Identifying inference configurations in applicability report

Abstract

Systems and methods are disclosed that relate to identifying inference configurations in applicability reports in a wireless communications network. In one embodiment, a method performed by a User Equipment (UE) for reporting applicability of an Artificial Intelligence or Machine Learning (AI / ML) functionality comprises transmitting, to a network node, a report comprising an applicability information for an inference configuration of an AI / ML functionality and identification information associated to the inference configuration of the AI / ML functionality. In this manner, the UE is enabled to receive one or more inference configuration (e.g. multiple inference configurations associated to multiple cells groups and / or multiple serving cells within the same cell group) and still report applicability and properly identify them in the report of applicability information.

Description

DETAILED DESCRIPTION

[0030] The embodiments set forth below represent information to enable those skilled in the art to practice the embodiments and illustrate the best mode of practicing the embodiments. Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the disclosure and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure.

[0031] 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.

[0032] Even if some of the terminology used to describe embodiments of the solution(s) proposed herein follows 5th Generation (5G) New Radio (NR) principles, the embodiments of the solution(s) described herein are also applicable for 6th Generation (6G) and beyond.

[0033] There currently exist certain challenge(s). In 3rd Generation Partnership Project (3GPP) Release (Rel)-19, it has been agreed that the User Equipment (UE) may receive a Radio Resource Control (RRC) Reconfiguration including an inference configuration for an Artificial Intelligence (AI) / Machine Learning (ML) functionality (e.g. time-domain and / or spatial domain beam prediction report) and, in response to the inference configuration, transmit applicability information about the AI / ML functionality e.g. applicable or non-applicable. This applicability information may be transmitted in a so-called applicability report. However, the UE may receive multiple inference configurations for an AI / ML functionality and, as such, there should be a way to enable the UE to indicate the exact inference configuration to which the applicability information is associated.

[0034] Note that while this is part of the Rel-19 work, the understanding is that this is also relevant for 6th Generation (6G) since AI / ML for Beam Management is one of the use cases that will very likely be proposed for 6G for the AI / ML specification in 3GPP. Notice that this is applicable for various AI / ML for Beam Management sub-use cases such as reporting of time-domain beam prediction(s) and / or reporting of spatial-domain beam prediction.

[0035] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. Embodiments of a method performed by a UE (and corresponding embodiments of a UE) are disclosed for identifying an AI / ML functionality configuration (e.g. an inference configuration of an AI / ML functionality) for which the UE transmits a report including applicability information (e.g. applicability indication) e.g. the UE indicates whether an AI / ML functionality is 'applicable' or 'not applicable' and one or more identifiers for a particular inference configuration of the AI / ML functionality. Based on this, the network can identify which inference configuration the UE is referring to as applicable or not applicable, out of the inference configuration(s) the UE may have been configured with. The report including applicability information (e.g. applicability indication) may correspond to an RRC message including applicability information such as an RRC Reconfiguration Complete message (or RRC Resume Complete, in case the message is an RRC Resume message), or a UE Assistance Information message (or an RRC Measurement Report), transmitted by the UE to the network in response to the AI / ML functionality configuration.

[0036] In this regard, Figure 5A illustrates the operation of a UE 500 and a network node 502 (e.g., a gNB or similar 6th Generation (6G) Radio Access Network (RAN) node) in accordance with embodiments of the present disclosure. Optional steps are represented by dashed lines / boxes. As illustrated, the UE 500 receives, from the network node 502, a first message (e.g., a first RRC message such as, e.g., an RRC Reconfiguration message) including one or more AI / ML functionality configurations (e.g., one or more inference configurations of an AI / ML functionality) and, for each of the AI / ML functionality configurations, associated identification information (step 504A). As discussed below in detail, for each of the AI / ML functionality configurations, the associated identification information includes any one or more of (e.g., any one or any combination of two or more of) the following identifications: (i) a CSI reporting configuration identification, (ii) a serving cell configuration identification, and (iii) a cell group configuration identification. For at least one AI / ML functionality configuration (e.g., for at least one of the AI / ML functionality configurations received in step 504A or for all of the AI / ML functionality configurations received in step 504A), the UE 500 performs an applicability determination (step 506A). The UE 500 transmits, to the network node 502, a second message (e.g., a second RRC message such as, e.g., an RRC Reconfiguration Complete message) including applicability information per AI / ML functionality configuration (e.g., per inference configuration) (step 508A). For each AI / ML functionality configuration for which the applicability information is included in the second message, the second message also includes the associated identification information for that AI / ML functionality configuration. Again, for each of the AI / ML functionality configurations, the associated identification information includes any one or more of (e.g., any one or any combination of two or more of) the following identifications: (i) a CSI reporting configuration identification, (ii) a serving cell configuration identification, and (iii) a cell group configuration identification.

[0037] Figure 5B illustrates one example of the procedure of Figure 5A in which the first message is an RRC Reconfiguration message, the second message is an RRC Reconfiguration complete message, and the AI / ML functionality configurations are inference configurations. Otherwise, steps 504B and 508B of Figure 5B are the same as steps 504A and 508A of Figure 5A, respectively. Now, further details regarding various aspects of the procedures of Figures 5A and 5B will be provided.

