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WO2025183612A1 - Sélection de ressources de mesure pour entraîner un modèle ml côté ue pour pour des prédictions de mesure radio par ia/ml - Google Patents

Sélection de ressources de mesure pour entraîner un modèle ml côté ue pour pour des prédictions de mesure radio par ia/ml

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Publication number
WO2025183612A1
WO2025183612A1 PCT/SE2025/050176 SE2025050176W WO2025183612A1 WO 2025183612 A1 WO2025183612 A1 WO 2025183612A1 SE 2025050176 W SE2025050176 W SE 2025050176W WO 2025183612 A1 WO2025183612 A1 WO 2025183612A1
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WIPO (PCT)
Prior art keywords
measurement resources
sets
measurement
resources
training
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Application number
PCT/SE2025/050176
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English (en)
Inventor
Reem KARAKI
Henrik RYDÉN
Marco BELLESCHI
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Telefonaktiebolaget LM Ericsson AB
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Telefonaktiebolaget LM Ericsson AB
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Application filed by Telefonaktiebolaget LM Ericsson AB filed Critical Telefonaktiebolaget LM Ericsson AB
Publication of WO2025183612A1 publication Critical patent/WO2025183612A1/fr
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Anticipated expiration legal-status Critical

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

Definitions

  • TECHNICAL FIELD T he present disclosure relates to a cellular communications system and, more specifically, training of a User Equipment (UE)-side Artificial Intelligence (AI) / Machine Learning (ML) model for AI/ML radio measurement predictions.
  • UE User Equipment
  • AI Artificial Intelligence
  • ML Machine Learning
  • 3GPP 3 rd Generation Partnership Project
  • NR New Radio
  • 3GPP 3 rd Generation Partnership Project
  • NR New Radio
  • the available large transmission bandwidths in these frequency ranges can potentially provide large data rates.
  • carrier frequency increases, both pathloss and penetration loss increase.
  • highly directional beams are required to focus the radio transmitter energy in a particular direction on the receiver.
  • large radio antenna arrays – at both receiver and transmitter sides — are needed to create such highly direction beams.
  • large antenna arrays for high frequencies use time-domain analog beamforming.
  • analog beamforming is to share a single radio frequency chain between many (or, potentially, all) of the antenna elements.
  • a limitation of analog beamforming 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.
  • 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 these reference signals at the receiver side, the best transmit and receive beams can be identified.
  • Beam management procedures in NR are defined by a set of Layer 1 (L1)/Layer 2 (L2) procedures that establish and maintain a suitable beam pairs for both transmitting and receiving data.
  • a beam management procedure can include the following sub procedures: beam determination, beam measurements, beam reporting, and beam sweeping.
  • P1/P2/P3 beam management procedures can be performed according to the NR Study Item (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.
  • SI NR Study Item
  • ⁇ P1 The P1 procedure is used to enable UE measurement on different transmission/reception point (TRP) Tx beams to support selection of TRP Tx beams/UE Rx beam(s).
  • TRP transmission/reception point
  • the NR base station i.e., the gNodeB, g NB
  • SS Synchronization Signal
  • PBCH Physical Broadcast Channel
  • the UE measures signal quality on corresponding SSB signals to detect and select an appropriate SSB beam, this i s shown in Figure 1 (SSB beam selection as part of Initial access procedure according to P1 scenario). Random access is then transmitted on the Random Access Channel ( RACH) resources indicated by the selected SSB.
  • RACH Random Access Channel
  • 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.
  • TRP For beamforming at TRP, it typically includes an intra/inter-TRP Tx beam sweep from a set of different beams.
  • For beamforming at UE it typically includes a UE Rx beam sweep from a set of different beams.
  • ⁇ P2 The P2 procedure is used to enable UE measurement on different TRP Tx beams to possibly 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- R S) 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.
  • CSI- R S Channel State Information Reference Signal
  • the UE m easures the Reference Signal Received Power (RSRP) and reports the result to the network.
  • RSRP Reference Signal Received Power
  • 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. o P2 procedure is performed on a possibly smaller set of beams for beam refinement than in P1.
  • P2 can be a special case of P1.
  • gNB configures the UE with different CSI-RSs and transmits each CSI-RS on corresponding beam.
  • UE measures the quality of each CSI- R S beam on its current RX beam and sends feedback about the quality of the measured beams. Thereafter, based on this feedback, gNB will decide and possibly indicates to the UE which beam will be used in future transmissions.
  • T his is shown in Figure 2 (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 beam in the case UE uses beamforming.
  • the UE is configured with a set of reference signals.
  • the UE determines which Rx beam is suitable to receive each reference signal in the set.
  • the network 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.
  • P3 can be used by the UE to find the best Rx beam for corresponding Tx beam.
  • 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.
  • Figure 3 illustrates UE Rx beam selection for corresponding CSI- RS Tx beam in DL according to P3 scenario.
  • 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.
  • SINR Signal to Interference plus Noise Ratio
  • UE measurement reports can be sent either over PUCCH or PUSCH to the network node, e.g., gNB.
  • 2 .1 Reference Signal Configurations in NR CSI-RS A CSI-RS is transmitted over each transmit (Tx) antenna port at the network node and for different antenna ports.
  • the CSI-RS are multiplexed in time, frequency, and code domain such that the channel between each Tx antenna port at the network node and each receive antenna port at a UE can be measured by the UE.
  • the time-frequency resource used for transmitting CSI-RS is referred to as a CSI-RS resource.
  • the CSI-RS for beam management is defined as a 1- or 2-port CSI-RS resource in a 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-RS transmission is semi-statically configured using Radio Resource Control (RRC) signaling with parameters such as CSI-RS resource, periodicity, and slot offset.
  • RRC Radio Resource Control
  • ⁇ 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 RE locations which consist of subcarrier locations and Orthogonal F requency 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 in uplink (UL) Downlink C ontrol Information (DCI), in the same DCI where the UL resources for the measurement report are scheduled.
  • DCI Downlink C ontrol Information
  • 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.
  • an SSB In NR, an SSB consists of a pair of synchronization signals (SSs), physical broadcast channel (PBCH), and Demodulation Reference Signal (DMRS) for PBCH.
  • An SSB is mapped to 4 consecutive OFDM symbols in the time domain and 240 contiguous subcarriers (20 resource blocks (RBs)) in the frequency domain.
  • NR supports beamforming and beam-sweeping for SSB transmission, by enabling a cell to transmit multiple SSBs in different narrow-beams multiplexed in time. The transmission of these SSBs is confined to a half frame time interval (5 milliseconds (ms)). It is also possible to configure a cell to transmit multiple SSBs in a single wide-beam with multiple repetitions.
  • the design of beamforming parameters for each of the SSBs within a half frame is up to network implementation.
  • the SSBs within a half frame are broadcasted periodically from each cell.
  • the periodicity of the half frames with SS/PBCH blocks is referred to as SSB periodicity, which is indicated by System Information Block (SIB) 1 (SIB1).
  • SIB System Information Block
  • 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.
  • a UE 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 NR A UE can be configured with the following: ⁇ N ⁇ 1 CSI reporting settings (CSI-ReportConfig) and ⁇ M ⁇ 1 resource settings (CSI-ResourceConfig). Each CSI reporting setting is linked to one or more resource setting for channel and/or interference 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 UE by RRC information element (IE) (CSI-ResourceConfigs).
  • One CSI-ResourceConfig contains several 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 Non-Zero Power (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 resource elements (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.
  • 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.
  • Parameters such 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.
  • 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 r eport are semi-statically configured by RRC but the triggering is dynamic
  • the content and time-domain behavior of the report is defined, along with the linkage to the associated Resource Settings.
  • the CSI-ReportConfig IE comprises the following configurations: ⁇ reportConfigType o Defines the time-domain behavior (periodic CSI reporting, semi-persistent CSI reporting, or aperiodic CSI reporting) along with the periodicity and slot offset of the report for periodic CSI reporting. ⁇ reportQuantity o Defines the reported CSI parameters -- the CSI content; for example, the Precoding Matrix Indicator (PMI), Channel Quality Indicator (CQI), Rank Indicator (RI), LI (layer indicator), CRI (CSI-RS resource index) and L1-RSRP.
  • PMI Precoding Matrix Indicator
  • CQI Channel Quality Indicator
  • RI Rank Indicator
  • LI layer indicator
  • CRI CSI-RS resource index
  • L1-RSRP L1-RSRP
  • codebookConfig o Defines the codebook used for PMI reporting, along with possible codebook subset restriction (CBSR).
  • CBSR codebook subset restriction
  • 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.
  • ⁇ reportFrequencyConfiguration o Define the frequency granularity of PMI and CQI (wideband or subband), if reported, 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 interference respectively
  • 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.
  • the report of L1-SINR for beam management has already been supported.
  • the use case of beam prediction which will be standardized as part of 3GPP Release 19 work item, consists of spatial beam prediction and temporal beam prediction.
  • the core idea of this use case is to predict the “best” beam (or beams) from a Set A of beams using measurement results from another Set B of beams.
  • TR Technical Report
  • the spatial-domain beam prediction for Set A of beams is based on measurement results of Set B of beams
  • the temporal beam prediction for Set A of beams is based on the historic measurement results of Set B of beams.
  • Set A and Set B of beams have not been defined yet (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.
