Methods for triggering data collection for UE-side ai / ML models

Abstract

Systems and methods are disclosed related to triggering of data collection for User Equipment (UE)-side Artificial Intelligence (AI) / Machine Learning (ML) models. In one embodiment, a method performed by a UE for UE-side data collection for training, retraining, or fine tuning a UE-side AI / ML model comprises transmitting, to a network nod, information related to one or more UE-side conditions related to an expected quality of data samples that can be collected by the UE. The method further comprises receiving, from the network node, an indication to start data collection at the UE using a predefined, preconfigured, or indicated radio resource configuration or an indication to send a request to the network node for a radio resource configuration for data collection for training, retraining, or fine tuning the UE-side AI / ML model.

Description

[0001] METHODS FOR TRIGGERING DATA COLLECTION FOR UE-SIDE AI / ML MODELS

[0002] RELATED APPLICATIONS

[0003] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 735,011, filed December 17, 2024, the disclosure of which is hereby incorporated herein by reference in its entirety.

[0004] TECHNICAL FIELD

[0005] The present disclosure relates to a wireless communication network such as, e.g., a 3rda Radio Access Network (RAN) of a 3rdGeneration Partnership Project (3GPP) system (e.g., a 5thGeneration (5G) system or 6thGeneration (6G) system) and, more specifically, data collection for User Equipment (UE)-side Artificial Intelligence (Al) / Machine Learning (ML) models.

[0006] BACKGROUND

[0007] One of the key features of 3rdGeneration Partnership Project (3GPP) New Radio (NR), compared to previous generations of wireless networks, is the ability to operate in higher frequencies (e.g., above 10 Gigahertz (GHz)). The available large transmission bandwidths in these frequency ranges can potentially provide large data rates. However, as carrier frequency increases, both pathloss and penetration loss increase. To maintain the coverage at the same level, highly directional beams are required to focus the radio transmitter energy in a particular direction on the receiver. However, large radio antenna arrays - at both receiver and transmitter sides - are needed to create such highly direction beams.

[0008] To reduce hardware costs, large antenna arrays for high frequencies use time-domain analog beamforming. The core idea of analog beamforming is to share a single radio 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 using one beam (in one direction) at a given time.

[0009] The above limitation requires the network (NW) and User Equipment (UE) to perform beam management procedures to establish and maintain suitable transmitter (Tx) / receiver (Rx) beam-pairs. For example, beam management procedures can be used by a transmitter to sweep a geographic area by transmitting reference signals on different candidate beams, during nonoverlapping 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. 1 NR Beam Management Procedures

[0010] Beam management procedures in NR are defined by a set of Layer 1 (Ll) / Layer 2 (L2) procedures that establish and maintain 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.

[0011] In case of downlink transmission from the NW to the UE, P1 / P2 / P3 beam management procedures can be performed according to the NR Study Item (SI) technical report (3GPP Technical Report (TR) 38.843 18.0.0) to overcome the challenges of establishing and maintaining the beam pairs when, for example, a UE moves or some blockage in the environment requires changing the beams. Although these scenarios are not directly mentioned in specifications, there are relevant procedures defined which enables the realization of these scenarios, examples of such realization are depicted in the corresponding figure of each scenario:

[0012] • Pl : The Pl 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). During initial access, for example, the NR base station (i.e., the gNodeB (gNB)) transmits Synchronization Signal (SS) / Physical Broadcast Channel (PBCH) block (SSB) beams in different directions to cover the whole cell. The UE measures signal quality on corresponding SSB signals to detect and select an appropriate SSB beam. This is shown in Figure 1. A random access preamble is then transmitted on the Random Access Channel (RACH) resources indicated by the selected SSB. The corresponding beam will be used by both the UE and the network to communicate until connected mode beam management is active. The network infers which SSB beam was chosen by the UE without any explicit signaling. o 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.

[0013] • 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-RS) beams to try; that is, the selected SSB beam can be used to define a candidate set of narrow CSI-RS beams for beam management. Once CSI-RS is transmitted, the UE measures the Reference Signal Received Power (RSRP) and reports the result to the network. If the network receives a CSI-RSRP report from the UE where a new CSI-RS beam is better than the old used to transmit Physical Downlink Control Channel (PDCCH) / Physical Downlink Shared Channel (PDSCH), the network updates the serving beam for the UE accordingly, and possibly also modifies the candidate set of CSI-RS beams. The network can also instruct the UE to perform measurements on SSBs. If the network receives a report from the UE where a new SSB beam is better than the previous best SSB beam, a corresponding update of the candidate set of CSI-RS beams for the UE may be motivated. o P2 procedure is performed on a possibly smaller set of beams for beam refinement than in Pl. Note that P2 can be a special case of Pl. For example, in connected mode gNB configures the UE with different CSI-RSs and transmits each CSI-RS on corresponding beam. UE then measures the quality of each CSI-RS beam on its current RX beam and sends feedback about the quality of the measured beams. Thereafter, based on this feedback, gNB will decide and possibly indicate to the UE which beam will be used in future transmissions. This is shown in Figure 2.

[0014] • P3: is used to enable UE measurement on the same TRP Tx beam to change UE Rx beam in the case UE uses beamforming. Once in connected mode, the UE is configured with a set of reference signals. Based on measurements, the UE determines which Rx beam is suitable to receive each reference signal in the set. The network then indicates which reference signals are associated with the beam that will be used to transmit PDCCH / PDSCH, and the UE uses this information to adjust its Rx beam when receiving PDCCH / PDSCH. o In connected mode, P3 can be used by the UE to find the best Rx beam for corresponding Tx beam. In this case gNB keeps one CSI-RS Tx beam at a time, and UE performs the sweeping and measurements on its own Rx beams for that specific Tx beam. UE then finds the best corresponding Rx beam based on the measurements and will use it in future for reception when gNB indicates the use of that Tx beam.

[0015] 2 Beam Measurement and Reporting in NR

[0016] For beam management, a UE can be configured to report RSRP, RSRQ and / or SINR for each one of up to four beams, either on CSI-RS or SSB. 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

[0017] CSI-RS:

[0018] A CSI-RS is transmited 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 transmiting CSI-RS is referred to as a CSI-RS resource.

[0019] In NR, 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:

[0020] • Periodic CSI-RS: CSI-RS is transmited periodically in certain slots. This CSI-RS transmission is semi-statically configured using RRC signaling with parameters such as CSI-RS resource, periodicity, and slot offset.

[0021] • 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.

[0022] • Aperiodic CSI-RS: This is a one-shot CSI-RS transmission that can happen in any slot. Here, one-shot means that CSI-RS transmission only happens once per trigger. The CSI- RS resources (i.e. , the Resource Element (RE) locations which consist of subcarrier locations and Orthogonal Frequency Division Multiplexing (OFDM) symbol locations) for aperiodic CSI-RS are semi-statically configured. The transmission of aperiodic CSI-RS is triggered by dynamic signaling through PDCCH using the CSI request field in uplink (UL) Downlink Control Information (DCI), in the same DCI where the UL resources for the measurement report are scheduled. Multiple aperiodic CSI-RS resources can be included in a CSI-RS resource set and the triggering of aperiodic CSI-RS is on a resource set basis. SSB:

[0023] 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.

[0024] 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).

[0025] 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 subcarrier spacing (SCS) of the SSBs. The L candidate SSBs within a half frame are indexed in an ascending order in time from 0 to L-l. 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.

[0026] 2.2 Measurement Resource Configurations in NR and Reporting

[0027] A UE can be configured with the following:

[0028] - N>1 CSI reporting settings (CSI-ReportConfig) and

[0029] M>1 resource settings (CSI-ResourceConfig).

[0030] 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.

[0031] The measurement resource configurations for beam management are provided to the UE by Radio Resource Control (RRC) information element (IE) (CSI-ResourceConfigs). One CSI- ResourceConfig contains several NZP-CSI-RS-ResourceSets and / or CSI-SSB-ResourceSets.

[0032] 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 REs,

[0033] - the number of antenna ports, and

[0034] - time-domain behavior.

[0035] 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.

[0036] Three types of CSI reporting are supported in NR as follows:

[0037] • Periodic CSI Reporting on PUCCH: CSI is reported periodically by a UE. Parameters such as periodicity and slot offset are configured semi-statically by higher layer RRC signaling from the network node to the UE

[0038] • Semi-Persistent CSI Reporting on Physical Uplink Shared Channel (PUSCH) or Physical Uplink Control Channel (PUCCH): similar to periodic CSI reporting, semi-persistent CSI reporting has a periodicity and slot offset which may be semi-statically configured. However, a dynamic trigger from network node to UE may be needed to allow the UE to begin semi-persistent CSI reporting. A dynamic trigger from network node to UE is needed to request the UE to stop the semi-persistent CSI reporting.

[0039] • Aperiodic CSI Reporting on PUSCH: This type of CSI reporting involves a single-shot (i.e., one time) CSI report by a UE which is dynamically triggered by the network node using DCI. Some of the parameters related to the configuration of the aperiodic CSI report is semi-statically configured by RRC but the triggering is dynamic

[0040] For beam management, a UE can be configured to report Ll-RSRP for up to four different CSI-RS / SSB resource indicators. The reported RSRP value corresponding to the first (best) CSI- RS Resource Indicator (CRI) / SSB Resource Indicator (SSBRI) requires 7 bits, using absolute values, while the others require 4 bits using encoding relative to the first. In NR release 16, the report of LI -Signal to Interference plus Noise Ratio (SINR) for beam management has already been supported.

[0041] 3 Beam Prediction

[0042] The use case of beam prediction which will be standardized as part of 3GPP Rel.19 work item (see RP-234036, “New WID on Artificial Intelligence (AI) / Machine Learning (ML) for NR Air Interface,” Dec. 2023. New 3GPP work item for Rel. 19) 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.

[0043] According to 3GPP TR 38.843, the spatial-domain beam prediction for Set A of beams is based on measurement results of Set B of beams, whereas the temporal beam prediction for Set A of beams is based on the historic measurement results of Set B of beams. Hence, the radio measurements on the Set B of resources would be the input of an AI / ML model / functionality, whereas the radio measurements on the set A of resources would be the output of the AI / ML model / functionality .

[0044] Set A and Set B of beams have not yet been defined (left for future study); however, the following two examples illustrate some scenarios that were studied in Release 18:

[0045] - Set B is a subset of a Set A. For example, 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.

[0046] - Set A and Set B correspond to two different sets of beams. For example, Set A is a set of 30 narrow CSI-RS beams, and Set B is a set of 8 wide SSB beams, as illustrated in the example of Figure 5. The UE measures beams in Set B, and the AI / ML model should predict the best beam(s) from Set A.

[0047] The beam prediction can be performed in the gNB and in the UE, and the gain is twofold. From the UE point of view, the UE would be able to generate good radio measurement estimations without really measuring certain resources, thereby saving energy, whereas from the gNB point of view, the gNB can get good radio measurements estimation from the UE without providing the measuring resources, thereby limiting the overhead over the air-interface.

[0048] Whether the UE can perform the beam prediction on a certain set of resources with a certain accuracy, depends on the applicability conditions of an AI / ML model / function. In particular, an AI / ML model / function may be trained to perform the beam prediction under certain applicability conditions. Such applicability conditions need to be fulfilled in order for the AIML model / function to generate the expected output, i.e. beam prediction for this use case, with enough accuracy. The applicability conditions may include a set of parameters / variables under which the AI / ML 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.

[0049] In order to determine whether an AI / ML 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). 4 AI / ML for NR Air Interface: Life Cycle Management (LCM)

[0050] In the physical layer, AI / ML has been introduced recently. The application of ML and Al for physical layer design and optimization is being deeply investigated to optimize the design of air-interface in wireless communications.

[0051] In 3GPP NR standardization work, the Release 18 study item (SI) on AI / ML for NR air interface (see 3GPP TR 38.343) started in May 2022. This study item has explored the benefits of augmenting the air-interface with features enabling improved support of AI / ML based algorithms for enhanced performance and / or reduced complexity / overhead. Through studying a few selected use cases (CSI feedback, beam management and positioning), this SI aims at laying the foundation for future air-interface use cases leveraging AI / ML techniques.

[0052] The 3GPP work is continuing with a follow-up specification work in the Release 19 AI / ML for NR air interface work item (WI) (see RP-234039). 6thGeneration (6G) systems based on 3GPP Release 20+ are expected to consider additional AI / ML use cases.

