Methods and apparatuses for enabling collection of machine learning or artificial intelligence training data in a network

Abstract

Embodiments described herein relate to methods and apparatuses for enabling machine learning / Artificial Intelligence, ML / AI, model training data collection for use in training a ML / AI, model in a network, A method performed by a user equipment comprises receiving one or more Layer 1, L1 measurement configurations; receiving a Layer 3 measurement reporting configuration; obtaining a first indication that the one or more L1 measurement configurations are to be used to perform measurements to be reported using the L3 measurement reporting configuration; performing measurements according to the one or more L1 measurement configurations to generate the ML / AI model training data, and reporting the ML / AI model training data according to the L3 measurement reporting configuration.

Description

METHODS AND APPARATUSES FOR ENABLING COLLECTION OF MACHINE LEARNING OR ARTIFICIAL INTELLIGENCE TRAINING DATA IN A NETWORKTECHNICAL FIELD[1] Embodiments described herein relate to methods and apparatuses for enabling machine learning / Artificial Intelligence, ML / AI, model training data collection for use in training a ML / AI, model in a network.BACKGROUND[2] AI / ML for PHY Study Item Rel-18[3] Artificial Intelligence (AI)ZMachine Learning (ML) have been investigated as promising tools to optimize the design of air-interface in wireless communication networks in both academia and industry. Example use cases include using autoencoders for Channel State Information (CSI) compression to reduce the feedback overhead and improve channel prediction accuracy; using deep neural networks for classifying Line of Sight (LOS) and No line of sight (NLOS) conditions to enhance the positioning accuracy; and using reinforcement learning for beam selection at the network side and / or the User Equipment (UE) side to reduce the signaling overhead and beam alignment latency; using deep reinforcement learning to learn an optimal precoding policy for complex Multiple input Multiple Output (MIMO) precoding problems.[4] In 3rdGeneration Partnership Project (3GPP) New Radio (NR) standardization work, a new release 18 study item on AI / ML for NR air interface has started since 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 (SI) aims at laying the foundation for future air-interface use cases leveraging AI / ML techniques.[5] A possible high-level description of AI / ML model life cycle management (LCM) for Al on the Physical Layer (PHY) can include the stages and data / signal flows depicted in Figure 1 . A detailed description of different LCM stages can be found in R1 2208908, Discussion on general aspects of Al ML framework, Ericsson.[6] Figure 1 illustrates a functional framework of model LCM for Al on PHY.[7] Data Collection is a stage that collects and provides input data (raw data or pre-processed data) for Model Training, Model inference and Model Monitoring. AI / ML algorithm specific data preparation (e.g., data ingestion and data refinement) is not carried out in the Data Collection stage.[8] Model Training is a process that uses featured data in terms of training datasets and validation datasets to train an AI / ML model.[9] Model deployment is a process of converting an AI / ML model into an executable form and delivering it to a target UE for inference where model inference is to be performed.

[0010] Model inference is a process of using a deployed AI / ML model to produce a set of outputs based on a set of featured inputs

[0011] Model monitoring is a process that monitoring drifts in data and model or monitoring performance metrics after the model has been deployed. Based on the monitored performance, decisions like model activation / deactivation / switching / fallback / selection can be taken.

[0012] It is also assumed in 3GPP the following terminology:The description of the terminologies may be further refined as the study progresses.New terminologies may be added as the study progresses..Working AssumptionMote: whether and how to indicate Functionality will be discussed separately.SUMMARY

[0013] There currently exist certain challenge(s).

[0014] In RAN2#123 meeting, proposals from R2-2308898 were discussed and the following was captured in the chairman’s notes.

[0015] For the network-side model, the discussions in the above capture how the network node (e.g. gNB) or the OAM node may initiate a data transfer session. It seems to be agreeable in RAN2 that the UE uses the Layer 3 (L3) (e.g. Radio Resource Control (RRC)) based reporting framework for transferring the network-side model related training data.

[0016] The use cases that are under discussion in the SI are Al for beam management, Al for CSI feedback compression / enhancement and Al for positioning. The above agreements were restricted to Al for beam management and Al for CSI feedback compression / enhancement use cases. Both these two use cases are performed based on the physical layer measurements. In other words, the measurements expected to be used for the training of the ML / AI model and for the inference are L1 measurements.

[0017] As the ML / AI models are expected to be built using the L1 measurements but the ML / AI model training data collection for the network-side model is based on L3 reporting framework, it is not currently clear how to create a link between a measurement configuration in L1 and a measurement reporting configuration in L3.

[0018] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges.

[0019] Embodiments described herein comprise a method at (e.g. performed by) a User Equipment (UE) for enabling data collection from a UE using a L3 reporting configuration for at least one AI / ML model associated to a functionality based on a L1 measurement configuration.

[0020] In some examples, the UE receives the L1 measurement configuration from a first network node (e.g., Central Unit Control Plane (CU-CP)).

[0021] In some examples, the UE receives the L3 reporting configuration from a first network node (e.g., CU- CP).

[0022] In some examples, the UE receives the L1 measurement configuration and the L3 reporting configuration in the same RRCReconfiguration message. In some examples, the said L1 measurement configuration is provided within the L3 reporting configuration, e.g. in the same container.

[0023] In some examples, the UE performs the collection of measurements (i.e., performing the measurements and storing the so performed measurements) according to the L1 measurement configuration when the UE receives the L1 measurement configuration.

[0024] In some examples, the UE performs the collection of measurements according to the L1 measurement configuration when the UE receives both the L1 measurement configuration and the corresponding L3 reporting configuration.

[0025] In some examples, the L3 reporting configuration includes an index associated to one or more of the L1 measurement configuration.

[0026] In some examples, the L3 reporting configuration includes an indication associated to the one or more of the L1 measurement configuration whose measurements are to be included in the training data related report.

[0027] In some examples, the UE transmits a report including the data as measured / collected to be used for the AI / ML model training associated to the functionality to the third network node.

[0028] Some embodiments described herein comprise a method at (e.g. performed by) a first network node (e.g., CU-CP) for enabling the data collection from a UE using a L3 reporting configuration for at least one AI / ML model associated to a functionality based on a L1 measurement configuration.

[0029] In some examples, the first network node receives a L1 measurement configuration from a second network node (e.g., a distributed unit (DU)) and sends the received L1 measurement configuration to the UE.

[0030] In some examples, the first network node sends a L3 reporting configuration to the UE.

[0031] In some examples, the L3 reporting configuration includes an index associated to one or more of the L1 measurement configuration.

[0032] In some examples, the L3 reporting configuration includes an indication associated to the one or more of the L1 measurement configuration whose measurements are to be included in the training data related report.

[0033] In some examples, the first network node receives a report including the data as measured / collected to be used for the AI / ML model training associated to the functionality from the UE.

[0034] In some embodiments a method is provided at (e.g. performed by) a second network node (e.g., DU) for enabling the data collection from a UE using a L3 reporting configuration for at least one AI / ML model associated to a functionality based on a L1 measurement configuration.

[0035] In some examples, the second network node sends a L1 measurement configuration to a first network node (e.g., CU-CP).

[0036] It will be appreciated that embodiments described herein provide a methods by which the UE is made aware of which L3 reporting configuration is associated to which L1 configuration for the training data collection of a AI / ML model based functionality.

