WO2025105998A1

WO2025105998A1 - Managing data collection

Info

Publication number: WO2025105998A1
Application number: PCT/SE2024/050950
Authority: WO
Inventors: Reem KARAKI; Henrik RYDÉN; Johan AXNÄS; Marco BELLESCHI; Pradeepa Ramachandra; Ali PARICHEHREHTEROUJENI
Original assignee: Telefonaktiebolaget LM Ericsson AB
Current assignee: Telefonaktiebolaget LM Ericsson AB
Priority date: 2023-11-13
Filing date: 2024-11-06
Publication date: 2025-05-22
Anticipated expiration: 2026-05-13

Abstract

The present disclosure provides a method performed by a user equipment for managing data for an artificial intelligence or machine learning (AI/ML) model. The method comprises transmitting (402) first information or second information to a network node when one or more first conditions are fulfilled. The first information comprises data collected for training the AI/ML model. The data is stored in a memory of the user equipment. The second information comprises an indication of an availability of the data in the memory.

Description

MANAGING DATA COLLECTION

TECHNICAL FIELD

[1] The present disclosure relates to methods for managing data for an artificial intelligence or machine learning (AI/ML) model, and a user equipment and network node configured to perform those methods.

BACKGROUND

[2] Artificial Intelligence (Al) and Machine Learning (ML) have been investigated, both in academia and industry, as promising tools to optimize the design of the air-interface in wireless communication networks. Example use cases include using autoencoders for Channel State Information (CSI) compression to reduce feedback overhead and improve channel prediction accuracy; using deep neural networks for classifying Line-of-Sight (LOS) and Non-LOS (NLOS) conditions to enhance positioning accuracy; using reinforcement learning for beam selection at the network (NW) side and/or the User Equipment (UE) side to reduce signalling overhead and beam alignment latency; and using deep reinforcement learning to leam an optimal precoding policy for complex Multiple Input Multiple Output (MIMO) precoding problems.

[3] In 3rd Generation Partnership Project (3 GPP) New Radio (NR) standardization work, a new Release 18 (Rel-18) study item on Al or ML (AI/ML) for the NR air interface started in May 2022. This study item will explore the benefits of augmenting the air-interface with features enabling improved support of AI/ML based algorithms for enhanced performance and/or reduced complexity/overhead. Through studying a few selected use cases (CSI feedback, beam management, and positioning), this study item aims at laying the foundation for future air-interface use cases leveraging AI/ML techniques.

[4] Building an Al model, or any ML model, includes several development steps and the actual training of the AI/ML model one step in the training pipeline. The AI/ML model can be trained at the UE-side by the UE itself or at the NW-side by a NW node. Irrespective of where the AI/ML model training is performed, a certain amount of data needs to be collected by the UE for use in training the AI/ML model and the collected data may need to be reported to the NW node.

[5] There exist techniques that are directed to enabling the data collection at the UE. However, these techniques are focused on defining the measurement configuration only without discussing the reporting characteristics.

[6] Therefore, several aspects remain unresolved.

SUMMARY

[7] As mentioned above, several aspects remain unresolved in relation to reporting data collected at the UE to the NW, specifically in relation to when to trigger the reporting for the data collected at the UE, and how to provide this information to the NW.

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

[9] Accordingly, in one aspect, there is provided a first method for managing data for an artificial intelligence or machine learning (AI/ML) model. The first method is performed by a user equipment. The first method comprises transmitting first information or second information to a network node when one or more first conditions are fulfilled. The first information comprises data collected for training the AI/ML model. The data is stored in a memory of the user equipment. The second information comprises an indication of an availability of the data in the memory.

[10] In another aspect, there is provided a second method for managing data for an AI/ML model. The second method is performed by a network node. The second method comprises receiving first information or second information from a user equipment when one or more first conditions are fulfilled. The first information comprises data collected for training the AI/ML model. The data is stored in a memory of the user equipment. The second information comprises an indication of an availability of the data in the memory.

[11] In another aspect, there is provided a UE comprising processing circuitry configured to cause the UE to perform the first method described earlier.

[12] In another aspect, there is provided a network node comprising processing circuitry configured to cause the network node to perform the second method described earlier.

[13] In another aspect, there is provided a computer program comprising instructions which, when executed by processing circuitry of a user equipment, cause the user equipment to perform the first method described earlier.

[14] In another aspect, there is provided a computer program comprising instructions which, when executed by processing circuitry of a network node, cause the network node to perform the second method described earlier. [15] In another aspect, there is provided a computer program product, embodied on a non- transitory machine-readable medium, comprising instructions which are executable by processing circuitry of a user equipment to cause the user equipment to perform the first method described earlier.

[16] In another aspect, there is provided a computer program product, embodied on a non- transitory machine-readable medium, comprising instructions which are executable by processing circuitry of a network node to cause the network node to perform the second method described earlier.

BRIEF DESCRIPTION OF THE DRAWINGS

[17] For a better understanding of the embodiments of the present disclosure, and to show how it may be put into effect, reference will now be made, by way of example only, to the accompanying drawings, in which:

[18] Fig. 1 shows a model lifecycle management (LCM) procedure;

[19] Fig. 2 shows a functional framework for studying AI/ML model LCM aspects;

[20] Fig. 3 shows an example autoencoder (AE)-based CSI report;

[21] Fig. 4 is a flow chart illustrating a method in accordance with some embodiments;

[22] Fig. 5 is a flow chart illustrating a method in accordance with some embodiments;

[23] Fig. 6 shows an example of a network in accordance with some embodiments;

[24] Fig. 7 shows an example of a communication system in accordance with some embodiments;

[25] Fig. 8 shows a UE in accordance with some embodiments;

[26] Fig. 9 shows a network node in accordance with some embodiments;

[27] Fig. 10 is a block diagram of a host;

[28] Fig. 11 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized; and

[29] Fig. 12 shows a communication diagram of a host communicating via a network node with a UE over a partially wireless connection in accordance with some embodiments.

DETAILED DESCRIPTION

[30] 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.

[31] Generally, all terms used herein are to be interpreted according to their ordinary meaning in the relevant technical field, unless a different meaning is clearly given and/or is implied from the context in which it is used. All references to a/an/the element, apparatus, component, means, step, etc. are to be interpreted openly as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any methods disclosed herein do not have to be performed in the exact order disclosed, unless a step is explicitly described as following or preceding another step and/or where it is implicit that a step must follow or precede another step. Any feature of any of the embodiments disclosed herein may be applied to any other embodiment, wherever appropriate. Likewise, any advantage of any of the embodiments may apply to any other embodiments, and vice versa.

[32] Other objectives, features and advantages of the enclosed embodiments will be apparent from the following description.

[33] Functional framework for AI/ML model lifecycle management (LCM)

[34] Building an artificial intelligence (Al) model, or any machine learning (ML) model, includes several development steps where the actual training of the AI/ML model is just one step in a training pipeline. An important part in Al development is the AI/ML model lifecycle management (LCM). This is illustrated in Fig. 1.

[35] Fig. l is an illustration of training and inference pipelines, and their interactions within a model lifecycle management procedure.

[36] The model lifecycle management typically consists of any one or more of the following:

• A training (re-training) pipeline 100 that may include: o Data Ingestion 102: Data ingestion refers to gathering raw (training) data from a data storage. After data ingestion, there may also be a step that controls the validity of the gathered data. o Data Pre-Processing 104: Data pre-processing refers to some feature engineering applied to the gathered data, e.g., it may include data normalization and possibly a data transformation required for the input data to the AI/ML model. o Model Training 106: Model training refers to the actual model training steps as previously outlined. o Model Evaluation 108: Model evaluation refers to benchmarking the performance to some model baseline. The iterative steps of model training and model evaluation may continue until an acceptable level of performance (as previously exemplified) is achieved. o Model Registration 110: Model registration refers to registering the AI/ML model, e.g. including any corresponding Al -metadata that provides information on how the AI/ML model was developed, and possibly AI/ML model evaluations performance outcomes.

• A deployment stage 120 to make the trained (or re-trained) AI/ML model part of the inference pipeline.

• An inference pipeline 130 that may include: o Data Ingestion 132: Data ingestion refers to gathering raw (inference) data from a data storage. o Data Pre-Processing 134: Data pre-processing stage is typically identical to corresponding processing that occurs in the training pipeline. o Model Operational 136: Model operational refers to using the trained and deployed model in an operational mode. o Data and Model Monitoring 138: Data and model monitoring refers to validating that the inference data are from a distribution that aligns well with the training data, as well as monitoring model outputs for detecting any performance, or operational, drifts.

• A drift detection stage 140 that informs about any drifts in the model operations.

[37] Fig. 2 illustrates a functional framework for studying AI/ML model LCM aspects. The functional framework of Fig. 2 can be used for studying different network and user equipment (NW-UE) collaboration levels for the Al for physical layer (PHY) use cases.

[38] As illustrated in Fig. 2, the functional framework may comprise any one or more of a data collection stage 202 in which data collection is performed, a model training stage 204 in which model training is performed, a management stage 206 in which management tasks are performed, an inference stage 208 in which inference is performed, and a model storage stage 210 in which model storage is performed. As illustrated by arrow 222 in Fig. 2, training data from the data collection stage 202 can be used for the model training. As illustrated by arrow 224 in Fig. 2, monitoring data from the data collection stage 202 can be used for the management tasks. As illustrated by arrow 226 in Fig. 2, inference data from the data collection can be used for the inference.

[39] As illustrated by arrow 228 in Fig. 2, the model storage stage 210 may comprise storing the trained or updated model from the model training stage 204. As illustrated by arrow 230 in Fig. 2, the model storage stage 210 may comprise receiving a model transfer or delivery request from the management stage 206. As illustrated by arrow 232 in Fig. 2, the model storage stage 210 may comprise transferring or delivering the model for the inference, and the inference stage 208 may comprise receiving the model for the inference. As illustrated by arrow 234 in Fig. 2, the management stage 206 may comprise receiving an inference output from the inference stage 208. As illustrated by arrow 236 in Fig. 2, the management stage 206 may comprise providing information for the inference, such as information on any one or more of selection, (de)activation, switching, and fallback. As illustrated by arrow 238 in Fig. 2, the model training stage 204 may comprise receiving, from the management stage 206, one or both of performance feedback and a retraining request.

[40] UE-NW collaboration levels for one- and two-sided AI/ML models

[41] The AI/ML models being discussed in the 3GPP Rel-18 study item on AI/ML for the NR air interface can be categorized into the following two types:

• One-side AI/ML model, which can be a UE-sided AI/ML model whose inference is performed entirely at the UE, or aNW-sided AI/ML model whose inference is performed entirely at the NW.

• Two-sided AI/ML model, which refers to a paired AI/ML Model(s) over which joint inference is performed across the UE and the NW, i.e., the first part of the inference is firstly performed by the UE and then the remaining part is performed by a gNodeB (gNB, which is a base station in NR), or vice versa. Fig. 3 shows an example use case of autoencoder (AE)-based CSI feedback or an AE-based CSI report. In Fig. 3, an encoder 302 (UE-part of the two-sided AE model) is operated at a UE to compress the estimated wireless channel, and the output of the encoder (the compressed wireless channel information estimates) is reported from the UE to a gNB. The gNB uses a decoder 304 (NW-part of the two-sided AE model) to reconstruct the estimated wireless channel information.

