Method for UE-assisted set a-b selection for beam prediction

Abstract

Systems and methods are disclosed for User Equipment (UE) assisted measurement resource selection for Artificial Intelligence (AI) / Machine Learning (ML) models or functions available at the UE In one embodiment, a method performed by a UE comprises determining a set(s) of recommended measurement resources from a first set(s) of measurement resources configured for the UE, the recommended set(s) comprising either or both of: a second set(s) of recommended measurement resources in which the UE is to perform radio measurements and a third set(s) of recommended measurement resources in which the UE is to perform radio measurement predictions using one or more AI / ML models or functions available at the UE. The method further comprises transmitting, to a network node, a first indication comprising information indicative of the set(s) of recommended measurement resources and, in response, receiving, from the network node, a second indication.

Description

[0001] METHOD FOR UE-ASSISTED SET A-B SELECTION FOR BEAM PREDICTION Related Applications This application claims the benefit of provisional patent application serial number 63 / 555,236, 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, morespecifically, to measurement resource selection when using User Equipment (UE) sideArtificial Intelligence (AI) / Machine Learning (ML) models or functionality for measurementprediction. Background One of the key features of 3rdGeneration Partnership Project (3GPP) New Radio (NR), compared to the previous generation of wireless networks, is the ability to operate in higher frequencies (e.g., above 10 Gigahertz (GHz)). The available large transmission bandwidths in these frequency ranges can potentially provide large data rates. However, as carrier frequency increases, both pathloss and penetration loss increase. To maintain the coverage at the same level, highly directional beams are required to focus the radio transmitter energy in a particulardirection on the receiver. However, large radio antenna arrays – at both receiver and transmittersides – are needed to create such highly direction beams.To reduce hardware costs, large antenna arrays for high frequencies use time-domain analog beamforming. The core idea of analog beamforming is to share a single radio frequencychain between many (or, potentially, all) of the antenna elements. A limitation of analogbeamforming is that it is only possible to transmit radio energy in using one beam (in one direction) at a given time. The above limitation requires the network (NW) and user equipment (UE) to perform beam management procedures to establish and maintain suitable transmitter (Tx) / receiver (Rx) beam-pairs. For example, beam management procedures can be used by a transmitter to sweep a geographic area by transmitting reference signals on different candidate beams, during non- overlapping time intervals, using a predetermined pattern. And by measuring the quality of thisreference signals at the receiver side, the best transmit and receive beams can be identified.1 NR Beam Management ProceduresBeam 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 receivingdata. A beam management procedure can include the following sub procedures: beamdetermination, 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 directly mentioned in specifications, there are relevant procedures defined which enables the realization of 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) Tx beams to support selection of TRP Tx beams / UE 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 1. 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. oFigure 1 illustrates SSB beam selection as part of Initial access procedureaccording to P1 scenario ^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, the NR base station (i.e., the gNodeB or 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 send feedback about the quality of the measured beams. Thereafter, based on this feedback, gNB will decide and possibly indicates to the UE which beam will be used in future transmissions. This is shown in Figure 2. Figure 2 illustrates CSI- RS Tx beam selection in Downlink according to P2 scenario^ 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 3 illustrates UE Rx beam selection for corresponding CSI-RS Tx beam inDL according to P3 scenario2 Beam Measurement and Reporting in NRFor 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. 2.1 Reference Signal Configurations in NRCSI-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) Downlink Control Information (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 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 depends on the SCS of the SSBs. The L candidate SSBs within a half frame are indexed 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. 2.2 Measurement Resource Configurations in NRA 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 NZP-CSI-RS-ResourceSets and / or CSI-SSB-ResourceSets. A UE can be configured to measure CSI-RSs using the RRC 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. 2.3 Measurement ReportingThree types of CSI reporting are supported in NR as follows: ^Periodic CSI Reporting on PUCCH: CSI is reported periodically by a UE. Parameterssuch as periodicity and slot offset are configured semi-statically by higher layer RRC signaling from the network node to the UE ^Semi-Persistent CSI Reporting on PUSCH or PUCCH: similar to periodic CSI reporting,semi-persistent CSI reporting has a periodicity and slot offset which may be semi- statically configured. However, a dynamic trigger from network node to UE may be needed to allow the UE to begin semi-persistent CSI reporting. A dynamic trigger from network node to UE is needed to request the UE to stop the semi-persistent CSI reporting. ^Aperiodic CSI Reporting on PUSCH: This type of CSI reporting involves a single-shot(i.e., one time) CSI report by a UE which is dynamically triggered by the network node using DCI. Some of the parameters related to the configuration of the aperiodic CSI report is semi-statically configured by RRC but the triggering is dynamic In each CSI reporting setting, the content and time-domain behavior of the report is defined, along with the linkage to the associated Resource Settings. The CSI-ReportConfig IE comprise the following configurations: ^reportConfigTypeo Defines the time-domain behavior (periodic CSI reporting, semi-persistentCSI reporting, or aperiodic CSI reporting) along with the periodicity and slot offset of the report for periodic CSI reporting. ^reportQuantityo Defines the reported CSI parameters -- the CSI content; for example, thePMI, CQI, RI, LI (layer indicator), CRI (CSI-RS resource index) and L1- RSRP. Only certain combinations are possible ; for example, ‘cri-RI-PMI- CQI’ is one possible value and ‘cri-RSRP’ is another) and each value of reportQuantity could be said to correspond to a certain CSI mode. ^codebookConfigo Defines the codebook used for PMI reporting, along with possiblecodebook subset restriction (CBSR). NR supported the following two types of PMI codebooks: Type I CSI and Type II CSI. Additionally, the Type I and Type II codebooks each have two different variants: regular and port selection. ^reportFrequencyConfigurationo Define the frequency granularity of PMI and CQI (wideband or subband), ifreported, along with the CSI reporting band, which is a subset of subbands of the bandwidth part (BWP) which the CSI corresponds to ^Measurement restriction in time domain (ON / OFF) for channel and interferencerespectively For beam management, a UE can be configured to report L1-RSRP for up to four different CSI-RS / SSB resource indicators. The reported RSRP value corresponding to the first (best) CRI / SSBRI requires 7 bits, using absolute values, while the others require 4 bits using encoding relative to the first. In NR release 16, the report of L1-SINR for beam management has already been supported. 3Agreements in 3GPPDuring the 3GPP meeting RAN1#109-e it was agreed to study Artificial Intelligence (AI) / Machine Learning (ML) based spatial beam prediction, the core idea of which is as follows:Predict the “best” beam (or beams) from a Set A of beams using measurement results fromanother Set B of beams. Set A and Set B of beams have not been defined yet (left for future study); 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 4 (both light and dark circles). The UE measures Set B (the 4 beams indicated by dark circles). The AI / ML model should predict the best beam (or beams) in Set A using only measurements from Set B. In other words, Figure 4 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. In other words, Figure 5 illustrates an example where Set A is a set of narrow beams and Set B isa set of wide beams. The spatial beam prediction can be performed in the gNB or the UE – the study item willcover both scenarios. During the 3GPP meeting RAN1#110, it was agreed to study AI / ML model training both at the NW and UE side. Which side that performs the training is expected to impact how data collection is performed, where another agreement is to study the aspect of data collection for beam management. Moreover, it was agreed to study the aspect of model monitoring and the standard impact on AI / ML model inference (e.g. reporting of predicted values). 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 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). 43GPP Study Item Technical ReportThe following text is an excerpt from 3GPP TR 38.843 regarding performancemonitoring. ***** START EXCERPT FROM 3GPP TR 38.843 ***** Performance monitoring: For the performance monitoring of BM-Case1 and BM-Case2: -Performance metric(s) with the following alternatives:- Alt.1: Beam prediction accuracy related KPIs, e.g., Top-K / 1 beam prediction accuracy- Alt.2: Link quality related KPIs, e.g., throughput, L1-RSRP, L1-SINR, hypothetical BLER- Alt.3: Performance metric based on input / output data distribution of AI / ML- Alt.4: The L1-RSRP difference evaluated by comparing measured RSRP and predicted RSRP- Benchmark / reference for the performance comparison, including:- Alt.1: The best beam(s) obtained by measuring beams of a set indicated by gNB (e.g., Beamsfrom Set A) -Alt.4: Measurements of the predicted best beam(s) corresponding to model output (e.g.,Comparison between actual L1-RSRP and predicted RSRP of predicted Top-1 / K Beams) -Signalling / configuration / measurement / report for model monitoring, e.g., signalling aspects relatedto assistance information (if supported), Reference signals For BM-Case1 and BM-Case2 with a UE-side AI / ML model: -Type1 performance monitoring:- Configuration / Signalling from gNB to UE for measurement and / or reporting- UE may have different operations- Option1: UE sends reporting to NW (e.g., for the calculation of performance metric at NW)- Option2: UE calculates performance metric(s), either reports it to NW or reports an event toNW based on the performance metric(s) -Indication from NW for UE to do LCM operations- Note: At least the performance and reporting overhead of model monitoring mechanism shouldbe considered -Type2 performance monitoring (UE-side performance monitoring):- Indication / request / report from UE to gNB for performance monitoring- Note: The indication / request / report may be not needed in some case(s)- Configuration / Signalling from gNB to UE for performance monitoring measurement and / orreporting -UE calculates performance metric(s), either reports it to NW or reports an event to NW based onthe performance metric(s) -If it is for UE-side model monitoring, UE makes decision(s) of model selection / activation / deactivation / switching / fallback operation - -Indication from NW to UE to do LCM operation- UE reporting of beam measurement(s) based on a set of beams indicated by gNB- Signalling, e.g., RRC-based, L1-based- Note: Performance and UE complexity, power consumption should be considered- Mechanism that facilitates the UE to detect whether the functionality / model is suitable orno longer suitable Table 7.2.3-1 summarizes applicability of various alternatives for performance metric(s) of AI / ML model monitoring for BM-Case1 and BM-Case2. Table 7.2.3-1: Alternatives for Performance metric(s) of AI / ML model monitoring for BM-Case 1 and BM-Case 2 Alt.1: Beam prediction Alt.2: Link quality Alt.3: Performance Alt.4: The L1-RSRP accuracy related KPIs, related KPIs, .e.g., metric based on difference evaluated by e.g., Top-K / 1 beam throughput, L1-RSRP, input / output data comparing measured RSRP prediction accuracy L1-SINR, hypothetical distribution of AI / ML and predicted RSRP BLER Applicable to all Applicable to all Applicable to all May not applicable to studied AI models studied AI models studied AI models some implementation of AI model (e.g., not output of predicted L1-RSRP) Reflect the prediction Reflect the system / link Reflect the change of Reflect accuracy of the accuracy of AI model performance the statics of the predicted 1-RSRP input / output data Not reflect the Not reflect the Not reflect the Not reflect the system / link system / link prediction accuracy of prediction performance directly performance directly AI model directly performance of AI model directly Not reflect the system / link performance directly Note1: The above analysis shall not give an indication about whether / which metric is supported or specified. Note2: Monitoring performance of the above alternatives are not addressed in the table. ***** END EXCERPT FROM 3GPP TR 38.843 *****5 NW-sided vs UE-sided modelAs mentioned above, the AI / ML model for beam prediction can be NW-sided or UE- sided (i.e. executed in the gNB or in the UE). 1. If the model is NW-sided, the UE makes RSRP (i.e., layer 1 RSRP or L1-RSRP) and / orSINR (i.e., layer 1 SINR or L1-SINR) measurements and reports the measurement results to the NW for input into the AI / ML model. 2. If the model is UE-sided, the UE both makes the measurements and the AI / ML-model-based prediction, and hence no reporting of the measurements is needed except for the final predicted beam(s). 6Data collectionA key part of AI / ML-based prediction is data collection. Data collection is performed in several stages of the life-cycle management (LCM). ^First, the model must be trained by collecting measurement data for a large set of UElocations / channel conditions representative for the UE locations / channel conditions that may be encountered during use of the model (i.e. inference). For each UE, preferably all possible narrow Tx beam directions should be swept, i.e. a fairly large set of beams. ^Second, when using the model for prediction (i.e. inference), measurement data for anyUE to predict beams for must be collected and fed to the AI / ML model. The set of beams to sweep for a UE is here much smaller than during training, since not all narrow beams are swept, only a few wide (or possibly narrow) beams are swept. ^Finally, measurements are needed to monitor that the model functions well, or otherwisedisable it or update it. For a NW-sided model, all three types of data collection (training, inference, monitoring) follow the same general procedure: ^The NW transmits some signal (e.g. CSI-RS or SSB) using a set of several different Txbeams on the DL ^The UE measures the RSRP (or some other quantity, for example, L1-SINR) of thedifferent transmissions oThe UE here typically does Rx beamforming; this beamforming is, however, animplementation detail that it is up to the UE to decide on. ^The UE reports the measured RSRP (or other quantity, for example, L1-SINR ) values tothe NW. Summary Systems and methods are disclosed for User Equipment (UE) assisted measurementresource selection for Artificial Intelligence (AI) / Machine Learning (ML) models or functionsavailable at the UE. In one embodiment, a method performed by a UE comprises determiningone or more sets of recommended measurement resources from one or more first sets of measurement resources configured for the UE, the one or more recommended sets comprisingeither or both of: one or more second sets of recommended measurement resources in which theUE is to perform radio measurements and one or more third sets of recommended measurementresources in which the UE is to perform radio measurement predictions using one or moreAI / ML models or functions available at the UE, upon performing radio measurements in one ormore second sets of measurement resources. The method further comprises transmitting, to anetwork node, a first indication comprising information indicative of the one or more sets ofrecommended measurement resources and, in response to transmitting the first indication,receiving, from the network node, a second indication comprising either or both of: informationindicative of a second set of measurement resources on which the UE is to perform the radio measurements in order to determine radio measurement predictions in a third set of measurementresources and information indicative of the third set of measurement resources on which the UEis to perform the radio measurements predictions based on the second set of measurement resources. The method further comprises, upon receiving the second indication, starting to perform radio measurement predictions on the resources included in the third set of measurement resources, based on radio measurements performed on the second set of measurement resources using the AI / ML models or functions available at the UE. In this manner, measurement resource selection is performed in a way that is both efficient