  • Set A is a set of 8 SSB/CSI-RS beams shown in Figure 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.
  • Figure 4 illustrates an example where Set B is a subset of Set A.
  • the figure illustrates a grid-of-beam type radiation pattern: Each row (resp. column) depicts a certain zenith (resp.
  • 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.
  • Set A is a set of 30 narrow CSI-RS beams
  • Set B is a set of 8 wide SSB beams.
  • the UE measures beams in Set B and the AI/ML model should predict the best beam(s) from Set A.
  • Figure 5 illustrates an example where Set A is a set of narrow beams and Set B is a set of wide beams.
  • the beam prediction can be performed in the gNB and in the UE, and the gain is twofold.
  • the UE 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.
  • the UE can perform the beam prediction on a certain set of resources with a certain accuracy, depends on the applicability conditions of an Artificial Intelligence (AI) / Machine Learning (ML) (also sometimes referred to herein as “AIML”) model/function.
  • AIML Artificial Intelligence
  • an AIML model/function may be trained to perform the beam prediction under certain applicability conditions.
  • the 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.
  • the UE 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).
  • the output beam prediction
  • received input e.g. radio measurement resources configured by the gNB.
  • 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., Beams from Set A) - Alt.4: Measurements of the predicted best beam(s) corresponding to model output (e.g., Comparison between
  • 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/or SINR (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 .
  • RSRP i.e., layer 1 RSRP or L1-RSRP
  • SINR i.e., layer 1 SINR or L1-SINR
  • AI/ML-based prediction is data collection, which is essential to train a model, since the model is trained/retrained/finetuned based on collected data. Data collection is performed in several stages of the Life-Cycle Management (LCM). i .
  • LCM Life-Cycle Management
  • the model must be trained by collecting measurement data for a large set of UE locations/channel conditions representative for the UE locations/channel conditions that may be encountered during use of the model (i.e. inference).
  • the UE may need to collect measurements from the gNB signals (e.g.
  • the UE-side model will be able to perform beam predictions on certain sets of beams, i.e. set A.
  • the training entity can be the UE itself (e.g. the application layer of the UE), or a network node, e.g. a radio access node like a gNB or a Core Network (CN) node (e.g.
  • NWDAF Network Data Analytics Function
  • OTT Over-the- Top
  • UE-vendor specific implementations e.g. software/hardware properties/capabilities
  • SUMMARY Systems and methods are disclosed for selecting measurement resources for training a User Equipment (UE)-sided model for Artificial Intelligence (AI)/Machine Learning (ML) radio measurement predictions are disclosed.
  • UE User Equipment
  • AI Artificial Intelligence
  • ML Machine Learning
  • a method performed by a UE for data collection for training of one or more AI/ML models or functionalities comprises receiving, from a network node, first information that indicates a first set of measurement resources. The method further comprises, based on the first set of measurement resources, selecting one or more second sets of recommended measurement resources in which the UE is to perform radio measurements and/or one or more third sets of measurement resources for prediction in which, once trained, one or more AI/ML models or functions available at the UE are to provide radio measurement predictions, upon performing radio measurements in one or more second sets of measurement resources. In this manner, the UE saves on performing measurements by selecting recommended measurement resources.
  • the one or more AI/ML models or functions comprise one or more AI/ML models that are part of an AI/ML functionality available at the UE or at a node performing UE-side model training of the one or more AI/ML models for the UE, that corresponds to radio measurement predictions.
  • receiving the first information that indicates the one or more first sets of measurement resources comprises receiving the first information that indicates the one or more first sets of measurement resources as part of a configuration for data collection for UE- side model training.
  • receiving the first information that indicates the one or more first sets of measurement resources comprises receiving the first information that indicates the one or more first sets of measurement resources as part of a measurement configuration for performing and reporting of the radio measurements.
  • receiving the first information that indicates the one or more first sets of measurement resources comprises receiving the first information that indicates the one or more first sets of measurement resources as part of a measurement configuration indicating a set of candidate measurement resources that can be configured by the network node to the UE to perform data collection for UE-side AI/ML model training.
  • selecting the one or more second recommended sets of measurement resources and/or the one or more third sets of measurement resources comprises selecting the one or more second recommended sets of measurement resources and the one or more third sets of measurement resources from resources included in the one or more first sets of measurement resources.
  • selecting the one or more second recommended sets of measurement resources and/or the one or more third sets of measurement resources is performed before training the one or more AI/ML models or functionalities.
  • selecting the one or more second recommended sets of measurement resources and/or the one or more third sets of measurement resources is performed upon fulfilling any one or more of the following conditions: receiving a request from the network node or from a training entity performing UE-side AI/ML model training, wherein the request is to start data collection for UE-side model training; receiving a request to indicate applicability of the one or more AI/ML models or functionalities available at the UE; receiving a request in response to the UE indicating capability to support the one or more AI/ML models or functionalities; receiving a request in response to UE indicating capability to collect data for AI/ML training.
  • selecting the one or more second recommended sets of measurement resources and/or the one or more third sets of measurement resources is performed upon fulfilling any one or more of the following conditions: upon determining that radio measurements for data collection for training of the one or more AI/ML models or functionalities has not been previously performed on at least part of the radio resources indicated in the one or more first sets of measurement resources; upon determining that radio measurements for data collection for training of the one or more AI/ML models or functionalities have not been previously performed for the network node transmitting the first information that indicates the one or more first sets of measurement resources; upon determining that radio measurements for data collection for training of the one or more AI/ML models or functionalities have not been previously performed in a geographic area in which the UE is located at the moment of performing the selecting, upon determining that radio measurements for data collection for training of AIML model/functionality have not been performed since a certain amount of time in the area in which the UE is located or in the network node to which the UE is connected at the moment of doing the selection.
  • the method further comprises transmitting to the network node a first indication comprising information that indicates the selected one or more second sets of recommended measurement resources and/or information that indicates the selected one or more third sets of measurement resources for predictions.
  • the first indication is transmitted via Radio Resource Control (RRC) signaling, Medium Access Control (MAC) Control Element (CE), or Uplink Control Information, UCI.
  • RRC Radio Resource Control
  • MAC Medium Access Control
  • CE Control Element
  • UCI Uplink Control Information
  • the UE for each of the selected one or more second sets of recommended measurement resources and each of the selected one or more third sets of measurement resources indicated by the information comprised in the first indication, the UE includes an associated set ID.
  • the UE for each of the selected one or more second sets of recommended measurement resources and each of the selected one or more third sets of measurement resources indicated by the information comprised in the first indication, includes one or more resource IDs associated to resources within that set of measurement resources.
  • the first indication is transmitted from the UE to the network node in response of any of: upon decision to retrain or finetune an existing model, wherein the decision is network-triggered; upon receiving a reconfiguration of the first set of measurement resources; being configured by the network node to perform AI/ML-based radio measurement prediction; upon receiving an activation request for the one or more AI/ML models or functionalities from the network node.
  • the first indication is transmitted from the UE to the network node in response of any of: upon decision to retrain or finetune an existing model, wherein the decision is initiated by the UE or triggered by a training entity performing UE-side model training of the one or more AI/ML models or functions; upon change in network or additional conditions; a new model is to be trained by the UE; one or more of the existing AI/ML models or functions at the UE are not fulfilling associated performance requirements.
  • the method further comprises, in response to transmitting the first indication, receiving, from the network node, a second indication comprising information that indicates a second set of measurement resources on which the UE is to perform radio measurements for data collection for the AI/ML model(s) in order to determine radio measurement predictions in the third set(s) of measurement resources and information that indicates a third set of measurement resources in which the trained AI/ML model(s) is to provide radio measurement predictions upon performing the radio measurements based on the second set of measurement resources.
  • the network is enabled to be involved in the selection of the measurement resources.
  • the second set of measurement resources is equal to or a subset of the one or more second sets of recommended measurement resources.
  • the third set of measurement resources indicated by the information comprised in the second indication received by the UE from the network node is equal to or a subset of the one or more third sets of measurement resources indicated by the information comprised in the first indication transmitted by the UE to the network node.
  • the method further comprises, upon selecting the one or more second sets of recommended measurement resources and the one more third sets of measurement resources for prediction, starting to perform the radio measurements on the resources included in the one or more second sets of recommended measurement resources and the one or more third sets of measurement resources for prediction.
  • the method further comprises sending the radio measurements and an associated set ID and / or resource ID of the radio resource for which data collection was performed to a training entity performing UE-side model training of the one or more AI/ML models/functionalities.
  • the collected measurements are used to train an AI/ML model at the training entity.
  • the UE or the training entity performing UE-side model training stores the associated set ID or resource ID for measurement resources used for performing the measurement.
  • the stored set ID or resource ID is used by the UE or by the training entity to determine the radio measurement resources for which the data collection for training has been performed for the network node that configured the UE with the one or more first sets of measurement resources.
  • the method further comprises, in response to transmitting the first indication, receiving a third indication indicating that the one or more second sets of recommended measurement resources and/or the one or more third sets of measurement resources are rejected.
  • one or more applicability conditions of the one or more AI/ML models or functionalities available at the UE are checked by the UE prior to transmitting the first indication.
  • receiving the first information that indicates the one or more first sets of measurement resources comprises receiving the first information via dedicated signaling or broadcast signaling.
  • a training entity for training the one or more AI/ML models or functionalities is a function located in the UE, and data collected is transmitted from one or more lower layers of the UE to the training entity within the UE.