[42] Functionality based LCM and model identifier (Model-ID) based LCM

[43] For UE-side models and UE-part of two-sided models, functionality based LCM and model identifier (model-ID) based LCM are discussed in 3GPP Rel-18. [44] In functionality-based LCM, the network indicates activation, deactivation, fallback, and/or switching of AI/ML functionality via 3GPP signalling (e.g., Radio Resource Control (RRC), medium access control- control element (MAC-CE), downlink control information (DCI)). Models may not be identified at the Network, and the UE may perform model-level LCM. Whether and how much awareness or interaction the NW should have about model -level LCM requires further study. For functionality identification, there may be either one or more than one functionality defined within an AI/ML-enabled feature, whereby AI/ML-enabled feature refers to a feature where AI/ML may be used. The UE may have one AI/ML model for the functionality, or the UE may have multiple AI/ML models for the functionality.

[45] For AI/ML functionality identification and functionality -based LCM of UE-side models and/or UE-part of two-sided models, functionality refers to an AI/ML-enabled feature (or feature group (FG)) enabled by configuration(s), where configuration(s) is(are) supported based on conditions indicated by UE capability. Correspondingly, functionality-based LCM operates based on, at least, one configuration of AI/ML-enabled feature (or FG) or specific configurations of an AI/ML-enabled feature (or FG).

[46] 3GPP also studies mechanisms for the UE to report information related to the applicability of functionality (or functionalities) (e.g. information on whether an AIML functionality is applicable or not, or updates of the applicability of such AIML functionality) among the configured or identified functionality (or functionalities). The applicable functionalities may be a subset of all (configured or identified) functionalities.

[47] In model-ID-based LCM, models are identified at the Network, and the Network or UE may activate, deactivate, select, and/or switch individual AI/ML models via the model ID.

[48] For AI/ML model identification and model-ID-based LCM of UE-side models and/or UE-part of two-sided models, model-ID-based LCM operates based on identified models, where a model may be associated with specific configurations or conditions associated with UE capability of an AI/ML-enabled feature (or FG) and additional conditions (e.g., scenarios, sites, and datasets) as determined or identified between the UE-side and NW-side.

[49] From a Radio Access Network 1 (RANI) perspective, an AI/ML model identified by a model ID may be logical, and how it maps to physical AI/ML model(s) may be up to implementation. When distinction is necessary for discussion purposes, companies may use the term “a logical AI/ML model” to refer to a model that is identified and assigned a model ID, and “physical AI/ML model(s)” to refer to an actual implementation of such a model. [50] Similar to the case of functionality-based LCM, 3GPP also studies mechanisms for the UE to report information related to the applicability of UE-side model(s) (or parts of the model), such as information on whether a UE-side model is applicable or not, or updates to the applicability of the UE-side model. The applicable models may be a subset of all identified models are studied.

[51] For AI/ML model identification of the UE-side or UE-part of two-sided models, model identification can be categorized in the following types:

• Type A: Model is identified to the NW (if applicable) and the UE (if applicable) without over-the-air signalling. o The model may be assigned with a model ID during the model identification, which may be referred to or used in over-the-air signalling after model identification.

• Type B: Model is identified via over-the-air signalling. o Type Bl:

- Model identification is initiated by the UE, and the NW assists the remaining steps (if any) of the model identification.

- The model may be assigned with a model ID during the model identification. o Type B2:

- Model identification is initiated by the NW, and the UE responds (if applicable) for the remaining steps (if any) of the model identification.

- The model may be assigned with a model ID during the model identification.

• Note: This study does not imply that model identification is necessary.

[52] Once models are identified, the UE can indicate supported AI/ML model IDs for a given AI/ML-enabled feature (or FG) in a UE capability report as a starting point. It is noted that model identification using a capability report is not precluded for type Bl and type B2.

[53] Model ID (in RANI discussions) may or may not be globally unique, and different types of model IDs may be created for a single model for various LCM purposes.

[54] For functionality or model-ID based LCM, once functionalities or models are identified, the same or similar procedures may be used for their activation, deactivation, switching, fallback, and monitoring.

[55] How to handle the impact of UE’s internal conditions such as memory, battery, and other hardware limitations on functionality or model operations and AI/ML-enabled feature is to be studied.

[56] UE-side model training

[57] There are several methods for the UE-side model monitoring. In one approach, the training of UE-side model is performed at the UE itself, i.e. the UE performs both the training and the inference. However, this approach might be too complex in practice or possibly not feasible given the limited computational resources of the UE, and the large computational complexity that the training operation might imply. Also, if models are dependent on location and/or region, a single UE may not cover an entire coverage area, so that models the UE trains by itself may always be limited to the areas the UE moves around, so that every time the UE enters a new area its trained AI/ML models could be outdated. Hence, alternative approaches for training UE-sided models include the possibility that a network node (e.g. a radio access node like a gNB or a core network (CN) node, such as a network data analytics function (NWDAF)) collects data from a UE and trains an AI/ML model that, at some point, should be delivered or transferred to that UE or other UEs which may then apply it. Further, an Over-the- Top (OTT) server, outside 3GPP, may be in charge of performing the training. This server could be for example a UE-vendor specific server. This latter approach might be a reasonable candidate 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).

[58] Irrespective of whether the UE-side model training is performed by a node outside the Radio Access Network (RAN), e.g. in a core network node, or even outside the 3GPP network, a certain amount of data needs to be collected by the UE, in order to enable such a node to perform model training. That is because for many use cases, such as Al-based CSI compression, Al-based CSI prediction, Al-based beam management, Al-based positioning, AI- based mobility predictions, Al-based traffic predictions, etc, the training node needs to receive inputs from the UE. Hence, one can envisage a protocol in which the UE does training (e.g. upon receiving a triggering from the training node) for a certain amount of time, it collects data, and once the data collection is completed, it transfers the collected data to the training node. [59] The following table showing the mapping between functionalities and entities was agreed in R2 -2308286.

[60] NW-side model training

[61] Related to NW-side model training, it has been assumed so far in 3GPP that the gNB and/or the Operations, Administration and Maintenance (0AM) may be in charge of that. If the gNB is responsible, it is assumed that the gNB may configure the UE with a set of resources, e.g. CSI-Reference Signal (RS) resources or Synchronization Signal Block (SSB) resource sets in which the UE should collect measurements for a certain amount of time. Then, the UE may report what is measured to the gNB, e.g. via RRC signalling. Then, the training can be performed in the gNB itself, or in another node controlled by the gNB-vendor, e.g. an OTT server handled by the gNB-vendor.

[62] A similar approach can apply for the case in which the 0AM does the NW-side training. In this case, the 0AM may request the gNB to provide to the UE a certain configuration according to which the UE should perform certain measurements, and collect data. Once the data collection is completed, the UE may transfer the collected data to the 0AM, e.g. using the Minimization of Drive Test (MDT) framework such as the immediate MDT or the logged MDT. [63] The following table showing the mapping between functionalities and entities was agreed in R2 -2308286.

[64] There are provided herein methods for managing data for an artificial intelligence or machine learning (AI/ML) model. More specifically, for example, there are provided methods for event based data reporting for AI/ML training.

[65] Fig.4 depicts a first method in accordance with particular embodiments. The first method 4 may be performed by a UE or wireless device (e.g. the UE 712 or UE 800 as described later with reference to Figs. 7 and 8 respectively). The first method begins at step 402 with transmitting first information or second information to a network node when one or more first conditions are fulfilled. The first information comprises data collected for training the AI/ML model. The data is stored in a memory of the user equipment. The second information comprises an indication of an availability of the data in the memory.

[66] The first method may comprise one or both of: transmitting the first information if the user equipment is configured to transmit the first information, and transmitting the second information if the user equipment is configured to transmit the second information. The UE may be configured by default (such as hardcoded) to transmit the first information, e.g. without any explicit NW configuration.

[67] The user equipment may be configured, by the network node, to transmit the first information and/or the user equipment may be configured, by the network node, to transmit the second information.

[68] The first method may comprise receiving, from the network node, an indication of the data that is to be collected for training the AI/ML model. Receiving the indication of the data that is to be collected may comprise receiving a first configuration associated with the AI/ML model, and the first configuration may comprise the indication of the data that is to be collected.

[69] The first configuration may comprise an instruction that instructs the UE to start collecting the data for training the AI/ML model.

[70] The first method may comprise monitoring whether the one or more first conditions are fulfilled. At least one of the one or more first conditions may be configured by the network node. The one or more first conditions may be received from the network node. Receiving the one or more first conditions may comprise receiving a second configuration, and the second configuration comprises the one or more first conditions.

[71] The second configuration may comprise any one or more of: one or more identifiers that each identify a respective first condition of the one or more first conditions; one or more parameters for use in identifying whether the one or more conditions are fulfilled; an indication of a minimum duration for which the one or more conditions are to be fulfilled prior to the user equipment transmitting the first information or the second information; an indication of whether the user equipment is allowed to transmit the first information or the second information more than once; and a bearer configuration to be used for transmitting the first information or the second information.

[72] The first information may be transmitted according to a third configuration. The first method may comprise receiving a third configuration according to which the user equipment is to transmit the first information. The third configuration may be received in response to transmitting the second information. The first configuration may comprise the third configuration. The third configuration may comprise a bearer configuration.

[73] The first method may comprise transmitting the second information when the one or more first conditions are fulfilled.

[74] The first method may comprise receiving, from the network node, a request for the user equipment to transmit the data collected for training the AI/ML model. The first information may be transmitted in response to receiving the request from the network node. The request may comprise the third configuration. The request may be received in response to transmitting the second information. The first information may be transmitted in response to receiving, from the network node, the request for the user equipment to transmit the data collected for training the AI/ML model. The request may be for the user equipment to transmit all or only part of the data collected for training the AI/ML model. [75] Fig. 5 depicts a second method in accordance with particular embodiments. The second method 5 may be performed by a network node (e.g. the network node 710 or network node 900 as described later with reference to Figs. 7 and 9 respectively). The second method begins at step 502 with receiving first information or second information from a user equipment when one or more first conditions are fulfilled. The first information comprises data collected for training the AI/ML model. The data is stored in a memory of the user equipment. The second information comprises an indication of an availability of the data in the memory.

[76] Herein, the one or more first conditions may also be referred to as one or more events.

[77] The second method may comprise one or both of: receiving the first information if the user equipment is configured to transmit the first information, and receiving the second information if the user equipment is configured to transmit the second information.

[78] The second method may comprise one or both of configuring the user equipment to transmit the first information and configuring the user equipment to transmit the second information.

[79] The second method may comprise transmitting, to the user equipment, an indication of the data that is to be collected for training the AI/ML model. Transmitting the indication of the data that is to be collected may comprise transmitting a first configuration associated with the AI/ML model, and the first configuration comprises the indication of the data that is to be collected.

[80] The first configuration may comprise an instruction that instructs the UE to start collecting the data for training the AI/ML model.

[81] The second method may comprise configuring at least one of the one or more first conditions. The second method may comprise transmitting the one or more first conditions to the user equipment. Transmitting the one or more first conditions may comprise transmitting a second configuration, and the second configuration may comprise the one or more first conditions.

[82] The first information may be received according to a third configuration. The second method may comprise transmitting, to the user equipment, a third configuration according to which the user equipment is to transmit the first information. The third configuration may be transmitted in response to receiving the second information. The first configuration may comprise the third configuration. The third configuration may comprise a radio bearer configuration. [83] The second method may comprise receiving the second information when the one or more first conditions are fulfilled.

[84] The second method may comprise transmitting, to the user equipment, a request for the user equipment to transmit the data collected for training the AI/ML model. The first information may be received in response to transmitting the request, to the user equipment. The request may comprise the third configuration. The request may be transmitted in response to receiving the second information. The first information may be received in response to transmitting, to the user equipment, the request for the data collected for training the AI/ML model. The request may be for the user equipment to transmit all or only part of the data collected for training the AI/ML model.