to the UE and desirable to the network. In one embodiment, determining the one or more sets of recommended measurementresources comprises evaluating one or more applicability conditions of one or more AI / MLmodels or functions available at the UE based on the one or more first sets of measurementresources configured for the UE and determining the one or more sets of recommendedmeasurement resources in response to the one or more applicability conditions of the one or more AI / ML models or functions being satisfied as determined by the evaluating. In one embodiment,evaluating the one or more applicability conditions comprises evaluating that the radiomeasurement prediction results can be determined by the AI / ML model / functionality with a certain accuracy. In one embodiment, the method further comprises receiving information, from a network node, that configures the UE with the one or more first sets of measurement resources. In one embodiment, receiving the information that configures the UE with the one or more first sets of measurement resources comprises receiving the information that configures the UE with the one or more first sets of measurement resources as part of a configuration for data collection for UE- side model training. In another embodiment, receiving the information that configures the UE with the one or more first sets of measurement resources comprises receiving the information that configures the UE with the one or more first sets of measurement resources as part of a configuration for performing radio measurements and associated radio measurement reporting. In another embodiment, receiving the information that configures the UE with the one or more first sets of measurement resources comprises receiving the information that configures the UE with the one or more first sets of measurement resources as part of a configuration indicating the one or more first sets of measurement resources as one or more sets of candidate measurement resources that can be currently configured by the network node to the UE to perform the radio measurements to determine radio measurement predictions. In one embodiment, the second indication is an indication to activate an AI / ML model(s) or function(s) at the UE according to the one or more second sets of recommended measurement resources and / or the one or more third sets of recommended measurement resources. In one embodiment, the information comprised in the second indication indicates that the one or more second sets of measurement resources is equal to the one or more second sets of recommended measurement resources and / or the one or more third sets of measurements resources is equal to the one or more third sets of recommended measurement resources. In one embodiment, the one or more second sets of recommended measurement resourcesare equal to the one or more first sets of measurement resources, a subset of the one or more firstsets of measurement resources, or a subset of at least one of the one or more first sets ofmeasurement resources. In one embodiment, the one or more third sets of recommended measurement resourcesare a subset of the one or more first sets of measurement resources or a subset of at least one ofthe one or more first sets of measurement resources. In one embodiment, determining the one or more sets of recommended measurementresources is done upon receiving a request from the network node to indicate any of the secondand third sets of recommended measurement resources. In one embodiment, the request is toindicate applicability of the one or more AI / ML models or functions available at the UE.In one embodiment, each of the one or more first sets of measurement resources isassociated to an identification ID.In one embodiment, each of the one or more first sets of measurement resources isassociated to a set ID. In one embodiment, each of the one or more resources comprised in the one or more firstsets of measurement resources is associated to a resource ID.In one embodiment, the set ID for one of the one or more first sets of measurement resources is unique within a cell. In one embodiment, the resource ID for one resource within one of the one or more first sets of measurement resources is unique within the cell. In one embodiment, for each of the one or more sets of recommended measurement resources, the first indication comprises an associated set ID for that set of recommended measurement resources. In one embodiment, for each of the one or more sets of recommended measurement resources, the first indication comprises one or more resource IDs associated to resources within that set of recommended measurement resources. In one embodiment, the first indication contains only information indicative of the one or more third sets of recommended measurement resources or the one or more second sets of recommended measurement resources indicated by the first indication is empty, and this implies that the UE can perform radio measurement predictions on the indicated one or more third sets of recommended measurement resources by performing radio measurements on resources indicated in the one or more first sets of measurement resources excluding resources indicated in the one or more third sets of recommended measurement resources. In one embodiment, the first indication contains only information indicative of the one or more second sets of recommended measurement resources or the one or more third sets of recommended measurement resources indicated in the first indication is empty, and this implies that the UE can perform radio measurement predictions on resources indicated in the one or more first sets of measurement resources excluding resources indicated in the one or more second sets of recommended measurement resources, by performing radio measurement on the indicated one or more second sets of recommended measurement resources. In one embodiment, the second set of measurement resources is equal to or a subset of theone or more second sets of recommended measurement resources or equal to or a subset of atleast one of the one or more second sets of recommended measurement resources. In one embodiment, the third set of measurement resources is equal or a subset of the oneor more third sets of recommended measurement resources or equal to or a subset of at least oneof the one or more second sets of recommended measurement resources. In one embodiment, the output of the AI / ML model or function available at the UE is the radio measurement predictions on the third set of measurement resources. In one embodiment, the second indication is received via Radio Resource Control (RRC)dedicated signaling, Medium Access Control (MAC) Control Element (CE), or PhysicalDownlink Control Channel (PDCCH).In one embodiment, transmitting the first indication comprises transmitting (602) the firstindication to the network node in response to any of: being configured by the network node toperform AI / ML-based radio measurement prediction, upon changing RRC state, upon entering atarget cell after a mobility procedure, upon determining that the applicability conditions of one ormore AI / ML models or functions available at the UE are fulfilled or not for one or moreresources included in the first set of measurement resources, upon being configured with a firstset of measurement resources, upon receiving a reconfiguration of the first set of measurementresources, upon AI / ML model / function activation request being received from the network node,upon applicability reporting of an AI / ML models / functions being requested by the network node. In one embodiment, the applicability conditions of one or more AIML models or functions available at the UE are checked by the UE prior to transmitting the first indication. In one embodiment, the first indication is transmitted via RRC signaling, MAC, or UplinkControl Information (UCI).In one embodiment, if the second indication contains only the third set of measurement resources or if the second set of measurement resources included in the second indication is empty, this implies that the UE can perform the radio measurement predictions on the indicated third set of resources by performing radio measurement on the resources indicated previously in the first set of measurement resources excluding the resources indicated in the said third set of resources. In one embodiment, if the first indication contains only the second set of measurement resources or if the third set of recommended measurement resources included in the first indication is empty, this implies that the UE can perform the radio measurement predictions on the resources indicated in the first set of measurement resources excluding the resources indicated in the said second set of resources, by performing radio measurement on the indicated second set of resources. Corresponding embodiments of a UE are also disclosed. In one embodiment, a UE comprises 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 determine one or more sets of recommended measurement resources from one or more first sets of measurement resources configured for the UE, the one or more recommendedsets comprising either or both of: one or more second sets of recommended measurementresources in which the UE is to perform radio measurements and one or more third sets ofrecommended measurement resources in which the UE is to perform radio measurementpredictions using one or more AI / ML models or functions available at the UE, upon performingradio measurements in one or more second sets of measurement resources. The processingcircuitry is further configured to cause the UE to transmit, to a network node, a first indicationcomprising information indicative of the one or more sets of recommended measurementresources and, in response to transmitting the first indication, receive, from the network node, asecond indication comprising either or both of: information indicative of a second set of measurement resources on which the UE is to perform the radio measurements in order todetermine radio measurement predictions in a third set of measurement resources andinformation indicative of the third set of measurement resources on which the UE is to perform the radio measurements predictions based on the second set of measurement resources. The processing circuitry is further configured to cause the UE to, upon receiving the secondindication, start to perform radio measurement predictions on the resources included in the thirdset of measurement resources, based on radio measurements performed on the second set of measurement resources using the AI / ML models or functions available at the UE. Embodiments of a method performed by a network node are also disclosed. In onembodiment, a method performed by a network node comprises receiving, from a UE, a firstindication comprising information indicative of one or more sets of recommended measurementresources, the one or more recommended sets comprising either or both of: one or more secondsets of recommended measurement resources in which the UE is to perform radio measurements; and one or more third sets of recommended measurement resources in which the UE is toperform radio measurement predictions using one or more AI / ML models or functions availableat the UE, upon performing radio measurements in one or more second sets of measurementresources. The method further comprises, in response to receiving the first indication,transmitting (604), to the UE, a second indication comprising either or both of: informationindicative of a second set of measurement resources on which the UE is to perform the radio measurements in order to determine radio measurement predictions in a third set of measurementresources; and information indicative of the third set of measurement resources on which the UEis to perform the radio measurements predictions based on the second set of measurement resources. Corresponding embodiments of a network node are also disclosed. In one embodiment, a network node comprises processing circuitry configured to cause the network node to receive,from a UE, a first indication comprising information indicative of one or more sets ofrecommended measurement resources, the one or more recommended sets comprising either orboth of: one or more second sets of recommended measurement resources in which the UE is toperform radio measurements; and one or more third sets of recommended measurement resourcesin which the UE is to perform radio measurement predictions using one or more AI / ML modelsor functions available at the UE, upon performing radio measurements in one or more second sets of measurement resources. The processing circuitry is further configured to cause the networknode to, in response to receiving the first indication, transmitting, to the UE, a second indicationcomprising either or both of: information indicative of a second set of measurement resources onwhich the UE is to perform the radio measurements in order to determine radio measurementpredictions in a third set of measurement resources; and information indicative of the third set ofmeasurement resources on which the UE is to perform the radio measurements predictions based on 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 illustrates Synchronization Signal (SS) / Physical Broadcast Channel (PBCH)Block (SSB) beam selection as part of an initial access procedure according to a first procedureused 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 2 illustrates Channel State Information (CSI) Reference Signal (CSI-RS) transmitbeam 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 3 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 4 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 resources for which the UE predicts measurements based on the measurements obtained for Set A); Figure 5 illustrates an example where Set A and Set B correspond to two different sets of beams and, in the particular example of Figure 5, Set A is a set of narrow beams and Set B is a set of wide beams; Figure 6A illustrates the operation of a network node (e.g., a gNB) and a UE for UE-assisted Set A and / or Set B selection and subsequent prediction and prediction reporting for the selected Set A, in accordance with an embodiment of the present disclosure; Figure 6B is a flow chart that illustrates one example embodiment of step 602 of Figure 6A; Figure 7 shows an example of a communication system in accordance with some embodiments; Figure 8 shows a UE in accordance with some embodiments; Figure 9 shows a network node in accordance with some embodiments; Figure 10 is a block diagram of a host, which may be an embodiment of the host of Figure 7, in accordance with various aspects described herein; Figure 11 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized; and Figure 12 shows a communication diagram of a host communicating via a network node with a UE over a partially wireless connection in accordance with some embodiments. Detailed Description 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. Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art. There currently exist certain challenge(s) related to Artificial Intelligence (AI) / Machine Learning (ML) based spatial beam prediction. For a User Equipment (UE) sided AI / ML model, if set A / B selection is completely up to the UE, several challenges can be observed. See Section 3 of the Background above for a description of set A and set B. First, set B requires certain network configuration (i.e. Reference Signal (RS) transmissions) for the UE to be able to perform those measurements for AI / ML model inference purposes. Each UE requests different network configuration, which would require an excessive amount of additional reference signal transmissions to accommodate for different UE needs. From the network (NW) perspective, this diminishes the benefits of running an AI / ML model at the UE side. Second, the NW has a better understanding of which beams are not likely to be picked within the deployment and predictions for those beams can be sufficient, while actual measurements are preferred and wanted for certain beams. Further, if set A / B selection is completely up to the UE, there is a risk that the NW deactivates the AI / ML model / functionality, and the UE misses on reduced measurements opportunities. Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. Embodiments of the present disclosure relate to enabling a UE with a flexibility on selecting which candidate beams in set B / A it uses via a new UE capability indication response. Some example embodiments related to a method performed by a UE are as follows: ^A1. A method performed by a UE comprises evaluating one or more applicabilityconditions of one or more AI / ML models or functions available at the UE based on one or more first sets of measurement resources that are configured by the network to the UE. The method further comprises, based on a result(s) of the evaluating, selecting any of thefollowing sets of radio measurement resources from the first set of measurement resources: oOne or more second sets of recommended measurement resources in which toperform radio measurements, which may be referred to herein as a set B of measurement resources, and / or oOne or more third sets of recommended measurement resources in which toperform radio measurement predictions (also referred to herein as a set A of measurement resources), upon performing radio measurements in the second set of measurement resources (set B). ^A2. The method according to A1, wherein the configuration of the one or more firstsets of measurement resources is received by the UE as part of a configuration for data collection for UE-side model training. ^A3. The method according to A1, wherein the configuration of the one or more firstsets of measurement resources is received by the UE as part of a configuration for performing radio measurements and associated radio measurement reporting.