[0053] A possible high-level description of model Life-Cycle Management (LCM) for Al on the Physical Layer (PHY) can include the stages and data / signal flows depicted in Figure 6.

[0054] 5 Data Collection

[0055] A key part of 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 LCM. i. First, 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). For each UE, preferably all possible narrow Tx beam directions should be swept, i.e. a fairly large set of beams. ii. Second, when using the model for prediction (i.e. inference), measurement data for any UE to predict beams for must be collected and fed to the AI / ML model. The set of beams to sweep for a UE is here much smaller than during training, since not all narrow beams are swept, only a few wide (or possibly narrow) beams are swept. iii. Finally, measurements are needed to monitor that the model functions well, or otherwise disable it or update it.

[0056] According to 3GPP TR 38.843, for aNW-sided model, the data collection procedure would imply the gNB transmitting some signal (e.g. CSI-RS or SSB) using a set of several different Tx beams on the DL, and the UE collecting and logging associated measurement results, e.g. RSRP. The UE will then report, e.g. periodically, upon events, or on NW-demand, the logged measurements results so that the NW can use this information to train, retrain, or finetune the NW- side model. The training of the NW-side model can occur in the gNB itself or in aNW-node such as the Operations, Administration, and Maintenance (OAM).

[0057] For the UE-side model, the UE may need to collect measurements from the gNB signals (e.g. CSI-RS or SSB), and once the data collection is completed such collected data should transferred to a training entity which will be in charge of training, retraining, and / or finetuning the UE-side model based on the collected measurements and possibly on network assistance information such that the collected data measurements can be categorized by the training entity. As an output of the training, the UE-side model will be able to perform beam predictions on certain sets of beams, i.e. set A. For example, the UE may train on a set of resources configured by the gNB, i.e. the UE performs radio measurements on such set of resources, and based on this training, the training entity will determine from this configured set of resources, the set of resources (set B) that the UE needs to measure to perform the predictions on another set of resources (set A). Alternatively, the gNB may configure the UE with the set A and B, wherein the set A would be the set of radio resources for which the gNB requests the UE to generate a UE-side model able to perform radio measurement predictions on such set, and the set B, being the set of radio resources that the UE has to measure to generate the radio measurement predictions on the set B.

[0058] For the case of UE-side model, the training entity can be the UE itself (e.g. the application layer of the UE), or a network node, e.g. a radio access node like a gNB or a Core Network (CN) node (e.g. like the Network Data Analytics Function (NWDAF)) or an Over-the-Top (OTT) server, outside 3GPP. This latter approach might be a reasonable solution, because in order to have optimal performances, the trained data set should fit the inference operations at the device which may depend on UE-vendor specific implementations (e.g. software / hardware properties / capabilities), that a NW node may not know entirely.

[0059] SUMMARY

[0060] Systems and methods are disclosed related to triggering of data collection for User Equipment (UE)-side Artificial Intelligence (AI) / Machine Learning (ML) models. In one embodiment, a method performed by a UE for UE-side data collection for training, retraining, or fine tuning a UE-side AI / ML model comprises transmitting, to a network nod, information related to one or more UE-side conditions related to an expected quality of data samples that can be collected by the UE. The method further comprises receiving, from the network node, an indication to start data collection at the UE using a predefined, preconfigured, or indicated radio resource configuration or an indication to send a request to the network node for a radio resource configuration for data collection for training, retraining, or fine tuning the UE-side AI / ML model.

[0061] In one embodiment, receiving the indication to start data collection at the UE using a predefined, preconfigured, or indicated radio resource configuration or the indication to send a request to the network node for a radio resource configuration for data collection comprises receiving the indication to start the data collection at the UE or the indication to send a request to the network node for a radio resource configuration for the data collection in response to the network node evaluating fulfillment of one or more events associated to the one or more UE-side conditions.

[0062] In one embodiment, the one or more UE-side conditions are one or more UE-side conditions for which one or more events for initiating the data collection are fulfilled. In one embodiment, the one or more events associated to the one or more UE-side conditions are configured by the network node. In one embodiment, the events associated to the UE-side conditions comprise any one or more of the following events: estimated accuracy of the UE-side model being less than a certain accuracy threshold, quality of data samples associated to inputs of the UE-side model being below a certain quality threshold, a speed of the UE being above a certain speed threshold, a speed of the UE being below a certain speed threshold, one or more radio measurements associated to a certain beam or cell being above a certain radio measurement threshold, and one or more radio measurements associated to a certain beam or cell being below a certain radio measurement threshold.

[0063] In one embodiment, the one or more UE-side conditions comprise any one or more of the following: a speed of the UE, a trajectory of the UE, information associated to a certain beam or to a certain cell, one or more radio measurement results associated to a certain beam or to a certain cell, information that indicates a hardware configuration update at the UE, one or more Signal to Noise Ratio (SNR) values, information about one or more weather conditions at the UE, information about a signal blockage at the UE, a performance metric for the UE-side AI / ML model, an estimated accuracy of the UE-side AI / ML model during UE-side model inference, an estimated accuracy of the UE-side AI / ML model prior to activation of UE-side AI / ML model inference, an estimated quality of the UE-side AI / ML model, information about a storage capacity at the UE, information about a battery status of a battery of the UE, information about a distance between the UE and an associated base station, and a location of the UE.

[0064] In one embodiment, the one or more UE-side conditions are associated to a UE-side AI / ML functionality, wherein the UE-side AI / ML model is associated to the UE-side AI / ML functionality. In one embodiment, the one or more UE-side conditions comprise information related to an expected quality of data samples that can be collected by the UE. In one embodiment, the information related to the expected quality of data samples that can be collected by the UE comprises information that directly indicates the expected quality of data samples.

[0065] In one embodiment, the radio resource configuration comprises a set A and a set B of radio resources each including any one or more of the following configurations: a set of Synchronization Signal (SS) / Physical Broadcast Channel (PBCH) blocks, a set of channel state information reference signal (CSI-RS) resources, a set of SS / PBCH block resource sets, a set of cells and / or frequencies, a multiple input multiple output (MIMO) layer configuration, a maximum aggregated bandwidth, and a subcarrier spacing.

[0066] In one embodiment, the request to the network node for a radio resource configuration to perform the data collection comprises a requested set A and / or a requested set B of resources in which the UE (700) requests performance of the UE-side data collection, wherein each of the requested set A and / or requested set B of resources comprises any one or more of the following: a set of SS / PBCH blocks, a set of CSI-RS resources, a set of SS / PBCH block resource sets, a set of cells and / or frequencies, a MIMO layer configuration, a maximum aggregated bandwidth, and a subcarrier spacing.

[0067] In one embodiment, receiving the indication to start data collection at the UE using a predefined, preconfigured, or indicated radio resource configuration or the indication to send a request to the network node for a radio resource configuration for data collection comprises receiving the indication to start the data collection at the UE. In one embodiment, the method further comprises performing UE-side data collection in response to receiving the indication to start the data collection at the UE.

[0068] In one embodiment, receiving the indication to start data collection at the UE using a predefined, preconfigured, or indicated radio resource configuration or the indication to send a request to the network node for a radio resource configuration for data collection comprises receiving the indication to send a request to the network node for a radio resource configuration for the data collection. In one embodiment, the method further comprises transmitting, to the network node, a request for a radio resource configuration for the data collection, receiving a radio resource configuration for the data collection from the network node in response to the request, and performing UE-side data collection based on the received radio resource configuration.

[0069] Corresponding embodiments of a UE are also disclosed. In one embodiment, a UE for UE- side data collection for training, retraining, or fine tuning a UE-side AI / ML model 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 transmit, to a network node, information related to one or more UE- side conditions related to an expected quality of data samples that can be collected by the UE and receive, from the network node, an indication to start data collection at the UE using a predefined, preconfigured, or indicated radio resource configuration or an indication to send a request to the network node for a radio resource configuration for data collection for training, retraining, or fine tuning the UE-side AI / ML model.

[0070] In another embodiment, a method performed by a UE for UE-side data collection for training, retraining, or fine tuning a UE-side AI / ML model comprises determining whether to initiate data collection for a UE-side AI / ML model based on an evaluation of one or more events associated to one or more UE-side conditions, the one or more UE-side conditions being related to an expected quality of data samples that can be collected by the UE. The method further comprises, upon determining to initiate data collection for the UE-side AI / ML model, either: transmitting, to a network node, a request for a radio resource configuration for performing the data collection or starting the data collection using predefined or preconfigured radio resource configuration.

[0071] Corresponding embodiments of a UE are also disclosed. In one embodiment, a UE for UE- side data collection for training, retraining, or fine tuning a UE-side AI / ML model 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 determine whether to initiate data collection for a UE-side AI / ML model based on an evaluation of one or more events associated to one or more UE-side conditions, the one or more UE-side conditions being related to an expected quality of data samples that can be collected by the UE. The processing circuitry is further configured to cause the UE to, upon determining to initiate data collection for the UE-side AI / ML model, either: transmit, to a network node, a request for a radio resource configuration for performing the data collection or start the data collection using predefined or preconfigured radio resource configuration.

[0072] Embodiments of a method performed by a network node are also disclosed. In one embodiment, a method performed by a network node of a wireless communications network for enabling UE-side data collection for training, retraining, or fine tuning a UE-side AI / ML model comprises receiving, from a UE, information related to one or more UE-side conditions related to an expected quality of data samples that can be collected by the UE for training, retraining, or finetuning a UE-side AI / ML model. The method further comprises determining to initiate data collection at the UE based on the received information and transmitting, to the UE, an indication to start data collection at the UE using a predefined, preconfigured, or indicated radio resource configuration or an indication for the UE to send a request to the network node for a radio resource configuration for data collection for training, retraining, or fine tuning the UE-side AI / ML model.

[0073] Corresponding embodiments of a network node are also disclosed. In one embodiment, a network node for a wireless communications network for enabling UE-side data collection for training, retraining, or fine tuning a UE-side AI / ML model comprises processing circuitry configured to cause the network node to receive, from a UE, information related to one or more UE-side conditions related to an expected quality of data samples that can be collected by the UE for training, retraining, or finetuning a UE-side AI / ML model. The processing circuitry is further configured to cause the network node to determine to initiate data collection at the UE based on the received information and transmit, to the UE, an indication to start data collection at the UE using a predefined, preconfigured, or indicated radio resource configuration or an indication for the UE to send a request to the network node for a radio resource configuration for data collection for training, retraining, or fine tuning the UE-side AI / ML model.

[0074] BRIEF DESCRIPTION OF THE DRAWINGS

[0075] The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the disclosure, and together with the description serve to explain the principles of the disclosure.

[0076] Figure 1 illustrates SSB beam selection as part of Initial access procedure according to Pl scenario;

[0077] Figure 2 illustrates CSI-RS Tx beam selection in Downlink according to P2 scenario;

[0078] Figure 3 illustrates UE Rx beam selection for corresponding CSI-RS Tx beam in DL according to P3 scenario;

[0079] 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. azimuth) angle from the antenna array. Set A has 8 beams and Set B has 4 beams (indicated by dark circles);

[0080] Figure 5 illustrates an example where Set A is a set of narrow beams and Set B is a set of wide beams;

[0081] Figure 6 illustrates a functional framework of model LCM for Al for Air Interface;

[0082] Figure 7 is a flowchart that illustrates an example of a procedure in accordance with embodiments of the present disclosure. It is considered the case in which the gNB configures the UE to report the expected quality of the UE-side model, as an input for the gNB to determine whether the UE should be allowed to initiate the data collection;

[0083] Figure 8 illustrates the operation of a UE and network node in accordance with an embodiment of the present disclosure;

[0084] Figure 9 shows an example of a communication system in accordance with some embodiments of the present disclosure;

[0085] Figure 10 is another example of a communication system according to some embodiments of the present disclosure;

[0086] Figure 11 shows a wireless device, which may be configured to operate in the communication system of Figure 9 or in the communication system of Figure 10;

[0087] Figure 12 shows a network node in accordance with some embodiments of the present disclosure; and

[0088] Figure 13 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments of the present disclosure may be virtualized.

[0089] DETAILED DESCRIPTION

[0090] 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.

