Methods for UE to determine purpose of sets of resources included in a CSI reporting configuration

Abstract

Systems and methods are disclosed herein that enable a User Equipment (UE) to determine a purpose for which a set of resources is to be used. In one embodiment, a method performed by a UE comprises receiving a Channel State Information (CSI) reporting configuration from a network node, the CSI reporting configuration comprising information that configures a certain set of resources. The method further comprises determining a purpose for which the certain set of resources is to be used from among a plurality of purposes, based on an implicit or explicit indication. In this manner, the UE is enabled to determine the purpose of a CSI measurement or reporting configuration and, By knowing the purpose of the CSI configuration, the UE can apply the correct procedures for that purpose.

Description

METHODS FOR UE TO DETERMINE PURPOSE OF SETS OF RESOURCES INCLUDED IN A CSI REPORTING CONFIGURATION RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Patent Application No.63 / 754,791, filed February 6, 2025, the disclosure of which is hereby incorporated herein by reference in its entirety.TECHNICAL FIELD

[0002] The present disclosure relates to a cellular communications system and, more specifically, to Channel State Information (CSI) reporting configuration in a cellular communications system in which User Equipment (UE)-assisted Artificial Intelligence (AI) / Machine Learning (ML) based measurements can be used.BACKGROUND

[0003] Artificial Intelligence (Al) and Machine Learning (ML) have been investigated, both in academia and industry, as promising tools to optimize the design of the air-interface in wireless communication networks. Example use cases include using autoencoders for Channel State Information (CSI) compression to reduce the feedback overhead and improve channel prediction accuracy; using deep neural networks for classifying Line-of-Sight (LOS) and Non-LOS (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; and using deep reinforcement learning to learn an optimal precoding policy for complex Multiple Input Multiple Output (MIMO) precoding problems.

[0004] In 3rd Generation Partnership Project (3GPP) New Radio (NR) standardization work, a new Release 18 study item on AI / ML for the NR air interface started in May 2022. This study item explored the benefits of augmenting the air-interface with features enabling improved support of AI / ML based algorithms for enhanced performance and / or reduced complexity / overhead. Through studying a few selected use cases (CSI feedback, beam management, and positioning), this study item aims at laying the foundation for future air-interface use cases leveraging AI / ML techniques. The analysis carried out during Release 18 is now considered in the context of a Release 19 work item. Additionally, during Release 19, a new study item addressing AI / ML for mobility has been approved. In the context of this new study item, 3GPP will investigate methods for cell-level measurement predictions, and mobility event predictions (e.g. Radio Link Failure (RLF), handover failure, mobility-related events predictions such as A3 / A5).

[0005] An important part in Al development and operation is the Lifecycle Management (LCM) of the AI / ML model (e.g., model training, model deployment, model inference, model monitoring, model updating) and AI / ML functionality.

[0006] In the NR Release 18 AI / ML for NR air interface study item, the LCM procedure is studied for the case that an AI / ML model has a model identifier (ID) with associated information and / or for the case that a given functionality is provided by some AI / ML operations.

[0007] Two types of LCM operations were studied in NR Release 18, namely, functionalitybased LCM and model-ID based LCM.- Functionality refers to an AI / ML-enabled Feature / Feature Group (FG) enabled by configuration(s), where configuration(s) is(are) supported based on conditions indicated by User Equipment (UE) capability. Correspondingly, functionality-based LCM operates based on, at least, one configuration of an AI / ML-enabled Feature / FG or specific configurations of an AI / ML-enabled Feature / FG. In functionality -based LCM, the network indicates activation / deactivation / fallback / switching of AI / ML functionality via 3GPP signalling (e.g., Radio Resource Control (RRC), Medium Access Control (MAC) Control Element (CE), Downlink Control Information (DCI)). Models may not be identified at the network, and the UE may perform model-level LCM. Whether and how much awareness / interaction the network should have about model-level LCM requires further study. For functionality identification, there may be either one or more than one functionalities defined within an AI / ML-enabled feature, whereby AI / ML-enabled Feature refers to a Feature where AI / ML may be used.In model-ID-based LCM, models are identified at the network, and the network / UE may activate / deactivate / select / switch individual AI / ML models via model ID. A model may be associated with specific configurations / conditions associated with UE capability of an AI / ML-enabled Feature / FG and additional conditions (e.g., scenarios, sites, and datasets) as determined / identified between the UE-side and network-side. An AI / ML model identified by a model ID may be logical, and how it maps to physical AI / ML model(s) may be up to implementation.

[0008] Figure 1 shows a functional framework that can be used for studying model LCM aspects for different Al for PHY use cases. The general framework consists of the following:- Data Collection: Data Collection is a function that provides input data to the Model Training, Management, and Inference functions.o Training Data: Data needed as input for the AI / ML Model Training function.o Monitoring Data: Data needed as input for the Management of AI / ML models or AI / ML functionalities.o Inference Data: Data needed as input for the AI / ML Inference function.Model Training: Model Training is a function that performs AI / ML model training, validation, and testing which may generate model performance metrics which can be used as part of the model testing procedure. The Model Training function is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on Training Data delivered by a Data Collection function, if required.o Trained / Updated Model: In case of having a Model Storage function, this is used to deliver trained, validated, and tested AI / ML models to the Model Storage function, or to deliver an updated version of a model to the Model Storage function. - Management: Management is a function that oversees the operation (e.g., selection / (de)activation / switching / fallback) and monitoring (e.g., performance) of AI / ML models or AI / ML functionalities. This function is also responsible for making decisions to ensure the proper inference operation based on data received from the Data Collection function and the Inference function.o Management Instruction: Information needed as input to manage the Inference function. Concerning information may include selection / (de)activation / switching of AI / ML models or AI / ML-based functionalities, fallback to non- AI / ML operation (i.e., not relying on inference process), etc.o Model Transfer / Delivery Request: Used to request model(s) to the Model Storage function.o Performance Feedback / Retraining Request: Information needed as input for the Model Training function, e.g., for model (re)training or updating purposes.Inference: Inference is a function that provides outputs from the process of applying AI / ML models or AI / ML functionalities, using the data that is provided by the Data Collection function (i.e., Inference Data) as an input. The Inference function is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on Inference Data delivered by a Data Collection function, if required.o Inference Output: Data used by the Management function to monitor the performance of AI / ML models or AI / ML functionalities.- Model Storage: Model Storage is a function responsible for storing trained / updated models that can be used to perform the Inference function.o Note: The Model Storage function in Figure 1 is only intended as a reference point (if any) when applicable for protocol terminations, model transfer / delivery, and related processes. It should be stressed that its purpose does not encompass restricting the actual storage locations of models. Therefore, the specification impact of all data / information / instruction flows (i.e., the arrows in Figure 1) to / from this function should be studied case by case.o Model Transfer / Delivery: Used to deliver an AI / ML model to the Inference function.

[0009] The AI / ML use cases considered in beam management which will be standardized as part of the 3GPP Release 19 work item consist of spatial beam prediction and temporal beam prediction. The core idea of AI / ML applied to the Radio Access Network (RAN) is to enable a UE to predict / infer certain performances or certain measurements on a given set A of resources based on experienced performances or performed measurements on a set B of resources, wherein the resources could be for example associated to reference signals (Synchronization Signal (SS) / Physical Broadcast Channel (PBCH) Block (SSB) / CSI Reference Signal (CSI-RS) or Positioning Reference Signal (P-RS) resources) or frequencies, depending on the specific AI / ML use case. For example, in the case of AI / ML-based beam management (which is considered by 3GPP in the context of Release 19), the use case is to predict / infer the “best” beam (or beams) from a Set A of beams (SSB / CSI-RS resources) using measurement results from another Set B of beams (SSB / CSI-RS resources). In particular, according to 3GPP Technical Report (TR) 38.843 VI 8.0.0, the spatial-domain beam prediction for Set A of beams is based on measurement results of Set B of beams, whereas the temporal beam prediction for Set A of beams is based on the historic measurement results of Set B of beams. Hence, the radio measurements on the Set B of resources would be the input of an AIML model / functionality, whereas the radio measurements on the set A of resources would be the output of the AIML model / functionality.

[0010] Regarding Set A and Set B of beams, the following two examples illustrate some scenarios that were studied in Release 18:- SetB is a subset of a Set A. For example, Set A is a set of 8 SSB / CSI-RS beams shown in Figure 2 (both light and dark circles). The UE measures Set B (the 4 beams indicated by dark circles). The AI / ML model should predict the best beam (or beams) in Set A using only measurements from Set B. In other words, Figure 2 illustrates an example where Set B is a subset of Set A. The figure illustrates a grid-of-beam type radiation pattern: Each row (resp. column) depicts a certain zenith (resp. azimuth) angle from the antenna array. Set A has 8 beams and Set B has 4 beams (indicated by dark circles).- Set A and Set B correspond to two different sets of beams. For example, Set A is a set of 30 narrow CSI-RS beams, and Set B is a set of 8 wide SSB beams, as shown in Figure 3. The UE measures beams in Set B and the AI / ML model should predict the best beam(s) from Set A.

[0011] The beam prediction can be performed in the gNodeB (gNB) and in the UE, and the gain is twofold. From the UE point of view, the UE would be able to generate good radio measurement estimations without really measuring certain resources, thereby saving energy, whereas from the gNB point of view, the gNB can get good radio measurements estimation from the UE without providing the measuring resources, thereby limiting the overhead over the airinterface.SUMMARY

[0012] Systems and methods are disclosed herein that enable a User Equipment (UE) to determine a purpose for which a set of resources is to be used. In one embodiment, a method performed by a UE comprises receiving a Channel State Information (CSI) reporting configuration from a network node, the CSI reporting configuration comprising information that configures a certain set of resources. The method further comprises determining a purpose for which the certain set of resources is to be used from among a plurality of purposes, based on an implicit or explicit indication. In this manner, the UE is enabled to determine the purpose of a CSI measurement or reporting configuration and, By knowing the purpose of the CSI configuration, the UE can apply the correct procedures for that purpose.

[0013] In one embodiment, the plurality of purposes comprises any two or more of the following purposes: as a set A of resources related to an inference configuration for an Artificial Intelligence (Al) or Machine Learning (ML) (i.e., AI / ML) model / functionality; as a set B of resources related to an inference configuration for an AI / ML model / functionality; as resources for non-AI / ML related channel measurements; as a set A of resources related to a data collection configuration for an AI / ML model / functionality training; as a set B of resources related to a data collection configuration for an AI / ML model / functionality training; and as a set of resources related to the monitoring of inference performed according to an AI / ML model / functionality. In one embodiment, a set A comprises a set of resources for which the UE should determine radio measurement predictions, and a set B comprises a set of resources in which the UE should perform radio measurements in order to determine the radio measurement predictions on the set A.