[0038] According to an embodiment of the method, the UE 500 includes in the second message of step 508A or the RRC Reconfiguration Complete message of Figure 508B (or within a report contained therein), for the (each) AI / ML functionality configuration (e.g. an inference configuration of an AI / ML functionality) for which applicability information is reported, one or more of the following identifiers (e.g., as the associated identification information for that AI / ML functionality configuration):An identification of a Channel State Information (CSI) reporting configuration in which the inference configuration (e.g. set A, set B, etc.) was received by the UE 500;An identification of a serving cell configuration (e.g. Serving cell index) of the serving cell in which the UE 500 is meant to report one or more inference / predicted values (e.g. predicted Reference Signal Received Power (RSRP)) according to the AI / ML functionality configuration (e.g. inference configuration). Such serving cell configuration received by the UE 500 includes the CSI reporting configuration in which the inference configuration is included. Hence in this case the UE 500 includes in the report the identification of the serving cell associated to the AI / ML functionality configuration (e.g. inference configuration).An identification of a cell group in which the inference configuration is associated with. That is a cell group in which the serving cell in which the CSI reporting configuration is included.

[0039] In one embodiment, for each inference configuration of an AI / ML functionality for which the UE 500 is to indicate the applicability information (e.g. whether that is applicable or not) or for each inference configuration of an AI / ML functionality for which the AI / ML functionality is applicable, the UE 500 includes in the second message of step 508A or the RRC Reconfiguration Complete message of step 508B (or the included report) the identification of the CSI reporting configuration in which the inference configuration was configured, with the indication of the serving cell associated to the included CSI reporting configuration, and / or an identification of the cell group of that serving cell e.g. Master Cell Group (MCG) or Secondary Cell Group (SCG).

[0040] Certain embodiments may provide one or more of the following technical advantage(s). One example of an advantage provided by embodiments of the present disclosure is that the UE 500 would be able to receive multiple inference configuration(s) (e.g. associated to multiple cells groups and / or multiple serving cells within the same cell group) and still report applicability and properly identify them in the report of applicability information.

[0041] In some embodiments, the UE 500 receives the first message (e.g. RRC Reconfiguration, RRC Resume) including the at least one AI / ML functionality configuration, including an inference configuration and / or an applicability reporting configuration, which may be simply called inference configuration, received by the UE 500 per cell group and / or per serving cell within the cell group. Furthermore, one inference configuration may be one inference configuration or may be one inference related parameter set which is configured for applicability report only. For example, for Al-based beam management, one inference configuration may include associated ID, CSI Reference Signal (CSI-RS) resource information of Set B for measurement, CSI-RS resource related information of Set A for prediction, report content related information; while one inference related parameter set may include associated ID, Set A related information, Set B related information, report content related information, time instances related information for measurements, time instances related information for prediction and so on.

[0042] In the context of the present disclosure, the term "AI / ML functionality" may be called a "supported functionality" the UE 500 can indicate by using UE capability signaling. A supported functionality is one or more functionalities for and / or associated to beam management and / or CSI reporting, or mobility operations, such as the reporting of time domain and / or spatial domain or frequency domain predictions (inference). It could be said as the ability the UE 500 has to produce an output of an inference function. For example, reporting of time-domain prediction(s) of SSB and / or CSI-RS measurement information (e.g. predicted RSRP) may be considered as an AI / ML functionality which is a "supported functionality" by the UE 500 when the UE 500 reports a capability associated to it (via RRC or Long Term Evolution (LTE) Positioning Protocol (LPP) signaling).

[0043] For example, "spatial domain prediction for beam management or for a mobility procedure e.g., handover or reconfiguration with sync, or Primary cell (PCell) change, or Primary Secondary Cell Group cell (PSCell) change" or a related functionality (e.g. reporting and inference of spatial domain info) may be a supported functionality in which the UE 500 may report that is capable of performing and reporting inference / prediction of a set A of beams or cells (e.g. predicted Layer 1 (L1) RSRP values of one or more beams or one or more Synchronization Signal Block (SSB) indexes of a cell or predicted L1 or Layer 3 (L3) RSRP values of one or more cells) based on measurements performed on a set B of beams (e.g. measured L1 / L3 RSRP values of one or more beams or one or more SSB indexes of a cell), in the case of spatial domain predictions.

[0044] For example, "frequency domain prediction for beam management or mobility procedure e.g., handover" or a related functionality (e.g. reporting and inference of frequency domain info) may be a supported functionality in which the UE 500 may indicate that is capable of performing and reporting inference (e.g., prediction of the radio link quality of a set A of beams or cells (e.g. predicted L1 RSRP values of one or more beams or one or more SSB indexes of a cell or predicted L1 or L3 RSRP values of one or more cells) based on measurements performed on a set B of beams (e.g. measured L1 / L3 RSRP values of one or more beams or one or more SSB indexes of a cell or one or more cells), in the case of frequency domain predictions.

[0045] As another example, "time domain prediction for beam management or a mobility procedure e.g., handover or reconfiguration with sync, or Primary cell (PCell) change, or Primary Secondary Cell Group cell (PSCell) change" or a related functionality (e.g. reporting and inference of time domain info) may be a supported functionality in which the UE 500 may report that is capable of performing and reporting inference of a set of A of beams (e.g. predicted L1 / L3 RSRP values of one or more beams or one or more SSB indexes of a cell in future time instances or the L1 / L3 RSRP value of one or more cells in the future time instances) based on measurements performed on a set B of beams or cells (e.g. measured L1 / L3 RSRP values of one or more beams or one or more SSB indexes of a cell and / or L1 / L3 RSRP value of one or more cells), in the case of time domain predictions.