[0091] Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0092] There currently exist certain challenge(s). Existing data collection methods for training User Equipment (UE)-side Artificial Intelligence (Al) or Machine Learning (ML) (i.e., AI / ML) models do not consider the quality of the prediction performed by the UE during the inference. This may lead to the UE triggering extensive data collections in scenarios or situations that may not be valuable for training or updating the UE AI / ML model, hence, causing waste of radio resources that are used for data collection. For example, the UE may start performing data collection, e.g. requesting the gNodeB (gNB) to allocate resources for the purpose of UE-side model training, unnecessarily even when the performance of the UE-side model inference is still acceptable, thereby negatively impacting not only the UE energy consumption but also the overhead over the air interface due to the unnecessary transmission of the reference signals (e.g. Channel State Information Reference Signals (CSI-RSs) or Synchronization Signal (SS) / Physical Broadcast Channel (PBCH) Blocks (SSBs)) that are needed for the UE to perform the data collection.

[0093] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. Embodiments of systems and methods for enabling a UE to perform data collection for the purpose of UE-side model training of an AI / ML model are disclosed.

[0094] In one embodiment of a first method, a UE first indicates a quality of data samples to the network (NW) (e.g., to a network node), e.g. the UE indicates an accuracy of the UE-side model inference. In response, the NW uses this information to enable the UE to perform the UE-side data collection. In one embodiment, a response is transmitted by the NW based on the UE-side model performance evaluated by the NW. The NW response can comprise any of the following:

[0095] • An indication indicating that the UE is allowed to perform data collection,

[0096] • A radio resource configuration that configures the UE to perform data collection.

[0097] In an embodiment of a second method, the network (e.g., a network node) configures a UE to perform data collection based on a certain event. For example, the network may configure the UE to start performing data collection or to send a request to the network for resources to perform the data collection, if a performance of the UE-side model inference drops below a certain level.

[0098] Some example embodiments of the present disclosure are as follows:

[0099] • Embodiment Al: A method for a UE to determine to perform data collection for the purpose of UE-side model training, in response of any of the following: o Evaluating the fulfillment of one or more events associated to a UE-side condition o Transmitting the one or more UE-side conditions to the network node, or the network node evaluating the said one or more UE-side conditions

[0100] • Embodiment A2. The method of Al, wherein in response of evaluating the fulfillment of one or more events associated to a UE-side condition: o the UE transmits a request to the network node to allow or enable the UE to perform the data collection, and in response it receives a radio resource configuration to start performing the data collection; or o the starts performing the data collection based on a radio resource configuration received by the UE.

[0101] Embodiment A3 : The method of embodiment Al , wherein in response to transmitting one or more UE-side conditions to the network node, the UE receives a radio resource configuration to start performing the data collection or an indication that allows the UE to request radio resources for the purpose of UE-side model training.

[0102] • Embodiment A4: The method of embodiment Al , wherein the UE receives a radio resource configuration to start performing the data collection or an indication that allows the UE to request radio resources for the purpose of UE-side model training, in response of the network node evaluating the fulfillment of one or more events associated to a UE-side condition.

[0103] • Embodiment A5: The method of embodiment Al, wherein the one or more events associated to a UE-side condition for which the UE determines whether to perform the data collection are configured by the network node.

[0104] • Embodiment A6: The method of embodiment Al, wherein the UE transmits to the gNB an indication of the one or more UE-side condition whose event for the triggering of the UE- side data collection is fulfilled.

[0105] • Embodiment A7: The method of embodiment Al, wherein the UE-side conditions may comprise: o The UE speed o The UE traj ectory o The radio measurement results (e.g. RSRP, RSRQ, SINR, RSSI) associated to a certain beam, or to a certain cell o The estimated accuracy of the UE-side model during UE-side model inference or prior to activate the UE-side model inference o The estimated quality of a UE-side model o The location of the UE o The direction of the UE

[0106] • Embodiment A8: The method of embodiment Al , wherein the events associated to the UE- side conditions may be: o Estimated accuracy of a UE-side model below a certain threshold o Quality of data samples associated to the inputs of UE-side model below a certain threshold o UE speed high or below a certain threshold. o The radio measurements associated to a certain beam or cell above or below a certain threshold.

[0107] • Embodiment A9: The method of embodiment Al, wherein the determination is performed during UE-side model inference • Embodiment Al 0: The method of embodiment Al, wherein the determination is performed as outcome of UE-side model monitoring at the UE or at the network node.

[0108] • Embodiment Al l: The method of any of the previous embodiments, wherein the determination to perform the data collection, the evaluations of the events, the reporting to the network, and the UE-side conditions are associated to a UE-side AIML functionality, wherein one or more UE-side AIML models may be associated to the UE-side AIML functionality.

[0109] • Embodiment A12: The method of any of the previous embodiments, wherein the radio resource configuration comprises a set A and a set B of radio resources including each set A / B: o A set of SSB(s) e.g. list of SSB indexes of a cell of the network node o A set of CSI-RS resources e.g. list of CSI-RS resources of a cell of the network node o A set of SS / PBCH block resource set o A set of cells / frequencies o A MIMO layer configuration o A maximum aggregated bandwidth o A subcarrier spacing.

[0110] • Embodiment Al 3: The method of any of the previous embodiments, wherein the request transmitted to the gNB to perform UE-side data collection comprises a set A / B of resources in which the UE request the gNB to perform the UE-side data collection: o A set of SSB(s) e.g. list of SSB indexes of a cell of the network node o A set of CSI-RS resources e.g. list of CSI-RS resources of a cell of the network node o A set of SS / PBCH block resource set o A set of cells / frequencies o A MIMO layer configuration o A maximum aggregated bandwidth o A subcarrier spacing

[0111] Certain embodiments may provide one or more of the following technical advantage(s). The proposed method enables data collection for UE AI / ML model training adaptive to the expected “data quality” (or ‘data priority’) which

[0112] • improves the efficiency of the data collection process e.g. in terms of radio resources to be allocated for over the air measurements, memory resources in the device / cloud for data storage, and computational resources in the device / cloud for pre-processing and / or training / updating the models;

[0113] • reduces the volume of the data to be collected by skipping collecting the data that may not have meaningful contribution in the training of the AI / ML model;

[0114] • ensures that the high quality data (or high priority data), e.g. from rare events, is collected for training or fine tuning of the AI / ML models;

[0115] • reduces the need for sending NW reference signals for the purpose of UE training, which leads to o more resources can be used for other purposes (e.g. data transmissions), and o improved energy efficiency since NW only transmits reference signals when needed.

[0116] Figure 7 illustrates the operation of a UE 700 and a network node 702 for enabling the UE 700 to perform data collection for the purpose of UE-side model training of an AI / ML model, in accordance with embodiments of the present disclosure.

[0117] The steps of the procedure of Figure 7 are described in more detail below:

[0118] Step S704: NW request / configuration to the UE

[0119] In one embodiment, the network requests or configures the UE 700 to report information related to expected data sample quality (i.e., the quality of data samples if data collection at the UE 700 is activated). In some embodiments, the information related to data sample quality includes one or more UE-side conditions (e.g., one or more conditions at the UE 700 that implicitly indicate a quality of the data samples used for the UE-side model or from which the quality of the data samples can be determined). As discussed below, the one or more UE-side conditions may include UE speed, UE trajectory, UE radio measurements associated to a certain beam or cell, estimated accuracy of the UE-side model during UE-side model inference or prior to activating the UE-side model inference, estimated quality of the UE-side model, UE location, and / or UE direction. Based on the UE-side condition(s), the network can determine whether to enable the UE 700 to perform the UE-side data collection for the purpose of UE-side model training. The full set of UE-conditions for which the NW can request / configure the UE 700 can be pre-defined or configured by the network node 702. For example, the network can configure the UE 700 to report the UE-side conditions via the UEAssistancelnformation. Examples of the UE-side conditions signaled by the UE 700 to the NW are described below with respect to step S706. In some other embodiments, the information related to data sample quality is information that directly indicates the data sample quality (e.g. , a value or metric that represents the data sample quality).

[0120] Step S706: UE information of expected data sample quality

[0121] Associated to the UE-side conditions, the UE 700 indicates to the network node 702 the information regarding the expected quality of the data samples. As discussed above, in some embodiments, the information regarding the expected quality of the data samples includes one or more UE-side conditions related to the data sample quality. The UE-side conditions may comprise:

[0122] • General UE-side Conditions when the “quality” of the data is high, such as:

[0123] O UE Speed: When the UE speed is high, the high UE speed leads to more diverse data set to be collected during certain period of time. In this scenario, the data samples that can be collected by the UE 700 can be treated as high quality data samples. Thus, in one embodiment, the indicated information regarding the expected quality of the data samples includes the UE’s current speed. The network can then enable the UE 700 to perform the data collection (see step S708 below). o UE Direction (i.e., UE Trajectory): When the UE 700 is moving in a certain direction, for example, one of a set of directions that are more exposed to interference from other base stations or other wireless communication systems (e.g. satellite ground stations), more accurate beam management is desired to deal with the interference. In this case, the quality of the data samples that can be obtained by the UE 700 when moving in such a direction can be considered as high. The UE 700 can acquire this information as follows:

[0124] ■ The UE 700 can make a list of interference and / or Signal to Interference plus Noise Ratio (SINR) measurements in different locations or directions A subset of these locations or directions for which interference is larger than a threshold or SINR is lower than a certain threshold can be marked as the directions or locations of interest for training for which collected data is treated of high quality.

[0125] ■ The UE 700 can use a sensing capability, e.g. in the form of integrated sensing and communication (ISAC), to acquire information about the surrounding area and the direction of blockage and interfering objects and, based on this information, identify the directions or locations for which the collected data is of high quality for training. o Information (e.g., Radio Measurements) associated to a Certain Beam (or Cell): When UE 700 is using a certain beam (or cell), for example, a beam for which a degraded performance is observed by the UE 700, the quality of data samples that can be collected is considered to be high. The UE 700 can, for example, indicate measurements results (e.g., RSRP measurements) associated to a certain, and the network can then, in step S708, enable the UE 700 to perform the data collection when the measurements are below a certain threshold. o Hardware Configuration Update: When UE 700 is performing an update in its hardware configurations, e.g. in the radio frequency (RF) chain and / or antenna such as update in power back-off leading to change in power amplifier’s non-linearity, or changing the bias power of the oscillator leading to the phase noise modification, or changing the resolution of the data convertors (Digital to Analog Converter (DAC), Analog to Digital Converter (ADC)), such update impact the quality of the beams, and hence, collecting new data becomes a priority for training / re-training the models. Thus, indicating a hardware configuration update at the UE 700 can imply a high data sample quality (i.e., data samples that would be collected by the UE 700 if data collection is activated would be high). o SNR Value: When UE 700 SNR values are larger than a certain threshold, the collected data samples are less noisy. Thus, SNR value(s) may be indicated to the network node 702 and used by the network node 702 to determine data sample quality. o Weather Condition (s): When UE 700 is operating on certain weather conditions that impact the signal propagation and hence beam quality, e.g. rain and fog, at higher frequency bands, the data sample quality may be considered to be high. o Signal Blockage: When UE 700 detects signal blockage, e.g. due to physical obstructions such as buildings in certain directions that impact signal quality using, e.g., sensing capabilities, the data sample quality may be considered to be high.

[0126] • Model Related UE-side Conditions o UE-side Model Performance Metric: The UE 700 reports a bad model performance metric, e.g. using the performance monitoring mechanism, which can be used as an indication that the data sample quality is high. For example, while performing the UE-side model inference, the UE 700 may report, as a result of the UE-side model monitoring, a low confidence score (high uncertainty) of the UE- side model. Upon receiving this information, the network node 702 may enable the UE 700 to perform UE-side data collection. o Quality of Input of the UE-Side Model: The UE 700 reports indication associated to the quality of the data used as input of the UE-side model. For example, considering the current radio configuration, the UE-side conditions (such as the radio measurements, the location, the speed, the trajectory etc.), the UE 700 may evaluate that the UE-side model is of high quality or low quality, e.g. in the cases that the UE 700 has collected very little data and more data collection is needed. Whether sufficient statistics are lacking can be evaluated by the UE 700 based on the following factors:

[0127] ■ Based on the change of the target value, and whether sufficiently large range of the target values are covered in the collected data, e.g. whether enough samples corresponding to RSRP ranges for certain beams (or best beams) are collected in the beam management use case.

[0128] ■ Based on the number of samples that were collected for one or more input / output values of the trained model, e.g. whether enough samples corresponding to certain beams (or best beams) were collected in the beam management use case.

[0129] • UE Status related UE-side Conditions related to the data collection when the UE 700 has suitable conditions to perform data collection, such as: o When the UE storage capacity allows logging more samples, (unlike the case when the UE storage is full) o When the UE battery status allows for performing and logging measurements. o When the UE distance from the base station is higher, hence, more accurate beam prediction is required.