  • a UE for data collection for training of one or more AI/ML models or functionalities comprises a communication interface comprising a transmitter and a receiver.
  • the UE further comprises processing circuitry associated with the communication interface.
  • the processing circuitry is configured to cause the UE to receive, from a network node, first information that indicates a first set of measurement resources.
  • the processing circuitry is further configured to cause the UE to, based on the first set of measurement resources, select one or more second sets of recommended measurement resources in which the UE is to perform radio measurements and/or one or more third sets of measurement resources for prediction in which, once trained, one or more AI/ML models or functions available at the UE are to provide radio measurement predictions, upon performing radio measurements in one or more second sets of measurement resources.
  • E mbodiments of a method performed by a network node are also disclosed.
  • a method performed by a network node comprises transmitting, to a UE, information indicative of one or more first sets of measurement resources and receiving, from the UE, a first indication comprising any of: information that indicates one or more second sets of recommended measurement resources in which the UE is to perform radio measurements; and information that indicates one or more third sets of measurement resources for prediction in which one or more trained AI/ML models or functionalities available at the UE are to provide radio measurement predictions.
  • the method further comprises determining (a) a second set of measurement resources on which the UE is to perform radio measurements for data collection for the one or more AI/ML models or functionalities in order to determine radio measurement predictions in a third set of measurement resources and (b) the third set of measurement resources, based on the one or more second sets of recommended measurement resources and the one or more third sets of measurement resources for prediction indicated by the information comprised in the first indication.
  • the method further comprises transmitting a second indication to the UE, the second indication comprising information that indicates the determined second set of measurement resources and information that indicates the determined third set of measurement resources.
  • the method further comprises transmitting reference signals associated to resources included in the determined second and third sets of measurement resources.
  • a network node comprises processing circuitry configured to cause the network node to transmit, to a UE, information indicative of one or more first sets of measurement resources and receive, from the UE, a first indication comprising any of: information that indicates one or more second sets of recommended measurement resources in which the UE is to perform radio measurements; and information that indicates one or more third sets of measurement resources for prediction in which one or more trained AI/ML models or functionalities available at the UE are to provide radio measurement predictions.
  • Figure 1 illustrates Synchronization Signal (SS) / Physical Broadcast Channel (PBCH) Block (SSB) selection as part of an initial access procedure.
  • Figure 2 illustrates Channel State Information (CSI) Reference Signal (CSI-RS) transmit beam selection in downlink.
  • F igure 3 illustrates User Equipment (UE) receives beam selection for a corresponding CSI-RS transmit beam in the downlink.
  • Figure 4 illustrates an example in which Set B is a subset of Set A.
  • Figure 5 illustrates an example in which Set A is a set of narrow beams and Set B is a set of wide beams.
  • Figure 6 illustrates the operation of a network node (NW) and a UE in accordance with embodiments of the present disclosure.
  • Figure 7 shows an example of a communication system in accordance with some embodiments.
  • NW network node
  • 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 illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized.
  • 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.
  • Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.
  • T here currently exist certain challenge(s) related to the use of Artificial Intelligence (AI) / Machine Learning (ML) model for predictions (e.g., beam predictions) in a wireless communication system such as, e.g., a 3rd Generation Partnership (3GPP) 5th Generation (5G) (or future generation) system.
  • 3GPP 3rd Generation Partnership
  • 5G 5th Generation
  • the training phase of an AI/ML model/functionality consists of the UE measuring the channel and collecting data to be used by the training entity to train/retrain/finetune the AI/ML model/functionality. In particular, during this phase, the UE measures the channel in certain radio resources, e.g.
  • CSI-RS Channel State Information
  • SS Synchronization Signal
  • PBSCH Physical Broadcast Channel blocks
  • the network may not be aware of the resources that the UE needs to measure in order to collect data for training purposes, especially if the UE-side model is trained in a node different than the New Radio (NR) base station (i.e., gNodeB, gNB). Additionally, each UE may request a different network configuration, which would require an excessive amount of additional reference signal transmissions to accommodate for different UE needs, especially because these reference signal transmissions are to be used by the UE to collect measurement for training purposes, rather than classical measurements to be reported to the gNB for data scheduling purposes. From the network (NW) perspective, this diminishes the benefits of running an AI/ML model at the UE side, and it also creates lots of extra overhead over the air interface.
  • NR New Radio
  • 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. If set A/B selection (see Section 3 of the Background section above for description of “set A” and “set B”) is completely up to the UE, there is a risk that the NW deactivates the AI/ML model/functionality, since it would generate radio measurement predictions on radio resources that are not of interest, and the UE would have just wasted power for the training. Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges.
  • Embodiments of systems and methods are disclosed for enabling a UE to transmit a recommended set A/B for training an AI/ML beam prediction model and obtain an early feedback from the NW concerning the selected beams in set A and/or set B before initiating model training and/or training data collection.
  • the early feedback enables the UE to obtain knowledge about the NW preferred set A/B prior to training and by that avoid wasting resources and energy to train a model that does not fit the NW needs.
  • receiving the information that indicates the one or more first sets of measurement resources comprises receiving the information that indicates the one or more first sets of measurement resources as part of a configuration for data collection for UE-side model training.
  • receiving the information that indicates the one or more first sets of measurement resources comprises receiving the information that indicates the one or more first sets of measurement resources as part of a configuration for data collection for UE-side model training.
  • receiving the information that indicates the one or more first sets of measurement resources comprises receiving the information that indicates the one or more first sets of measurement resources as part of a measurement configuration for performing and reporting of the radio measurements.
  • receiving the information that indicates the one or more first sets of measurement resources comprises receiving the information that indicates the one or more first sets of measurement resources as part of a measurement configuration indicating a set of candidate measurement resources that can be configured by the network node to the UE to perform data collection for UE side model training.
  • selecting the one or more second recommended sets of measurement resources and the one or more third sets of measurement resources is performed upon fulfilling any one or more of the following conditions: o Receiving a request from the network node or from a training entity performing the UE-side model training, wherein the request is to start data collection for UE- side model training, o Receiving a request to indicate applicability of the AIML models/functionalities available at the UE, o Receiving a request in response to UE indicating capability to support the AIML models/functionalities, o Receiving a request in response to UE indicating capability to collect data for training, o Upon determining that radio measurements for data collection for training of AIML model/functionality has not been previously performed on at least part of t he radio resources indicated in the one or more first sets of measurement resources, o Upon determining that radio measurements for data collection for training of AIML model/functionality has not been previously performed for the network n ode transmitting the one or more first sets of measurement resources,
  • each of the one or more first sets of measurement resources is associated to an identification ID.
  • each of the one or more first sets of measurement resources is associated to a set ID ⁇ A11.
  • each of one or more resources comprised in the one or more first sets of measurement resources is associated to a resource ID.
  • the set ID for one of the one or more first sets of measurement resources is unique within the cell.
  • the resource ID for one resource within the one or more first sets of measurement resources is unique within the cell. ⁇ A14.
  • the method according to A1 further comprising transmitting to the network node in a first indication any of: o information that indicates the selected one or more second sets of recommended measurement resources (set B) o information that indicates the selected one or more third sets of measurement resources for predictions (set A) ⁇ A15.
  • the method according to A14 wherein for each of the selected one or more second sets of recommended measurement resources and each of the selected one or more third s ets of measurement resources indicated by the information comprised in the first indication, the UE includes an associated set ID. ⁇ A16.
  • the UE includes one or more resource IDs associated to resources within that s et of (recommended) measurement resources.
  • the method according to A14 further comprising, in response to transmitting the first indication, receiving (e.g., from the network node) a second indication comprising: o information that indicates a second set of measurement resources on which the UE is to perform radio measurements for data collection for the AI/ML model(s) i n order to determine radio measurement predictions in the third set(s) of measurement resources; and o information that indicates a third set of measurement resources in which the trained AI/ML Model is to provide radio measurement predictions upon performing the radio measurements based on the second set of measurement resources.
  • ⁇ A17A The method according to A17, wherein the second set of measurement resources is equal to or a subset of the one or more second sets of recommended measurement resources.
  • the third set of measurement resources indicated by the information comprised in the second indication received by the UE from the network node is equal to or a subset of the one or more third sets of measurement resources indicated by the information comprised in the first indication transmitted by the UE to the network node.
  • the second indication is received by the UE as part of a configuration for performing radio measurements.
  • the second indication is received by the UE as part of a configuration for data collection for UE-side model training.
  • the method according to A17 further comprising, upon receiving the second indication, transmitting (e.g., to the network node) a request for radio transmissions required for performing the radio measurements on the resources included in the second and third sets of measurement resources indicated by the information comprised in the second indication.
  • transmitting e.g., to the network node
  • the request comprises an associated set ID for the second set of measurement resources and an associated set ID for the third set of measurement resources.
  • A23 The method according to A17, further comprising, upon receiving the second indication, starting to perform the radio measurement on the resources included in the second and third sets of measurement resources.
  • the method according to A1 or A14 further comprising, upon selecting the one or more second sets of recommended measurement resources and the one more third sets of measurement resources for prediction (and optionally transmitting the first indication to the network node), starting to perform the radio measurements on the resources included in the one or more second sets of recommended measurement resources and the one or m ore third sets of measurement resources for prediction.