^ A4. The method according to A1, wherein the configuration of the one or more firstsets of measurement resources is received by the UE as part of a configuration indicating the set of candidate measurement resources that can be currently configured by the NW tothe UE to perform the radio measurements to determine the radio measurement predictions.^ A5. A method according to any of A1 to A4, wherein the one or more second sets ofrecommended measurement resources is equal to the one or more first sets of measurement resources or a subset of the one or more first set of resources or a subset of at least one of the one or more first sets of measurement resources.^ A6. A method according to any of A1 to A5, wherein the one or more third sets ofrecommended measurement resources is equal to the one or more first sets of measurement resources or a subset of the one or more first set of resources or a subset of at least one of the one or more first sets of measurement resources.^ A7. The method according to any of A1 to A6, wherein the one or more AI / MLmodels or functions available at the UE support radio measurement predictions.^ A8. The method according to any of A1 to A7, wherein the selecting is done uponreceiving a request from the NW to indicate any of the second and third set of measurement resources.^ A9. The method according to A8, wherein the request is to indicate the applicability ofthe one or more AI / ML models or functions available at the UE.^ A10. The method according to any of A1 to A9, wherein each of the one or more firstsets of measurement resources is associated to an identification ID.^ A11. The method according to A10, wherein each of the one or more first sets ofmeasurement resources is associated to a set ID^ A12. The method according to A10 or A11, wherein each of the one or more resourcescomprised in the one or more first sets of measurement resources is associated to a resource ID^ A13. The method according to A11, wherein the set ID for one first set of measurementresources is unique within the cell.^ A14. The method according to A12, wherein the resource ID for one resource withinthe first set of measurement resources is unique within the cell.^ A15. The method according to any of A1 to A14, further comprising transmitting, to thegNB, a first indication comprising information indicative of any of the selected one or more second sets of recommended measurement resources and third set of recommended measurement resources.^ A16. The method according to A15, wherein for each of the selected second sets ofrecommended measurement resources and third set of recommended measurement resources indicated by the information comprised in the first indication transmitted to the gNB, the UE includes the associated set ID(s) for the second set of measurement resources and the associated set ID(s) for the third set of measurement resources.^ A17. The method according to A15, wherein for each of the selected second sets ofrecommended measurement resources and third set of recommended measurement resources indicated by the information comprised in the first indication transmitted to the gNB, the UE includes the one or more resource IDs associated to the resources within the second set of measurement resources, and the one or more resource IDs associated to the resources within the third set of measurement resources.^ A18. The method according to A15, wherein if the first indication contains only thethird set of recommended measurement resources or if the second set of recommended measurement resources included in the first indication is empty, this implies that the UE can perform the radio measurement predictions on the indicated third set of recommended resources by performing radio measurement on the resources indicated in the first set of measurement resources excluding the resources indicated in the said third set of recommended resources.^ A19. The method according to A15, wherein if the first indication contains only thesecond set of recommended measurement resources or if the third set of recommended measurement resources included in the first indication is empty, this implies that the UE can perform the radio measurement predictions on the resources indicated in the first set of measurement resources excluding the resources indicated in the said second set of recommended resources, by performing radio measurement on the indicated second set of recommended resources.^ A20. The method according to A15, wherein in response to transmitting the firstindication, the UE receives (e.g., from the gNB) a second indication comprising any of: oinformation indicative of a second set of measurement resources for the UE toperform the radio measurements in order to determine the radio measurement predictions in the third set of measurement resources.o information indicative of a third set of measurement resources for the UE toperform the radio measurements predictions based on the received second set of measurement resources^ A21. The method according to A20, wherein the second set of measurement resourcesis equal to or a subset of the one or more second sets of recommended measurement resources or equal to or a subset of at least one of the one or more second sets of recommended measurement resources.^ A22. The method according to A20, wherein the third set of measurement resources isequal or a subset of the one or more third sets of recommended measurement resources or equal to or a subset of at least one of the one or more second sets of recommended measurement resources.^ A23. The method according to A20, wherein the third set of measurement resources isempty indicating that the UE should not perform radio measurement predictions.^ A24. The method according to A20, further comprising, upon receiving the secondindication, starting to perform radio measurement predictions on the resources included in the third set of measurement resources, based on the second set of measurement resources whose associated reference signals are transmitted by the gNB.^ A25. The method according to A15, wherein the first indication is transmitted to thegNB in response to any of: oBeing configured by the gNB to perform AI / ML-based radio measurementprediction oUpon changing Radio Resource Control (RRC) stateo Upon entering a target cell after a mobility procedureo Upon determining that the applicability conditions of one or more AI / ML modelsor functions available at the UE are fulfilled or not for one or more resources included in the first set of measurement resources oUpon being configured with a first set of measurement resourceso Upon receiving a reconfiguration of the first set of measurement resourceso Upon AI / ML models / functionalities activation is request received from the gNBo Upon applicability reporting of an AI / ML models / functionalities is request by thegNB^ A26. The method according to A15, further comprising, in response to transmitting thefirst indication, receiving (e.g., from the gNB) a third indication indicating that AI / ML models / functionalities should be deactivated, and UE should not perform radio measurement predictions.^ A27. The method according to A25, wherein the applicability conditions of one or moreAIML models / functionalities available at the UE are checked by the UE prior to transmitting the first indication.^ A28. The method according to A1, wherein the first set of measurement resources maycomprise any of: oA set of SSB for a cello A set of CSI-RS resources for a cello A set of SS / PBCH block or CSI-RS resource set for a cello A set of cellso A set of frequencieso A set of SSB for a list of cellso A set of CSI-RS resources for a list of cellso A set of SS / PBCH block resource set for a list of cells^ A29. A method according to A20, wherein the output of the AI / ML model / functionalityavailable at the UE is the radio measurement predictions on the third set of measurement resources comprising the prediction results for one or more 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.^ A30. A method according to A29, wherein the radio measurement prediction resultscomprise any of: oThe measured quantities for each of the one or more resources in the third set ofmeasurement resources oThe measured quantities for the best resources in terms of measured quantitiesamong the resources in the second and / or third set of measurement resources, wherein the number of best resources can be a fixed or configured number oThe measured quantities for the worst resources in terms of measured quantitiesamong the resources in the second and / or third set of measurement resources, wherein the number of worst resources can be a fixed or configured number oThe average measured quantities for the resources in the second and / or third set ofmeasurement resources.o The variance of the measured quantities for the resources in the second and / orthird set of measurement resources. oThe accuracy of the reported predictions results^ A31 A method according to A30, further comprising reporting the radio measurementprediction results to the gNB. ^A32. A method according to A15, wherein the first indication is transmitted via RRCsignaling (UEAssistanceInformation), or MAC (MAC CE), or UCI. ^A33. A method according to A20, wherein the second indication is received via RRCdedicated signalling or MAC (MAC CE), PDCCH. ^A34. A method according to A1, wherein evaluating the one or more applicabilityconditions comprises evaluating that the radio measurement prediction results can be determined by the AIML model / functionality with a certain accuracy. ^A35. The method according to A20, wherein if the second indication contains only thethird set of measurement resources or if the second set of measurement resources included in the second indication is empty, this implies that the UE can perform the radio measurement predictions on the indicated third set of resources by performing radio measurement on the resources indicated previously in the first set of measurement resources excluding the resources indicated in the said third set of resources. ^A36. The method according to A15, wherein if the first indication contains only thesecond set of measurement resources or if the third set of recommended measurement resources included in the first indication is empty, this implies that the UE can perform the radio measurement predictions on the resources indicated in the first set of measurement resources excluding the resources indicated in the said second set of resources, by performing radio measurement on the indicated second set of resources. Some example embodiments related to a method performed by a network node, which in these examples is a gNB, are as follows: ^B1. A method performed by a network node (a gNB in this example) todetermine one or more first sets of measurement resources. ^B2. The method according to B1, wherein the one or more first sets ofmeasurement resources are configured to the UE as part of a configuration for performing radio measurements and associated reporting ^B3. The method according to B1, wherein the one or more first sets ofmeasurement resources are configured to the UE as part of a configuration indicating a set of candidate measurement resources that can be currently configured by the network to the UE to perform the radio measurements to determine radio measurement predictions. ^B4. The method according to B1, wherein in response to configuration the firstof measurement resources, receiving a first indication from the UE ^B5. The method according to B4, further comprising, based on a second set ofrecommended resources indicated by information comprised in the first indication, determining a set of resources associated to which the gNB is to transmit reference signals in order for the UE to perform radio measurement predictions in a third set of recommended resources. ^B6. The method according to B5, wherein a second set of measurementresources is configured to the UE as part of a configuration indicating a set of measurement resources on which the UE is to perform the radio measurements to determine radio measurement predictions. ^B7. The method according to B4, further comprising, based on a third set ofrecommended resources indicated by information comprised in the first indication, determining a set of resources associated to which the gNB does not need to transmit reference signals in order for the UE to perform radio measurement predictions in the third set of recommended resources. ^B8. The method according to B7, wherein the third set of measurementresources is configured to the UE as part of a configuration indicating the set of measurement resources to perform the radio measurement predictions. ^B9. The method according to B5, further comprising transmitting a secondindication to the UE in response to receiving the first indication. Certain embodiments may provide one or more of the following technical advantage(s). The benefits of embodiments of proposed solution are two folded. On one hand, the UE saves on performing measurements by offering different alternatives for set A,B to accommodate for the network need, and if configured correctly, the gNB can save on reference signal transmissions. Moreover, the network is the consumer of the UE sided predictions, so it is natural that the network has some level of involvement in the selection of set A / B for the UE sided model in order to ensure that the model developed by the UE is aligned with the network’s needs. This does not mean that the network has to dictate set A and B for the training of the UE sided model. The UE can train for different alternatives of set A, B. In this case, the UE can optimize based on its own AI / ML capabilities, and the network has the final decision on which alterative of set A / B should be used by the UE. this way, the network can potentially reduce on RS transmissions by aligning the set B among different UEs. Figure 6A illustrates the operation of a network node (e.g., a gNB) and a UE inaccordance with an embodiment of the present disclosure. Note that not all of the stepsillustrated in Figure 6A are required. Further, while the steps are illustrated in Figure 6A asoccurring in a particular order, the steps may be performed in any desired order and steps may be performed in parallel. In step 600 of Figure 6A, the network node configures the UE with measurements thatcan be used by the UE to train an AI / ML model. For example, the UE may configure the UE to perform measurements on a set of NZP CSI-RS resources. Relevant excepts from 3GPP TS 38.331 regarding the configuration of NZP CSI-RS resources are reproduced below. 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, resourceMapping CSI-RS-ResourceMapping, …… 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)) OF NZP-CSI-RS-ResourceId, repetition ENUMERATED { on, off }…. OPTIONAL, -- Need SCSI-ResourceConfig information element-- ASN1START-- TAG-CSI-RESOURCECONFIG-START CSI-ResourceConfig ::= SEQUENCE { csi-ResourceConfigId CSI-ResourceConfigId, csi-RS-ResourceSetList CHOICE { nzp-CSI-RS-SSB SEQUENCE { …..The IE CSI-ReportConfig is used to configure a periodic or semi-persistent report sent on PUCCH on thecell in which the CSI-ReportConfig is included, or to configure a semi-persistent or aperiodic report senton 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-STARTCSI-ReportConfig ::= SEQUENCE {reportConfigId CSI-ReportConfigId, carrier ServCellIndex OPTIONAL, -- Need SresourcesForChannelMeasurement CSI-ResourceConfigId, csi-IM-ResourcesForInterference CSI-ResourceConfigI d OPTIONAL, -- Need R ….. A potential issue with a data-driven approach for training a beam prediction AI / ML model is that different sites / cells may have different antenna and / or beam configurations (denoted herein as 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 receives the same beam configuration / pattern ID over two or more cells, the UE can assume that the CSI resources are using the same beams / precoders. This can be achieved via that the 3GPP specifications introduces a “consistency” identifier for its CSI resources, that 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- Consistency-nzp-CSI- An ID that indicates Resource RS-ResourceId (other could whether the UE can assume comprise: that the resource is using the -beamID same spatial TX-filter across -precoderID) time and cells NZP-CSI-RS- Consistency-nzp-CSI- An ID that indicates ResourceSet RS-ResourceSetId whether the UE can assume (other could comprise: that the set of resources is -beamSetID using the same spatial TX- -precoderSetID) filters across time and cells CSI-ReportConfig Consistency-csi- An ID that indicates ReportConfigId whether the UE can assume that reported resources are using the same spatial TX- filters across time and cells csi-ResourceConfig Consistency-csi-An ID that indicates ResourceConfigID whether the UE can assume that configured resources are using the same spatial TX- filters across time and cells The 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 In some embodiments, in step 600, the network configures the UE with a first set of measurement resources. In one embodiment, the configuration of the one or more first sets of measurement resources is received by the UE as part of a