[0014] In one embodiment, the implicit or explicit indication is associated to one or more of the plurality of purposes.

[0015] In one embodiment, the implicit or explicit indication of the purpose(s) of the certain set of resources is comprised in the CSI reporting configuration.

[0016] In one embodiment, the indication is an explicit indication. In one embodiment, the explicit indication of the purpose(s) of the certain set of resources is a flag or a dedicated information element that explicitly indicates one of the plurality of purposes.

[0017] In one embodiment, the indication is an implicit indication. In one embodiment, the implicit indication is the presence or absence of a certain parameters, value of a parameter, or information element in the CSI reporting configuration. In another embodiment, the implicit indication is: the presence or absence of an explicit indication associated to a first purpose implicitly indicates that purpose of the certain set of resources is a second purpose or one or more other purposes or a report type or report quantity comprised in the CSI reporting configuration, where this report type or report quantity implicitly indicates one of the plurality of purposes.

[0018] In one embodiment, reporting parameters such as report quantity and report type included in one CSI reporting configuration are different depending on the purpose of the set of resources.

[0019] In one embodiment, the implicit or explicit indication comprises a report quantity parameter comprised in the CSI reporting configuration. In one embodiment, the CSI reporting configuration comprises first information that identifies resources for channel prediction and second information that identifies resources for channel measurement. Further, when the report quantity parameter is set to any one of ‘cri-RSRP’ or ‘ssb-Index-RSRP’, the report quantity parameter thereby indicates that one or more set B of resources indicated by one of the first and second information are for channel measurements and that one or more set A of resources indicated by one of the first and second information are for channel measurement predictions. In one embodiment, when the report quantity parameter is set to a value of none, the report quantity parameter thereby indicates that one or more set B of resources indicated by one of the first and second information are for a first set of channel measurements and that one or more set A of resources indicated by one of the first and second information are for a second set of channel measurements.

[0020] In another embodiment, the CSI reporting configuration comprises first information that identifies resources for channel prediction and second information that identifies resources for channel measurement. Further, when the report quantity parameter is set to a value of none, the report quantity parameter thereby indicates that one or more set B of resources indicated by one of the first and second information are for a first set of channel measurements and that one or moreset A of resources indicated by one of the first and second information are for a second set of channel measurements.

[0021] In one embodiment, the method further comprises performing one or more actions based on the determined purpose of the certain set of resources and the CSI reporting configuration.

[0022] Corresponding embodiments of a UE are also disclosed. In one embodiment, a UE comprises a communication interface comprising a transmitter and a receiver. The UE further comprises processing circuitry associated with the communication interface. The processing circuitry is configured to cause the UE to receive a CSI reporting configuration from a network node, the CSI reporting configuration comprising information that configures a certain set of resources. The processing circuitry is further configured to cause the UE to determine a purpose for which the certain set of resources is to be used from among a plurality of purposes, based on an implicit or explicit indication.

[0023] Embodiments of a method performed by a network node are also disclosed. In one embodiment, a method performed by a network node comprises transmitting a CSI reporting configuration to a UE, wherein the CSI reporting configuration comprises information that configures a certain set of resources and an implicit or explicit indication of a purpose for which the certain set of resources is to be used and the purpose is from among a plurality of purposes.

[0024] Corresponding embodiments of a network node are also disclosed. In one embodiment, a network node comprises processing circuitry configured to cause the network node to transmit a CSI reporting configuration to a UE, wherein the CSI reporting configuration comprises information that configures a certain set of resources and an implicit or explicit indication of a purpose for which the certain set of resources is to be used and the purpose is from among a plurality of purposes.BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the disclosure, and together with the description serve to explain the principles of the disclosure.

[0026] Figure 1 shows a functional framework that can be used for studying model Life Cycle Management (LCM) aspects for different Artificial Intelligence (AI) / Machine Learning (ML) for physical layer (PHY) use cases.

[0027] Figure 2 illustrates an example where Set B of beams for channel measurement is a subset of Set A of beams for measurement prediction.

[0028] Figure 3 illustrates an example where Set B of beams for channel measurement and Set A of beams for measurement prediction are different sets of beams.

[0029] Figure 4 illustrates a Channel State Information (CSI) report configuration in which Set A and Set B are signaled.

[0030] Figures 5A and 5B illustrate an exemplary implementation of the CSI report configuration in accordance with an embodiment of the present disclosure.

[0031] Figure 6 illustrates another exemplary embodiment in which Set A and Set B for AI / ML purposes are signaled in an extension of a legacy CSI report configuration.

[0032] Figure 7 illustrates another exemplary embodiment that is similar to that of Figure 6 but where the Set B configuration in the report extension also points to a legacy CSI report configuration.

[0033] Figure 8 illustrates another exemplary embodiment in which Set A and Set B are signaled in a CSI report configuration of a new type, fully independent of the legacy CSI report configuration.

[0034] Figures 9A and 9B illustrate another exemplary implementation of the CSI report configuration in accordance with an embodiment of the present disclosure.

[0035] Figures 10A and 10B illustrate yet another exemplary implementation of the CSI report configuration in accordance with an embodiment of the present disclosure.

[0036] Figure 11 illustrates the operation of a User Equipment (UE) and a network node, in accordance with the embodiments of the present disclosure.

[0037] Figure 12 shows an example of a communication system in accordance with some embodiments.

[0038] Figure 13 is another example of a communication system according to some embodiments.

[0039] Figure 14 shows an exemplary embodiment of a wireless device, which may be configured to operate in communication system of Figure 12 or in communication system of Figure 13.

[0040] Figure 15 shows a network node in accordance with some embodiments.

[0041] Figure 16 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized.DETAILED DESCRIPTION

[0042] The embodiments set forth below represent information to enable those skilled in the art to practice the embodiments and illustrate the best mode of practicing the embodiments. Uponreading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the disclosure and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure.

[0043] 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.

[0044] There currently exist certain challenge(s). For Artificial Intelligence (AI) / Machine Learning (ML) based beam management, it has been agreed in 3rdGeneration Partnership Project (3GPP) Radio Access Network (RAN) Working Group (WG) 1 (i.e., RANI) that the User Equipment (UE) will be configured with reference signals for set A and set B using the existing Channel State Information (CSI) framework. The following agreements have been reached in RANI discussions so far:Agreement: For UE-sided model at least for BM Case-1, for inference results report • Two resource sets can be configured for Set A and Set B separately in the CSI report configuration for the reporto FFS whether support only resource set for Set B is configured • UE performs measurement on the resource set for Set B for inference, and UE is not expected to measure resource set for Set A for inference,• The beam information in the inference report refers to the resource set for Set A Agreement: For both BM-Case 1 and BM-Case 2, for UE-sided model for inference, when Set A and Set B are configured within CSI report configuration,• Two CSI-ResourceConfigld s are configured for Set A and Set B separately

[0045] For beam management with UE-sided AI / ML models, it is not yet clear how the UE can determine whether a given CSI measurement or reporting configuration, e.g., an instance of the Information Element (IE) CSI-ReportConfig, (including information on Set A and Set B) is intended for inference, or performance monitoring, or UE-side data collection for training. In other words, it is not clear as to whether the CSI reporting configuration includes an inference configuration and how the fields and / or IE(s) in the CSI reporting configuration are to be interpreted by the UE. This ambiguity needs to be solved so the UE can follow the correct procedures in response to an inference configuration (i.e. for performing inferences and report them). Additionally, the CSI reporting configuration is also used for legacy non-AI / ML purposes, i.e. to provide the UE with CSI Reference Signal (CSLRS) resources for a serving cell related to which the UE should transmit the CSI reports (e.g. on Physical Uplink Control Channel (PUCCH)or Physical Uplink Shared Channel (PUSCH)) to aid next-generation Node B (gNB) scheduling decisions. How to distinguish a CSI reporting configuration for an AI / ML purpose from that for a non-AI / ML purpose is another problem to be solved.

[0046] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. Systems and methods are disclosed for a UE to determine a purpose, out of a plurality of purposes, of one or more sets of resources included in a CSI reporting configuration or CSI measurement configuration. The determination can be based on explicit or implicit indications included in the CSI reporting configuration. In one embodiment, the purpose is indicated via an explicit indication, where this explicit indication may be a flag included in the CSI reporting configuration or an IE including a list of resources explicitly associated to one of the plurality of purposes. In another embodiment, the purpose is indicated via an implicit indication. An example of this implicit indication is as follows: if the UE determines from the presence or absence of a parameter (e.g., an information element or a value of a parameter), this implicitly indicates the purpose of the radio resource set.

[0047] The plurality of purposes for a set of resources from which the purpose of the set(s) of resources includes, for example, the purpose of AI / ML model or functionality (model / functionality) inference, the purpose of data collection for AI / ML model / functionality training at the UE, the purpose of monitoring of a performances of the AI / ML model / functionality inference, and the purpose of conventional non-AI / ML radio measurements. Note that this plurality of purposes (also referred to herein as a set of purposes) from which the purpose of a set of resources can be indicated is only an example. As another example, the set of purposes includes any combination of two or more of the aforementioned purposes.

[0048] Some exemplary embodiments of the present disclosure, denoted as “Embodiments Al, A2, etc.”, are as follows:

[0049] AL A method performed by a UE comprises:• receiving a CSI reporting configuration from a network node, the CSI reporting configuration comprising information that configures a certain set of resources;• determining a purpose(s) for which the certain set of resources configured by the information comprised in the CSI reporting configuration is to be used from a plurality of purposes, based on an implicit or explicit indication (e.g., received from the network node, e.g., in the CSI reporting configuration).

[0050] A2. The method of Al, wherein the plurality of purposes comprises any one or more (and preferably any two or more) of the following purposes:• as a set A of resources related to an inference configuration for an AI / ML model / functionality;• as a set B of resources related to an inference configuration for an AI / ML model / functionality;• as resources for non- AI / ML related channel measurements;• as a set A of resources related to a data collection configuration for an AI / ML model / functionality training;• as a set B of resources related to a data collection configuration for an AI / ML model / functionality training;• as a set of resources related to the monitoring of inference performed according to an AI / ML model / functionality.

[0051] A3. The method of Al or A2, wherein the implicit or explicit indication is associated to one or more of the above purposes.

[0052] A4. The method of any of Al to A3, wherein the implicit or explicit indication of the purpose(s) of the certain set of resources is comprised in the CSI reporting configuration.

[0053] A5. The method of any of Al to A4, wherein the indication is an explicit indication.

[0054] A6. The method of A5, wherein the explicit indication of the purpose(s) of the set of resources is a flag or a dedicated information element (including a specific set of resources) that explicitly indicates one of the plurality of purposes.

[0055] A7. The method of any of Al to A4, wherein the indication is an implicit indication.

[0056] A8. Th method of A7, wherein the implicit indication is the presence or absence of a certain parameters, value of a parameter, or information element in the CSI reporting configuration.