  • A25 The method according to any of A23 or A24, further comprising sending the radio measurements and an associated set ID and / or resource ID of the radio resource for which data collection was performed to a training entity performing UE-side model t raining of the one or more AI/ML models/functionalities.
  • the method according to any of A23, A24, A25 wherein the UE or the training entity performing the UE-side model training logs/stores the associated set ID or resource ID for measurement resources used for performing the measurement.
  • A27 The method according to A25, wherein the collected measurements are used to train an AI/ML model at the training entity.
  • the logged set ID or resource ID is used by the UE or by the training entity to determine the radio measurement resources for which the data collection for training has been performed for the network node that configured t he UE with the one or more first sets of measurement resources.
  • A29 The method according to any of A23 or A24, wherein the collected measurements are not reported to the network node.
  • ⁇ A30 The method according to A14, wherein the first indication is transmitted from the UE to the network node in response of any of: o upon decision to retrain/finetune an existing model, wherein the decision is triggered by the NW or initiated by the UE, or triggered by the training entity performing the UE-side model training.
  • o Upon receiving a reconfiguration of the first set of measurement resources.
  • o Upon change in the NW configuration or additional conditions. As non-limiting example, a change in the NW antenna configuration or antenna pattern, the existing model at the UE becomes no longer applicable, and a UE initiates training of a new model.
  • a new model is to be trained by the UE.
  • One or more of the existing models at the UE are not fulfilling the performance requirements.
  • each of the one or more first sets of measurement resources comprise any of: o a set of SSB for a cell o a set of CSI-RS resources for a cell o a set of SS/PBCH block resource set for a cell o a set of cells o a set of frequencies o a set of SSB for a list of cells o a set of CSI-RS resources for a list of cells o a set of SS/PBCH block resource set for a list of cells ⁇ A35.
  • an output of the trained one or more AI/ML models/functionalities at the UE is radio measurement predictions on the third set of measurement resources comprising prediction results for one or more measurement quantities, such as the RSRP, RSRQ, SINR, RSSI level, associated to the second set of measurement resources.
  • measurement quantities such as the RSRP, RSRQ, SINR, RSSI level
  • the radio measurement prediction results comprise any of: o the measured quantities for each of the one or more resources in the third set of measurement resources o the measured quantities for the best resources in terms of measured quantities among 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 o the measured quantities for the worst resources in terms of measured quantities among 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 o the average measured quantities for the resources in the second and/or third set of measurement resources. o the variance of the measured quantities for the resources in the second and/or third set of measurement resources. o the accuracy of the reported predictions results ⁇ A37.
  • the radio measurement prediction results can further include: o a time instance when the prediction results are valid, for example indicated in an absolute UTC time, or in a NR time-unit in respect to when the second set of measurement resources are measured.
  • a time-index relative to when the first measurement of the second set of measurement resources are performed ⁇
  • a time-index relative to when the last measurement of the second set of measurement resources are performed o a time-window for how long the radio measurement prediction results are valid, for example a certain number of NR-time units from the NW receives the radio measurement prediction results. Or in respect to the time-instance in bullet above.
  • o a time stamp indicating the point in time (indicated in an absolute UTC time, or in a NR time-unit) in which the data collection for the training purposes started or stopped.
  • o a time stamp indicating the point in time (indicated in an absolute UTC time, or in a NR time-unit) in which a radio measurement prediction was performed
  • the location indicating the point in time indicated in an absolute UTC time, or in a NR time-unit
  • the location indicating the point in time indicated in an absolute UTC time, or in a NR time-unit in which a radio measurement prediction was performed ⁇ A38.
  • a method further comprising receiving (e.g., from the network node) information that indicates the one or more first sets of measurement resources via RRC signaling dedicated to the UE, or broadcast signaling (e.g., SIB).
  • RRC signaling dedicated to the UE
  • broadcast signaling e.g., SIB
  • the first indication is transmitted via RRC signaling (UEAssistanceInformation), or MAC (MAC CE), or UCI.
  • MAC CE MAC dedicated signaling
  • UCI e.g., MAC CE
  • a method is transmitted via RRC signaling (UEAssistanceInformation), or MAC (MAC CE), or UCI.
  • the second and/or third indication is received via RRC dedicated signaling or MAC (MAC CE), PDCCH.
  • the UE indicates availability of AL/ML model/functionality that is applicable to conditions comprising training based on the second and third set for measurements resources.
  • ⁇ A42 A method according to any of the previous methods, wherein the training entity is a function located in a RAN network node, gNB, or core network node, or OTT server, or UE.
  • ⁇ A43 A method according to A42, wherein if the training entity is a function located in the UE, the data collected are transmitted from the UE lower layers to the said training entity within the UE.
  • a method performed by a network node comprises transmitting, to a UE (e.g., via dedicated or broadcast signaling) i nformation indicative of one or more first sets of measurement resources.
  • a network node e.g., a RAN node such, e.g., a gNB
  • the method comprises transmitting, to a UE (e.g., via dedicated or broadcast signaling) i nformation indicative of one or more first sets of measurement resources.
  • a network node e.g., a RAN node such, e.g., a gNB
  • the method comprises transmitting, to a UE (e.g., via dedicated or broadcast signaling) i nformation indicative of one or more first sets of measurement resources.
  • a UE e.g., via dedicated or broadcast signaling
  • a method according to B2 further comprising: determining: o a second set of measurement resources on which the UE is to perform radio measurements for data collection for the one or more AI/ML models/functionalities in order to determine radio measurement predictions in a third set of measurement resources, and o the third set of measurement resources, based on the one or more second sets of recommended measurement resources and the one or more third sets of measurement resources for prediction indicated by the information comprised in the first indication.
  • a method according to B4 further comprising transmitting reference signals associated to resources included in the determined second and third sets of measurement resources (e.g., for the UE to perform radio measurements).
  • a method according to B1 further comprising transmitting reference signals associated to resources included in the one or more first sets of measurement resources (e.g., for the UE to perform radio measurements).
  • Certain embodiments may provide one or more of the following technical advantage(s). The benefits of the proposed solution are twofold. On one hand, the UE saves on performing measurements by offering different alternatives for set A,B to accommodate for the networks need. On the other hand, if configured correctly, the network (e.g., gNB) can save on reference signal transmissions.
  • the UE can provide information on certain alternatives that fits onto its own hardware, and alternatives that provide the UE with its own optimal trade-off between energy saving and prediction performance in finding the best beam.
  • the network is the consumer of the UE sided prediction; therefore, it is beneficial that the network has some level of involvement in the selection of set A/B for the UE sided model 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.
  • 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. In this way, the network can potentially reduce on reference signal transmissions by aligning the set B among different UEs.
  • TS 38.331 shows the definition of a Non-Zero Power (NZP) Channel State Information (CSI) Reference Signal (CSI- RS) Resource Information Element (IE), the NZP CSI-RS Resource Set IE, the CSI Resource Config IE, and the CSI Report Config IE, which may be beneficial for understanding certain embodiments of the present disclosure.
  • NZP Non-Zero Power
  • CSI- RS Channel State Information
  • IE Resource Information Element
  • 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 [19], clause 5.2.2.3.1).
  • NZP Non-Zero-Power
  • SI-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 the cell in which the CSI-ReportConfig is included, or to configure a semi- persistent or aperiodic report sent on PUSCH triggered by DCI received on the cell in which the CSI-ReportConfig is included (in this case, the cell on which the report is sent is determined by the received DCI). See TS 38.214 [19], clause 5.2.1.
  • C SI-ReportConfig information element -- ASN1START -- TAG-CSI-REPORTCONFIG-START CSI-ReportConfig :: SEQUENCE ⁇ reportConfigId CSI-ReportConfigId, carrier ServCellIndex OPTIONAL, -- Need S resourcesForChannelMeasurement CSI- ResourceConfigId, csi-IM-ResourcesForInterference CSI-ResourceConfigI d OPTIONAL, -- Need R ; ***** END EXCERPT FROM 3GPP TS 38.331 *****
  • 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/beam configurations.
  • an identifier is provided to the UE indicating the beam configuration/pattern. For example, in case the UE receive the same beam configuration/pattern ID over two or more cells, it can assume that the CSI resources are using the same beams/precoders. This can be achieved via introduction in 3GPP specifications of a “consistency” identifier for its CSI resources, that is then valid over a longer duration than a normal ResourceID, and possibly over multiple cells.
  • F igure 6 illustrates the operation of a network node (NW) and a UE in accordance with embodiments of the present disclosure.
  • the network node e.g., a RAN node such as, e.g., a gNB
  • the network node configures the UE with measurements that can be used by the UE to train an AI/ML model, say a first set of radio measurement resources.
  • the network node sends, to the UE, information that indicates the first set of radio measurement resources configured to the UE.
  • a single first set of radio measurement resources is used for this discussion, it is to be understood that there may be one or more first sets of radio measurement resources.
  • the configuration, or information that indicates the first set of measurement resources can be provided to the UE in dedicated signaling (e.g., RRC signaling).
  • the UE may indicate its capability to support AI/ML model/functionality or the UE may indicate the need to perform model training to the network node.
  • the network node may then provide the configuration of the first set of measurement resources to the UE.
  • the configuration of the first set of measurement resources may be provided via broadcasting signaling (e.g., SIB) to the UEs in the cell.