[85] There is also provided a method performed by a system comprising the UE and the network node. The method performed by the system comprises the first method and the second method.

[86] In more detail, there is provided a method for a UE to:

Perform data collection or logging according to one or more parameters to be measured for the purpose of data collection for AI/ML model training, wherein the parameters to be measured may be included in a first configuration provided by the network (e.g. network node).

Monitor the fulfilment of a first set of one or more event(s), wherein upon the fulfilment of one or more events in the first set of events, the UE may perform one of the following actions: o indicating to the network (e.g. network node) the availability in the UE memory of data logged according to the parameters to be measured included in the first configuration; and o transmitting to the network (e.g. network node) the data logged in the UE memory according to the parameters to be measured included in the first configuration.

[87] There is also provided a UE comprising processing circuitry configured to cause the UE to perform the first method described earlier. The UE may comprise at least one memory for storing instructions which, when executed by the processing circuitry of the UE, cause the UE to operate according to the first method.

[88] There is also provided a network node comprising processing circuitry configured to cause the network node to perform the second method described earlier. The network node may comprise at least one memory for storing instructions which, when executed by the processing circuitry of the network node, cause the network node to operate according to the second method.

[89] There is also provided a system (or network) comprising the UE and the network node.

[90] There is also provided a computer program comprising instructions which, when executed by processing circuitry of a user equipment, cause the user equipment to perform the first method.

[91] There is also provided a computer program comprising instructions which, when executed by processing circuitry of a network node, cause the network node to perform the second method.

[92] There is also provided a computer program product, embodied on a non-transitory machine-readable medium, comprising instructions which are executable by processing circuitry of a user equipment to cause the user equipment to perform the first method.

[93] There is also provided a computer program product, embodied on a non-transitory machine-readable medium, comprising instructions which are executable by processing circuitry of a network node to cause the network node to perform the second method.

[94] Certain embodiments may provide one or more of the following technical advantage(s).

[95] The approaches described herein can allow the network (e.g. network node) to better optimize the network resource usage, particularly in case of overload in the network. Using the available measurement indication, the network is aware of the logged measurements at the UE(s). Nonetheless, the network can prioritize serving other traffic over data collection for training if there are not enough resources. When there is less demand on network resources, the network can fetch the logged measurements from the UE. The approaches described herein also provide means to configure the UE to send the logged information without additional delays, when needed.

[96] Initial considerations

[97] In this disclosure, the terms “ML-model”, “Al-model” or “AI/ML model” can be used interchangeably. An AI/ML model can be defined as a functionality or be part of a functionality that is deployed or implemented in a first node, e.g. a User Equipment (UE) in the case of a UE-sided model. An AI/ML model can be defined as a feature or part of a feature that is implemented or supported in a first node. This first node can indicate the feature version to a second node. If the AI/ML-model is updated, the feature version may be changed by the first node.

[98] An AI/ML-model may correspond to a function which receives one or more inputs (e.g. measurements, configuration(s)) and provide as outcome one or more predict! on(s) or estimates of a certain type (e.g. time-domain and/or spatial domain predictions of beam measurements).

[99] In one example, an AI/ML-model may correspond to a function receiving as input the measurement of a reference signal at time instance tO (e.g. transmitted in beam-X) and provide as outcome the prediction of the reference signal in timer tO+T. In another example, an AI/ML- model may correspond to a function receiving as input the measurement of a reference signal X (e.g. transmitted in beam-x), such as an SSB whose index is ‘x’, and provide as outcome the prediction of other reference signals transmitted in different beams e.g. reference signal Y (e.g. transmitted in beam-x), such as an SSB whose index is ‘x’.

[100] Another example is an AI/ML model for use in (or for aiding) CSI estimation, in such a setup the AI/ML-model may be a specific AI/ML-model with a UE and an AI/ML-model within the NW side. Jointly both AI/ML-models provide a joint network. The function of the AI/ML-model at the UE may be to compress a channel input and the function of the AI/ML- model at the NW side may be to decompress the received output from the UE. It is further possible to apply something similar for positioning wherein the input may be a channel impulse in some form related to a certain reference point (typically a TP (transmit point)) in time. The purpose on the NW side may be to detect different peaks within the impulse response, that reflect the multipath experienced by the radio signals arriving at the UE side. For positioning, another way is to input multiple sets of measurements into an ML network and based on that derive an estimated position of the UE. Another AI/ML-model is an AI/ML-model that is able to aid the UE in channel estimation or interference estimation for channel estimation. The channel estimation may, for example, be for the Physical Downlink Shared Channel (PDSCH) and may be associated with a specific set of reference signals patterns that are transmitted from the network (e.g. network node) to the UE. The AI/ML-model may then be part of the receiver chain within the UE and may not be directly visible within the reference signal pattern as such that is configured/scheduled to be used between the network (e.g. network node) and UE. Another example of an AI/ML-model for CSI estimation is to predict a suitable Channel Quality Information (CQI), Precoder Matrix Indicator (PMI), Rank Indicator (RI), Channel State Information reference signal (CSI-RS) Resource Indicator (CRI) or similar value into the future.

[101] Another way to describe an AI/ML model can be as follows:

In terms of the time, frequency or spatial domain, the output of the AI/ML model may be in a different time instance, or at a different frequency location, or at a different spatial direction, or a combination of time, frequency or space, than those of the model input. In one example (time domain), an AI/ML-model may correspond to a function receiving as input the measurement of a reference signal at time instance tO (e.g. transmitted in beam-X) and provide as outcome the prediction of the reference signal in time instance tO+T. In another example (spatial domain), an AI/ML-model may correspond to a function receiving as input the measurement of a reference signal X (e.g. transmitted in beam-x), such as an S SB whose index is ‘x’, and provide as outcome the estimation or prediction of the link quality of other reference signals transmitted in different beams e.g. reference signal Y (e.g. transmitted in beam-y).

In terms of model structure, the AI/ML model may be fully contained within the UE, or split between the UE and network (e.g. network node).

One example of split structure is an AI/ML model for use in (or for aiding) CSI estimation, where a possible setup of the AI/ML-model is a split model, which comprises a specific sub- AI/ML-model within a UE and a sub- AI/ML-model within the NW side which collaborate to generate a desired outcome for the overall AI/ML model. The function of the sub- AI/ML-model at the UE may be to compress a channel input and the function of the sub- AI/ML-model at the NW side may be to decompress the received output from the UE. It is further possible to apply something similar for positioning wherein the input may be a channel impulse in some form related to a certain reference point in time. The purpose on the NW side may be to detect different peaks within the impulse response, that corresponds to different reception directions of radio signals at the UE side.

One example of ML contained within the UE is ML enhanced positioning, e.g., an AI/ML model implemented in the UE takes as input multiple sets of measurements (each corresponding to a down link (DL) signal from a different network node), and based on that derive an estimated position of the UE. • In terms of utility for the physical layer, the AI/ML model can be used for many functions, including: channel estimation, Line Of Sight (LOS) or Non-Line Of Sight (NLOS) classification, beam selection, position estimation of the UE, link adaption, etc. For example, an AI/ML-model that is able to aid the UE in channel estimation which may or may not incorporate interference estimation. The channel estimation could for example be for the PDSCH and be associated with specific set of reference signals patterns that are transmitted from the network (e.g. network node) to the UE. The AI/ML-model may then be part of the receiver chain within the UE and may not be directly visible within the reference signal pattern as such that is configured/ scheduled to be used between the network (e.g. network node) and UE. Another example of an AI/ML-model for CSI estimation is to predict a suitable CQI, PMI, RI or similar value into the future. The future may be a certain number of slots after the UE has performed the last measurement or targeting a specific slot in time within the future.

[102] According to the method, the UE can be connected to the network or network node (e.g. it may receive and transmit data and/or control information) i.e. in an RRC CONNECTED state, and may be configured to perform a specific function by using an AI/ML-model (which may be referred as an AI/ML-model functionality e.g. beam measurement predictions in timedomain). The specific functionality or function of an AI/ML model can, for example, be for one of the following examples, which can also be grouped as a functionality area (one or more AI/ML-model functionality per area), as follows:

• CSI reporting

• Beam management (BM) o In one option, there may be a BM functionality of an AI/ML-model(s), wherein an AI/ML model (e.g. at the UE) is capable of performing the inference of one or more time-domain predictions related to beam management. For example, the UE may be configured by the network (e.g. network node) to report (e.g. on Physical Uplink Control Channel (PUCCH) and/or Physical Uplink Shared Channel (PUSCH)) one or more time-domain predictions of SSB and/or CSI-RS and/or Phase Tracking reference signal (PTRS) measurements, e.g., by receiving a reporting configuration for AI/ML. o In one option, there may be a BM functionality of an AI/ML-model(s), wherein an AI/ML model (e.g. at the UE) is capable of performing the inference of one or more spatial-domain predictions related to beam management. o In one option, there may be a BM functionality of an AI/ML-model(s), wherein an AI/ML model (e.g. at the UE) is capable of performing the inference of both time and spatial-domain predictions related to beam management. o The UE may be configured with an AI/ML functionality, when at least one action related to that functionality is configured e.g. the UE may be configured to report predictions of beam measurement to one of its configured serving cell(s) and/or CSI(s) and/or SSB(s) of a serving cell.

Radio Resource Management (RRM) measurement o Such as mobility measurement, i.e., Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Received Signal Strength Indicator (RSSI), but also aspects related to radio link failure e.g. Radio Link Failure (RLF) predictions. Further, Radio Link Monitoring (RLM) related timers (T310) and counters (N310 and N311) related predictions could also be considered here. o Such as the measurement framework defined in Section 5.5 of 3GPP Technical Standard (TS) 38.331 comprising how the UE perform measurements (e.g. measurement configuration), what triggers measurement reports (e.g. event-triggered reports, periodic reports), and content to be included in measurement reports.

Link adaptation

Hybrid Automatic Repeat Request (HARQ) transmission

Data transmission

Data reception

Power control

Positioning of the UE

Random access transmission

Energy efficiency (e.g. Discontinuous Reception (DRX) settings) [103] The methods disclosed above may be applicable to the AI/ML model(s) associated to an AI/ML model functionality, or to the AI/ML model functionalities interchangeably.

[104] Herein, the terms collected data and logged data can be used interchangeably. The terms can refer to the operations at the UE for storing, in the UE memory, data associated to (or with) performed measurements. When transmitting the collected data to the network (e.g. a network node), the UE may transmit parts of the collected data, such as depending on the physical radio resources scheduled by the network (e.g. network node). This means that parts of the collected data may remain in the UE memory until they are transmitted.

[105] There is provided a method for a UE to:

Monitor the fulfilment of a first set of one or more event(s), wherein upon the fulfilment of one or more events in the first set of events, the UE may perform one of the following actions: o indicating to the network (e.g. a network node) the availability in the UE memory of data logged according to the parameters to be measured included in the first configuration (which can be referred to herein as an “availability indication”); and o transmitting to the network (e.g. the network node) the data logged in the UE memory according to the parameters to be measured included in the first configuration.

[106] It may be the case that which action to perform is configured by the network (e.g. the network node).

[107] One or more events in the first set may be configured by the network (e.g. the network node) in a second configuration. Thus, the events may be configured by the network (e.g. network node), but they can also be specified in the procedural text in the specification, without any network configuration.

[108] The transmission of the logged data may be performed according to a third configuration, comprising for example a radio bearer configuration (e.g. a signalling radio bearer (SRB)). [109] The third configuration may be provided as part of the first configuration.