[0037] According to some embodiments there is therefore provided a method performed by a user equipment for enabling machine learning / Artificial Intelligence, ML / AI, model training data collection for use in training a ML / AI, model in a network. The method comprises receiving one or more Layer 1, L1 measurement configurations and receiving a Layer 3 measurement reporting configuration. The method further comprises obtaining a first indication that the one or more L1 measurement configurations are to be used to perform measurements to be reported using the L3 measurement reporting configuration; performing measurements according to the one or more L1 measurement configurations to generate the ML / AI model training data, and reporting the ML / AI model training data according to the L3 measurement reporting configuration.

[0038] According to some embodiments there is provided a method performed by a first network node for enabling Machine Learning / Artificial Intelligence, ML / AI, model training data collection for use in training an ML / AI model in a network. The method comprises transmitting one or more Layer 1 , L1 , measurement configurations to the UE and transmitting a Layer 3 measurement reporting configuration to the UE. The method further comprises providing, to the UE, a first indication that the one or more L1 measurement configurations are to be used to perform measurements to be reported using the L3 measurement reporting configuration; and receiving a report comprising the ML / AI model training data according to the L3 measurement reporting configuration.

[0039] According to some embodiments there is provided a method performed by a second network node for enabling Machine Learning / Artificial Intelligence, ML / AI, model training data collection for use in training an ML / AI model in a network. The method comprises transmitting one or more layer 1 , L1 , measurement configurations to a first network node, wherein each L1 measurement configuration comprises a secondindication that the L1 measurement configuration is to be used to perform measurements to generate ML / AI model training data.

[0040] According to some embodiments there is provided a user equipment for enabling Machine Learning / Artificial Intelligence, ML / AI model training data collection for use in training an ML / AI model in a network. The user equipment comprising processing circuitry and memory. The memory contains instructions executable by the processing circuitry whereby the user equipment is operable to: receive one or more Layer 1 , L1 measurement configurations; receive a Layer 3 measurement reporting configuration; obtain a first indication that the one or more L1 measurement configurations are to be used to perform measurements to be reported using the L3 measurement reporting configuration; perform measurements according to the one or more L1 measurement configurations to generate the ML / AI model training data, and report the ML / AI model training data according to the L3 measurement reporting configuration.

[0041] According to some embodiments there is provided a first network node for enabling Machine Learning / Artificial Intelligence, ML / AI, model training data collection for use in training an ML / AI model in a network. The first network node comprises processing circuitry and memory. The memory contains instructions executable by the processing circuitry whereby the first network node is operable to: transmit one or more Layer 1 , L1 , measurement configurations to the UE; transmit a Layer 3 measurement reporting configuration to the UE; provide, to the UE, a first indication that the one or more L1 measurement configurations are to be used to perform measurements to be reported using the L3 measurement reporting configuration; and receive a report comprising the ML / AI model training data according to the L3 measurement reporting configuration.

[0042] According to some embodiments there is provided a second network node for enabling Machine Learning / Artificial Intelligence, ML / AI, model training data collection for use in training an ML / AI model in a network. The second network node comprises processing circuitry and memory. The memory contains instructions executable by the processing circuitry whereby second first network node is operable to: transmit one or more layer 1 , L1 , measurement configurations to a first network node, wherein each L1 measurement configuration comprises a second indication that the L1 measurement configuration is to be used to perform measurements to generate ML / AI model training data.

[0043] Certain embodiments may provide one or more of the following technical advantage(s). By enabling the linking between a L1 measurement configuration and a L3 reporting configuration for reporting collected data at the UE to the network, the UE can reuse one or more of the L1 measurement configuration that is already configured to the UE for L1 operations and thus avoiding the over-the-air overhead of transmitting the L1 measurement configuration twice.

[0044] Embodiments described herein also enable the UE to identify which L1 measurement configurations are relevant for AI / ML model related training data collection and which of the L1 measurement configurations are relevant for normal L1 operations (e.g. operations other than ML / AI model training, e.g. classical Uplink Control Information (UCI) reporting, beam management, reporting of CSI measurements, etc.)

[0045] Brief Description of the Drawings

[0046] 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:

[0047] Fig. 1 illustrates a Functional framework of model LCM for Al on PHY

[0048] Fig. 2 is a flow chart illustrating a method in accordance with some embodiments;

[0049] Fig. 3 is a flow chart illustrating a method in accordance with some embodiments;

[0050] Fig. 4 is a flow chart illustrating a method in accordance with some embodiments;

[0051] Fig. 5 is a signalling diagram illustrating an example implementation of the methods of Fig. 2 and Fig. 3;

[0052] Fig. 6 is a signalling diagram illustrating an example implementation of the method of Fig. 4;

[0053] Fig. 7 shows an example of a communication system in accordance with some embodiments;

[0054] Fig. 8 shows a UE in accordance with some embodiments;

[0055] Fig. 9 shows a network node in accordance with some embodiments;

[0056] Fig. 10 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized.DETAILED DESCRIPTION

[0057] 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.

[0058] Herein, the terms “ML-model”, “Al-model” or “AI / ML model” are interchangeable. An AI / ML model may be defined as a functionality or be part of a functionality that is deployed / implemented in a first node e.g. a User Equipment, in the case of a UE-sided model or a Network node in the case of a network-sided model. An AI / ML model can be defined as a feature or part of a feature that is implemented / supported in a first node. This first node can indicate the feature version to a second node. If the ML-model is updated, the feature version maybe changed by the first node.

[0059] 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 prediction(s) / estimates of a certain type (e.g. timedomain and / or spatial domain predictions of beam measurements). In one example, an ML-model may correspond to a function receiving as an input the measurement of a reference signal at time instance tO (e.g. transmitted in beam-X) and providing as an outcome the prediction of the reference signal at time tO+T. In another example, an ML-model may correspond to a function receiving as an input the measurement of a reference signal X (e.g. transmitted in beam-x), such as an Synchronization Signal Block (SSB) whose index is ‘x’, and providing as an 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’. Another example is a ML model for aid in CSI estimation. In such a setup the ML-model may be specific ML-model within a UE and another ML-model within the Network (NW) side. Jointly both ML-models provide a joint network. The functionof the ML-model at the UE would be to compress a channel input and the function of the ML-model at the NW side would 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 would be to detect different peaks within the impulse response, which reflects the multipath experienced by the radio signals arriving at the UE side. For positioning, another way may be to input multiple sets of measurements into an ML network and based on that derive an estimated position of the UE. Another ML-model would be an ML-model to be able to aid the UE in channel estimation or interference estimation for channel estimation. The channel estimation could for example be for the Physical Downlink Shared Channel (PDSCH) and be associated with specific set of reference signals patterns that are transmitted from the NW to the UE. The ML-model will 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 NW and UE. Another example of an ML-model for CSI estimation is to predict a suitable Channel Quality Indicator (CQI), Precoder Matrix Indicator (PMI), Rank Indicator (Rl), Channel State Information Reference Signal (CSI-RS) resource indicator (CRI) or similar value into the future.