  • SIB broadcasting signaling
  • Step 602 the UE selects resources in which to perform radio measurement predictions (referred to herein as a third set of measurement resources for predictions, or set A) and resources in which to perform radio measurements necessary for the UE to perform the radio measurement predictions (referred to herein as a second set of recommended measurement resources, or recommended set B) from a set of resources configured by the network node (i.e., the first set of measurement resources from Step 600).
  • the first set of measurement resources may be resources that the network node has configured to the UE to indicate CSI-RS (and/or other 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.
  • the first set of measurement resources may be resources indicated separately from the resources in which the UE has to perform radio measurements.
  • 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 (candidate second set of measurement resources), and a candidate set of measurement resources for which the UE can determine the radio measurement predictions (candidate third set of measurement resources).
  • the first set of measurement resources can be associated by the network node to an identifier.
  • an identifier may be associated to each resource set (set ID) included in the first set of measurement resources, or to each individual resource (resource ID) included by the network node in a resource set (e.g., the first set of measurement resources). Such ID may be unique within the cell, and it may be associated to a specific network configuration.
  • the UE Based on received reference signal transmissions and/or information related to the first set of measurement resources, in Step 602, the UE selects one or more set B (second set of recommended measurement resources) to train the one or more AI models/functionalities available at the UE.
  • the one or more selected set B may compromise one or more of the following beams: - one or more SSB beams - one or more narrow beams, e.g.
  • the UE may associate a set A (third set of measurement resources for prediction), wherein the set A may be a subset of the beams listed above.
  • 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 certain accuracy level - Cost of measurements, in case the UE is battery constrained, it can select to measure on less 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 o Note that reducing the number of beams in set B, leads to less model inputs and thereby typically simpler models.
  • Set A and B indicated by the network - Historical information at the UE for example the UE have been configured with a certain consistencyID and/or resource ID a threshold number of times, and would hence benefit in creating a model for predicting instead of measuring such resources.
  • the UE would in this case, for example use the N most frequent beams in set B, and the other beams in set A. In this way, the UE would at least get measurement of the typically N strongest beams.
  • the UE estimates the correlation among beams and select the N beams that are most uncorrelated as part of set B.
  • T he selection of such resources by the UE in Step 602 may be triggered by any of the following events: ⁇ Receiving a request from the network node or from the training entity performing the UE-side model training, wherein the request is to start data collection for UE-side model training. o For example, the training entity may determine that the UE needs to perform data collection for training given that an AIML model/functionality is not applicable when the UE is connected to such network node , or no AIML model/functionality has been previously trained for this network node . ⁇ Receiving a request to indicate the applicability of the AIML models/functionalities available at the UE.
  • the UE may receive a request from the network node to determine the applicability of an existing AIML model/functionality, and in response to that the UE may trigger data collection for training.
  • the training entity may for example determine that no data collection for training was previously performed associated to the area in which this network node is located, and hence it may trigger the UE to perform data collection for training.
  • ⁇ Upon determining that radio measurements for data collection for training of AIML model/functionality has not been performed since a certain amount of time in the area in which the UE is located or in the network node to which the UE is connected at the moment of doing the selection. o The training entity may for example determine that the AIML model/functionality that may be applicable for this area may be outdated, i.e.
  • the training entity involved in the above steps may be a logical function located in a RAN node, such as the network node, or in a core network node, e.g. the Network Data Analytics Function (NWDAF), or in an over-the-top server, or in the application layer of the UE.
  • the UE may select one or more set B to train one or more AI/ML models, due to: - A new model is to be trained by the UE, e.g. there is no model available at the UE that is applicable to the NW configuration.
  • - Upon decision to retrain/finetune an existing model, wherein the decision is triggered by the NW or initiated by the UE. - Upon receiving a reconfiguration of the first set of measurement resources. - Upon change in the NW configuration or additional conditions. As non-limiting example, a change in the NW antenna configuration or antenna pattern, the existing model at the UE becomes no longer applicable, and a UE initiate training of a new/updated model. - One or more of the existing models at the UE are not fulfilling the performance requirements. - Being configured by the network node to perform AIML-based radio measurement prediction.
  • the UE may start performing radio measurements on the selected resources from the first set of measurement resources, i.e. on the second sets of recommended measurement resources to generated predictions on the third set of measurement resources for prediction (Step 608).
  • the UE may log and store the collected data and transmit it to the training entity.
  • the UE may also log and store the resources, e.g. the set and/or resource ID associated to which data collection for training was performed. For example, the UE may store the IDs associated to the resources of second sets of recommended measurement resources and of the third set of measurement resources for prediction.
  • This information is important for the UE and/or for the training entity to determine whether training was previously performed when the UE was connected to the network node, and the resources associated to which data collection was performed. Hence, in this step, the UE along with the collected measurement results, the UE may collect additional information from the network node that is used for training purposes and transmit that to the training entity.
  • Such information may include: - The associated consistency ID and/or set ID and/or resource IDs for each of measurement resource for which the measurements are collected - Time stamps at which the measurements are collected - Non-radio information to be used by the UE-sided model, for example o Geolocation information o Sensor information o UE orientation information
  • the network node transmits the reference signals associated to the resources included in the first set of measurement resources (Step 606). For example, the network node may start transmitting the reference signals associated to the resources included in the first set of measurement resources upon transmitting the first set of measurement resources.
  • the training entity trains one or more AI models corresponding to each of the approved set (A,B) combination (Step 610).
  • the UE may then report the availability of an AI/ML model that is applicable to the set A/B used for training to the network node.
  • the UE upon doing the selection in Step 602, the UE indicates the second sets of recommended measurement resources and/or third set of measurement resources for prediction to the network node in a first indication, as described below in Step 602a.
  • S tep 602a In this step, the UE signals a first indication that indicates a recommended set B and/or set A to the network node and asks for confirmation before training a corresponding AI/ML model(s). In the first indication, the UE can report the said set IDs or resource IDs associated to the selected set B and set A.
  • the UEW can indicate which nzp-CSI-RS-ResourceId in case only a subset of the beams in each set are included - Which nzp-CSI-RS-ResourceId that are part of setB/A.
  • 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.
  • 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 the above methods.
  • the UE may just report either only the second set of recommended measurement resources or the third set of recommended measurement resources.
  • the UE would train a model that uses measurements on 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.
  • the UE model would be trained to perform the radio measurement predictions on the resources indicated in the said third set using measure radio measurements according to the first set of measurement resources previously configured by the network node excluding the resources indicated in the said third set.
  • the UE can report this information in the existing framework on capabilities in 3GPP. Alternatively, the UE can report this information via any one or more of RRC, uplink (UL) Medium Access Control (MAC) Control Elements (CE), or Uplink Control Information (UCI).
  • RRC uplink
  • MAC Medium Access Control
  • CE Control Elements
  • UCI Uplink Control Information
  • the UE can report the set 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.
  • 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.
  • S tep 604 Upon receiving the first indication in Step 602a, the network node can, based on the reported alternatives of the recommended set (A, B), decide on (i.e., determine) one or more preferred sets (A,B), i.e. second set of measurement resources and third set of measurement resources, and send a second indication that indicates the one or more preferred sets (A,B) to the UE.
  • the UE receives the second indication from the network that indicates the set B/set A. If the network approves any of the recommended set A/set B that is suggested by the UE in the first indication of Step 602a, the UE can proceed with Step 608 to start performing measurements.
  • the indication of the approved set A and set B can be signaled to the UE in the second indication via any one or more of RRC messages (e.g. RRC reconfiguration procedure), DL MAC CE, or PDCCH.
  • RRC messages e.g. RRC reconfiguration procedure
  • DL MAC CE e.g. DL MAC CE
  • PDCCH Physical Downlink Control Channel
  • the network node 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 network node of the set A and set B, or two different MAC CEs associated to different logical channels identities may be indicated by the network node 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.
  • the message may contain two separate lists indicating the resource IDs or set IDs associated to the selected set A and set B. T he second and third sets of measurements indicated by the information contained in the second indication may be signaled as part of different information element, e.g. an IE including information that indicates the second set, and another IE including information that indicates the third set. In another embodiment, the same IE can be used to transmit both. In such case, it is the UE implementation that the determines the second set and the third set from such IE.
  • the second set corresponds to the second sets of recommended measurement resources selected from the first set and included in this IE in the second indication
  • the third set corresponds to the third set of measurement resources for prediction selected from the first set included in this IE in the second indication.
  • the decision on whether the UE recommended set A/B are valid can be based on: -
  • the time instance for such prediction is not useful for the NW, e.g. the NW anyway cannot schedule data in a certain time window (e.g. due to Time Division Duplex configuration)
  • the beams in set B or set A is seldom used, they may be occasional beam due to some event (e.g.
  • the network node transmits reference signals on both set A and set B.
  • the network node may transmit, to the UE, information that configures the UE to perform measurements on both set A and set B. Further, in some embodiments, the UE may request the network to configure measurements corresponding to the reference signal transmissions on the approved set A and set B for the purpose of training data collection.
  • S tep 608 The UE performs measurements corresponding to the reference signal transmissions on the approved set A and set B. The measurements are logged/stored by the UE for the purpose of training the one or more AI/ML models.
  • the network node may start transmitting the reference signals associated to the resources included in the indicated second and third set of measurement resources for the UE to perform the corresponding data collection.