[0046] As another example, beam management - downlink (DL) transmit (Tx) beam prediction for both UE-sided model and network (NW)-sided model, including:Spatial-domain DL Transmitted (Tx) beam prediction for Set A of beams based on measurement results of Set B of beams ("BM-Case1")Temporal DL Tx beam prediction for Set A of beams based on the historic measurement results of Set B of beams ("BM-Case2")

[0047] As another example, positioning accuracy enhancements, including:Direct AI / ML positioning, such as:Ο UE-based positioning with UE-side model, direct AI / ML positioningUE-assisted / Location Management Function (LMF)-based positioning with LMF- side model, direct AI / ML positioningNext Generation Radio Access Network (NG-RAN) node assisted positioning with LMF-side model, direct AI / ML positioningAI / ML assisted positioning, such as:Ο UE-assisted / LMF-based positioning with UE-side model, AI / ML assisted positioningΟ NG-RAN node assisted positioning with gNB-side model, AI / ML assisted positioning

[0048] As another example, CSI compression e.g., considering extending the spatial / frequency compression to spatial / temporal / frequency compression, cell / site specific models, CSI compression plus prediction (compared to Rel-18 non-AI / ML based approach)

[0049] According to embodiments of the present disclosure, the UE 500 receives (e.g., in step 504A or step 504B) from the network the first message (e.g. RRC Reconfiguration, RRC resume), including the at least one AI / ML functionality configuration (e.g. including an inference configuration in a CSI reporting configuration) based on which the UE 500 is configured to report back to the network applicability information (e.g. including an applicability indication for an AI / ML functionality) for the at least one AI / ML functionality configuration (e.g., in step 508A or step 504B), wherein the report (e.g., included in or that forms the message of step 508A or step 508B) includes one or more of: (i) an identification of the CSI reporting configuration, (ii) an identification of a serving cell (or identification of a serving cell configuration), and (iii) an identification of a cell group associated to the AI / ML functionality configuration (or an identification of a cell group configuration), associated to the AI / ML functionality configuration.

[0050] Prior to reporting the applicability information (e.g., in step 508A or 508B), the UE 500 determines (e.g., in step 506A or step 506B) whether an AI / ML functionality is applicable or not applicable. An AI / ML functionality determined to be applicable is an "applicable AI / ML functionality" i.e. is a functionality the UE 500 is ready to apply for model inference, or, in other words, the UE 500 is able to perform the inference and / or report the inference and / or perform further actions based on the AI / ML functionality configuration (e.g., the inference configuration and / or applicability reporting configuration). So, when the UE 500 is provided with an inference configuration ("at least one inference configuration") for performing inference(s) using an AI / ML model (e.g. perform predicted L1 RSRP for beams and / or SSB indexes and / o CSI-RS resource indicator(s) and / or L1 RSRP measurements for beams and / or SSB indexes and / or CSI-RS resource indicator(s) to be used as input to an AI / ML model) and report inference information derived from the inference(s)), whether the UE 500 can perform inference(s) using an AI / ML model and report inference information derived from the inference(s)) according to the at least one inference related configuration. In this context, the at least one inference related configuration may include one or more parameters for CSI resources (e.g. a CSI resource configuration) to be measured and / or predicted and / or one or more parameters for reporting (e.g. in a CSI reporting configuration); thus, it may be said that an inference related configuration includes a measurement configuration.

[0051] According to embodiments of the present disclosure, the UE 500 may use one or more "applicability condition(s)" which represent a set of conditions for determining whether an AI / ML model / functionality (also denoted a "supported functionality") is applicable or not. An AI / ML functionality (and / or AI / ML model) is applicable when there is at least an inference related configuration (or simply inference configuration) received by the UE 500 (provided by the gNB) out of multiple inference related configurations received (e.g. in a single RRC Reconfiguration message) for which the AIML model / functionality (the supported functionality) is applicable i.e. the UE 500 is able to produce outputs of an AI / ML model, wherein the outputs are called inference(s).

[0052] An AI / ML functionality is not applicable (or non-applicable) when there is no "inference related configuration" (which may also be simply called an inference configuration) received by the UE 500 for which the AI / ML model / functionality (the supported functionality) is applicable. As stated earlier an "inference configuration" may include one or more parameters for CSI resources (e.g. a CSI resource configuration) to be measured and / or predicted and / or one or more parameters for reporting (e.g. in a CSI reporting configuration); thus, it could be said that the "inference configuration" includes at least one radio measurement configuration and inference configuration.

[0053] According to one option, the UE 500 determines whether an AI / ML functionality is applicable or not possibly based on one or more UE-side additional condition(s), such as UE speed, scenario, location, cell the UE is connected to, hardware capabilities, etc.

[0054] According to one option, the UE 500 determines whether an AI / ML functionality is applicable or not possibly based on one or more network (NW)-side additional conditions, such as:Set A and / or Set BMapping relationship of Set A and Set B, including ordering to (a set of ID, or resource)Consistency of downlink spatial domain transmission filters corresponding to the beams in Set A and Set B.Quasi Co-Located (QCL) assumptionThe order of model input and model output between reference signal (RS) and Tx beams can be pre-defined.Transmission powerUE distributionantenna height and / or other antenna propertiesDeployment scenarios (e.g., Inter-Site Distance (ISD), Urban micro (Umi) / Urban macro (Uma))NW-side resource configuration(s) which may be considered as NW implementation-based configurations which may possibly impact the inference performance for a UE sided model. For instance, beam and Tx port mapping relationship in the gNodeB (gNB) for a given cell, NW antenna shape, Antenna dip angle, height of the tower / gNB, etc.

[0055] The NW-side additional conditions, configured for the UE 500 to determine the applicability of an AI / ML functionality, may also be characterized as network implementation- based configurations (settings) which can impact the consistency between training and inference for UE sided model. For example, if the UE 500 has performed training for an AI / ML model and / or functionality in the first and / or the second cell for a given set of network configuration(s) (settings), the inference is expected to produce accurate outputs under similar conditions.

[0056] Each NW-side additional condition may be identified by an associated ID.