[HO] The availability indication may comprise one or more of the following: a flag indicator (e.g. 1 bit field that is set to one to indicate available logged data); an indication related to the fulfilled event(s), e.g. an event identifier; an indication of the size of the logged data; an indication of the number of samples collected; information about the logged measurements, e.g. identifier(s) of the measurement configuration for which measurements are available; an indication of the use case of the collected measurements, e.g. for the positioning purpose, or for beam management, or CSI compression, or the mobility related prediction, etc.; and a time indication for how long the UE can keep the logged information after which the UE can no longer report it (e.g. the UE discards the logged information).

[Hl] The availability indication may be transmitted to the network (e.g. network node) and, in response to that, the UE may receive a request to report the collected data for AI/ML model training.

[112] In response to the transmission of the availability indication, the UE may receive a third configuration to report the collected data for AI/ML model training. The third configuration may comprise, for example, a radio bearer configuration (e.g. SRB).

[113] In response to the received request, the UE may transmit the one or more (e.g. all or part of the) information which it has logged. The information may comprise any one or more of the following: the logged data; the location information associated to the location in which the UE started the data collection and/or the location information associated to the location in which the UE stopped the data collection; the radio channel conditions (e.g. RSRP, RSRQ, RSSI, and/or Signal to Interference and Noise Ratio (SINR)) at the moment in which the UE started the data collection; the radio channel conditions (e.g. RSRP, RSRQ, RSSI, and/or SINR) at the moment in which the UE stopped the data collection; the time gap between the point in time in which the UE reports the collected data, and the point in time in which the UE reported the availability indication to the network (e.g. network node); a flag indicator (e.g. 1 bit field that is set to one to indicate available logged data), such as for the case in which more collected data not yet transmitted is available for transmission at the UE; an indication related to the fulfilled event(s), e.g. an event identifier; an indication of the size of the remaining logged data not yet transmitted; an indication of the number of samples collected and not yet transmitted; information about the logged measurements, e.g. identifier(s) of the measurement configuration associated to which logged data are available and not yet transmitted; an indication of the use case of the collected measurements, e.g. for the positioning purpose, or for beam management, or CSI compression, or the mobility related prediction, etc.; and a time indication for how long the UE can keep the logged data not yet transmitted, after which the UE can no longer report it (e.g. the UE discards the logged information).

[114] In response to the received request, the UE may transmit the one or more (e.g. all or part of the) information which it has logged according to the third configuration.

[115] The transmission of the data logged according to the first configuration may comprise any one or more of the following: the logged data; the location information associated to the location in which the UE started the data collection and/or the location information associated to the location in which the UE stopped the data collection; the radio channel conditions (e.g. RSRP, RSRQ, RSSI, and/or SINR) at the moment in which the UE started the data collection; the radio channel conditions (e.g. RSRP, RSRQ, RSSI, and/or SINR) at the moment in which the UE stopped the data collection; a flag indicator (e.g. 1 bit field that is set to one to indicate available logged data), such as for the case in which more collected data not yet transmitted is available for transmission at the UE; an indication related to the fulfilled event(s), e.g. an event identifier; an indication of the size of the remaining logged data not yet transmitted; an indication of the remaining number of samples collected but not yet transmitted; information about the logged measurements, e.g. identifier(s) of the measurement configuration associated to which logged data are available and not yet transmitted; an indication of the use case of the collected measurements, e.g. for the positioning purpose, or for beam management, or CSI compression, or the mobility related prediction, etc.; and a time indication for how long the UE can keep the logged data not yet transmitted after which the UE can no longer report it (e.g. the UE discards the logged information).

[116] The transmission of the data logged according to the first configuration may be performed according to the third configuration.

[117] Procedure to report availability of the data upon fulfilment of a configured event.

[118] There is provided a method at a UE for the data reporting from a UE for measurements collected (e.g. using a Layer 3 (L3) configuration), such that the reported data is for AI/ML model training purpose. The method may comprise any one or more of the following steps:

[119] Step 1

[120] The UE (e.g. device) may receive, from a network node, a first message. The first message may comprise a first configuration. The first configuration may comprise a data collection configuration. The data collection configuration may comprise the following:

A measurement configuration that instructs the UE to perform, log, or collect measurements for the AI/ML model training for the associated data collection, o The UE can be configured with what measurements to be logged and how (e.g. periodic measurements, or event triggered), which may correspond to one or more parameters for the UE to perform lower layer measurements. o The UE may be configured with more than one data collection configurations.

[121] The UE may monitor one or more reporting events, upon fulfilling which the UE may perform one of the following actions:

• Indicating the availability of the data logged in the UE memory according to the measurement configuration included in the first configuration.

• Transmitting to the network (e.g. network node) the data logged in the UE memory according to the parameters to be measured included in the first configuration. o In some embodiments, the one or more of the reporting events may be configured by the network (e.g. network node) in a second configuration included in the first configuration for the concerned data collection configuration. o In another embodiment, the one or more of the reporting events may be configured by the network (e.g. network node) in a second configuration, and it may be applicable to more than one data collection configuration. For instance, the UE may be configured with multiple measurement configurations associated to different data collections, but one or same reporting event configuration that is applicable to all measurement and data collection configurations. o In some embodiments, the one or more of the reporting events may be specified in a specification procedure. Examples are provided later in the disclosure.

[122] In some embodiments, the UE may be configured by the network (e.g. network node) to perform one of the actions listed above.

[123] The reporting event may comprise one or more events upon which the UE reports the availability of the measurements or the actual AI/ML related measurements. The configuration may comprise any one or more of the following: identifier(s) for the one or more events; indications of observed metrics, executed actions, predicted or planned actions, aborted or reverted actions, radio procedures, notifications, indexes, thresholds, timers, state transitions, locations, for each of the event(s) to be used for detecting fulfilment of the event(s); conditions applied to observed metrics, measurements, executed actions, predicted or planned actions, aborted or reverted actions, radio procedures, notifications, indexes, thresholds, timers, state transitions, locations, for each of the event(s) for detecting that the event(s) is(are) fulfilled; a minimum time interval or duration for which the event should be fulfilled before the UE triggers the reporting of the availability of the measurements; an indication of whether the UE is allowed to report the availability of the measurements only once upon the fulfilment of an event, or multiple times (For instance, the network node may configure the UE with a timer which starts after the first reporting of the availability of the measurement. The UE may not be allowed to retrigger the reporting of the availability of the measurements before the timer expires); and certain signalling radio bearer (e.g. SRB) configurations to be used for transmitting the AI/ML related data. In one embodiment, the radio bearer configuration may be provided by the network (e.g. network node) in response of receiving from the UE the availability indication of logged data. Hence, upon receiving from the network (e.g. network node) the request to transmit the available logged data, the UE may start transmitting the logged data using the configured radio bearer configuration. In another embodiment, the radio bearer configuration may be provided by the network (e.g. network node), e.g. as part of the configuration for the AI/ML-related data collection, such as the first configuration. In such a case, upon fulfilling one or more of the reporting events, the UE may start transmitting the logged data using the configured radio bearer configuration.

[124] How to configure the events of UE AI/ML measurements availability or the events of the UE AI/ML measurement reporting

[125] Any one or more of the following events may be used by the UE for the reporting to the network (e.g. network node) of the availability in the UE memory of collected data, or for the reporting to the network (e.g. network node) of collected data. Some of these events may be configured by the network (e.g. network node), for example as part of the AI/ML data collection configuration. Some other events instead may be specified in a procedural text in the standard, or they may be left to the UE implementation.

[126] Events related to the UE aspects, which may comprise any one or more of:

UE’s storage capacity. For instance, the condition can be represented as a threshold (e.g. in percentage), where the condition may be fulfilled if the remaining UE storage capacity drops below this threshold.

UE battery level. For instance, the condition can be represented as a threshold (e.g. in percentage), where the condition may be fulfilled if the UE battery level drops below this threshold.

UE orientation change. After changed orientation, it may no longer be possible to relate further measurements to existing measurements, and reporting may be warranted. Similar for changed UE location, change in detected beam directions at UE side.

UE mobility. For instance, the condition can be represented as mobility state, for example the UE reports availability when moving from static to moving state. UE traffic information. For instance, the UE may expect to be transitioning into idle mode within a certain time window.

Any combination of the above.

[127] Events related to the network (e.g. network node) model information, which may comprise the following:

When the network (e.g. network node) has a scenario-specific model, the network (e.g. network node) may need to retrieve the measurement data collected by the UE in the served cell. The network (e.g. network node) can configure the UE to report availability of measurements when the UE is expected to be handed over in a certain time window. In case the model is valid over a certain number of cells, the network (e.g. network node) can configure an event that reports availability of measurements when the UE is in any of the valid cells, such as those illustrated in Fig. 6.

[128] Events related to the dataset (measurement properties), which may comprise the following:

Data importance. The network (e.g. network node) can trigger a measurement availability event based on the importance of the measurements at the UE. For example, the network (e.g. network node) might have bad performance of predicting a certain range of values, the network (e.g. network node) can configure a UE reporting event when it has measurements in said range of values. One example could be for beam management, when the network (e.g. network node) has bad prediction performance of a certain beam indices, when the observes data (measurement samples) where the beam indices are strongest. The UE can trigger an availability report.

Number of omitted samples. In case the UE has omitted many samples. It can indicate that the UE is not observing any new data and can report the collected measurements.

[129] Events related to logged measurement size, which may comprise any one or more of the following: number of samples collected; size [e.g. in bytes] of the logged measurements; and any combination of the above.

[130] Events related to measurement events, which may comprise any one or more of the following:

Channel quality (e.g. small path loss or little interference) for the link between the UE and the serving network node (e.g. gNB). If the UE channel quality varies over time, it may be useful to report data primarily when the channel quality is in a period of good quality, such as in order to minimize the UE power and spectral resources needed for the transfer of the report. The channel quality can be configured in absolute terms, or relative to average channel quality (over some time period) for the UE.

Fulfilling of certain measurement events, such as A1-A2-A3-A4-A5-A6, etc.: o Upon fulfilling one or more the legacy events such as A1-A2-A3-A4-A5-A6, the UE may indicate the availability of the data logged as part of the measurement results associated to the said fulfilled one or more legacy events. Alternatively, the UE may indicate in the measurement results the logged data.

[131] Events related to the parameters being measured, which may comprise any one or more of the following:

Signal quality values. For instance, the network (e.g. network node) may configure the UE to indicate the availability of the logged signal quality values if there are at least X samples for which the signal quality values of a measured beam is higher than a threshold. o Signal quality can be represented as RSRP, SINR, and/or RSRQ.

Strongest Beam indices. For instance, the network (e.g. network node) may configure the UE to indicate the availability of the logged Layer 1 (LI)- RSRP values if there are at least X samples with different unique strongest beam indices. UE location information. For instance, the network (e.g. network node) may configure the UE to indicate the availability of the logged values if there are at least X samples with different unique UE location. o This can, for example, be beneficial for the positioning use case, or in general to ensure the network (e.g. network node) has collected samples over a wide geographical area.

Reception of an explicit indication from the network node. o Such an indication could be an RRC message indicating the UE to transmit the collected training data. Such an indication could also be a downlink (DL) MAC CE or a DCI message. o In some embodiments, such an indication from the network node could be in terms of configuration of a specific data radio bearer (DRB) or SRB to transmit the AI/ML model training data. For example, upon receiving the configuration for the SRB2, the UE might initiate the transmission of the stored AIML training data using the SRB2. o Such embodiments can be useful when the network (e.g. network node) is already aware that the UE has stored training data (e.g. a certain time has expired since configuring the UE with a periodic logging of the training data) and the network node currently has the uplink (UL) resources to receive the training data from the UE.