[0057] A9. The method of A7, wherein the implicit indication is:• the presence or absence of an explicit indication associated to a first purpose implicitly indicates that purpose of the certain set of resources is a second purpose or one or more other purposes; or• a report type or report quantity comprised in the CSI reporting configuration, where this report type or report quantity implicitly indicates one of the plurality of purposes.

[0058] A10. The method of any of Al to A9, wherein a set A comprises a set of resources for which the UE should determine radio measurement predictions, and a set B comprises a set of resources in which the UE should perform radio measurements in order to determine the radio measurement predictions on the set A.

[0059] All. The method of any of Al to A10, wherein reporting parameters such as report quantity and report type included in one CSI reporting configuration are different depending on the purpose of the set of resources.

[0060] Certain embodiments may provide one or more of the following technical advantage(s). Embodiments of the solution(s) disclosed herein may enable the UE to determine the purpose of a CSI measurement or reporting configuration out of the following purposes: AI / ML inference, AI / ML performance monitoring, AI / ML UE data collection for training, or non- AI / ML (legacy). By knowing the purpose of the CSI configuration, the UE can apply the correct procedures for that purpose, such as measuring the correct resources on which the network actually sends measurement signals, comparing measured values with AI / ML predicted values for the correct set of resources, and reporting the type of value that the network expects from the UE (measured or predicted).

[0061] The teachings of certain embodiments may improve, e.g., data rate, latency, and / or power consumption.

[0062] Now a more detailed description of some exemplary embodiments of the present disclosure will be provided.

[0063] First, embodiments related to configuration of resources for the purpose of Set A / Set B configuration will be described. In this regard, three possible approaches for how to configure Set A and Set B for AI / ML configurations are disclosed herein. These three approaches are described below.

[0064] Approach 1

[0065] Set A and Set B are signaled to a UE in a CSI report configuration as shown in the example of Figure 4. In Figure 4, fields that are present in a legacy CSI report configuration are shown in regular text while the new fields introduced for AI / ML based beam management are shown in bold text. Descriptions of the new fields are provided below.

[0066] The presence of any of the new fields in CSI-ReportConfig (e.g. resourceForChannelPrediction, or referenceReportForPrediction) indicates that the purpose of the CSI-ReportConfig is for AI / ML, i.e. either inference, monitoring, or data collection. Otherwise, legacy behavior can be configured by ensuring that none of the new fields are included in CSL ReportConfig.

[0067] When a CSI-ReportConfig includes both the fields ‘resourcesForChannelMeasurement’ and ‘resourcesForChannelPrediction’, then information on set A and set B of resources are obtained as follows:• ‘resourcesForChannelMeasurement’ points to an identifier (i.e., CSI-ResourceConfigld) of a CSI resource configuration (as specified in CSI-ResourceConfig IE in 3GPP Technical Specification (TS) 38.331 V18.4.0) that contains one or more set A of resources; the one or more set A of resources can be either set(s) of Non-Zero Power (NZP) CSI-RS resources or set(s) of Synchronization Signal (SS)ZPhysical Broadcast Channel (PBCH) Block (SSB) resources. In another example, the ‘resourcesForChannelMeasurement’ represents one or more set B of resources which can also be either set(s) of NZP CSI-RS resources or set(s) of SSB resources• ‘resourcesForChannelPrediction’ points to an identifier of a second CSI resource configuration that contains one or more set B of resources (if ‘resourcesForChannelMeasurement’ points to set A); the one or more set B of resources can be either set(s) of NZP CSI-RS resources or set(s) of SSB resources. In another example (in case the ‘resourcesForChannelMeasurement’ represents one or more set B of resources) the ‘resourcesForChannelPrediction’ points to an identifier of a second CSI resource configuration that contains one or more set A of resources.

[0068] According to this embodiment, if resourcesForChannelPrediction is included in the CSI reporting configuration, then resourcesForChannelMeasurement points to an identifier of a CSI resource configuration that contains one or more set A of resources (or set B of resources in another embodiment), say for a first purpose, and the resourcesForChannelPrediction points to an identifier of a CSI resource configuration that contains one or more set B of resources (or set A of resources in another embodiment), say for a second purpose. Otherwise, if the resourceForChannelPrediction is absent (i.e. not included in the CSI reporting configuration), then resourcesForChannelMeasurement points to an identifier of a CSI resource configuration that contains one or more of resources used for non-AI.ML related purposes (e.g. resources for conventional non-AIML channel measurements), say for a third purpose. Hence the resource sets identified by the field resourcesForChannelMeasurement can be associated to a first or third purpose, depending on the presence or absence in the same CSI reporting configuration of a second field resourceForChannelPrediction associated to a second purpose.

[0069] Accordingly, one or more of the reporting configuration parameters included in the same reporting configuration, e.g. the reportConfigType, or the reportQuantity, can be associated to the reporting of measurements (or predictions) associated to resources of a first or second purpose. The reportConfigType, or the reportQuantity may be associated to specific procedures related to the reporting of radio measurements results associated to predictions, such as the predicted Reference Signal Received Power (RSRP) in the CSI-RS / SSB.

[0070] As an example, this embodiment can be captured as per the signaling implementation in 3GPP TS 38.331 illustrated in Figures 5A and 5B (new aspects are shown in bold text), wherein the resourcesForChannelMeasurement is assumed to represent the set A of resources if the resourceForChannelPrediction is included in the same reporting configuration, and wherein the resourceForChannelPrediction represents the set B of resources.

[0071] In another embodiment, when ‘reportQuantity’ in the CSI-ReportConfig is set to any one of ‘cri-RSRP’ or ‘ssb-Index-RSRP’, then the UE performs one or more of the following actions:• the UE performs a first set of channel measurements on the one or more set B of resources pointed to by one of the identifiers ‘resourcesForChannelPrediction’ or ‘resourcesForChannelMeasurement’ in the same CSI-ReportConfig,• the UE predicts the channel on one or more set A resources pointed to by one of the identifiers resourceForChannelMeasurement or ‘resourcesForChannelPrediction’ in the same CSI-ReportConfig, and,o In one special case of this step, in case a parameter “predictionDelay” is configured, the UE is expected to predict the set A channel for a reference resource as indicated by the prediction delay. The predictionDelay parameter could comprise a higher layer parameter, or be part of the reportQuantity, or codebook configuration. • reports the predicted one or more set A resources to the network. In some embodiments, the UE reports the requested ‘cri-RSRP’ or ‘ssb-Index-RSRP’. In some embodiments, the predicted Reference Signal Received Power (RSRP) of the corresponding predicted one or more set A resources are also reported to the network.• In one embodiment of this approach, in case the UE only can predict a CSI-RS Resource Indicator (CRI) and not RSRP. The UE reports the CRI value and sets the corresponding RSRP value to a fixed value or reserved value. For example, the fixed value can be the lowest or highest possible quantized RSRP values that the UE can report.

[0072] In a set of embodiments, related to approach 1, the UE receives an RRC Reconfiguration including a CSI reporting configuration (e.g. instance of a IE CSI-ReportConfig) and determines whether the CSI reporting configuration is configuring the UE for reporting one or more CSI measurements (e.g. Channel Quality Indicator (CQI), Layer 1 (LI) RSRP, etc.) or for reporting one or more prediction(s) / inferences (e.g. time domain and / or spatial domain prediction of Ll-RSRP). In other words, the UE determines whether the CSI reporting configuration comprises (and / or corresponds to) an inference configuration.

[0073] The UE determines that the CSI reporting configuration (e.g. instance of a IE CSI-ReportConfig) comprises (and / or corresponds to) an inference configuration when the CSI reporting configuration includes a field (parameter and / or information element) pointing to a configuration of a set of resource(s) assumed by the UE to be transmitted by the network (e.g. beams, reference signal identifiers / indexes, SSB indexes, CSI-RS resource indicators), such as a CSI-ResourceConfigld, or assumed to be transmitted by the network upon reception by the UE of an activation command (e.g. received via a lower layer signaling, such as MAC CE and / or DCI), wherein the set of resources are to be used by the UE to be measured so these measurements performed on these resources are to be used as input to an Al / AML model and / or inference function, so the UE can perform one or more predictions and / or inferences as outputs.- In one option, the field (parameter and / or information element) pointing to the configuration of the set of resource(s) (e.g. beams, reference signal identifiers / indexes, SSB indexes, CSI-RS resource indicators) may correspond to a field called ‘resourcesForChannelPrediction’ of IE CSI-ResourceConfigld (or one or more IES in which the field is nested within). In other words, it is the presence of that reference to Set B which makes the UE to determine that a CSI reporting configuration comprises or / and corresponds to an inference configuration.- In other words, the set of resource referred herein may also be called resources for channel prediction, since the UE uses measurements based on them to perform predictions (on resources identified by another parameter / field e.g. ‘resourcesForChannelMeasuremenf .

[0074] In another embodiment, related to approach 1, the UE receives an RRC Reconfiguration including a CSI reporting configuration (e.g. instance of a IE CSI-ReportConfig) including the field ‘resourcesForChannelMeasuremenf of IE CSI-ResourceConfigld, and, when the CSI reporting configuration comprises and / or corresponds to an inference configuration, the UE interprets the field ‘resourcesForChannelMeasuremenf of IE CSI-ResourceConfigld as a pointer to a set of resources to be predicted and / or inferred. In other words, in that case, in response to that the UE determined that to be an inference configuration, the UE does not have to measure and / or report measurements on resources indicated by the field ‘resourcesForChannelMeasuremenf of IE CSI-ResourceConfigld, but instead, it needs to perform inferences on them and report them (or consider a subset of them to be reported).

[0075] In another embodiment, related to approach 1, the UE receives an RRC Reconfiguration including a CSI reporting configuration (e.g. instance of a IE CSI-ReportConfig) including a field ‘resourcesForChannelMeasuremenf (of IE CSI-ResourceConfigld) and, when the CSI reporting configuration comprises a field pointing to a set B (e.g.‘resourcesForChannelPrediction’), the UE interprets the field ‘resourcesForChannelMeasuremenf of IE CSI-ResourceConfigld as a pointer to a set of resources to be predicted and / or inferred (e.g. a pointer to a set A).The pointer to a set B may correspond to a pointer or indicator (parameter and / or information element) pointing to a configuration of a set of resource(s) (e.g. beams, reference signal identifiers / indexes, SSB indexes, CSI-RS resource indicators) assumed by the UE to be transmitted by the network (or to be transmitted upon activation e.g. via a lower layer signaling, such as MAC CE and / or DCI) to be measured so these measurements performed on these resources are to be used as input to an AI / AML model and / or inference function, so the UE can perform one or more predictions and / or inferences as outputs. - In other words, when ‘resourcesForChannelPrediction’ is included within the same CSI- ReportConfig, the field ‘resourcesForChannelMeasuremenf identifies a set of resources for which the UE performs radio measurement predictions, based on measurements performed on the resources included in ‘resourcesForChannelPrediction’ within the same CSI-ReportConfig.