  • the collected measurements may also be delivered the training entity along with additional information such as the associated consistency ID and/or set ID and/or resource IDs for each of measurement resource for which the measurements are collected, the time stamps at which the measurements are collected, non-radio information to be used by the UE-sided model, for example Geolocation information, Sensor information, UE orientation information S tep 610:
  • the training entity trains one or more AI models corresponding to each of the approved set (A,B) combination.
  • T he UE may, in some embodiments, report the availability of an AI/ML model that is applicable to the set A/B used for training.
  • the UE can simply acknowledge that it has trained a model according to the network configuration in Step 606.
  • this Step 602 and 4 can be performed by training entity that decides on the selection of set B based on the reported measurements from one or more UEs.
  • the training entity trains one or more AL models and delivers the one or more models with the additional information about set A and B to the UE.
  • An example implementation of an embodiment of the present disclosure may be provided via the following changes to 3GPP specifications.
  • the network node can for example, when configuring CSI-RS and/or SSB-related measurements, indicate that the UE can use a certain configuration for data collection for training (e.g., indicate the first set of measurement resources).
  • the bold, italicized information in the CSI-MeasConfig IE shown below may be used for this purpose: nzp- ResourceSets)) OF NZP-CSI-RS-ResourceSetId OPTIONAL, -- Need N csi-IM-ResourceToAddModList SEQUENCE (SIZE (1..maxNrofCSI-IM-Resources)) OF CSI-IM- Resource OPTIONAL, -- Need N csi-IM-ResourceToReleaseList SEQUENCE (SIZE (1..maxNrofCSI-IM-Resources)) OF CSI-IM- ResourceId OPTIONAL, -- Need N csi-IM-ResourceSetToAddModList SEQUENCE (SIZE (1..maxNrofCSI-IM-Re
  • the network node e.g., gNB
  • the network node already provides the reference signals/SSBs for the UE to perform the measurement.
  • the UE indicates in the first indication the resources that it needs to perform the training.
  • the UE may indicate the second sets of recommended measurement resources and/or the third set of measurement resources for prediction.
  • This first indication may be transmitted for example via RRC (UEAssistanceInformation), or via MAC CE.
  • the network node may transmit a second indication including the second set of measurement resources (setA) and/or a third set of measurement resources(setB) in which the UE should perform the radio measurements as shown by the bold, italicized text in CSI-MeasConfig below:
  • CSI-MeasConfig :: SEQUENCE ⁇ nzp-CSI-RS-ResourceToAddModList SEQUENCE (SIZE (1..maxNrofNZP-CSI-RS-Resources)) OF NZP- CSI-RS-Resource OPTIONAL, -- Need N nzp-CSI-RS-ResourceToReleaseList SEQUENCE (SIZE (1..maxNrofNZP-CSI-RS-Resources)) OF NZP- CSI-RS-ResourceId OPTIONAL, -- Need N nzp-CSI-RS-ResourceSetToAddModListForTraining SEQUENCE (SIZE (1..maxNrofNZ
  • 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).
  • 3GPP Third Generation Partnership Project
  • 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.
  • network nodes include disaggregated implementations or portions thereof.
  • the telecommunication network 702 includes one or more Open-RAN (ORAN) network nodes.
  • ORAN Open-RAN
  • 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.
  • ORAN specification e.g., a specification published by the O-RAN Alliance, or any similar organization
  • 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).
  • a near-real time control application e.g., xApp
  • rApp non-real time control application
  • 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.
  • an ORAN access node may be a logical node in a physical node.
  • an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized.
  • the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an O-2 interface defined by the O-RAN Alliance or comparable technologies.
  • the network nodes 710 facilitate 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.
  • UE User Equipment
  • 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.
  • 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.
  • 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.
  • 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.
  • the communication system 700 of Figure 7 enables connectivity between the UEs, network nodes, and hosts.
  • 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.
  • GSM Global System for Mobile Communications
  • UMTS Universal Mobile Telecommunications System
  • LTE Long Term Evolution
  • 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.
  • the UEs 712 are configured to transmit and/or receive information without direct human interaction.
  • 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.
  • a UE may be configured for operating in single- or multi-Radio Access Technology (RAT) or multi-standard mode.
  • RAT Radio Access Technology
  • a UE may operate with any one or combination of WiFi, New Radio (NR), and LTE, i.e. being configured for Multi-Radio Dual Connectivity (MR-DC), such as Evolved UMTS Terrestrial RAN (E-UTRAN) NR - Dual Connectivity (EN-DC).
  • MR-DC Multi-Radio Dual Connectivity
  • E-UTRAN Evolved UMTS Terrestrial RAN
  • EN-DC Dual Connectivity
  • 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).
  • the hub 714 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs.
  • the hub 714 may be a broadband router enabling access to the core network 706 for the UEs.
  • 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.
  • 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.
  • 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.
  • VR Virtual Reality
  • the hub 714 may be configured to connect to a Machine-to-Machine (M2M) service provider over the access network 704 and/or to another UE over a direct connection.
  • M2M Machine-to-Machine
  • UEs may establish a wireless connection with the network nodes 710 while still connected via the hub 714 via a wired or wireless connection.
  • the hub 714 may be a dedicated hub – that is, a hub whose primary function is to route communications to/from the UEs from/to the network node 710B.
  • the hub 714 may be a non-dedicated hub – that is, a device which is capable of operating to route communications between the UEs and the network node 710B, but which is additionally capable of operating as a communication start and/or end point for certain data channels.
  • F igure 8 shows a UE 800 in accordance with some embodiments.
  • 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.
  • NB-IoT Narrowband Internet of Things
  • MTC Machine Type Communication
  • eMTC enhanced MTC
  • 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).
  • D2D Device-to-Device
  • DSRC Dedicated Short-Range Communication
  • V2V Vehicle-to-Vehicle
  • V2I Vehicle-to-Infrastructure
  • V2X Vehicle- to-Everything
  • a UE may not necessarily have a user in the sense of a human user who owns and/or operates the relevant device.
  • a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller).
  • 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.
  • the processing circuitry 802 may include multiple Central Processing Units (CPUs).
  • 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, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like.
  • the presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user.
  • a sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof.
  • An output device may use the same type of interface port as an input device.
  • a Universal Serial Bus (USB) port may be used to provide an input device and an output device.
  • 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.
  • 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.
  • RAID Redundant Array of Independent Disks
  • 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).
  • 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.
  • 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.
  • GPS Global Positioning System
  • 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.
  • 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).
  • 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.
  • 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, a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot.
  • UAV Unmanned Ae
  • a UE in the form of an IoT device comprises circuitry and/or software in dependence of the intended application of the IoT device in addition to other components as described in relation to the UE 800 shown in Figure 8.
  • a UE may represent a machine or other device that performs monitoring and/or measurements and transmits the results of such monitoring and/or measurements to another UE and/or a network node.
  • the UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device.
  • the UE may implement the 3GPP NB-IoT standard.
  • 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.
  • 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.
  • 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.
  • a UE might comprise the sensor and the actuator and handle communication of data for both the speed sensor and the actuators.
  • F igure 9 shows a network node 900 in accordance with some embodiments.
  • 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.
  • 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).
  • APs e.g., radio APs
  • BSs Base Stations
  • eNBs evolved Node Bs
  • gNBs NR Node Bs
  • O-RAN nodes or components of an O-RAN node e.g., O-RU, O-DU, O- CU.
  • Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations.
  • a base station may be a relay node or a relay donor node controlling a relay.
  • a network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an O-RAN access node), and/or Remote Radio Units (RRUs), sometimes referred to as Remote Radio Heads (RRHs).
  • RRUs Remote Radio Units
  • RRHs Remote Radio Heads
  • RRUs Remote Radio Heads
  • 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).
  • DAS Distributed Antenna System
  • 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).
  • MSR Transmission Point
  • MSR Multi-Standard Radio
  • RNCs Radio Network Controllers
  • BSCs Base Transceiver Stations
  • MCEs Multi-Cell/Multicast Coordination Entities
  • OFM Operation and Maintenance
  • OSS Operations Support System
  • SON Self-Organizing Network
  • positioning nodes
  • 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.
  • the network node 900 comprises multiple separate components (e.g., BTS and BSC components)
  • one or more of the separate components may be shared among several network nodes.
  • a single RNC may control multiple NodeBs.
  • each unique NodeB and RNC pair may in some instances be considered a single separate network node.
  • 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 Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within the network node 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.
  • the processing circuitry 902 includes a System on a Chip (SOC).
  • the processing circuitry 902 includes one or more of Radio Frequency (RF) transceiver circuitry 912 and baseband processing circuitry 914.
  • RF Radio Frequency
  • the RF 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.
  • 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)
  • 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.
  • 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 the filters 920 and/or the amplifiers 922.
  • the radio signal may then be transmitted via the antenna 910.
  • 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.
  • the communication interface 906 may comprise different components and/or different combinations of components.
  • the network node 900 does not include separate radio front-end circuitry 918; instead, the processing circuitry 902 includes radio front-end circuitry and is connected to the antenna 910.
  • all or some of the RF transceiver circuitry 912 is part of the communication interface 906.
  • 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.
  • 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 for performing the functionality described herein.
  • 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.
  • 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.
  • 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.
  • providing a core network node, such as core network node 708 of Figure 7 some components, such as the radio front-end circuitry 918 and the RF transceiver circuitry 912 may be omitted.