[0060] An AI / ML model may also be defined follows:

[0061] - In terms of the time / frequency / 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 / space, than those of the model input. In one example (time domain), an ML-model may correspond to a function receiving as input the measurementof a reference signal at time instance tO (e.g. transmitted in beam- X) and providing as an outcome the prediction of the reference signal in time instance tO+T. In another example (spatial domain), an 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 providing as an outcome the estimation / prediction of the link quality of other reference signals transmitted in different beams e.g. reference signal Y (e.g. transmitted in beam-y).

[0062] - In terms of model structure, the ML model may be fully contained within the UE, or split between the UE and network.

[0063] - One example of split structure is a ML model for aid in CSI estimation, where a possible setup of the ML- model is a split model, which comprise a specific sub- ML-model within a UE and a sub- ML-model within the NW side which collaborate to generate a desired outcome for the overall ML model. The function of the sub- ML-model at the UE would be to compress a channel input and the function of the sub- ML-model at the NW side would 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 would be to detect different peaks within the impulse response, that correspond to different reception directions of radio signals at the UE side.

[0064] - One example of ML contained within the UE is ML enhanced positioning, e.g., an ML model implemented in the UE takes as input multiple sets of measurements (each corresponding to a downlink (DL) signal from a different network node), and based on that derive an estimated position of the UE.

[0065] - In terms of utility for physical layer, the ML model can be used for many functions, including: channel estimation, Line Of Sight (LOS)ZNon-Line Of Sight (NLOS) classification, beam selection, position estimation of the UE, link adaption, etc. For example, an 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 NW to the UE. The ML- model will 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 NW and UE. Another example of an ML-model for CSI estimation is to predict a suitable CQI, PMI, Rank Indicator (Rl) 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.

[0066] It will be appreciated that in embodiments described herein the UE is connected to the network (e.g. it may receive and transmit data and / or control information) i.e. in RRC_CONNECTED state, and, being 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 time-domain). The specific functionality or function of an AI / ML model can for example be for one of the following examples, which could also be grouped to as a functionality area (one or more AI / ML-model functionality per area), as follows:• CSI reporting• Beam management (BM)■ In one option there may be a BM functionality of an AIZML-model(s) wherein an AI / ML model (e.g. at the UE) is capable of performing the inference of one or more timedomain predictions related to beam management. For example, the UE may be configured by the network to report (e.g. on Physical Uplink Control Channel (PUCCH) and / or Physical Uplink Shared Channel (PUSCH)) one or more timedomain 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.■ In one option there may be a BM functionality of an AIZML-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.■ In one option there may be a BM functionality of an AIZML-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.■ The UE is considered to be configured with AI / ML functionality when at least one action related to that functionality is configured e.g. the UE is 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■ Such as mobility measurement, i.e., RSRP, RSRQ, RSSI, but also aspects related to radio link failure e.g. RLF predictions. Further, RLM related timers (T310) and counters (N310 and N311) related predictions could also be considered here.■ Such as the measurement framework defined in §5.5., 3GPP 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• HARQ transmission• Data transmission• Data reception• Power control• Positioning of the UE• Random access transmission• Energy efficiency (e.g Discontinuous Reception (DRX) settings)• L3 mobility e.g. predictions of one or more cells in which the UE moves to• Lower Layer triggered mobility (LTM)■ In one option there may be an LTM related 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 inter-cell beam measurements and / or SSB indexes of one or more LTM candidate cells. For example, the UE may be configured by the network to report (e.g. on PUCCH and / or PUSCH) one or more time-domain predictions of SSB and / or CSI-RS and / or PTRS measurements), of one or more LTM candidate cells.

[0067] The methods disclosed herein may be applicable to the AI / ML model(s) associated to an AI / ML model functionality, or to the AI / ML model functionalities interchangeably.

[0068] Figure 2 depicts a method in accordance with particular embodiments. The method of Figure 2 may be performed by a UE or wireless device (e.g. the UE 712 or UE 800 as described later with reference to Figures 7 and 8 respectively). The method may be for enabling Machine Learning / Artificial Intelligence, ML / AI, model training data collection for use in training a ML / AI, model in a network. The method begins at step 202 with receiving one or more Layer 1, L1 , measurement configurations. The one or more L1 measurement configurations may bereceived from a first network node (e.g. a central unit control plane (CU-CP)). The terms L1 measurement configuration and L1 configuration are used herein interchangeably.

[0069] In step 204 the method comprises receiving a Layer 3 measurement reporting configuration. The L3 measurement reporting configuration may be received from the first network node. The terms L3 measurement reporting configuration and L3 reporting configuration are used herein interchangeably.

[0070] In step 206 the method comprises obtaining a first indication that the one or more L1 measurement configurations are to be used to perform measurements to be reported using the L3 measurement reporting configuration. The first indication may be received from the first network node (e.g. as part of the L3 measurement reporting configuration and / or the L1 measurement configuration). The first indication may, for example, be an explicit indication or may be implied in or derived by the UE from information in the one or more L1 measurement configurations and / or the L3 measurement reporting configuration.

[0071] In step 208 the method comprises performing measurements according to the one or more L1 measurement configurations to generate the ML / AI model training data.

[0072] In step 210 the method comprises reporting the ML / AI model training data according to the L3 measurement reporting configuration.

[0073] It will be appreciated that L1 measurement configuration may correspond to one or more parameters for the UE to perform lower layer measurements, such as one or more parameters in the information element (IE) CSI-MeasConfig.

[0074] In some examples there may be no explicit indication in the one or more L1 measurement configurations regarding which of the one or more L1 measurement configurations corresponds to the configuration whose associated measurements are to be collected for AI / ML model training purposes. For example, the one or more L1 measurement configurations may not comprise a second indication as described in paragraph

[0071] below.

[0075] However, in some examples, the one or more L1 measurement configurations each comprise a second indication that the L1 measurement configuration is to be used to perform measurements to generate ML / AL model training data. In other words, each of the one or more L1 measurement configurations of step 202 may comprise a second indication regarding whether the L1 measurement configuration is to be used for AI / ML model training related measurements. For example, the second indication may comprise a flag. An example implementation of such a second indication associated to CSI configuration for AI / ML model training is given below:NZ P-CS I-RS-Res ource : : = SEQUENCE { nzp-CS I-RS-Resourceld NZ P-CSI -RS-Res ourceld, resour ceMappi ng CSI -RS-Res ourceMapping , powerControlO f f set INTEGER ( - 8 . . 15 ) , powerControlO f f setS S ENUMERATED { db- 3 , dbO , db3 , db 6 } OPTIONAL-- Need R s cramblingID S cramblingld, periodicityAndO f f set CSI -Res our cePeriodicityAndOf f set OPTIONAL-- Cond PeriodicOrSemiPersi stent qcl- In foPeriodi cCSI -RS TCI -Stateld OPTIONAL-- Cond Periodic_ L purpos e _ ENUMERATED } onlyTrain , onlyNormalOp , both }OPTIONAL _ 11}

[0076] In this example, the UE receives a CSI-RS resource configuration (L1 measurement configuration) in which the UE receives an second indication (purpose) that indicates whether the L1 configuration is pertaining only to ML model training purposes (set to onlyTrain) or is it pertaining only to normal L1 operation purposes (set to onlyNormalOp)or pertaining to both ML model training purposes and normal L1 operation purposes(set to both). When the UE receives an L1 configuration that is indicated to be for ML model training purposes only or both for ML model training purposes and for normal L1 operation, then the UE may determine that the measurements derived based on such a L1 configuration are to be stored for ML model training purposes.