[0076] In one embodiment, the UE receives multiple CSI reporting configuration(s) (e.g. multiple instances of the IE CSI-ReportConfig), each associated to a CSI reporting configuration identifier (e.g. CSI-ReportConfigld) and determines to evaluate the applicability of a subset of the received multiple CSI reporting configuration(s), wherein the subset is the subset for which the CSI reporting configuration includes a field (parameter and / or information element) pointing to a configuration of a set of resource(s) (e.g. beams, reference signal identifiers / indexes, SSB indexes, CSI-RS resource indicators) assumed by the UE to be transmitted by the network (or to be transmitted upon activation e.g. via a lower layer signaling, such as MAC CE and / or DCI) to be measured (e.g. a pointer to a set B, a pointer to a resource configuration in which set B is configured) so these measurements performed on these resources are to be used as input to an AI / ML model and / or inference function, so the UE can perform one or more predictions and / or inferences as outputs.

[0077] Approach 2a

[0078] In this alternative approach, Set A and Set B for AI / ML purposes are signaled in an extension (CSI-ReportConfig-r 19) of a legacy CSI report configuration, as shown in Figure 6 (new elements shown in bold text).• The configuration for Set A (reportForChannelPr ediction) in the extension CSI- ReportConfig-rl9 points to a legacy CSI report configuration and inherits (part of) the configuration from the legacy CSI report configuration.• The configuration for Set B (resourcesForChannelMeasurement) in the extension CSI- ReportConfig-rl9 points to a set of channel measurement resources.• Other needed fields or information elements for AI / ML purposes can be included in the extension CSI-ReportConfig-r 19.

[0079] In this alternative approach, a legacy CSI report configuration can be configured simultaneously for AI / ML and non-AI / ML based CSI reporting. The presence of the extension configuration CSI-ReportConfig-r 19 indicates that the CSI report configuration is intended for an AI / ML purpose. For the configuration of Set A (reportForChannelPrediction) in the extension CSI-ReportConfig-r 19. which inherits the configuration from the legacy CSI report configuration, it needs to be ensured that there is no ambiguity in whether some fields in the legacy CS1-ReportConfig are used for non-AI / ML purposes or for AI / ML purposes. To solve this ambiguity:• New fields can be added in the extension configuration for AI / ML purposes, corresponding to those legacy fields that could cause conflicts if configured at the same time for non-AI / ML purposes, or for which different values may be wanted for AI / ML purposes and non-AI / ML purposes. Some examples are the reportQuantity and reportType from the legacy report, which could refer only to non-AI / ML purposes, and for which the respective reportQuantity-r 19 and reportType-rl9 can be added to the extension report, for AI / ML purposes.• Some fields in the legacy configuration can be repurposed to refer only to the AI / ML purposes, if the configuration extension is present. For instance, a rule can be described in 3 GPP specifications according to which the reporting of the predicted values for Set A for the inference purpose will follow the reportType and reportQuantity indicated in reportForChannelPrediction (set A), as inherited from the legacy configuration.

[0080] If the extension configuration CSI-ReportConfig-r 19 is present, based on further fields (or field values) in the extension configuration, the UE can determine for which specific AI / ML purpose this configuration is intended (inference, or performance monitoring, or training).

[0081] Approach 2b

[0082] This approach is similar to Approach 2a, except that the Set B configuration in the report extension (CSI-ReportConfig-r 19) also points to a legacy CSI report configuration, as shown in Figure 7 (new elements are shown in bold text).

[0083] Either one of the following alternative behavior could be considered in this case:- In this approach, the configured extension CSI report to indicate inference report will follow the reportType and ReportQuantity indicated in reportForChannelPrediction (setA). The reportType and ReportQuantity indicated in reportForChannelMeasurement (set B) is ignored.- In another approach, the configured extension CSI report to indicate inference report will follow the reportType and ReportQuantity indicated in reportForChannelPrediction (set A). A separate CSI report corresponding to the set B measurements is signalled according to the reportType and ReportQuantity indicated in reportForChannelMeasurement (set B).

[0084] Approach 3

[0085] In this approach, Set A and Set B are signaled in a CSI report configuration of a new type (e.g. Prediction-CSI-ReportConfig-r 19), fully independent of the legacy CSI report configuration, as shown in the example in Figure 8. For this, a new list of report configurations is added for AI / ML purposes in CSI-MeasConfig (e.g. prediction-CSI-ReportConfigToAddModList-rl9), in addition to the report configuration list for non-AI / ML purposes (csi-ReportConfigToAdd ModLisf). In this case, the presence of the new CSI report configuration in the new list indicates that this configuration is intended for AI / ML purposes only. As in the previous approaches, the UE can distinguish which exact AI / ML purpose is intended based on which fields / information elements are present, and / or which values they take.

[0086] Now, embodiment related to configuration of resources for the purpose of monitoring will be described. In another embodiment, a resource set can be used for the purpose of monitoring the AIML model / functionality which is operating according to an inference configuration, e.g. a RRC configuration including the resourceForChannelPrediction as per the above embodiments, or including a reportType / Quantity associated to the reporting of radio measurement predictions / inference. For example, if a CSI reporting configuration includes an indication that refers to an inference configuration, then the resourcesForChannelMeasurements included in the said CSI reporting configuration can be associated to the set of resources that the UE measures to evaluate the performances (e.g. the accuracy and / or the applicability) of an AI / ML model / functionality. Further, the UE may determine that the resourcesForChannelMeasurements are intended to be used for the purpose of monitoring by the absence of a field associated to resources for channel prediction.

[0087] Hence by combining this embodiment with the first embodiment, the resource sets identified by the field ‘resourcesForChannelMeasuremenf can be associated to a first purpose (set A of resources related to an inference configuration) or second purpose (set of non-AIML related resources for conventional non-AIML channel measurement reports), or third purpose (set of resources for the monitoring of the AI / ML model / functionality inference) depending on thepresence or absence in the same CSI reporting configuration of a field ‘refToPredictionConfiguration’ associated to the said third purpose.

[0088] According to this embodiment, the reportQuantity and the reportType included in the same CSI-reporting configuration comprising a resource for inference monitoring, are for the reporting of the associated monitoring results.

[0089] An example of this embodiment can captured as the signaling implementation in 3GPP TS 38.331 illustrated in Figures 9A and 9B (new / changed elements shown in bold text), which considers the impact of both the first and second embodiment. According to this example, if the refToPredictionConfiguration is included, then the resourceForChannelMeasurements represent the set of monitoring resources, otherwise the methods as per the first embodiments are applied.

[0090] Further, the said CSI reporting configuration may include reporting configurations, included e.g. in the fields reportQuantity or reportType which are specific for the reporting of monitoring results. The reportQuantity could include for example the ‘cri-RSRP-monitoring’ or ‘ssb-Index-RSRP-monitoring’ which are associated to specific metric for the reporting of monitoring results, such as the accuracy of the RSRP of the CSI-RS / SSB inference results.

[0091] In another detailed embodiment, when the “reportQuantity” is set to any of “CRT or SSB-Index” and the UE is configured with a monitoring reporting configuration that is linked a CSI reporting configuration including the resourceForChannelPrediction (i.e. inference configuration)- the monitoring reporting configuration is linked with the inference reporting configuration, and- the UE may be configured to estimate a beam prediction accuracy, whereino the configuration may be explicit in the reportQuantity for the reporting configuration, oro the configuration may be implicit based on the reportQuantity in the inference configuration

[0092] In another detailed embodiment, when the “reportQuantity” is set to any of “CRI-RSRP” or SSB-Index-RSRP” and the UE is configured with a monitoring reporting configuration that is linked to the inference reporting configuration:- the UE may be configured to estimate a beam prediction accuracy and an RSRP prediction error metric,o the configuration may be explicit in the reportQuantity for the reporting configuration, oro the configuration may be implicit based on the reportQuantity in the inference configuration

[0093] Now, embodiments related to configuration of resources for the purpose of training will be described. In another embodiment, a resource set can be used for the purpose of data collection for training an AI / ML model / functionality. For example, if a CSI reporting configuration includes an explicit indication indicating a list of resources to be used for the purpose of data collection for of UE-side model training, then the resourcesForChannelMeasurements included in the said CSI reporting configuration can be associated to the set of A resources according to which a UE-side model should be trained, and the resourceForChannelDataCollection included in the said CSI reporting configuration can be associated to the set of B resources in which the UE should perform measurements in order for a UE-side model to perform predictions on the set A.

[0094] Hence by combining this embodiment with the first embodiment, the resource sets identified by the field ‘resourcesForChannelMeasuremenf can be associated to a first purpose (set A of resources related to an inference configuration) or second purpose (set non-AIML related resources for conventional non-AIML channel measurement reports), or third purpose (set A of resources related to a data collection configuration for UE-side model training) depending on the presence or absence in the same CSI reporting configuration of a field resourceForChannelDataCollection associated to a fourth purpose (set B of resources related to a data collection configuration for UE-side model training).

[0095] According to this method, the reportQuantity and the reportType included in the CSI reporting configuration comprising the said indication, are associated to the reporting of data collection related to UE-side model training.

[0096] An example of this embodiment can captured as per the signaling implementation in 3GPP TS 38.331 shown in Figures 10A and 10B (new / changed elements shown in bold text), which considers the impact of both the first and second embodiment, and third embodiment.

[0097] In another embodiment, an explicit flag is included indicating that the CSI reporting configuration is referred to a data collection configuration for the purpose of UE-side training. In this case the resourcesForChannelMeasurements included in the said CSI reporting configuration can be associated to the set of A resources according to which a UE-side model should be trained, and the resourceForChannelPrediction included in the said CSI reporting configuration can be associated to the set B of resources in which the UE should perform measurements in order for a UE-side model to perform predictions on the set A. The indication above could be for example a flag indicating that the CSI reporting configuration is referred to a data collection configurationfor the purpose of UE-side training, or a field included in the reportType specifically for data collection. In another method, if this flag is included, one resource set could contain both the set A of resources and the set B, and it is up to the UE implementation to determine the said set A and set B from such set of resources for the purpose of data collection for UE-side model training.

[0098] In one embodiment, when a CSI reporting configuration (i.e., CSI-ReportConfig) includes both the fields ‘resourcesForChannelMeasuremenf and ‘resourcesForChannelPrediction’, the UE determines the corresponding procedures based on the value of the ‘reportQuantity’ field indicated as part of the CSI-ReportConfig.