configuration for data collection for UE-side model training. In another embodiment, the configuration of the one or more first sets of measurement resources is received by the UE as part of a configuration for performing radio measurements and associated radio measurement reporting. In another embodiment, the configuration of the one or more first sets of measurement resources is received by the UE as part of a configuration indicating the set of candidate measurement resources that can be currently configured by the network node to the UE to perform the radio measurements to determine the radio measurement predictions. In step 602 of Figure 6A, the UE may determines resources in which to perform radiomeasurement predictions (referred to herein as a third set(s) of recommended measurementresources or one or more Set A(s)), and resources in which to perform radio measurementsnecessary for the UE to perform the radio measurement predictions (referred to herein as asecond set(s) of recommended measurement resources or one or more Set B(s)) from a set ofresources configured by the gNB (i.e., a first set(s) of measurement resources configured in step600 of Figure 6A). The said first set of measurement resources may be the resources that the gNBhas configured to the UE to indicate CSI-RS (reference signals) belonging to the serving cell that the UE has to measure and for which measurement results should be reported, e.g. via PUCCH. In another embodiment, such first set of measurement resources may be indicated by theresources separately from the resources in which the UE has to perform radio measurements. Insuch case, this first set of measurement resources represents a candidate set of measurement resources in which the UE may perform the radio measurements to determine the radio measurement predictions according to one or more AI / ML models / functionalities (second set of measurement resources), and a candidate set of measurement resources for which the UE can determine the radio measurement predictions (third set of measurement resources). The first set(s) of measurement resources can be associated by the gNB to an identifier as proposed in previous embodiments. In particular, an identifier may be associated to each resource set (set ID) included in the first set(s) of measurement resources, or to each individual resource (resource ID) included by the gNB in a resource set. Such ID may be unique within the cell, and it may be associated to a specific network configuration, as per the previous embodiments. In step 602 of Figure 6A, based on the received reference signal transmissions andpreviously collected measurements during the training, the UE selects one or more set Bs (secondset of recommended measurement resources) to train one or more AI models / functionalities. Theone or more selected set Bs may compromise one or more of the following beams:- One or more SSB beams- One or more narrow beams, e.g. the beams configured for CSI measurements, and / or- the beams that the NW may activate to aid the UE prediction.For each of the sets B, the UE may associate a set A (third set of recommended measurement resources), wherein the set A may be a subset of the beams listed above. For temporal beam prediction, set B, or subset of set B can be included in set A. The selection of set A and B can be based on:- Performance of the combination of setB / setA, for example it should be above a certainaccuracy level -Cost of measurements, in case the UE is battery constrained, it can select to measure onless set B beams to save energy -Battery type / service type / QoS target / device type- Estimate of achievable power savings from different omission patterns- UE computational capabilities, for instance in terms of number of operations per seconds,type of processor (CPU, GPU), number of CPUs. This could be reported specifically for executing machine learning model or more generally associated to the UE oNote that reducing the number of beams in set B, leads to less model inputs andthereby typically simpler models. -Possible alternatives of Set A and B indicated by the NW- Applicability conditions of the one or more AI / ML models / functionalities with respect tothe selected set A and set B, i.e. the AIML model / functionality is applicable if it can determine with a certain accuracy radio measurement predictions on the resources associated to the set A, given the radio measurements performed in the set B. It should be noted that one or more of these steps can be performed by the UE itself, or delegated to another external entity (e.g., training server) that decides on the selection of set B based on the reported measurements from one or more UEs. Upon selecting the second and third set of recommended measurement resources (i.e. set B and set A respectively), the UE signals support of AI / ML based beam prediction based on theone or more set A / B alternatives, i.e. in a first indication to the gNB, in step 602 of Figure 6A.In step 602 of Figure 6A, the UE can report the said set IDs or resource IDs associated tothe selected set B and set A. For example, it can report: -Which nzp-CSI-RS-ResourceId that are part of setB / A, or in the “Consistency-nzp-CSI-RS-ResourceId” -Which nzp-CSI-RS-ResourceSetIds (or consistencynzp-CSI-RS-ResourceSetIds ) thatare part of setB / A. oAdditionally, the UEW can indicate which nzp-CSI-RS-ResourceId in case only asubset of the beams in each set are included -Which nzp-CSI-RS-ResourceId that are part of setB / A.o The UE can also include above information on specific -CSI-RS-ResourceId nzp-CSI-RS-ResourceSetIds that are part of setB / A -Which reportConfigId that are part of setB / A.o The UE can also include above information on specific -CSI-RS-ResourceId nzp-CSI-RS-ResourceSetIds that are part of setB / A -That it only need the SSB beams in set B, and the set A beams according to any of theabove methods. In this embodiment, the UE may also report how many or which of the set A / B alternatives can be supported / run simultaneously. In some embodiments, the UE may just report either only the second set of recommendedmeasurement resources (or the third set of recommend measurement resources is empty) or onlythe third set of recommended measurement resources (or the second set of measurementresources is empty). In the first case, it means that the UE recommends the gNB to configure the resources for radio measurements according to the said second set, whereas it can perform the radio measurement predictions on the other resources included in the first set of measurement resources excluding the resources indicated in the said second set. In other words, this implies that the UE can perform radio measurement predictions on resources indicated in the one or more first sets of measurement resources excluding resources indicated in the one or more second sets of recommended measurement resources, by performing radio measurement on the indicated one or more second sets of recommended measurement resources. In the second case, it means that the UE recommends the gNB to configure the resources for radio measurements according to the first set of measurement resources previously configured by the gNB excluding the resources indicated in the said third set, whereas it can perform the radio measurement predictions on the resources indicated in the said third set. In other words, this implies that the UE can perform radio measurement predictions on the indicated one or more third sets of recommended measurement resources by performing radio measurements on resources indicated in the one or more first sets of measurement resources excluding resources indicated in the one or more third sets of recommended measurement resources. In this step (i.e., step 602 of Figure 6A), the UE can report this information in the existingframework on capabilities in 3GPP. Alternatively, the UE can report this information via any oneor more of RRC, uplink (UL) Medium Access Control (MAC) Control Element (CE), or UplinkControl Information (UCI). The UE can further also include the performance in terms ofaccuracy for each of the combination above. For example, in a MAC CE, the UE can report theset IDs or resource IDs associated to the selected set A and set B. Two different fields in the MAC CE can be used for the reporting of the set A and set B, or two different MAC CEs associated to different logical channels identities may be reported for the set A and set B. Each bit in the MAC CEs may be associated to a specific set ID or resource ID, or one octet can be used to represent the binary value of a specific set ID or resource ID. In case RRC is used, e.g. the UEAssistanceInformation message, the message may contain two separate lists indicating the resource IDs or set IDs associated to the selected set A and set B. In another embodiment, the “capability” in this step is conveyed as partly in “UEAssistance Information” procedure specified in 3GPP TS 38.331. The capability indicating thesupported set (A,B) alternatives could in this framework both support a proactive and reactive reporting, where the NW could request a UE to report which sets (A, B) it can support. Upon receiving the first indication, the network node can, based on the reported alternatives of the recommended set (A, B), decide on a one or more preferred set (A,B). As non-limiting examples, a preferred set (A,B) can be selected targeting: -aligning set B across different UEs, so that the NW has opportunities to reduce RStransmissions by requesting the UEs to measure on the same set B beams, -Aligning set A across different UEs , so that the NW has opportunities to minimize RStransmissions on non-measured set A beams, -Maximizing the alignment on non-measured beams (i.e. predicted beams) across differentUEs. In particular, the network will determine which are the resources that needs to be configured (second set of measurement resources) and transmitted to the UE to perform the radio measurements, and the resources (third set of measurement resources) in which the UE will determine the radio measurement predictions and that consequently will not need to be provided to the UE, which means no reference signals associated to those resources should be transmitted by the gNB. In step 604 of Figure 6A, in response of receiving the first indication, the network nodecan request the UE in a second indication to activate the beam prediction functionality according to the preferred one or more sets (A,B) decided in the previous step, i.e. second and third set ofmeasurement resources. The indication of the configured set A and set B can be signaled to theUE via any one or more of RRC messages (e.g. RRC reconfiguration procedure), downlink (DL) MAC CE, or Physical Downlink Control Channel (PDCCH). For example, in a MAC CE the gNB can indicate the set IDs or resource IDs associated to the set A and set B. Two different fields in the MAC CE can be used for the indication by the gNB of the set A and set B, or two different MAC CEs associated to different logical channels identities may be indicated by the gNB for the set A and set B. Each bit in the MAC CEs may be associated to a specific set ID or resource ID, or one octet can be used to represent the binary value of a specific set ID or resource ID. In case RRC is used, e.g. an RRC message containing a CSI measurement configuration, the message may contain two separate lists indicating the resource IDs or set IDs associated to the selected set A and set B. Note that, in step 604, in one embodiment, if the second indication contains only the third set of measurement resources or if the second set of measurement resources included in the second indication is empty, this implies that the UE can perform the radio measurement predictions on the indicated third set of resources by performing radio measurement on the resources indicated previously in the first set of measurement resources excluding the resources indicated in the said third set of resources. In another embodiment, if the first indication contains only the second set of measurement resources or if the third set of recommended measurement resources included in the first indication is empty, this implies that the UE can perform the radio measurement predictions on the resources indicated in the first set of measurement resources excluding the resources indicated in the said second set of resources, by performing radio measurement on the indicated second set of resources. Optionally, in step 606 of Figure 6A, the UE confirms the activation of the MLfunctionality according to the preferred one or more sets (A,B) indicated by the NW. In step 608 of Figure 6A, the network node transmits reference signals on set B beams.In step 610 of Figure 6A, on the basis of the received configuration of set A and set B, i.e. thirdand second set of measurement resources, the UE starts performing the radio predictions on the resources associated to the set A based on the radio measurements performed on set B. The UE may then report the prediction results associated to the set A and or set B to the gNB. The first indication transmitted from the UE to the network node / gNB in step 602 ofFigure 6A may be transmitted to the gNB in response to any of:^ Being configured by the gNB to perform AI / ML-based radio measurement prediction^ Upon changing RRC stateo In this case the first indication may be send in an RRC complete message, such asRRCReconfigurationComplete, RRCResumeComplete ^Upon entering a target cell after a mobility procedureo In this case the first indication may be send in an RRC complete message, such asRRCReconfigurationComplete ^Upon determining that the applicability conditions of one or more AI / MLmodel / functionality available at the UE are fulfilled or not for one or more resources included in the first set of measurement resources. In other words, in one embodiment as illustrated in Figure 6B, step 602 includes evaluating one or more applicability conditions of one or more AI / ML models / functions available at the UE based on the first set of measurement resources, which are configured by the network (step 602A). Thisevaluating includes, in one embodiment, evaluating that the radio measurement prediction results can be determined by the AI / ML model / functionality with a certain accuracy. Based on this evaluation, if the applicability conditions are satisfied (step 602B, YES), the UE determines the recommended Set A(s) and / or the recommended Set B(s) (step 602C) and transmits the first indication to the network node in step 602 (step 602D). oFor example, the UE during the inference execution may realize that theprediction results on the configured set A are not within a certain accuracy criteria, in which case the UE may indicate in the first indication a new set of recommended set A and / or set B. ^Upon being configured with a first set of measurement resources^ Upon receiving a reconfiguration of the first set of measurement resourceso For example, the network may transmit a reconfiguration of the first set ofmeasurement resources, upon which the UE should perform a check of the applicability conditions according to which the UE may need to transmit a new set of recommended set A and / or setB^ Upon AI / ML models / functionalities activation is request received from the gNB^ Upon applicability reporting of an AIML models / functionalities is request by the gNBThe network may also transmit, to the UE, a third indication (not shown in Figure 6A) indicating the deactivation or deconfiguration of a previously configured set A and set B upon which the UE may stop the AI / ML model / functionality operations, or it may transmit a first indication indicating a new recommended set of set A and / or set B. The third indication may also contain an indication indicating that the UE should stop the AIML model / functionality operations. The radio measurements predictions results determined on the basis of the second and third set of measurement configuration, i.e. set A / set B, may be transmitted to the gNB (in step610 of Figure 6A), and it may comprise:^ The measured quantities for each of the one or more resources in the third set ofmeasurement resources^ The measured quantities for the best resources in terms of measured quantities among theresources in the second and / or third 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 second and / or third 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 second and / or third set ofmeasurement resources. ^The variance of the measured quantities for the resources in the second and / or third set ofmeasurement resources. ^The accuracy of the reported predictions resultsTechnical Specification Impact: One example implementation of an example embodiments of the present disclosure is shown below as modifications to existing 3GPP specifications: In one possible implementation method, the first set of measurement resources can be configured as part of the CSI-RS measurement configuration or SS / PBCH block resourceconfiguration, i.e. as part of the nzp-CSI-RS-Resource or nzp-CSI-RS-ResourceSet, or csi-SSB-ResourceSet. In another method, the first set of measurement resources may be configured as part of a separate resource configuration including the candidate resources that the network can configure to the UE for performing the radio measurements needed to the AIML models / functionalities to determine the radio measurement predictions results. Such candidate resources can be associated to a CSI-RS measurement configuration or SS / PBCH block resourceconfiguration, i.e. candidate-nzp-CSI-RS-Resource or candidate-nzp-CSI-RS-ResourceSet, orcandidate-csi-SSB-ResourceSet. OPTIONAL, -- Need Nnzp-CSI-RS-ResourceSetToReleaseList SEQUENCE (SIZE (1..maxNrofNZP-CSI-RS-ResourceSets)) OFNZP-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-ResourceConfigcandidate-nzp-CSI-RS-ResourceToAddModList SEQUENCE (SIZE (1..maxNrofNZP-CSI-RS-Resources)) OFNZP-CSI-RS-Resource OPTIONAL, -- Need Ncandidate-nzp-CSI-RS-ResourceToReleaseList SEQUENCE (SIZE (1..maxNrofNZP-CSI-RS-Resources)) OF NZP-CSI-RS-ResourceId OPTIONAL, -- Need Ncandidate-nzp-CSI-RS-ResourceSetToAddModList SEQUENCE (SIZE (1..maxNrofNZP-CSI-RS-ResourceSets)) OF NZP-CSI-RS-ResourceSetOPTIONAL, -- Need Ncandidate -nzp-CSI-RS-ResourceSetToReleaseList SEQUENCE (SIZE (1..maxNrofNZP-CSI-RS-ResourceSets)) OF NZP-CSI-RS-ResourceSetId candidate-csi-SSB-ResourceSetToAddModList SEQUENCE (SIZE (1..maxNrofCSI-SSB-ResourceSets)) OFCSI-SSB-ResourceSet OPTIONAL, -- Need Ncandidate-csi-SSB-ResourceSetToReleaseList SEQUENCE (SIZE (1..maxNrofCSI-SSB-ResourceSets)) OFCSI-SSB-ResourceSetId OPTIONAL, -- Need Ncsi-ResourceConfigToAddModList SEQUENCE (SIZE (1..maxNrofCSI-ResourceConfigurations)) OFCSI-ResourceConfigOPTIONAL, -- Need Ncsi-ResourceConfigToReleaseList SEQUENCE (SIZE (1..maxNrofCSI-ResourceConfigurations)) OF CSI-ResourceConfigIdOPTIONAL, -- Need Ncsi-ReportConfigToAddModList SEQUENCE (SIZE (1..maxNrofCSI-ReportConfigurations)) OFCSI-ReportConfig OPTIONAL, -- Need Ncsi-ReportConfigToReleaseList SEQUENCE (SIZE (1..maxNrofCSI-ReportConfigurations)) OFCSI-ReportConfigId <Text Omitted> }-- TAG-CSI-MEASCONFIG-STOP-- ASN1STOPEach of this resource configuration element can be associated by the network to a specific ID, e.g. an ID that uniquely identifies a certain configuration at cell level or at network level, e.g.Consistency-nzp-CSI-RS-ResourceId as in the following. This ID can then be used by the UE inthe first indication to indication the resources included in the second set of recommended measurement resources and third set of recommended measurement resources.