[0130] Based on the above, the UE 700 can for example report to the network node 702 that the accuracy of a UE-side model is low, and based on that the network node 702 can determine to configure the UE 700 to perform data collection, e.g. the network node 702 provides the UE 700 with a radio resource configuration for the purpose of UE-side data collection or to enables the UE 700 to send a request to the network node 702 for the necessary radio resources for the purpose of UE-side data collection (see step S708 below).

[0131] The UE 700 can report the above information while performing UE-side model inference, i.e. as a result of the UE-side model monitoring, or before activating the UE-side model inference. For example, the accuracy of the UE-side model can be evaluated by the UE 700 on the basis of any other data collection previously performed by the UE 700 and on a plurality of UE-side conditions (such as the radio measurements currently experienced, the frequencies / cells / beams in which the UE 700 is currently operating, the UE-speed, the UE location, the UE trajectory, etc.) that the UE 700 is currently experiencing. For example, the UE 700 may determine considering the current UE-side conditions, whether the amount of samples used as input for the generation of the UE-side model are sufficient, thereby being the model of a certain quality. If not, the UE 700 may report an indication indicating low accuracy of the UE-side model. In order to determine the accuracy of the UE-side model, the UE 700 may receive information from the UE-side training entity, i.e. the entity that trained the concerned UE-side model.

[0132] In some other embodiments, rather than the information about the expected data sample quality sent to the network node 702 in step S706 including the UE-side condition(s), the information about the expected data sample sent to the network node 702 in step S706 includes information that directly indicates the data sample quality. For example, the UE 700 may determine whether an event(s) related to a UE-side condition(s) is(are) fulfilled (i.e., has occurred), where fulfillment of the event(s) indicates high data sample quality (or low data sample quality), and send, to the network node 702, an indication that the data sample quality is high (or low) (e.g., an indication that the event(s) is fulfilled). In other words, the UE 700 may determine the data sample quality based on any one or any combination of two or more of the UE-side conditions described above and indicate the determined data sample quality to the network node 702.

[0133] In another embodiment, the information related to the expected quality of data samples that can be collected by the UE 700 comprises one or more UE-side conditions for which one or more events for initiating the data collection are fulfilled. In one embodiment, the one or more events associated to the one or more UE-side conditions are configured by the network node 702. In one embodiment, the events associated to the UE-side conditions comprise any one or more of the following events:

[0134] • estimated accuracy of the UE-side model being less than a certain accuracy threshold;

[0135] • quality of data samples associated to inputs of the UE-side model being below a certain quality threshold;

[0136] • a speed of the UE 700 being above a certain speed threshold;

[0137] • a speed of the UE 700 being below a certain speed threshold;

[0138] • one or more radio measurements associated to a certain beam or cell being above a certain radio measurement threshold; • one or more radio measurements associated to a certain beam or cell being below a certain radio measurement threshold.

[0139] In another embodiment, the information related to the expected data sample quality sent by the UE 700 to the network node 702 in step S706 includes (a) one or more UE conditions, (b) information that indicates whether one or more events related to one or more UE-conditions are fulfilled (i.e., have occurred), and / or (c) an indication of the expected data sample quality. For example, the UE 700 can report, in step S706 as or as part of the indicated information related to the expected data sample quality, the expected quality as a tuple value, where the first value includes the UE-condition information and the second value includes the data sample quality information or a quality score. For example, in case the UE 700 wants to collect the data when its AI / ML model outputs high uncertainty, the UE 700 can report according to Example 1 in the following. Other examples could comprise that the UE 700 does not need to get more data when the speed is low. Or that the UE 700 has no data, hence, the quality is high for all conditions.

[0140] Example 1: o Condition= model-uncertainty -high, o expected data quality= high

[0141] Example 2: o Condition=UE-speed-low, o expected data quality=low

[0142] Example 3: o Condition=all, o expected data quality= high

[0143] Along with the information related to the expected data sample quality, the UE 700 may report to the network (e.g., in step S706 or a separate step) a set of one or more resources in which the UE 700 requests to perform the data collection. For example, this set of resources may comprise the radio resources, e.g. CSI-RS / SSB, in which the UE 700 evaluates the UE-side model to be less accurate or for which the UE-side model has not collected yet enough samples to generate a UE-side model that guarantees certain quality.

[0144] The information sent from the UE 700 in step S706 can be sent by the UE 700 either immediately after the network request / configuration in step S704, or later, when the UE 700 wants to update the data quality information provided to the NW.

[0145] In addition to the information in step S706, the network can further include one or more NW-side related conditions when estimating the expected data sample quality. In one embodiment, the NW-side conditions could comprise: • UE is in an important NW region: When the UE 700 is in a certain area where the NW requires high prediction performance at the UE 700, or an area for which less data has been collected previously o For BM use case, this could comprise an area where beam failures are frequent, the area could correspond to a certain SNR level and / or a certain direction of the cell where a certain beam is serving the UE 700. o Another example is an area where adjacent wireless services exist, e.g. satellite ground station and for which more accurate interference avoidance / mitigation is desired, hence, more accurate beams are desired.

[0146] • NW hardware configurations that impact data quality: When the NW performs certain changes in the hardware, e.g. antenna or RF chain that impact the performance. o For BM use case, this could comprise changes in the back-off value of the transmitter at the base station leading to the changes in the power amplifier nonlinearity, or the resolution of the DAC / ADC converters, or change in the bias power of the oscillator that impacts the phase noise, that impacts the quality of the beam alignment and new data need to be collected for training or re-training the AI / ML models.

[0147] Step S708: Network Node Initiates Data Collection

[0148] After the network has received the information related to the expected data sample quality in step S706, the network node 702 determines whether to enable the UE 700 to perform the data collection. In one embodiment, the information related to the expected data sample quality received by the network node 702 in step S706 includes one or more UE-side conditions, as described above. In this case, the network node 702 determines whether to enable the UE 700 to perform the data collection based on the one or more UE-side conditions. For example, the network node 702 may determine whether one or more events indicative of high data sample quality have been fulfilled (e.g., have occurred), based on the indicated UE-side condition(s), and, if so, decides to enable the UE 700 to perform the data collection. In another embodiment, the information related to the expected data sample quality received by the network node 702 in step S706 includes information that directly indicates the expected data sample quality, as described above. In this case, the network node 702 determines whether to enable the UE 700 to perform the data collection based on the indicated expected data sample quality and, optionally, one or more network-side conditions, as described above. For example, if the indicated expected data sample quality is high, then the network node 702 decides to enable the UE 700 to perform the data collection.

[0149] In one embodiment, enabling the UE 700 to perform the data collection may include the network node 702 configuring radio resources for the UE 700 to perform UE-side data collection, e.g. the network node 702 configures the UE 700 with resource sets comprising CSI-RSs and / or SSBs in which the UE 700 should perform data collection. In another embodiment, the network node 702 configures the UE 700 to request radio resources for the UE-side data collection. For example, according to this latter embodiment, the network node 702 may configure the UE 700 to request resources via UEAssistancelnformation. In one example, upon receiving indication from the UE 700 indicating low expected accuracy of the UE-side model (while or before performing the UE-side model inference), the network node 702 configures the UE 700 to perform the UE- side data collection.

[0150] In one embodiment, the radio resource configuration provided from the network node 702 to the UE 700 to enable the UE 700 to perform UE-side data collection comprises:

[0151] • a set of Synchronization Signal (SS) / Physical Broadcast Channel (PBCH) blocks (SSBs);

[0152] • a set of channel state information reference signal (CSI-RS) resources;

[0153] • a set of SSB resource sets;

[0154] • a set of cells and / or frequencies;

[0155] • a multiple input multiple output (MIMO) layer configuration;

[0156] • a maximum aggregated bandwidth;

[0157] • a subcarrier spacing.

[0158] As a result of receiving the information related to the expected data sample quality, the network node 702 may further request the UE 700 to deactivate (or deconfigure) the UE-side model inference, if currently activated (or configured).

[0159] NW triggered data collection (step S708 -embodiment A)

[0160] In one embodiment, The network node 702 can for example based on step S706, i.e. UE report about the expected data sample quality, and optionally network-side conditions available in the network node 702 (denoted as NW-side conditions) configure data collection (Step 1 of Step S708 - Embodiment A). In this case, for events / conditions that are detected by the network node 702, the network node 702 can configure data collection based on predefined events and / or information from the UE 700, e.g. UE 700 informs the network node 702 that its interested in collecting data corresponding to certain beams / performance metric, etc., and once the network node 702 detects the fulfilment of the event (i.e. in case one or more of the NW-side conditions listed in section above is / are met), the network node 702 configures the UE 700 for data collection, accordingly. In this case, the UE 700 does not need to request data collection again after the fulfilment of event; hence, this might reduce the delays for configuring. For example, the network node 702 can configure periodic measurements in case the UE 700 has indicated that all data is of high quality and hence is important.

[0161] UE triggered data collection (step S708 -embodiment B)

[0162] In another embodiment, the UE 700 triggers the data collection autonomously upon fulfilling one or more events associated to the UE-side conditions (step 2 of step S708 - Embodiment B). Such one or more events are, for example, either predefined or configured by the network (see, e.g., step 1 of step S708 - Embodiment B). For example, the network configures the UE 700 to perform data collection upon fulfilling an event associated to the accuracy of the UE-side model, e.g. the network can configure the UE 700 to perform the data collection if the accuracy of the UE-side model is below a certain percentage, or if it is determined by the UE 700 to be low. In another example, the network node 702 configures the UE 700 to perform data collection if the UE 700 determines that the quality of the data considered for the input of the UE- side model is low, e.g. the UE 700 has not acquired yet enough data for the UE-side model training entity to generate a UE-side model that is accurate enough considering the current UE-side conditions. Other examples of events associated to the UE-sided conditions could comprise events related to the UE speed, UE direction, UE RX-beam information, UE hardware configurations, and / or radio measurements, etc.

[0163] Upon fulfilling one or more of the events, the UE 700 may send a request to the network node 702 to start performing the data collection (step 2 of step S708 - Embodiment B), and as part of this request the UE 700 may report to the network node 702 an indication of the event that is fulfilled, e.g. when the UE 700 speed is higher than a certain threshold, or the accuracy is low or the data quality is low, or if the radio measurement results are comprise within a certain range, or they are above or below a certain threshold, etc. Then the network node 702 configures the related data collection, e.g. the network node 702 configures the UE 700 with the resource sets comprising CSI-RSs / SSBs in which the UE 700 should perform data collection. For example, the UE 700 may be configured by the network node 702 to transmit a UEAssistancelnformation requesting the radio resources for the UE-side data collection, the configuration comprising the one or more events associated to the UE-side conditions.

[0164] In one embodiment, the request sent from the UE 700 to the network node 702 in step 2 of step S708 - Embodiment B comprises a requested set A and / or a requested set B of resources in which the UE (700) requests performance of the UE-side data collection, wherein each of the requested set A and / or requested set B of resources comprises any one or more of the following:

[0165] • a set of SSBs;

[0166] • a set of CSI-RS resources;

[0167] • a set of SSB resource sets;

[0168] • a set of cells and / or frequencies;

[0169] • a multiple input multiple output (MIMO) layer configuration;

[0170] • a maximum aggregated bandwidth;

[0171] • a subcarrier spacing.

[0172] In another embodiment, upon fulfilling one or more of the events, the UE 700 starts performing the data collection according to a radio resource configuration previously received from the network node 702 for the sake of UE-side model data collection. Hence, according to this embodiment, the UE 700 does not need to transmit a request to the network node 702 to perform the data collection upon fulfillment of the event.

[0173] Along with the UE-side conditions whose events for the data collection are fulfilled, the UE 700 may report to the network node 702 a set of one or more resources in which the UE 700 requests to perform the data collection. For example, this set of resources may comprise the radio resources, e.g. CSI-RSs / SSBs resources sets, for which the event for the triggering of the UE-side data collection are fulfilled, e.g. those radio resources in which the UE 700 deems the UE-side model to be less accurate, or for which the UE-side model has not collected yet enough samples to generate an UE-side model that guarantees certain quality, or those radio resources in which the radio measurement results are comprised within a certain range, or they are above / below a certain threshold. Related to the latter method, the UE 700 for example should not request data collection on radio resources on which the UE 700 measured too low radio channel quality, e.g. too low RSRP, or that the UE 700 has not been able to detect, e.g. for at least a certain time, or during the last performed radio measurement. This is to avoid that the network node 702 configures the UE 700 to perform data collection for a radio resource that is too poor.