[0099] In one detailed embodiment, when ‘reportQuantity’ in the CSI-ReportConfig is set to a value of ‘none’, then the UE performs one or more of the following actions:• the UE performs a first set of channel measurements on the one or more set B of resources pointed to by the identifier ‘resourcesForChannelMeasurement’ in the same CSI- ReportConfig,• the UE performs a second set of channel measurements on the one or more set A of resources pointed to by the identifier ‘resourcesForChannelPrediction’ in the same CSI- ReportConfig, and• the first set and second set of channel measurements is used to train an AIML model / function to predict one or more resources from set A of resources based on measurements of the one or more set B of resources by the UE or the UE training entity. In this case, the UE does not report the measurement on neither the first nor second set of resources (i.e. set A / B) to the Network. In some embodiments, the collected measurements could be used for other purposes such as retraining / updating AIML model / functionality or additionally monitoring the performance of the AIML model / functionality.

[0100] In another detailed embodiment, a new parameter is signaled in the CSI configuration to the UE to indicate that the UE may use the performed measurements for training. Here, the UE data collection is enabled while the UE is still be expected to report the measurements to the network. In one variant of this embodiment, a new flag is introduced to the CSI configuration that indicates that data collection for UE sided model training is expected from the UE. When ‘reportQuantity’ in the CSI-ReportConfig is set to any one of ‘cri-RSRP’ or ‘ssb-Index-RSRP’, and the new flag for UE data collection is enabled then the UE performs one or more of the following actions:• the UE performs a first set of channel measurements on the one or more set B of resources pointed to by the identifier ‘resourcesForChannelMeasurement’ in the same CSL ReportConfig,• the UE performs a second set of channel measurements on the one or more set A of resources pointed to by the identifier ‘resourcesForChannelPrediction’ in the same CSI- ReportConfig, and• reports according to the reportConfigType the requested ‘cri-RSRP’ or ‘ssb-Index-RSRP’ based on the channel measurements on the one or more set B and one or more set A resources to the network, the first set and second set of channel measurements is additionally used to train an AIML model / function to predict one or more resources from set A of resources based on measurements of the one or more set B of resources by the UE or the UE training entity.

[0101] Further methods include the UE determining that a certain CSI reporting configuration is associated to a data collection configuration for the purpose of UE-side model training. In particular, in all these cases, the UE determine from various information included in the ‘reportQuantity’ field that the CSI reporting configuration includes resource for data collection e.g. the resourceForChannelMeasurements is associated to a set of A and / or B of resource for the data collection purposes.:- When the “reportQuantity” is set to any of “CRI-train” or S SB -Index-train”■ the UE trains a beam prediction classifier model and the UE is not expected to report any measurement result.- When the “reportQuantity” is set to any of “CRI-train” or S SB -Index-train” , and the UE is further configured with a “predictionDelay” parameter indicating for which time instances the UE should predict the resources of set A.■ the UE trains a beam prediction classifier model, to predict the channel according to the predictionDelay parameter, and the UE is not expected to report any measurement result- When the “reportQuantity” is set to any of “CRI-RSRP -train” or SSB-Index-RSRP -train”■ the UE trains a beam prediction regression model, and the UE is not expected to report any measurement result- When the “reportQuantity” is set to any of “CRI-RSRP -train” or SSB-Index-RSRP -train”, and the UE is further configured with a “predictionDelay” parameter indicating for which time instances the UE should predict the resources of set A. In this case, the UE trains a beam prediction regression model, and the UE is not expected to report any measurement result.

[0102] Now, further description is provided that is related to all embodiments described above. In this regard, Figure 11 illustrates the operation of a UE 1100 and a network node 1102,in accordance with the embodiments described above. Note that the details above, while not all repeated here in the description of Figure 11, are equally applicable to the corresponding steps or actions shown in Figure 11.

[0103] As illustrated in Figure 11, the UE 1100 receives, from the network node 1102, a CSI reporting configuration that includes information that configures one or more sets of resources (step 1104). The UE 1100 determines a purpose(s) of a certain set of resources from among the one or more sets of resources configured by the information comprised in the CSI reporting configuration, based on an implicit or explicit indication (step 1106). The implicit or explicit indication of the purpose(s) of the certain set of resources is, in some embodiments, included in the CSI reporting configuration. The implicit or explicit indication of the purpose(s) of the certain set of resources is associated to one or more purposes from a predefined set of purposes. The predefined set of purposes includes any one or more (and preferably any two or more or potentially all) of the following purposes:• as a set A of resources related to an inference configuration for an AI / ML model / functionality;• as a set B of resources related to an inference configuration for an AI / ML model / functionality;• as resources for non- AI / ML related channel measurements;• as a set A of resources related to a data collection configuration for an AI / ML model / functionality training;• as a set B of resources related to a data collection configuration for an AI / ML model / functionality training;• as a set of resources related to the monitoring of inference performed according to an AI / ML model / functionality.

[0104] A set A comprises a set of resources for which the UE should determine radio measurement predictions, and a set B comprises a set of resources in which the UE should perform radio measurements in order to determine the radio measurement predictions on the set A.

[0105] In some embodiments, the indication of the purpose(s) of the certain set of resources is an explicit indication. For example, the explicit indication of the purpose(s) of the certain set of resources is a flag or a dedicated information element (including a specific set of resources) that explicitly indicates one of the predefined set of resources.

[0106] In another embodiment, the indication of the purpose(s) of the certain set of resources is an implicit indication. For example, in one embodiment, the implicit indication is the presence or absence of a certain parameters, value of a parameter, or information element in the CSIreporting configuration. In another embodiment, the implicit indication is: the presence or absence of an explicit indication associated to a first purpose implicitly indicates that purpose of the certain set of resources is a second purpose or one or more other purposes; or a report type or report quantity comprised in the CSI reporting configuration, where this report type or report quantity implicitly indicates one of the plurality of purposes.

[0107] In one embodiment, reporting parameters such as report quantity and report type included in one CSI reporting configuration are different depending on the purpose of the set of resources.

[0108] The UE 1100 performs one or more actions in accordance with the determined purpose(s) of the certain set of resources and the CSI reporting configuration (step 1108). Note that different action(s) may be performed by the UE 1100 for different purposes.

[0109] Again, further details regarding various aspects of the process of Figure 11 are described in above and are equally applicable here to Figure 11.

[0110] Figure 12 shows an example of a communication system 1200 in accordance with some embodiments.[OHl] In the example, the communication system 1200 includes a telecommunications network 1202 that includes an access network 1204, such as a radio access network (RAN), and a core network 1206, which includes one or more core network nodes 1208. The access network 1204 includes one or more access network nodes or base stations of various types, access network nodes 1210A and 1210B are depicted (which may be collectively referred to as network nodes 1210), or any other similar 3rdGeneration Partnership Project (3GPP) access nodes or non-3GPP access points (APs). Some embodiments of the access network 1204 may include more than one access network technology. The network nodes 1210 of access network 1204 facilitate direct or indirect connection of wireless devices, also referred to as user equipments (UEs), such as by connecting UEs 1212A, 1212B, 1212C, and 1212D (one or more of which may be generally referred to as UEs 1212) to the core network 1206 over one or more wireless connections.

[0112] Moreover, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunications network 1202 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a network node in the telecommunications network 1202 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other network nodes to implement one or more functionalities of any network node in thetelecommunications network 1202, including one or more access network nodes 1210 and / or core network nodes 1208.

[0113] 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). An ORAN network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an Al, Fl, Wl, El, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN network node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an O-2 interface defined by the 0-RAN Alliance or comparable technologies.

[0114] The network nodes 1210 facilitate direct or indirect connection of one or more UEs 1212 to the core network 1206 over one or more wireless connections. Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 1200 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 1200 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0115] The UEs 1212 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 1210 and other communication devices. Similarly, the network nodes 1208, 1210 are arranged, capable, configured, and / or operable to communicate directly or indirectly (e.g., via other devices of telecommunications network 1202) with the UEs 1212 and / or with other network nodes or equipment in the telecommunications network 1202 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administrationin the telecommunications network 1202. More specifically, UEs 1212 may send messages, data, and / or other signals to network nodes 1208, 1210 or other elements of the telecommunications network 1202 by transmitting such signals to the relevant device directly without the signals passing through any intervening devices or by transmitting such signals to the relevant device indirectly through an intervening device (or multiple intervening devices) that then transmit the signal to the relevant device. Similarly, network nodes 1208, 1210 may send messages, data, and other signals to UEs 12122, other network nodes 1208, 1210, and other devices in telecommunications network 1202 directly or indirectly. As one specific example, a core network node 108 may transmit a particular message to a UE 1212 by transmitting the message to an access network node 1210 that will then transmit the message to the intended UE 1212. Similarly, a core network node 108 may receive a particular message from a UE 1212 by receiving the message from an access network node 1210 that itself received the message from the UE 1212.

[0116] In the depicted example, the core network 1206 connects elements of the access network 1204 (e.g., one or more of the network nodes 1210) to one or more host computing systems, such as host 1216. 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 1206 includes one or more core network nodes (e.g., core network node 1208) of various types, one or more of which may be generally referred to as network nodes 1208. Network nodes 1208 are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, access network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 1208. Example core network nodes provide functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0117] The host 1216 may be under the ownership or control of a service provider other than an operator or provider of the access network 1204 and / or the telecommunications network 1202. The host 1216 may be operated by the service provider or on behalf of the service provider. The host 1216 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs,analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0118] As a whole, the communication system 1200 of Figure 12 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system 1200 may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (Wi-Fi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (Wi-Max), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, Li-Fi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox. Moreover, the communication system 1200 may be configured to support multiple different standards, protocols, or other rule sets, with individual components supporting all of the relevant rule sets or with different components or sub-systems within the communication system 1200 supporting different standards, protocols, or rule sets.

[0119] As one example, in certain embodiments, access network 1204 may contain some access network nodes 1210 that support 3GPP radio access technologies (RAT), such as LTE or NR, while other access network nodes 1210 support (or the same access network nodes 1210 additionally support) non-3GPP RATs, such as Wi-Fi or a proprietary RAT. As another example, telecommunications network 1202 may support multiple generations of related communication standards (e.g., 4G and 5G 3GPP communication standards) and, as a result, may include an access network 104 and / or a core network 106 that supports multiple different standard generations or may include multiple access networks 104 and / or multiple core networks 106 with individual networks 104, 106 supporting different standard generations.

[0120] Telecommunications network 1202 may support network slicing to provide different logical networks to different devices that are connected to the telecommunications network 1202. For example, the telecommunications network 1202 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC) / Massive loT services to yet further UEs.

[0121] In some examples, one or more of the UEs 1212 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed totransmit information to the access network 1204 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 1204. 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).

