Methods for selecting and configuring measurement resources based on configured prediction resources for AIML radio measurement predictions

Abstract

Systems and methods are disclosed for User Equipment (UE) assisted resource set selection for Artificial Intelligence (AI) / Machine Learning (ML) model or functionality such as beam prediction In one embodiment, a method performed by a UE comprises receiving, from a network node, first information that indicates a first set of measurement resources on which the UE is to perform measurement predictions and receiving, from the network node, second information that indicates a second set of measurement resources with which the UE can be configured to perform measurements, based on which the measurement predictions can be generated. The method further comprises determining whether an applicability condition(s) for using an AI / ML model(s) or function(s) at the UE to generate measurement predictions are satisfied for the first and second sets of measurement resources and sending a first indication to the network node indicating whether the one or more applicability conditions are satisfied.

Description

[0001] METHODS FOR SELECTING AND CONFIGURING MEASUREMENT RESOURCES BASED ON CONFIGURED PREDICTION RESOURCES FOR AIML RADIO MEASUREMENT PREDICTIONS Related Applications This application claims the benefit of provisional patent application serial number 63 / 555,232, filed February 19, 2024, the disclosure of which is hereby incorporated herein by reference in its entirety. Technical Field The present disclosure relates to a cellular communications system and, more specifically, to measurement resource configuration in relation to measurement predictions using a User Equipment (UE) side Artificial Intelligence (AI) / Machine Learning (ML) model or functionality. Background Artificial Intelligence (AI) 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 learn an optimal precoding policy for complex Multiple Input Multiple Output (MIMO) precoding problems. 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 will explore the benefits of augmenting the air-interface with features enabling improved support of AI / ML based algorithms for enhanced performance and / or reduced complexity / overhead. Through studying a few selected use cases (CSI feedback, beam management, and positioning), this study item aims at laying the foundation for future air- interface use cases leveraging AI / ML techniques. Functional Framework for AI / ML Model LCM Building the AI model, or any machine learning model, includes several development steps where the actual training of the AI model is just one step in a training pipeline. An important part in AI 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 that may include:o Data Ingestion: Data ingestion refers to gathering raw (training) data from a datastorage. After data ingestion, there may also be a step that controls the validity of the gathered data. oData Pre-Processing: Data pre-processing refers to some feature engineering appliedto the gathered data, e.g., it may include data normalization and possibly a data transformation required for the input data to the AI model. oModel Training: Model training refers to the actual model training steps aspreviously outlined. oModel Evaluation: Model evaluation refers to benchmarking the performance tosome model baseline. The iterative steps of model training and model evaluation continue until the acceptable level of performance (as previously exemplified) is achieved. oModel Registration: Model registration refers to registering the AI model, includingany corresponding AI-metadata that provides information on how the AI model was developed, and possibly AI model evaluations performance outcomes.^ A deployment stage to make the trained (or re-trained) AI model part of the inferencepipeline.^ An inference pipeline that may include:o Data Ingestion: Data ingestion refers to gathering raw (inference) data from a datastorage. oData Pre-Processing: Data pre-processing stage is typically identical to correspondingprocessing that occurs in the training pipeline. oModel Operational: Model operational refers to using the trained and deployed modelin an operational mode. oData and Model Monitoring: Data & model monitoring refers to validating that theinference 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.UE-NW Collaboration Levels for One- and Two-Sided AI / ML ModelsThe AI / ML models being discussed in the Rel-18 study item on AI / ML for the NR air interface can be categorized into the following two types:^ One-side AI / ML model, which can be a UE-sided AI / ML model whose inference isperformed entirely at the UE, or a NW-sided AI / ML model whose inference is performed entirely at the NW.^ Two-sided AI / ML model, which refers to a paired AI / ML Model(s) over which jointinference is performed across the UE and the NW, i.e., the first part of the inference is firstly performed by UE and then the remaining part is performed by NR base station (i.e., a gNodeB or gNB), or vice versa. Figure 3 shows an example use case of autoencoder (AE)- based CSI feedback / report, where an encoder (UE-part of the two-sided AE model) is operated at a UE to compress the estimated wireless channel, and the output of the encoder (the compressed wireless channel information estimates) is reported from the UE to a gNB.The gNB uses a decoder (NW-part of the two-sided AE model) to reconstruct the estimated wireless channel information. Functionality based LCM and Model-ID based LCM For UE-side models and UE-part of two-sided models, functionality based LCM and model-ID based LCM are discussed in 3GPP Rel-18. In functionality-based LCM, network indicates activation / deactivation / fallback / switching of AI / ML functionality via 3GPP signaling (e.g., Radio Resource Control (RRC), Medium Access Control (MAC)-Control Element (CE), Downlink Control Information (DCI)). Models may not be identified at the Network, and UE may perform model-level LCM. Whether and how much awareness / interaction NW should have about model-level LCM requires further study. For functionality identification, there may be either one or more Functionalities defined within an AI / ML-enabled feature, whereby AI / ML-enabled Feature refers to a Feature where AI / ML may be used. Note: UE may have one AI / ML model for the functionality, or UE may have multiple AI / ML models for the functionality. For AI / ML functionality identification and functionality-based LCM of UE-side models and / or UE-part of two-sided models, functionality refers to an AI / ML-enabled Feature / FG enabled by configuration(s), where configuration(s) is(are) supported based on conditions indicated by UE capability. Correspondingly, functionality-based LCM operates based on, at least, one configuration of AI / ML-enabled Feature / FG or specific configurations of an AI / ML- enabled Feature / FG. After functionality identification, necessity, mechanisms, for UE to report updates on applicable functionality(es) among [configured / identified] functionality(es), where the applicable functionalities may be a subset of all [configured / identified] functionalities are studied. Applicable functionalities / models can be reported by the UE. In model-ID-based LCM, models are identified at the Network, and Network / UE may activate / deactivate / select / switch individual AI / ML models via model ID. For AI / ML model identification and model-ID-based LCM of UE-side models and / or UE-part of two-sided models, model-ID-based LCM operates based on identified models, where a model may be associated with specific configurations / conditions associated with UE capability of an AI / ML-enabled Feature / FG and additional conditions (e.g., scenarios, sites, and datasets) as determined / identified between UE-side and NW-side. From RAN1 perspective, an AI / ML model identified by a model ID may be logical, and how it maps to physical AI / ML model(s) may be up to implementation. When distinction is necessary for discussion purposes, companies may use the term a logical AI / ML model to refer to a model that is identified and assigned a model ID, and physical AI / ML model(s) to refer to an actual implementation of such a model. After model identification, necessity, mechanisms, for UE to report updates on applicable UE part / UE-side model(s), where the applicable models may be a subset of all identified models are studied. For AI / ML model identification of UE-side or UE-part of two-sided models, model identification is categorized in the following types:^ Type A: Model is identified to NW (if applicable) and UE (if applicable) without over-the-airsignaling oThe model may be assigned with a model ID during the model identification, whichmay be referred / used in over-the-air signaling after model identification.^ Type B: Model is identified via over-the-air signaling,o Type B1:- Model identification initiated by the UE, and NW assists the remaining steps(if any) of the model identification -the model may be assigned with a model ID during the model identificationo Type B2:- Model identification initiated by the NW, and UE responds (if applicable)for the remaining steps (if any) of the model identification -the model may be assigned with a model ID during the model identification^ Note: This does not imply that model identification is necessary. Once models are identified, UE can indicate supported AI / ML model IDs for a given AI / ML-enabled Feature / FG in a UE capability report as starting point. Note: model identification using capability report is not precluded for type B1 and type B2. Model ID [in RAN1 discussion] may or may not be globally unique, and different types of model IDs may be created for a single model for various LCM purposes. Note: Details can be studied in the WI phase. For functionality / model-ID based LCM, once functionalities / models are identified, the same or similar procedures may be used for their activation, deactivation, switching, fallback, and monitoring. How to handle the impact of UE’s internal conditions such as memory, battery, and other hardware limitations on functionality / model operations and AI / ML-enabled Feature is to be studied. NR Beam Management Procedures Beam management procedures in NR are defined by a set of Layer 1 (L1) / Layer 2 (L2) procedures that establish and maintain a suitable beam pairs for both transmitting and receiving data. A beam management procedure can include the following sub procedures: beam determination, beam measurements, beam reporting, and beam sweeping. In case of downlink transmission from the NW to the UE, P1 / P2 / P3 beam management procedures can be performed according to the NR SI technical report to overcome the challenges of establishing and maintaining the beam pairs when, for example, a UE moves or some blockage in the environment requires changing the beams. Although these scenarios are not directlymentioned in specifications, there are relevant procedures defined which enables the realizationof these scenarios, examples of such realization are depicted in the corresponding figure of each scenario: ^P1: The P1 procedure is used to enable UE measurement on differenttransmission / reception point (TRP) transmit (Tx) beams to support selection of TRP Tx beams / UE receive (Rx) beam(s). During initial access, for example, the gNB transmits Synchronization Signal (SS) / Physical Broadcast Channel (PBCH) block (SSB) beams in different directions to cover the whole cell. The UE measures signal quality on corresponding SSB signals to detect and select an appropriate SSB beam, this is shown in Figure 4. Random access is then transmitted on the Random Access Channel (RACH) resources indicated by the selected SSB. The corresponding beam will be used by both the UE and the network to communicate until connected mode beam management is active. The network infers which SSB beam was chosen by the UE without any explicit signaling. oFor beamforming at TRP, it typically includes an intra / inter-TRP Tx beam sweepfrom a set of different beams. For beamforming at UE, it typically includes a UE Rx beam sweep from a set of different beams.^ P2: The P2 procedure is used to enable UE measurement on different TRP Tx beams topossibly change inter / intra-TRP Tx beam(s). The network can use the SSB beam as an indication of which (narrow) Channel State Information (CSI) Reference Signal (CSI-RS) beams to try; that is, the selected SSB beam can be used to define a candidate set of narrow CSI-RS beams for beam management. Once CSI-RS is transmitted, the UE measures the Reference Signal Received Power (RSRP), and reports the result to the network. If the network receives a CSI-RSRP report from the UE where a new CSI-RS beam is better than the old used to transmit Physical Downlink Control Channel (PDCCH) / Physical Downlink Shared Channel (PDSCH), the network updates the serving beam for the UE accordingly, and possibly also modifies the candidate set of CSI-RS beams. The network can also instruct the UE to perform measurements on SSBs. If the network receives a report from the UE where a new SSB beam is better than the previous best SSB beam, a corresponding update of the candidate set of CSI-RS beams for the UE may be motivated. oP2 procedure is performed on a possibly smaller set of beams for beam refinementthan in P1. Note that P2 can be a special case of P1. For example, in connected mode gNB configures the UE with different CSI-RSs and transmits each CSI-RS on corresponding beam. UE then measures the quality of each CSI-RS beam on its current RX beam and sends feedback about the quality of the measured beams. Thereafter, based on this feedback, gNB will decide and possibly indicate to the UE which beam will be used in future transmissions. This is shown in Figure 5.^ P3: is used to enable UE measurement on the same TRP Tx beam to change UE Rx beamin the case UE uses beamforming. Once in connected mode, the UE is configured with a set of reference signals. Based on measurements, the UE determines which Rx beam is suitable to receive each reference signal in the set. The network then indicates which reference signals are associated with the beam that will be used to transmit PDCCH / PDSCH, and the UE uses this information to adjust its Rx beam when receiving PDCCH / PDSCH. oIn connected mode, P3 can be used by the UE to find the best Rx beam forcorresponding Tx beam. In this case gNB keeps one CSI-RS Tx beam at a time, and UE performs the sweeping and measurements on its own Rx beams for that specific Tx beam. UE then finds the best corresponding Rx beam based on the measurements and will use it in future for reception when gNB indicates the use of that Tx beam. oFigure 6 illustrates UE Rx beam selection for corresponding CSI-RS Tx beam inDL according to P3 scenario. Beam Measurement and Reporting in NR For beam management, a UE can be configured to report RSRP or / and Signal to Interference plus Noise Ratio (SINR) for each one of up to four beams, either on CSI-RS or SSB. UE measurement reports can be sent either over Physical Uplink Control Channel (PUCCH) or Physical Uplink Shared Channel (PUSCH) to the network node, e.g., gNB. CSI-RS: A CSI-RS is transmitted over each transmit (Tx) antenna port at the network node and for different antenna ports. The CSI-RS are multiplexed in time, frequency, and code domain such that the channel between each Tx antenna port at the network node and each receive antenna port at a UE can be measured by the UE. The time-frequency resource used for transmitting CSI-RS is referred to as a CSI-RS resource. In NR, the CSI-RS for beam management is defined as a 1- or 2-port CSI-RS resource ina CSI-RS resource set where the filed repetition is present. The following three types of CSI-RS transmissions are supported: ^Periodic CSI-RS: CSI-RS is transmitted periodically in certain slots. This CSI-RStransmission is semi-statically configured using RRC signaling with parameters such as CSI-RS resource, periodicity, and slot offset. ^Semi-Persistent CSI-RS: Similar to periodic CSI-RS, resources for semi-persistent CSI-RS transmissions are semi-statically configured using RRC signaling with parameters such as periodicity and slot offset. However, unlike periodic CSI-RS, dynamic signaling is needed to activate and deactivate the CSI-RS transmission. ^Aperiodic CSI-RS: This is a one-shot CSI-RS transmission that can happen in any slot.Here, one-shot means that CSI-RS transmission only happens once per trigger. The CSI- RS resources (i.e., the Resource Element (RE) locations which consist of subcarrier locations and Orthogonal Frequency Division Multiplexing (OFDM) symbol locations) for aperiodic CSI-RS are semi-statically configured. The transmission of aperiodic CSI- RS is triggered by dynamic signaling through PDCCH using the CSI request field inuplink (UL) DCI, in the same DCI where the UL resources for the measurement report are scheduled. Multiple aperiodic CSI-RS resources can be included in a CSI-RS resource set and the triggering of aperiodic CSI-RS is on a resource set basis. SSB: In NR, an SSB consists of a pair of synchronization signals (SSs), physical broadcast channel (PBCH), and Demodulation Reference Signal (DMRS) for PBCH. An SSB is mapped to 4 consecutive OFDM symbols in the time domain and 240 contiguous subcarriers (20 resource blocks (RBs)) in the frequency domain. NR supports beamforming and beam-sweeping for SSB transmission, by enabling a cell to transmit multiple SSBs in different narrow-beams multiplexed in time. The transmission of these SSBs is confined to a half frame time interval (5 milliseconds (ms)). It is also possible to configure a cell to transmit multiple SSBs in a single wide-beam with multiple repetitions. The design of beamforming parameters for each of the SSBs within a half frame is up to network implementation. The SSBs within a half frame are broadcasted periodically from each cell. The periodicity of the half frames with SS / PBCH blocks is referred to as SSB periodicity, which is indicated by SIB1. The maximum number of SSBs within a half frame, denoted by L, depends on the frequency band, and the time locations for these L candidate SSBs within a half frame dependson the subcarrier spacing (SCS) of the SSBs. The L candidate SSBs within a half frame areindexed in an ascending order in time from 0 to L-1. By successfully detecting PBCH and its associated DMRS, a UE knows the SSB index. A cell does not necessarily transmit SS / PBCH blocks in all L candidate locations in a half frame, and the resource of the un-used candidate positions can be used for the transmission of data or control signaling instead. It is up to network implementation to decide which candidate time locations to select for SSB transmission within a half frame, and which beam to use for each SSB transmission. Measurement resource configurations in NR: A UE can be configured with the following: -N≥1 CSI reporting settings (CSI-ReportConfig) and- M≥1 resource settings (CSI-ResourceConfig).Each CSI reporting setting is linked to one or more resource setting for channel and / orinterference measurement. The CSI framework is modular in the sense that several CSI reporting settings may be associated with the same Resource Setting. The measurement resource configurations for beam management are provided to the UEby RRC information element (IE) (CSI-ResourceConfigs). One CSI-ResourceConfig containsseveral Non-Zero Power (NZP)-CSI-RS-ResourceSets and / or CSI-SSB-ResourceSets. A UE can be configured to measure CSI-RSs using the RRC Information Element (IE) NZP-CSI-RS-ResourceSet. A NZP CSI-RS resource set contains the configurations of Ks ≥1 CSI-RS resources. Each CSI-RS resource configuration resource includes at least the following: -mapping to REs,- the number of antenna ports, and- time-domain behavior.Up to 64 CSI-RS resources can be grouped together in an NZP-CSI-RS-ResourceSet. A UE can be configured to measure SSBs using the RRC IE CSI-SSB-ResourceSet. Resource sets comprising SSB resources are defined in a similar manner to the CSI-RS resources defined above. In the case of aperiodic CSI-RS and / or aperiodic CSI reporting, the network node configures the UE with ^^CSI triggering states. Each triggering state contains the aperiodic CSI report setting to be triggered along with the associated aperiodic CSI-RS resource sets. Periodic and semi-persistent resource settings can only comprise a single resource set(i.e., S=1). Aperiodic resource settings can have many resources sets (S>=1), because one out ofthe S resource sets defined in the resource setting is indicated by the aperiodic triggering statethat triggers a CSI report. Beam Prediction The use case of beam prediction which will be standardized as part of 3GPP Rel.19 workitem consists of spatial beam prediction, and temporal beam prediction. The core idea of this usecase is to predict the “best” beam (or beams) from a Set A of beams using measurement resultsfrom another Set B of beams. According to 3GPP Technical Report (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. Set A and Set B of beams have not been defined yet; however, the following two examples illustrate some scenarios that will likely be studied in Release 18: -Set B is a subset of a Set A. For example, Set A is a set of 8 SSB / CSI-RS beams shown inFigure 7 (both light and dark circles). The UE measures Set B (the 4 beams indicated bydark circles). The AI / ML model should predict the best beam (or beams) in Set A using only measurements from Set B. In other words, Figure 7 illustrates an example where SetB 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). -Set A and Set B correspond to two different sets of beams. For example, Set A is a set of30 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. Figure 8illustrates an example where Set A is a set of narrow beams and Set B is a set of widebeams. The beam prediction can be performed in the gNB and 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, 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 air-interface. Whether the UE can perform the beam prediction on a certain set of resources with a certain accuracy depends on the applicability conditions of an AIML model / function. In particular, an AI / ML model or function (also referred to herein as “AIML model / function”) may be trained to perform the beam prediction under certain applicability conditions. Such applicability conditions need to be fulfilled in order for the AIML 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 AIML model / function was trained. 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 TX power, etc. In order to determine whether an AIML model / function is applicable or not, the UE needs to assess the applicability conditions of such AIML model / function with respect to the output (beam prediction) that need to be generated and received input (e.g. radio measurement resources configured by the gNB). Data Collection A key part of AI / ML-based prediction is collection of data that is used to train the AI / ML models at the UE or at the NW. Data collection is performed in several parts of the life-cycle management (LCM). First, a large amount of measurements must be collected in order to train the model. Second, when using the model for inference (i.e., beam prediction), the UE measurement data to feed into it must be collected; this is typically a smaller set of data at a time, but such collection typically happens more frequently than training. Finally, measurements are needed to monitor whether the model functions well. If not, actions need to be taken to ensure proper functioning of the system, for example, the action may be to disable the model or update the model. According to 3GPP TR 38.843, for a NW-sided model, the data collection procedure would imply the gNB transmitting some signal (e.g., CSI-RS or SSB) using a set of several different Tx beams on the downlink (DL), and the UE collecting and logging associatedmeasurement results, e.g. RSRP. The UE will then report, e.g. periodically, upon events, or onNW-demand, the logged measurements results so that the NW can use this information to train / retrain / finetune the NW-side model. The training of the NW-side model can occur in the gNB itself or in a NW-node such as the Operations, Administration, and Maintenance (OAM) node. For the UE-side model, the UE may need to collect measurements from the gNB signals (e.g. CSI-RS or SSB) and, once the data collection is completed, such collected data should transferred to a training entity which will be in charge of training / retraining / finetune the UE-side model based on the collected measurements and possibly on network assistance information such that the collected data measurements can be categorized by the training entity. As an output of the training, the UE-side model will be able to perform beam predictions on certain sets of beams, i.e. set A. For the case of UE-side mode, the training entity can be the UE itself (e.g., the application layer of the UE), or a network node, e.g. a radio access node like a gNB or a core network (CN) node (e.g., like the Network Data Analytics Function (NWDAF)), or an Over-the-Top (OTT) server, outside 3GPP. This latter approach might be a reasonable solution, because in order to have optimal performances, the trained data set should fit the inference operations at the device which may depend on UE-vendor specific implementations (e.g. software / hardware properties / capabilities), that a NW node may not know entirely. Summary Systems and methods are disclosed for User Equipment (UE) assisted resource set selection for Artificial Intelligence (AI) / Machine Learning (ML) model or functionality such asbeam prediction. In one embodiment, a method performed by a UE comprises receiving, from anetwork node, first information that indicates a first set of measurement resources on which theUE is to perform measurement predictions and receiving, from the network node, secondinformation that indicates a second set of measurement resources with which the UE can be configured to perform measurements, based on which the measurement predictions can be generated. The method further comprises determining whether one or more applicabilityconditions for using one or more AI / ML models or functions at the UE to generate measurementpredictions are satisfied for the first set of measurement resources and the second set ofmeasurement resources and sending a first indication to the network node, the first indicationcomprising information indicative of whether the one or more applicability conditions are satisfied. In this manner, measurement resources to be used for AI / ML models or functions can be selected in a manner that is efficient and improves network performance. In one embodiment, the first indication