[0077] In examples in which the L1 configuration is pertaining only training, one or more of the fields in the L1 configuration that are associated with normal operation (e.g. any operation that is not for the purpose of training data collection) may be not provided by the network in the L1 configuration, or if provided, the UE may ignore them. For example, in some cases there are mandatory legacy parameters already included in legacy configuration that cannot be removed if the same IE is reused to convey the AI / ML L1 configuration. Hence in such cases, the UE may ignore those mandatory parameters if received in a L1 configuration which comprises a second indication indicating that the L1 configuration is to be used only for the purpose of collecting ML / AI model training data.

[0078] In another embodiment, if the L1 configuration is pertaining only to L1 normal operation, one or more of the fields in the L1 configuration that are associated with training data collection operation may be not provided by the network in the L1 configuration, or if provided, the UE may ignore them.

[0079] In some examples, the second indication be provided implicitly in the L1 configuration. For example, the second indication may comprise one or more empty fields in the L1 measurement configuration that are associated with normal measurement collection. In other words, as these fields are missing from the L1 measurement configuration, the UE may assume that the L1 measurement configuration is for the purpose of collecting ML / AI model training data.

[0080] In some examples, the second indication comprises one or more non-empty fields in the L1 measurement configuration that are associated with generating ML / AI model training data.

[0081] For example, the network may provide in the L1 configuration fields that are only applicable for the purpose of collecting ML / AI training data, and may not provide fields that are only applicable for normal operation. Hence, the UE will figure out that the concerned L1 configuration is pertaining only to the purpose of collecting ML / AI training data.

[0082] It will also be appreciated that for any L1 configuration received by the UE, if the L1 configuration is pertaining only normal operation, this may be indicated implicitly in the L1 configuration. For example, the network may provide in the L1 configuration fields that are only applicable for normal operation, and may notprovide fields that are only applicable for the purpose of collecting ML / AI training data. Hence, the UE will figure out that the concerned L1 configuration is pertaining only normal operation.

[0083] It will also be appreciated that for any L1 configuration received by the UE, if the L1 configuration is pertaining to both normal operation and training operation this information may be carried implicitly in the L1 configuration. For example, the network may provide in the L1 configuration fields that are applicable for normal operation, and fields that are applicable to the purpose of collecting ML / AI training data. Hence, the UE will figure out that the concerned L1 configuration is pertaining both to normal operation and to the purpose of collecting ML / AI training data.

[0084] In some examples, the L3 measurement reporting configuration of step 204 comprises the first indication of step 206. For example, the indication that the one or more L1 measurement configurations are to be used to perform measurements to be reported using the L3 measurement reporting configuration is included in the L3 measurement reporting configuration. For example, the L3 reporting configuration may comprise an indication indicating which L1 configurations are to be used for generating the measurements to be collected for the AI / ML model training.

[0085] For example, the L3 measurement reporting configuration may comprise an indication indicating that a certain L1 configuration is to be used for generating the measurements to be collected for the AI / ML model training. In this example, the certain L1 measurement configuration need not comprise any indication regarding whether the corresponding L1 measurement configuration is to be used for AI / ML model training related measurements

[0086] When the UE receives the L3 measurement reporting configuration, the UE may then apply the one or more L1 measurement configurations indicated in the first indication only for ML model training purposes.

[0087] The first indication may for example comprise one or more indexes to the one or more L1 configurations. For example, as disclosed in the following embodiments, the L3 reporting configuration may include indexes associated to the SSBs / CSIs resources configuration to be used for the AI / ML model training; or the L3 reporting configuration may include the SSBs / CSIs resources configuration to be used for the AI / ML model training.

[0088] In some examples, the first indication may be implicit. For example, the first indication may be that the L3 measurement reporting configuration comprises the one or more L1 measurement configurations (e.g. within the same container). In another example, the first indication may be that the L3 measurement reporting configuration is received by the UE in the same message as the one or more L1 measurement configurations.

[0089]

[0090] Detailed description of when the UE starts collecting the data / measurements based on the received L1 configuration associated to an AI / ML model training

[0091] In some examples, the method of Figure 2 further comprises initiating the step of performing measurements responsive to receiving the L3 measurement reporting configuration. For example, when the UE receives a L1 measurement configuration indicating that the associated configuration is only for AI / ML model training aspects, then the UE does not initiate performing the collection of measurements (i.e., performing themeasurements and storing the so performed measurements) associated to such a configuration until the associated L3 measurement reporting configuration is received. Such an embodiment would aid the L3 to act as the trigger for initiating the data collection session at the UE but being configured with the corresponding configuration earlier. This is useful for the network when the node creating the L1 configuration (e.g. the DU) and the node creating the L3 reporting configuration (e.g. the CU-CP) are different and thus at the time of initiating the data collection session, the CU-CP need not check with the DU to trigger the measurement collection session at the UE.

[0092] In some examples, the method of Figure 2 further comprises initiating the step of performing measurements further responsive to one or more conditions in the L3 measurement reporting configuration being met. For example, when the UE receives a L1 measurement configuration indicating that the associated configuration is only for AI / ML model training aspects, then the UE may not initiate performing the collection of measurements (i.e., performing the measurements and storing the so performed measurements) associated to such a configuration until the conditions as configured in the associated L3 measurement reporting configuration are fulfilled. Such an embodiment may aid the node hosting the L3 (e.g. CU-CP) to control when (e.g., when none of the CSI-RSs are above a threshold or at least X CSI-RSs are above a threshold etc.) the UE shall start performing the data collection for the AI / ML model training.

[0093] In some examples, the method of Figure 2 further comprises further comprising, for each L1 measurement configuration, initiating the step of performing measurements according to the L1 measurement configuration responsive to receiving the L1 measurement configuration. For example, when the UE receives a L1 measurement configuration indicating that the associated configuration is only for AI / ML model training aspects, then the UE may immediately start performing the collection of measurements (i.e., performing the measurements and storing the so performed measurements) associated to such a configuration. Such an embodiment may be useful when the network wants to collect data for building an AI / ML model from any and every possible location.

[0094] In some examples, the method of Figure 2 further comprises initiating the step of performing measurements further responsive to one or more conditions in the L1 measurement configuration being met. For example, when the UE receives a L1 measurement configuration indicating that the associated configuration is only for AI / ML model training aspects, then the UE may not initiate performing the collection of measurements (i.e., performing the measurements and storing the so performed measurements) associated to such a configuration until the conditions as configured in the L1 configuration are fulfilled. Such an embodiment may aid the node hosting the inference function in the future (DU) to control when and / or where (e.g., when none of the CSI-RSs are above a threshold or at least X CSI-RSs are above a threshold etc.) the UE shall start performing the data collection for the AI / ML model training.