[0122] In the example, the hub 1214 communicates with the access network 1204 to facilitate indirect communication between one or more UEs (e.g., UE 1212C and / or 1212D) and network nodes (e.g., network node 1210B). In some examples, the hub 1214 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 1214 may be a broadband router enabling access to the core network 1206 for the UEs. As another example, the hub 1214 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 1210, or by executable code, script, process, or other instructions in the hub 1214.

[0123] As another example, the hub 1214 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 1214 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 1214 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 1214 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 1214 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0124] The hub 1214 may have a constant / persistent or intermittent connection to the network node 1210B. The hub 1214 may also allow for a different communication scheme and / or schedule between the hub 1214 and UEs (e.g., UE 1212C and / or 1212D), and between the hub 1214 and the core network 1206. In other examples, the hub 1214 is connected to the core network 1206 and / or one or more UEs via a wired connection. Moreover, the hub 1214 may be configured to connect to an M2M service provider over the access network 1204 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 1210 while still connected via the hub 1214 via a wired or wireless connection. In some embodiments, the hub 1214 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 1210B. In other embodiments,the hub 1214 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 1210B, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0125] Figure 13 is another example of a communication system 1300 according to some embodiments. As used herein, the communication system 1300 includes multiple access points (APs) 1310 (with four exemplary APs 1310A, 1310B, 1310C, and 1310D being depicted) and multiple wireless devices, referred to in the context of communication system 1300 as stations (ST As) 1312 (referred to individually as ST A 1312A, ST A 1312B, ST A 1312C, STA 1312D, and STA 1312E). STA 1312A is served by AP 1310A in a first basic service set (BSS) 1320A. STA 1310B and STA 1310C are served by AP 1310B in a second BSS, BSS 1320B. STA 1312D is served by AP 1310C in a third BSS, BSS 1320C. STA 1312E is served by AP 1310D in a fourth BSS, BSS 1320D. Stations 1312 may be non-AP STAs and correspond to various kinds of wireless devices, for example, user terminals, such as mobile or stationary computing devices like smartphones, laptop computers, desktop computers, tablet computers, gaming devices, headmounted displays (HMDs) for Augmented Reality (AR) or Virtual Reality (VR), or the like. Further, stations 1312 could, for example, correspond to other kinds of equipment like smart home devices, printers, multimedia devices, data storage devices, or the like.

[0126] Each of STAs 1312 may connect through a radio link to one of APs 1310. For example, depending on location or channel conditions experienced by a given STA 1312, the STA may select an appropriate AP and BSS for establishing the radio link. The radio link may be based on one or more orthogonal frequency-division multiplexing (OFDM) carriers from a frequency spectrum that is shared on the basis of a contention-based mechanism, e.g., an unlicensed or license exempt band like 2.4 GHz Industrial, Scientific, and Medical (ISM) band, the 5 GHz band, the 6 GHz band, or the 60 GHz band.

[0127] Each AP 1310 may provide data connectivity to STAs 1312 connected to a particular AP 1310. As illustrated, APs 1310 may be connected to a data network 1330. In this way, APs 1310 may also provide data connectivity between STAs 1312 and other entities, e.g., to one or more servers, service providers, data sources, data sinks, user terminals, or the like. Accordingly, the radio link established between a given STA 1312 and its serving AP 1310 may be used for providing various kinds of services to STA 1312, e.g., a voice service, a multimedia service, or other data service. Such services may be based on applications that are executed on STA 1312 and / or on a device linked to STA 1312. By way of example, Figure 13 illustrates an application service platform 1332 provided in data network 1330. The application(s) executed on STA 1312 and / or on one or more other devices linked to STA 1312 may use the radio link for datacommunication with one or more other STA 1312 and / or the application service platform 1332, thereby enabling utilization of the corresponding service(s) at STA 1312.

[0128] Figure 14 shows a wireless device 1400, which may be configured to operate in communication system 1200 of Figure 12 or in communication system 1300 of Figure 13. The wireless device 1400 may be alternatively referred to as a UE 1400, like a UE 1212 within the context of communication system 1200, or as a station (STA) 1400 or as a non-access-point station (non-AP STA) 1400, like a STA 1312 within the context of the communication system 1300, in accordance with respective embodiments. As used herein, a wireless device refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other wireless devices. Examples of a wireless device include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle, vehicle-mounted or vehicle embedded / integrated wireless device, and wireless terminal. Other examples include any type of UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0129] A wireless device 1400 may support device-to-device (D2D) communication, for example by implementing a 3 GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to-everything (V2X). In other examples, wireless device 1400 may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, wireless device 1400 may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, wireless device 1400 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).

[0130] In particular embodiments, wireless device 1400 includes processing circuitry 1402 that is operatively coupled via a bus 1404 to an input / output interface 1406, a power source 1408, a memory 1410, a communication interface 1412, and / or any other component, or any combination thereof. Certain embodiments of wireless device 1400 may include all or a subset of the components shown in Figure 14. The level of integration between the components may vary fromone embodiment of wireless device 1400 to another. In general, in a particular embodiment of wireless device 1400, processing circuitry 1402, input / output interface 1406, power source 1408, memory 1410, and communication interface 1412 may, in whole or in part, represent or include physical components common to or shared by one or more of the other elements of wireless device 1400. Further, certain embodiments of wireless devices 1400 may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0131] The processing circuitry 1402 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 1410. The processing circuitry 1402 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 1402 may include multiple central processing units (CPUs).

[0132] In the example, the input / output interface 1406 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into wireless device 1400. 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.

[0133] In some embodiments, the power source 1408 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used to supply power to circuitry or to charge an associated battery. The power source 1408 may further include power circuitry for delivering power from the power source 1408 itself, and / or an external power source, to the various parts of wireless device 1400 via input circuitry or an interface such as an electrical power cable. Powersource 1408 may perform any formatting, converting, or other modification to make accessible power suitable for the respective components of the wireless device 1400 to which power is supplied.

[0134] The memory 1410 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 1410 includes one or more programs 1414, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 1416. The memory 1410 may store, for use by wireless device 1400, any of a variety of various operating systems or combinations of operating systems.

[0135] The memory 1410 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 1410 may allow wireless device 1400 to access instructions, programs, and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 1410, which may be or comprise a device-readable storage medium.

[0136] The processing circuitry 1402 may be configured to communicate with an access network or other network via or using the communication interface 1412. The communication interface 1412 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 1422. The communication interface 1412 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another wireless device or a network node in an access network). Each transceiver may include a transmitter 1418 and / or a receiver 1420 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 1418 and receiver 1420 may be coupled toone or more antennas (e.g., antenna 1422) and may share circuit components, software, or firmware, or alternatively be implemented separately.

[0137] In the illustrated embodiment, communication functions of the communication interface 1412 may include cellular communication, Wi-Fi communication (e.g., according to an IEEE 802.11 family standard), LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / intemet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0138] In particular embodiments, wireless device 1400 may provide an output of data captured via a sensor, through its communication interface 1412, via a wireless connection to a network node, and / or in any appropriate manner. Data captured by sensors of a wireless device 1400 can be communicated through a wireless connection to a network node via another wireless device 1400. In particular embodiments, such output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0139] As another example, wireless device 1400 comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, wireless device 1400 may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

[0140] Wireless device 1400, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voicecontrolled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. In particular embodiments, wireless device 1400 represents an loT device that comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the example embodiment of wireless device 1400 shown in Figure 14.

[0141] As yet another specific example, in an loT scenario, wireless device 1400 may represent a machine or other device that performs monitoring and / or measurements, and transmits the results of such monitoring and / or measurements to another wireless device and / or a network node. Wireless device 1400 may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, wireless device 1400 may implement the 3 GPP NB-IoT standard. In other scenarios, wireless device 1400 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.

[0142] In practice, any number of wireless devices 1400 may be used together with respect to a single use case. For example, a first wireless device 1400 might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second wireless device 1400 that is a remote controller operating the drone. When a user makes changes from the remote controller, the first wireless device 1400 may adjust the throttle on the drone (e.g., by controlling an actuator) to increase or decrease the drone’s speed. The first and / or the second wireless device 1400 can also include more than one of the functionalities described above. For example, wireless device 1400 might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0143] Figure 15 shows a network node 1500 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunications network. In accordance with respective embodiments, network node 1500 may be configured to operate in communication system 1200 of Figure 12, like network nodes 1208 or 1210, or in communication system 1300 of Figure 13, like an AP 1310 or a station 1312.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)), 0-RAN nodes or components of an 0-RAN node (e.g., 0-RU, 0-DU, O-CU).

[0144] Network nodes 1500 may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. Network node 1500 may be a relay node or a relay donor node controlling a relay. Network nodes 1500 may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an 0-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).

[0145] Other examples of network nodes 1500 include multiple transmission point (multi -TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0146] In particular embodiments, network node 1500 includes a processing circuitry 1502, a memory 1504, a communication interface 1506, and a power source 1508. In general, in a particular embodiment of network node 1500, processing circuitry 1502, memory 1504, communication interface 1506, and power source 1508 may, in whole or in part, represent or include physical components common to or shared by one or more of the other elements of network node 1500.

[0147] The network node 1500 may be composed of multiple distinct network entities (e.g., a NodeB entity and an RNC entity, or a BTS entity and a BSC entity, etc.), which may each have or utilize their own respective physical components. In certain scenarios in which the network node 1500 comprises multiple such entities (e.g., BTS and BSC), one or more of the separate entities may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 1500 may be configured to support multiple radio access technologies (RATs). In such embodiments, somecomponents may be duplicated (e.g., separate memories 1504 or portions of memory 1504 for different RATs) and some components may be reused (e.g., a same antenna 1510 may be shared by different RATs). The network node 1500 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1500, for example GSM, WCDMA, LTE, NR, Wi-Fi (e.g., according to an IEEE 802.11 family standard), Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 1500.

[0148] The processing circuitry 1502 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other components, such as the memory 1504, to provide network node 1500 functionality.

[0149] In some embodiments, the processing circuitry 1502 includes a system on a chip (SOC). In some embodiments, the processing circuitry 1502 includes one or more of radio frequency (RF) transceiver circuitry 1512 and baseband processing circuitry 1514. In some embodiments, the RF transceiver circuitry 1512 and the baseband processing circuitry 1514 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 1512 and baseband processing circuitry 1514 may be on the same chip or set of chips, boards, or units.

[0150] The memory 1504 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 1502. The memory 1504 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 1502 and utilized by the network node 1500. The memory 1504 may be used to store any calculations made by the processing circuitry 1502 and / or any data received via the communication interface 1506. In some embodiments, the processing circuitry 1502 and memory 1504 is integrated.