comprises information that indicates that the oneor more applicability conditions are satisfied, and the method further comprises performingmeasurements on the second set of measurement resources and generating a measurementprediction for the first set of measurement resources using the one or more AI / ML models or functions with the measurements performed on the second set of measurement resources as inputs to the one or more AI / ML models or functions. In one embodiment, the first indication comprises information that indicates that the one or more applicability conditions are satisfied, and the method further comprises receiving, from the network node, an indication to perform measurements on the second set of measurementresources, performing measurements on the second set of measurement resources, and generatinga measurement prediction for the first set of measurement resources using the one or more AI / ML models or functions with the measurements performed on the second set of measurement resources as inputs to the one or more AI / ML models or functions. In one embodiment, the first indication comprises information that indicates that the one or more applicability conditions are satisfied and further comprises information that indicates a third set of measurement resources, wherein the third set of measurement resources is a subset ofthe second set of measurement resources, and the method further comprises performingmeasurements on the third set of measurement resources and generating a measurementprediction for the first set of measurement resources using the one or more AI / ML models or functions with the measurements performed on the third set of measurement resources as inputs to the one or more AI / ML models or functions. In one embodiment, the first indication comprises information that indicates that the one or more applicability conditions are satisfied and further comprises information that indicates a third set of measurement resources, wherein the third set of measurement resources is a subset ofthe second set of measurement resources, and the method further comprises receiving, from thenetwork node, an indication to perform measurements on the third set of measurement resources, performing measurements on the third set of measurement resources, and generating a measurement prediction for the first set of measurement resources using the one or more AI / ML models or functions with the measurements performed on the third set of measurement resources as inputs to the one or more AI / ML models or functions. In one embodiment, the third set of measurement resources is a recommendation for the network to configure the resources in a second of measurement resources such that the applicability conditions of one or more AIML models / functionalities are fulfilled to determine the radio measurement predictions in the first set of measurement resources. In one embodiment, the first indication indicates that the one or more applicability conditions are not satisfied. In one embodiment, the second set of measurement resources is a set of measurement resources already configured to the UE for radio measurement reporting, or a set of candidate measurement resources that can be configured to the UE for radio measurement reporting. In one embodiment, the measurement prediction is a spatial-domain beam prediction.In one embodiment, the first indication comprises any one or more of: information thatindicates one or more third sets of measurement resources that the UE has to measure in order to determine the measurement prediction on the first set of measurement resources, wherein the one or more third sets of measurement resources are thereby recommendation to the network node forconfiguration to the UE for radio measurements; information related to whether the one or moreapplicability conditions of the one or more AI / ML models or functions available at the UE are fulfilled or not fulfilled, indicating respectively that the UE can perform or not perform the measurement prediction on the first set of measurement resources based on radio measurementson the second set of measurement resources; and information related to whether the one or moreapplicability conditions of the one or more AI / ML models or functions available at the UE are fulfilled or not fulfilled, indicating respectively that the UE can perform or not performmeasurement prediction on the first set of measurement resources based on radio measurementson a selected third set of measurement resources. In one embodiment, the one or more third sets of measurement resources selected by the UE are equal to the second set of measurement resources or a subset of the second set of measurement resources. In one embodiment, the second and first set of measurement resources consists of a pool of multiple sets of measurement resources, and the first indication comprises information for each of first and second set of measurements resources in the pool. In one embodiment, the second set of measurementresources consists of a pool of multiple sets of measurement resources. In one embodiment, theselected one or more third sets of measurement resources is equal to one of the said sets. In one embodiment, each of the multiple sets of measurement resources is associated to an index, and the one or more selected third sets of measurement resources are indicated to the network node via one of the indices. In another embodiment, the selected third set of measurement resources fulfills the one or more applicability conditions of the one or more AI / ML models or function available at the UE with respect to the first set of measurement resources. In one embodiment, by being satisfied, the applicability conditions allow the UE to determine the measurement prediction on the first set of measurement resources with a certain accuracy. In one embodiment, determining whether the one or more applicability conditions aresatisfied is in response to in one or more of the following: receiving the first information thatindicates the first set of measurement resources after previously receiving the second informationindicative of the second set of measurement resources; receiving the second informationindicative of the second set of measurement resources after previously receiving the firstinformation indicative of the first set of measurement resources; receiving the first and secondinformation indicative of the first and second sets of measurement resources in a same message;receiving a successive indication of a new first set of measurement resources; receiving asuccessive indication of a new second set of measurement resources; receiving a successiveindication that the UE should select another set of measurement resources as a third set ofmeasurement resources; determining that the UE can no longer provide a measurement predictionon the first set of measurement resources based on measurements on the second set of measurement resources, as a result of the one or more applicability conditions not being fulfilledfor the one or more AI / ML models or functions available at the UE; determining that the UE canprovide the measurement prediction on the first set of measurement resources based on measurements on the second set of measurement resources, as a result of the one or more applicability conditions being fulfilled for the one or more AIML models or functions availableat the UE; determining that the UE can provide the measurement prediction on the first set ofmeasurement resources based on measurements on a different third set of measurement resourcespreviously selected, as a result of the one or more applicability conditions not being fulfilled forthe one or more AI / ML models or function available at the UE with a previously selected third set of measurement resources. In one embodiment, each of the first and second sets of measurement resources compriseany one or more of the following: a set of Synchronization Signal (SS) / Physical BroadcastChannel (PBCH) Blocks (SSBs) for a cell; a set of Channel State Information Reference Signal(CSI-RS) resources for a cell; a set of SS / PBCH block or CSI-RS resource sets; a set of cells; aset of frequencies; a set of SSBs for a list of cells that can comprise two or more cells; a set ofCSI-RS resources for a list of cells that can comprise two or more cells; a set of SS / PBCH blockresource sets for a list of cells that can comprise two or more cells.In one embodiment, the method further comprises receiving a second indication from thenetwork node, the second indication comprising any one or more of: a configuration to activatethe one or more AI / ML models or functions available at the UE to determine the measurementprediction on the first set of measurement resources; a configuration to configure the second setof measurement resources in response to transmitting in the first indication with information that indicates that the one or more applicability conditions for the second set of measurements arefulfilled; a configuration to configure the second set of measurement resources in response totransmitting in the first indication information indicative of a third set of measurement resources;a configuration to deconfigure or deactivate the first set of measurement resources; aconfiguration to deconfigure or deactivate the second set of measurement resources or one or more sets of measurement resources within the second set of measurement resources; aconfiguration including a successive first set of measurement resources; a configurationincluding a successive second set of measurement resources; a configuration including indicationthat the UE should select another set of measurement resources as a third set of measurement resources. In one embodiment, the second indication is received from the network node in response to transmitting the first indication to the network node. In one embodiment, in response to receiving a configuration that deconfigures the first or second set of measurement resources, the UE deactivates the one or more AI / ML models or functions available at the UE. In anotherembodiment, a successive second set of measurement resources indicated in the secondindication is equal to or a subset of the third set of measurement resources included by the UE in the first indication. In one embodiment, the second indication is received via Radio ResourceControl (RRC) signaling, Medium Access Control (MAC), Uplink Control Information (UCI), orPhysical Downlink Control Channel (PDCCH).In one embodiment, the UE starts performing the radio measurement predictions on theresources included in the first set of measurement resources, in response to: evaluating that theone or more AI / ML models or functions available at the UE fulfill the one or more applicabilityconditions; selecting a third set of measurement resources in which case the radio measurementpredications are performed based on the radio measurements performed in the third set ofmeasurement resources; receiving a configuration associated to the first, second or third set ofmeasurement resources, in which case the radio measurement predictions are performed based on the radio measurements performed in the second set of radio measurements or third set of radio measurements. In one embodiment, the second set of measurement resources, or the selected third set of measurement resources, is the input of one or more AI / ML models or functions available at the UE. In another embodiment, an output of the one or more AI / ML models or functions available at the UE is the measurement predictions comprising prediction results for one or more measurement quantities. In one embodiment, the measurement prediction resultscomprise any of: measured quantities for each of measurement resource in the first set ofmeasurement resources and / or second set of measurement resources; measured quantities for oneor more best measurement resources in terms of measured quantities among the measurement resources in the first set of measurement resources and / or second set of measurement resources,wherein a number of best measurement resources can be a fixed or configured number; measuredquantities for one or more worst measurement resources in terms of measured quantities among the measurement resources in the first set of measurement resources and / or second set of measurement resources, wherein a number of worst measurement resources can be a fixed orconfigured number; average measured quantities for the measurement resources in the first set ofmeasurement resources and / or second set of measurement resources; variance of measuredquantities for the measurement resources in the first set of measurement resources and / or secondset of measurement resources; accuracy of the measurement predictions results.In one embodiment, the first and second information indicative of the first and second sets of measurement resources are received via broadcast or system information signaling or via dedicated signaling. In one embodiment, the first indication is transmitted via Radio Resource Control (RRC)signaling, Medium Access Control (MAC), or Uplink Control Information (UCI).In one embodiment, in response to transmitting the first indication to the network node, the UE receives information indicative of a successive second set of measurement resources based on which the UE should evaluate the one or more applicability conditions. In one embodiment, the measurement predictions are transmitted to the network node. In one embodiment, the measurement predictions are transmitted to another network node performing the UE-side model training. In one embodiment, the step of determining whether the one or more applicability conditions are satisfied is performed during an inference for the one or more AI / ML models or functions. In one embodiment, the step of determining whether the one or more applicability conditions are satisfied is performed during data collection for training of one or more AI / ML models or functions. Corresponding embodiments of a UE are also disclosed. In one embodiment, a UEcomprises a communication interface comprising a transmitter and a receiver, and processingcircuitry associated with the communication interface. The processing circuitry is configured tocause the UE to receive, from a network node, first information that indicates a first set ofmeasurement resources on which the UE is to perform measurement predictions and receive, from the network node, second information that indicates a second set of measurement resources with which the UE can be configured to perform measurements, based on which themeasurement predictions can be generated. The processing circuitry is further configured tocause the UE to determine whether one or more applicability conditions for using one or moreAI / ML models or functions at the UE to generate measurement predictions are satisfied for thefirst set of measurement resources and the second set of measurement resources and send a firstindication to the network node, the first indication comprising information indicative of whether the one or more applicability conditions are satisfied. Embodiments of a method performed by a network node are also disclosed. In one embodiment, a method performed by a network node comprises sending, to a UE, first information that indicates a first set of measurement resources on which the UE is to performmeasurement predictions and sending, to the UE, second information that indicates a second setof measurement resources with which the UE can be configured to perform measurements, based on which the measurement predictions can be generated. The method further comprises receiving a first indication from the UE, the first indication comprising information indicative ofwhether one or more applicability conditions for using one or more AI / ML models or functionsat the UE to generate a measurement prediction are satisfied for the first set of measurement resources and the second set of measurement resources. Corresponding embodiments of a network node are also disclosed. In one embodiment, anetwork node comprises processing circuitry configured to cause the network node to send, to aUE, first information that indicates a first set of measurement resources on which the UE is toperform measurement predictions and send, to the UE, second information that indicates asecond set of measurement resources with which the UE can be configured to perform measurements, based on which the measurement predictions can be generated. The processing circuitry is further configured to cause the network node to receive a first indication from the UE, the first indication comprising information indicative of whether one or more applicability conditions for using one or more AI / ML models or functions at the UE to generate a measurement prediction are satisfied for the first set of measurement resources and the second set of measurement resources. Brief Description of the Drawings 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.Figure 1 is an illustration of training and inference pipelines, and their interactions withina model lifecycle management procedure; Figure 2 shows a functional framework that can be used for studying different network(NW)-UE collaboration levels for the Artificial Intelligence (AI) for Physical Layer (PHY) usecases; Figure 3 illustrates an autoencoder (AE)-based Channel State Information (CSI) report;Figure 4 illustrates Synchronization Signal (SS) / Physical Broadcast Channel (PBCH) Block (SSB) beam selection as part of an initial access procedure according to a first procedure used to enable User Equipment (UE) measurement on different Transmission / Reception Point (TRP) transmit beams to support selection of TRP transmit beams and UE receive beam(s); Figure 5 illustrates Channel State Information (CSI) Reference Signal (CSI-RS) transmit beam selection in downlink according to a second procedure used to enable UE measurement on different TRP transmit beams to possibly change inter / intra-TRP transmit beam(s); Figure 6 illustrates UE receive beam selection for corresponding CSI-RS transmit beam in downlink according to a procedure used to enable UE measurement on the same TRP transmit beam to change UE receive beam in the case the UE uses beamforming; Figure 7 illustrates an example where Set B (i.e., set of beams on which measurement results are measured by the UE) is a subset of Set A (i.e., set of beams or measurement resourcesfor which the UE predicts measurements based on the measurements obtained for Set A);Figure 8 illustrates an example where Set A and Set B correspond to two different sets of beams and, in the particular example of Figure 8, Set A is a set of narrow beams and Set B is a set of wide beams; Figure 9 illustrates the operation of a network node and a UE, in accordance with embodiments of the present disclosure; Figure 10 shows an example of a communication system in accordance with someembodiments; Figure 11 shows a UE in accordance with some embodiments;Figure 12 shows a network node in accordance with some embodiments;Figure 13 is a block diagram of a host, which may be an embodiment of the host ofFigure 10, in accordance with various aspects described herein; Figure 14 is a block diagram illustrating a virtualization environment in which functionsimplemented by some embodiments may be virtualized; and Figure 15 shows a communication diagram of a host communicating via a network nodewith a UE over a partially wireless connection in accordance with some embodiments. Detailed Description 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. There currently exist certain challenge(s) in regard to beam prediction. In order for a User Equipment (UE)-side model to generate as output the predictions in a set of beams (so called set A of beams, see e.g., the examples of Figures 7 and 8 and the corresponding description above), the UE needs to perform measurements on another set of beams (so called set B of beams, see e.g., the examples of Figures 7 and 8 and the corresponding description above). However, the network node (i.e., the gNB in the example embodiments described herein) might not know beforehand which beams form the set of beams in which the UE can generate accurate enough predictions, and the beams 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 whether it is worthwhile to configure the UE to enable its Artificial Intelligence (AI) / Machine Learning (ML) models / functions. For example, the UE might be capable of generating good predictions (e.g., predictions having a predefined threshold accuracy level) on certain resources that are not of interest for the gNB, e.g. resources that are already reserved to other users. In another example, the UE might be capable to generate good predictions on resources that are of interest for the gNB, but in order to do that, the UE would need to perform measurements on resources that the gNB cannot provide at the moment. This problem might become particularly severe if the set of beams in which the UE may potentially perform the prediction (set A) is very large, i.e. the UE may not be capable of performing accurate enough predictions (e.g., predictions having a predefined threshold accuracylevel) on such a large set of beams. Also, if the set of beams that the UE needs to measure (set B)to generate accurate enough predictions is very large, i.e. the gNB would need to sweep a large set of beams which will increase gNB complexity, and overhead. Certain aspects of the present disclosure and their embodiments may provide solutions to these or other challenges. Systems and methods are disclosed herein for a network node, such as the gNB, to determine a set of resources (e.g., set of Synchronization Signal (SS) / PhysicalBroadcast Channel (PBCH) Block (SSB) beams, or set of Channel State Information (CSI)Reference Signal (CSI-RS) beams) in which a UE should provide measurement predictions, i.e.set A, and to indicate such set of resources to the UE. On the basis of the received said set of resources, the UE may determine a set of resources in which the UE should perform the measurements, i.e. set B, in order for the UE to provide the measurement predictions according to the set of resources indicated by the gNB. The UE will then signal the determined set of resources to the gNB. The gNB on the basis of the received set of resources may determine whether to configure the UE with the requested set of resources in order for the UE to provide the measurement prediction on the first set of resources, or not. Some example embodiments of a method performed by a UE are as follows: A1. The method at a User Equipment (UE) to evaluate the applicability conditions of the one or more AI / ML models / functionalities available at the UE based on a received first set of measurement resources in relation to which the UE has to perform radio measurement predictions, and on a received second set of measurement resources in which the UE can perform the radio measurements in order to determine radio measurement predictions on the first set of measurement resources. A2. A method according to A1, wherein the second set of measurement resources is a set of measurement resources already configured to the UE for radio measurement reporting, or a set of candidate measurement resources that can be configured to the UE for radio measurement reporting. A3. A method according to A1, wherein in response of evaluating the applicability conditions of the one or more AIML models / functionalities available at the UE, the UE transmits to the gNB a first indication, the first indication comprising any of: •A selected one or more third set of measurement resources that the UE has tomeasure in order to determine the radio measurement predictions on the first set of measurement resources, wherein the one or more third set of measurement resources is recommendation to the gNB for the configuration of a second set of measurement resources. •Information related to whether the applicability conditions of the one ormore AI / ML models / functionalities available at the UE are fulfilled or not fulfilled, indicating respectively that the UE can perform or not perform the radio measurement predictions on the first set of measurement resources based on a second set of measurement resources. •Information related to whether the applicability conditions of the one ormore AI / ML models / functionalities available at the UE are fulfilled or not fulfilled, indicating respectively that the UE can perform or not perform the radio measurement predictions on the first set of measurement resources based on a selected third set of measurement resources. A4. A method according to A3, wherein the one or more third set of measurement resources selected by the UE are equal to the second set of measurement resources or it is a subset of it. A5. A method according to A1, A3, wherein the second set of measurement resources consists of a pool of one or more sets of measurement resources, and the selected one or more third set of measurement resources is equal to one of the said sets. A6. A method according to A5, wherein each of the one or more sets of measurement resources or each of the resources included in the first and second set of measurement resources are associated to an index, and the one or more selected third set of measurement resources are indicated to the gNB including one of the said indexes. A7. A method according to A3, wherein the selected third set of measurement resources fulfills the applicability conditions one or more AIML models / functionalities available at the UE with respect to the received first set of measurement resources in which the UE should perform the radio measurement predictions. A8. A method according to A1, wherein the fulfilled applicability conditions allows the UE to determine the prediction results on the first set of measurement resources with a certain accuracy. A9. A method according to A1, wherein the applicability conditions are evaluated in response of:^ Receiving the first set of measurement resources after being previously configured with thesecond set of measurement resources.^ Receiving the second set of measurement resources after being previously configured withthe first set of measurement resources.^ Receiving the first and second set of measurement resources in the same message.^ Receiving a successive first set of measurement resources^ Receiving a successive second set of measurement resources^ Receiving a successive indication that the UE should select another set of measurementresources as third set of measurement resources.^ Determining that the UE cannot provide any longer the prediction results on the first set ofmeasurement resources based on the second set of measurement resources, as a result of the applicability conditions not being fulfilled for the one or more AI / ML models / functionalities available at the UE.