  • F igure 10 is a block diagram illustrating a virtualization environment 1000 in which functions implemented by some embodiments may be virtualized.
  • virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices, and networking resources.
  • virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components.
  • Some or all of the functions described herein may be implemented as virtual components executed by one or more Virtual Machines (VMs) implemented in one or more virtualization environments 1000 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, a UE, a core network node, or a host.
  • VMs Virtual Machines
  • the node may be entirely virtualized.
  • the virtualization environment 1000 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. Virtualization may facilitate distributed implementations of a network node, a UE, a core network node, or a host.
  • Applications 1002 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 1000 to implement some of the features, functions, and/or benefits of some of the embodiments disclosed herein.
  • Hardware 1004 includes processing circuitry, memory that stores software and/or instructions executable by hardware processing circuitry, and/or other hardware devices as described herein, such as a network interface, an input/output interface, and so forth.
  • Software may be executed by the processing circuitry to instantiate one or more virtualization layers 1006 (also referred to as hypervisors or Virtual Machine Monitors (VMMs)), provide VMs 1008A and 1008B (one or more of which may be generally referred to as VMs 1008), and/or perform any of the functions, features, and/or benefits described in relation with some embodiments described herein.
  • the virtualization layer 1006 may present a virtual operating platform that appears like networking hardware to the VMs 1008.
  • the VMs 1008 comprise virtual processing, virtual memory, virtual networking, or interface and virtual storage, and may be run by a corresponding virtualization layer 1006.
  • a virtualization layer 1006 may be implemented on one or more of VMs 1008, and the implementations may be made in different ways.
  • Virtualization of the hardware is in some contexts referred to as Network Function Virtualization (NFV).
  • NFV Network Function Virtualization
  • 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.
  • a VM 1008 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine.
  • Each of the VMs 1008, and that part of the hardware 1004 that executes that VM forms separate virtual network elements.
  • a virtual network function is responsible for handling specific network functions that run in one or more VMs 1008 on top of the hardware 1004 and corresponds to the application 1002.
  • the hardware 1004 may be implemented in a standalone network node with generic or specific components.
  • the hardware 1004 may implement some functions via virtualization.
  • the hardware 1004 may be part of a larger cluster of hardware (e.g., such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 1010, which, among others, oversees lifecycle management of the applications 1002.
  • the hardware 1004 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station.
  • some signaling can be provided with the use of a control system 1012 which may alternatively be used for communication between hardware nodes and radio units.
  • a control system 1012 which may alternatively be used for communication between hardware nodes and radio units.
  • the computing devices described herein e.g., UEs, network nodes
  • 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.
  • processing circuitry 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.
  • computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components.
  • 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.
  • 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.
  • some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer- readable storage medium.
  • some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner.
  • the processing circuitry can be configured to perform the described functionality.
  • Embodiment 1 A method performed by a user equipment for data collection for training of one or more Artificial Intelligence, AI, / Machine Learning, ML, models or functionalities, the method comprising: receiving (600), from a network node, first information that indicates a first set of measurement resources; selecting (602) one or more second sets of recommended measurement resources in which the UE is to perform radio measurements and/or one or more third sets of measurement resources for prediction in which, once trained, one or more AI/ML models or functions available at the UE are to provide radio measurement predictions, upon performing radio measurements in one or more second sets of measurement resources.
  • E mbodiment 2 The method of embodiment 1, wherein the one or more AI/ML models or functions comprise one or more AI/ML models that are part of an AI/ML functionality available at the UE or at a node performing UE-side model training of the one or more AI/ML models for the UE, that corresponds to radio measurement predictions.
  • Embodiment 3 The method of embodiment 1, wherein receiving the first information that indicates the one or more first sets of measurement resources comprises receiving the first information that indicates the one or more first sets of measurement resources as part of a configuration for data collection for UE-side model training.
  • Embodiment 4 The method of embodiment 1, wherein receiving the first information that indicates the one or more first sets of measurement resources comprises receiving the first information that indicates the one or more first sets of measurement resources as part of a measurement configuration for performing and reporting of the radio measurements.
  • E mbodiment 5 The method of embodiment 1, wherein receiving the first information that indicates the one or more first sets of measurement resources comprises receiving the first information that indicates the one or more first sets of measurement resources as part of a measurement configuration indicating a set of candidate measurement resources that can be configured by the network node to the UE to perform data collection for UE-side AI/ML model training.
  • E mbodiment 6 The method of embodiment 1, wherein selecting the one or more second recommended sets of measurement resources and/or the one or more third sets of measurement resources comprises selecting the one or more second recommended sets of measurement resources and the one or more third sets of measurement resources from resources included in the one or more first sets of measurement resources.
  • E mbodiment 7 The method of embodiment 1, wherein selecting the one or more second recommended sets of measurement resources and/or the one or more third sets of measurement resources is performed before training the one or more AI/ML models or functionalities.
  • Embodiment 8 The method of embodiment 7, wherein selecting the one or more second recommended sets of measurement resources and/or the one or more third sets of measurement resources is performed upon fulfilling any one or more of the following conditions: receiving a request from the network node or from a training entity performing UE-side AI/ML model training, wherein the request is to start data collection for UE-side model training, receiving a request to indicate applicability of the one or more AI/ML models or functionalities available at the UE, receiving a request in response to the UE indicating capability to support the one or more AI/ML models or functionalities, receiving a request in response to UE indicating capability to collect data for AI/ML training, upon determining that radio measurements for data collection for training of the one or more AI/ML models or functionalities has not been previously performed on at least part of the radio resources indicated in the one or more first sets of measurement resources, upon determining that radio measurements for data collection for training of the one or more AI/ML models or functionalities has not been previously performed for the network node transmitting the first information that indicates
  • E mbodiment 9 The method of embodiment 1, wherein each of the one or more first sets of measurement resources is associated to an identifier, ID.
  • Embodiment 10 The method of embodiment 1, wherein each of the one or more first sets of measurement resources is associated to a set ID
  • E mbodiment 11 The method of embodiment 1, wherein each of one or more resources comprised in the one or more first sets of measurement resources is associated to a resource ID.
  • E mbodiment 12 The method of embodiment 10, wherein the set ID for one of the one or more first sets of measurement resources is unique within the cell.
  • E mbodiment 13 The method of embodiment 11, wherein the resource ID for one resource within the one or more first sets of measurement resources is unique within the cell.
  • E mbodiment 14 The method of embodiment 1, further comprising transmitting (602a) to the network node a first indication comprising: information that indicates the selected one or more second sets of recommended measurement resources; and/or information that indicates the selected one or more third sets of measurement resources for predictions.
  • E mbodiment 15 The method of embodiment 14, wherein for each of the selected one or more second sets of recommended measurement resources and each of the selected one or more third sets of measurement resources indicated by the information comprised in the first indication, the UE includes an associated set ID.
  • E mbodiment 16 The method of embodiment 14, wherein for each of the selected one or more second sets of recommended measurement resources and each of the selected one or more third sets of measurement resources indicated by the information comprised in the first indication, the UE includes one or more resource IDs associated to resources within that set of measurement resources.
  • E mbodiment 17 The method of embodiment 14, further comprising, in response to transmitting the first indication, receiving (604) (e.g., from the network node) a second indication comprising: information that indicates a second set of measurement resources on which the UE is to perform radio measurements for data collection for the AI/ML model(s) in order to determine radio measurement predictions in the third set(s) of measurement resources; and information that indicates a third set of measurement resources in which the trained AI/ML model(s) is to provide radio measurement predictions upon performing the radio measurements based on the second set of measurement resources.
  • E mbodiment 18 The method of embodiment 17, wherein the second set of measurement resources is equal to or a subset of the one or more second sets of recommended measurement resources.
  • E mbodiment 19 The method of embodiment 17, wherein the third set of measurement resources indicated by the information comprised in the second indication received by the UE from the network node is equal to or a subset of the one or more third sets of measurement resources indicated by the information comprised in the first indication transmitted by the UE to the network node.
  • E mbodiment 20 The method of embodiment 17, wherein the second indication is received by the UE as part of a configuration for performing radio measurements.
  • E mbodiment 21 The method of embodiment 17, wherein the second indication is received by the UE as part of a configuration for data collection for UE-side model training.
  • E mbodiment 22 The method of embodiment 17, further comprising, upon receiving the second indication, transmitting (e.g., to the network node) a request for radio transmissions required for performing the radio measurements on the resources included in the second and third sets of measurement resources indicated by the information comprised in the second indication.
  • E mbodiment 23 The method of embodiment 22, wherein the request comprises an associated set ID for the second set of measurement resources and an associated set ID for the third set of measurement resources.
  • E mbodiment 24 The method of embodiment 17, further comprising, upon receiving the second indication, starting (608) to perform the radio measurement on the resources included in the second and third sets of measurement resources.
  • E mbodiment 25 The method of embodiment 1 or 14, further comprising, upon selecting the one or more second sets of recommended measurement resources and the one more third sets of measurement resources for prediction (and optionally transmitting the first indication to the network node), starting (608) to perform the radio measurements on the resources included in the one or more second sets of recommended measurement resources and the one or more third sets of measurement resources for prediction.
  • Embodiment 26 The method of embodiment 24 or 25, further comprising sending the radio measurements and an associated set ID and / or resource ID of the radio resource for which data collection was performed to a training entity performing UE-side model training of the one or more AI/ML models/functionalities.