[0095] Detailed description for L3 reporting configurations associated to an AI / ML model training

[0096] As described above, in some examples, the L3 reporting configuration comprises an index (e.g. the first indication) to the one or more L1 configurations whose measurements are to be collected for the AI / ML model training purposes. When the UE obtains such an index, the UE may collect the measurements based on the indexed one or more L1 configurations and reports so collected measurements as per the L3 reporting configuration. An example implementation of such an index is given below. In the example below, the UE receives a L3 reporting configuration in which the network can indicate which SSBs and / or CSI-RSs belonging to which serving cell should be part of the data collection session for this AI / ML model training.

[0097] Upon receiving such a first indication, the UE is able to determine which measurements are to be collected and reported in the report generated based on such a L3 reporting configuration. A single L3 reporting configuration could request the UE to collect measurements associated to more than one serving cells, more than one SSB of the same serving cell (in which case a list of indexes associated to the SSBs resources may be included in the L3 reporting configuration), more than one CSI-RS of the same serving cell (in which case a list of indexes associated to the CSIs resources may be included in the L3 reporting configuration) or few SSBs from one serving cell and few SSBs from another serving cell etc.ServingCellL lConf iglndex-r 18- I Es : : = SEQUENCE { cellType CHOI CE { sCelllndex SCelllndex pCel l ENUMERATED { true }

[0098] In some examples, the first indication comprises an indication in both the L1 measurement configuration and the L3 measurement reporting configuration of a functionality of the ML / AI model. For example, the L3 reporting configuration may include an indication of the functionality for which the measurements are to be collected for the AI / ML model training purposes. When the UE obtains such an indication, the UE collects the measurements based on any L1 configuration associated to the AI / ML training for the corresponding functionality and reports so collected measurements as per the L3 reporting configuration.

[0099] An example implementation of such a first indication is given below. In the example below, the UE receives a L3 reporting configuration in which the network can indicate that it is interested in CSI compression functionality (CSI-compression) to be of interest. Upon receiving such an indication, the UE gets to know that any CSI configuration in which there is an indication that the configuration can be used for AI / ML model training purposes is of relevance for this reporting configuration. A single reporting configuration could request the UE to collect measurements associated to a specific serving cell or more than one serving cells. For example, in the exampleimplementation below, if the field servCelllndex is not included, then the UE would collect the measurements associated to the configured mlFunctionality or all the serving cells. If the field servCelllndex is included, then the UE would collect the measurements associated to the configured mIFunctionality in only those serving cells. Further, as mentioned above, the L1 configurations can further indicate if a L1 configuration is of relevance for data collection for AI / ML model training or not (e.g., with the field purpose in L1 configuration being set to either onlyTrain or both).AIMLReportingConfiguration-rl 8 -I Es : : = SEQUENCE { ml Functi ali ty _ ENUMERATED { CSI - compr es s ion , CSI-enahcnement,_ Lr1

[0100] In some examples, the first indication comprises an indication of one or more serving cells on which to collect training data in the L3 measurement reporting configuration, wherein the one or more L1 measurement configurations are each associated with at least one of the one or more serving cells. For example, the L3 reporting configuration may include an indication of the serving cell for which the measurements are to be collected for the AI / ML model training purposes. When the UE obtains such an indication, the UE collects the measurements based on any L1 configuration associated to the AI / ML training for the corresponding serving cell and reports so collected measurements as per the L3 reporting configuration.

[0101] An example implementation of such a first indication is given below. In the example below, the UE receives a L3 reporting configuration in which the network can indicate that it is interested in a particular serving cell to be of interest. Upon receiving such an indication, the UE gets to know that any L1 configuration for AI / ML model training purposes associated to that serving is of relevance for this reporting configuration. A single reporting configuration could request the UE to collect measurements associated to a specific serving cell or more than one serving cells. Further, as mentioned above, the L1 configurations can further indicate if a L1 configuration is of relevance for data collection for AI / ML model training or not (e.g., with the field purpose in L1 configuration being set to either onlyTrain or both).cellType _ CHOI CE { sCelllndex SCelllndex pCel l _ ENUMERATED { true ) pSCell ENUMERATED { true )_ Lr)

[0102] In some examples, rather than an index associated to the specific L1 measurement configuration to be used for AI / ML model training purposes, the L3 reporting configuration includes the L1 measurement configuration to be used for AI / ML model training purposes. In this case, the L3 reporting configuration may comprise the one or more SSBs and / or CSI-RSs resources for one or more cells in which the UE should perform AI / ML training, for example as illustrated below.ServingCellL lConf iglndex-r 18- I Es : : = SEQUENCE { cellType CHOI CE { sCelllndex SCelllndex pCel l ENUMERATED { true ) pSCell ENUMERATED { true )) OPTIONAL , n zp-CSI - RS-Res ourceToAddModLis t _ SEQUENCE ( SI Z E ( 1 . . maxNro fNZ P- CS I -RS-Resources ) ) OF NZ P- CSI -RS- Res ource OPTIONAL , — Need N cs i-SSB- Res ourceSetToAddModLis t _ SEQUENCE ( SI Z E ( 1 . . maxNro f CSI - SS B-ResourceSets ) ) OF CSI -SSB- Res ourceS et OPTIONAL , — Need N )

[0103] Upon receiving such a L3 reporting configuration, in some embodiments the UE starts collecting the measurements as per the L1 configuration (e.g., when the L1 configuration is dedicated only for the AI / ML model training purposes by an explicit indication in the L1 configuration)

[0104] The L3 reporting configuration may also include indication to release one or more of the L1 measurement configuration. For example, the L3 reporting configuration may include a list of indexes associated to SSBs / CSI- RSs resources to be released. In another embodiment, the L3 reporting configuration may include indication to release L1 measurement configurations for one or more serving cells, in which case all the L1 measurement configurations (SSBs / CSI-RSs resources) associated to such cells may be released.

[0105] Upon receiving such a L3 reporting configuration including indication of the release of one or more of the L1 measurement configurations (i.e. SSBs / CSI-RSs resources), the UE may stop collecting the measurements according to such L1 measurement configuration(s) for the AI / ML model training purposes.

[0106] Upon receiving such a L3 reporting configuration, in some embodiments the UE may start evaluating the criterion for collecting the measurements as per the L1 configuration. The criterion for collecting the measurements could be in terms of certain conditions being met (e.g., when none of the CSI-RSs are above a threshold or at leastX CSI-RSs are above a threshold etc.) or a timer meeting a certain condition (e.g., when T310 is above a threshold etc.).

[0107] Figure 3 depicts a method in accordance with particular embodiments. The method of Figure 3 may be performed by a network node (e.g. the network node 710 or network node 900 as described later with reference to Figures 7 and 9 respectively). The method may be for enabling Machine Learning / Artificial Intelligence, ML / AI, model training data collection for use in training an ML / AI model in a network. The method begins at step 302 with transmitting one or more Layer 1, L1 , measurement configurations to the UE. Step 302 corresponds to step 202 of Figure 2. In step 304 the method comprises transmitting a Layer 3 measurement reporting configuration to the UE. Step 304 corresponds to step 204 of Figure 2. In step V206 the method comprises providing, to the UE, a first indication that the one or more L1 measurement configurations are to be used to perform measurements to be reported using the L3 measurement reporting configuration. It will be appreciated, as described above, that the first indication may be provided explicitly or implicitly to the UE. The first indication may be provided by the network and obtained by the UE in any of the manners described above. In step 308 the method comprises receiving a report comprising the ML / AI model training data according to the L3 measurement configuration. Step 308 corresponds to step 210 of Figure 2.