^ Determining that the UE can provide the prediction results on the first set of measurementresources based on the second set of measurement resources, as a result of the applicability conditions being fulfilled for the one or more AIML models / functionalities available at the UE.^ Determining that the UE can provide the prediction results on the first set of measurementresources based on a different third set of measurement resources previously selected, as a result of the applicability conditions not being fulfilled for the one or more AI / ML models / functionalities available at the UE with the previously selected third set of measurement resources. A10. A method according to A1, wherein the first and second set of measurement resources may comprise any of: •A set of SSB for a cell• A set of CSI-RS resources for a cell• A set of SS / PBCH block resource set 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• A set of SS / PBCH block resource set for a list of cellsA11. A method, wherein a second indication is received from the gNB in the second indication comprising any of: ^A configuration to activate the one or more AI / ML models / functionalities available at theUE to determine the prediction results in relation to first set of measurement resources ^A configuration to configure the second set of measurement resources in response oftransmitting in the first indication and indication indicating that the applicability conditions for the said second set of measurements are fulfilled. ^A configuration to configure the second set of measurement resources in response oftransmitting in the first indication and indication indicating the third set of measurement resources. ^A configuration to deconfigure or deactivate the first set of measurement resources^ A configuration to deconfigure or deactivate the second set of measurement resources orone or more sets of measurement resources within the second set of measurement resources (third set of measurement resources) ^A configuration including a successive first set of measurement resources^ A configuration including a successive second set of measurement resources^ A configuration including indication that the UE should select another set ofmeasurement resources as third set of measurement resources. A12. A method according to A11, A3, wherein the second indication is received from the gNB in response of transmitting the first indication to the gNB. A13. A method according to A11, wherein in response of receiving a configuration that deconfigures the first or second set of measurement resources, the UE deactivates the one or more AIML models / functionalities available at the UE to determine the prediction results in relation to first set of measurement resources. A14. A method according to A11, wherein second set of measurement resources included in the second indication is equal or a subset of the third set of measurement resources included by the UE in the first indication. A15: A method according to A1, A3, A11 wherein the UE starts performing the radio measurement predictions on the resources included in the first set of measurement resources, in response of: ^evaluating that the one or more AI / ML models / functionalities available at the UE fulfillthe applicability conditions in which case the radio measurements predications are performed based on the radio measurements performed in the second set of measurement resources ^Selecting the third set of measurement resources in which case the radio measurementpredications are performed based on the radio measurements performed in the third set of measurement resources. ^Receiving a configuration in the second indication associated to the first, second or thirdset of measurement resources, in which case the radio measurement predictions are performed based on the radio measurements performed in the second set of radio measurements or third set of radio measurements. A16. A method according to A15 wherein the second set of measurement resources, or the selected third set of measurement resources, is the input of one or more AIML models / functionalities available at the UE. A17. A method according to A15, wherein the output of the AIML model / functionality available at the UE is the radio measurement predictions comprising the prediction results for one or more measurement quantities, such as the RSRP, RSRQ, SINR, RSSI level, associated to the first set of measurement resources. A18. A method according to A17, wherein the radio measurement prediction results comprise any of:^ The measured quantities for each of the one or more resources in the first set ofmeasurement resources and / or second set of measurement resources ^The measured quantities for the best resources in terms of measured quantities among theresources in the first set of measurement resources and / or second set of measurement resources, wherein the number of best resources can be a fixed or configured number ^The measured quantities for the worst resources in terms of measured quantities amongthe resources in the first set of measurement resources and / or second set of measurement resources, wherein the number of worst resources can be a fixed or configured number ^The average measured quantities for the resources in the first set of measurementresources and / or second set of measurement resources. ^The variance of the measured quantities for the resources in the first set of measurementresources and / or second set of measurement resources. ^The accuracy of the reported predictions resultsA19. A method according to A1, wherein the first and second set of measurement resources are received via SIB signaling or via dedicated RRC signaling. A20. A method according to A3, wherein the first indication is transmitted via RRC signaling (UEAssistanceInformation), or MAC (MAC CE), or UCI. A21. A method according to A11, wherein the second indication is received via RRC dedicated signaling or MAC (MAC CE), or PDCCH (DCI). A22. A method according to A19, A20, A21, wherein in a first step the UE evaluates the applicability conditions based on the second set of measurement resources received via SIB signaling, and in a second step, in response of transmitting the first indication to the gNB, the UE receives a successive second set of measurement resources via RRC dedicated signalling based on which the UE should evaluate the applicability conditions. A23. A method according to A18, wherein the prediction results are transmitted to the gNB. A24. A method according to A18, wherein the prediction results are transmitted to a network node performing the UE-side model training. A25. A method according to A1, wherein the evaluation of the applicability conditions is performed during the inference for one or more AIML models / functionalities. A26. A method according to A1, wherein the evaluation of the applicability conditions is performed during the data collection for the training of one or more AIML models / functionalities. A27. A method according to A3, wherein the third set of measurement resources is a recommendation for the network to configure the resources in a second of measurement resources such that the applicability conditions of one or more AIML models / functionalities are fulfilled to determine the radio measurement predictions in the first set of measurement resources. A28. The method according to A6, wherein the index of a resource or resource set included in the first and second set of measurement resources is unique within the cell. Some example embodiments of a method performed by a network node, which in these example embodiments is a gNB, are as follows: B1. The method at a gNB to configure the UE with a first set of measurement resources in relation to which the UE has to perform radio measurement predictions, and with a second set of measurement resources. B2. A method according to B1, wherein the second set of measurement resources is a set of measurement resources already configured to the UE for radio measurement reporting, or a candidate set of measurement resources that can be configured to the UE for radio measurement reporting. B3. A method according to B1, wherein the configuration of the first and second set is based on any of: ^Availability of the resources in the first set of measurement resources^ Radio measurement predictions based on AIML models / functionalities at NW-side^ Load of the resources in the first set of measurement resources^ UE capabilities^ UE location^ UE trajectory^ UE mobility conditions^ Load balancing policiesB4. A method according to B1, wherein the first and second set of measurement resources are configured independent of each other. B5. A method according to B1, wherein a successive second set of measurement resources is transmitted to the UE in response of receiving a first indication from the UE that for the previously configured second set of measurement resources or UE selected third set of measurement resources, the applicability conditions of one or more AIML model / functionalities available at the UE are not fulfilled. B6. A method according to B1, wherein a configuration to activate the one or more AIML models / functionalities available at the UE in relation to first set of measurement resources is transmitted to the UE B7. A method according to B1, wherein a configuration to deactivate or deconfigure the one or more AIML models / functionalities available at the UE in relation to first set of measurement resources is transmitted to the UE. B8. A method according to B1, wherein a configuration to configure a second set of measurement resources is transmitted to the UE in response of receiving indication from the UE in the first indication that the applicability conditions of one or more AIML model / functionalities available at the UE are fulfilled in relation to the second set of measurement resources. B9. A method according to B1, wherein a configuration to configure a second set of measurement resources is transmitted to the UE in response of receiving indication from the UE in the first indication that the applicability conditions of one or more AIML model / functionalities available at the UE are fulfilled for a third set of measurement resources. B10. A method according to B8, wherein the configured second set of measurement resources is equal to third set of measurement resources indicated in the first indication. B11. A method according to B5, wherein the gNB determines the reference signals to be transmitted in association to the resources included in the successive second set of measurement resources. Certain embodiments may provide one or more of the following technical advantage(s). Embodiments of the present disclosure provide a mechanism by which a Radio Access Network(RAN) node (e.g., gNodeB (gNB) in the case of New Radio (NR)) can control activation of anAI / ML model / function for beam prediction at the UE, on the basis of whether the UE can generate as output of such AI / ML model / function a beam prediction on a set of resources requested by the RAN node. This allows the RAN node to request the UE to provide the prediction on a limited set of requested resources, thereby limiting the UE complexity, and the air interface overhead due to the transmissions of prediction results on resources not of interest for the network. Additionally, by requesting the UE to generate the prediction results just on limited set of resources (set A) and by enabling the UE to indicate to the RAN node the necessary resources in which to perform the measurements (set B) to generate the requested prediction results, the RAN node would just need to provide the UE with a minimum set of measurement resources (e.g., SSB, CSI-RS) necessary to generate the prediction results on the desired resources. This would reduce the RAN node complexity needed to sweep a potentially very large set of beams, and also reduce the air interface overhead due to the transmission of a potential large set of measurement resources, set B. As used herein, the term “applicability conditions” is used to represent a set of conditions that determine whether an AIML model / function is applicable or not. For much of the following description, the RAN node or network node is, as an example, a gNB. However, the present disclosure is not limited thereto. Figure 9 illustrates the operation of a network node, which in the following is a gNB 900 as an example, and a UE 902, in accordance with embodiments of the present disclosure. As illustrated, the gNB 900 configures the UE 902 with a first set of measurement resources in which the UE 902 should perform radio measurement predictions (step 904). In other words, the gNB 900 sends, to the UE 902, first information that indicates, or configures, the first set ofmeasurement resources. Such measurement resources may be a set of CSI-RS resources for acell, or a set of SS / PBCH block resource set for a cell, or a list of cells, or frequencies, or a set of CSI-RS resources for a list of cells, or a set of SS / PBCH block resource set for a list of cells. The first set of measurement resources may be selected by the gNB 900 on the basis of UE mobility predictions evaluated at the network, and / or antenna array(s) of one or moreTransmission and Reception Points (TRPs), and / or antenna deployment(s) of one or more TRPs,or in order to evaluate the radio coverage. For example, the gNB 900 may determine that the UE 902 is entering the radio coverage of certain beams for which it may request the UE 902 to provide radio measurement predictions. In another example, the gNB 900, in order to reduce the overhead over the air interface and the gNB complexity, may identify a set of beams that it does not need to sweep (e.g. beam sweeping or CSI-RS transmission sweeping within beams) and request instead the UE 902 to provide the radio measurement predictions on such resources. In yet another example, the gNB 900 may identify a set of beams for which the reference signals can be transmitted with longer periodicity, and request instead the UE 902 to provide the radio measurement predictions which shorter periodicities for the said set of beams. In order to allow the UE 902 to perform the radio measurement predictions on the first set of measurement resources, the gNB 900 also configures the UE 902 with a second set of measurement resources on which the UE 902 should perform ordinary radio measurements (step 906). In other words, the gNB 900 sends, to the UE 902, second information that indicates, or configures, the second set of measurement resources. The UE 902 uses part or all of the second set of measurement resources as an input to one or more AI / ML models / functionalities. This second set of measurement resources may correspond to the SSBs (wide beams) transmitted by the gNB 900 which configuration is provided as part of System Information Block(SIB) signaling. In another example, this set of beams may correspond to CSI-RS resources(narrow beams) configured in dedicated Radio Resource Control (RRC) signaling as part of Non-Zero Power CSI-RS resource set (NZP-CSI-RS-ResourceSet) or CSI-SSB Resource Set (CSI-SSB-ResourceSet). In one example, the second set of measurement resources are the ones already configured specifically to the UE 902 for ordinary CSI reporting or they may be the SSBs common to the cell. In another example, this second set of measurement resources may be indicated separately to the UE 902 and indicates the candidate set of measurement resources that the gNB 900 can configure to the UE so that the UE can perform the necessary radio measurements to provide the radio measurement predictions (output of the AI / ML model / functionality) in the first set of measurement resources. The second set of measurement resources might be a single set of measurement resources, or it might consist of a pool of multiple sets of measurement resources. Each set of resources within the second set of multiple resources can be associated by the gNB 900 to an index. The second set of measurement resources may be selected by the gNB 900 on the basis of UE mobility predictions evaluated at the network, and / or antenna array(s) of one or more TRPs, and / or antenna deployment(s) of one or more TRPS, or in order to evaluate the radio coverage. Upon receiving the configuration of the first set of measurement resources and the configuration of the second set of measurement resources, the UE 902 determines whether one or more applicability conditions of the one or more AI / ML models / functionalities available at the UE 902 are satisfied (i.e., the UE 902 evaluates the one or more applicability conditions), based on the first set of measurement resources and the second set of measurement resources (step 908). As a result of the applicability condition determination, the UE 902 may send to the gNB 900 a first indication (step 910). For example, the second set of measurement resources which is a candidate set of measurement resources that the gNB 900 can configure for actual measurements by the UE 902 are indicated in system information (e.g., SIB signaling), the UE 902 evaluates the applicability conditions based on this second set of measurement resources, and the UE 902 indicates in the first indication whether for this second (candidate) set of measurement resources the applicability conditions are fulfilled in relation to the first set of measurement resources. In another example, the second set of measurement resources which is a candidate set of measurement resources that the gNB 900 can configure for the UE for actual measurements are indicated in the UE dedicated signaling (e.g., RRC signaling), the UE 902 evaluates the applicability conditions based on this second set of measurement resources indicated in the dedicated signaling, and the UE 902 indicates in the first indication whether for second (candidate) set of measurement resources the applicability conditions are fulfilled in relation to the first set of measurement resources. In case the applicability conditions of one or more AI / ML models / functionalities available at the UE 902 are fulfilled, the UE 902 may reply to the network (i.e., to the gNB 900 in this example) with the first indication indicating that the applicability conditions are fulfilled, implying that the candidate second set of measurement resources can be used as input of one or more AI / ML models / functionalities in order to determine the radio measurement predictions (output of an AI / ML model / functionality) on the first set of measurement resources. In response to receiving this information, the gNB 900 may configure the second set of measurement resources so that the UE 902 can start performing the radio measurement on this second set of measurement resources in order to perform the radio measurement predictions in the first set (step 912). In case the applicability conditions of one or more AI / ML models / functionalities available at the UE 902 are not fulfilled, the UE 902 may reply to the network with the first indication indicating that the applicability conditions are not fulfilled, implying that the second set of measurement resources cannot be used as input of one or more AI / ML models / functionalities in order to determine the radio measurement predictions (output of an AI / ML model / functionality) on the first set of measurement resources. In another example, in case the applicability conditions of one or more AI / ML models / functionalities available at the UE 902 are not fulfilled, the UE 902 may reply to the gNB 900 with the first indication indicating that the applicability conditions are not fulfilled, implying that the first set of measurement resources cannot be used as output of one or more AI / ML models / functionalities based on the given second set of measurement resources. In another example, the applicability conditions are fulfilled even if only a subset of the resources indicated in the second set of measurement resources is used by the UE 902 to determine the radio measurement predictions (output of an AI / ML model / functionality) on the first set of measurement resources. In such a case, the first indication may contain an indication, say third set of measurement resources, of the subset of second set of measurement resources that the UE 902 should use to determine the radio measurement predictions in the first set of measurement resources. This third set of measurement resources is thus recommendation to the gNB 900 to configure a second set of measurement resources for radio measurements such that the UE 902 can perform the radio measurement predictions in the first set of measurement resources. The third set of measurement resources may contain a list of the measurement resources included in the second set of measurement resources that the UE 902 can use. In an alternative method, in case the second set of measurement resources comprises multiple sets of resources, the third set of measurement resources may contain one or more indexes referring to one or more of the sets within the second set of measurement resources. In response to receiving this information, the gNB 902 may configure the UE 902 a set of measurement resources for measurement according to the indicated the third set of measurement resources so that the UE 902 can start performing the radio measurement on this set of measurement resources in order to perform the radio measurement predictions in the first set of measurement resources (step 912).In this case, the configured set of measurement resources may be the same as the third set ofmeasurements or a subset of the third set of measurement resources. The first indication of step 910 may be transmitted to the gNB 900 via an RRC signaling, e.g. UEAssistanceInformation, or a Medium Access Control (MAC) Control Element (CE), or Uplink Control Information (UCI). A field in the RRC signaling, or a bit in the MAC CE, or a bitin the UCI, or multiple bits in the MAC CE, or multiple bits in the UCI could be used to signalthat the applicability or non-applicability (with respect to the first or second set of measurement resources) of one or more AI / ML models / functionalities available at the UE are fulfilled. In case of multiple possible sets of resources associated to the second set of measurement resources, the MAC CE format may contain a bit associated to each of the indexes associated to the multiple sets, wherein a bit is set to ‘0’ for a certain index if the applicability conditions are not fulfilled if the set associated to the said index is used, ‘1’ otherwise. The RRC signaling may instead contain a list of indexes for each of the said sets with indication of applicability or non-applicability of the AI / ML models / functionalities for the corresponding set. In another case, the first indication transmitted from the UE 902 to the gNB 900 may include the reason, for example the reason of non-applicability. In one embodiment, once the UE 902 determines that the applicability conditions of the one or more AI / ML models / functionalities available at the UE 902 are fulfilled, the UE 902 may send the first indication such that it only indicates the applicability conditions without other information. In one embodiment, once the UE 902 determines that the applicability conditions of the one or more AI / ML models / functionalities available at the UE 902 are fulfilled, the UE 902 may send the first indication such that it not only indicates the applicability conditions but also includes other information, e.g., the radio measurement predictions on the first set of measurement resources and additional info if supported. Based on the early report (including the prediction within the first indication), the gNB 900 could determine whether to apply the prediction in the following transmission. In addition, the gNB 900 may not indicate the candidate second or third set of measurement resources, which reduces the DL overhead. Upon receiving the first indication in step 910, the gNB 900 may configure the second set of measurement resources or the third set of measurement resources that the UE 902 has selected (step 911) so that the UE 902 can start performing the radio measurement on the second set of measurement resources or third set of measurement resources in order to perform the radio measurement predictions in the first set (step 912). For example, the UE 902 may first evaluate the applicability conditions on the basis of the SSBs common to the cell (second set of measurement resources) and on a configured first set of measurement resources, and if the applicability conditions are not fulfilled, the UE 902 may transmit the first indication to the gNB indicating the non-applicability of the AI / ML models / functionalities. The gNB 900 may then provide another second set of measurement resources in step 911 (referred to as a successive second set of measurement resources), e.g. the gNB may perform beam sweeping of the SSBs, or it may provide such second set of measurement resources via dedicated signaling. Hence, from the first indication, the gNB 900 determines the reference signals that needs to be transmitted to the UE 902 to perform the necessary radio measurements, i.e. the reference signals associated the successive second set of measurement resources. At the same time, the gNB 900 determines the reference signals that do not need to be transmitted to the UE 902 for the UE 902 to perform the radio measurement predictions, i.e. the reference signals associated to resources that the UE 902 does not need to measure in order to perform the radio measurement predictions. For example, if the successive second set of measurement resources is a subset of the second set of measurement resources initially configured to the UE 902, the gNB 900 determines that the reference signals associated to the resources included in the second set of measurement resources and not included in the successive second set of measurement resources, do not need to be transmitted to the UE 902. Upon determining that the applicability conditions of the one or more AI / ML models / functionalities are fulfilled, the UE 902 may start performing the radio measurement predictions (in step 912) on the resources included in the first set of measurement resourcesbased on radio measurement on the second set of measurement resources, or third set ofmeasurement resources selected by the UE within the second set of measurement resources indicated by the network. In another case, the UE may start performing the radio measurement predictions (in step 912) on the resources included in the first set of measurement resources upon receiving a configuration included in a second indication transmitted by the gNB 900 (in step 911). The second indication may be transmitted by the gNB 900 in the following cases: ^A configuration to activate the one or more AI / ML models / functionalities available at theUE to determine the prediction results in relation to first set of measurement resources oThis can be an acknowledgment to a first indication transmitted by the UE 902indicating that the second set of measurement resources can be used as model inputs of one or more AI / ML model / functionalities^ A configuration to configure the second set of measurement resources in response to thegNB 9090 receiving in the first indication an indication that the applicability conditions for the said second set of measurements are fulfilled. oAccording to this method, the gNB 900 first indicates the candidate second set ofmeasurement resources, and in case the UE indicates in the first indication that the applicability conditions are fulfilled for the said second set of measurement resources, then the gNB 900 configures the UE 902 to perform the radio measurements on the said second set of measurement resources to determine the radio measurement predictions on the first set of measurement resources.