[0174] Data collection during inference (step S708 -embodiment C)

[0175] In one embodiment, in case the data sample quality is high for conditions related to inaccurate models, the network node 702 configures data collection using the inference related procedure described in embodiment C (step 1 of step S708 - Embodiment C). The inference procedure (step 2 of step S708 - Embodiment C) can indicate the data quality of a possible data collection instance. For example, in the case where the UE 700 reports a highly uncertain prediction (step 3 of step S708 - Embodiment C), this indicates that the UE 700 needs to collect more data for such inference input. One example is for the beam management use case, where the UE 700 can indicate a low probability that any beam is being the strongest beam, then the network node 702 could configure a subsequent data collection procedure for enabling the UE 700 to get a new sample with the uncertain condition (step 4 of step S708 - Embodiment C).

[0176] In one related embodiment, the configuration of the subsequent data collection is considered implicit, meaning that in case the UE 700 reports an uncertainty value above / below a certain threshold, the UE 700 can expect to receive a subsequent data collection occasion. In case of beam management use case, this data collection occasion corresponds to the transmission of the set A beams.

[0177] Step S710: UE-Side Data Collection

[0178] The UE-side data collection can comprise collecting data for training a model and / or for monitoring the performance of a model. In case of monitoring, the reported information related to the expected data sample quality in step S706 reflects the data samples where the UE 700 would like to monitor its model, i.e. areas where it is uncertain of its performance.

[0179] For the data collection using the CSI-Report framework, the following CSI-Report types could be configured:

[0180] • Aperiodic CSI reporting, o Could be used in case UE 700 enters a specific environment with high data quality for a short period of time

[0181] • Periodic or semi-persistent CSI reporting o Could be used in for example when all data is of high quality, where the periodicity of the report in the periodic CSI-resourceConfig can be adapted according to the UE assistance information (UAI), e.g. the UE speed, where the lower periodicity is set when the UE speed is high.

[0182] For the aPeriodic or semi-persistent reporting, the network node 702 sends a command to the UE 700 to initiate data collection, where this command could be part of the L1 / L2 signaling. For periodic, this is done using RRC signaling (using layer 3).

[0183] Step S712: UE optional report of the quality information of the collected data

[0184] In one embodiment, the UE 700 indicates the quality of the data collected to the network node 702, for example that the data quality is inline with the expected quality reported in step S706. The network node 702 can use such information when configure subsequent data collection instances to the UE 700. For example, in case the collected data quality is much lower than expected, the network node 702 can stop configuring the UE 700 with more data collection time instances.

[0185] The quality information of the collected information can be reported via a single integer value in range of 1-100, where a high value can indicate that it is crucial for the UE 700 to get such data in order to train an accurate ML model. The quality information can further comprise a value reflecting the expected loss value if using an existing model at the UE 700, where a high loss value can indicate that it is important data to be collected by the UE 700 since it can train to mitigate such high loss values.

[0186] Figure 8 illustrates the operation of a UE 800 and network node 802 in accordance with an embodiment in which the UE 800 autonomously determines when to initiate data collection for training, retraining, and / or finetuning of the UE-side AI / ML model. Optionally, the network node 802 sends, to the UE 800, configuration information includes, for example, information that indicates one or more UE-side conditions, information that indicates one or more events associated to the one or more UE-side conditions, and / or a radio resource configuration to be used for UE- side data collection (step S804). The UE 800 determines whether to initiate UE-side data collection for training, retraining, and / or finetuning the UE-side AI / ML model based on an evaluation of one or more events associated to one or more UE-side conditions related to the quality of data samples that can be collected by the UE 800 (step S806). In one embodiments, the UE-side condition(s) to be considered and / or the associated event(s) is(are) configured by the network node 802, e.g., in step S804. In another embodiment, the UE-side condition(s) to be considered and / or the associated event(s) is(are) predefined. The details above regarding the UE- side conditions and associated events are equally applicable here.

[0187] Upon determining to initiate UE-side data collection (e.g., upon fulfillment of one or more events associated to one or more UE-side conditions related to data sample quality), the UE 800 initiates UE-side data collection (step S808). In one embodiment (Option A), the UE 800 sends a request to the network node 802 for a radio resource configuration to start UE-side data collection (step S808A-1) and, in response, receives a radio resource configuration from the network node 802 (step S808A-2). The UE 800 then starts performing UE-side data collection using the received radio resource configuration (step S808A-3). In another embodiment (Option B), the UE 800 starts performing UE-side data collection, e.g., using a predefined or preconfigured radio resource configuration (e.g., the radio resource configuration configured in step S804) (step S808B-1).

[0188] Figure 9 shows an example of a communication system 900 in accordance with some embodiments. Note that the UE in the embodiments described above (e.g., UE 700 or UE 800) may be one of the UEs 912 of Figure 9, and the network node in the embodiments described above (e.g., network node 702 or network node 802) may be one of the network nodes 910 of Figure 9, as will be appreciated by one of skill in the art.

[0189] In the example, the communication system 900 includes a telecommunications network 902 that includes an access network 904, such as a radio access network (RAN), and a core network 906, which includes one or more core network nodes 908. The access network 904 includes one or more access network nodes or base stations of various types, access network nodes 910A and 910B are depicted (which may be collectively referred to as network nodes 910), or any other similar 3rdGeneration Partnership Project (3GPP) access nodes or non-3GPP access points (APs). Some embodiments of the access network 904 may include more than one access network technology. The network nodes 910 of access network 904 facilitate direct or indirect connection of wireless devices, also referred to as user equipments (UEs), such as by connecting UEs 912A, 912B, 912C, and 912D (one or more of which may be generally referred to as UEs 912) to the core network 906 over one or more wireless connections.

[0190] Moreover, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunications network 902 includes one or more Open- RAN (ORAN) network nodes. An ORAN network node is a network node in the telecommunications network 902 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other network nodes to implement one or more functionalities of any network node in the telecommunications network 902, including one or more access network nodes 910 and / or core network nodes 908.

[0191] Examples of an ORAN network node include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O-CU- CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or anon-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). An ORAN network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an Al, Fl, Wl, El, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN network node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an 0-2 interface defined by the 0-RAN Alliance or comparable technologies.

[0192] The network nodes 910 facilitate direct or indirect connection of one or more UEs 912 to the core network 906 over one or more wireless connections. Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 900 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system 900 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0193] The UEs 912 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes 910 and other communication devices. Similarly, the network nodes 908, 910 are arranged, capable, configured, and / or operable to communicate directly or indirectly (e.g., via other devices of telecommunications network 902) with the UEs 912 and / or with other network nodes or equipment in the telecommunications network 902 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunications network 902. More specifically, UEs 912 may send messages, data, and / or other signals to network nodes 908, 910 or other elements of the telecommunications network 902 by transmitting such signals to the relevant device directly without the signals passing through any intervening devices or by transmitting such signals to the relevant device indirectly through an intervening device (or multiple intervening devices) that then transmit the signal to the relevant device. Similarly, network nodes 908, 910 may send messages, data, and other signals to UEs 9122, other network nodes 908, 910, and other devices in telecommunications network 902 directly or indirectly. As one specific example, a core network node 108 may transmit a particular message to a UE 912 by transmitting the message to an access network node 910 that will then transmit the message to the intended UE 912. Similarly, a core network node 108 may receive a particular message from a UE 912 by receiving the message from an access network node 910 that itself received the message from the UE 912. In the depicted example, the core network 906 connects elements of the access network 904 (e.g., one or more of the network nodes 910) to one or more host computing systems, such as host 916. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 906 includes one or more core network nodes (e.g., core network node 908) of various types, one or more of which may be generally referred to as network nodes 908. Network nodes 908 are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, access network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 908. Example core network nodes provide 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).

[0194] The host 916 may be under the ownership or control of a service provider other than an operator or provider of the access network 904 and / or the telecommunications network 902. The host 916 may be operated by the service provider or on behalf of the service provider. The host 916 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0195] As a whole, the communication system 900 of Figure 9 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system 900 may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (Wi-Fi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (Wi-Max), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, Li-Fi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox. Moreover, the communication system 900 may be configured to support multiple different standards, protocols, or other rule sets, with individual components supporting all of the relevant rule sets or with different components or sub-systems within the communication system 900 supporting different standards, protocols, or rule sets.

[0196] As one example, in certain embodiments, access network 904 may contain some access network nodes 910 that support 3GPP radio access technologies (RAT), such as LTE or NR, while other access network nodes 910 support (or the same access network nodes 910 additionally support) non-3GPP RATs, such as Wi-Fi or a proprietary RAT. As another example, telecommunications network 902 may support multiple generations of related communication standards (e.g., 4G and 5G 3GPP communication standards) and, as a result, may include an access network 104 and / or a core network 106 that supports multiple different standard generations or may include multiple access networks 104 and / or multiple core networks 106 with individual networks 104, 106 supporting different standard generations.

[0197] Telecommunications network 902 may support network slicing to provide different logical networks to different devices that are connected to the telecommunications network 902. For example, the telecommunications network 902 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC) / Massive loT services to yet further UEs.

[0198] In some examples, one or more of the UEs 912 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 904 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 904. Additionally, a UE may be configured for operating in single- or multi-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0199] In the example, the hub 914 communicates with the access network 904 to facilitate indirect communication between one or more UEs (e.g., UE 912C and / or 912D) and network nodes (e.g., network node 910B). In some examples, the hub 914 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 914 may be a broadband router enabling access to the core network 906 for the UEs. As another example, the hub 914 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 910, or by executable code, script, process, or other instructions in the hub 914.

[0200] As another example, the hub 914 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 914 may be a content source. For example, for aUE that is a VRheadset, display, loudspeaker or other media delivery device, the hub 914 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 914 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 914 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0201] The hub 914 may have a constant / persistent or intermittent connection to the network node 910B. The hub 914 may also allow for a different communication scheme and / or schedule between the hub 914 and UEs (e.g., UE 912C and / or 912D), and between the hub 914 and the core network 906. In other examples, the hub 914 is connected to the core network 906 and / or one or more UEs via a wired connection. Moreover, the hub 914 may be configured to connect to an M2M service provider over the access network 904 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 910 while still connected via the hub 914 via a wired or wireless connection. In some embodiments, the hub 914 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 910B. In other embodiments, the hub 914 may be a nondedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 910B, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0202] Figure 10 is another example of a communication system 1000 according to some embodiments. As used herein, the communication system 1000 includes multiple access points (APs) 1010 (with four exemplary APs 1010A, 1010B, 1010C, and 1010D being depicted) and multiple wireless devices, referred to in the context of communication system 1000 as stations (STAs) 1012 (referred to individually as STA 1012A, STA 1012B, STA 1012C, STA 1012D, and STA 1012E). STA 1012A is served by AP 1010A in a first basic service set (BSS) 1020A. STA 1010B and STA 1010C are served by AP 1010B in a second BSS, BSS 1020B. STA 1012D is served by AP 1010C in a third BSS, BSS 1020C. STA 1012E is served by AP 1010D in a fourth BSS, BSS 1020D. Stations 1012 may be non- AP STAs and correspond to various kinds ofwireless devices, for example, user terminals, such as mobile or stationary computing devices like smartphones, laptop computers, desktop computers, tablet computers, gaming devices, head- mounted displays (HMDs) for Augmented Reality (AR) or Virtual Reality (VR), or the like. Further, stations 1012 could, for example, correspond to other kinds of equipment like smart home devices, printers, multimedia devices, data storage devices, or the like.

[0203] Each of STAs 1012 may connect through a radio link to one of APs 1010. For example, depending on location or channel conditions experienced by a given STA 1012, the STA may select an appropriate AP and BSS for establishing the radio link. The radio link may be based on one or more orthogonal frequency-division multiplexing (OFDM) carriers from a frequency spectrum that is shared on the basis of a contention-based mechanism, e.g., an unlicensed or license exempt band like 2.4 GHz Industrial, Scientific, and Medical (ISM) band, the 5 GHz band, the 6 GHz band, or the 60 GHz band.