-- ASN1START-- TAG-CSI-SSB-RESOURCESET-STARTCSI-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]], [[ consistency-CSI-SSB-ResourceSetId Consistency-CSI-SSB-ResourceSetId ]] } ServingAdditionalPCIIndex-r17 ::= INTEGER(0..maxNrofAdditionalPCI-r17) -- TAG-CSI-SSB-RESOURCESET-STOP-- ASN1STOP-- ASN1START-- TAG-NZP-CSI-RS-RESOURCE-STARTNZP-CSI-RS-Resource ::= SEQUENCE { nzp-CSI-RS-ResourceId NZP-CSI-RS-ResourceId, resourceMapping CSI-RS-ResourceMapping, powerControlOffset INTEGER (-8..15), powerControlOffsetSS ENUMERATED{db-3, db0, db3, db6} OPTIONAL,-- Need R -- Cond PeriodicOrSemiPersistentqcl-InfoPeriodicCSI-RS TCI-StateId OPTIONAL,-- Cond Periodic..., [[ subcarrierSpacing-r18 SubcarrierSpacing OPTIONAL,-- Cond LTMabsoluteFrequencyPointA-r18 ARFCN-ValueNR OPTIONAL,-- Cond LTMcyclicPrefix-r18 ENUMERATED {extended} OPTIONAL-- Cond LTM]], [[ consistency-nzp-CSI-RS-ResourceId Consistency-nzp-CSI-RS-ResourceId]] }-- TAG-NZP-CSI-RS-RESOURCE-STOP-- ASN1STOP-- ASN1START-- TAG-NZP-CSI-RS-RESOURCESET-START 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]],[[ consistency-nzp-CSI-ResourceSetId Consistency-nzp-CSI-ResourceSetId ]] } Figure 7 shows an example of a communication system 700 in accordance with some embodiments. In the example, the communication system 700 includes a telecommunication network 702 that includes an access network 704, such as a Radio Access Network (RAN), and a core network 706, which includes one or more core network nodes 708. The access network 704 includes one or more access network nodes, such as network nodes 710A and 710B (one or more of which may be generally referred to as network nodes 710), 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 702 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network 702 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 702, including one or more network nodes 710 and / or core network nodes 708. Examples of an ORAN network node include an Open Radio Unit (O-RU), an Open Distributed Unit (O-DU), an Open Central Unit (O-CU), including an O-CU Control Plane (O- CU-CP) or an O-CU User Plane (O-CU-UP), a RAN intelligent controller (near-real time or non- real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an 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 710facilitate direct or indirect connection of User Equipment (UE), such as by connecting UEs 712A, 712B, 712C, and 712D (one or more of which may be generally referred to as UEs 712) to the core network 706 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 700 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 700 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system. The UEs 712 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 710 and other communication devices. Similarly, the network nodes 710 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 712 and / or with other network nodes or equipment in the telecommunication network 702 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 702. In the depicted example, the core network 706 connects the network nodes 710 to one or more hosts, such as host 716. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 706 includes one more core network nodes (e.g., core network node 708) 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 708. 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 716 may be under the ownership or control of a service provider other than an operator or provider of the access network 704 and / or the telecommunication network 702, and may be operated by the service provider or on behalf of the service provider. The host 716 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 700 of Figure 7 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system 700 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 702 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunication network 702 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 702. For example, the telecommunication network 702 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 712 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 704 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 704. 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 714 communicates with the access network 704 to facilitate indirect communication between one or more UEs (e.g., UE 712C and / or 712D) and network nodes (e.g., network node 710B). In some examples, the hub 714 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 714 may be a broadband router enabling access to the core network 706 for the UEs. As another example, the hub 714 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 710, or by executable code, script, process, or other instructions in the hub 714. As another example, the hub 714 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 714 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 714 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 714 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 714 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 714 may have a constant / persistent or intermittent connection to the network node 710B. The hub 714 may also allow for a different communication scheme and / or schedule between the hub 714 and UEs (e.g., UE 712C and / or 712D), and between the hub 714 and the core network 706. In other examples, the hub 714 is connected to the core network 706 and / or one or more UEs via a wired connection. Moreover, the hub 714 may be configured to connectto a Machine-to-Machine (M2M) service provider over the access network 704 and / or to anotherUE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 710 while still connected via the hub 714 via a wired or wireless connection.In some embodiments, the hub 714 may be a dedicated hub – that is, a hub whose primaryfunction is to route communications to / from the UEs from / to the network node 710B. In otherembodiments, the hub 714 may be a non-dedicated hub – that is, a device which is capable ofoperating to route communications between the UEs and the network node 710B, but which is additionally capable of operating as a communication start and / or end point for certain data channels. Figure 8 shows a UE 800 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 ahuman user who owns and / or operates the relevant device. Instead, a UE may represent a devicethat is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter). The UE 800 includes processing circuitry 802 that is operatively coupled via a bus 804 to an input / output interface 806, a power source 808, memory 810, a communication interface 812, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 8. 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 802 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 810. The processing circuitry 802 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 802 may include multiple Central Processing Units (CPUs). In the example, the input / output interface 806 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 800. 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, adirectional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitivedisplay may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device. In some embodiments, the power source 808 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 808 may further include power circuitry for delivering power from the power source 808 itself, and / or an external power source, to the various parts of the UE 800 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 808. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 808 to make the power suitable for the respective components of the UE 800 to which power is supplied. The memory 810 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 810 includes one or more application programs 814, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 816. The memory 810 may store, for use by the UE 800, any of a variety of various operating systems or combinations of operating systems. The memory 810 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 810 may allow the UE 800 to access instructions, application programs, and the like stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system, may be tangibly embodied as or in the memory 810, which may be or comprise a device-readable storage medium. The processing circuitry 802 may be configured to communicate with an access network or other network using the communication interface 812. The communication interface 812 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 822. The communication interface 812 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter 818 and / or a receiver 820 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 818 and receiver 820 may be coupled to one or more antennas (e.g., the antenna 822) and may share circuit components, software, or firmware, or alternatively be implemented separately. In the illustrated embodiment, communication functions of the communication interface 812 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 812, 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 remotecontrolled surgical robot. A UE in the form of an IoT device comprises circuitry and / or softwarein dependence of the intended application of the IoT device in addition to other components as described in relation to the UE 800 shown in Figure 8. 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 UE may represent a vehicle, such as a car, a bus, a truck, a ship, an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation. In practice, any number of UEs may be used together with respect to a single use case. For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g., by controlling an actuator) to increase or decrease the drone’s speed. The first and / or the second UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the actuator and handle communication of data for both the speed sensor and the actuators. Figure 9 shows a network node 900 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, ormacro base stations. A base station may be a relay node or a relay donor node controlling arelay. 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 900 includes processing circuitry 902, memory 904, a communication interface 906, and a power source 908. The network node 900 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 900 comprises multiple separate components(e.g., BTS and BSC components), one or more of the separate components may be shared amongseveral 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 900 may be configured to support multiple RATs. In such embodiments, some components may be duplicated (e.g., separate memory 904 for different RATs) and some components may be reused (e.g., a same antenna 910 may be shared by different RATs). The network node 900 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 900, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, Long Range Wide Area Network (LoRaWAN), Radio Frequency Identification (RFID), or Bluetoothwireless technologies. These wireless technologies may be integrated into the same or differentchip or set of chips and other components within the network node 900. The processing circuitry 902 may comprise a combination of one or more of a microprocessor, controller, microcontroller, CPU, DSP, ASIC, FPGA, or any other suitable computing device, resource, or combination of hardware, software, and / or encoded logic operable to provide, either alone or in conjunction with other network node 900 components, such as the memory 904, to provide network node 900 functionality. In some embodiments, the processing circuitry 902 includes a System on a Chip (SOC). In some embodiments, the processing circuitry 902 includes one or more of Radio Frequency (RF) transceiver circuitry 912 and baseband processing circuitry 914. In some embodiments, theRF transceiver circuitry 912 and the baseband processing circuitry 914 may be on separate chips(or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of the RF transceiver circuitry 912 and the baseband processing circuitry 914 may be on the same chip or set of chips, boards, or units. The memory 904 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 902. The memory 904 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 902 and utilized by the network node 900. The memory 904 may be used to store any calculations made by the processing circuitry 902 and / or any data received via the communication interface 906. In some embodiments, the processing circuitry 902 and the memory 904 are integrated. The communication interface 906 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, the communication interface 906 comprises port(s) / terminal(s) 916 to send and receive data, for example to and from a network over a wired connection. The communication interface 906 also includes radio front-end circuitry 918 that may be coupled to, or in certain embodiments a part of, the antenna 910. The radio front-end circuitry 918 comprises filters 920 and amplifiers 922. The radio front-end circuitry 918 may be connected to the antenna 910 and the processing circuitry 902. The radio front-end circuitry 918 may be configured to condition signals communicated between the antenna 910 and the processing circuitry 902. The radio front-end circuitry 918 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 918 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of thefilters 920 and / or the amplifiers 922. The radio signal may then be transmitted via the antenna910. Similarly, when receiving data, the antenna 910 may collect radio signals which are then converted into digital data by the radio front-end circuitry 918. The digital data may be passed to the processing circuitry 902. In other embodiments, the communication interface 906 may comprise different components and / or different combinations of components. In certain alternative embodiments, the network node 900 does not include separate radio front-end circuitry 918; instead, the processing circuitry 902 includes radio front-end circuitryand is connected to the antenna 910. Similarly, in some embodiments, all or some of the RFtransceiver circuitry 912 is part of the communication interface 906. In still other embodiments, the communication interface 906 includes the one or more ports or terminals 916, the radio front- end circuitry 918, and the RF transceiver circuitry 912 as part of a radio unit (not shown), and the communication interface 906 communicates with the baseband processing circuitry 914, which is part of a digital unit (not shown). The antenna 910 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 910 may be coupled to the radio front-end circuitry 918 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 910 is separate from the network node 900 and connectable to the network node 900 through an interface or port. The antenna 910, the communication interface 906, and / or the processing circuitry 902 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node 900. Any information, data, and / or signals may be received from a UE, another network node, and / or any other network equipment. Similarly, the antenna 910, the communication interface 906, and / or the processing circuitry 902 may be configured to perform any transmitting operations described herein as being performed by the network node 900. Any information, data, and / or signals may be transmitted to a UE, another network node, and / or any other network equipment. The power source 908 provides power to the various components of the network node 900 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 908 may further comprise, or be coupled to,power management circuitry to supply the components of the network node 900 with power forperforming the functionality described herein. For example, the network node 900 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 908. As a further example, the power source 908 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 900 may include additional components beyond those shown in Figure 9 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 900 may include user interface equipment to allow input of information into the network node 900 and to allow output of information from the network node 900. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 900. Figure 10 is a block diagram of a host 1000, which may be an embodiment of the host 716 of Figure 7, in accordance with various aspects described herein. As used herein, the host 1000 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 1000 may provide one or more services to one or more UEs. The host 1000 includes processing circuitry 1002 that is operatively coupled via a bus 1004 to an input / output interface 1006, a network interface 1008, a power source 1010, andmemory 1012. Other components may be included in other embodiments. Features of thesecomponents may be substantially similar to those described with respect to the devices of previous figures, such as Figures 8 and 9, such that the descriptions thereof are generally applicable to the corresponding components of the host 1000. The memory 1012 may include one or more computer programs including one or more host application programs 1014 and data 1016, which may include user data, e.g. data generated by a UE for the host 1000 or data generated by the host 1000 for a UE. Embodiments of the host 1000 may utilize only a subset or all of the components shown. The host application programs 1014 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), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, and heads-up display systems). The host application programs 1014 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 1000 may select and / or indicate a different host for Over-The-Top (OTT) services for a UE. The host application programs 1014 may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real-Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), Dynamic Adaptive Streaming over HTTP (DASH or MPEG-DASH), etc. Figure 11 is a block diagram illustrating a virtualization environment 1100 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices, and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more Virtual Machines (VMs) implemented in one or more virtual environments 1100 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 1100 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 1102 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 1100 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein. Hardware 1104 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 maybe executed by the processing circuitry to instantiate one or more virtualization layers 1106 (alsoreferred to as hypervisors or VM Monitors (VMMs)), provide VMs 1108A and 1108B (one or more of which may be generally referred to as VMs 1108), and / or perform any of the functions, features, and / or benefits described in relation with some embodiments described herein. The virtualization layer 1106 may present a virtual operating platform that appears like networking hardware to the VMs 1108. The VMs 1108 comprise virtual processing, virtual memory, virtual networking, or interface and virtual storage, and may be run by a corresponding virtualization layer 1106. Different embodiments of the instance of a virtual appliance 1102 may be implemented on one or more of the VMs 1108, 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 1108 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 1108, and that part of the hardware 1104 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs 1108, 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 1108 on top of the hardware 1104 and corresponds to the application 1102. The hardware 1104 may be implemented in a standalone network node with generic or specific components. The hardware 1104 may implement some functions via virtualization. Alternatively, the hardware 1104 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 1110, which, among others, oversees lifecycle management of the applications 1102. In some embodiments, the hardware 1104 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 1112 which may alternatively be used for communication between hardware nodes and radio units. Figure 12 shows a communication diagram of a host 1202 communicating via a network node 1204 with a UE 1206 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as the UE 712A of Figure 7 and / or the UE 800 of Figure 8), the network node (such as the network node 710A of Figure 7 and / or the network node 900 of Figure 9), and the host (such as the host 716 of Figure 7 and / or the host 1000 of Figure 10) discussed in the preceding paragraphs will now be described with reference to Figure 12. Like the host 1000, embodiments of the host 1202 include hardware, such as a communication interface, processing circuitry, and memory. The host 1202 also includes software, which is stored in or is accessible by the host 1202 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 1206 connecting via an OTT connection 1250 extending between the UE 1206 and the host 1202. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection 1250. The network node 1204 includes hardware enabling it to communicate with the host 1202 and the UE 1206. The connection 1260 may be direct or pass through a core network (like thecore network 706 of Figure 7) 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 1206 includes hardware and software, which is stored in or accessible by the UE 1206 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 1206 with the support of the host 1202. In the host 1202, an executing host application may communicate with the executing client application via the OTT connection 1250 terminating at the UE 1206 and the host 1202. 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 1250 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 1250. The OTT connection 1250 may extend via the connection 1260 between the host 1202 and the network node 1204 and via a wireless connection 1270 between the network node 1204 and the UE 1206 to provide the connection between the host 1202 and the UE 1206. The connection 1260 and the wireless connection 1270, over which the OTT connection 1250 may be provided, have been drawn abstractly to illustrate the communication between the host 1202 and the UE 1206 via the network node 1204, 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 1250, in step 1208, the host 1202 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 1206. In other embodiments, the user data is associated with a UE 1206 that shares data with the host 1202 without explicit human interaction. In step 1210, the host 1202 initiates a transmission carrying the user data towards the UE 1206. The host 1202 may initiate the transmission responsive to a request transmitted by the UE 1206. The request may be caused by human interaction with the UE 1206 or by operation of the client application executing on the UE 1206.The transmission may pass via the network node 1204 in accordance with the teachings of theembodiments described throughout this disclosure. Accordingly, in step 1212, the network node 1204 transmits to the UE 1206 the user data that was carried in the transmission that the host 1202 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 1214, the UE 1206 receives the user data carried in the transmission, which may be performed by a client application executed on the UE 1206 associated with the host application executed by the host 1202. In some examples, the UE 1206 executes a client application which provides user data to the host 1202. The user data may be provided in reaction or response to the data received from the host 1202. Accordingly, in step 1216, the UE 1206 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 1206. Regardless of the specific manner in which the user data was provided, the UE 1206 initiates, in step 1218, transmission of the user data towards the host 1202 via the network node 1204. In step 1220, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 1204 receives user data from the UE 1206 and initiates transmission of the received user data towards the host 1202. In step 1222, the host 1202 receives the user data carried in the transmission initiated by the UE 1206. One or more of the various embodiments improve the performance of OTT services provided to the UE 1206 using the OTT connection 1250, in which the wireless connection 1270 forms the last segment. More precisely, the teachings of these embodiments may improve, e.g., power consumption and thereby provide benefits such as, e.g., extended battery lifetime. In an example scenario, factory status information may be collected and analyzed by the host 1202. As another example, the host 1202 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host 1202 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host 1202 may store surveillance video uploaded by a UE. As another example, the host 1202 may store or control access to media content such as video, audio, VR, or AR which it can broadcast, multicast, or unicast to UEs. As other examples, the host 1202 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 1250 between the host 1202 and the UE 1206 in response to variations in the measurement results. The measurement procedure and / or the network functionality for reconfiguring the OTT connection 1250 may be implemented in software and hardware of the host 1202 and / or the UE 1206. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection 1250 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 1250 may include messageformat, retransmission settings, preferred routing, etc.; the reconfiguring need not directly alter the operation of the network node 1204. 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 1202. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 1250 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, the method comprising: determining (602) one or more sets of recommended measurement resources from one or more first sets of measurement resources configured for the UE, the one or more recommended sets of radio comprising either or both of: one or more second sets of recommended measurement resources (recommended set B) in which the UE is to perform radio measurements; one or more third sets of recommended measurement resources (recommended set A) in which the UE is to perform radio measurement predictions (e.g., using one or more AI / ML models or functions available at the UE), upon performing radio measurements in one or more second sets of measurement resources ((configured) set B). Embodiment 2: The method of embodiment 1, wherein determining (602) the one or more sets of recommended measurement resources comprises: evaluating one or more applicability conditions of one or more AI / ML models or functions available at the UE based on the one or more first sets of measurement resources configured (by a network node) for the UE; and determining the one or more sets of recommended measurement resources based on a result(s) of the evaluating. Embodiment 3: The method of embodiment 2, wherein evaluating the one or moreapplicability conditions comprises evaluating that the radio measurement prediction results can be determined by the AIML model / functionality with a certain accuracy. Embodiment 4: The method of any of embodiments 1 to 3, further comprises receiving (602) information, from a network node, that configures the UE with the one or more first sets of measurement resources. Embodiment 5: The method of embodiment 4, wherein receiving (602) the information that configures the UE with the one or more first sets of measurement resources comprises receiving the information that configures the UE with the one or more first sets of measurement resources as part of a configuration for data collection for UE-side model training. Embodiment 6: The method of embodiment 4, wherein receiving (602) the information that configures the UE with the one or more first sets of measurement resources comprises receiving the information that configures the UE with the one or more first sets of measurement resources as part of a configuration for performing radio measurements and associated radio measurement reporting. Embodiment 7: The method of embodiment 4, wherein receiving (602) the information that configures the UE with the one or more first sets of measurement resources comprises receiving the information that configures the UE with the one or more first sets of measurement resources as part of a configuration indicating the one or more first sets of measurement resources as one or more sets of candidate measurement resources that can be currently configured by the network node to the UE to perform the radio measurements to determine radio measurement predictions. Embodiment 8: The method of any of embodiments 1 to 7, wherein the one or moresecond sets of recommended measurement resources are equal to the one or more first sets ofmeasurement resources, a subset of the one or more first sets of measurement resources, or asubset of at least one of the one or more first sets of measurement resources.Embodiment 9: The method of any of embodiments 1 to 8, wherein the one or more third sets of recommended measurement resources are a subset of the one or more first sets ofmeasurement resources or a subset of at least one of the one or more first sets of measurementresources. Embodiment 10: The method of any of embodiments 1 to 9, wherein the radiomeasurement predictions are generated by the one or more AI / ML models or functions availableat the UE. Embodiment 11: The method of any of embodiments 1 to 10, wherein the determining(602) the one or more sets of recommended measurement resources is done upon receiving arequest from the network node to indicate any of the second and third set of recommended measurement resources. Embodiment 12: The method of embodiment 11, wherein the request is to indicateapplicability of the one or more AI / ML models or functions available at the UE.Embodiment 13: The method of any of embodiments 1 to 12, wherein each of the one ormore first sets of measurement resources is associated to an identification ID.Embodiment 14: The method of any of embodiments 1 to 12, wherein each of the one ormore first sets of measurement resources is associated to a set ID.Embodiment 15: The method of embodiment 13 or 14, wherein each of the one or moreresources comprised in the one or more first sets of measurement resources is associated to aresource ID. Embodiment 16: The method of embodiment 14, wherein the set ID for one of the one or more first sets of measurement resources is unique within a cell. Embodiment 17: The method of embodiment 15, wherein the resource ID for one resource within one of the one or more first sets of measurement resources is unique within the cell. Embodiment 18: The method of any of embodiments 1 to 17, further comprisingtransmitting (602), to a network node, a first indication comprising information indicative of theone or more sets of recommended measurement resources. Embodiment 19: The method of embodiment 18, wherein, for each of the one or more sets of recommended measurement resources, the first indication comprises an associated set ID for that set of recommended measurement resources. Embodiment 20: The method of embodiment 18, wherein, for each of the one or more sets of recommended measurement resources, the first indication comprises one or more resource IDs associated to resources within that set of recommended measurement resources. Embodiment 21: The method of embodiment 18, wherein the first indication contains only information indicative of the one or more third sets of recommended measurement resources or the one or more second sets of recommended measurement resources indicated by the first indication is empty, and this implies that the UE can perform radio measurement predictions on the indicated one or more third sets of recommended measurement resources by performing radio measurements on resources indicated in the one or more first sets of measurement resources excluding resources indicated in the one or more third sets of recommended measurement resources. Embodiment 22: The method of embodiment 18, wherein the first indication contains only information indicative of the one or more second sets of recommended measurement resources or the one or more third sets of recommended measurement resources indicated in the first indication is empty, and this implies that the UE can perform radio measurement predictions on resources indicated in the one or more first sets of measurement resources excluding resources indicated in the one or more second sets of recommended measurement resources, by performing radio measurement on the indicated one or more second sets of recommended measurement resources. Embodiment 23: The method of embodiment 18, further comprising, in response totransmitting the first indication, receiving (604), from the network node, a second indicationcomprising either or both of: ^information indicative of a second set of measurement resources on which the UE is toperform the radio measurements in order to determine radio