[0057] It may also be said that for an AI / ML- functionality (or AI / ML-enabled feature / feature group (FG)), additional conditions refer to any aspects that are assumed for the training of the model but are not a part of UE capability for the AI / ML-enabled feature / FG. It does not imply that additional conditions are necessarily specified. Additional conditions can be divided into two categories: NW-side additional conditions and UE-side additional conditions. Note: whether specification impact is needed is a separate discussion

[0058] To determine whether an AI / ML functionality is applicable or not the UE 500 may receive one or more AI / ML functionality configuration(s) which may include one or more NW-side additional conditions, such as the ones listed above and / or based on UE-side additional conditions, known at the UE 500 e.g. the cell the UE is connected to, its current location, UE speed, etc.

[0059] In regard to the AI / ML functionality configuration (e.g., inference configuration) of step 504A or step 504B, in the context of the present disclosure, an AI / ML functionality configuration may in one option include one or more parameters, Information Element(s) (IE(s)), fields and / or configuration(s) necessary and / or sufficient for the UE 500 to operate the AI / ML functionality, such as an inference configuration or an inference related configuration (which may also be considered a full and / or complete inference configuration, sufficient for the operation of the AI / ML functionality in the second cell). In other words, when the UE 500 receives the inference configuration or an inference related configuration for an AI / ML functionality for a given serving cell in a given cell group, the UE 500 can generate inference information (e.g. as output of an AI / ML model associated with the AI / ML functionality) and possibly report to the serving cell.

[0060] In the context of the present disclosure, an inference configuration or an inference related configuration may correspond to a Channel State information (CSI) measurement configuration (e.g. in an IE CSI-MeasConfig, CSI-ReportConfig, CSI-ResourceConfig) associated to a set A and or set B of beams for a beam management AI / ML functionality. The inference configuration may further include one or more of:Synchronization Signal Block (SSB) identifiers associated to a serving cell and / or a neighbor cell;CSI-RS resource identifiers associated to a serving cell and / or a neighbor cell;Beam identifiers associated to a serving cell and / or a neighbor cell;Mobility Reference Signal(s) identifiers associated to a serving cell and / or a neighbor cell;Candidate inference configuration(s) Set A and / or B (1); Set A and / or B (2); Set A and / or B (3), etc.

[0061] In the context of the present disclosure, an inference configuration or an inference related configuration may include and / or point to or indicate a first set (set A) of measurement resources (e.g. beams, SSB indexes and / or CSI-RS resource identifiers, Mobility Refence Signal identifiers) in which the UE 500 performs radio measurement predictions (inferences, such as predicted RSRP values), and a second set (set B) of radio measurement resources (e.g. beams, SSB indexes and / or CSI-RS resource identifiers, Mobility Refence Signal identifiers) in which the UE 500 can perform radio measurement in order to determine the radio measurement predictions on the first set. That may also include one or more configuration(s) associated to network side (NW-side) additional conditions reflecting the NW operational properties, such as:• Mapping relationship of Set A and Set B, including ordering to (a set of IDs, or resources)• Consistency of downlink spatial domain transmission filters corresponding to the beams in Set A and Set B.• QCL assumption• The order of model input and model output.• between RS and Tx beams can be pre-defined.• Transmission power• UE distribution• antenna height• Deployment scenarios (e.g., ISD, Umi / Uma / rural / indoor / indoor office / indoor factory, specific area(s))• UE speed

[0062] The inference configuration or an inference related configuration may include a list of IDs referring to the set A and set B (or to the resources within the set A / B) and referring to one or more NW-side additional conditions.

[0063] In the context of the present disclosure, the AI / ML functionality configuration may include one or more of the following:An inference configuration for a beam management functionality (e.g. time-domain prediction of beam information)Ο In one example, the inference configuration includes the CSI reporting configuration including parameters indicating how the UE 500 is to report time- domain predictions of beam information (e.g. beam indexes and / or SSB indexes and / or time-domain prediction of beam measurements) and / or spatial-domain predictions of beam information.Ο In one example, the inference configuration includes the CSI resource configuration for resources (e.g. SSB indexes and / or CSI-RS resources) which the UE 500 measures and provides as input to an AI / ML model (or inference function) to produce inference outputs e.g. the actual time-domain predictions of beam information (e.g. beam indexes and / or SSB indexes and / or time-domain prediction of beam measurements) and / or spatial-domain predictions of beam information to be included in a report.An inference configuration for positioning functionalityAn inference configuration for CSI reporting functionalityA configuration enabling the UE 500 to determine whether the AI / ML functionality is applicable or not.Network conditions such as Set A / set B configuration(s).A state indication for the AI / ML functionality, e. g., activated', inactivated', deactivated'.An indication on whether the UE 500 is allowed to consider the AI / ML functionality as 'activated' when the functionality is determined by the UE 500 to be applicable.

[0064] The AI / ML functionality configuration may include also an identifier of the AI / ML functionality to which the configuration (e.g. inference configuration) is referred to, wherein the AI / ML functionality could be for example, beam management functionality, spatial beam management functionality, temporal beam management functionality, L3 mobility functionality, positioning functionality, CSI compression functionality, CSI prediction functionality, etc.

[0065] According to one option, the AI / ML functionality configuration includes an identifier of that configuration (i.e., includes the associated identification information for that configuration), which is later to be reported by the UE 500 to the network when the UE 500 indicates whether that particular AI / ML functionality configuration is applicable or not (e.g., in step 508A or step 508B). The identifier of the configuration of the AI / ML functionality includes any one of or any combination of two or more of the following identifiers: i) a CSI reporting configuration identifier in which the inference configuration is received by the UE 500, ii) an identification of the serving cell in which the UE 500 is to report the inference, iii) a cell group identification for the cell group (e.g. MCG, SCG) of that serving cell.