[0204] Each AP 1010 may provide data connectivity to STAs 1012 connected to a particular AP 1010. As illustrated, APs 1010 may be connected to a data network 1030. In this way, APs 1010 may also provide data connectivity between STAs 1012 and other entities, e.g., to one or more servers, service providers, data sources, data sinks, user terminals, or the like. Accordingly, the radio link established between a given STA 1012 and its serving AP 1010 may be used for providing various kinds of services to STA 1012, e.g., a voice service, a multimedia service, or other data service. Such services may be based on applications that are executed on STA 1012 and / or on a device linked to STA 1012. By way of example, Figure 10 illustrates an application service platform 1032 provided in data network 1030. The application(s) executed on STA 1012 and / or on one or more other devices linked to STA 1012 may use the radio link for data communication with one or more other STA 1012 and / or the application service platform 1032, thereby enabling utilization of the corresponding service(s) at STA 1012.

[0205] Figure 11 shows a wireless device 1100, which may be configured to operate in communication system 900 of Figure 9 or in communication system 1000 of Figure 10. The wireless device 1100 may be alternatively referred to as a UE 1100, like a UE 912 within the context of communication system 900, or as a station (STA) 1100 or as a non-access-point station (non-AP STA) 1100, like a STA 1012 within the context of the communication system 1000, in accordance with respective embodiments. As used herein, a wireless device refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other wireless devices. Examples of a wireless device include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage 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, and wireless terminal. Other examples include any type of UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0206] A wireless device 1100 may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to- everything (V2X). In other examples, wireless device 1100 may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, wireless device 1100 may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, wireless device 1100 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).

[0207] In particular embodiments, wireless device 1100 includes processing circuitry 1102 that is operatively coupled via a bus 1104 to an input / output interface 1106, a power source 1108, a memory 1110, a communication interface 1112, and / or any other component, or any combination thereof. Certain embodiments of wireless device 1100 may include all or a subset of the components shown in Figure 11. The level of integration between the components may vary from one embodiment of wireless device 1100 to another. In general, in a particular embodiment of wireless device 1100, processing circuitry 1102, input / output interface 1106, power source 1108, memory 1110, and communication interface 1112 may, in whole or in part, represent or include physical components common to or shared by one or more of the other elements of wireless device 1100. Further, certain embodiments of wireless devices 1100 may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0208] The processing circuitry 1102 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 1110. The processing circuitry 1102 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field- programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 1102 may include multiple central processing units (CPUs).

[0209] In the example, the input / output interface 1106 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into wireless device 1100. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0210] In some embodiments, the power source 1108 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used to supply power to circuitry or to charge an associated battery. The power source 1108 may further include power circuitry for delivering power from the power source 1108 itself, and / or an external power source, to the various parts of wireless device 1100 via input circuitry or an interface such as an electrical power cable. Power source 1108 may perform any formatting, converting, or other modification to make accessible power suitable for the respective components of the wireless device 1100 to which power is supplied.

[0211] The memory 1110 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 1110 includes one or more programs 1114, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 1116. The memory 1110 may store, for use by wireless device 1100, any of a variety of various operating systems or combinations of operating systems.

[0212] The memory 1110 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 1110 may allow wireless device 1100 to access instructions, 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 1110, which may be or comprise a device-readable storage medium.

[0213] The processing circuitry 1102 may be configured to communicate with an access network or other network via or using the communication interface 1112. The communication interface 1112 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 1122. The communication interface 1112 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 wireless device or a network node in an access network). Each transceiver may include a transmitter 1118 and / or a receiver 1120 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 1118 and receiver 1120 may be coupled to one or more antennas (e.g., antenna 1122) and may share circuit components, software, or firmware, or alternatively be implemented separately.

[0214] In the illustrated embodiment, communication functions of the communication interface 1112 may include cellular communication, Wi-Fi communication (e.g., according to an IEEE 802.11 family standard), LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / intemet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0215] In particular embodiments, wireless device 1100 may provide an output of data captured via a sensor, through its communication interface 1112, via a wireless connection to a network node, and / or in any appropriate manner. Data captured by sensors of a wireless device 1100 can be communicated through a wireless connection to a network node via another wireless device 1100. In particular embodiments, such 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).

[0216] As another example, wireless device 1100 comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, wireless device 1100 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.

[0217] Wireless device 1100, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. In particular embodiments, wireless device 1100 represents an loT device that comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the example embodiment of wireless device 1100 shown in Figure 11. As yet another specific example, in an loT scenario, wireless device 1100 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 wireless device and / or a network node. Wireless device 1100 may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, wireless device 1100 may implement the 3GPP NB-IoT standard. In other scenarios, wireless device 1100 may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0218] In practice, any number of wireless devices 1100 may be used together with respect to a single use case. For example, a first wireless device 1100 might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second wireless device 1100 that is a remote controller operating the drone. When a user makes changes from the remote controller, the first wireless device 1100 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 wireless device 1100 can also include more than one of the functionalities described above. For example, wireless device 1100 might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0219] Figure 12 shows a network node 1200 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunications network. In accordance with respective embodiments, network node 1200 may be configured to operate in communication system 900 of Figure 9, like network nodes 908 or 910, or in communication system 1000 of Figure 10, like an AP 1010 or a station 1012. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)), O-RAN nodes or components of an O-RAN node (e.g., O-RU, O-DU, O-CU).

[0220] Network nodes 1200 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. Network node 1200 may be a relay node or a relay donor node controlling a relay. Network nodes 1200 may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an O-RAN access node) and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0221] Other examples of network nodes 1200 include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0222] In particular embodiments, network node 1200 includes a processing circuitry 1202, a memory 1204, a communication interface 1206, and a power source 1208. In general, in a particular embodiment of network node 1200, processing circuitry 1202, memory 1204, communication interface 1206, and power source 1208 may, in whole or in part, represent or include physical components common to or shared by one or more of the other elements of network node 1200.

[0223] The network node 1200 may be composed of multiple distinct network entities (e.g., a NodeB entity and a RNC entity, or a BTS entity and a BSC entity, etc.), which may each have or utilize their own respective physical components. In certain scenarios in which the network node 1200 comprises multiple such entities (e.g., BTS and BSC), one or more of the separate entities may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 1200 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memories 1204 or portions of memory 1204 for different RATs) and some components may be reused (e.g., a same antenna 1210 may be shared by different RATs). The network node 1200 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1200, for example GSM, WCDMA, LTE, NR, Wi-Fi (e.g., according to an IEEE 802.11 family standard), Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 1200.

[0224] The processing circuitry 1202 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other components, such as the memory 1204, to provide network node 1200 functionality.

[0225] In some embodiments, the processing circuitry 1202 includes a system on a chip (SOC). In some embodiments, the processing circuitry 1202 includes one or more of radio frequency (RF) transceiver circuitry 1212 and baseband processing circuitry 1214. In some embodiments, the RF transceiver circuitry 1212 and the baseband processing circuitry 1214 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 1212 and baseband processing circuitry 1214 may be on the same chip or set of chips, boards, or units.

[0226] The memory 1204 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 1202. The memory 1204 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 1202 and utilized by the network node 1200. The memory 1204 may be used to store any calculations made by the processing circuitry 1202 and / or any data received via the communication interface 1206. In some embodiments, the processing circuitry 1202 and memory 1204 is integrated.

[0227] The communication interface 1206 is used in wired or wireless communication of signaling and / or data with UEs, other network nodes, and / or any other network equipment. In the illustrated embodiment, communication interface 1206 comprises port(s) / terminal(s) 1216 to send and receive data, for example to and from a network over a wired connection. In particular embodiments, network node 1100 may be capable of wireless communication and communication interface 1206 may also include radio front-end circuitry 1218 that may be coupled to, or in certain embodiments a part of, an antenna 1210. Particular embodiments of radio front-end circuitry 1218 include filter(s) 1220 and amplifier(s) 1222. The radio front-end circuitry 1218 may be connected to an antenna 1210 and processing circuitry 1202. The radio front-end circuitry may be configured to condition signals communicated between antenna 1210 and processing circuitry 1202. The radio front-end circuitry 1218 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 1218 may convert the digital data into a radio signal(s) having the appropriate channel and bandwidth parameters using a combination of filters 1220 and / or amplifiers 1222. The radio signal(s) may then be transmitted via the antenna 1210. Similarly, when receiving data, the antenna 1210 may collect radio signals which are then converted into digital data by the radio front-end circuitry 1218. The digital data may be passed to the processing circuitry 1202. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0228] In certain alternative embodiments, network node 1200 may be capable of wireless communication but does not include separate radio front-end circuitry 1218, instead, the processing circuitry 1202 includes radio front-end circuitry and is connected to the antenna 1210. Similarly, in some embodiments, all or some of the RF transceiver circuitry 1212 is part of the communication interface 1206. In still other embodiments, the communication interface 1206 includes one or more ports or terminals 1216, the radio front-end circuitry 1218, and the RF transceiver circuitry 1212, as part of a radio unit (not shown), and the communication interface 1206 communicates with the baseband processing circuitry 1214, which is part of a digital unit (not shown).

[0229] The antenna 1210 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 1210 may be coupled to the radio front-end circuitry 1218 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 1210 is separate from the network node 1200 and connectable to the network node 1200 through one or more interfaces or ports.

[0230] The antenna 1210, communication interface 1206, and / or the processing circuitry 1202 may be configured to perform some or all of the receiving operations and / or obtaining operations described herein as being performed by the network node 1200. Any information, data, and / or signals may be received from a UE, another network node, and / or any other network equipment. Similarly, the antenna 1210, the communication interface 1206, and / or the processing circuitry 1202 may be configured to perform some or all of the transmitting or sending operations described herein as being performed by the network node 1200. Any information, data and / or signals may be transmitted to a UE, another network node, and / or any other network equipment.

[0231] The power source 1208 provides power to the various components of network node 1200 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 1208 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 1200 with power for performing the functionality described herein. For example, the network node 1200 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 1208. As a further example, the power source 1208 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0232] Embodiments of the network node 1200 may include additional components beyond those shown in Figure 12 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node 1200 may include user interface equipment to allow input of information into the network node 1200 and to allow output of information from the network node 1200. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 1200.

[0233] Figure 13 is a block diagram illustrating a virtualization environment 1300 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 1300 hosted by one or more of hardware nodes, such as a hardware computing device that operates as an access network node, UE, core network node, or host. Further, in embodiments in which a virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment 1300 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.

[0234] Applications 1302 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 1200 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0235] Hardware 1304 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 1306 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VM 1308A and VM 1308B (which may be collectively referred to as VMs 1308), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 1306 may present a virtual operating platform that appears like networking hardware to one or more of the VMs 1308.

[0236] The VMs 1308 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by virtualization layer 1306. Different embodiments of the instance of a virtual appliance 1302 may be implemented on one or more of VMs 1308, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

[0237] In the context of NFV, each of the VMs 1308 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 1308, and that part of hardware 1304 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more of the VMs 1308 on top of the hardware 1304 and corresponds to an application 1302.

[0238] Hardware 1304 may be implemented in a standalone network node with generic or specific components. Hardware 1304 may implement some functions via virtualization. Alternatively, hardware 1304 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 1310, which, among others, oversees lifecycle management of applications 1302. In some embodiments, hardware 1304 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 1312 which may alternatively be used for communication between hardware nodes and radio units. Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions, and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

[0239] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer-readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer-readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and / or by end users and a wireless network generally.

[0240] Those skilled in the art will recognize improvements and modifications to the embodiments of the present disclosure. All such improvements and modifications are considered within the scope of the concepts disclosed herein.

[0241] Some exemplary embodiments of the present disclosure are as follows: Group A Embodiments

[0242] Embodiment 1 : A method performed by a user equipment, UE, for UE-side data collection for training, retraining, or fine tuning a UE-side Artificial Intelligence, Al, / Machine Learning, ML, model, the method comprising: transmitting (S706), to a network node, information related to an expected quality of data samples that can be collected by the UE; receiving (S708), from the network node, an indication to start data collection at the UE using a predefined, preconfigured, or indicated radio resource configuration or an indication to send a request to the network node for a radio resource configuration for data collection for training, retraining, or fine tuning the UE- side AI / ML model.

[0243] Embodiment 2: The method of embodiment 1, wherein the information related to the expected quality of data samples that can be collected by the UE comprises one or more UE-side conditions.

[0244] Embodiment 3: The method of embodiment 2, wherein receiving (S708) the indication to start data collection at the UE using a predefined, preconfigured, or indicated radio resource configuration or the indication to send a request to the network node for a radio resource configuration for data collection comprises receiving the indication to start the data collection at the UE or the indication to send a request to the network node for a radio resource configuration for the data collection in response to the network node evaluating fulfillment of one or more events associated to the one or more UE-side conditions.