[0108] The one or more L1 measurement configurations and the L3 measurement reporting configurations may be configured as described above with reference to Figure 2.

[0109] In some examples, the method comprises receiving each of the one or more L1 measurement configurations from a second network node. The second network node may comprise a distributed unit of a network node.

[0110] Figure 4 depicts a method in accordance with particular embodiments. The method of Figure 4 may be performed by a network node (e.g. the network node 710 or network node 900 as described later with reference to Figures 7 and 9 respectively). The method may be for enabling Machine Learning / Artificial Intelligence, ML / AI, model training data collection for use in training an ML / AI model in a network. The method begins at step 402 with transmitting one or more layer 1, L1 , measurement configurations to a first network node, wherein each L1 measurement configuration comprises a second indication that the L1 measurement configuration is to be used to perform measurements to generate ML / AI model training data.

[0111] The one or more L1 measurement configurations and the second indication may be configured as described above with reference to Figure 2.

[0112] Figure 5 is a signalling diagram illustrating an example implementation of Figures 2 and 3.

[0113] In step 502, the first network node (e.g. CU-CP) transmits the one or more L1 measurement configurations to the UE. Step 502 corresponds to steps 202 and 302.

[0114] In step 504, the first network node transmits the Layer 3 measurement reporting configuration to the UE. Step 504 corresponds to steps 204 and 304

[0115] In step 506, transmits the report comprising the ML / AI model training data according to the L3 measurement reporting configuration. Step 506 corresponds to steps 210 and 308.

[0116] Figure 6 is a signalling diagram illustrating an example implementation of Figure 4.

[0117] In step 602 the second network node (e.g. the DU) transmits one or more layer 1, L1 , measurement configurations to a first network node, wherein each L1 measurement configuration comprises a second indication that the L1 measurement configuration is to be used to perform measurements to generate ML / AI model training data. Step 602 corresponds to step 402.

[0118] Figure 7 shows an example of a communication system 700 in accordance with some embodiments.

[0119] 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 3rdGeneration 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.

[0120] Examples of an ORAN network node include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O-CU-CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near- real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an A1 , F1 , W1, E1 , E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN access node may be a logical 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 O-2 interface defined by the O-RAN Alliance or comparable technologies. The network nodes 710 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 712a, 712b, 712c, and 712d (one or more of which may be generally referred to as UEs 712) to the core network 706 over one or more wireless connections.

[0121] 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 differentembodiments, 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] As a whole, the communication system 700 of Figure 7 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and / or any other appropriate wireless communication standard, such as the WorldwideInteroperability 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.

[0126] 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)ZMassive loT services to yet further UEs.

[0127] 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-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0128] In the example illustrated in Figure 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.

[0129] The hub 714 may have a constant / persistent or intermittent connection to the network node 710b. The hub 714 may also allow for a different communication scheme and / or schedule between the hub 714 and UEs (e.g., UE 712c and / or 712d), and between the hub 714 and the core network 706. In other examples, the hub 714 is connected to the core network 706 and / or one or more UEs via a wired connection. Moreover, the hub 714 may be configured to 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 adedicated 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.

[0130] Figure 8 shows a UE 800 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over 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-loT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0131] A UE may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to- infrastructure (V2I), or vehicle-to-everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0132] 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 Figure 8. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0133] 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 Figure 2.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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 USIM and / or ISIM, other memory, or any combination thereof. TheU ICC may for example be an embedded UICC (eUlCC), integrated U ICC (iUICC) or a removable U ICC 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.

[0138] 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.

[0139] 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, locationbased 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 / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0140] 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).

[0141] 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.

[0142] 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 industrialapplication 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 Figure 8.

[0143] 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-loT 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.

[0144] 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.

[0145] Figure 9 shows a network node 900 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, 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).

[0146] 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 distributedradio 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).

[0147] Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multistandard 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).

[0148] 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., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 900 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared 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.

[0149] 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 Figure 3 or 4.

[0150] In some embodiments, the processing circuitry 902 includes a system on a chip (SOO). 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 asradio 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.

[0151] 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.

[0152] The communication interface 906 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, the communication interface 906 comprises port(s) / terminal(s) 916 to send and receive data, for example to and from a network over a wired connection. The communication interface 906 also includes radio front-end circuitry 918 that may be coupled to, or in certain embodiments a part of, the antenna 910. 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.

[0153] 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 front-end circuitry 918, and the RF transceiver circuitry 912, as part of a radio unit (not shown), and the communication interface 906 communicates with the baseband processing circuitry 914, which is part of a digital unit (not shown).

[0154] 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 ofantenna 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.

[0155] 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.

[0156] 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.

[0157] Embodiments of the network node 900 may include additional components beyond those shown in Figure 9 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node 900 may include user interface equipment to allow input of information into the network node 900 and to allow output of information from the network node 900. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 900.

[0158] Figure 10 is a block diagram illustrating a virtualization environment 1000 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 1000 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 1000 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an O-2 interface.

[0159] Applications 1002 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0160] Hardware 1004 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 1006 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 1008a and 1008b (one or more of which may be generally referred to as VMs 1008), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 1006 may present a virtual operating platform that appears like networking hardware to the VMs 1008.

[0161] The VMs 1008 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 1006. Different embodiments of the instance of a virtual appliance 1002 may be implemented on one or more of VMs 1008, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV 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.

[0162] In the context of NFV, a VM 1008 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 1008, and that part of hardware 1004 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 1008 on top of the hardware 1004 and corresponds to the application 1002.

[0163] Hardware 1004 may be implemented in a standalone network node with generic or specific components. Hardware 1004 may implement some functions via virtualization. Alternatively, hardware 1004 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 1010, which, among others, oversees lifecycle management of applications 1002. In some embodiments, hardware 1004 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 1012 which may alternatively be used for communication between hardware nodes and radio units.

[0164] 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 withdifferent 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.