- Upon expiry of a timer o A timer may be initiated by the UE at the time of receiving an AI/ML training data collection related configuration and this timer may be checked for expiry against a configured duration value. Upon meeting the expiry condition, the UE may initiate the transmission of the stored AI/ML training data. o A timer may be initiated by the UE at the time of starting the logging of the first sample associated to the received configuration for AI/ML training data collection and this timer may be checked for expiry against a configured duration value. Upon meeting the expiry condition, the UE may initiate the transmission of the stored AI/ML training data. - Upon receiving a new data collection configuration o A UE may be capable of performing data collection for one or more AI/ML model training. If the network (e.g. network node) configures a new training data collection configuration while the UE already has stored training data for a previously configured AI/ML training data collection configuration, then the UE may initiate the transmission of the stored training data. o For example, a UE may be capable of performing data collection for beam management or data collection for CSI enhancement but not for both at the same time. A network node may configure the UE with an event triggered data collection (it is to be noted that the term event here refers to when the UE shall initiate the logging of the data, not the event associated to when the reporting is triggered) for CSI-enhancement at time-Tl . At a later point in time, T2, the UE may receive a training data collection for beam management. At this time, if the UE has already logged any measurement associated to the first data collection configuration i.e. for CSI-enhancement, then the UE may initiate the transmission of this stored data before initiating the data collection form the beam management. This embodiment enables the network (e.g. network node) to use event triggered logging and event triggered reporting of the AI/ML training data collection in a flexible manner.

- Upon fulfilling the leaving criterion for logging the event triggered logging configuration o A UE may be configured with an event criterion on when to perform the logging and only upon fulfilling this criterion, the UE may perform the logging of the measurements associated to the training of the AI/ML model. The UE may perform periodical logging of measurements while the measurement logging event criterion continues to be fulfilled. When the UE fulfills the corresponding ‘leaving condition for data collection’ (i.e. the UE no more stores the measurements associated to the training of an AI/ML model), then the UE may also initiate the transmission of the stored data associated to the training of an AI/ML model.

- Upon receiving an RRCReconfiguration message: o For example, upon receiving an RRCReconfiguration message, the UE may indicate to the network (e.g. network node) the availability of logged data in the RRCReconfigurationComplete message. o The RRCReconfiguration message may be associated to any type of RRC configuration, e.g. dual-connectivity configuration, serving cell configuration, measurement configurations, etc.

- Upon receiving an RRCReconfiguration message including a reconfiguration with sync: o For example, upon receiving an RRCReconfiguration message including the reconfiguration with sync, the UE may indicate to the network (e.g. network node) the availability of logged data in the RRCReconfigurationComplete message to the target node upon successfully executing the handover.

- In some embodiments, the configuration for AI/ML measurement reporting can be related to the configuration of a certain radio bearer to be used for the AI/ML data collection: o In one embodiment, the configuration can be associated to a certain signalling radio bearer (e.g. SRB) that the UE is to use to transmit the AI/ML measurements to the network (e.g. network node). The UE may be allowed to transmit the AI/ML measurement to the network (e.g. network node) only if such signalling radio bearer is configured. o In another embodiment, the configuration can be associated to a certain data radio bearer (DRB) that the UE is to use to transmit the AI/ML measurements to the network (e.g. network node). The UE may be allowed to transmit the AI/ML measurement to the network (e.g. network node) only if such DRB is configured.

[132] Step 2

[133] When the measurement reporting event is satisfied, the UE may send an indication of the availability of measurement or the AI/ML related measurements. The indication may comprise any one or more of the following:

- a flag indicator;

- an indication related to the fulfilled event(s), e.g. an event identifier;

- an indication of the size of the logged data;

- a number of samples; - information about the logged measurements, e.g. identifier(s) of the measurement configuration for which measurements are available;

- a time indication for how long the UE can keep the logged information after which the UE can no longer report it (e.g. the UE discards the logged information); and

- the AI/ML related measurements collected based on the AI/ML configuration.

[134] Step 3

[135] The UE (e.g. device) may optionally receive a second message from the network node in response to the indicated measurements availability requesting to report the logged measurements.

- In some embodiments, the second message comprising the request to retrieve the measurements may comprise the configuration on the reporting. The configuration may indicate one or more parts of the AI/ML measurements, e.g. the AI/ML measurements collected based on a specific measurement configuration identified by a measurement identity or by a specific purpose or use-case of the measurements, e.g. a request to retrieve the measurements for the AI/ML based positioning or CSI compression related measurement or beam management, or mobility prediction etc. The configuration may comprise a radio bearer configuration for the reporting of the logged data

[136] Step 4

[137] The UE may report the logged data to the network node in response of receiving the request from the network or network node (which may be sent in response to receiving from the UE the availability indication). In another embodiment, the UE may report the logged data to the network node in response to fulfilling the one or more events for the reporting.

[138] The report comprising the logged data may comprise any one or more of the following:

- the logged data;

- the location information associated to the location in which the UE started the data collection and/or the location information associated to the location in which the UE stopped the data collection;

- the radio channel conditions (RSRP, RSRQ, RS SI, and/or SINR) at the moment in which the UE started the data collection;

- the radio channel conditions (RSRP, RSRQ, RS SI, and/or SINR) at the moment in which the UE stopped the data collection; - the time gap between the point in time in which the UE reports the collected data, and the point in time in which the UE reported the availability indication to the network (e.g. network node);

- a flag indicator (e.g. 1 bit field that is set to one to indicate available logged data), such as for the case in which more collected data not yet transmitted are available for transmission at the UE;

- an indication related to the fulfilled event(s), e.g. an event identifier;

- an indication of the size of the remaining logged data not yet transmitted;

- an indication of the number of samples collected and not yet transmitted;

- information about the logged measurements, e.g. identifier(s) of the measurement configuration associated to which logged data are available and not yet transmitted;

- an indication of the use case of the collected measurements, e.g. for the positioning purpose, or for beam management, or CSI compression, or the mobility related prediction, etc.;

- a time indication for how long the UE can keep the logged data not yet transmitted, after which the UE can no longer report it (e.g. the UE discards the logged information).

[139] Reporting the measurement to the network (e.g. network node) can be based on certain events the UE may receive at Step 1 or at Step 3 (if the UE receives it).

[140] The selection on whether to report the logged measurements can be based on signal strength of the UE. For example, if the UE is in a bad coverage region, the network (e.g. network node) may wait until the UE is in a better signal quality region. The decision may comprise of whether one or more of RSRP, RSRQ, SINR, and CQI measurements are above a certain threshold.

[141] Alternatively or in addition, the selection on whether to report the logged measurements can be based on cell load. In case the network (e.g. network node) is highly loaded, the logged measurements may be marked as a low-priority traffic, and only sent when there is no other data to be transmitted and/or received to the network node (e.g. base station) connected UEs. For example, in case the cell is highly loaded, the network (e.g. network node) may decide upon receiving the availability indication to not request that the UE transmits the logged data. In another example, in case the cell is not highly loaded, the network (e.g. network node) may decide upon receiving the availability indication to request that the UE transmits the logged data.

[142] In some embodiments, the UE may report the measurements based on criteria communicated to the UE by the UE vendor (or proxy, or some other central node), e.g. over- the-top or via RRC signalling (in the latter case, for example, using an indicator or number that is not necessarily understood by the serving network (e.g. network node)). The criteria may be any of the criteria listed above, or some other (vendor-specific) criteria.

[143] In one example, the UE may collect measurements that are not reported until the UE is in another cell. In this example, the network node may report the collected measurements over an Xn interface to (e.g. a network node of) the cell that needs the measurements.

[144] Fig. 6 illustrates an example network comprising a UE 602 and a network node 604. As illustrated by arrow 606 in Fig. 6, the UE 602 is moving in a deployment where a model trained at the network (e.g. network node 604) is valid over a first cell 608 (“cell 1”) and a second cell 610 (“cell 2”). The model is invalid over athird cell 612 (“cell 3”). The model is trained using collected measurements at the UE 602.

[145] Fig. 7 shows an example of a communication system 700 in accordance with some embodiments.

[146] In the example, the communication system 700 includes a telecommunication network 702 that includes an access network 704, such as a radio access network (RAN), and a core network 706, which includes one or more core network nodes 708. The access network 704 includes one or more access network nodes, such as network nodes 710a and 710b (one or more of which may be generally referred to as network nodes 710), or any other similar 3 ^rd Generation Partnership Project (3GPP) access nodes or non-3GPP access points. Moreover, as will be appreciated by those of skill in the art, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunication network 702 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network 702 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other nodes to implement one or more functionalities of any node in the telecommunication network 702, including one or more network nodes 710 and/or core network nodes 708.

[147] Examples of an ORAN network node include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O- CU-CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an Al, Fl, Wl, El, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN access node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an 0-2 interface defined by the O-RAN Alliance or comparable technologies. The network nodes 710 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 712a, 712b, 712c, and 712d (one or more of which may be generally referred to as UEs 712) to the core network 706 over one or more wireless connections.

[148] Example wireless communications over a wireless connection include transmitting and/or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and/or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 700 may include any number of wired or wireless networks, network nodes, UEs, and/or any other components or systems that may facilitate or participate in the communication of data and/or signals whether via wired or wireless connections. The communication system 700 may include and/or interface with any type of communication, telecommunication, data, cellular, radio network, and/or other similar type of system.

[149] The UEs 712 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and/or operable to communicate wirelessly with the network nodes 710 and other communication devices. Similarly, the network nodes 710 are arranged, capable, configured, and/or operable to communicate directly or indirectly with the UEs 712 and/or with other network nodes or equipment in the telecommunication network 702 to enable and/or provide network access, such as wireless network access, and/or to perform other functions, such as administration in the telecommunication network 702.

[150] In the depicted example, the core network 706 connects the network nodes 710 to one or more hosts, such as host 716. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 706 includes one more core network nodes (e.g., core network node 708) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and/or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 708. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and/or a User Plane Function (UPF).

[151] The host 716 may be under the ownership or control of a service provider other than an operator or provider of the access network 704 and/or the telecommunication network 702, and may be operated by the service provider or on behalf of the service provider. The host 716 may host a variety of applications to provide one or more services. Examples of such applications include the provision of live and/or pre-recorded audio/video content, data collection services, for example, 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.

[152] As a whole, the communication system 700 of Fig. 7 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system 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 2^nd Generation (2G), 3^rd Generation (3G), 4^th Generation (4G), 5^th Generation (5G) standards, or any applicable future generation standard (e.g., 6^th Generation (6G)); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and/or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and/or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox.

[153] In some examples, the telecommunication network 702 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 702 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 702. For example, the telecommunications network 702 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and/or Massive Machine Type Communication (mMTC)/Massive loT services to yet further UEs.

[154] In some examples, the UEs 712 are configured to transmit and/or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 704 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 704. Additionally, a UE may be configured for operating in single- or multi- Radio Access Technology (RAT) or multistandard 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).