  • E mbodiment 27 The method of any of embodiments 24, 25, 26, wherein the UE or the training entity performing the UE-side model training logs/stores the associated set ID or resource ID for measurement resources used for performing the measurement.
  • E mbodiment 28 The method of embodiment 26, wherein the collected measurements are used to train an AI/ML model at the training entity.
  • E mbodiment 29 The method of embodiment 27, wherein the logged set ID or resource ID is used by the UE or by the training entity to determine the radio measurement resources for which the data collection for training has been performed for the network node that configured the UE with the one or more first sets of measurement resources.
  • E mbodiment 30 The method of embodiment 24 or 25, wherein the collected measurements are not reported to the network node.
  • E mbodiment 31 The method of embodiment 14, wherein the first indication is transmitted from the UE to the network node in response of any of: upon decision to retrain/finetune an existing model, wherein the decision is triggered by the network (e.g., by the network node) or initiated by the UE, or triggered by the training entity performing UE-side model training of the one or more AI/ML models or functions, upon receiving a reconfiguration of the first set of measurement resources, upon change in network or additional conditions (e.g., a change in an antenna configuration or antenna pattern of the network node), a new model is to be trained by the UE, one or more of the existing AI/ML models or functions at the UE are not fulfilling associated performance requirements, being configured by the network node to perform AIML-based radio measurement prediction, upon receiving an activation request for the one or more AI/ML models or functionalities from the network node.
  • the network e.g., by the network node
  • the training entity performing UE-side model training of the one or
  • Embodiment 32 The method of embodiment 14, further comprising, in response to transmitting the first indication, receiving (604) a third indication indicating that the one or more second sets of recommended measurement resources and/or the one or more third sets of measurement resources are rejected.
  • E mbodiment 33 The method of embodiment 32, wherein in response to receiving the third indication, the UE does not proceed with the AI/ML model training based on the selected second and third measurement resources.
  • Embodiment 34 The method of embodiment 14, wherein one or more applicability conditions of the one or more AIML models or functionalities available at the UE are checked by the UE prior to transmitting the first indication.
  • each of the one or more first sets of measurement resources comprise any of: a set of SSB for a cell; a set of CSI-RS resources for a cell; a set of SS/PBCH block resource set for a cell; a set of cells; a set of frequencies; a set of SSB for a list of cells; a set of CSI-RS resources for a list of cells; a set of SS/PBCH block resource set for a list of cells.
  • E mbodiment 36 The method of embodiment 24 or 25, wherein an output of the trained one or more AI/ML models/functionalities at the UE is radio measurement predictions on the third set of measurement resources comprising prediction results for one or more measurement quantities, such as the RSRP, RSRQ, SINR, RSSI level, associated to the second set of measurement resources.
  • an output of the trained one or more AI/ML models/functionalities at the UE is radio measurement predictions on the third set of measurement resources comprising prediction results for one or more measurement quantities, such as the RSRP, RSRQ, SINR, RSSI level, associated to the second set of measurement resources.
  • the radio measurement prediction results comprise any of: the measured quantities for each of the one or more resources in the third set of measurement resources the measured quantities for the best resources in terms of measured quantities among 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 the measured quantities for the worst resources in terms of measured quantities among 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 the average measured quantities for the resources in the second and/or third set of measurement resources the variance of the measured quantities for the resources in the second and/or third set of measurement resources the accuracy of the reported predictions results.
  • E mbodiment 38 The method of embodiment 37, wherein the radio measurement prediction results can further include: a time instance when the prediction results are valid, for example indicated in an absolute UTC time, or in a NR time-unit in respect to when the second set of measurement resources are measured. For example, a time-index relative to when the first measurement of the second set of measurement resources are performed. For example, a time- index relative to when the last measurement of the second set of measurement resources are performed a time-window for how long the radio measurement prediction results are valid, for example a certain number of NR-time units from the NW receives the radio measurement prediction results.
  • a time stamp indicating the point in time (indicated in an absolute UTC time, or in a NR time-unit) in which the data collection for the training purposes started or stopped; a time stamp indicating the point in time (indicated in an absolute UTC time, or in a NR time-unit) in which a radio measurement prediction was performed; the location indicating the point in time (indicated in an absolute UTC time, or in a NR time-unit) in which the data collection for the training purposes started or stopped; the location indicating the point in time (indicated in an absolute UTC time, or in a NR time-unit) in which a radio measurement prediction was performed.
  • E mbodiment 39 The method of embodiment 1, wherein receiving the first information that indicates the one or more first sets of measurement resources comprises receiving the first information via dedicated signaling (e.g., RRC signaling dedicated to the UE) or broadcast signaling (e.g., SIB).
  • E mbodiment 40 The method of embodiment 14, wherein the first indication is transmitted via RRC signaling (UEAssistanceInformation), or MAC (MAC CE), or UCI.
  • E mbodiment 41 The method of embodiment 17 or 34, wherein the second and/or third indication is received via RRC dedicated signaling or MAC (MAC CE), PDCCH.
  • E mbodiment 42 The method of embodiment 34, the UE indicates availability of the one or more AL/ML models or functionalities that are applicable to conditions comprising training based on the second and third set for measurements resources.
  • E mbodiment 43 The method of any of embodiments 1 to 42, wherein a training entity for training the one or more AI/ML models or functionalities is a function located in a RAN network node, gNB, or core network node, or OTT server, or UE.
  • E mbodiment 44 The method of embodiment 43, wherein if the training entity is a function located in the UE, the data collected are transmitted from the UE lower layers to the said training entity within the UE.
  • Embodiment 45 A method performed by a network node (e.g., a RAN node such, e.g., a gNB), the method comprises: transmitting (600), to a UE (e.g., via dedicated or broadcast signaling) information indicative of one or more first sets of measurement resources.
  • a network node e.g., a RAN node such, e.g., a gNB
  • the method comprises: transmitting (600), to a UE (e.g., via dedicated or broadcast signaling) information indicative of one or more first sets of measurement resources.
  • E mbodiment 46 The method of embodiment 45, further comprising receiving (602a), from the UE, a first indication comprising any of: information that indicates one or more second sets of recommended measurement resources (set B) in which the UE is to perform radio measurements; and information that indicates the selected one or more third sets of measurement resources for prediction (set A) in which one or more trained AI/ML models/functionalities available at the UE are to provide radio measurement predictions.
  • Embodiment 47 The method of embodiment 46, further comprising determining (604) (a) a second set of measurement resources on which the UE is to perform radio measurements for data collection for the one or more AI/ML models or functionalities in order to determine radio measurement predictions in a third set of measurement resources and (b) the third set of measurement resources, based on the one or more second sets of recommended measurement resources and the one or more third sets of measurement resources for prediction indicated by the information comprised in the first indication.
  • Embodiment 48 A method of embodiment 47, further comprising transmitting (604) a second indication to the UE, the second indication comprising information that indicates the determined second set of measurement resources and information that indicates the determined third set of measurement resources.
  • E mbodiment 49 The method of embodiment 48, further comprising transmitting (606) reference signals associated to resources included in the determined second and third sets of measurement resources (e.g., for the UE to perform radio measurements).
  • Embodiment 50 The method of embodiment 45, further comprising transmitting reference signals associated to resources included in the one or more first sets of measurement resources (e.g., for the UE to perform radio measurements).
  • Group C Embodiments E mbodiment 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.
  • E mbodiment 52 A network node comprising: processing circuitry configured to perform any of the steps of any of the Group B embodiments; power supply circuitry configured to supply power to the processing circuitry.
  • E mbodiment 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.
  • UE user equipment

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Abstract

L'invention concerne des systèmes et des procédés pour sélectionner des ressources de mesure afin d'entraîner un modèle ML côté équipement utilisateur (UE) pour des prédictions de mesure radio par intelligence artificielle (IA) ou d'apprentissage automatique (ML). Dans un mode de réalisation, un procédé mis en œuvre par un UE pour la collecte de données d'apprentissage d'un ou de plusieurs modèles ou fonctionnalités d'IA/ML consiste à recevoir, en provenance d'un nœud de réseau, des premières informations qui indiquent un premier ensemble de ressources de mesure. Le procédé consiste en outre, sur la base du premier ensemble de ressources de mesure, à sélectionner un ou plusieurs seconds ensembles de ressources de mesure recommandées dans lesquelles l'UE doit effectuer des mesures radio et/ou un ou plusieurs troisièmes ensembles de ressources de mesure pour une prédiction dans laquelle, une fois entraîné, un ou plusieurs modèles ou fonctions d'IA/ML disponibles au niveau de l'UE doivent fournir des prédictions de mesure radio, lors de la réalisation de mesures radio dans un ou plusieurs seconds ensembles de ressources de mesure.
PCT/SE2025/050176 2024-02-26 2025-02-26 Sélection de ressources de mesure pour entraîner un modèle ml côté ue pour pour des prédictions de mesure radio par ia/ml Pending WO2025183612A1 (fr)

Applications Claiming Priority (2)

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WO2024030604A1 (fr) * 2022-08-05 2024-02-08 Interdigital Patent Holdings, Inc. Validation d'intelligence artificielle (ia)/apprentissage automatique (ml) dans une gestion de faisceau et une prédiction de faisceau hiérarchique
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