[0165] 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.EMBODIMENTSGroup A Embodiments1. A method performed by a user equipment for enabling machine learning / Artificial Intelligence, ML / AI, model training data collection for use in training a ML / AI, model in a network, the method comprising: receiving one or more Layer 1 , L1 measurement configurations; receiving a Layer 3 measurement reporting configuration; obtaining a first indication that the one or more L1 measurement configurations are to be used to perform measurements to be reported using the L3 measurement reporting configuration; performing measurements according to the one or more L1 measurement configurations to generate the ML / AI model training data, and reporting the ML / AI model training data according to the L3 measurement reporting configuration.The method of embodiment 1 , wherein the one or more L1 measurement configurations each comprise a second indication that the L1 measurement configuration is to be used to perform measurements to generate ML / AI model training data. The method of embodiment 2 wherein the second indication comprises a flag in the L1 measurement configuration. The method of embodiment 2, wherein the second indication comprises one or more empty fields in the L1 measurement configuration that are associated with normal measurement collection The method of embodiment 2, wherein the second indication comprises one or more non-empty fields in the L1 measurement configuration that are associated with generating ML / AI model training data. The method of embodiment 2 to 5, further comprising initiating the step of performing measurements responsive to receiving the L3 measurement reporting configuration. The method of embodiment 6, further comprising initiating the step of performing measurements further responsive to one or more conditions in the L3 measurement reporting configuration being met. The method of embodiment 2 to 5, further comprising, for each L1 measurement configuration, initiating the step of performing measurements according to the L1 measurement configuration responsive to receiving the L1 measurement configuration. The method of embodiment 8, further comprising initiating the step of performing measurements further responsive to one or more conditions in the L1 measurement configuration being met. The method of embodiment 1 to 9, wherein the L3 measurement reporting configuration comprises the first indication. The method of embodiment 10, wherein the first indication comprises an index to the L1 measurement configuration. The method of embodiment 10, wherein the first indication is that the L3 measurement reporting configuration comprises the one or more L1 measurement configurations. The method of embodiment 1 to 9, wherein the first indication comprises an indication in both the L1 measurement configuration and the L3 measurement reporting configuration of a functionality of the ML / AI model. The method of any one of embodiment 2 to 9, wherein the first indication comprises:an indication of one or more serving cells on which to collect training data in the L3 measurement reporting configuration, wherein the one or more L1 measurement configurations are each associated with at least one of the one or more serving cells.15. The method of any one of embodiment 1 to 9 wherein the first indication is that the L1 measurement configuration and the L3 measurement reporting configuration are received in the same message.16. The method of any one of embodiments 1 to 15, wherein the L3 measurement reporting configuration comprises one of: an indication of one or more SSBs to collect ML / AI model training data on; an indication of one or more Channel State Information Reference Signals (CSI-RSs) to collect ML / AI model training data on; and an indication of one or more serving cells on which to collect ML / AI model training data.17. 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 Embodiments18. A method performed by a first network node for enabling Machine Learning / Artificial Intelligence, ML / AI, model training data collection for use in training an ML / AI model in a network, the method comprising: transmitting one or more Layer 1 , L1, measurement configurations to the UE; transmitting a Layer 3 measurement reporting configuration to the UE; providing, to the UE, a first indication that the one or more L1 measurement configurations are to be used to perform measurements to be reported using the L3 measurement reporting configuration; receiving a report comprising the ML / AI model training data according to the L3 measurement reporting configuration.19. The method of embodiment 18, further comprising: receiving each of the one or more L1 measurement configurations from a second network node.20. The method of embodiment 18 or 19, wherein the one or more L1 measurement configurations each comprise a second indication that the L1 measurement configuration is to be used to perform measurements to generate ML / AI model training data.21 . The method of embodiment 20, wherein the second indication comprises a flag in the L1 measurement configuration.22. The method of embodiment 20, wherein the second indication comprises one or more empty fields inthe L1 measurement configuration that are associated with normal measurement collection. The method of embodiment 20, wherein the second indication comprises one or more non-empty fields in the L1 measurement configuration that are associated with generating ML / AI model training data. The method of embodiment 18 to 23, wherein the L3 measurement reporting configuration comprises the first indication. The method of embodiment 24, wherein the first indication comprises an index to the L1 measurement configuration. The method of embodiment 24, wherein the first indication is that the L3 measurement reporting configuration comprises the one or more L1 measurement configurations. The method of embodiment 18 to 23, wherein the first indication comprises an indication in both the L1 measurement configuration and the L3 measurement reporting configuration of a first functionality of the ML / AI model. The method of any one of embodiment 20 to 23, wherein the first indication comprises: an indication of one or more serving cells on which to collect training data in the L3 measurement reporting configuration, wherein the one or more L1 measurement configurations are each associated with at least one of the one or more serving cells. The method of any one of embodiment 18 to 23, wherein the first indication is that the L1 measurement configuration and the L3 measurement reporting configuration are transmitted in the same message. The method of any one of embodiments 18 to 29, wherein the L3 measurement reporting configuration comprises one of: an indication of one or more SSBs to collect ML / AI model training data on; an indication of one or more Channel State Information Reference Signals (CSI-RSs) to collect ML / AI model training data on; and an indication of one or more serving cells on which to collect ML / AI model training data.31 . A method performed by a second network node for enabling Machine Learnin g / Artif icial Intelligence, ML / AI, model training data collection for use in training an ML / AI model in a network, the method comprising: transmitting one or more layer 1, L1 , measurement configurations to a first network node, wherein each L1 measurement configuration comprises a second indication that the L1 measurement configuration is to be used to perform measurements to generate ML / AI model training data.32. The method of embodiment 31, wherein the second indication comprises a flag in the L1 measurement configuration.33. The method of embodiment 31, wherein the second indication comprises one or more empty fields in the L1 measurement configuration that are associated with normal measurement collection.34. The method of embodiment 31 , wherein the second indication comprises one or more non-empty fields in the L1 measurement configuration that are associated with generating ML / AI model training data.35. The method of any of the previous embodiments, further comprising: obtaining user data; and forwarding the user data to a host or a user equipment.Group C Embodiments36. A user equipment for enabling Machine Learning / Artificial Intelligence, ML / AI, model training data collection for use in training an ML / AI model in a network, 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.37. A network node for enabling Machine Learning / Artificial Intelligence, ML / AI, model training data collection for use in training an ML / AI model in a network, 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.38. A user equipment (UE) for enabling Machine Learning / Artificial Intelligence, ML / AI, model training data collection for use in training an ML / AI model in a network, 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.

Claims

CLAIMS1 . A method performed by a user equipment for enabling machine learning / Artificial Intelligence, ML / AI, model training data collection for use in training an ML / AI, model in a network, the method comprising: receiving (202) one or more Layer 1, L1 measurement configurations; receiving (204) a Layer 3, L3, measurement reporting configuration; obtaining (206) a first indication that the one or more L1 measurement configurations are to be used to perform measurements to be reported using the L3 measurement reporting configuration; performing (208) measurements according to the one or more L1 measurement configurations to generate the ML / AI model training data, and reporting (210) the ML / AI model training data according to the L3 measurement reporting configuration.

2. The method of claim 1 , wherein the one or more L1 measurement configurations each comprise a second indication that the L1 measurement configuration is to be used to perform measurements to generate ML / AI model training data.

3. The method of claim 2 wherein the second indication comprises one or more of: a flag in the L1 measurement configuration; one or more empty fields in the L1 measurement configuration that are associated with normal measurement collection; one or more non-empty fields in the L1 measurement configuration that are associated with generating ML / AI model training data.

4. The method of claim 2 to 3, further comprising initiating the step of performing measurements responsive to receiving the L3 measurement reporting configuration.

5. The method of claim 4, further comprising initiating the step of performing measurements further responsive to one or more conditions in the L3 measurement reporting configuration being met.

6. The method of claim 2 to 3, further comprising, for each L1 measurement configuration, initiating the step of performing measurements according to the L1 measurement configuration responsive to receiving the L1 measurement configuration.

7. The method of claim 6 wherein the one or more L1 measurement configuration are indicated as being for ML / AI training purposes, the method further comprising storing the measurements performed according to the one or more L1 measurement configurations for ML / AI model training purposes.

8. The method of claim 6 or 7 , further comprising initiating the step of performing measurements further responsive to one or more conditions in the L1 measurement configuration being met.