^ A configuration to configure the second set of measurement resources in response to thegNB 900 receiving in the first indication an indication of the third set of measurementresources. oAccording to this method, the gNB 900 first indicates the candidate second set ofmeasurement resources, and in case the UE indicates in the first indication that the applicability conditions are fulfilled for a third set of measurement resources, e.g. a subset of the candidate set of measurement resources, then the gNB 900 configures the UE 902 to perform the radio measurements on the configured second set of measurement resources to determine the radio measurement predictions on the first set of measurement resources, wherein the configured second set of measurement resources can be equal to the indicated third set of measurement resources.^ To deconfigure or deactivate the first set of measurement resources.o For example, due to UE-mobility reasons, UE or gNB measurements, network(NW)-side prediction, the gNB may require the UE to stop performing the radio measurement predictions on the first set of measurement resources. oIn response of receiving this message, the UE 902 may stop using the associatedAI / ML models / functionalities^ To deconfigure or deactivate the second set of measurement resources or one or more setsof measurement resources within the second set of measurement resources (third set of measurement resources) oFor example, due to UE-mobility reasons, UE or gNB measurements, NW-sideprediction, or resource availability at the gNB, the gNB 900 may require the UE to stop performing the radio measurement predictions based on radio measurement performed in a previously configured second set of measurement resources, or in a third set of measurement resources previously selected by the UE 902.o In response of receiving this message, the UE 902 may stop using the associatedAI / ML models / functionalities^ To configure a successive first set of measurement resourceso For example, due to UE-mobility reasons, UE or gNB measurements, NW-sideprediction, the gNB 900 may require the UE 902 to perform the radio measurement predictions on a different set of first set of measurement resources compared to the one previously configured. oIn response to receiving this message, the UE 902 may evaluate the applicabilityconditions and start performing the radio measurement predictions on the resources included in the successive first set of measurement resources based on radio measurements on the second set of measurement resources, as per the previous embodiments. In another method, the UE may select another third set of measurement resources. The UE 902 may also send the first indication to the gNB as per the previous embodiments.^ To configure a successive second set of measurement resourceso For example, due to UE-mobility reasons, UE or gNB measurements, NW-sideprediction, or resource availability at the gNB 900, the gNB 900 may require the UE 902 to perform the radio measurement predictions based on radio measurement performed in a second set of measurement resources different from the one previously configured, or in a third set of measurement resources different from the one previously selected by the UE 902. oIn response to receiving this message, the UE 902 may evaluate the applicabilityconditions and start performing the radio measurement predictions on the resources included in the first set of measurement resources based on radio measurements on the successive second set of measurement resources, as per theprevious embodiments. Alternatively, the UE 902 may select another third set of measurement resources in which to perform the radio measurements, wherein the third set of measurement resources are a subset of the resources included in the successive second set of measurement resources. The UE 902 may also send thefirst indication to the gNB 900as per the previous embodiments.^ To indicate to the UE should select another set of measurement resources as third set ofmeasurement resources oFor example, if multiple sets of resources were previously configured as part ofthe second set of measurement resources, the gNB 900 may indicate to the UE 902 to select another set of measurement resources (i.e., another third set of measurement resources) among the second set of measurement resources previously configured. oIn response of receiving this message, the UE 902 may evaluate the applicabilityconditions and start performing the radio measurement predictions on the resources included in the first set of measurement resources based on radio measurements performed on the said third set of measurement resources. The UE 902 may also send the first indication to the gNB as per the previous embodiments. The applicability conditions are evaluated by the UE 902 response to the following events: ^Receiving the first set of measurement resources after being previously configured withthe second set of measurement resources. oAccording to this method, the UE 902 evaluates the applicability conditions uponreceiving the first set of measurement resources, and the evaluation is based on such first set of measurement resources and on the second set of measurement resources previously configured. ^Receiving the second set of measurement resources after being previously configuredwith the first set of measurement resources. oAccording to this method, the UE 902 evaluates the applicability conditions uponreceiving the second set of measurement resources, and the evaluation is based on such second set of measurement resources and on the first set of measurement resources previously configured. ^Receiving the first and second sets of measurement resources in the same message.o According to this method, the same RRC message may convey both the first andsecond set of measurement resources. oAccording to this method, the UE 902 evaluates the applicability conditions uponreceiving the first and second sets of measurement resources. ^Receiving a successive first set of measurement resourceso According to this method, the applicability conditions are checked again if there isa change in the configuration of the first set of measurement resources. The applicability conditions should be checked with respect to a previously configured second set of measurement resources and the successive first set of measurement resources. ^Receiving a successive second set of measurement resourceso According to this method, the applicability conditions are checked again if there isa change in the configuration of the second set of measurement resources. The applicability conditions should be checked with respect to a previously configured first set of measurement resources and the successive second set of measurement resources.^ Receiving a successive indication that the UE 902 should select another set ofmeasurement resources as third set of measurement resources. oAccording to this method, the applicability conditions are checked again if thegNB indicates that the third set of measurement resource previously selected by the UE 902 should be any longer used, and another third set of measurement resources from a previously configured second set of measurement resources should be selected by the UE 902. The evaluation of the applicability conditionsshould be checked with respect to such new third set of measurement resources that the UE 902 should select and the first set of measurement resources previously configured.^ Determining that the UE 902 cannot provide any longer the prediction results on the firstset of measurement resources based on the second set of measurement resources, as a result of the applicability conditions not being fulfilled for the one or more AI / ML models / functionalities available at the UE 902.o According to this method, the UE 902 may determine after starting performing theradio measurement predictions, that the applicability conditions are not any longer fulfilled with respect to the current first and second set of measurement resources. oThis method may imply the UE 902 periodically determining whether theapplicability conditions are fulfilled or not fulfilled.^ Determining that the UE 902 can provide the prediction results on the first set ofmeasurement resources based on the second set of measurement resources, as a result of the applicability conditions being fulfilled for the one or more AI / ML models / functionalities available at the UE 902.o According to this method, the UE 902 may first determine that the applicabilityconditions are not fulfilled with respect to the current first and second set of measurement resources. The UE 902 may continue the evaluation of the applicability conditions, and it may then determine, e.g. due to a change in the conditions, that the applicability conditions are now fulfilled in relation with the first and second set of measurement resources.o This method may imply the UE 902 periodically determining whether theapplicability conditions are fulfilled or not fulfilled. This method also can be usedin the case that the UE 902 determines whether the applicability conditions, for example, based on the performance monitoring and / or event trigger setting which may be configured by network, or may be preconfigured by UE 902, or may bepre-defined and known by network and UE 902.^ Determining that the UE 902 can provide the prediction results on the first set ofmeasurement resources based on a different third set of measurement resources previously selected, as a result of the applicability conditions not being fulfilled for the one or more AI / ML models / functionalities available at the UE 902 with the previously selected third set of measurement resources. oAccording to this method, the UE 902 may first determine that the applicabilityconditions are not any longer fulfilled with respect to the current first and third set of measurement resources previously selected. The UE may then continue the evaluation of the applicability conditions, and it may then determine, e.g. due to achange in the conditions, that the applicability conditions are now fulfilled in relation with the first set of measurement resources and with another third set of measurement resources that the UE can select from a previously configured second set of measurement resources. oThis method may imply the UE 902 periodically determining whether theapplicability conditions are fulfilled or not fulfilled. This method also can be usedin the case that the UE 902 determines whether the applicability conditions, for example, based on the performance monitoring and / or event trigger setting which may be configured by network, or may be preconfigured by UE 902, or may bepre-defined and known by network and UE 902.The determination of applicability conditions at the UE 902 may be considered as a part of the UE-sided performance monitoring (e.g., model monitoring, functionality monitoring). If applicability conditions are fulfilled, the model or the functionality could be activated. Otherwise, the model or the functionality may be deactivated. In one embodiment, the evaluation results of application conditions may be transmitted to the training entity in charge of performing the UE-side model training, wherein the training entitycould be a network node such as the Core Network (CN) node, or an Operations, Administration,and Maintenance (OAM) node, or an Over-The-Top (OTT) server.In response of the applicability condition evaluation, the UE 902 may start performing the radio measurement predictions on the resources included in the first set of measurement resources if the applicability conditions are fulfilled, and it may send a first indication to the gNB, as described in the previous methods. The said radio measurement predictions may be based on the case on the radio measurement performed in the following resources: ^In the second set of measurement resources, in response of evaluating that the one ormore AI / ML models / functionalities available at the UE fulfill the applicability conditions oIn this case, the radio measurements performed on the second set of measurementresources are the input of the AI / ML model / functionality ^In the second set of measurement resources, in response of receiving indication in thesecond indication to activate the one or more AI / ML models / functionalities (acknowledgment to the usage by the UE of the second set of measurement resources) oIn this case, the radio measurements performed on the second set of measurementresources are the input of the AI / ML model / functionality ^In the third set of measurement resources, in response of selecting the third set ofmeasurement resources oIn this case, the radio measurements performed on the third set of measurementresources are the input of the AI / ML model / functionality ^In the third set of measurement resources, in response of receiving indication in thesecond indication to activate the one or more AI / ML models / functionalities (acknowledgment to the usage by the UE of the third set of measurement resources) oIn this case, the radio measurements performed on the third set of measurementresources are the input of the AI / ML model / functionality The radio measurement predictions results are the output of the AI / ML models / functionalities, and they may consist of: ^The measured quantities for each of the one or more resources in the first set ofmeasurement resources ^The measured quantities for the best resources in terms of measured quantities among theresources in the first set of measurement resources, wherein the number of best resources can be a fixed or configured number ^The measured quantities for the worst resources in terms of measured quantities amongthe resources in the first set of measurement resources, wherein the number of worst resources can be a fixed or configured number ^The average measured quantities for the resources in the first set of measurementresources. ^The variance of the measured quantities for the resources in the first set of measurementresources.^ The accuracy of the reported predictions resultsThe prediction results may be one or more of the following measurement quantities, such as the Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Signal to Interference plus Noise Ratio (SINR), Received Strength of Signal Indicator (RSSI) level, associated to the first set of measurement resources. The prediction results may be one or more of the following outcomes, such as ID of the best beam, or IDs of the best X beams, within the beams of the first set of measurement resources. In one embodiment, such prediction results may be transmitted to the gNB (step 914). Inanother embodiment the predictions results may be transmitted to the training entity in charge of performing the UE-side model training, wherein the training entity could be a network node such as the CN node, or an OAM node, or an OTT server. The above methods can be executed during the inference of one or more AI / ML models / functionalities. In another method they are executed during the data collection for training. In one example implementation, an embodiment of the present disclosure may include any one of the following changes to the 3GPP specifications: The NW can for example when configuring CSI-RS measurements indicate that the UE can use a certain configuration for set B (second set of measurement resources as per the embodiments above) or set A beams (first set of measurement resources as per the embodiments above). In one possible implementation method, the set A and set B can be configured as follows as part of the CSI measurement configuration: CSI-MeasConfig ::= SEQUENCE { nzp-CSI-RS-ResourceToAddModList SEQUENCE (SIZE (1..maxNrofNZP-CSI-RS-Resources)) OF NZP-CSI-RS-Resource OPTIONAL, -- Need Nnzp-CSI-RS-ResourceToReleaseList SEQUENCE (SIZE (1..maxNrofNZP-CSI-RS-Resources)) OF NZP-CSI-RS-ResourceId OPTIONAL, -- Need Nnzp-CSI-RS-ResourceSetToAddModList SEQUENCE (SIZE (1..maxNrofNZP-CSI-RS-ResourceSets)) OF NZP-CSI-RS-ResourceSetOPTIONAL, -- Need Nnzp-CSI-RS-ResourceSetToReleaseList SEQUENCE (SIZE (1..maxNrofNZP-CSI-RS-ResourceSets)) OF NZP-CSI-RS-ResourceSetIdOPTIONAL, -- Need Ncsi-IM-ResourceToAddModList SEQUENCE (SIZE (1..maxNrofCSI-IM-Resources)) OF CSI-IM-Resource OPTIONAL, -- Need Ncsi-IM-ResourceToReleaseList SEQUENCE (SIZE (1..maxNrofCSI-IM-Resources)) OF CSI-IM-ResourceId OPTIONAL, -- Need Ncsi-IM-ResourceSetToAddModList SEQUENCE (SIZE (1..maxNrofCSI-IM-ResourceSets)) OF CSI- IM-ResourceSet OPTIONAL, -- Need N csi-IM-ResourceSetToReleaseList SEQUENCE (SIZE (1..maxNrofCSI-IM-ResourceSets)) OF CSI-IM-ResourceSetId OPTIONAL, -- Need Ncsi-SSB-ResourceSetToAddModList SEQUENCE (SIZE (1..maxNrofCSI-SSB-ResourceSets)) OF CSI-SSB-ResourceSet OPTIONAL, -- Need Ncsi-SSB-ResourceSetToReleaseList SEQUENCE (SIZE (1..maxNrofCSI-SSB-ResourceSets)) OF CSI-SSB-ResourceSetId OPTIONAL, -- Need Ncsi-ResourceConfigToAddModList SEQUENCE (SIZE (1..maxNrofCSI-ResourceConfigurations)) OF CSI-ResourceConfigsetB-nzp-CSI-RS-ResourceSetToAddModList SEQUENCE (SIZE (1..maxNrofNZP-CSI-RS-ResourceSets)) OFNZP-CSI-RS-ResourceSetOPTIONAL, -- Need N setB-nzp-CSI-RS-ResourceSetToReleaseList SEQUENCE (SIZE (1..maxNrofNZP-CSI-RS-ResourceSets)) OF NZP-CSI-RS-ResourceSetIdsetB-csi-SSB-ResourceSetToAddModList SEQUENCE (SIZE (1..maxNrofCSI-SSB-ResourceSets)) OF CSI-SSB-ResourceSet OPTIONAL, -- Need NsetB-csi-SSB-ResourceSetToReleaseList SEQUENCE (SIZE (1..maxNrofCSI-SSB-ResourceSets)) OF CSI-SSB-ResourceSetId OPTIONAL, -- Need Ncsi-ResourceConfigToAddModList SEQUENCE (SIZE (1..maxNrofCSI-ResourceConfigurations)) OF CSI-ResourceConfig OPTIONAL, -- Need Ncsi-ReportConfigToAddModList SEQUENCE (SIZE (1..maxNrofCSI-ReportConfigurations)) OFCSI-ReportConfig OPTIONAL, -- Need Ncsi-ReportConfigToReleaseList SEQUENCE (SIZE (1..maxNrofCSI-ReportConfigurations)) OF CSI-ReportConfigId setA-nzp-CSI-RS-ResourceSetToAddModList SEQUENCE (SIZE (1..maxNrofNZP-CSI-RS-ResourceSets)) OF NZP-CSI-RS-ResourceSetOPTIONAL, -- Need NsetA-nzp-CSI-RS-ResourceSetToReleaseList SEQUENCE (SIZE (1..maxNrofNZP-CSI-RS-ResourceSets)) OF NZP-CSI-RS-ResourceSetId setA-csi-SSB-ResourceSetToAddModList SEQUENCE (SIZE (1..maxNrofCSI-SSB-ResourceSets)) OFCSI-SSB-ResourceSet OPTIONAL, -- Need NsetA-csi-SSB-ResourceSetToReleaseList SEQUENCE (SIZE (1..maxNrofCSI-SSB-ResourceSets)) OFCSI-SSB-ResourceSetId OPTIONAL, -- Need N<Text Omitted> }-- TAG-CSI-MEASCONFIG-STOP-- ASN1STOPAccording to this method, the set B can be considered as the legacy configuration of the CSI-RS resources (nzp-CSI-RS, csi-SSB), whereas the set A of resources can be represented by setA-nzp-CSI-RS or setA-csi-SSB. According to this method, the set A of resources ( first set of measurement resources) may comprise different resources than the one included in the set B of resources (second set of measurement resources). In another method, the set B can be designed as a separate set of resources (nzp-CSI-RS, csi-SSB), compared with the legacy configuration. For example, this method can be used in case the network does not want the UE to report the legacy CSI reports over the said set B of resources. This method would allow the gNB to first configure a set of candidate measurement resources, and then wait for the UE reply on which of these candidate sets (if any) can be used for the prediction, before configuring them for radio measurements. In another example, this method can be used in case the set B is configured by the gNB for the purpose of UE data collection during the training phase. In yet another embodiment, both the set B and the set A can be represented byconsidering the same IE, e.g. the legacy nzp-CSI-RS, csi-SSB resource configuration list. In such case, the gNB may indicate as part of the configuration list whether a certain entry is meant to belong to a set A, or set B, or if that shall be used for legacy measurement configuration (inthis case the aiml-setType will be empty). This embodiment is illustrated in the following:NZP-CSI-RS-ResourceSet ::= SEQUENCE { nzp-CSI-ResourceSetId NZP-CSI-RS-ResourceSetId, nzp-CSI-RS-Resources SEQUENCE (SIZE (1..maxNrofNZP-CSI-RS-ResourcesPerSet)) OF NZP-CSI-RS-ResourceId, repetition ENUMERATED { on, off }OPTIONAL, -- Need SaperiodicTriggeringOffset INTEGER(0..6)OPTIONAL, -- Need Strs-Info ENUMERATED {true}OPTIONAL, -- Need R..., [[ aperiodicTriggeringOffset-r16 INTEGER(0..31)OPTIONAL -- Need S]], [[ pdc-Info-r17 ENUMERATED {true}OPTIONAL, -- Need RcmrGroupingAndPairing-r17 CMRGroupingAndPairing-r17OPTIONAL, -- Need RaperiodicTriggeringOffset-r17 INTEGER (0..124)OPTIONAL, -- Need SaperiodicTriggeringOffsetL2-r17 INTEGER(0..31)OPTIONAL -- Need R]], [[resourceType-r18 ENUMERATED {periodic}OPTIONAL -- Cond LTM]] aiml-setType ENUMERATED {setA, setB}} CSI-SSB-ResourceSet ::= SEQUENCE { csi-SSB-ResourceSetId CSI-SSB-ResourceSetId, csi-SSB-ResourceList SEQUENCE (SIZE(1..maxNrofCSI-SSB-ResourcePerSet)) OF SSB-Index,..., [[ servingAdditionalPCIList-r17 SEQUENCE (SIZE(1..maxNrofCSI-SSB-ResourcePerSet)) OFServingAdditionalPCIIndex-r17 OPTIONAL -- Need R]] aiml-setType ENUMERATED {setA, setB}} If the set B configuration consists of a list of multiple entries, i.e. multiple nzp-CSI- RS / csi-SSB resources sets are configured, the UE can select the entry / ies in the list that fulfills the applicability conditions for the radio measurement predictions on the set A. In particular, the UE can select the one or more nzp-CSI-ResourceSetId (if CSI-RS based configuration) or CSI- SSB-ResourceSetId (if SSB-based configuration) that fulfill the applicability conditions. The one or more identities can be reported in a MAC CE or via another RRC message (e.g., UEAssistanceInformation). This information corresponds to the third set of measurement resources selected by the UE which will be included in a first indication. In case the set B only contains one set of resources, the UE may select the resources (e.g. via the related resource identities NZP-CSI-RS-ResourceId), and indicate the combination of resources that are needed for the radio measurement predictions on the first set of measurement resources. This information also would correspond to the third set of measurement resources selected by the UE which will be included in a first indication, and they can be reported in a MAC CE or via another RRC message (e.g., UEAssistanceInformation). The second indication may comprise a measurement report configuration for the UE to report the radio measurement predictions, based on the first indication transmitted by the UE. Such measurement report configuration could be as follows: reportSlotConfig ENUMERATED {sl5, sl10, sl20, sl40, sl80, sl160, sl320}, reportSlotOffsetList SEQUENCE (SIZE (1.. maxNrofUL-Allocations)) OF INTEGER(0..32), p0alpha P0-PUSCH-AlphaSetId }, aperiodic SEQUENCE { reportSlotOffsetList SEQUENCE (SIZE (1..maxNrofUL-Allocations)) OFINTEGER(0..32) } }, reportQuantity CHOICE { none NULL, cri-RI-PMI-CQI NULL, cri-RI-i1 NULL, cri-RI-i1-CQI SEQUENCE { pdsch-BundleSizeForCSI ENUMERATED {n2, n4} OPTIONAL -- Need S }, cri-RI-CQI NULL, cri-RSRP NULL, ssb-Index-RSRP NULL, cri-RI-LI-PMI-CQI NULL}, Wherein the CRI resource configuration for the reporting can be configured as follows.The aimlSetB-csi-RS-ResourceSetList contains the configuration in which the UE shouldperform the radio measurements in order to determine the radio measurement prediction results in the resources included in aimlSetA-csi-RS-ResourceSetList-- ASN1START-- TAG-CSI-RESOURCECONFIG-STARTCSI-ResourceConfig ::= SEQUENCE { csi-ResourceConfigId CSI-ResourceConfigId, csi-RS-ResourceSetList CHOICE { nzp-CSI-RS-SSB SEQUENCE { nzp-CSI-RS-ResourceSetList SEQUENCE (SIZE (1..maxNrofNZP-CSI-RS- ResourceSetsPerConfig)) OF NZP-CSI-RS-ResourceSetIdOPTIONAL, -- Need Rcsi-SSB-ResourceSetList SEQUENCE (SIZE (1..maxNrofCSI-SSB-ResourceSetsPerConfig))OF CSI-SSB-ResourceSetId OPTIONAL -- Need R}, csi-IM-ResourceSetList SEQUENCE (SIZE (1..maxNrofCSI-IM-ResourceSetsPerConfig)) OF CSI-IM-ResourceSetId }, bwp-Id BWP-Id, resourceType ENUMERATED { aperiodic, semiPersistent, periodic }, ..., [[ csi-SSB-ResourceSetListExt-r17 CSI-SSB-ResourceSetIdOPTIONAL -- Need R]], [[ aimlSetB-csi-RS-ResourceSetList CHOICE { nzp-CSI-RS-SSB SEQUENCE {nzp-CSI-RS-ResourceSetList SEQUENCE (SIZE (1..maxNrofNZP-CSI-RS- ResourceSetsPerConfig)) OF NZP-CSI-RS-ResourceSetIdOPTIONAL, -- Need Rcsi-SSB-ResourceSetList SEQUENCE (SIZE (1..maxNrofCSI-SSB-ResourceSetsPerConfig))OF CSI-SSB-ResourceSetId OPTIONAL -- Need R}, }, aimlSetA-csi-RS-ResourceSetList CHOICE { setA-nzp-CSI-RS-SSB SEQUENCE { nzp-CSI-RS-ResourceSetList SEQUENCE (SIZE (1..maxNrofNZP-CSI-RS- ResourceSetsPerConfig)) OF NZP-CSI-RS-ResourceSetIdOPTIONAL, -- Need RsetA-csi-SSB-ResourceSetList SEQUENCE (SIZE (1..maxNrofCSI-SSB-ResourceSetsPerConfig)) OF CSI-SSB-ResourceSetId OPTIONAL -- Need R},}, ]] }-- TAG-CSI-RESOURCECONFIG-STOP-- ASN1STOPAs per the above configuration, the UE shall provide radio measurement predictions on the resources indicated in aimlSetA-csi-RS-ResourceSetList, as a result of performing the radio measurement in the resources included in aimlSetB-csi-RS-ResourceSetList. This aimlSetB-csi-RS-ResourceSetList is in this case the second set of measurement resources included in thesecond indication, wherein the second set of measurement resources may be equal or a subset of the resources selected by the UE and indicated in the first indication (third set of measurement resources) In one example, the indication of whether a beam is a setB / A beam is part of the current measurement RS resource configuration. For example, indicated with yellow as a part of the NZP-CSI-RS-Resource information element (IE). From 38.331 NZP-CSI-RS-Resource The IE NZP-CSI-RS-Resource is used to configure Non-Zero-Power (NZP) CSI-RS transmitted in the cell where the IE is included, which the UE may be configured to measure on (see TS 38.214