[0066] In the context of the present disclosure, an AI / ML functionality configuration may in another option include one or more parameters, IE(s), fields and / or configuration(s) necessary and / or sufficient for the UE 500 to report the applicability of the AI / ML functionality in the second cell, such as an applicability reporting configuration. In other words, when the UE 500 receives the applicability reporting configuration for an AI / ML functionality the UE 500 can determine whether the AI / ML functionality, supported by the UE 500, and / or associated configuration(s) of that AI / ML functionality, is applicable or not applicable. The applicability reporting configuration may include one or more of the following:An indication that the UE 500 is allowed to do UE assistance information reporting to the second cell e.g. by configuring it in the IE OtherConfig in the RRCReconfiguration message and / or the HandOver (HO) command.An indication of the AI / ML functionality for which the UE 500 transmits the applicability report e.g. indications of the applicability associated with the indicated AIML functionality.One or more NW-side additional condition(s) (included e.g. in the IE OtherConfig in the RRCReconfiguration message and / or the HO command), e.g. for the UE 500 to determine whether the AI / ML model / functionality has been trained under similar conditions, such as one or more of the following:• Configuration(s) related to the mapping relationship of Set A and Set B, including ordering to (a set of IDs, or resources)• Configuration(s) related to the consistency of downlink spatial domain transmission filters corresponding to the beams in Set A and Set B. In that context, consistency may correspond to one or more of:Set size consistency for Set B, Set A: consistency in number of beams and / or associated resources for Set B and Set A, across training and inferenceperiodicity consistency for Set B, Set A: consistency in periodicity of beams and / or associated resources for Set B and Set A, across training and inferencerelationship of Set A / Set B (Set B is a subset of Set A or not): consistency in relationship of beams and / or associated resources for Set B and Set A, i.e., whether Set B is a subset of Set A, across training and inference• Configuration(s) related to the QCL assumption(s)Beam configuration(s) of the network such as:Beam characteristics, e.g., beam boresight direction (azimuth and elevation), 3dB beamwidth. In one sub-option the beam characteristics may be associated to an identifier indicated to the UE 500 during training and AI / ML configuration, for checking of the consistency between training and inference.Set A / Set B related info, e.g., the beam index of set B.Information about the beam codebook and / or indexing / mapping of Set A and Set B i.e. info on whether the AI / ML Model / functionality is trained with a data set with a certain beam codebook and index / mapping of Set A / Set B, inference works for the same beam codebook and index / mapping of Set A / Set B.• Configuration(s) related to the order of model input and model output between RS and Tx beams can be pre-defined.• Configuration(s) related to the transmission power and / or power levels the gNodeB and / or the serving cells are operating• Configuration(s) related to the UE distribution• Configuration(s) related to Antenna height• Configuration(s) related to the deployment scenarios (e.g., ISD, Umi / Uma / rural / indoor / indoor office / indoor factory, specific area(s))• Configuration(s) related to UE speedAn indication of an identifier (associated ID) associated to one or more network conditions, so that the UE 500 assumes that NW-side additional conditions with the same associated ID are consistent at least within a cell.In one option, NW-side additional condition may be associated to an inference configuration (e.g. resource set A, to be inferred and / or estimated and / or predicted, and / or resource set B, in which the UE 500 should perform the measurement to infer / estimate / predict the radio measurement associated to the set A resources) and / or one training configuration (e.g. resource of CSI resources configured by the gNB at the time of the UE 500 performing UE-side model training) identified by the same associated ID, for the second cell (which is a neighbor cell which may become the target cell in a handover). The UE 500 may perform training of one or AI / ML functionalities / models with different sets of collected data via training configuration identified by its associated ID (i.e. one associated ID->one training configuration->one AI model).

[0067] An inference configuration and / or an indication or pointer to an inference configuration for which the UE 500 is to report the applicability information.

[0068] As illustrated in Figures 5A and 5B and described above, embodiments of a procedure are disclosed herein in which a UE identifies an AI / ML functionality configuration (e.g. an inference configuration of an AI / ML functionality) for which the UE 500 transmits a report including applicability information (e.g. applicability indication) e.g. the UE 500 indicates whether an AI / ML functionality is 'applicable' or 'not applicable' and one or more identifiers for a particular inference configuration of the AI / ML functionality. In one option, rather than transmitting an indication indicating whether an AI / ML functionality is 'applicable' or 'not applicable', the UE 500 only transmits applicability indication associated to those AI / ML functionalities that are applicable out of the configured AIML functionalities. Based on this, the network can identify which inference configuration the UE 500 is referring to as applicable or not applicable, out of the inference configuration(s) the UE 500 may have been configured with. The report including applicability information (e.g. applicability indication) (e.g., of step 508A or 508B) may correspond to an RRC message including applicability information which as an RRC Reconfiguration Complete message, or a UE Assistance Information message (or an RRC Measurement Report), transmitted by the UE 500 to the network in response to the AI / ML functionality configuration.