measurement predictions in a third set of measurement resources; ^information indicative of the third set of measurement resources on which the UE is toperform the radio measurements predictions based on the second set of measurement resources. Embodiment 24: The method of embodiment 23, wherein the second set of measurement resources is equal to or a subset of the one or more second sets of recommended measurementresources or equal to or a subset of at least one of the one or more second sets of recommendedmeasurement resources. Embodiment 25: The method of embodiment 23 or 24, wherein the third set ofmeasurement resources is equal or a subset of the one or more third sets of recommendedmeasurement resources or equal to or a subset of at least one of the one or more second sets ofrecommended measurement resources. Embodiment 26: The method of embodiment 23 or 24, wherein the third set of measurement resources is empty indicating that the UE should not perform radio measurement predictions. Embodiment 27: The method of any of embodiments 23 to 25, further comprising, uponreceiving the second indication, starting to perform (610) radio measurement predictions on theresources included in the third set of measurement resources, based on the second set of measurement resources whose associated reference signals are transmitted by the network node. Embodiment 28: The method of embodiment 23, wherein the output of the AI / ML model / function available at the UE is the radio measurement predictions on the third set of measurement resources 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. Embodiment 29: The method of embodiment 28, wherein the radio measurement prediction results comprise any of: ^The measured quantities for each of the one or more resources in the third set ofmeasurement resources ^The measured quantities for the best resources in terms of measured quantities among theresources in the second and / or third 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 second and / or third 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 second and / or third set ofmeasurement resources. ^The variance of the measured quantities for the resources in the second and / or third set ofmeasurement resources. ^The accuracy of the reported predictions resultsEmbodiment 30: The method of embodiment 29, further comprising reporting the radiomeasurement prediction results to the network node. Embodiment 31: The method of embodiment 23, wherein the second indication isreceived via RRC dedicated signaling or MAC (MAC CE), PDCCH.Embodiment 32: The method of embodiment 18, wherein transmitting the first indicationcomprises transmitting the first indication to the network node in response to any of:^ being configured by the network node to perform AI / ML-based radio measurementprediction, ^upon changing Radio Resource Control (RRC) state,^ upon entering a target cell after a mobility procedure,^ upon determining that the applicability conditions of one or more AI / ML models orfunctions available at the UE are fulfilled or not for one or more resources included in the first set of measurement resources, ^upon being configured with a first set of measurement resources,^ upon receiving a reconfiguration of the first set of measurement resources,^ upon AI / ML model / function activation request being received from the network node,^ upon applicability reporting of an AI / ML models / functions being requested by thenetwork node. Embodiment 33: The method of embodiment 32, wherein the applicability conditions of one or more AIML models / functions available at the UE are checked by the UE prior to transmitting the first indication. Embodiment 34: The method of embodiment 18, further comprising, in response totransmitting the first indication, receiving, from the network node, a third indication indicating that AI / ML models / functions should be deactivated, and UE should not perform radio measurement predictions. Embodiment 35: The method of embodiment 18, wherein the first indication istransmitted via RRC signaling (UEAssistanceInformation), or MAC (MAC CE), or UCI.Embodiment 36: The method of any of embodiments 1 to 35, wherein the first set of measurement resources comprises any one or more of: •a set of SSB for a cell,• a set of CSI-RS resources for a cell,• a set of SS / PBCH block or CSI-RS resource set,• 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 cells.Embodiment 37: The method of embodiment 23, wherein if the second indication contains only the third set of measurement resources or if the second set of measurement resources included in the second indication is empty, this implies that the UE can perform the radio measurement predictions on the indicated third set of resources by performing radio measurement on the resources indicated previously in the first set of measurement resources excluding the resources indicated in the said third set of resources. Embodiment 38: The method of embodiment 18, wherein if the first indication contains only the second set of measurement resources or if the third set of recommended measurement resources included in the first indication is empty, this implies that the UE can perform the radio measurement predictions on the resources indicated in the first set of measurement resources excluding the resources indicated in the said second set of resources, by performing radio measurement on the indicated second set of resources. Embodiment 39: 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 40: A method performed by a network node, the method comprising: determining one or more first sets of measurement resources; and sending, to a UE, information that configures the one or more first set of measurement resources for the UE. Embodiment 41: The method of embodiment 40, wherein the one or more first sets ofmeasurement resources are configured to the UE as part of a configuration for performing radio measurements and associated reporting. Embodiment 42: The method of embodiment 40, wherein the one or more first sets ofmeasurement resources are configured to the UE as part of a configuration indicating a set ofcandidate measurement resources that can be currently configured by the network to the UE toperform the radio measurements to determine radio measurement predictions. Embodiment 43: The method of embodiment 40, further comprising receiving a first indication from the UE, the first indication comprising information that indicates one or more sets of recommended measurement resources related to measurement prediction (e.g., using one or more AI / ML models or functions available at the UE). Embodiment 44: The method of embodiment 43, wherein the one or more sets of recommended measurement resources comprising either or both of: one or more second sets of recommended measurement resources (recommended set B) in which the UE is to perform radio measurements; one or more third sets of recommended measurement resources (recommended set A) in which the UE is to perform radio measurement predictions (e.g., using one or more AI / ML models or functions available at the UE), upon performing radio measurements in one or more second sets of measurement resources ((configured) set B). Embodiment 45: The method of embodiment 43, further comprising, based on a secondset of recommended measurement resources indicated by information comprised in the firstindication, determining a set of resources associated to which the network node is to transmitreference signals in order for the UE to perform radio measurement predictions in a third set ofrecommended resources. Embodiment 46: The method of embodiment 45, wherein a second set of measurementresources is configured to the UE as part of a configuration indicating a set of measurementresources on which the UE is to perform the radio measurements to determine radio measurementpredictions.Embodiment 47: The method of embodiment 43, further comprising, based on a third setof recommended resources indicated by information comprised in the first indication,determining a set of resources associated to which the network node does not need to transmitreference signals in order for the UE to perform radio measurement predictions in the third set of recommended resources. Embodiment 48: The method of embodiment 47, wherein the third set of measurement resources is configured to the UE as part of a configuration indicating the set of measurement resources to perform the radio measurement predictions. Embodiment 49: The method of embodiment 43, further comprising transmitting asecond indication to the UE in response to receiving the first indication.Embodiment 50: 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 51: A user equipment comprising: processing circuitry configured to perform any of the steps of any of the Group A embodiments; and power supply circuitry configured to supply power to the processing circuitry. Embodiment 52: A network node comprising: processing circuitry configured to perform any of the steps of any of the Group B embodiments; and power supply circuitry configured to supply power to the processing circuitry. Embodiment 53: A user equipment (UE) comprising: an antenna configured to send and receive wireless signals; radio front-end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry; the processing circuitry being configured to perform any of the steps of any of the Group A embodiments; an input interface connected to the processing circuitry and configured to allow input of information into the UE to be processed by the processing circuitry; an output interface connected to the processing circuitry and configured to output information from the UE that has been processed by the processing circuitry; and a battery connected to the processing circuitry and configured to supply power to the UE. Embodiment 54: A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a network node in a cellular network for transmission to a user equipment (UE), the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B embodiments to transmit the user data from the host to the UE. Embodiment 55: The host of the previous embodiment, wherein: the processing circuitry of the host is configured to execute a host application that provides the user data; and the UE comprises processing circuitry configured to execute a client application associated with the host application to receive the transmission of user data from the host. Embodiment 56: A method implemented in a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: providing user data for the UE; and initiating a transmission carrying the user data to the UE via a cellular network comprising the network node, wherein the network node performs any of the operations of any of the Group B embodiments to transmit the user data from the host to the UE. Embodiment 57: The method of the previous embodiment, further comprising, at the network node, transmitting the user data provided by the host for the UE. Embodiment 58: 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 59: A communication system configured to provide an over-the-top (OTT) service, the communication system comprising: a host comprising: processing circuitry configured to provide user data for a user equipment (UE), the user data being associated with the over-the-top service; and a network interface configured to initiate transmission of the user data toward a cellular network node for transmission to the UE, the network node having a communication interface and processing circuitry, the processing circuitry of the network 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 60: The communication system of the previous embodiment, further comprising: the network node; and / or the UE. 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 initiate receipt of user data; and a network interface configured to receive the user data from a network node in a cellular network, the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B embodiments to receive the user data from a user equipment (UE) for the host. Embodiment 62: The host of the previous 2 embodiments, wherein: the processing circuitry of the host is configured to execute a host application that receives the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application. Embodiment 62: The host of the any of the previous 2 embodiments, wherein the initiating receipt of the user data comprises requesting the user data. Embodiment 63: A method implemented by a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: at the host, initiating receipt of user data from the UE, the user data originating from a transmission which the network node has received from the UE, wherein the network node performs any of the steps of any of the Group B embodiments to receive the user data from the UE for the host. Embodiment 64: The method of the previous embodiment, further comprising at the network node, transmitting the received user data to the host. Embodiment 65: A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a cellular network for transmission to a user equipment (UE), wherein the UE comprises a communication interface and processing circuitry, the communication interface and processing circuitry of the UE being configured to perform any of the operations of any of the Group A embodiments to receive the user data from the host. Embodiment 66: 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 67: The host of the previous 2 embodiments, wherein: the processing circuitry of the host is configured to execute a host application, thereby providing the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application. Embodiment 68: A method implemented by a host operating in a communication system that further includes a network node and a user equipment (UE), the method comprising: providing user data for the UE; and initiating a transmission carrying the user data to the UE via a cellular network comprising the network node, wherein the UE performs any of the operations of any of the Group A embodiments to receive the user data from the host. Embodiment 69: The method of the previous embodiment, further comprising: at the host, executing a host application associated with a client application executing on the UE to receive the user data from the host application. Embodiment 70: The method of the previous embodiment, further comprising: at the host, transmitting input data to the client application executing on the UE, the input data being provided by executing the host application, wherein the user data is provided by the client application in response to the input data from the host application. Embodiment 71: A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a cellular network for transmission to a user equipment (UE), wherein the UE comprises a communication interface and processing circuitry, the communication interface and processing circuitry of the UE being configured to perform any of the steps of any of the Group A embodiments to transmit the user data to the host. Embodiment 72: 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 73: The host of the previous 2 embodiments, wherein: the processing circuitry of the host is configured to execute a host application, thereby providing the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application. Embodiment 74: 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 75: The method of the previous embodiment, further comprising: at the host, executing a host application associated with a client application executing on the UE to receive the user data from the UE. Embodiment 76: The method of the previous 2 embodiments, further comprising: at the host, transmitting input data to the client application executing on the UE, the input data being provided by executing the host application, wherein the user data is provided by the client application in response to the input data from the host application. 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, the method comprising:^ determining (602) one or more sets of recommended measurement resourcesfrom one or more first sets of measurement resources configured for the UE, the one or more recommended sets of comprising either or both of: -one or more second sets of recommended measurement resources in which theUE is to perform radio measurements; -one or more third sets of recommended measurement resources in which the UEis to perform radio measurement predictions using one or more Artificial Intelligence, AI, / Machine Learning, ML, models or functions available at theUE, upon performing radio measurements in one or more second sets of measurement resources; ^transmitting (602), to a network node, a first indication comprising informationindicative of the one or more sets of recommended measurement resources; ^in response to transmitting the first indication, receiving (604), from the networknode, a second indication comprising either or both of:- information indicative of a second set of measurement resources on which theUE is to perform the radio measurements in order to determine radio measurement predictions in a third set of measurement resources; -information indicative of the third set of measurement resources on which theUE is to perform the radio measurements predictions based on the second set of measurement resources; and ^upon receiving the second indication, starting to perform (610) radio measurementpredictions on the resources included in the third set of measurement resources, based onradio measurements performed on the second set of measurement resources using theAI / ML models or functions available at the UE.