[155] In the example illustrated in Fig. 7, the hub 714 communicates with the access network 704 to facilitate indirect communication between one or more UEs (e.g., UE 712c and/or 712d) and network nodes (e.g., network node 710b). In some examples, the hub 714 may be a controller, router, a content source and analytics node, or any of the other communication devices described herein regarding UEs. For example, the hub 714 may be a broadband router enabling access to the core network 706 for the UEs. As another example, the hub 714 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 710, or by executable code, script, process, or other instructions in the hub 714. As another example, the hub 714 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 714 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 714 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 714 then provides to the UE either directly, after performing local processing, and/or after adding additional local content. In still another example, the hub 714 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[156] The hub 714 may have a constant/persi stent or intermittent connection to the network node 710b. The hub 714 may also allow for a different communication scheme and/or schedule between the hub 714 and UEs (e.g., UE 712c and/or 712d), and between the hub 714 and the core network 706. In other examples, the hub 714 is connected to the core network 706 and/or one or more UEs via a wired connection. Moreover, the hub 714 may be configured to connect to an M2M service provider over the access network 704 and/or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 710 while still connected via the hub 714 via a wired or wireless connection. In some embodiments, the hub 714 may be a dedicated hub - that is, a hub whose primary function is to route communications to/from the UEs from/to the network node 710b. In other embodiments, the hub 714 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 710b, but which is additionally capable of operating as a communication start and/or end point for certain data channels.

[157] Fig. 8 shows a UE 800 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and/or operable to communicate wirelessly with network nodes and/or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless camera, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle, vehicle-mounted or vehicle embedded/integrated wireless device, etc. Other examples include any UE identified by the 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.

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

[159] The UE 800 includes processing circuitry 802 that is operatively coupled via a bus 804 to an input/ output interface 806, a power source 808, a memory 810, a communication interface 812, and/or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Fig. 8. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[160] The processing circuitry 802 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 810. The processing circuitry 802 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 802 may include multiple central processing units (CPUs). The processing circuitry 802 may be operable to provide, either alone or in conjunction with other UE 800 components, such as the memory 810, UE 800 functionality. For example, the processing circuitry 802 may be configured to cause the UE 802 to perform the methods as described with reference to Fig. 4.

[161] In the example, the input/ output interface 806 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and/or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE 800. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, 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.

[162] In some embodiments, the power source 808 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source 808 may further include power circuitry for delivering power from the power source 808 itself, and/or an external power source, to the various parts of the UE 800 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 808. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 808 to make the power suitable for the respective components of the UE 800 to which power is supplied.

[163] The memory 810 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 810 includes one or more application programs 814, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 816. The memory 810 may store, for use by the UE 800, any of a variety of various operating systems or combinations of operating systems.

[164] The memory 810 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic 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 Universal Subscriber Identity Module (USIM) and/or Integrated Subscriber Identity Module (ISIM), other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 810 may allow the UE 800 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 810, which may be or comprise a device-readable storage medium.

[165] The processing circuitry 802 may be configured to communicate with an access network or other network using the communication interface 812. The communication interface 812 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 822. The communication interface 812 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter 818 and/or a receiver 820 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 818 and receiver 820 may be coupled to one or more antennas (e.g., antenna 822) and may share circuit components, software or firmware, or alternatively be implemented separately.

[166] In some embodiments, communication functions of the communication interface 812 may include cellular communication, Wi-Fi communication, 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 in 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.

[167] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 812, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[168] As another example, a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or controls a robotic arm performing a medical procedure according to the received input.

[169] A UE, 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, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are devices which are or which are 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 head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and/or software in dependence on the intended application of the loT device in addition to other components as described in relation to the UE 800 shown in Fig. 8. [170] As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and/or measurements, and transmits the results of such monitoring and/or measurements to another UE and/or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship 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.

[171] In practice, any number of UEs may be used together with respect to a single use case. For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed. The first and/or the second UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[172] Fig. 9 shows a network node 900 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and/or operable to communicate directly or indirectly with a UE and/or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, 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).

[173] Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an O-RAN access node) and/or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such 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).

[174] Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or 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).

[175] The network node 900 includes processing circuitry 902, a memory 904, a communication interface 906, and a power source 908, and/or any other component, or any combination thereof. The network node 900 may be composed of multiple physically separate components (e.g., aNodeB component and aRNC component, or aBTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 900 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared 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 900 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 904 for different RATs) and some components may be reused (e.g., a same antenna 910 may be shared by different RATs). The network node 900 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 900, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z- wave, 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 900.

[176] The processing circuitry 902 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 network node 900 components, such as the memory 904, network node 900 functionality. For example, the processing circuitry 902 may be configured to cause the network node to perform the methods as described with reference to Fig. 5.

[177] In some embodiments, the processing circuitry 902 includes a system on a chip (SOC). In some embodiments, the processing circuitry 902 includes one or more of radio frequency (RF) transceiver circuitry 912 and baseband processing circuitry 914. In some embodiments, the radio frequency (RF) transceiver circuitry 912 and the baseband processing circuitry 914 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 912 and baseband processing circuitry 914 may be on the same chip or set of chips, boards, or units.

[178] The memory 904 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, 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 902. The memory 904 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and/or other instructions capable of being executed by the processing circuitry 902 and utilized by the network node 900. The memory 904 may be used to store any calculations made by the processing circuitry 902 and/or any data received via the communication interface 906. In some embodiments, the processing circuitry 902 and memory 904 is integrated.

[179] The communication interface 906 is used in wired or wireless communication of signalling and/or data between a network node, access network, and/or UE. As illustrated, the communication interface 906 comprises port(s)/terminal(s) 916 to send and receive data, for example to and from a network over a wired connection. The communication interface 906 also includes radio front-end circuitry 918 that may be coupled to, or in certain embodiments a part of, the antenna 910. Radio front-end circuitry 918 comprises filters 920 and amplifiers 922. The radio front-end circuitry 918 may be connected to an antenna 910 and processing circuitry 902. The radio front-end circuitry may be configured to condition signals communicated between antenna 910 and processing circuitry 902. The radio front-end circuitry 918 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 918 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 920 and/or amplifiers 922. The radio signal may then be transmitted via the antenna 910. Similarly, when receiving data, the antenna 910 may collect radio signals which are then converted into digital data by the radio front-end circuitry 918. The digital data may be passed to the processing circuitry 902. In other embodiments, the communication interface may comprise different components and/or different combinations of components.

[180] In certain alternative embodiments, the network node 900 does not include separate radio front-end circuitry 918, instead, the processing circuitry 902 includes radio front-end circuitry and is connected to the antenna 910. Similarly, in some embodiments, all or some of the RF transceiver circuitry 912 is part of the communication interface 906. In still other embodiments, the communication interface 906 includes one or more ports or terminals 916, the radio frontend circuitry 918, and the RF transceiver circuitry 912, as part of a radio unit (not shown), and the communication interface 906 communicates with the baseband processing circuitry 914, which is part of a digital unit (not shown).

[181] The antenna 910 may include one or more antennas, or antenna arrays, configured to send and/or receive wireless signals. The antenna 910 may be coupled to the radio front-end circuitry 918 and may be any type of antenna capable of transmitting and receiving data and/or signals wirelessly. In certain embodiments, the antenna 910 is separate from the network node 900 and connectable to the network node 900 through an interface or port.

[182] The antenna 910, communication interface 906, and/or the processing circuitry 902 may be configured to perform any receiving operations and/or certain obtaining operations described herein as being performed by the network node. Any information, data and/or signals may be received from a UE, another network node and/or any other network equipment. Similarly, the antenna 910, the communication interface 906, and/or the processing circuitry 902 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and/or signals may be transmitted to a UE, another network node and/or any other network equipment.

[183] The power source 908 provides power to the various components of network node 900 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 908 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 900 with power for performing the functionality described herein. For example, the network node 900 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 908. As a further example, the power source 908 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[184] Embodiments of the network node 900 may include additional components beyond those shown in Fig. 9 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and/or any functionality necessary to support the subject matter described herein. For example, the network node 900 may include user interface equipment to allow input of information into the network node 900 and to allow output of information from the network node 900. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 900.

[185] Fig. 10 is a block diagram of a host 1000, which may be an embodiment of the host 716 of Fig. 7, in accordance with various aspects described herein. As used herein, the host 1000 may be or comprise various combinations hardware and/or software, including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm. The host 1000 may provide one or more services to one or more UEs.

[186] The host 1000 includes processing circuitry 1002 that is operatively coupled via a bus 1004 to an input/ output interface 1006, a network interface 1008, a power source 1010, and a memory 1012. Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices of previous figures, such as Figs. 8 and 9, such that the descriptions thereof are generally applicable to the corresponding components of host 1000.

[187] The memory 1012 may include one or more computer programs including one or more host application programs 1014 and data 1016, which may include user data, e.g., data generated by a UE for the host 1000 or data generated by the host 1000 for a UE. Embodiments of the host 1000 may utilize only a subset or all of the components shown. The host application programs 1014 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (VVC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), MPEG, VP9) and audio codecs (e.g., FL AC, Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems). The host application programs 1014 may also provide for user authentication and licensing checks and may periodically report health, routes, and content availability to a central node, such as a device in or on the edge of a core network. Accordingly, the host 1000 may select and/or indicate a different host for over-the-top services for a UE. The host application programs 1014 may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real-Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), Dynamic Adaptive Streaming over HTTP (MPEG-DASH), etc.

[188] Fig. 11 is a block diagram illustrating a virtualization environment 1100 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 1100 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment 1100 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an O-2 interface.

[189] Applications 1102 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and/or benefits of some of the embodiments disclosed herein.

[190] Hardware 1104 includes processing circuitry, memory that stores software and/or instructions executable by hardware processing circuitry, and/or other hardware devices as described herein, such as a network interface, input/output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 1106 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 1108a and 1108b (one or more of which may be generally referred to as VMs 1108), and/or perform any of the functions, features and/or benefits described in relation with some embodiments described herein. The virtualization layer 1106 may present a virtual operating platform that appears like networking hardware to the VMs 1108.

[191] The VMs 1108 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 1106. Different embodiments of the instance of a virtual appliance 1102 may be implemented on one or more of VMs 1108, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

[192] In the context of NFV, a VM 1108 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 1108, and that part of hardware 1104 that executes that VM, be it hardware dedicated to that VM and/or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs 1108 on top of the hardware 1104 and corresponds to the application 1102.

[193] Hardware 1104 may be implemented in a standalone network node with generic or specific components. Hardware 1104 may implement some functions via virtualization. Alternatively, hardware 1104 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 1110, which, among others, oversees lifecycle management of applications 1102. In some embodiments, hardware 1104 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signalling can be provided with the use of a control system 1112 which may alternatively be used for communication between hardware nodes and radio units.

[194] Fig. 12 shows a communication diagram of a host 1202 communicating via a network node 1204 with a UE 1206 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as a UE 712a of Fig. 7 and/or UE 800 of Fig. 8), network node (such as network node 710a of Fig. 7 and/or network node 900 of Fig. 9), and host (such as host 716 of Fig. 7 and/or host 1000 of Fig. 10) discussed in the preceding paragraphs will now be described with reference to Fig. 12.

[195] Like host 1000, embodiments of host 1202 include hardware, such as a communication interface, processing circuitry, and memory. The host 1202 also includes software, which is stored in or accessible by the host 1202 and executable by the processing circuitry. The software includes a host application that may be operable to provide a service to a remote user, such as the UE 1206 connecting via an over-the-top (OTT) connection 1250 extending between the UE 1206 and host 1202. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection 1250.

[196] The network node 1204 includes hardware enabling it to communicate with the host 1202 and UE 1206. The connection 1260 may be direct or pass through a core network (like core network 706 of Fig. 7) and/or one or more other intermediate networks, such as one or more public, private, or hosted networks. For example, an intermediate network may be a backbone network or the Internet.