[0151] The communication interface 1506 is used in wired or wireless communication of signaling and / or data with UEs, other network nodes, and / or any other network equipment. In the illustrated embodiment, communication interface 1506 comprises port(s) / terminal(s) 1516 to send and receive data, for example to and from a network over a wired connection. In particular embodiments, network node 1400 may be capable of wireless communication and communication interface 1506 may also include radio front-end circuitry 1518 that may be coupled to, or in certain embodiments a part of, an antenna 1510. Particular embodiments of radio front-end circuitry 1518 include filter(s) 1520 and amplifier(s) 1522. The radio front-end circuitry 1518 may be connected to an antenna 1510 and processing circuitry 1502. The radio front-end circuitry may be configured to condition signals communicated between antenna 1510 and processing circuitry 1502. The radio front-end circuitry 1518 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 1518 may convert the digital data into a radio signal(s) having the appropriate channel and bandwidth parameters using a combination of filters 1520 and / or amplifiers 1522. The radio signal(s) may then be transmitted via the antenna 1510. Similarly, when receiving data, the antenna 1510 may collect radio signals which are then converted into digital data by the radio front-end circuitry 1518. The digital data may be passed to the processing circuitry 1502. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0152] In certain alternative embodiments, network node 1500 may be capable of wireless communication but does not include separate radio front-end circuitry 1518, instead, the processing circuitry 1502 includes radio front-end circuitry and is connected to the antenna 1510. Similarly, in some embodiments, all or some of the RF transceiver circuitry 1512 is part of the communication interface 1506. In still other embodiments, the communication interface 1506 includes one or more ports or terminals 1516, the radio front-end circuitry 1518, and the RF transceiver circuitry 1512, as part of a radio unit (not shown), and the communication interface 1506 communicates with the baseband processing circuitry 1514, which is part of a digital unit (not shown).

[0153] The antenna 1510 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 1510 may be coupled to the radio front-end circuitry 1518 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 1510 is separate from the network node 1500 and connectable to the network node 1500 through one or more interfaces or ports.

[0154] The antenna 1510, communication interface 1506, and / or the processing circuitry 1502 may be configured to perform some or all of the receiving operations and / or obtaining operationsdescribed herein as being performed by the network node 1500. Any information, data, and / or signals may be received from a UE, another network node, and / or any other network equipment. Similarly, the antenna 1510, the communication interface 1506, and / or the processing circuitry 1502 may be configured to perform some or all of the transmitting or sending operations described herein as being performed by the network node 1500. Any information, data and / or signals may be transmitted to a UE, another network node, and / or any other network equipment.

[0155] The power source 1508 provides power to the various components of network node 1500 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 1508 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 1500 with power for performing the functionality described herein. For example, the network node 1500 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 1508. As a further example, the power source 1508 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.

[0156] Embodiments of the network node 1500 may include additional components beyond those shown in Figure 15 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 1500 may include user interface equipment to allow input of information into the network node 1500 and to allow output of information from the network node 1500. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 1500.

[0157] Figure 16 is a block diagram illustrating a virtualization environment 1600 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 1600 hosted by one or more of hardware nodes, such as a hardware computing device that operates as an access network node, UE, core network node, or host. Further, inembodiments in which a virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment 1600 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an 0-2 interface.

[0158] Applications 1602 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 1500 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0159] Hardware 1604 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 1606 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VM 1608 A and VM 1608B (which may be collectively referred to as VMs 1608), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 1606 may present a virtual operating platform that appears like networking hardware to one or more of the VMs 1608.

[0160] The VMs 1608 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by virtualization layer 1606. Different embodiments of the instance of a virtual appliance 1602 may be implemented on one or more of VMs 1608, 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.

[0161] In the context of NFV, each of the VMs 1608 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 1608, and that part of hardware 1604 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more of the VMs 1608 on top of the hardware 1604 and corresponds to an application 1602.

[0162] Hardware 1604 may be implemented in a standalone network node with generic or specific components. Hardware 1604 may implement some functions via virtualization. Alternatively, hardware 1604 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 1610, which, among others, oversees lifecycle management of applications 1602. In some embodiments, hardware 1604 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 1612 which may alternatively be used for communication between hardware nodes and radio units.

[0163] Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions, and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

[0164] 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.

[0165] Those skilled in the art will recognize improvements and modifications to the embodiments of the present disclosure. All such improvements and modifications are considered within the scope of the concepts disclosed herein.

[0166] Some exemplary embodiments of the present disclosure are as follows:Group A Embodiments

[0167] Embodiment 1: A method performed by a User Equipment, UE, (1100), the method comprising: receiving (1104) a Channel State Information, CSI, reporting configuration from a network node (1102), the CSI reporting configuration comprising information that configures a certain set of resources; and determining a purpose(s) for which the certain set of resources is(are) to be used from among a plurality of (e.g., predefined) purposes, based on an implicit or explicit indication (e.g., received from the network node, e.g., in the CSI reporting configuration).

[0168] Embodiment 2: The method of embodiment 1, wherein the plurality of purposes comprises any one or more (and preferably any two or more) of the following purposes:• as a set A of resources related to an inference configuration for an AI / ML model / functionality;• as a set B of resources related to an inference configuration for an AI / ML model / functionality;• as resources for non- AI / ML related channel measurements;• as a set A of resources related to a data collection configuration for an AI / ML model / functionality training;• as a set B of resources related to a data collection configuration for an AI / ML model / functionality training;• as a set of resources related to the monitoring of inference performed according to an AI / ML model / functionality.

[0169] Embodiment 3: The method of embodiment 1 or 2, wherein the implicit or explicit indication is associated to one or more of the plurality of purposes.

[0170] Embodiment 4: The method of any of embodiments 1 to 3, wherein the implicit or explicit indication of the purpose(s) of the certain set of resources is comprised in the CSI reporting configuration.

[0171] Embodiment 5: The method of any of embodiments 1 to 4, wherein the indication is an explicit indication.

[0172] Embodiment 6: The method of embodiment 5, wherein the explicit indication of the purpose(s) of the certain set of resources is a flag or a dedicated information element (including a specific set of resources) that explicitly indicates one of the plurality of purposes.

[0173] Embodiment 7: The method of any of embodiments 1 to 4, wherein the indication is an implicit indication.

[0174] Embodiment 8: The method of embodiment 7, wherein the implicit indication is the presence or absence of a certain parameters, value of a parameter, or information element in the CSI reporting configuration.

[0175] Embodiment 9: The method of embodiment 7, wherein the implicit indication is: the presence or absence of an explicit indication associated to a first purpose implicitly indicates that purpose of the certain set of resources is a second purpose or one or more other purposes; or a report type or report quantity comprised in the CSI reporting configuration, where this report type or report quantity implicitly indicates one of the plurality of purposes.

[0176] Embodiment 10: The method of any of embodiments 1 to 9, wherein a set A comprises a set of resources for which the UE should determine radio measurement predictions, and a set B comprises a set of resources in which the UE should perform radio measurements in order to determine the radio measurement predictions on the set A.

[0177] Embodiment 11: The method of any of embodiments 1 to 10, wherein reporting parameters such as report quantity and report type included in one CSI reporting configuration are different depending on the purpose of the set of resources.

[0178] Embodiment 12: The method of any of embodiments 1 to 11, further comprising performing (1108) one or more actions based on the determined purpose(s) of the certain set of resources and the CSI reporting configuration.

[0179] Embodiment 13: The method of any of the previous embodiments, further comprising: providing user data; and forwarding the user data to a host via the transmission to the network node.Group B Embodiments

[0180] Embodiment 14: A method performed by a network node (1102), the method comprising: transmitting (1104) a Channel State Information, CSI, reporting configuration to a User Equipment, UE, (1100), wherein: the CSI reporting configuration comprising information that configures a certain set of resources and an implicit or explicit indication of a purpose(s) for which the certain set of resources is(are) to be used; and the purpose(s) is from among a plurality of (e.g., predefined) purposes.

[0181] Embodiment 15: The method of embodiment 14, wherein the plurality of purposes comprises any one or more (and preferably any two or more) of the following purposes:• as a set A of resources related to an inference configuration for an AI / ML model / functionality;• as a set B of resources related to an inference configuration for an AI / ML model / functionality;• as resources for non- AI / ML related channel measurements;• as a set A of resources related to a data collection configuration for an AI / ML model / functionality training;• as a set B of resources related to a data collection configuration for an AI / ML model / functionality training;• as a set of resources related to the monitoring of inference performed according to an AI / ML model / functionality.

[0182] Embodiment 16: The method of embodiment 14 or 15, wherein the implicit or explicit indication is associated to one or more of the plurality of purposes.

[0183] Embodiment 17: The method of any of embodiments 14 to 16, wherein the implicit or explicit indication of the purpose(s) of the certain set of resources is comprised in the CSI reporting configuration.

[0184] Embodiment 18: The method of any of embodiments 14 to 17, wherein the indication is an explicit indication.

[0185] Embodiment 19: The method of embodiment 18, wherein the explicit indication of the purpose(s) of the certain set of resources is a flag or a dedicated information element (including a specific set of resources) that explicitly indicates one of the plurality of purposes.

[0186] Embodiment 20: The method of any of embodiments 14 to 17, wherein the indication is an implicit indication.

[0187] Embodiment 21 : The method of embodiment 20, wherein the implicit indication is the presence or absence of a certain parameters, value of a parameter, or information element in the CSI reporting configuration.

[0188] Embodiment 22: The method of embodiment 20, wherein the implicit indication is: the presence or absence of an explicit indication associated to a first purpose implicitly indicates that purpose of the certain set of resources is a second purpose or one or more other purposes; or a report type or report quantity comprised in the CSI reporting configuration, where this report type or report quantity implicitly indicates one of the plurality of purposes.

[0189] Embodiment 23: The method of any of embodiments 14 to 22, wherein a set A comprises a set of resources for which the UE should determine radio measurement predictions, and a set B comprises a set of resources in which the UE should perform radio measurements in order to determine the radio measurement predictions on the set A.

[0190] Embodiment 24: The method of any of embodiments 14 to 23, wherein reporting parameters such as report quantity and report type included in one CSI reporting configuration are different depending on the purpose of the set of resources.

[0191] Embodiment 25: The method of any of the previous embodiments, further comprising: obtaining user data; and forwarding the user data to a host or a user equipment.Group C Embodiments

[0192] Embodiment 26: A wireless device comprising: processing circuitry configured to perform any of the operations of any of the Group A embodiments; and a power source configured to supply power to the processing circuitry.

[0193] Embodiment 27: A network node comprising: processing circuitry configured to perform any of the operations of any of the Group B embodiments; a power source circuitry configured to supply power to the processing circuitry.

[0194] Embodiment 28: A wireless device comprising: one or more antennas; communication interface connected to the one or more antennas and to processing circuitry; the processing circuitry being configured to perform any of the operations of any of the Group A embodiments; an input interface connected to the processing circuitry and configured to allow input of information into the UE to be processed by the processing circuitry; an output interface connected to the processing circuitry and configured to output information from the UE that has been processed by the processing circuitry; and a power source connected to the processing circuitry and configured to supply power to the UE.

