candidate AI / ML radio measurement configurations.

[0154] Embodiment 3 : The method of embodiment 2, wherein the configuration flag is signalled by the network node (910) explicitly.

[0155] Embodiment 4: The method of any of embodiments 1 to 3, wherein in response to transmitting the set of one or more recommended AI / ML radio measurement configurations to the network node (910), the method further comprises: receiving (706), from the network node (910), a second AI / ML inference configuration to perform AI / ML inference comprising the indicated recommended AI / ML radio measurement configurations or a subset thereof.

[0156] Embodiment 5: The method of any of embodiments 1 to 4, further comprising: activating (710) the AI / ML functionality / model with one of the recommended AI / ML radio measurement configurations of the set of recommended AI / ML radio measurement configurations, if the recommended AI / ML radio measurement configurations is selected from the one or more candidate radio configurations.

[0157] Embodiment 6: The method of any of embodiments 1 to 3, wherein in response to transmitting the set of one or more recommended AI / ML radio measurement configurations to the network node (910), the method further comprises: activating (710) the AI / ML functionality / model in response to receiving (708) an activation command from the network node (910).

[0158] Embodiment 7: The method of any of embodiments 1 to 6, wherein the UE (912) does not signal the set of recommended AI / ML radio measurement configurations to the network node (910), if no AI / ML model is available for a concerned AI / ML functionality indicated in the first AI / ML inference configuration.

[0159] Embodiment 8: The method of any of embodiments 1 to 7, wherein the set of recommended AI / ML radio measurement configurations are indicated by the UE (912) only if none of the candidate radio configurations can make the AI / ML functionality / model indicated in the inference configuration applicable.

[0160] Embodiment 9: The method of any of embodiments 1 to 8, wherein the set of recommended AI / ML radio measurement configurations or candidate AI / ML radio measurement configurations comprise one or more of a set A and / or set B of radio resources where the radio resources comprise real radio resources; a network-side operation property; and a UE-side operation property.

[0161] Embodiment 10: The method of embodiment 9, wherein the set A and / or set B of radio resources comprise one or more of: a set of Synchronization Signal Blocks, SSB, for a cell; a set of Channel State Information Reference Signal, CSI-RS, resources for a cell; a set of Synchronization Signal, SS, Physical Broadcast Channel, PBCH, block resource sets for a cell; a set of cells; a set of frequencies; a set of SSB for a list of cells; a set of CSI-RS resources for a list of cells; and a set of SS / PBCH block resource sets for a list of cells.

[0162] Embodiment 11: The method of embodiment 9, wherein the network-side / UE-side operational properties comprises one or more of: a mapping relationship of Set A and Set B; an indication of consistency of downlink spatial domain transmission filters corresponding to the beams in Set A and Set B; a quasi co-location, QCL, assumption; an order of model input andmodel output; transmission power; a UE distribution; antenna height; a deployment scenario information; and a UE speed.

[0163] Embodiment 12: The method of embodiments 9 to 11, wherein the set of recommended AI / ML radio measurement configurations comprise a list of identifiers each referring to a specific configuration.

[0164] Embodiment 13: The method of any of embodiments 1 to 12, wherein the embodiments are executed for each of the AI / ML models / functionalities for which AI / ML radio measurement configurations are configured.

[0165] Embodiment 14: The method of any of embodiments 1 to 13, wherein the set of recommended AI / ML radio measurement configurations comprises one or more of: a measurement to prediction pattern; a measurement periodicity configuration or the time interval between measurement samples; and a configuration of observation window.

[0166] Embodiment 15: A user equipment for signaling recommended radio measurement configurations based on Artificial Intelligence Machine Learning, AI / ML, inference configurations, the UE (912) comprising processing circuitry configured to perform any of the steps of embodiments 1 to 14.

[0167] Embodiment 16: A method performed by a network node (910) for facilitating signaling recommended radio measurement configurations based on Artificial Intelligence Machine Learning, AI / ML, inference configurations, the method comprising: receiving (704), from a User Equipment, UE, (912) a set of one or more recommended AI / ML radio measurement configurations for an AI / ML inference for at least one AI / ML functionality / model, wherein the set of one or more recommended AI / ML radio measurement configurations are selected from a set of candidate AI / ML radio measurement configurations included in a first AI / ML inference configuration provided (702) to the UE (912) or from a different set of candidate AI / ML radio measurement configurations not included in the first AI / ML inference configuration.

[0168] Embodiment 17: A network node (910) for facilitating signaling recommended radio measurement configurations based on Artificial Intelligence Machine Learning, AI / ML, inference configurations, the network node (910) comprising processing circuitry configured to perform any of the steps of embodiment 16.

Claims

CLAIMS1. A method performed by a user equipment, UE, (912) for signaling recommended radio measurement configurations based on Artificial Intelligence Machine Learning, AI / ML, inference configurations, the method comprising: transmitting (704), to a network node (910), a set of one or more recommended AI / ML radio measurement configurations for an AI / ML inference or for an AI / ML training for at least one AI / ML functionality / model, wherein the set of one or more recommended AI / ML radio measurement configurations are selected from a set of candidate AI / ML radio measurement configurations included in a first AI / ML inference configuration received (702) from the network node (910) or from a different set of candidate AI / ML radio measurement configurations not included in the first AI / ML inference configuration received from the network node (910).