[0019] , clause 5.2.2.3.1). A change of configuration between periodic, semi-persistent or aperiodic for an NZP-CSI-RS- Resource is not supported without a release and add.NZP-CSI-RS-Resource information element-- ASN1START-- TAG-NZP-CSI-RS-RESOURCE-STARTNZP-CSI-RS-Resource ::= SEQUENCE {nzp-CSI-RS-ResourceId NZP-CSI-RS-ResourceId, isSetBbeam Boolean(True,False)isSetAbeam Boolean(True,False)resourceMapping CSI-RS-ResourceMapping, …… Similarly, the indication of setA / B could in another embodiment be part of the IE NZP-CSI-RS-ResourceSet is a set of Non-Zero-Power (NZP) CSI-RS resources (their IDs) and set-specific parameters. This enables the NW to indicate a set of resources that all are part of the setB or set A. NZP-CSI-RS-ResourceSet information element-- ASN1START-- TAG-NZP-CSI-RS-RESOURCESET-STARTNZP-CSI-RS-ResourceSet ::= SEQUENCE { nzp-CSI-ResourceSetId NZP-CSI-RS-ResourceSetId, nzp-CSI-RS-Resources SEQUENCE (SIZE (1..maxNrofNZP-CSI-RS-ResourcesPerSet)) OFNZP-CSI-RS-ResourceId, repetition ENUMERATED { on, off } isSetBbeams Boolean(True,False)….. isSetAbeams Boolean(True,False)…. OPTIONAL, -- Need SSimilarly, the indication of setA / B could in another embodiment be part of the IE CSI-ResourceConfig. The IE CSI-ResourceConfig defines a group of one or more NZP-CSI-RS-ResourceSet, CSI-IM-ResourceSet and / or CSI-SSB-ResourceSet.CSI-ResourceConfig information element-- ASN1START-- TAG-CSI-RESOURCECONFIG-STARTCSI-ResourceConfig ::= SEQUENCE { csi-ResourceConfigId CSI-ResourceConfigId, isSetBbeams Boolean(True,False)….. isSetAbeams Boolean(True,False)csi-RS-ResourceSetList CHOICE { nzp-CSI-RS-SSB SEQUENCE { ….. Similarly, the indication of setA / B could in another embodiment be part of the IE CSI-ReportConfig .The IE CSI-ReportConfig is used to configure a periodic or semi-persistent report sent on PUCCH onthe cell in which the CSI-ReportConfig is included, or to configure a semi-persistent or aperiodicreport sent on PUSCH triggered by DCI received on the cell in which the CSI-ReportConfig is included (in this case, the cell on which the report is sent is determined by the received DCI). See TS 38.214