[197] The UE 1206 includes hardware and software, which is stored in or accessible by UE 1206 and executable by the UE’s processing circuitry. The software includes a client application, such as a web browser or operator-specific “app” that may be operable to provide a service to a human or non-human user via UE 1206 with the support of the host 1202. In the host 1202, an executing host application may communicate with the executing client application via the OTT connection 1250 terminating at the UE 1206 and host 1202. In providing the service to the user, the UE's client application may receive request data from the host's host application and provide user data in response to the request data. The OTT connection 1250 may transfer both the request data and the user data. The UE's client application may interact with the user to generate the user data that it provides to the host application through the OTT connection 1250. [198] The OTT connection 1250 may extend via a connection 1260 between the host 1202 and the network node 1204 and via a wireless connection 1270 between the network node 1204 and the UE 1206 to provide the connection between the host 1202 and the UE 1206. The connection 1260 and wireless connection 1270, over which the OTT connection 1250 may be provided, have been drawn abstractly to illustrate the communication between the host 1202 and the UE 1206 via the network node 1204, without explicit reference to any intermediary devices and the precise routing of messages via these devices.

[199] As an example of transmitting data via the OTT connection 1250, in step 1208, the host 1202 provides user data, which may be performed by executing a host application. In some embodiments, the user data is associated with a particular human user interacting with the UE 1206. In other embodiments, the user data is associated with a UE 1206 that shares data with the host 1202 without explicit human interaction. In step 1210, the host 1202 initiates a transmission carrying the user data towards the UE 1206. The host 1202 may initiate the transmission responsive to a request transmitted by the UE 1206. The request may be caused by human interaction with the UE 1206 or by operation of the client application executing on the UE 1206. The transmission may pass via the network node 1204, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step 1212, the network node 1204 transmits to the UE 1206 the user data that was carried in the transmission that the host 1202 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 1214, the UE 1206 receives the user data carried in the transmission, which may be performed by a client application executed on the UE 1206 associated with the host application executed by the host 1202.

[200] In some examples, the UE 1206 executes a client application which provides user data to the host 1202. The user data may be provided in reaction or response to the data received from the host 1202. Accordingly, in step 1216, the UE 1206 may provide user data, which may be performed by executing the client application. In providing the user data, the client application may further consider user input received from the user via an input/output interface of the UE 1206. Regardless of the specific manner in which the user data was provided, the UE 1206 initiates, in step 1218, transmission of the user data towards the host 1202 via the network node 1204. In step 1220, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 1204 receives user data from the UE 1206 and initiates transmission of the received user data towards the host 1202. In step 1222, the host 1202 receives the user data carried in the transmission initiated by the UE 1206.

[201] One or more of the various embodiments improve the performance of OTT services provided to the UE 1206 using the OTT connection 1250, in which the wireless connection 1270 forms the last segment. More precisely, the teachings of these embodiments may improve the data rate, latency and/or power consumption and thereby provide benefits such as reduced user waiting time, better responsiveness, and/or extended battery lifetime.

[202] In an example scenario, factory status information may be collected and analyzed by the host 1202. As another example, the host 1202 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host 1202 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host 1202 may store surveillance video uploaded by a UE. As another example, the host 1202 may store or control access to media content such as video, audio, VR or AR which it can broadcast, multicast or unicast to UEs. As other examples, the host 1202 may be used for energy pricing, remote control of non-time critical electrical load to balance power generation needs, location services, presentation services (such as compiling diagrams etc. from data collected from remote devices), or any other function of collecting, retrieving, storing, analyzing and/or transmitting data.

[203] In some examples, a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring the OTT connection 1250 between the host 1202 and UE 1206, in response to variations in the measurement results. The measurement procedure and/or the network functionality for reconfiguring the OTT connection may be implemented in software and hardware of the host 1202 and/or UE 1206. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection 1250 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software may compute or estimate the monitored quantities. The reconfiguring of the OTT connection 1250 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of the network node 1204. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signalling that facilitates measurements of throughput, propagation times, latency and the like, by the host 1202. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 1250 while monitoring propagation times, errors, etc.

[204] 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.

[205] 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.

[206] Other embodiments of the present disclosure are defined in the following numbered statements:

Group A Embodiments

Embodiment 1. A method performed by a user equipment for managing data for an artificial intelligence or machine learning, AI/ML, model, the method comprising: transmitting first information or second information to a network node when one or more first conditions are fulfilled, wherein the first information comprises data collected for training the AI/ML model, wherein the data is stored in a memory of the user equipment, and wherein the second information comprises an indication of an availability of the data in the memory.

Embodiment 2. The method of Embodiment 1, comprising one or both of: transmitting the first information if the user equipment is configured to transmit the first information; and transmitting the second information if the user equipment is configured to transmit the second information.

Embodiment 3. The method of Embodiment 1 or 2, wherein: the user equipment is configured, by the network node, to transmit the first information; and/or the user equipment is configured, by the network node, to transmit the second information.

Embodiment 4. The method of any of the previous Embodiments, comprising: receiving, from the network node, an indication of the data that is to be collected for training the AI/ML model.

Embodiment 5. The method of Embodiment 4, wherein: receiving the indication of the data that is to be collected comprises receiving a first configuration associated with the AI/ML model; and the first configuration comprises the indication of the data that is to be collected.

Embodiment 6. The method of Embodiment 5, wherein: the first configuration comprises an instruction that instructs the UE to start collecting the data for training the AI/ML model.

Embodiment 7. The method of any of the previous Embodiments, comprising: monitoring whether the one or more first conditions are fulfilled.

Embodiment 8. The method of any of the previous Embodiments, wherein: at least one of the one or more first conditions is configured by the network node.

Embodiment 9. The method of any of the previous Embodiments, wherein: receiving the one or more first conditions from the network node.

Embodiment 10. The method of Embodiment 9, wherein: receiving the one or more first conditions comprises receiving a second configuration; and the second configuration comprises the one or more first conditions.

Embodiment 11. The method of Embodiment 10, wherein: the second configuration comprises any one or more of: one or more identifiers that each identify a respective first condition of the one or more first conditions; one or more parameters for use in identifying whether the one or more conditions are fulfilled; an indication of a minimum duration for which the one or more conditions are to be fulfilled prior to the user equipment transmitting the first information or the second information; an indication of whether the user equipment is allowed to transmit the first information or the second information more than once; and a bearer configuration to be used for transmitting the first information or the second information.

Embodiment 12. The method of any of the previous Embodiments, wherein: the first information is transmitted according to a third configuration.

Embodiment 13. The method of any of the previous Embodiments, comprising: receiving a third configuration according to which the user equipment is to transmit the first information.

Embodiment 14. The method of Embodiment 13, wherein: the third configuration is received in response to transmitting the second information.

Embodiment 15. The method of Embodiment 13 or 14, when Embodiment 13 is directly or indirectly dependent on Embodiment 5, wherein: the first configuration comprises the third configuration.

Embodiment 16. The method of any of Embodiments 12 to 15, wherein: the third configuration comprises a bearer configuration.

Embodiment 17. The method of any of the previous Embodiments, comprising: transmitting the second information when the one or more first conditions are fulfilled.

Embodiment 18. The method of any of the previous Embodiments, comprising: receiving, from the network node, a request for the user equipment to transmit the data collected for training the AI/ML model.

Embodiment 19. The method of Embodiment 18, wherein: the request is received in response to transmitting the second information. Embodiment 20. The method of any of the previous Embodiments, wherein: the first information is transmitted in response to receiving, from the network node, a request for the user equipment to transmit the data collected for training the AI/ML model.

Embodiment 21. The method of Embodiment 20, wherein: the request is for the user equipment to transmit all or only part of the data collected for training the AI/ML model.

Embodiment 22. The method of any of the previous Embodiments, further comprising: providing user data; and forwarding the user data to a host via the transmission to the network node.

Group B Embodiments

Embodiment 23. A method performed by a network node for managing data for an artificial intelligence or machine learning, AI/ML, model, the method comprising: receiving first information or second information from a user equipment when one or more first conditions are fulfilled, wherein the first information comprises data collected for training the AI/ML model, wherein the data is stored in a memory of the user equipment, and wherein the second information comprises an indication of an availability of the data in the memory.

Embodiment 24. The method of Embodiment 23, comprising one or both of: receiving the first information if the user equipment is configured to transmit the first information; and receiving the second information if the user equipment is configured to transmit the second information.

Embodiment 25. The method of Embodiment 23 or 24, comprising one or both of: configuring the user equipment to transmit the first information; or configuring the user equipment to transmit the second information. Embodiment 26. The method of any of Embodiments 23 to 25, comprising: transmitting, to the user equipment, an indication of the data that is to be collected for training the AI/ML model.

Embodiment 27. The method of Embodiment 26, wherein: transmitting the indication of the data that is to be collected comprises transmitting a first configuration associated with the AI/ML model; and the first configuration comprises the indication of the data that is to be collected.

Embodiment 28. The method of Embodiment 27, wherein: the first configuration comprises an instruction that instructs the UE to start collecting the data for training the AI/ML model.

Embodiment 29. The method of any of Embodiments 23 to 28, comprising: configuring at least one of the one or more first conditions.

Embodiment 30. The method of any Embodiments 23 to 29, comprising: transmitting the one or more first conditions to the user equipment.

Embodiment 31. The method of Embodiment 30, wherein: transmitting the one or more first conditions comprises transmitting a second configuration; and the second configuration comprises the one or more first conditions.

Embodiment 32. The method of any of Embodiments 23 to 31, wherein: the first information is received according to a third configuration.

Embodiment 33. The method of any of Embodiments 23 to 32, comprising: transmitting, to the user equipment, a third configuration according to which the user equipment is to transmit the first information, Embodiment 34. The method of Embodiment 33, wherein: the third configuration is transmitted in response to receiving the second information.

Embodiment 35. The method of Embodiment 33 or 34, when Embodiment 33 is directly or indirectly dependent on Embodiment 27, wherein: the first configuration comprises the third configuration.

Embodiment 36. The method of any of Embodiments 32 to 35, wherein: the third configuration comprises a radio bearer configuration.

Embodiment 37. The method of any of Embodiments 23 to 36, comprising: receiving the second information when the one or more first conditions are fulfilled.

Embodiment 38. The method of any of Embodiments 23 to 37, comprising: transmitting, to the user equipment, a request for the user equipment to transmit the data collected for training the AI/ML model.

Embodiment 39. The method of Embodiment 38, wherein: the request is transmitted in response to receiving the second information.

Embodiment 40. The method of any of Embodiments 23 to 39, wherein: the first information is received in response to transmitting, to the user equipment, a request for the data collected for training the AI/ML model.

Embodiment 41. The method of Embodiment 40, wherein: the request is for the user equipment to transmit all or only part of the data collected for training the AI/ML model.

Embodiment 42. The method of any of Embodiments 23 to 41, further comprising: obtaining user data; and forwarding the user data to a host or a user equipment.

Group C Embodiments

Embodiment 43. A user equipment for managing data for an artificial intelligence or machine learning, AI/ML, model, the user equipment comprising: processing circuitry configured to cause the user equipment to perform any of the steps of any of the Group A embodiments; and power supply circuitry configured to supply power to the processing circuitry.

Embodiment 44. A network node for managing data for an artificial intelligence or machine learning, AI/ML, model, the network node comprising: processing circuitry configured to cause the network node to perform any of the steps of any of the Group B embodiments; power supply circuitry configured to supply power to the processing circuitry.