2. The method of claim 1, wherein the first AI / ML inference configuration comprises a configuration flag that indicates whether the UE (912) is allowed to transmit the set of recommended AI / ML radio measurement configurations different than the candidate AI / ML radio measurement configurations.

3. The method of claim 2, wherein the configuration flag is signalled by the network node (910) explicitly.

4. The method of any of claims 1 to 3, wherein in response to transmitting the set of one or more recommended AI / ML radio measurement configurations to the network node (910), the method further comprises: receiving (706), from the network node (910), a second AI / ML inference configuration to perform AI / ML inference comprising the indicated recommended AI / ML radio measurement configurations or a subset thereof.

5. The method of any of claims 1 to 4, further comprising: activating (710) the AI / ML functionality / model with one of the recommended AI / ML radio measurement configurations of the set of recommended AI / ML radio measurement configurations, if the recommended AI / ML radio measurement configurations is selected from the one or more candidate radio configurations.

6. The method of any of claims 1 to 3, wherein in response to transmitting the set of one or more recommended AI / ML radio measurement configurations to the network node (910), the method further comprises: activating (710) the AI / ML functionality / model in response to receiving (708) an activation command from the network node (910).

7. The method of any of claims 1 to 6, wherein the UE (912) does not signal the set of recommended AI / ML radio measurement configurations to the network node (910), if no AI / ML model is available for a concerned AI / ML functionality indicated in the first AI / ML inference configuration.

8. The method of any of claims 1 to 7, wherein the set of recommended AI / ML radio measurement configurations are indicated by the UE (912) only if none of the candidate radio configurations can make the AI / ML functionality / model indicated in the inference configuration applicable.

9. The method of any of claims 1 to 8, wherein the different set of candidate AI / ML radio measurement configurations not included in the first AI / ML inference configuration are also received from the network node (910).

10. The method of any of claims 1 to 9, wherein the set of recommended AI / ML radio measurement configurations or candidate AI / ML radio measurement configurations comprise one or more of: a set A and / or set B of radio resources where the radio resources comprise real radio resources; a network-side operation property; and a UE-side operation property.

11. The method of claim 10, wherein the set A and / or set B of radio resources comprise one or more of: a set of Synchronization Signal Blocks, SSB, for a cell; a set of Channel State Information Reference Signal, CSI-RS, resources for a cell;a set of Synchronization Signal, SS, Physical Broadcast Channel, PBCH, block resource sets for a cell; a set of cells; a set of frequencies; a set of SSB for a list of cells; a set of CSI-RS resources for a list of cells; and a set of SS / PBCH block resource sets for a list of cells.

12. The method of claim 10, wherein the network-side / UE-side operational properties comprises one or more of: a mapping relationship of Set A and Set B; an indication of consistency of downlink spatial domain transmission filters corresponding to the beams in Set A and Set B; a quasi co-location, QCL, assumption; an order of model input and model output; transmission power; a UE distribution; antenna height; a deployment scenario information; and a UE speed.

13. The method of claims 10 to 12, wherein the set of recommended AI / ML radio measurement configurations comprise a list of identifiers each referring to a specific configuration.

14. The method of any of claims 1 to 13, wherein the set of recommended AI / ML radio measurement configurations comprises one or more of: a measurement to prediction pattern; a measurement periodicity configuration or the time interval between measurement samples; and a configuration of observation window.

15. The method of any of claims 1 to 14, further comprising: providing to the network node (910) a request to perform data collection for training anAI / ML functionality / model.

16. A User Equipment, UE, (912) for signaling recommended radio measurement configurations based on Artificial Intelligence Machine Learning, AI / ML, inference configurations, the UE (912) comprising processing circuitry configured to: transmit (704), to a network node (910), a set of one or more recommended AI / ML radio measurement configurations for an AI / ML inference or for an AI / ML training for at least one AI / ML functionality / model, wherein the set of one or more recommended AI / ML radio measurement configurations are selected from a set of candidate AI / ML radio measurement configurations included in a first AI / ML inference configuration received (702) from the network node (910) or from a different set of candidate AI / ML radio measurement configurations not included in the first AI / ML inference configuration received from the network node (910).

17. The UE (912) of claim 16, wherein the processing circuitry is further configured to perform any of the steps of claims 1 to 15.

18. A method performed by a network node (910) for facilitating signaling recommended radio measurement configurations based on Artificial Intelligence Machine Learning, AI / ML, inference configurations, the method comprising: receiving (704), from a User Equipment, UE, (912) a set of one or more recommended AI / ML radio measurement configurations for an AI / ML inference or for an AI / ML training for at least one AI / ML functionality / model, wherein the set of one or more recommended AI / ML radio measurement configurations are selected from a set of candidate AI / ML radio measurement configurations included in a first AI / ML inference configuration provided (702) to the UE (912) or from a different set of candidate AI / ML radio measurement configurations not included in the first AI / ML inference configuration.

19. A network node (910) for facilitating signaling recommended radio measurement configurations based on Artificial Intelligence Machine Learning, AI / ML, inference configurations, the network node (910) comprising processing circuitry configured to: receive (704), from a User Equipment, UE, (912) a set of one or more recommended AI / ML radio measurement configurations for an AI / ML inference or for an AI / ML training for at least one AI / ML functionality / model, wherein the set of one or more recommended AI / MLradio measurement configurations are selected from a set of candidate AI / ML radio measurement configurations included in a first AI / ML inference configuration provided (702) to the UE (912) or from a different set of candidate AI / ML radio measurement configurations not included in the first AI / ML inference configuration.

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