[0245] Embodiment 4: The method of embodiment 1, the information related to the expected quality of data samples that can be collected by the UE comprises one or more UE-side conditions for which one or more events for initiating the data collection are fulfilled.

[0246] Embodiment 5: The method of embodiment 4, wherein the one or more events associated to the one or more UE-side conditions are configured by the network node.

[0247] Embodiment 6: The method of embodiment 4 or 5, wherein the events associated to the UE-side conditions comprise any one or more of the following events:

[0248] • estimated accuracy of the UE-side model being less than a certain accuracy threshold;

[0249] • quality of data samples associated to inputs of the UE-side model being below a certain quality threshold;

[0250] • a speed of the UE being above a certain speed threshold;

[0251] • a speed of the UE being below a certain speed threshold;

[0252] • one or more radio measurements associated to a certain beam or cell being above a certain radio measurement threshold; • one or more radio measurements associated to a certain beam or cell being below a certain radio measurement threshold.

[0253] Embodiment 7 : The method of any of embodiments 2 to 6, wherein the one or more UE- side conditions comprise any one or more of the following UE-side conditions:

[0254] • a speed of the UE;

[0255] • a traj ectory of the UE;

[0256] • one or more radio measurement results (e.g. RSRP, RSRQ, SINR, RSSI) associated to a certain beam or to a certain cell;

[0257] • an estimated accuracy of the UE-side model during UE-side model inference;

[0258] • an estimated accuracy of the UE-side model prior to activation of UE-side model inference;

[0259] • an estimated quality of the UE-side model;

[0260] • a location of the UE;

[0261] • a direction of movement of the UE.

[0262] Embodiment 8: The method of any of embodiments 2 to 7, wherein transmitting (S706) the information related to the expected quality of data samples that can be collected by the UE and the one or more UE-side conditions are associated to a UE-side AI / ML functionality, wherein the UE-side model is associated to the UE-side AI / ML functionality.

[0263] Embodiment 9: The method of embodiments 1 to 8, wherein the radio resource configuration comprises a set A and a set B of radio resources each including any one or more of the following configurations:

[0264] • a set of SSB(s) e.g. list of SSB indexes of a cell of the network node

[0265] • a set of CSI-RS resources e.g. list of CSI-RS resources of a cell of the network node

[0266] • a set of SS / PBCH block resource sets

[0267] • a set of cells / frequencies

[0268] • a MIMO layer configuration

[0269] • a maximum aggregated bandwidth

[0270] • a subcarrier spacing.

[0271] Embodiment 10: The method of embodiments 1 to 9, wherein the request to the network node for a radio resource configuration to perform the data collection comprises a requested set A and / or a requested set B of resources in which the UE requests performance of the UE-side data collection, wherein each of the requested set A and / or requested set B of resources comprises any one or more of the following:

[0272] • a set of SSB(s) e.g. list of SSB indexes of a cell of the network node • a set of CSI-RS resources e.g. list of CSI-RS resources of a cell of the network node

[0273] • a set of SS / PBCH block resource set

[0274] • a set of cells / frequencies

[0275] • a MIMO layer configuration

[0276] • a maximum aggregated bandwidth

[0277] • a subcarrier spacing

[0278] Embodiment 11 : A method performed by a user equipment, UE, for UE-side data collection for training, retraining, or fine tuning a UE-side Artificial Intelligence, Al, / Machine Learning, ML, model, the method comprising: determining (S806) whether to initiate data collection for a UE-side AI / ML model based on an evaluation of one or more events associated to one or more UE-side conditions, the one or more UE-side conditions being related to an expected quality of data samples that can be collected by the UE; and upon determining (S806) to initiate data collection for the UE-side AI / ML model, either: transmitting (S808A-1), to a network node, a request for a radio resource configuration for performing the data collection; or starting (S808B- 1) the data collection using predefined or preconfigured radio resource configuration.

[0279] Embodiment 12: The method of embodiment 11 , wherein the one or more events associated to the one or more UE-side conditions are configured by the network node.

[0280] Embodiment 13: The method of embodiment 11 or 12, wherein the one or more UE-side conditions comprise any one or more of the following UE-side conditions:

[0281] • a speed of the UE;

[0282] • a traj ectory of the UE;

[0283] • one or more radio measurement results (e.g. RSRP, RSRQ, SINR, RSSI) associated to a certain beam or to a certain cell;

[0284] • an estimated accuracy of the UE-side model during UE-side model inference;

[0285] • an estimated accuracy of the UE-side model prior to activation of UE-side model inference;

[0286] • an estimated quality of the UE-side model;

[0287] • a location of the UE;

[0288] • a direction of movement of the UE.

[0289] Embodiment 14: The method of any of embodiments 11 to 13, wherein the one or more events associated to the one or more UE-side conditions comprise any one or more of the following events: estimated accuracy of the UE-side model being less than a certain accuracy threshold; • quality of data samples associated to inputs of the UE-side model being below a certain quality threshold;

[0290] • a speed of the UE being above a certain speed threshold;

[0291] • a speed of the UE being below a certain speed threshold;

[0292] • one or more radio measurements associated to a certain beam or cell being above a certain radio measurement threshold;

[0293] • one or more radio measurements associated to a certain beam or cell being below a certain radio measurement threshold.

[0294] Embodiment 15: The method of any of embodiments 11 to 14, wherein determining whether to initiate data collection for the UE-side AI / ML model is performed during UE-side model inference.

[0295] Embodiment 16: The method of any of embodiments 11 to 14, wherein determining whether to initiate data collection for the UE-side AI / ML model is performed as an outcome of UE-side model monitoring at the UE.

[0296] Embodiment 17: The method of any of embodiments 11 to 16, wherein determining whether to initiate data collection for the UE-side AI / ML model, the evaluations of the one or more events, and the UE-side conditions are associated to a UE-side AI / ML functionality, wherein the UE-side model is associated to the UE-side AI / ML functionality.

[0297] Embodiment 18: The method of any of embodiments 11 to 17, wherein the radio resource configuration comprises a set A and a set B of radio resources each comprising any one or more of the following configurations:

[0298] • a set of SSB(s) e.g. list of SSB indexes of a cell of the network node

[0299] • a set of CSI-RS resources e.g. list of CSI-RS resources of a cell of the network node

[0300] • a set of SS / PBCH block resource set

[0301] • a set of cells / frequencies

[0302] • a MIMO layer configuration

[0303] • a maximum aggregated bandwidth

[0304] • a subcarrier spacing.

[0305] Embodiment 19: The method of any of embodiments 11 to 17, wherein the request transmitted to the network node for a radio resource configuration to perform UE-side data collection comprises a requested set A and / or a request set B of resources in which the UE requests the network node to allow the UE-side data collection, wherein each of the requested set A and / or requested set B of resources comprises any one or more of the following: • a set of SSB(s) e.g. list of SSB indexes of a cell of the network node

[0306] • a set of CSI-RS resources e.g. list of CSI-RS resources of a cell of the network node

[0307] • a set of SS / PBCH block resource set

[0308] • a set of cells / frequencies

[0309] • a MIMO layer configuration

[0310] • a maximum aggregated bandwidth

[0311] • a subcarrier spacing.

[0312] Embodiment 20: The method of any of the previous embodiments, further comprising: providing user data; and forwarding the user data to a host via the transmission to the network node.

[0313] Group B Embodiments

[0314] Embodiment 21: A method performed by a network node of a wireless communications network for enabling UE-side data collection for training, retraining, or fine tuning a UE-side Artificial Intelligence, Al, / Machine Learning, ML, model, the method comprising: receiving (S706), from a UE, information related to an expected quality of data samples that can be collected by the UE for training, retraining, or finetuning a UE-side AI / ML model; determining (S708) to initiate data collection at the UE based on the information related to the expected quality of data samples that can be collected by the UE; and transmitting (S708), to the UE, an indication to start data collection at the UE using a predefined, preconfigured, or indicated radio resource configuration or an indication to send a request to the network node for a radio resource configuration for data collection for training, retraining, or fine tuning the UE-side AI / ML model.

[0315] Embodiment 22: The method of embodiment 21, wherein the information related to the expected quality of data samples that can be collected by the UE comprises one or more UE-side conditions.

[0316] Embodiment 23: The method of embodiment 22, wherein determining (S708) to initiate data collection at the UE comprises determining (S708) that one or more events associated to the one or more UE-side conditions are fulfilled.

[0317] Embodiment 24: The method of embodiment 21, the information related to the expected quality of data samples that can be collected by the UE comprises one or more UE-side conditions for which one or more events for initiating the data collection are fulfilled.

[0318] Embodiment 25 : The method of embodiment 24, wherein the one or more events associated to the one or more UE-side conditions are configured by the network node. Embodiment 26: The method of embodiment 23 to 25, wherein the events associated to the UE-side conditions comprise any one or more of the following events:

[0319] • estimated accuracy of the UE-side model being less than a certain accuracy threshold;

[0320] • quality of data samples associated to inputs of the UE-side model being below a certain quality threshold;

[0321] • a speed of the UE being above a certain speed threshold;

[0322] • a speed of the UE being below a certain speed threshold;

[0323] • one or more radio measurements associated to a certain beam or cell being above a certain radio measurement threshold;

[0324] • one or more radio measurements associated to a certain beam or cell being below a certain radio measurement threshold.

[0325] Embodiment 27 : The method of any of embodiments 22 to 26, wherein the one or more UE-side conditions comprise any one or more of the following UE-side conditions:

[0326] • a speed of the UE;

[0327] • a traj ectory of the UE;

[0328] • one or more radio measurement results (e.g. RSRP, RSRQ, SINR, RSSI) associated to a certain beam or to a certain cell;

[0329] • an estimated accuracy of the UE-side model during UE-side model inference;

[0330] • an estimated accuracy of the UE-side model prior to activation of UE-side model inference;

[0331] • an estimated quality of the UE-side model;

[0332] • a location of the UE;

[0333] • a direction of movement of the UE.

[0334] Embodiment 28: The method of any of embodiments 22 to 27, wherein receiving (S706) the information related to the expected quality of data samples that can be collected by the UE and the one or more UE-side conditions are associated to a UE-side AI / ML functionality, wherein the UE-side model is associated to the UE-side AI / ML functionality.

[0335] Embodiment 29: The method of embodiments 21 to 28, wherein the radio resource configuration comprises a set A and a set B of radio resources each including any one or more of the following configurations:

[0336] • a set of SSB(s) e.g. list of SSB indexes of a cell of the network node

[0337] • a set of CSI-RS resources e.g. list of CSI-RS resources of a cell of the network node

[0338] • a set of SS / PBCH block resource sets

[0339] • a set of cells / frequencies • a MIMO layer configuration

[0340] • a maximum aggregated bandwidth

[0341] • a subcarrier spacing.

[0342] Embodiment 30: The method of embodiments 21 to 29, wherein the request to the network node for a radio resource configuration to perform the data collection comprises a requested set A and / or a requested set B of resources in which the UE requests performance of the UE-side data collection, wherein each of the requested set A and / or requested set B of resources comprises any one or more of the following:

[0343] • a set of SSB(s) e.g. list of SSB indexes of a cell of the network node

[0344] • a set of CSI-RS resources e.g. list of CSI-RS resources of a cell of the network node

[0345] • a set of SS / PBCH block resource set

[0346] • a set of cells / frequencies

[0347] • a MIMO layer configuration

[0348] • a maximum aggregated bandwidth

[0349] • a subcarrier spacing

[0350] Embodiment 31: The method of embodiments 21 to 30, wherein determining (S708) to initiate data collection at the UE is further based on one or more network-side conditions.

[0351] Embodiment 32: The method of any of the previous embodiments, further comprising: obtaining user data; and forwarding the user data to a host or a user equipment.

[0352] Group C Embodiments

[0353] Embodiment 33: A User Equipment, UE, comprising: processing circuitry configured to perform any of the operations of any of the Group A embodiments; and a power source configured to supply power to the processing circuitry.

[0354] Embodiment 34: A network node comprising: processing circuitry configured to perform any of the operations of any of the Group B embodiments; a power source circuitry configured to supply power to the processing circuitry.

[0355] Embodiment 35: A User Equipment, UE, comprising: one or more antennas; communication interface connected to the one or more antennas and to processing circuitry; the processing circuitry being configured to perform any of the operations 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 power source connected to the processing circuitry and configured to supply power to the UE.