2. The method of claim 1, wherein determining (602) the one or more sets of recommendedmeasurement resources comprises: evaluating (602A) one or more applicability conditions of one or more AI / ML models or functions available at the UE based on the one or more first sets of measurement resources configured for the UE; anddetermining (602C) the one or more sets of recommended measurement resourcesin response to the one or more applicability conditions of the one or more AI / ML models orfunctions being satisfied (602B, YES) as determined by the evaluating (602A).

3. The method of claim 2, wherein evaluating (602A) the one or more applicabilityconditions comprises evaluating that the radio measurement prediction results can be determined by the AI / ML model / functionality with a certain accuracy.

4. The method of any of claims 1 to 3, further comprises receiving (600) information, from anetwork node, that configures the UE with the one or more first sets of measurement resources.

5. The method of claim 4, wherein receiving (600) the information that configures the UEwith the one or more first sets of measurement resources comprises receiving (600) the information that configures the UE with the one or more first sets of measurement resources as part of a configuration for data collection for UE-side model training.

6. The method of claim 4, wherein receiving (602) the information that configures the UEwith the one or more first sets of measurement resources comprises receiving the information that configures the UE with the one or more first sets of measurement resources as part of a configuration for performing radio measurements and associated radio measurement reporting.

7. The method of claim 4, wherein receiving (602) the information that configures the UEwith the one or more first sets of measurement resources comprises receiving the information that configures the UE with the one or more first sets of measurement resources as part of a configuration indicating the one or more first sets of measurement resources as one or more sets of candidate measurement resources that can be currently configured by the network node to the UE to perform the radio measurements to determine radio measurement predictions.

8. The method of any of claims 1 to 7, wherein the second indication is an indication toactivate an AI / ML model(s) or function(s) at the UE according to the one or more second sets ofrecommended measurement resources and / or the one or more third sets of recommended measurement resources.

9. The method of any of claims 1 to 7, wherein the information comprised in the secondindication indicates that the one or more second sets of measurement resources is equal to the oneor more second sets of recommended measurement resources and / or the one or more third sets ofmeasurements resources is equal to the one or more third sets of recommended measurement resources.

10. The method of any of claims 1 to 9, wherein the one or more second sets of recommendedmeasurement resources are equal to the one or more first sets of measurement resources, a subsetof the one or more first sets of measurement resources, or a subset of at least one of the one ormore first sets of measurement resources.

11. The method of any of claims 1 to 10, wherein the one or more third sets of recommendedmeasurement resources are a subset of the one or more first sets of measurement resources or asubset of at least one of the one or more first sets of measurement resources.

12. The method of any of claims 1 to 11, wherein the determining (602) the one or more setsof recommended measurement resources is done upon receiving a request from the network nodeto indicate any of the second and third sets of recommended measurement resources.

13. The method of claim 12, wherein the request is to indicate applicability of the one ormore AI / ML models or functions available at the UE.

14. The method of any of claims 1 to 13, wherein each of the one or more first sets ofmeasurement resources is associated to an identification ID.

15. The method of any of claims 1 to 13, wherein each of the one or more first sets ofmeasurement resources is associated to a set ID.

16. The method of claim 14 or 15, wherein each of the one or more resources comprised inthe one or more first sets of measurement resources is associated to a resource ID.

17. The method of claim 15, wherein the set ID for one of the one or more first sets ofmeasurement resources is unique within a cell.

18. The method of claim 16, wherein the resource ID for one resource within one of the oneor more first sets of measurement resources is unique within the cell.

19. The method of any of claims 1 to 18, wherein, for each of the one or more sets ofrecommended measurement resources, the first indication comprises an associated set ID for that set of recommended measurement resources.

20. The method of any of claims 1 to 18, wherein, for each of the one or more sets ofrecommended measurement resources, the first indication comprises one or more resource IDs associated to resources within that set of recommended measurement resources.

21. The method of any of claims 1 to 18, wherein the first indication contains onlyinformation indicative of the one or more third sets of recommended measurement resources or the one or more second sets of recommended measurement resources indicated by the first indication is empty, and this implies that the UE can perform radio measurement predictions on the indicated one or more third sets of recommended measurement resources by performing radio measurements on resources indicated in the one or more first sets of measurement resources excluding resources indicated in the one or more third sets of recommended measurement resources.

22. The method of any of claims 1 to 18, wherein the first indication contains onlyinformation indicative of the one or more second sets of recommended measurement resources or the one or more third sets of recommended measurement resources indicated in the first indication is empty, and this implies that the UE can perform radio measurement predictions on resources indicated in the one or more first sets of measurement resources excluding resources indicated in the one or more second sets of recommended measurement resources, by performing radio measurement on the indicated one or more second sets of recommended measurement resources.

23. The method of any of claims 1 to 22, wherein the second set of measurement resources isequal to or a subset of the one or more second sets of recommended measurement resources orequal to or a subset of at least one of the one or more second sets of recommended measurement resources.

24. The method of any of claims 1 to 23, wherein the third set of measurement resources isequal or a subset of the one or more third sets of recommended measurement resources or equalto or a subset of at least one of the one or more second sets of recommended measurement resources.

25. The method of any of claims 1 to 24, wherein the output of the AI / ML model or functionavailable at the UE is the radio measurement predictions on the third set of measurement resources.

26. The method of any of claims 1 to 25, wherein the second indication is received via RadioResource Control, RRC, dedicated signaling or Medium Access Control, MAC, Control Element,CE, or Physical Downlink Control Channel, PDCCH.

27. The method of any of claims 1 to 26, wherein transmitting (602) the first indicationcomprises transmitting (602) the first indication to the network node in response to any of:^ being configured by the network node to perform AI / ML-based radio measurementprediction, ^upon changing Radio Resource Control, RRC, state,^ upon entering a target cell after a mobility procedure,^ upon determining that the applicability conditions of one or more AI / ML models orfunctions available at the UE are fulfilled or not for one or more resources included in the first set of measurement resources, ^upon being configured with a first set of measurement resources,^ upon receiving a reconfiguration of the first set of measurement resources,^ upon AI / ML model / function activation request being received from the network node,^ upon applicability reporting of an AI / ML models / functions being requested by thenetwork node.

28. The method of claim 27, wherein the applicability conditions of one or more AIMLmodels or functions available at the UE are checked by the UE prior to transmitting the firstindication.

29. The method of any of claims 1 to 28, wherein the first indication is transmitted via RadioResource Control, RRC, signaling or Medium Access Control, MAC, or Uplink ControlInformation, UCI.

30. The method of any of claims 1 to 29, wherein if the second indication contains only thethird set of measurement resources or if the second set of measurement resources included in the second indication is empty, this implies that the UE can perform the radio measurement predictions on the indicated third set of resources by performing radio measurement on the resources indicated previously in the first set of measurement resources excluding the resources indicated in the said third set of resources.

31. The method of any of claims 1 to 29, wherein if the first indication contains only thesecond set of measurement resources or if the third set of recommended measurement resources included in the first indication is empty, this implies that the UE can perform the radio measurement predictions on the resources indicated in the first set of measurement resources excluding the resources indicated in the said second set of resources, by performing radio measurement on the indicated second set of resources.

32. A User Equipment, UE, adapted to:^ determine (602) one or more sets of recommended measurement resources fromone or more first sets of measurement resources configured for the UE, the one or more recommended sets comprising either or both of: -one or more second sets of recommended measurement resources in which theUE is to perform radio measurements; -one or more third sets of recommended measurement resources in which the UEis to perform radio measurement predictions using one or more Artificial Intelligence, AI, / Machine Learning, ML, models or functions available at the UE, upon performing radio measurements in one or more second sets of measurement resources; ^transmit (602), to a network node, a first indication comprising informationindicative of the one or more sets of recommended measurement resources; ^in response to transmitting the first indication, receive (604), from the networknode, a second indication comprising either or both of:- information indicative of a second set of measurement resources on which theUE is to perform the radio measurements in order to determine radio measurement predictions in a third set of measurement resources;- information indicative of the third set of measurement resources on which theUE is to perform the radio measurements predictions based on the second set of measurement resources; and ^upon receiving the second indication, start to perform (610) radio measurementpredictions on the resources included in the third set of measurement resources, based on radio measurements performed on the second set of measurement resources using the AI / ML models or functions available at the UE.

33. The UE of claim 32, further adapted to perform the method of any of claims 2 to 31.

34. A User Equipment, UE, (800) comprising:^ a communication interface (812) comprising a transmitter (818) and a receiver(820); and ^processing circuitry (802) associated with the communication interface (8012),the processing circuitry (802) configured to cause the UE (800) to: -determine (602) one or more sets of recommended measurement resources fromone or more first sets of measurement resources configured for the UE, the one or more recommended sets comprising either or both of: ^one or more second sets of recommended measurement resources in which theUE is to perform radio measurements; ^one or more third sets of recommended measurement resources in which the UEis to perform radio measurement predictions using one or more Artificial Intelligence, AI, / Machine Learning, ML, models or functions available at the UE, upon performing radio measurements in one or more second sets of measurement resources; -transmit (602), to a network node, a first indication comprising informationindicative of the one or more sets of recommended measurement resources; -in response to transmitting the first indication, receive (604), from the networknode, a second indication comprising either or both of:^ information indicative of a second set of measurement resources on which theUE is to perform the radio measurements in order to determine radio measurement predictions in a third set of measurement resources;^ information indicative of the third set of measurement resources on which theUE is to perform the radio measurements predictions based on the second set of measurement resources; and -upon receiving the second indication, start to perform (610) radio measurementpredictions on the resources included in the third set of measurement resources, based on radio measurements performed on the second set of measurement resources using the AI / ML models or functions available at the UE.

35. The UE (800) of claim 34, wherein the processing circuitry (802) is further configured tocause the UE (800) to perform the method of any of claims 2 to 31.

36. A method performed by a network node, the method comprising:^ receiving (602) from a User Equipment, UE, a first indication comprisinginformation indicative of one or more sets of recommended measurement resources, the one or more recommended sets comprising either or both of: -one or more second sets of recommended measurement resources in which theUE is to perform radio measurements; -one or more third sets of recommended measurement resources in which the UEis to perform radio measurement predictions using one or more Artificial Intelligence, AI, / Machine Learning, ML, models or functions available at the UE, upon performing radio measurements in one or more second sets of measurement resources; ^in response to receiving the first indication, transmitting (604), to the UE, asecond indication comprising either or both of: -information indicative of a second set of measurement resources on which theUE is to perform the radio measurements in order to determine radio measurement predictions in a third set of measurement resources; -information indicative of the third set of measurement resources on which theUE is to perform the radio measurements predictions based on the second set of measurement resources.

37. The method of claim 36, further comprising sending (600), to the UE, information thatconfigures the one or more first set of measurement resources for the UE.

38. The method of claim 37, wherein the one or more first sets of measurement resources areconfigured to the UE as part of a configuration for performing radio measurements and associated reporting.

39. The method of claim 37, wherein the one or more first sets of measurement resources areconfigured to the UE as part of a configuration indicating a set of candidate measurementresources that can be currently configured by the network to the UE to perform the radiomeasurements to determine radio measurement predictions.

40. The method of any of claims 36 to 39, wherein the second set of measurement resourcesis a set of measurement resources associated to which the network node is to transmit referencesignals in order for the UE to perform radio measurement predictions in the third set ofmeasurement resources.

41. The method of claim 40, wherein the second indication is sent to the UE as part of aconfiguration indicating a set of measurement resources on which the UE is to perform the radiomeasurements to determine radio measurement predictions.

42. The method of any of claims 36 to 41, wherein the third set of measurement resources is aset of measurement resources comprising measurement resources associated to which the network node does not need to transmit reference signals in order for the UE to perform radio measurement predictions in the third set of recommended resources.

43. The method of claim 42, wherein the second indication is sent to the UE as part of aconfiguration indicating a set of measurement resources on which the UE is to perform the radiomeasurements to determine radio measurement predictions.

44. A network node adapted to:^ receive (602) from a User Equipment, UE, a first indication comprisinginformation indicative of one or more sets of recommended measurement resources, the one or more recommended sets comprising either or both of: -one or more second sets of recommended measurement resources in which theUE is to perform radio measurements;- one or more third sets of recommended measurement resources in which the UEis to perform radio measurement predictions using one or more Artificial Intelligence, AI, / Machine Learning, ML, models or functions available at the UE, upon performing radio measurements in one or more second sets of measurement resources; ^in response to receiving the first indication, transmitting (604), to the UE, asecond indication comprising either or both of: -information indicative of a second set of measurement resources on which theUE is to perform the radio measurements in order to determine radio measurement predictions in a third set of measurement resources; -information indicative of the third set of measurement resources on which theUE is to perform the radio measurements predictions based on the second set of measurement resources.

45. The network node of claim 44, further adapted to perform the method of any of claims 37to 43.

46. A network node (900) comprising processing circuitry (902) configured to cause thenetwork node (900) to:^ receive (602) from a User Equipment, UE, a first indication comprisinginformation indicative of one or more sets of recommended measurement resources, the one or more recommended sets comprising either or both of: -one or more second sets of recommended measurement resources in which theUE is to perform radio measurements; -one or more third sets of recommended measurement resources in which the UEis to perform radio measurement predictions using one or more Artificial Intelligence, AI, / Machine Learning, ML, models or functions available at the UE, upon performing radio measurements in one or more second sets of measurement resources; ^in response to receiving the first indication, transmitting (604), to the UE, asecond indication comprising either or both of: -information indicative of a second set of measurement resources on which theUE is to perform the radio measurements in order to determine radio measurement predictions in a third set of measurement resources;- information indicative of the third set of measurement resources on which theUE is to perform the radio measurements predictions based on the second set of measurement resources.

47. The network node of claim 46, wherein the processing circuitry is further configured tocause the network node to perform the method of any of claims 37 to 43.

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