9. The method of claim 1 to 8, wherein the L3 measurement reporting configuration comprises the first indication.

10. The method of claim 9, wherein the first indication comprises: an index to the L1 measurement configuration; or that the L3 measurement reporting configuration comprises the one or more L1 measurement configurations.

11. The method of claim 1 to 8, wherein the first indication comprises an indication in both the L1 measurement configuration and the L3 measurement reporting configuration of a functionality of the ML / AI model.

12. The method of any one of claim 2 to 8, wherein the first indication comprises: an indication of one or more serving cells or beams on which to collect training data in the L3 measurement reporting configuration, wherein the one or more L1 measurement configurations are each associated with at least one of the one or more serving cells or beams.

13. The method of any one of claim 1 to 8, wherein the first indication is that the L1 measurement configuration and the L3 measurement reporting configuration are received in the same message.

14. The method of any one of claims 1 to 13, wherein the L3 measurement reporting configuration comprises one of: an indication of one or more SSBs to collect ML / AI model training data on; an indication of one or more Channel State Information Reference Signals (CSI-RSs) to collect ML / AI model training data on; and an indication of one or more serving cells on which to collect ML / AI model training data; beam .

15. The method of any one of claims 1 to 14 wherein the L3 measurement reporting configuration comprises an indication to release / suspend one or more other L1 measurement configurations.

16. The method of any one of claims 1 to 15, wherein the ML / AI model training data is reported over Radio Resource Control, RRC, signalling.

17. The method of any one of claims 1 to 16 wherein the L3 measurement reporting configuration and the one or more L1 measurement configurations are received in the same RRCReconfiguration message.

18. A method performed by a first network node for enabling Machine Learning / Artificial Intelligence, ML / AI, model training data collection for use in training an ML / AI model in a network, the method comprising: transmitting (302) one or more Layer 1 , L1, measurement configurations to the UE; transmitting (304) a Layer 3 measurement reporting configuration to the UE; providing (306) , to the UE, a first indication that the one or more L1 measurement configurations are to be used to perform measurements to be reported using the L3 measurement reporting configuration; and receiving (308) a report comprising the ML / AI model training data according to the L3 measurement reporting configuration.

19. The method of claim 18, further comprising: receiving each of the one or more L1 measurement configurations from a second network node.

20. The method of claim 18 or 19, wherein the one or more L1 measurement configurations each comprise a second indication that the L1 measurement configuration is to be used to perform measurements to generate ML / AI model training data.

21. The method of claim 20, wherein the second indication comprises one of: a flag in the L1 measurement configuration; one or more empty fields in the L1 measurement configuration that are associated with normal measurement collection; one or more non-empty fields in the L1 measurement configuration that are associated with generating ML / AI model training data.

22. The method of claim 18 to 21 , wherein the L3 measurement reporting configuration comprises the firstindication.

23. The method of claim 22, wherein the first indication comprises: an index to the L1 measurement configuration; or that the L3 measurement reporting configuration comprises the one or more L1 measurement configurations.

24. The method of claim 18 to 21 , wherein the first indication comprises an indication in both the L1 measurement configuration and the L3 measurement reporting configuration of a first functionality of the ML / AI model.

25. The method of any one of claim 20 to 21, wherein the first indication comprises: an indication of one or more serving cells or beams on which to collect training data in the L3 measurement reporting configuration, wherein the one or more L1 measurement configurations are each associated with at least one of the one or more serving cells or beams.

26. The method of any one of claim 18 to 21 , wherein the first indication is that the L1 measurement configuration and the L3 measurement reporting configuration are transmitted in the same message.

27. The method of any one of claims 18 to 26, wherein the L3 measurement reporting configuration comprises one of: an indication of one or more SSBs to collect ML / AI model training data on; an indication of one or more Channel State Information Reference Signals (CSI-RSs) to collect ML / AI model training data on; and an indication of one or more serving cells on which to collect ML / AI model training data.

28. The method of any one of claims 18 to 27 wherein the L3 measurement reporting configuration comprises an indication to release / suspend one or more other L1 measurement configurations.

29. The method of claim 18 to 28, wherein the report comprising the ML / AI model training data is received over Radio Resource Control, RRC, signalling.

30. The method of any one of claims 18 to 29 wherein the L3 measurement reporting configuration and the one or more L1 measurement configurations are transmitted in the same RRCReconfiguration message.

31. A method performed by a second network node for enabling Machine Learn i n g / Artificial Intelligence, ML / AI, model training data collection for use in training an ML / AI model in a network, the method comprising: transmitting (401) one or more layer 1 , L1 , measurement configurations to a first network node, wherein each L1 measurement configuration comprises a second indication that the L1 measurement configuration is to be used to perform measurements to generate ML / AI model training data.

32. The method of claim 31, wherein the second indication comprises one of: a flag in the L1 measurement configuration; one or more empty fields in the L1 measurement configuration that are associated with normal measurement collection; one or more non-empty fields in the L1 measurement configuration that are associated with generating ML / AI model training data.

33. A user equipment for enabling Machine Learning / Artificial Intelligence, ML / AI model training data collection for use in training an ML / AI model in a network, the user equipment comprising processing circuitry and memory, the memory containing instructions executable by the processing circuitry whereby the user equipment is operable to: receive one or more Layer 1 , L1 measurement configurations; receive a Layer 3 measurement reporting configuration; obtain a first indication that the one or more L1 measurement configurations are to be used to perform measurements to be reported using the L3 measurement reporting configuration; perform measurements according to the one or more L1 measurement configurations to generate the ML / AI model training data, and report the ML / AI model training data according to the L3 measurement reporting configuration.

34. A user equipment as claimed in claim 33 wherein the memory contains further instructions executable b the processing circuitry whereby the user equipment is operable to perform the method as claimed in any one of claims 2 to 17.

35. A first network node for enabling Machine Learning / Artificial Intelligence, ML / AI, model training data collection for use in training an ML / AI model in a network, the first network node comprising processing circuitry and memory, the memory containing instructions executable by the processing circuitry whereby the first network node is operable to: transmit one or more Layer 1, L1 , measurement configurations to the UE;transmit a Layer 3 measurement reporting configuration to the UE; provide, to the UE, a first indication that the one or more L1 measurement configurations are to be used to perform measurements to be reported using the L3 measurement reporting configuration; and receive a report comprising the ML / AI model training data according to the L3 measurement reporting configuration.

36. The first network node as claimed in claim 35, wherein the memory contains further instructions executable by the processing circuitry whereby the first network node is operable to perform the method as claimed in any one of claims 19 to 30.

37. A second network node for enabling Machine Learning / Artificial Intelligence, ML / AI, model training data collection for use in training an ML / AI model in a network, the second network node comprising processing circuitry and memory, the memory containing instructions executable by the processing circuitry whereby second first network node is operable to: transmit one or more layer 1 , L1 , measurement configurations to a first network node, wherein each L1 measurement configuration comprises a second indication that the L1 measurement configuration is to be used to perform measurements to generate ML / AI model training data.

38. The second network node as claimed in claim 37, wherein the memory contains further instructions executable by the processing circuitry whereby the second network node is operable to perform the method as claimed in claim 32.

Images