[0019] , clause 5.2.1. CSI-ReportConfig information element-- ASN1START-- TAG-CSI-REPORTCONFIG-START A potential issue with a data-driven approach for training a beam prediction AI / M model is that different sites / cells may have different antenna / beam configurations. Moreover, even within the same cell, there can be scenarios where antenna / beam configurations are semi- dynamically adjusted to better fit to the current traffic load situations. To enable a trained AI / ML model at the UE-side to generalize to many different scenarios and / or antenna / beam configurations. In one embodiment, an identifier is provided to the UE indicating the beam configuration / pattern. For example, in case the UE receive the same beam configuration / pattern ID over two or more cells, it can assume that the CSI resources are using the same beams / precoders. This can be achieved via that the standard introduces a “consistency”identifier for its CSI resources, which can be associated to a set of measurement resources and / orto the resources included in a set of measurement resources as expressed in claim A6. This identifier is then valid over a longer duration than a normal ResourceID, and possibly over multiple cells. This can be introduced via any of the options above via extending its respective IE according to below: Existing IE (38.331) New element CommentNZP-CSI-RS-Resource Consistency-nzp-CSI-RS- An ID that indicates whether the UE ResourceId can assume that the resource is using the same spatial TX-filter across time and cells NZP-CSI-RS-ResourceSet Consistency-nzp-CSI-RS-An ID that indicates whether the UE ResourceSetId can assume that the set of resources ( is using the same spatial TX-filters across time and cells CSI-ReportConfig Consistency-csi-ReportConfigId An ID that indicates whether the UEcan assume that reported resources are using the same spatial TX-filters across time and cells csi-ResourceConfig Consistency-csi-ResourceConfigID An ID that indicates whether the UEcan assume that configured resources are using the same spatial TX-filters across time and cellsThe consistency identifier could be generated using any of the following information:^ Consistency Cell ID^ PLMN-ID^ NW Vendor infoo Vendor ID^ Deployment infoo Antenna physical tilto Antenna physical directiono Antenna Position^ Beam pattern informationo For example, time when beam pattern or specific beam that impacts a resourceIDor was changed Figure 10 shows an example of a communication system 1000 in accordance with some embodiments. In the example, the communication system 1000 includes a telecommunication network 1002 that includes an access network 1004, such as a Radio Access Network (RAN), and a core network 1006, which includes one or more core network nodes 1008. The access network 1004 includes one or more access network nodes, such as network nodes 1010A and 1010B (one or more of which may be generally referred to as network nodes 1010), 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 1002 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network 1002 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other nodes to implement one or more functionalities of any node in the telecommunication network 1002, including one or more network nodes 1010 and / or core network nodes 1008. Examples of an ORAN network node include an Open Radio Unit (O-RU), an Open Distributed Unit (O-DU), an Open Central Unit (O-CU), including an O-CU Control Plane (O- CU-CP) or an O-CU User Plane (O-CU-UP), a RAN intelligent controller (near-real time or non- real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node may supporta specification by, for example, supporting an interface defined by the ORAN specification, suchas an A1, F1, W1, E1, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN access node may be a logical nodein a physical node. Furthermore, an ORAN network node may be implemented in avirtualization 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-2interface defined by the O-RAN Alliance or comparable technologies. The network nodes 1010facilitate direct or indirect connection of User Equipment (UE), such as by connecting UEs 1012A, 1012B, 1012C, and 1012D (one or more of which may be generally referred to as UEs 1012) to the core network 1006 over one or more wireless connections. 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 1000 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 1000 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system. The UEs 1012 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 1010 and other communication devices. Similarly, the network nodes 1010 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 1012 and / or with other network nodes or equipment in the telecommunication network 1002 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network 1002. In the depicted example, the core network 1006 connects the network nodes 1010 to one or more hosts, such as host 1016. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled tohosts. The core network 1006 includes one more core network nodes (e.g., core network node1008) 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 1008. 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). The host 1016 may be under the ownership or control of a service provider other than an operator or provider of the access network 1004 and / or the telecommunication network 1002, and may be operated by the service provider or on behalf of the service provider. The host 1016 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. As a whole, the communication system 1000 of Figure 10 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system 1000 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. In some examples, the telecommunication network 1002 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunication network 1002 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 1002. For example, the telecommunication network 1002 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 (IoT) services to yet further UEs. In some examples, the UEs 1012 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 1004 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 1004. Additionally, a UE may beconfigured for operating in single- or multi-Radio Access Technology (RAT) or multi-standardmode. 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 EvolvedUMTS Terrestrial RAN (E-UTRAN) NR - Dual Connectivity (EN-DC).In the example, a hub 1014 communicates with the access network 1004 to facilitate indirect communication between one or more UEs (e.g., UE 1012C and / or 1012D) and network nodes (e.g., network node 1010B). In some examples, the hub 1014 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 1014 may be a broadband router enabling access to the core network 1006 for the UEs. As another example, the hub 1014 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions maybe received from the UEs, network nodes 1010, or by executable code, script, process, or otherinstructions in the hub 1014. As another example, the hub 1014 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 1014 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 1014 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 1014 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 1014 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy IoT devices. The hub 1014 may have a constant / persistent or intermittent connection to the network node 1010B. The hub 1014 may also allow for a different communication scheme and / or schedule between the hub 1014 and UEs (e.g., UE 1012C and / or 1012D), and between the hub 1014 and the core network 1006. In other examples, the hub 1014 is connected to the core network 1006 and / or one or more UEs via a wired connection. Moreover, the hub 1014 may be configured to connect to a Machine-to-Machine (M2M) service provider over the access network 1004 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 1010 while still connected via the hub 1014 via awired or wireless connection. In some embodiments, the hub 1014 may be a dedicated hub – thatis, a hub whose primary function is to route communications to / from the UEs from / to thenetwork node 1010B. In other embodiments, the hub 1014 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 1010B, but which is additionally capable of operating as a communication start and / or end point for certain data channels. Figure 11 shows a UE 1100 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged, and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, Voice over Internet Protocol (VoIP) phone, wireless local loop phone, desktop computer, Personal Digital Assistant (PDA), wireless camera, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, Laptop Embedded Equipment (LEE), Laptop Mounted Equipment (LME), smart device, wireless Customer Premise Equipment (CPE), vehicle, vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3GPP, including a Narrowband Internet of Things (NB-IoT) UE, a Machine Type Communication (MTC) UE, and / or an enhanced MTC (eMTC) UE. 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 device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter). The UE 1100 includes processing circuitry 1102 that is operatively coupled via a bus 1104 to an input / output interface 1106, a power source 1108, memory 1110, a communication interface 1112, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 11. 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. The processing circuitry 1102 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 1110. The processing circuitry 1102 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 1102 may include multiple Central Processing Units (CPUs). In the example, the input / output interface 1106 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 1100. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device. In some embodiments, the power source 1108 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 1108 may further include power circuitry for delivering power from the power source 1108 itself, and / or an external power source, to the various parts of the UE 1100 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 1108. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 1108 to make the power suitable for the respective components of the UE 1100 to which power is supplied. The memory 1110 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 1110 includes one or more application programs 1114, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 1116. The memory 1110 may store, for use by the UE 1100, any of a variety of various operating systems or combinations of operating systems. The memory 1110 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 1110 may allow the UE 1100 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 memory1110, which may be or comprise a device-readable storage medium. The processing circuitry 1102 may be configured to communicate with an access network or other network using the communication interface 1112. The communication interface 1112 may comprise one or more communication subsystems and may include or be communicativelycoupled to an antenna 1122. The communication interface 1112 may include one or moretransceivers 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 1118 and / or a receiver 1120 appropriate to provide network communications (e.g., optical, electrical, frequencyallocations, and so forth). Moreover, the transmitter 1118 and receiver 1120 may be coupled toone or more antennas (e.g., the antenna 1122) and may share circuit components, software, or firmware, or alternatively be implemented separately. In the illustrated embodiment, communication functions of the communication interface 1112 may include cellular communication, WiFi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, NFC, location-based communication such as the use of the Global Positioning System (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband CDMA (WCDMA), GSM, LTE, NR, UMTS, WiMax, Ethernet, Transmission Control Protocol / Internet Protocol (TCP / IP), Synchronous Optical Networking (SONET), Asynchronous Transfer Mode (ATM), Quick User Datagram Protocol Internet Connection (QUIC), Hypertext Transfer Protocol (HTTP), and so forth. Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 1112, 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). As another example, a UE comprises an actuator, a motor, or a switch related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input. A UE, when in the form of an IoT 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 IoT 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, awearable 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 IoT device comprises circuitry and / or software in dependence of the intended application of the IoT device in addition to other components as described in relation to the UE 1100 shown in Figure 11. As yet another specific example, in an IoT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements and transmits the results of suchmonitoring and / or measurements to another UE and / or a network node. The UE may in this casebe 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 UEmay represent a vehicle, such as a car, a bus, a truck, a ship, an airplane, or other equipment thatis capable of monitoring and / or reporting on its operational status or other functions associated with its operation. 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 speedinformation (obtained through a speed sensor) to a second UE that is a remote controlleroperating 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. Figure 12 shows a network node 1200 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged, and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment in a telecommunication network. Examples of network nodes include, but are not limited to, APs (e.g., radio APs), Base Stations (BSs) (e.g., radio BSs, Node Bs, evolved Node Bs (eNBs), NR Node Bs (gNBs)), and O-RAN nodes or components of an O-RAN node (e.g., O-RU, O-DU, O- CU). 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). 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). The network node 1200 includes processing circuitry 1202, memory 1204, a communication interface 1206, and a power source 1208. The network node 1200 may be composed of multiple physically separate components (e.g., a NodeB component and an RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 1200 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 1200 may be configured to support multiple RATs. In such embodiments, some components may be duplicated (e.g., separate memory 1204 for different RATs) and some components may be reused (e.g., a same antenna 1210 may be shared by different RATs). The network node 1200 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1200, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z- wave, Long Range Wide Area Network (LoRaWAN), Radio Frequency Identification (RFID), orBluetooth wireless technologies. These wireless technologies may be integrated into the same ordifferent chip or set of chips and other components within the network node 1200. The processing circuitry 1202 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 logicoperable to provide, either alone or in conjunction with other network node 1200 components,such as the memory 1204, to provide network node 1200 functionality. In some embodiments, the processing circuitry 1202 includes a System on a Chip (SOC). In some embodiments, the processing circuitry 1202 includes one or more of Radio Frequency (RF) transceiver circuitry 1212 and baseband processing circuitry 1214. In some embodiments,the RF transceiver circuitry 1212 and the baseband processing circuitry 1214 may be on separatechips (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 1212 and the baseband processing circuitry 1214 may be on the same chip or set of chips, boards, or units. The memory 1204 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 1202. The memory 1204 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 1202 and utilized by the network node 1200. The memory 1204 may be used to store any calculations made by the processing circuitry 1202 and / or any data received via the communication interface 1206. In some embodiments, the processing circuitry 1202 and the memory 1204 are integrated. The communication interface 1206 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, thecommunication interface 1206 comprises port(s) / terminal(s) 1216 to send and receive data, forexample to and from a network over a wired connection. The communication interface 1206 also includes radio front-end circuitry 1218 that may be coupled to, or in certain embodiments a part of, the antenna 1210. The radio front-end circuitry 1218 comprises filters 1220 and amplifiers 1222. The radio front-end circuitry 1218 may be connected to the antenna 1210 and the processing circuitry 1202. The radio front-end circuitry 1218 may be configured to condition signals communicated between the antenna 1210 and the processing circuitry 1202. The radio front-end circuitry 1218 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 1218 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of the filters 1220 and / or the amplifiers 1222. The radio signal may then be transmitted via the antenna 1210. Similarly, when receiving data, the antenna 1210 may collect radio signals which are then converted into digital data by the radio front-end circuitry 1218. The digital data may be passed to the processing circuitry 1202. In other embodiments, the communication interface 1206 may comprise different components and / or different combinations of components. In certain alternative embodiments, the network node 1200 does not include separate radio front-end circuitry 1218; instead, the processing circuitry 1202 includes radio front-end circuitry and is connected to the antenna 1210. Similarly, in some embodiments, all or some of the RF transceiver circuitry 1212 is part of the communication interface 1206. In still other embodiments, the communication interface 1206 includes the one or more ports or terminals 1216, the radio front-end circuitry 1218, and the RF transceiver circuitry 1212 as part of a radio unit (not shown), and the communication interface 1206 communicates with the baseband processing circuitry 1214, which is part of a digital unit (not shown). The antenna 1210 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 1210 may be coupled to the radio front-end circuitry 1218 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 1210 is separate from the network node 1200 and connectable to the network node 1200 through an interface or port. The antenna 1210, the communication interface 1206, and / or the processing circuitry 1202 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node 1200. Any information, data, and / or signals may be received from a UE, another network node, and / or any other network equipment. Similarly, the antenna 1210, the communication interface 1206, and / or the processing circuitry 1202 may be configured to perform any transmitting operations described herein as being performed by the network node 1200. Any information, data, and / or signals may be transmitted to a UE, another network node, and / or any other network equipment. The power source 1208 provides power to the various components of the network node 1200 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 1208 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 1200 with power for performing the functionality described herein. For example, the network node 1200 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 1208. As a further example, the power source 1208 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. Embodiments of the network node 1200 may include additional components beyond those shown in Figure 12 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 1200 may include user interface equipment to allow input of information into the network node 1200 and to allow output of information from the network node 1200. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 1200. Figure 13 is a block diagram of a host 1300, which may be an embodiment of the host 1016 of Figure 10, in accordance with various aspects described herein. As used herein, the host 1300 may be or comprise various combinations of hardware and / or software including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm. The host 1300 may provide one or more services to one or more UEs. The host 1300 includes processing circuitry 1302 that is operatively coupled via a bus 1304 to an input / output interface 1306, a network interface 1308, a power source 1310, and memory 1312. Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices of previous figures, such as Figures 11 and 12, such that the descriptions thereof are generally applicable to the corresponding components of the host 1300. The memory 1312 may include one or more computer programs including one or more host application programs 1314 and data 1316, which may include user data, e.g. data generated by a UE for the host 1300 or data generated by the host 1300 for a UE. Embodiments of the host 1300 may utilize only a subset or all of the components shown. The host application programs 1314 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (VVC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), Moving Picture Experts Group (MPEG), VP9) and audio codecs (e.g., Free Lossless Audio Codec (FLAC), Advanced Audio Coding (AAC), MPEG, G.711), includingtranscoding for multiple different classes, types, or implementations of UEs (e.g., handsets,desktop computers, wearable display systems, and heads-up display systems). The host application programs 1314 may also provide for user authentication and licensing checks and may periodically report health, routes, and content availability to a central node, such as a device in or on the edge of a core network. Accordingly, the host 1300 may select and / or indicate adifferent host for Over-The-Top (OTT) services for a UE. The host application programs 1314may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real-Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), Dynamic Adaptive Streaming over HTTP (DASH or MPEG-DASH), etc. Figure 14 is a block diagram illustrating a virtualization environment 1400 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 morevirtual components. Some or all of the functions described herein may be implemented as virtualcomponents executed by one or more Virtual Machines (VMs) implemented in one or more virtual environments 1400 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment 1400 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an O-2 interface. Applications 1402 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 1400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein. Hardware 1404 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 1406 (also referred to as hypervisors or VM Monitors (VMMs)), provide VMs 1408A and 1408B (one or more of which may be generally referred to as VMs 1408), and / or perform any of the functions, features, and / or benefits described in relation with some embodiments described herein. The virtualization layer 1406 may present a virtual operating platform that appears like networking hardware to the VMs 1408. The VMs 1408 comprise virtual processing, virtual memory, virtual networking, or interface and virtual storage, and may be run by a corresponding virtualization layer 1406. Different embodiments of the instance of a virtual appliance 1402 may be implemented on one or more of the VMs 1408, 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. In the context of NFV, a VM 1408 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 1408, and that part of the hardware 1404 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs 1408, 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 1408 on top of the hardware 1404 and corresponds to the application 1402. The hardware 1404 may be implemented in a standalone network node with generic or specific components. The hardware 1404 may implement some functions via virtualization. Alternatively, the hardware 1404 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 1410, which, among others, oversees lifecycle management of the applications 1402. In some embodiments, the hardware 1404 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 RAN or a base station. In some embodiments, some signaling can be provided with the use of a control system 1412 which may alternatively be used for communication between hardware nodes and radio units. Figure 15 shows a communication diagram of a host 1502 communicating via a network node 1504 with a UE 1506 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as the UE 1012A of Figure 10 and / or the UE 1100 of Figure 11), the network node (such as the network node 1010A of Figure 10 and / or the network node 1200 of Figure 12), and the host (such as the host 1016 of Figure 10 and / or the host 1300 of Figure 13) discussed in the preceding paragraphs will now be described with reference to Figure 15. Like the host 1300, embodiments of the host 1502 include hardware, such as a communication interface, processing circuitry, and memory. The host 1502 also includes software, which is stored in or is accessible by the host 1502 and executable by the processing circuitry. The software includes a host application that may be operable to provide a service to a remote user, such as the UE 1506 connecting via an OTT connection 1550 extending between the UE 1506 and the host 1502. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection 1550. The network node 1504 includes hardware enabling it to communicate with the host 1502 and the UE 1506. The connection 1560 may be direct or pass through a core network (like the core network 1006 of Figure 10) and / or one or more other intermediate networks, such as one or more public, private, or hosted networks. For example, an intermediate network may be a backbone network or the Internet. The UE 1506 includes hardware and software, which is stored in or accessible by the UE 1506 and executable by the UE’s processing circuitry. The software includes a client application, such as a web browser or operator-specific “app” that may be operable to provide a service to a human or non-human user via the UE 1506 with the support of the host 1502. In the host 1502, an executing host application may communicate with the executing client application via the OTT connection 1550 terminating at the UE 1506 and the host 1502. In providing the service to the user, the UE's client application may receive request data from the host's host application and provide user data in response to the request data. The OTT connection 1550 may transfer both the request data and the user data. The UE's client application may interact with the user to generate the user data that it provides to the host application through the OTT connection 1550. The OTT connection 1550 may extend via the connection 1560 between the host 1502 and the network node 1504 and via a wireless connection 1570 between the network node 1504 and the UE 1506 to provide the connection between the host 1502 and the UE 1506. The connection 1560 and the wireless connection 1570, over which the OTT connection 1550 may be provided, have been drawn abstractly to illustrate the communication between the host 1502 and the UE 1506 via the network node 1504, without explicit reference to any intermediary devices and the precise routing of messages via these devices. As an example of transmitting data via the OTT connection 1550, in step 1508, the host 1502 provides user data, which may be performed by executing a host application. In some embodiments, the user data is associated with a particular human user interacting with the UE 1506. In other embodiments, the user data is associated with a UE 1506 that shares data with the host 1502 without explicit human interaction. In step 1510, the host 1502 initiates a transmissioncarrying the user data towards the UE 1506. The host 1502 may initiate the transmissionresponsive to a request transmitted by the UE 1506. The request may be caused by human interaction with the UE 1506 or by operation of the client application executing on the UE 1506. The transmission may pass via the network node 1504 in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step 1512, the network node 1504 transmits to the UE 1506 the user data that was carried in the transmission that the host 1502 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 1514, the UE 1506 receives the user data carried in the transmission, which may be performed by a client application executed on the UE 1506 associated with the host application executed by the host 1502. In some examples, the UE 1506 executes a client application which provides user data to the host 1502. The user data may be provided in reaction or response to the data received from the host 1502. Accordingly, in step 1516, the UE 1506 may provide user data, which may be performed by executing the client application. In providing the user data, the client application may further consider user input received from the user via an input / output interface of the UE 1506. Regardless of the specific manner in which the user data was provided, the UE 1506 initiates, in step 1518, transmission of the user data towards the host 1502 via the network node 1504. In step 1520, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 1504 receives user data from the UE 1506 and initiates transmission of the received user data towards the host 1502. In step 1522, the host 1502 receives the user data carried in the transmission initiated by the UE 1506. One or more of the various embodiments improve the performance of OTT services provided to the UE 1506 using the OTT connection 1550, in which the wireless connection 1570 forms the last segment. In an example scenario, factory status information may be collected and analyzed by the host 1502. As another example, the host 1502 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host 1502 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host 1502 may store surveillance video uploaded by a UE. As another example, the host 1502 may store or control access to media content such asvideo, audio, VR, or AR which it can broadcast, multicast, or unicast to UEs. As other examples,the host 1502 may be used for energy pricing, remote control of non-time critical electrical load to balance power generation needs, location services, presentation services (such as compiling diagrams etc. from data collected from remote devices), or any other function of collecting, retrieving, storing, analyzing, and / or transmitting data. In some examples, a measurement procedure may be provided for the purpose of monitoring data rate, latency, and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring the OTT connection 1550 between the host 1502 and the UE 1506 in response to variations in the measurement results. The measurement procedure and / or the network functionality for reconfiguring the OTT connection 1550 may be implemented in software and hardware of the host 1502 and / or the UE 1506. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection 1550 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or by supplying values of other physical quantities from which software may compute or estimatethe monitored quantities. The reconfiguring of the OTT connection 1550 may include messageformat, retransmission settings, preferred routing, etc.; the reconfiguring need not directly alter the operation of the network node 1504. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signaling that facilitates measurements of throughput, propagation times, latency, and the like by the host 1502. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 1550 while monitoring propagation times, errors, etc. Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions, and methods disclosed herein. Determining, calculating, obtaining, or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box or nested within multiple boxes, in practice computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware. In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer-readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device- readable storage medium, such as in a hardwired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer-readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole and / or by end users and a wireless network generally. 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. Some example embodiments of the present disclosure are as follows: Group A Embodiments Embodiment 1: A method performed by a User Equipment, UE, comprising any one or more of the following: ^receiving (904), from a network node, first information that indicates a first set ofmeasurement resources (e.g., a first set of measurement response on which the UE is to perform measurement predictions); ^receiving (906), from the network node, second information that indicates a second set ofmeasurement resources (e.g., a second set of measurement resources with which the UE can be configured to perform measurements, based on which the measurement predictions can be generated); ^determining (908) whether one or more applicability conditions for using one or moreArtificial Intelligence, AI, / Machine Learning, ML, models or functions at the UE to generate a measurement prediction for the first set of measurement resources based on radio measurements performed on the second set of measurement resources; and ^sending (910) a first indication to the network node, the first indication comprisinginformation indicative of whether the one or more applicability conditions are satisfied. Embodiment 2: The method of embodiment 1, wherein: ^the first indication comprises information that indicates that the one or more applicabilityconditions are satisfied; and ^the method further comprises:o performing (912) measurements on the second set of measurement resources; ando generating (912) a measurement prediction for the first set of measurementresources using the one or more AI / ML models or functions with the measurements performed on the second set of measurement resources as inputs to the one or more AI / ML models or functions. Embodiment 3: The method of embodiment 1, wherein: ^the first indication comprises information that indicates that the one or more applicabilityconditions are satisfied; and ^the method further comprises:o receiving (911), from the network node, an indication to perform measurements on the second set of measurement resources; operforming (912) measurements on the second set of measurement resources; ando generating (912) a measurement prediction for the first set of measurementresources using the one or more AI / ML models or functions with the measurements performed on the second set of measurement resources as inputs to the one or more AI / ML models or functions. Embodiment 4: The method of embodiment 1, wherein: ^the first indication comprises information that indicates that the one or more applicabilityconditions are satisfied and further comprises information that indicates a third set of measurement resources, wherein the third set of measurement resources is a subset of thesecond set of measurement resources; and ^the method further comprises:o performing (912) measurements on the third set of measurement resources; ando generating (912) a measurement prediction for the first set of measurementresources using the one or more AI / ML models or functions with the measurements performed on the third set of measurement resources as inputs to the one or more AI / ML models or functions. Embodiment 5: The method of embodiment 1, wherein: ^the first indication comprises information that indicates that the one or more applicabilityconditions are satisfied and further comprises information that indicates a third set of measurement resources, wherein the third set of measurement resources is a subset of the second set of measurement resources; and ^the method further comprises:o receiving (911), from the network node, an indication to perform measurementson the third set of measurement resources; operforming (912) measurements on the third set of measurement resources; ando generating (912) a measurement prediction for the first set of measurementresources using the one or more AI / ML models or functions with the measurements performed on the third set of measurement resources as inputs to the one or more AI / ML models or functions. Embodiment 6: The method of embodiment 1, wherein the first indication indicates that the one or more applicability conditions are not satisfied. Embodiment 7: The method of any of embodiments 1 to 6, wherein the second set of measurement resources is a set of measurement resources already configured to the UE for radio measurement reporting, or a set of candidate measurement resources that can be configured to the UE for radio measurement reporting. Embodiment 8: The method of any of embodiments 1 to 7, wherein the measurement prediction is a spatial-domain beam prediction. Embodiment 9: The method of embodiment 1, wherein the first indication comprises any one or more of: ^information that indicates one or more third sets of measurement resources that theUE has to measure in order to determine the measurement prediction on the first set of measurement resources, wherein the one or more third sets of measurement resources are thereby recommendation to the network node for configuration to the UE for radio measurements; ^information related to whether the one or more applicability conditions of the one ormore AI / ML models or functions available at the UE are fulfilled or not fulfilled, indicating respectively that the UE can perform or not perform the measurement prediction on the first set of measurement resources based on radio measurements on the second set of measurement resources; ^information related to whether the one or more applicability conditions of the one ormore AI / ML models or functions available at the UE are fulfilled or not fulfilled, indicating respectively that the UE can perform or not perform measurement prediction on the first set of measurement resources based on a selected third set of measurement resources. Embodiment 10: The method of embodiment 9, wherein the one or more third sets of measurement resources selected by the UE are equal to the second set of measurement resources or a subset of the second set of measurement resources. Embodiment 11: The method of embodiment 9 or 10, wherein the second set of measurement resources consists of a pool of one or more sets of measurement resources, and the selected one or more third sets of measurement resources is equal to one of the said sets. Embodiment 12: The method of embodiment 11, wherein each of the one or more sets of measurement resources or each of the resources included in the first and second set of measurement resources is associated to an index, and the one or more selected third sets of measurement resources are indicated to the network node via one of the said indexes. Embodiment 13: The method of embodiment 9 or 10, wherein the selected third set of measurement resources fulfills the one or more applicability conditions of the one or more AI / ML models or function available at the UE with respect to the first set of measurement resources. Embodiment 14: The method of any of embodiments 1 to 13, wherein by being satisfied, the applicability conditions allow the UE to determine the measurement prediction on the first set of measurement resources with a certain accuracy. Embodiment 15: The method of any of embodiments 1 to 14, determining whether the one or more applicability conditions are satisfied is in response to: ^receiving the first information that indicates the first set of measurement resourcesafter previously receiving with the information indicative of the second set of measurement resources; ^receiving the second information indicative of the second set of measurementresources after previously receiving the first information indicative of the first set of measurement resources; ^receiving the first and second information indicative of the first and second set ofmeasurement resources in a same message; ^receiving a successive indication of a new first set of measurement resources;^ receiving a successive indication of a new second set of measurement resources;^ receiving a successive indication that the UE should select another set ofmeasurement resources as a third set of measurement resources; ^determining that the UE can no longer provide a measurement prediction on the firstset of measurement resources based on measurements on the second set of measurement resources, as a result of the one or more applicability conditions not being fulfilled for the one or more AI / ML models or functions available at the UE; ^determining that the UE can provide the measurement prediction on the first set ofmeasurement resources based on measurements on the second set of measurement resources, as a result of the one or more applicability conditions being fulfilled for the one or more AIML models or functions available at the UE; ^determining that the UE can provide the measurement prediction on the first set ofmeasurement resources based on measurements on a different third set of measurement resources previously selected, as a result of the one or more applicability conditions not being fulfilled for the one or more AI / ML models or function available at the UE with a previously selected third set of measurement resources. Embodiment 16: The method of any of embodiments 1 to 15, wherein each of the first and second sets of measurement resources comprise any of: ^a set of SSB for a cell;^ a set of CSI-RS resources for a cell;^ a set of SS / PBCH block resource sets for a cell;^ a set of cells;^ a set of frequencies;^ a set of SSBs for a list of cells that can comprise two or more cells;^ a set of CSI-RS resources for a list of cells that can comprise two or more cells;^ a set of SS / PBCH block resource sets for a list of cells that can comprise two ormore cells. Embodiment 17: The method of any of embodiments 1 and 9 to 16, further comprising receiving a second indication from the network node, the second indication comprising any one or more of: ^a configuration to activate the one or more AI / ML models or functions available at theUE to determine the measurement prediction on the first set of measurement resources; ^a configuration to configure the second set of measurement resources in response totransmitting in the first indication with information that indicates that the one or more applicability conditions for the second set of measurements are fulfilled. ^a configuration to configure the second set of measurement resources in response totransmitting in the first indication with information indicative of the third set of measurement resources; ^a configuration to deconfigure or deactivate the first set of measurement resources;^ a configuration to deconfigure or deactivate the second set of measurement resources orone or more sets of measurement resources within the second set of measurement resources; ^a configuration including a successive first set of measurement resources;^ a configuration including a successive second set of measurement resources;^ a configuration including indication that the UE should select another set of measurementresources as a third set of measurement resources. Embodiment 18: The method of embodiment 17, wherein the second indication is received from the network node in response to transmitting the first indication to the network node. Embodiment 19: The method of embodiment 17 or 18, wherein in response to receiving a configuration that deconfigures the first or second set of measurement resources, the UE deactivates the one or more AI / ML models or functions available at the UE. Embodiment 20: The method of embodiment 17 or 18, wherein a successive second set of measurement resources indicated in the second indication is equal to or a subset of the third set of measurement resources included by the UE in the first indication. Embodiment 21: The method of any of embodiments 1 or 9 to 20, wherein the UE starts performing the radio measurement predictions on the resources included in the first set of measurement resources, in response to: ^evaluating that the one or more AI / ML models or functions available at the UE fulfill theone or more applicability conditions; ^selecting a third set of measurement resources in which case the radio measurementpredications are performed based on the radio measurements performed in the third set of measurement resources; ^receiving a configuration in the second indication associated to the first, second or thirdset of measurement resources, in which case the radio measurement predictions are performed based on the radio measurements performed in the second set of radio measurements or third set of radio measurements. Embodiment 22: The method of embodiment 21, wherein the second set of measurement resources, or the selected third set of measurement resources, is the input of one or more AI / ML models or functions available at the UE. Embodiment 23: The method of embodiment 21, wherein the output of the one or more AI / ML models or functions available at the UE is the measurement predictions comprising prediction results for one or more measurement quantities, such as, e.g., RSRP, RSRQ, SINR, or RSSI level, associated to the first set of measurement resources. Embodiment 24: The method of embodiment 23, wherein the measurement prediction results comprise any of: ^measured quantities for each of the one or more resources in the first set of measurementresources and / or second set of measurement resources; ^measured quantities for one or more best resources in terms of measured quantitiesamong the resources in the first set of measurement resources and / or second set of measurement resources, wherein the number of best resources can be a fixed or configured number; ^measured quantities for one or more worst resources in terms of measured quantitiesamong the resources in the first set of measurement resources and / or second set of measurement resources, wherein the number of worst resources can be a fixed or configured number; ^average measured quantities for the resources in the first set of measurement resourcesand / or second set of measurement resources; ^variance of the measured quantities for the resources in the first set of measurementresources and / or second set of measurement resources;^ accuracy of the predictions results.Embodiment 25: The method of any of embodiments 1 to 24, wherein the first and second information indicative of the first and second sets of measurement resources are received via broadcast or system information signaling or via dedicated signaling. Embodiment 26: The method of any of embodiments 1 to 25, wherein the first indication is transmitted via RRC signaling (UEAssistanceInformation), or MAC (MAC CE), or UCI. Embodiment 27: The method of any of embodiments 17 to 20, wherein the second indication is received via RRC dedicated signaling or MAC (MAC CE), or PDCCH (DCI). Embodiment 28: The method embodiment 1, wherein in response to transmitting the first indication to the network node, the UE receives information indicative of a successive second set of measurement resources (e.g., via RRC dedicated signaling) based on which the UE should evaluate the one or more applicability conditions. Embodiment 29: The method of any of embodiments 1 to 28, wherein the measurement predictions are transmitted to the network node. Embodiment 30: The method of any of embodiments 1 to 28, wherein the measurement predictions are transmitted to another network node performing the UE-side model training. Embodiment 31: The method of any of embodiments 1 to 30, wherein the step of determining whether the one or more applicability conditions are satisfied is performed during an inference for the one or more AI / ML models or functions. Embodiment 32: The method of any of embodiments 1 to 30, wherein the step of determining whether the one or more applicability conditions are satisfied is performed during data collection for training of one or more AI / ML models or functions. Embodiment 33: The method of embodiment 9, wherein the third set of measurement resources is a recommendation for the network to configure the resources in a second of measurement resources such that the applicability conditions of one or more AIML models / functionalities are fulfilled to determine the radio measurement predictions in the first set of measurement resources. Embodiment 34: The method of embodiment 12, wherein the index of a resource or resource set included in the first and second set of measurement resources is unique within the cell. Embodiment 35: The method of any of the previous embodiments, further comprising: ^providing user data; and^ forwarding the user data to a host via the transmission to the network node. Group B Embodiments Embodiment 36: A method performed by a network node (e.g., gNB), the method comprising: ^sending (904), to a UE, first information that indicates a first set of measurementresources (e.g., a first set of measurement response on which the UE is to perform measurement predictions); ^sending (906), to the UE, second information that indicates a second set of measurementresources (e.g., a second set of measurement resources with which the UE can be configured to perform measurements, based on which the measurement predictions can be generated); and ^receiving (910) a first indication from the UE, the first indication comprising informationindicative of whether one or more applicability conditions for using one or more Artificial Intelligence, AI, / Machine Learning, ML, models or functions at the UE to generate a measurement prediction for the first set of measurement resources based on radio measurements performed on the second set of measurement resources are satisfied. Embodiment 37: The method of embodiment 36, wherein the second set of measurement resources is a set of measurement resources already configured to the UE for radio measurement reporting, or a candidate set of measurement resources that can be configured to the UE for radio measurement reporting. Embodiment 38: The method of embodiment 36, wherein the configuration of the first and second sets, via the first and second information, is based on any of: ^Availability of the resources in the first set of measurement resources^ Radio measurement predictions based on AIML models / functionalities at NW-side^ Load of the resources in the first set of measurement resources^ UE capabilities^ UE location^ UE trajectory^ UE mobility conditions^ Load balancing policiesEmbodiment 39: The method of embodiment 36, wherein the first and second set of measurement resources are configured, via the first and second information, independent of each other. Embodiment 40: The method of embodiment 36, wherein a successive second set of measurement resources is transmitted to the UE in response to receiving the first indication from the UE that indicates, for the previously configured second set of measurement resources or UE selected third set of measurement resources, the one or more applicability conditions of one or more AI / ML models or functions available at the UE are not fulfilled. Embodiment 41: The method of embodiment 36, further comprising transmitting, to the UE, a configuration to activate the one or more AI / ML models or functions available at the UE in relation to first set of measurement resources. Embodiment 42: The method of embodiment 36, further comprising transmitting, to the UE, a configuration to deactivate or deconfigure the one or more AI / ML models or functions available at the UE in relation to the first set of measurement resources. Embodiment 43: The method of embodiment 36, further comprising transmitting, to the UE, a configuration to configure the second set of measurement resources in response of receiving the first indication from the UE that indicates that the one or more applicability conditions of one or more AI / ML models or functions available at the UE are fulfilled in relation to the second set of measurement resources. Embodiment 44: The method of embodiment 36, further comprising transmitting, to the UE, a configuration to configure a third set of measurement resources in response to receiving the first indication that indicates that the one or more applicability conditions of one or more AI / ML models or functions available at the UE are fulfilled for the third set of measurement resources. Embodiment 45: The method of any of the previous embodiments, further comprising: ^obtaining user data; and^ forwarding the user data to a host or a user equipment.Group C Embodiments Embodiment 46: A user equipment comprising: ^processing circuitry configured to perform any of the steps of any of the Group Aembodiments; and ^power supply circuitry configured to supply power to the processing circuitry.Embodiment 47: A network node comprising: ^processing circuitry configured to perform any of the steps of any of the Group Bembodiments; and ^power supply circuitry configured to supply power to the processing circuitry.Embodiment 48: A user equipment (UE) comprising: ^an antenna configured to send and receive wireless signals;^ radio front-end circuitry connected to the antenna and to processing circuitry, andconfigured to condition signals communicated between the antenna and the processing circuitry; ^the processing circuitry being configured to perform any of the steps of any of the GroupA embodiments; ^an input interface connected to the processing circuitry and configured to allow input ofinformation into the UE to be processed by the processing circuitry; ^an output interface connected to the processing circuitry and configured to outputinformation from the UE that has been processed by the processing circuitry; and ^a battery connected to the processing circuitry and configured to supply power to the UE.Embodiment 49: A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: ^processing circuitry configured to provide user data; and^ a network interface configured to initiate transmission of the user data to a network nodein a cellular network for transmission to a user equipment (UE), the network node having acommunication interface and processing circuitry, the processing circuitry of thenetwork node configured to perform any of the operations of any of the Group B embodiments to transmit the user data from the host to the UE. Embodiment 50: The host of the previous embodiment, wherein: ^the processing circuitry of the host is configured to execute a host application thatprovides the user data; and ^the UE comprises processing circuitry configured to execute a client applicationassociated with the host application to receive the transmission of user data from the host. Embodiment 51: A method implemented in a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: ^providing user data for the UE; and^ initiating a transmission carrying the user data to the UE via a cellular networkcomprising the network node, wherein the network node performs any of the operations of any of the Group B embodiments to transmit the user data from the host to the UE. Embodiment 52: The method of the previous embodiment, further comprising, at the network node, transmitting the user data provided by the host for the UE. Embodiment 53: The method of any of the previous 2 embodiments, wherein the user data is provided at the host by executing a host application that interacts with a client application executing on the UE, the client application being associated with the host application. Embodiment 54: A communication system configured to provide an over-the-top (OTT) service, the communication system comprising:^ a host comprising:^ processing circuitry configured to provide user data for a user equipment (UE), the userdata being associated with the over-the-top service; and ^a network interface configured to initiate transmission of the user data toward a cellularnetwork node for transmission to the UE, the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B embodiments to transmit the user data from the host to the UE. Embodiment 55: The communication system of the previous embodiment, further comprising: ^the network node; and / or^ the UE.Embodiment 56: A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: ^processing circuitry configured to initiate receipt of user data; and^ a network interface configured to receive the user data from a network node in a cellularnetwork, the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B embodiments to receive the user data from a user equipment (UE) for the host. Embodiment 57: The host of the previous 2 embodiments, wherein: ^the processing circuitry of the host is configured to execute a host application thatreceives the user data; and ^the host application is configured to interact with a client application executing on theUE, the client application being associated with the host application. Embodiment 58: The host of the any of the previous 2 embodiments, wherein the initiating receipt of the user data comprises requesting the user data. Embodiment 59: A method implemented by a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: ^at the host, initiating receipt of user data from the UE, the user data originating from atransmission which the network node has received from the UE, wherein the network node performs any of the steps of any of the Group B embodiments to receive the user data from the UE for the host. Embodiment 60: The method of the previous embodiment, further comprising at the network node, transmitting the received user data to the host. Embodiment 61: A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: ^processing circuitry configured to provide user data; and^ a network interface configured to initiate transmission of the user data to a cellularnetwork for transmission to a user equipment (UE), wherein the UE comprises a communication interface and processing circuitry, the communication interface and processing circuitry of the UE being configured to perform any of the operations of any of the Group A embodiments to receive the user data from the host. Embodiment 62: The host of the previous embodiment, wherein the cellular network further includes a network node configured to communicate with the UE to transmit the user data to the UE from the host. Embodiment 63: The host of the previous 2 embodiments, wherein: ^the processing circuitry of the host is configured to execute a host application, therebyproviding the user data; and ^the host application is configured to interact with a client application executing on theUE, the client application being associated with the host application. Embodiment 64: A method implemented by a host operating in a communication system that further includes a network node and a user equipment (UE), the method comprising: ^providing user data for the UE; and^ initiating a transmission carrying the user data to the UE via a cellular networkcomprising the network node, wherein the UE performs any of the operations of any of the Group A embodiments to receive the user data from the host. Embodiment 65: The method of the previous embodiment, further comprising: ^at the host, executing a host application associated with a client application executing onthe UE to receive the user data from the host application. Embodiment 66: The method of the previous embodiment, further comprising: ^at the host, transmitting input data to the client application executing on the UE, the inputdata being provided by executing the host application, ^wherein the user data is provided by the client application in response to the input datafrom the host application. Embodiment 67: A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: ^processing circuitry configured to provide user data; and^ a network interface configured to initiate transmission of the user data to a cellularnetwork for transmission to a user equipment (UE), wherein the UE comprises a communication interface and processing circuitry, the communication interface and processing circuitry of the UE being configured to perform any of the steps of any of the Group A embodiments to transmit the user data to the host. Embodiment 68: The host of the previous embodiment, wherein the cellular network further includes a network node configured to communicate with the UE to transmit the user data from the UE to the host. Embodiment 69: The host of the previous 2 embodiments, wherein: ^the processing circuitry of the host is configured to execute a host application, therebyproviding the user data; and ^the host application is configured to interact with a client application executing on theUE, the client application being associated with the host application. Embodiment 70: A method implemented by a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: ^at the host, receiving user data transmitted to the host via the network node by the UE,wherein the UE performs any of the steps of any of the Group A embodiments to transmit the user data to the host. Embodiment 71: The method of the previous embodiment, further comprising: ^at the host, executing a host application associated with a client application executing onthe UE to receive the user data from the UE. Embodiment 72: The method of the previous 2 embodiments, further comprising: ^at the host, transmitting input data to the client application executing on the UE, the inputdata being provided by executing the host application, ^wherein the user data is provided by the client application in response to the input datafrom the host application. Those skilled in the art will recognize improvements and modifications to the embodiments of the present disclosure. All such improvements and modifications are considered within the scope of the concepts disclosed herein.