[0069] According to embodiments of the present disclosure, the UE 500 includes in the report (e.g., in the report included in or formed by the second message of step 508A or 508B), for an AI / ML functionality configuration (e.g. an inference configuration of an AI / ML functionality), one or more of the following identifiers:An identification of a CSI reporting configuration in which the inference configuration (e.g. set A, set B, etc.) was received by the UE 500;Ο In one example, the identification of a CSI reporting configuration corresponds to the CSI-ReportConfigId of the instance of the IE CSI-ReportConfig in which the inference configuration was received by the UE 500.In another example, the identification corresponds to a csi-ResourceConfigId associated to group of one or more NZP-CSI-RS-ResourceSet, and / or CSI-SSB- ResourceSet.An identification of a serving cell configuration (e.g. Serving cell index) of the serving cell in which the UE 500 is meant to report one or more inference / predicted values (e.g. predicted RSRP) according to the AI / ML functionality configuration (e.g. inference configuration). Such serving cell configuration received by the UE 500 includes the CSI reporting configuration in which the inference configuration is included. Hence, this is the identifier of the serving cell associated to the concerned CSI reporting configuration included in the report.○ In one option, the identification of the serving cell configuration corresponds to a serving cell index (per cell group) which is unique within a given cell group the UE 500 may be configured with e.g. MCG or SCG. In such an option, the inclusion of the serving cell index per cell group indicates a serving cell.Ο In one option, the identification of the serving cell configuration corresponds to a serving cell index which is unique across multiple cell groups the UE 500 may be configured with e.g. MCG and / or SCG. In such an option, the inclusion of the serving cell index indicates both a serving cell and its associated cell group.In one option, the identification of the serving cell configuration corresponds to an identification of a serving cell associated to that serving cell configuration, such as a cell identifier and / or an associated frequency information, e.g., Synchronization Signal Sequence (SSB) frequency.An identification of a cell group in which the inference configuration is associated with. That is a cell group in which the serving cell in which the CSI reporting configuration is included.In the option in which the identification of the serving cell configuration corresponds to a serving cell index which is unique across multiple cell groups the UE 500 may be configured with (e.g. MCG and / or SCG, the serving cell index also corresponds to an identification of a cell group since the reporting of its value indicate to the network a particular serving cell of a particular cell group.CSI reporting configuration Id

[0070] In one set of embodiments, the UE 500 receives a message (e.g., the first message of step 504A or 504B) from the network (e.g. RRC Reconfiguration, RRC Resume) including one or more AI / ML functionality configurations, wherein the AI / ML functionality configuration comprises an inference configuration(s) for an AI / ML functionality within an instance of a Channel State Information (CSI) reporting configuration information element (e.g. in an instance of the Information Element (IE) CSI-ReportConfig), wherein each of the instances has an associated identification of the CSI reporting configuration, such as a CSI reporting configuration identifier (e.g. reportConfigId of IE CSI-ReportConfigId). In other words, the message is configuring the UE 500 with an AI / ML functionality, such as AI / ML for Beam Management (BM), e.g., reporting of time-domain beam prediction(s) and / or reporting of spatial-domain beam prediction. In response to receiving the message including the one or more inference configuration(s) for an AI / ML functionality, the UE 500 transmits a report (e.g. in an RRC Reconfiguration Complete, or UE Assistance Information message, or RRC Resume Complete) (e.g., in step 508A or 508B) including applicability information (e.g. applicability indication) for one or more of the received inference configurations, wherein for each applicability information to be reported the UE 500 includes the associated CSI reporting configuration identifier. In one option, prior to transmitting the report, the UE 500 includes a first field (e.g. inferenceId) in the report, wherein the UE 500 sets the first field to the CSI reporting configuration identifier (e.g. CSI-ReportConfigId) associated with the CSI reporting configuration in which the inference configuration was received by the UE 500 and for which the UE 500 is transmitting the associated applicability information.Serving cell configuration Id

[0071] In one set of embodiments, the UE 500 receives (e.g., in step 504A or 504B) a message from the network (e.g. RRC Reconfiguration, RRC Resume) including one or more _AI / ML functionality configuration(s), wherein the AI / ML functionality configuration comprises an inference configuration(s) for an AI / ML functionality within a serving cell configuration associated to a serving cell configuration identifier (e.g. servCellIndex, ServCellIndex, sCellIndex, or IE SCellIndex). In response to receiving the message including the one or more inference configuration(s) for an AI / ML functionality, the UE 500 transmits a report (e.g. in an RRC Reconfiguration Complete, or UE Assistance Information message) (e.g., in step 508A or 508B) including applicability information (e.g. applicability indication) one or more of the received inference configurations, wherein for each applicability information to be reported the UE 500 includes the associated serving cell configuration identifier (e.g. servCellIndex, ServCellIndex, sCellIndex, or IE SCellIndex). Thanks to the inclusion of the serving cell configuration identifier, it is possible to configure the UE 500 with inference configuration(s) for multiple serving cells (e.g. within a cell group, like the MCG or the SCG and / or for multiple cell groups) and still be able to report applicability information for the associated inference configuration.In one option, the identification of the serving cell configuration (i.e. the serving cell configuration identifier e.g. servCellIndex, ServCellIndex, sCellIndex, or IE SCellIndex) corresponds to a serving cell index (per cell group) which is unique within a given cell group the UE 500 may be configured with e.g. MCG or SCG. In such an option, the inclusion of the serving cell index per cell group indicates a serving cell.

[0072] In one option, the identification of the serving cell configuration corresponds to a serving cell index (i.e. the serving cell configuration identifier e.g. servCellIndex, ServCellIndex, sCellIndex, or IE SCellIndex) which is unique across multiple cell groups the UE 500 may be configured with e.g. MCG and / or SCG. In such an option, the inclusion of the serving cell index indicates both a serving cell and its associated cell group. Or, in other words, the indication indicates both the serving cell and its associated cell group.

[0073] In one option, prior to transmitting the report, the UE 500 includes a second field (e.g. ServingCellInferenceId) in the report, wherein the UE 500 sets the second field to the serving cell configuration identifier (e.g. servCellIndex, ServCellIndex, sCellIndex, or IE SCellIndex) associated with the cell configuration in which the inference configuration was received by the UE 500.