Claims

CLAIMS1. A method performed by a User Equipment, UE, (1100), the method comprising:receiving (1104) a Channel State Information, CSI, reporting configuration from a network node (1102), the CSI reporting configuration comprising information that configures a certain set of resources; anddetermining (1106) a purpose for which the certain set of resources is to be used from among a plurality of purposes, based on an implicit or explicit indication.

2. The method of claim 1, wherein the plurality of purposes comprises any two or more of the following purposes:• as a set A of resources related to an inference configuration for an Artificial Intelligence or Machine Learning, AI / ML, model / functionality;• as a set B of resources related to an inference configuration for an AI / ML model / functionality;• as resources for non- AI / ML related channel measurements;• as a set A of resources related to a data collection configuration for an AI / ML model / functionality training;• as a set B of resources related to a data collection configuration for an AI / ML model / functionality training;• as a set of resources related to the monitoring of inference performed according to an AI / ML model / functionality.

3. The method of claim 2, wherein a set A comprises a set of resources for which the UE should determine radio measurement predictions, and a set B comprises a set of resources in which the UE should perform radio measurements in order to determine the radio measurement predictions on the set A.

4. The method of any of claims 1 to 3, wherein the implicit or explicit indication is associated to one or more of the plurality of purposes.

5. The method of any of claims 1 to 4, wherein the implicit or explicit indication of the purpose(s) of the certain set of resources is comprised in the CSI reporting configuration.

6. The method of any of claims 1 to 5, wherein the indication is an explicit indication.

7. The method of claim 6, wherein the explicit indication of the purpose(s) of the certain set of resources is a flag or a dedicated information element that explicitly indicates one of the plurality of purposes.

8. The method of any of claims 1 to 5, wherein the indication is an implicit indication.

9. The method of claim 8, wherein the implicit indication is the presence or absence of a certain parameters, value of a parameter, or information element in the CSI reporting configuration.

10. The method of claim 8, wherein the implicit indication is:the presence or absence of an explicit indication associated to a first purpose implicitly indicates that purpose of the certain set of resources is a second purpose or one or more other purposes; ora report type or report quantity comprised in the CSI reporting configuration, where this report type or report quantity implicitly indicates one of the plurality of purposes.

11. The method of any of claims 1 to 10, wherein reporting parameters included in one CSI reporting configuration are different depending on the purpose of the set of resources.

12. The method of claim 1, wherein the implicit or explicit indication comprises a report quantity parameter comprised in the CSI reporting configuration.

13. The method of claim 12, wherein:the CSI reporting configuration comprises first information that identifies resources for channel prediction and second information that identifies resources for channel measurement; and when the report quantity parameter is set to any one of ‘cri-RSRP’ or ‘ssb-Index-RSRP’, the report quantity parameter thereby indicates that one or more set B of resources indicated by one of the first and second information are for channel measurements and that one or more set A of resources indicated by one of the first and second information are for channel measurement predictions.

14. The method of claim 13, wherein when the report quantity parameter is set to a value ofnone, the report quantity parameter thereby indicates that one or more set B of resources indicated by one of the first and second information are for a first set of channel measurements and that one or more set A of resources indicated by one of the first and second information are for a second set of channel measurements.

15. The method of claim 12, wherein:the CSI reporting configuration comprises first information that identifies resources for channel prediction and second information that identifies resources for channel measurement; and when the report quantity parameter is set to a value of none, the report quantity parameter thereby indicates that one or more set B of resources indicated by one of the first and second information are for a first set of channel measurements and that one or more set A of resources indicated by one of the first and second information are for a second set of channel measurements.

16. The method of any of claims 1 to 15, further comprising performing (1108) one or more actions based on the determined purpose of the certain set of resources and the CSI reporting configuration.

17. A User Equipment, UE, (1100; 1400), comprising:a communication interface (1412) comprising a transmitter (1418) and a receiver (1420); andprocessing circuitry (1402) associated with the communication interface (1412), the processing circuitry (1402) configured to cause the UE (1100; 1400) to:receive (1104) a Channel State Information, CSI, reporting configuration from a network node (1102), the CSI reporting configuration comprising information that configures a certain set of resources; anddetermine (1106) a purpose for which the certain set of resources is to be used from among a plurality of purposes, based on an implicit or explicit indication.

18. The UE of claim 17, wherein the plurality of purposes comprises any two or more of the following purposes:• as a set A of resources related to an inference configuration for an AI / ML model / functionality;• as a set B of resources related to an inference configuration for an AI / ML model / functionality;• as resources for non-AI / ML related channel measurements;• as a set A of resources related to a data collection configuration for an AI / ML model / functionality training;• as a set B of resources related to a data collection configuration for an AI / ML model / functionality training;• as a set of resources related to the monitoring of inference performed according to an AI / ML model / functionality.

19. The UE of claim 17 or 18, wherein the implicit or explicit indication of the purpose(s) of the certain set of resources is comprised in the CSI reporting configuration.

20. The UE of claim 17, wherein the implicit or explicit indication comprises a report quantity parameter comprised in the CSI reporting configuration.

21. The UE of claim 20, wherein:the CSI reporting configuration comprises first information that identifies resources for channel prediction and second information that identifies resources for channel measurement; and when the report quantity parameter is set to any one of ‘cri-RSRP’ or ‘ssb-Index-RSRP’, the report quantity parameter thereby indicates that one or more set B of resources indicated by one of the first and second information are for channel measurements and that one or more set A of resources indicated by one of the first and second information are for channel measurement predictions.

22. The UE of claim 21, wherein when the report quantity parameter is set to a value of none, the report quantity parameter thereby indicates that one or more set B of resources indicated by one of the first and second information are for a first set of channel measurements and that one or more set A of resources indicated by one of the first and second information are for a second set of channel measurements.

23. The UE of claim 20, wherein:the CSI reporting configuration comprises first information that identifies resources for channel prediction and second information that identifies resources for channel measurement; and when the report quantity parameter is set to a value of none, the report quantity parameter thereby indicates that one or more set B of resources indicated by one of the first and secondinformation are for a first set of channel measurements and that one or more set A of resources indicated by one of the first and second information are for a second set of channel measurements.

24. The UE of any of claims 17 to 23, wherein the processing circuitry is further configured to cause the UE to perform (1108) one or more actions based on the determined purpose of the certain set of resources and the CSI reporting configuration.

25. A method performed by a network node (1102), the method comprising:transmitting (1104) a Channel State Information, CSI, reporting configuration to a User Equipment, UE, (1100), wherein:the CSI reporting configuration comprises information that configures a certain set of resources and an implicit or explicit indication of a purpose for which the certain set of resources is to be used; andthe purpose is from among a plurality of purposes.

26. The method of claim 25, wherein the plurality of purposes comprises any two or more of the following purposes:• as a set A of resources related to an inference configuration for an AI / ML model / functionality;• as a set B of resources related to an inference configuration for an AI / ML model / functionality;• as resources for non- AI / ML related channel measurements;• as a set A of resources related to a data collection configuration for an AI / ML model / functionality training;• as a set B of resources related to a data collection configuration for an AI / ML model / functionality training;• as a set of resources related to the monitoring of inference performed according to an AI / ML model / functionality.

27. The method of claim 25 or 26, wherein the implicit or explicit indication of the purpose(s) of the certain set of resources is comprised in the CSI reporting configuration.

28. The method of claim 25, wherein the implicit or explicit indication comprises a report quantity parameter comprised in the CSI reporting configuration.

29. The method of claim 28, wherein:the CSI reporting configuration comprises first information that identifies resources for channel prediction and second information that identifies resources for channel measurement; and when the report quantity parameter is set to any one of ‘cri-RSRP’ or ‘ssb-Index-RSRP’, the report quantity parameter thereby indicates that one or more set B of resources indicated by one of the first and second information are for channel measurements and that one or more set A of resources indicated by one of the first and second information are for channel measurement predictions.

30. The method of claim 29, wherein when the report quantity parameter is set to a value of none, the report quantity parameter thereby indicates that one or more set B of resources indicated by one of the first and second information are for a first set of channel measurements and that one or more set A of resources indicated by one of the first and second information are for a second set of channel measurements.

31. The method of claim 28, wherein:the CSI reporting configuration comprises first information that identifies resources for channel prediction and second information that identifies resources for channel measurement; and when the report quantity parameter is set to a value of none, the report quantity parameter thereby indicates that one or more set B of resources indicated by one of the first and second information are for a first set of channel measurements and that one or more set A of resources indicated by one of the first and second information are for a second set of channel measurements.

32. A network node (1102; 1500), comprising processing circuitry (1502) configured to cause the network node (1102; 1500) to:transmit (1104) a Channel State Information, CSI, reporting configuration to a User Equipment, UE, (1100), wherein:the CSI reporting configuration comprises information that configures a certain set of resources and an implicit or explicit indication of a purpose for which the certain set of resources is to be used; andthe purpose is from among a plurality of purposes.

33. The network node of claim 32, wherein the plurality of purposes comprises any two ormore of the following purposes:• as a set A of resources related to an inference configuration for an AI / ML model / functionality;• as a set B of resources related to an inference configuration for an AI / ML model / functionality;• as resources for non- AI / ML related channel measurements;• as a set A of resources related to a data collection configuration for an AI / ML model / functionality training;• as a set B of resources related to a data collection configuration for an AI / ML model / functionality training;• as a set of resources related to the monitoring of inference performed according to an AI / ML model / functionality.

34. The network node of claim 32 or 33, wherein the implicit or explicit indication of the purpose(s) of the certain set of resources is comprised in the CSI reporting configuration.

35. The network node of claim 32, wherein the implicit or explicit indication comprises a report quantity parameter comprised in the CSI reporting configuration.

36. The network node of claim 35, wherein:the CSI reporting configuration comprises first information that identifies resources for channel prediction and second information that identifies resources for channel measurement; and when the report quantity parameter is set to any one of ‘cri-RSRP’ or ‘ssb-Index-RSRP’, the report quantity parameter thereby indicates that one or more set B of resources indicated by one of the first and second information are for channel measurements and that one or more set A of resources indicated by one of the first and second information are for channel measurement predictions.

37. The network node of claim 36, wherein when the report quantity parameter is set to a value of none, the report quantity parameter thereby indicates that one or more set B of resources indicated by one of the first and second information are for a first set of channel measurements and that one or more set A of resources indicated by one of the first and second information are for a second set of channel measurements.

38. The network node of claim 35, wherein:the CSI reporting configuration comprises first information that identifies resources for channel prediction and second information that identifies resources for channel measurement; and when the report quantity parameter is set to a value of none, the report quantity parameter thereby indicates that one or more set B of resources indicated by one of the first and second information are for a first set of channel measurements and that one or more set A of resources indicated by one of the first and second information are for a second set of channel measurements.

Images