Claims

Claims1. A method performed by a User Equipment, UE, comprising:receiving (904), from a network node, first information that indicates a first set of measurement resources on which the UE is to perform measurement predictions; receiving (906), from the network node, second information that indicates a second set of measurement resources with which the UE can be configured to perform measurements, based on which the measurement predictions can be generated; determining (908) whether one or more applicability conditions for using one or more Artificial Intelligence, AI, / Machine Learning, ML, models or functions at the UE to generatemeasurement predictions are satisfied for the first set of measurement resources and the secondset of measurement resources; and sending (910) a first indication to the network node, the first indication comprising information indicative of whether the one or more applicability conditions are satisfied.

2. The method of claim 1, wherein:the first indication comprises information that indicates that the one or more applicability conditions are satisfied; and the method further comprises: performing (912) measurements on the second set of measurement resources; and generating (912) a measurement prediction for the first set of measurement resources using the one or more AI / ML models or functions with the measurements performed on the second set of measurement resources as inputs to the one or more AI / ML models or functions.

3. The method of claim 1, wherein:the first indication comprises information that indicates that the one or more applicability conditions are satisfied; and the method further comprises: receiving (911), from the network node, an indication to perform measurements on the second set of measurement resources; performing (912) measurements on the second set of measurement resources; and generating (912) a measurement prediction for the first set of measurement resources using the one or more AI / ML models or functions with the measurements performed on the second set of measurement resources as inputs to the one or more AI / ML models or functions.

4. The method of claim 1, wherein:the first indication comprises information that indicates that the one or more applicability conditions are satisfied and further comprises information that indicates a third set of measurement resources, wherein the third set of measurement resources is a subset of the second set of measurement resources; and the method further comprises: performing (912) measurements on the third set of measurement resources; and generating (912) a measurement prediction for the first set of measurement resources using the one or more AI / ML models or functions with the measurements performed on the third set of measurement resources as inputs to the one or more AI / ML models or functions.

5. The method of claim 1, wherein:the first indication comprises information that indicates that the one or more applicability conditions are satisfied and further comprises information that indicates a third set of measurement resources, wherein the third set of measurement resources is a subset of the second set of measurement resources; and the method further comprises: receiving (911), from the network node, an indication to perform measurements on the third set of measurement resources; performing (912) measurements on the third set of measurement resources; and generating (912) a measurement prediction for the first set of measurement resources using the one or more AI / ML models or functions with the measurements performed on the third set of measurement resources as inputs to the one or more AI / ML models or functions.

6. The method of claim 4 or 5, wherein the third set of measurement resources is arecommendation for the network to configure the resources in a second of measurement resources such that the applicability conditions of one or more AIML models / functionalities are fulfilled to determine the radio measurement predictions in the first set of measurement resources.

7. The method of claim 1, wherein the first indication indicates that the one or moreapplicability conditions are not satisfied.

8. The method of any of claims 1 to 7, wherein the second set of measurement resources is aset of measurement resources already configured to the UE for radio measurement reporting, or a set of candidate measurement resources that can be configured to the UE for radio measurement reporting.

9. The method of any of claims 1 to 8, wherein the measurement prediction is a spatial-domain beam prediction.

10. The method of claim 1, wherein the first indication comprises any one or more of:• information that indicates one or more third sets of measurement resourcesthat the UE has to measure in order to determine the measurement predictionon the first set of measurement resources, wherein the one or more third sets of measurement resources are thereby recommendation to the network node for configuration to the UE for radio measurements; •information related to whether the one or more applicability conditions ofthe one or more AI / ML models or functions available at the UE are fulfilled or not fulfilled, indicating respectively that the UE can perform or not perform the measurement prediction on the first set of measurement resources based on radio measurements on the second set of measurement resources; •information related to whether the one or more applicability conditions ofthe one or more AI / ML models or functions available at the UE are fulfilled or not fulfilled, indicating respectively that the UE can perform or not perform measurement prediction on the first set of measurement resources based on radio measurements on a selected third set of measurementresources.

11. The method of claim 10, wherein the one or more third sets of measurement resourcesselected by the UE are equal to the second set of measurement resources or a subset of the second set of measurement resources.

12. The method of claim 10 or 11, wherein the second and first set of measurement resourcesconsists of a pool of multiple sets of measurement resources, and the first indication comprises information for each of first and second set of measurements resources in the pool.

13. The method of claim 10 or 11, wherein the second set of measurement resources consistsof a pool of multiple sets of measurement resources.

14. The method of claim 13, wherein the selected one or more third sets of measurementresources is equal to one of the said sets.

15. The method of claim 14, wherein each of the multiple sets of measurement resources isassociated to an index, and the one or more selected third sets of measurement resources are indicated to the network node via one of the indices.

16. The method of claim 10 or 11, wherein the selected third set of measurement resourcesfulfills the one or more applicability conditions of the one or more AI / ML models or function available at the UE with respect to the first set of measurement resources.

17. The method of any of claims 1 to 16, wherein by being satisfied, the applicabilityconditions allow the UE to determine the measurement prediction on the first set of measurement resources with a certain accuracy.

18. The method of any of claims 1 to 17, determining (908) whether the one or moreapplicability conditions are satisfied is in response to any one or more of the following:^ receiving the first information that indicates the first set of measurement resources afterpreviously receiving the second information indicative of the second set of measurement resources;^ receiving the second information indicative of the second set of measurement resources afterpreviously receiving the first information indicative of the first set of measurement resources;^ receiving the first and second information indicative of the first and second sets ofmeasurement resources in a same message;^ receiving a successive indication of a new first set of measurement resources;^ receiving a successive indication of a new second set of measurement resources;^ receiving a successive indication that the UE should select another set of measurementresources as a third set of measurement resources;^ determining that the UE can no longer provide a measurement prediction on the first set ofmeasurement resources based on measurements on the second set of measurement resources,as a result of the one or more applicability conditions not being fulfilled for the one or more AI / ML models or functions available at the UE;^ determining that the UE can provide the measurement prediction on the first set ofmeasurement resources based on measurements on the second set of measurement resources, as a result of the one or more applicability conditions being fulfilled for the one or more AIML models or functions available at the UE;^ determining that the UE can provide the measurement prediction on the first set ofmeasurement resources based on measurements on a different third set of measurement resources previously selected, as a result of the one or more applicability conditions not being fulfilled for the one or more AI / ML models or function available at the UE with a previously selected third set of measurement resources.

19. The method of any of claims 1 to 18, wherein each of the first and second sets ofmeasurement resources comprise any one or more of the following:• a set of Synchronization Signal, SS, / Physical Broadcast Channel, PBCH,Blocks, SSBs, for a cell;• a set of Channel State Information Reference Signal, CSI-RS, resources for acell; •a set of SS / PBCH block or CSI-RS resource sets;• a set of cells;• a set of frequencies;• a set of SSBs for a list of cells that can comprise two or more cells;• a set of CSI-RS resources for a list of cells that can comprise two or morecells; •a set of SS / PBCH block resource sets for a list of cells that can comprise twoor more cells.

20. The method of claim 1, further comprising receiving (911) a second indication from thenetwork node, the second indication comprising any one or more of: ^a configuration to activate the one or more AI / ML models or functions available at theUE to determine the measurement prediction on the first set of measurement resources; ^a configuration to configure the second set of measurement resources in response totransmitting in the first indication with information that indicates that the one or more applicability conditions for the second set of measurements are fulfilled.^ a configuration to configure the second set of measurement resources in response totransmitting in the first indication information indicative of a third set of measurementresources; ^a configuration to deconfigure or deactivate the first set of measurement resources;^ a configuration to deconfigure or deactivate the second set of measurement resources orone or more sets of measurement resources within the second set of measurement resources; ^a configuration including a successive first set of measurement resources;^ a configuration including a successive second set of measurement resources;^ a configuration including indication that the UE should select another set of measurementresources as a third set of measurement resources.

21. The method of claim 20, wherein the second indication is received from the network nodein response to transmitting the first indication to the network node.

22. The method of claim 20 or 21, wherein in response to receiving a configuration thatdeconfigures the first or second set of measurement resources, the UE deactivates the one or more AI / ML models or functions available at the UE.

23. The method of claim 20 or 21, wherein a successive second set of measurement resourcesindicated in the second indication is equal to or a subset of the third set of measurement resources included by the UE in the first indication.

24. The method of any of claims 20 to 23, wherein the second indication is received viaRadio Resource Control, RRC, signaling or Medium Access Control, MAC, or Uplink Control Information, or Physical Downlink Control Channel, PDCCH.

25. The method of claim 1, wherein the UE starts performing the radio measurementpredictions on the resources included in the first set of measurement resources, in response to: ^evaluating that the one or more AI / ML models or functions available at the UE fulfill theone or more applicability conditions; ^selecting a third set of measurement resources in which case the radio measurementpredications are performed based on the radio measurements performed in the third set of measurement resources;^ receiving a configuration associated to the first, second or third set of measurementresources, in which case the radio measurement predictions are performed based on the radio measurements performed in the second set of radio measurements or third set of radio measurements.

26. The method of claim 25, wherein the second set of measurement resources, or theselected third set of measurement resources, is the input of one or more AI / ML models or functions available at the UE.

27. The method of claim 25, wherein an output of the one or more AI / ML models orfunctions available at the UE is the measurement predictions comprising prediction results for one or more measurement quantities.

28. The method of claim 27, wherein the measurement prediction results comprise any of:^ measured quantities for each of measurement resource in the first set of measurementresources and / or second set of measurement resources; ^measured quantities for one or more best measurement resources in terms of measuredquantities among the measurement resources in the first set of measurement resourcesand / or second set of measurement resources, wherein a number of best measurement resources can be a fixed or configured number; ^measured quantities for one or more worst measurement resources in terms of measuredquantities among the measurement resources in the first set of measurement resourcesand / or second set of measurement resources, wherein a number of worst measurement resources can be a fixed or configured number; ^average measured quantities for the measurement resources in the first set ofmeasurement resources and / or second set of measurement resources; ^variance of measured quantities for the measurement resources in the first set ofmeasurement resources and / or second set of measurement resources; ^accuracy of the measurement predictions results.

29. The method of any of claims 1 to 28, wherein the first and second information indicativeof the first and second sets of measurement resources are received via broadcast or system information signaling or via dedicated signaling.

30. The method of any of claims 1 to 29, wherein the first indication is transmitted via RadioResource Control, RRC, signaling or Medium Access Control, MAC, or Uplink ControlInformation, UCI.

31. The method claim 1, wherein in response to transmitting the first indication to thenetwork node, the UE receives information indicative of a successive second set of measurement resources based on which the UE should evaluate the one or more applicability conditions.

32. The method of any of claims 1 to 31, wherein the measurement predictions aretransmitted to the network node.

33. The method of any of claims 1 to 31, wherein the measurement predictions aretransmitted to another network node performing the UE-side model training.

34. The method of any of claims 1 to 33, wherein the step of determining whether the one ormore applicability conditions are satisfied is performed during an inference for the one or more AI / ML models or functions.

35. The method of any of claims 1 to 33, wherein the step of determining whether the one ormore applicability conditions are satisfied is performed during data collection for training of one or more AI / ML models or functions.

36. A User Equipment, UE, adapted to:receive (904), from a network node, first information that indicates a first set of measurement resources on which the UE is to perform measurement predictions; receive (906), from the network node, second information that indicates a second set of measurement resources with which the UE can be configured to perform measurements, based on which the measurement predictions can be generated; determine (908) whether one or more applicability conditions for using one or more Artificial Intelligence, AI, / Machine Learning, ML, models or functions at the UE to generate measurement predictions are satisfied for the first set of measurement resources and the second set of measurement resources; and send (910) a first indication to the network node, the first indication comprising information indicative of whether the one or more applicability conditions are satisfied.

37. The UE of claim 36, further adapted to perform the method of any of claims 2 to 35.

38. A User Equipment, UE, (902; 1100), comprising:a communication interface (1112) comprising a transmitter (1118) and a receiver (1120);and processing circuitry (1102) associated with the communication interface (1112), the processing circuitry (1102) configured to cause the UE (902; 1100) to: receive (904), from a network node, first information that indicates a first set of measurement resources on which the UE is to perform measurement predictions; receive (906), from the network node, second information that indicates a second set of measurement resources with which the UE can be configured to perform measurements, based on which the measurement predictions can be generated; determine (908) whether one or more applicability conditions for using one or more Artificial Intelligence, AI, / Machine Learning, ML, models or functions at the UE to generate measurement predictions are satisfied for the first set of measurement resources and the second set of measurement resources; and send (910) a first indication to the network node, the first indication comprising information indicative of whether the one or more applicability conditions are satisfied.

39. The UE (902; 1100) of claim 38, wherein the processing circuitry (1102) is furtherconfigured to cause the UE (902; 1100) to perform the method of any of claims 2 to 35.

40. A method performed by a network node, the method comprising:sending (904), to a UE, first information that indicates a first set of measurement resources on which the UE is to perform measurement predictions; sending (906), to the UE, second information that indicates a second set of measurement resources with which the UE can be configured to perform measurements, based on which the measurement predictions can be generated; and receiving (910) a first indication from the UE, the first indication comprising information indicative of whether one or more applicability conditions for using one or more ArtificialIntelligence, AI, / Machine Learning, ML, models or functions at the UE to generate ameasurement prediction are satisfied for the first set of measurement resources and the second setof measurement resources.

41. The method of claim 40, wherein the second set of measurement resources is a set ofmeasurement resources already configured to the UE for radio measurement reporting, or acandidate set of measurement resources that can be configured to the UE for radio measurement reporting.

42. The method of claim 40, wherein the configuration of the first and second sets ofmeasurement resources, via the first and second information, is based on any of: ^availability of the measurement resources in the first set of measurement resources;^ radio measurement predictions based on AI / ML models or functionalities at the networkside; ^load of the measurement resources in the first set of measurement resources;^ UE capabilities of the UE;^ location of the UE;^ trajectory of the UE;^ mobility conditions of the UE;^ load balancing policies.

43. The method of claim 40, wherein the first and second sets of measurement resources areconfigured, via the first and second information, independent of each other.

44. The method of claim 40, wherein a successive second set of measurement resources istransmitted to the UE in response to receiving the first indication from the UE that indicates, for the previously configured second set of measurement resources or a UE selected third set of measurement resources, the one or more applicability conditions of one or more AI / ML models or functions available at the UE are not fulfilled.

45. The method of claim 40, further comprising transmitting (906), to the UE, a configurationto activate the one or more AI / ML models or functions available at the UE in relation to the firstset of measurement resources.

46. The method of claim 40, further comprising transmitting (906), to the UE, a configurationto deactivate or deconfigure the one or more AI / ML models or functions available at the UE in relation to the first set of measurement resources.

47. The method of claim 40, further comprising transmitting (911), to the UE, a configurationto configure the second set of measurement resources in response of receiving the first indication from the UE that indicates that the one or more applicability conditions of one or more AI / MLmodels or functions available at the UE are fulfilled in relation to the second set of measurement resources.

48. The method of claim 40, further comprising transmitting (911), to the UE, a configurationto configure a third set of measurement resources in response to receiving the first indication that indicates that the one or more applicability conditions of one or more AI / ML models or functions available at the UE are fulfilled for the third set of measurement resources.

49. A network node adapted to:send (904), to a UE, first information that indicates a first set of measurement resources on which the UE is to perform measurement predictions; send (906), to the UE, second information that indicates a second set of measurement resources with which the UE can be configured to perform measurements, based on which the measurement predictions can be generated; and receive (910) a first indication from the UE, the first indication comprising information indicative of whether one or more applicability conditions for using one or more Artificial Intelligence, AI, / Machine Learning, ML, models or functions at the UE to generate a measurement prediction are satisfied for the first set of measurement resources and the second set of measurement resources.

50. The network node of claim 49, further adapted to perform the method of any of claims 41to 48.

51. A network node (900; 1200) comprising processing circuitry (1202) configured to causethe network node (900; 1200) to: send (904), to a UE, first information that indicates a first set of measurement resources on which the UE is to perform measurement predictions; send (906), to the UE, second information that indicates a second set of measurement resources with which the UE can be configured to perform measurements, based on which the measurement predictions can be generated; and receive (910) a first indication from the UE, the first indication comprising information indicative of whether one or more applicability conditions for using one or more Artificial Intelligence, AI, / Machine Learning, ML, models or functions at the UE to generate a measurement prediction are satisfied for the first set of measurement resources and the second set of measurement resources.

52. The network node (900; 1200) of claim 51, wherein the processing circuitry (1202) isfurther configured to cause the network node (900; 1200) to perform the method of any of claims41 to 48.

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