[0074] This may be useful, for example, when the UE 500 receives one inference configuration per serving cell in a set or sub-set of serving cells the UE 500 is configured with.CSI reporting configuration Id + serving cell configuration Id

[0075] In one set of embodiments, the UE 500 receives a message (e.g., in step 504A or 504B) from the network (e.g. RRC Reconfiguration, RRC Resume) including one or more AI / ML functionality configuration, wherein the AI / ML functionality configuration comprises an inference configuration(s) for an AI / ML functionality within an instance of a CSI reporting configuration information element (e.g. in an instance of the IE CSI-ReportConfig) within a serving cell configuration, wherein each of the instances has an associated CSI reporting configuration identifier (e.g. reportConfigId of IE CSI-ReportConfigId), and the serving cell configuration in which the CSI reporting configurations with the inference configurations are included is associated to a serving cell configuration identifier (e.g. servCellIndex, ServCellIndex, sCellIndex, or IE SCellIndex). In response to receiving the message including the one or more inference configuration(s) for an AI / ML functionality, the UE 500 transmits a report (e.g. in an RRC Reconfiguration Complete, or UE Assistance Information message) (e.g., in step 508A or 508B) including applicability information (e.g. applicability indication) one or more of the received inference configurations, wherein for each applicability information to be reported the UE 500 includes the associated CSI reporting configuration identifier and the associated serving cell configuration identifier (e.g. servCellIndex, ServCellIndex, sCellIndex, or IE SCellIndex). Thanks to the inclusion of both the CSI reporting configuration and the serving cell configuration identifier it would be possible to configure the UE 500 with inference configuration(s) for multiple serving cells (e.g. within a cell group, like the MCG or the SCG and / or for multiple cell groups) and still be able to report applicability information for the associated inference configuration.

[0076] In one option, the identification of the serving cell configuration (i.e. the serving cell configuration identifier e.g. servCellIndex, ServCellIndex, sCellIndex, or IE SCellIndex) corresponds to a serving cell index (per cell group) which is unique within a given cell group the UE 500 may be configured with e.g. MCG or SCG. In such an option, the inclusion of the serving cell index per cell group indicates a serving cell.

[0077] In one option, the identification of the serving cell configuration corresponds to a serving cell index (i.e. the serving cell configuration identifier e.g. servCellIndex, ServCellIndex, sCellIndex, or IE SCellIndex) which is unique across multiple cell groups the UE 500 may be configured with e.g. MCG and / or SCG. In such an option, the inclusion of the serving cell index indicates both a serving cell and its associated cell group. Or, in other words, the indication indicates both the serving cell and its associated cell group.

[0078] In one option, prior to transmitting the report, the UE 500 includes a second field (e.g. ServingCellInferenceId) in the report, wherein the UE 500 sets the second field to the serving cell configuration identifier (e.g. servCellIndex, ServCellIndex, sCellIndex, or IE SCellIndex) associated with the cell configuration in which the inference configuration was received by the UE 500.

[0079] In one option, prior to transmitting the report, the UE 500 includes both a first field (e.g. inferenceId) and second field in the report, wherein the UE 500 sets the first field to the CSI reporting configuration identifier (e.g. CSI-ReportConfigId) associated with the CSI reporting configuration in which the inference configuration was received by the UE 500 AND the UE 500 sets the second field to the serving cell configuration identifier (e.g. servCellIndex, ServCellIndex, sCellIndex, or IE SCellIndex) associated with the cell configuration in which the inference configuration was received by the UE 500. Thanks to both fields, the UE 500 identifies an inference configuration of a given serving cell, for which the applicability information is being reported.Inference configuration Id

[0080] In one set of embodiments, the UE 500 receives (e.g., in step 504A or 504B) a message from the network (e.g. RRC Reconfiguration, RRC Resume) including one or more _AI / ML functionality configuration, wherein the AI / ML functionality configuration comprises an inference configuration(s) for an AI / ML functionality associated with an inference configuration identifier. In other words, the message is configuring the UE 500 with an AI / ML functionality, such as AI / ML for Beam Management (BM), e.g., reporting of time-domain beam prediction(s) and / or reporting of spatial-domain beam prediction. In response to receiving the message including the one or more inference configuration(s) for an AI / ML functionality, the UE 500 transmits a report (e.g. in an RRC Reconfiguration Complete, or UE Assistance Information message, or RRC Resume Complete) (e.g., in step 508A or 508B) including applicability information (e.g. applicability indication) one or more of the received inference configurations, wherein for each applicability information to be reported the UE 500 includes the associated inference configuration identifier. In one option, prior to transmitting the report, the UE 500 includes a first field (e.g. inferenceId) in the report, wherein the UE 500 sets the first field to the inference configuration identifier associated with the inference configuration received by the UE 500 and for which applicability information is being reported. This may be a new identifier set in a new information element and / or field defined for including an inference configuration.Inference configuration Id + serving cell configuration Id

[0081] In one set of embodiments, the UE 500 receives (e.g., in step 504A or 504B) a message from the network (e.g. RRC Reconfiguration, RRC Resume) including one or more AI / ML functionality configuration, wherein the AI / ML functionality configuration comprises an inference configuration(s) for an AI / ML functionality within an instance of a CSI reporting configuration information element (e.g. in an instance of the IE CSI-ReportConfig) within a serving cell configuration, wherein each of the instances has an associated inference configuration identifier, and the serving cell configuration in which the inference configuration is included is associated to a serving cell configuration identifier (e.g. servCellIndex, ServCellIndex, sCellIndex, or IE SCellIndex). In response to receiving the message including the one or more inference configuration(s) for an AI / ML functionality, the UE 500 transmits a report (e.g. in an RRC Reconfiguration Complete, or UE Assistance Information message) (e.g., in step 508A or 508B) including applicability information (e.g. applicability indication) one or more of the received inference configurations, wherein for each applicability information to be reported the UE 500 includes the associated inference configuration identifier and the associated serving cell configuration identifier (e.g. servCellIndex, ServCellIndex, sCellIndex, or IE SCellIndex). Thanks to the inclusion of both the inference reporting configuration and the serving cell configuration identifier it would be possible to configure the UE 500 with inference configuration(s) for multiple serving cells (e.g. within a cell group, like the MCG or the SCG and / or for multiple cell groups) and still be able to report applicability information for the associated inference configuration.