Embodiment 45. A user equipment (UE) for managing data for an artificial intelligence or machine learning, AI/ML, model, the UE comprising: an antenna configured to send and receive wireless signals; radio front-end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry; the processing circuitry being configured to perform any of the steps of any of the Group A embodiments; an input interface connected to the processing circuitry and configured to allow input of information into the UE to be processed by the processing circuitry; an output interface connected to the processing circuitry and configured to output information from the UE that has been processed by the processing circuitry; and a battery connected to the processing circuitry and configured to supply power to the UE.

Embodiment 46. A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a network node in a cellular network for transmission to a user equipment (UE), the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B embodiments to transmit the user data from the host to the UE.

Embodiment 47. The host of the previous embodiment, wherein: the processing circuitry of the host is configured to execute a host application that provides the user data; and the UE comprises processing circuitry configured to execute a client application associated with the host application to receive the transmission of user data from the host.

Embodiment 48. A method implemented in a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: providing user data for the UE; and initiating a transmission carrying the user data to the UE via a cellular network comprising the network node, wherein the network node performs any of the operations of any of the Group B embodiments to transmit the user data from the host to the UE.

Embodiment 49. The method of the previous embodiment, further comprising, at the network node, transmitting the user data provided by the host for the UE.

Embodiment 50. The method of any of the previous 2 embodiments, wherein the user data is provided at the host by executing a host application that interacts with a client application executing on the UE, the client application being associated with the host application.

Embodiment 51. A communication system configured to provide an over-the-top (OTT) service, the communication system comprising: a host comprising: processing circuitry configured to provide user data for a user equipment (UE), the user data being associated with the over-the-top service; and a network interface configured to initiate transmission of the user data toward a cellular network node for transmission to the UE, the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B embodiments to transmit the user data from the host to the UE.

Embodiment 52. The communication system of the previous embodiment, further comprising: the network node; and/or the UE.

Embodiment 53. A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to initiate receipt of user data; and a network interface configured to receive the user data from a network node in a cellular network, the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of any of the Group B embodiments to receive the user data from a user equipment (UE) for the host.

Embodiment 54. The host of the previous 2 embodiments, wherein: the processing circuitry of the host is configured to execute a host application that receives the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application.

Embodiment 55. The host of the any of the previous 2 embodiments, wherein the initiating receipt of the user data comprises requesting the user data. Embodiment 56. A method implemented by a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: at the host, initiating receipt of user data from the UE, the user data originating from a transmission which the network node has received from the UE, wherein the network node performs any of the steps of any of the Group B embodiments to receive the user data from the UE for the host.

Embodiment 57. The method of the previous embodiment, further comprising at the network node, transmitting the received user data to the host.

Embodiment 58. A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a cellular network for transmission to a user equipment (UE), wherein the UE comprises a communication interface and processing circuitry, the communication interface and processing circuitry of the UE being configured to perform any of the operations of any of the Group A embodiments to receive the user data from the host.

Embodiment 59. The host of the previous embodiment, wherein the cellular network further includes a network node configured to communicate with the UE to transmit the user data to the UE from the host.

Embodiment 60. The host of the previous 2 embodiments, wherein: the processing circuitry of the host is configured to execute a host application, thereby providing the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application. Embodiment 61. A method implemented by a host operating in a communication system that further includes a network node and a user equipment (UE), the method comprising: providing user data for the UE; and initiating a transmission carrying the user data to the UE via a cellular network comprising the network node, wherein the UE performs any of the operations of any of the Group A embodiments to receive the user data from the host.

Embodiment 62. The method of the previous embodiment, further comprising: at the host, executing a host application associated with a client application executing on the UE to receive the user data from the host application.

Embodiment 63. The method of the previous embodiment, further comprising: at the host, transmitting input data to the client application executing on the UE, the input data being provided by executing the host application, wherein the user data is provided by the client application in response to the input data from the host application.

Embodiment 64. A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a cellular network for transmission to a user equipment (UE), wherein the UE comprises a communication interface and processing circuitry, the communication interface and processing circuitry of the UE being configured to perform any of the steps of any of the Group A embodiments to transmit the user data to the host.

Embodiment 65. The host of the previous embodiment, wherein the cellular network further includes a network node configured to communicate with the UE to transmit the user data from the UE to the host.

Embodiment 66. The host of the previous 2 embodiments, wherein: the processing circuitry of the host is configured to execute a host application, thereby providing the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application.

Embodiment 67. A method implemented by a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: at the host, receiving user data transmitted to the host via the network node by the UE, wherein the UE performs any of the steps of any of the Group A embodiments to transmit the user data to the host.

Embodiment 68. The method of the previous embodiment, further comprising: at the host, executing a host application associated with a client application executing on the UE to receive the user data from the UE.

Embodiment 69. The method of the previous 2 embodiments, further comprising: at the host, transmitting input data to the client application executing on the UE, the input data being provided by executing the host application, wherein the user data is provided by the client application in response to the input data from the host application.

[207] It should be noted that the above-mentioned embodiments illustrate rather than limit the idea, and that those skilled in the art will be able to design many alternative embodiments without departing from the scope of the appended claims. The word “comprising” does not exclude the presence of elements or steps other than those listed in a claim, “a” or “an” does not exclude a plurality, and a single processor or other unit may fulfil the functions of several units recited in the claims. Any reference signs in the claims shall not be construed so as to limit their scope.

Claims

1. A method performed by a user equipment for managing data for an artificial intelligence or machine learning, AI/ML, model, the method comprising: transmitting (402) first information or second information to a network node when one or more first conditions are fulfilled, wherein the first information comprises data collected for training the AI/ML model, wherein the data is stored in a memory of the user equipment, and wherein the second information comprises an indication of an availability of the data in the memory.

2. The method of claim 1, comprising one or both of: transmitting the first information if the user equipment is configured to transmit the first information; and transmitting the second information if the user equipment is configured to transmit the second information.

3. The method of claim 1 or 2, wherein: the user equipment is configured, by the network node, to transmit the first information; and/or the user equipment is configured, by the network node, to transmit the second information.

4. The method of any of the previous claims, comprising: receiving, from the network node, an indication of the data that is to be collected for training the AI/ML model, wherein receiving the indication of the data that is to be collected comprises receiving a first configuration associated with the AI/ML model, and the first configuration comprises the indication of the data that is to be collected.

5. The method of claim 4, wherein: the first configuration comprises an instruction that instructs the UE to start collecting the data for training the AI/ML model.

6. The method of any of the previous claims, wherein: at least one of the one or more first conditions is configured by the network node.

7. The method of any of the previous claims, wherein: receiving the one or more first conditions from the network node, wherein receiving the one or more first conditions comprises receiving a second configuration, and the second configuration comprises the one or more first conditions.

8. The method of claim 7, wherein: the second configuration comprises any one or more of: one or more identifiers that each identify a respective first condition of the one or more first conditions; one or more parameters for use in identifying whether the one or more conditions are fulfilled; an indication of a minimum duration for which the one or more conditions are to be fulfilled prior to the user equipment transmitting the first information or the second information; an indication of whether the user equipment is allowed to transmit the first information or the second information more than once; and a bearer configuration to be used for transmitting the first information or the second information.

9. The method of any of the previous claims, wherein: the first information is transmitted according to a third configuration.

10. The method of any of the previous claims, comprising: receiving a third configuration according to which the user equipment is to transmit the first information.

11. The method of claim 10, wherein: the third configuration is received in response to transmitting the second information.

12. The method of claim 10 or 11, when claim 10 is directly or indirectly dependent on claim 4, wherein: the first configuration comprises the third configuration.

13. The method of any of claims 9 to 12, wherein: the third configuration comprises a bearer configuration.

14. The method of any of the previous claims, comprising: transmitting the second information when the one or more first conditions are fulfilled.

15. The method of any of the previous claims, comprising: receiving, from the network node, a request for the user equipment to transmit the data collected for training the AI/ML model, wherein the first information is transmitted in response to receiving the request from the network node.

16. The method of claim 15, when directly or indirectly dependent on any of claims 9 to 13, wherein: the request comprises the third configuration.

17. The method of claim 15 or 16, wherein: the request is received in response to transmitting the second information.

18. A method performed by a network node for managing data for an artificial intelligence or machine learning, AI/ML, model, the method comprising: receiving (502) first information or second information from a user equipment when one or more first conditions are fulfilled, wherein the first information comprises data collected for training the AI/ML model, wherein the data is stored in a memory of the user equipment, and wherein the second information comprises an indication of an availability of the data in the memory.

19. The method of claim 18, comprising one or both of: receiving the first information if the user equipment is configured to transmit the first information; and receiving the second information if the user equipment is configured to transmit the second information.

20. The method of claim 18 or 19, comprising one or both of: configuring the user equipment to transmit the first information; and configuring the user equipment to transmit the second information.

21. The method of any of claims 18 to 20, comprising: transmitting, to the user equipment, an indication of the data that is to be collected for training the AI/ML model, wherein transmitting the indication of the data that is to be collected comprises transmitting a first configuration associated with the AI/ML model, and the first configuration comprises the indication of the data that is to be collected.

22. The method of claim 21, wherein: the first configuration comprises an instruction that instructs the UE to start collecting the data for training the AI/ML model.

23. The method of any of claims 18 to 22, comprising: configuring at least one of the one or more first conditions.

24. The method of any claims 18 to 23, comprising: transmitting the one or more first conditions to the user equipment, wherein transmitting the one or more first conditions comprises transmitting a second configuration, and the second configuration comprises the one or more first conditions.

25. The method of any of claims 18 to 24, wherein: the first information is received according to a third configuration.

26. The method of any of claims 18 to 25, comprising: transmitting, to the user equipment, a third configuration according to which the user equipment is to transmit the first information.

27. The method of claim 26, wherein: the third configuration is transmitted in response to receiving the second information.

28. The method of claim 26 or 27, when claim 26 is directly or indirectly dependent on claim 21, wherein: the first configuration comprises the third configuration.

29. The method of any of claims 25 to 28, wherein: the third configuration comprises a radio bearer configuration.

30. The method of any of claims 18 to 29, comprising: receiving the second information when the one or more first conditions are fulfilled.

31. The method of any of claims 18 to 30, comprising: transmitting, to the user equipment, a request for the user equipment to transmit the data collected for training the AI/ML model, wherein the first information is received in response to transmitting the request, to the user equipment.

32. The method of claim 31, when directly or indirectly dependent on any of claims 25 to 29, wherein: the request comprises the third configuration.

33. The method of claim 31 or 32, wherein: the request is transmitted in response to receiving the second information.

34. A user equipment, UE (800), comprising: processing circuitry (802) configured to cause the UE (800) to: transmit first information or second information to a network node when one or more first conditions are fulfilled, wherein the first information comprises data collected for training the AI/ML model, wherein the data is stored in a memory of the user equipment, and wherein the second information comprises an indication of an availability of the data in the memory.

35. The UE (800) of claim 34, wherein: the processing circuitry (802) is configured to cause the UE (800) to perform the method according to any of claims 2-17.

36. A network node (900), comprising: processing circuitry (902) configured to cause the network node (900) to: receive first information or second information from a user equipment when one or more first conditions are fulfilled, wherein the first information comprises data collected for training the AI/ML model, wherein the data is stored in a memory of the user equipment, and wherein the second information comprises an indication of an availability of the data in the memory.

37. The network node (900) of claim 36, wherein: the processing circuitry (902) is configured to cause the network node (900) to perform the method according to any of claims 19-33.

38. A computer program comprising instructions which, when executed by processing circuitry of a user equipment, cause the user equipment to perform the method according to any of claims 1-17.

39. A computer program comprising instructions which, when executed by processing circuitry of a network node, cause the network node to perform the method according to any of claims 18-33.