Claims

CLAIMS1. A method performed by a user equipment, UE, (700) for UE-side data collection for training, retraining, or fine tuning a UE-side Artificial Intelligence, Al, / Machine Learning, ML, model, the method comprising: transmitting (S706), to a network node (702), information related to one or more UE- side conditions related to an expected quality of data samples that can be collected by the UE (700); and receiving (S708), from the network node (702), an indication to start data collection at the UE (700) using a predefined, preconfigured, or indicated radio resource configuration or an indication to send a request to the network node (702) for a radio resource configuration for data collection for training, retraining, or fine tuning the UE-side AI / ML model.

2. The method of claim 1 , wherein receiving (S708) the indication to start data collection at the UE (700) using a predefined, preconfigured, or indicated radio resource configuration or the indication to send a request to the network node (702) for a radio resource configuration for data collection comprises receiving (S708) the indication to start the data collection at the UE (700) or the indication to send a request to the network node (702) for a radio resource configuration for the data collection in response to the network node (702) evaluating fulfillment of one or more events associated to the one or more UE-side conditions.

3. The method of claim 1, wherein the one or more UE-side conditions are one or more UE- side conditions for which one or more events for initiating the data collection are fulfilled.

4. The method of claim 3, wherein the one or more events associated to the one or more UE- side conditions are configured by the network node (702).

5. The method of claim 3 or 4, wherein the events associated to the UE-side conditions comprise any one or more of the following events:• estimated accuracy of the UE-side model being less than a certain accuracy threshold;• quality of data samples associated to inputs of the UE-side model being below a certain quality threshold;• a speed of the UE being above a certain speed threshold;• a speed of the UE being below a certain speed threshold;• one or more radio measurements associated to a certain beam or cell being above a certain radio measurement threshold;• one or more radio measurements associated to a certain beam or cell being below a certain radio measurement threshold.

6. The method of any of claims 1 to 5, wherein the one or more UE-side conditions comprise any one or more of the following:• a speed of the UE (700);• a traj ectory of the UE (700);• information associated to a certain beam or to a certain cell;• one or more radio measurement results associated to a certain beam or to a certain cell;• information that indicates a hardware configuration update at the UE (700);• one or more Signal to Noise Ratio, SNR, values;• information about one or more weather conditions at the UE (700);• information about a signal blockage at the UE (700);• a performance metric for the UE-side AI / ML model;• an estimated accuracy of the UE-side AI / ML model during UE-side model inference;• an estimated accuracy of the UE-side AI / ML model prior to activation of UE-side AI / ML model inference;• an estimated quality of the UE-side AI / ML model;• information about a storage capacity at the UE (700);• information about a battery status of a battery of the UE (700);• information about a distance between the UE (700) and an associated base station;• a location of the UE (700).

7. The method of any of claims 1 to 6, wherein the one or more UE-side conditions are associated to a UE-side AI / ML functionality, wherein the UE-side AI / ML model is associated to the UE-side AI / ML functionality.

8. The method of any of claims 1 to 7, wherein the one or more UE-side conditions comprise information related an expected quality of data samples that can be collected by the UE (700)9. The method of claim 8, wherein the information related to the expected quality of data samples that can be collected by the UE (700) comprises information that directly indicates the expected quality of data samples.

10. The method of claims 1 to 9, wherein the radio resource configuration comprises a set A and a set B of radio resources each including any one or more of the following configurations:• a set of Synchronization Signal, SS, / Physical Broadcast Channel, PBCH, blocks;• a set of channel state information reference signal, CSI-RS, resources;• a set of SS / PBCH block resource sets;• a set of cells and / or frequencies;• a multiple input multiple output, MIMO, layer configuration;• a maximum aggregated bandwidth;• a subcarrier spacing.

11. The method of claims 1 to 10, wherein the request to the network node for a radio resource configuration to perform the data collection comprises a requested set A and / or a requested set B of resources in which the UE (700) requests performance of the UE-side data collection, wherein each of the requested set A and / or requested set B of resources comprises any one or more of the following:• a set of Synchronization Signal, SS, / Physical Broadcast Channel, PBCH, blocks;• a set of channel state information reference signal, CSI-RS, resources;• a set of SS / PBCH block resource sets;• a set of cells and / or frequencies;• a multiple input multiple output, MIMO, layer configuration;• a maximum aggregated bandwidth;• a subcarrier spacing.

12. The method of any of claims 1 to 11, wherein receiving (S708) the indication to start data collection at the UE (700) using a predefined, preconfigured, or indicated radio resource configuration or the indication to send a request to the network node (702) for a radio resource configuration for data collection comprises receiving (S708) the indication to start the data collection at the UE (700).

13. The method of claim 12, further comprising performing (S710) UE-side data collection in response to receiving the indication (S708) to start the data collection at the UE (700).

14. The method of any of claims 1 to 11, wherein receiving (S708) the indication to start data collection at the UE (700) using a predefined, preconfigured, or indicated radio resource configuration or the indication to send a request to the network node (702) for a radio resource configuration for data collection comprises receiving (S708) the indication to send a request to the network node (702) for a radio resource configuration for the data collection.

15. The method of claim 14, further comprising: transmitting, to the network node (702), a request for a radio resource configuration for the data collection; receiving a radio resource configuration for the data collection from the network node (702), in response to the request; and performing (S710) UE-side data collection based on the received radio resource configuration.

16. A user equipment, UE, (700; 1100) for UE-side data collection for training, retraining, or fine tuning a UE-side Artificial Intelligence, Al, / Machine Learning, ML, model, the UE (700; 1100) comprising: a communication interface (1112) comprising a transmitter (1118) and a receiver (1120); and processing circuitry (1102) associated with the communication interface (1112), the processing circuitry (1102) configured to cause the UE (700; 1100) to: transmit (S706), to a network node (702), information related to one or more UE-side conditions related to an expected quality of data samples that can be collected by the UE; and receive (S708), from the network node (702), an indication to start data collection at the UE (700) using a predefined, preconfigured, or indicated radio resource configuration or an indication to send a request to the network node (702) for a radio resource configuration for data collection for training, retraining, or fine tuning the UE-side AI / ML model.

17. The UE (700; 1100) of claim 16, wherein the processing circuitry (1102) is further configured to cause the UE (700; 1100) to perform the method of any of claims 2 to 15.

18. A method performed by a user equipment, UE, (800) for UE-side data collection for training, retraining, or fine tuning a UE-side Artificial Intelligence, Al, / Machine Learning, ML, model, the method comprising: determining (S806) whether to initiate data collection for a UE-side AI / ML model based on an evaluation of one or more events associated to one or more UE-side conditions, the one or more UE-side conditions being related to an expected quality of data samples that can be collected by the UE; and upon determining (S806) to initiate data collection for the UE-side AI / ML model, either: transmitting (S808A-1), to a network node, a request for a radio resource configuration for performing the data collection; or starting (S808B-1) the data collection using predefined or preconfigured radio resource configuration.

19. The method of claim 18, wherein the one or more events associated to the one or more UE- side conditions are configured by the network node.

20. The method of claim 18 or 19, wherein the one or more UE-side conditions comprise any one or more of the following UE-side conditions:• a speed of the UE (800);• a trajectory of the UE (800);• information associated to a certain beam or to a certain cell;• one or more radio measurement results associated to a certain beam or to a certain cell;• information that indicates a hardware configuration update at the UE (800);• one or more Signal to Noise Ratio, SNR, values;• information about one or more weather conditions at the UE (800);• information about a signal blockage at the UE (800);• a performance metric for the UE-side AI / ML model;• an estimated accuracy of the UE-side AI / ML model during UE-side model inference;• an estimated accuracy of the UE-side AI / ML model prior to activation of UE-side AI / ML model inference;• an estimated quality of the UE-side AI / ML model;• information about a storage capacity at the UE (800);• information about a battery status of a battery of the UE (800);information about a distance between the UE (800) and an associated base station; a location of the UE (800).

21. The method of any of claims 18 to 20, wherein the one or more events associated to the one or more UE-side conditions comprise any one or more of the following events:• estimated accuracy of the UE-side model being less than a certain accuracy threshold;• quality of data samples associated to inputs of the UE-side model being below a certain quality threshold;• a speed of the UE being above a certain speed threshold;• a speed of the UE being below a certain speed threshold;• one or more radio measurements associated to a certain beam or cell being above a certain radio measurement threshold;• one or more radio measurements associated to a certain beam or cell being below a certain radio measurement threshold.

22. The method of any of claims 18 to 21 , wherein determining whether to initiate data collection for the UE-side AI / ML model is performed during UE-side model inference.

23. The method of any of claims 18 to 21 , wherein determining whether to initiate data collection for the UE-side AI / ML model is performed as an outcome of UE-side model monitoring at the UE.

24. The method of any of claims 18 to 23, wherein determining whether to initiate data collection for the UE-side AI / ML model, the evaluations of the one or more events, and the UE-side conditions are associated to a UE-side AI / ML functionality, wherein the UE-side model is associated to the UE-side AI / ML functionality.

25. The method of any of claims 18 to 24, wherein the radio resource configuration comprises a set A and a set B of radio resources each comprising any one or more of the following configurations:• a set of Synchronization Signal, SS, / Physical Broadcast Channel, PBCH, blocks;• a set of channel state information reference signal, CSI-RS;• a set of SS / PBCH block resource sets;• a set of cells and / or frequencies;a multiple input multiple output, MIMO, layer configuration; a maximum aggregated bandwidth; a subcarrier spacing.

26. The method of any of claims 18 to 24, wherein the request transmitted to the network node for a radio resource configuration to perform UE-side data collection comprises a requested set A and / or a request set B of resources in which the UE requests the network node to allow the UE- side data collection, wherein each of the requested set A and / or requested set B of resources comprises any one or more of the following:• a set of Synchronization Signal, SS, / Physical Broadcast Channel, PBCH, blocks;• a set of channel state information reference signal, CSI-RS;• a set of SS / PBCH block resource sets;• a set of cells and / or frequencies;• a multiple input multiple output, MIMO, layer configuration;• a maximum aggregated bandwidth;• a subcarrier spacing.

27. A user equipment, UE, (800; 1100) for UE-side data collection for training, retraining, or fine tuning a UE-side Artificial Intelligence, Al, / Machine Learning, ML, model, the UE (800; 1100) comprising: a communication interface (1112) comprising a transmitter (1118) and a receiver (1120); and processing circuitry (1102) associated with the communication interface (1112), the processing circuitry (1102) configured to cause the UE (800; 1100) to: determine (S806) whether to initiate data collection for a UE-side AI / ML model based on an evaluation of one or more events associated to one or more UE-side conditions, the one or more UE-side conditions being related to an expected quality of data samples that can be collected by the UE; and upon determining (S806) to initiate data collection for the UE-side AI / ML model, either: transmit (S808A-1), to a network node, a request for a radio resource configuration for performing the data collection; or start (S808B-1) the data collection using predefined or preconfigured radio resource configuration.

28. The UE (800; 1100) of claim 27, wherein the processing circuitry (1102) is further configured to cause the UE (800; 1100) to perform the method of any of claims 19 to 26.

29. A method performed by a network node (702) of a wireless communications network for enabling user equipment, UE,-side data collection for training, retraining, or fine tuning a UE-side Artificial Intelligence, Al, / Machine Learning, ML, model, the method comprising: receiving (S706), from a UE (700), information related to one or more UE-side conditions related to an expected quality of data samples that can be collected by the UE (700) for training, retraining, or finetuning a UE-side AI / ML model; determining (S708) to initiate data collection at the UE (700) based on the received information; and transmitting (S708), to the UE (700), an indication to start data collection at the UE (700) using a predefined, preconfigured, or indicated radio resource configuration or an indication for the UE (700) to send a request to the network node (702) for a radio resource configuration for data collection for training, retraining, or fine tuning the UE-side AI / ML model.

30. A network node (702; 1200) for a wireless communications network for enabling user equipment, UE,-side data collection for training, retraining, or fine tuning a UE-side Artificial Intelligence, Al, / Machine Learning, ML, model, the network node (702; 1200) comprising processing circuitry (1202) configured to cause the network node (702; 1200) to: receive (S706), from a UE (700), information related to one or more UE-side conditions related to an expected quality of data samples that can be collected by the UE (700) for training, retraining, or finetuning a UE-side AI / ML model; determine (S708) to initiate data collection at the UE (700) based on the received information; and transmit (S708), to the UE (700), an indication to start data collection at the UE (700) using a predefined, preconfigured, or indicated radio resource configuration or an indication for the UE (700) to send a request to the network node (702) for a radio resource configuration for data collection for training, retraining, or fine tuning the UE-side AI / ML model.

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