[0082] In one option, the identification of the serving cell configuration (i.e. the serving cell configuration identifier e.g. servCellIndex, ServCellIndex, sCellIndex, or IE SCellIndex) corresponds to a serving cell index (per cell group) which is unique within a given cell group the UE 500 may be configured with e.g. MCG or SCG. In such an option, the inclusion of the serving cell index per cell group indicates a serving cell.

[0083] In one option, the identification of the serving cell configuration corresponds to a serving cell index (i.e. the serving cell configuration identifier e.g. servCellIndex, ServCellIndex, sCellIndex, or IE SCellIndex) which is unique across multiple cell groups the UE 500 may be configured with e.g. MCG and / or SCG. In such an option, the inclusion of the serving cell index indicates both a serving cell and its associated cell group. Or, in other words, the indication indicates both the serving cell and its associated cell group.

[0084] In one option, prior to transmitting the report, the UE 500 includes a second field (e.g. ServingCellInferenceId) in the report, wherein the UE 500 sets the second field to the serving cell configuration identifier (e.g. e.g. servCellIndex, ServCellIndex, sCellIndex, or IE SCellIndex) associated with the cell configuration in which the inference configuration was received by the UE 500.

[0085] In one option, prior to transmitting the report, the UE 500 includes both a first field (e.g. inferenceId) and second field in the report, wherein the UE 500 sets the first field to the inference configuration identifier associated with the inference configuration received by the UE 500 AND the UE 500 sets the second field to the serving cell configuration identifier (e.g. servCellIndex, ServCellIndex, sCellIndex, or IE SCellIndex) associated with the cell configuration in which the inference configuration was received by the UE 500. Thanks to both fields, the UE 500 identifies an inference configuration of a given serving cell, for which the applicability information is being reported.Cell group Id

[0086] In one set of embodiments, the UE 500 receives (e.g., in step 504A or 504B) a message from the network (e.g. RRC Reconfiguration, RRC Resume) including one or more AI / ML functionality configuration, wherein the AI / ML functionality configuration comprises an inference configuration(s) for an AI / ML functionality as part of a Cell Group associated to a cell group identifier. In response to receiving the message including the one or more inference configuration(s) for an AI / ML functionality, the UE 500 transmits a report (e.g. in an RRC Reconfiguration Complete, or UE Assistance Information message) (e.g., in step 508A or 508B) including applicability information (e.g. applicability indication) one or more of the received inference configurations, wherein for each applicability information to be reported the UE 500 includes an identification of the cell group in which that inference configuration is received by the UE 500. Thanks to the inclusion of the identification of the cell group it would be possible to configure the UE 500 with inference configuration(s) for multiple cell groups (e.g., MCG and SCG) and still be able to report applicability information for the associated inference configuration. In one option, this is used when the UE 500 needs to select one inference configuration (when configured with multiple inference configuration(s)) per cell group to report the applicability information.

[0087] In one option, the identification of the cell group corresponds to a cell group identifier. When the UE 500 transmits the applicability information for a given inference configuration, the UE 500 includes the cell group identifier for the cell group of the inference configuration for which the UE 500 is reporting the applicability.

[0088] In one option, the identification of the cell group corresponds to a field name e.g. masterCellGroupApplicabilityinfo for the information about the MCG, and e.g. masterCellGroupApplicabilityinfo for the information about the SCG.CSI reporting configuration Id + serving cell configuration Id + cell group Id

[0089] In one set of embodiments the UE 500 receives (e.g., in step 504A or 504B) a message from the network (e.g. RRC Reconfiguration, RRC Resume) including one or more AI / ML functionality configuration, wherein the AI / ML functionality configuration comprises an inference configuration(s) for an AI / ML functionality within an instance of a CSI reporting configuration information element (e.g. in an instance of the IE CSI-ReportConfig) within a serving cell configuration of a Cell Group Configuration (e.g. MCG or SCG), wherein each of the instances has an associated CSI reporting configuration identifier (e.g. reportConfigId of IE CSI- ReportConfigId), and the serving cell configuration in which the CSI reporting configurations with the inference configurations are included is associated to a serving cell configuration identifier (e.g. servCellIndex, ServCellIndex, sCellIndex, or IE SCellIndex), and the Cell Group in which the serving cell configuration is included has a cell group identifier. In response to receiving the message including the one or more inference configuration(s) for an AI / ML functionality, the UE 500 transmits a report (e.g. in an RRC Reconfiguration Complete, or UE Assistance Information message) (e.g., in step 508A or 508B) including applicability information (e.g. applicability indication) one or more of the received inference configurations, wherein for each applicability information to be reported the UE 500 includes the associated CSI reporting configuration identifier, the associated serving cell configuration identifier (e.g. servCellIndex, ServCellIndex, sCellIndex, or IE SCellIndex) and an identification of the cell group in which that serving cell configuration is included e.g. a cell group identifier, or a field name which indicates the cell group. Thanks to the inclusion of the CSI reporting configuration, the serving cell configuration identifier and an identification of the cell group of the serving cell it would be possible to configure the UE 500 with inference configuration(s) for multiple serving cells (e.g. within a cell group, like the MCG or the SCG) for multiple cell groups (e.g., MCG and SCG) and still be able to report applicability information for the associated inference configuration.

[0090] In one option, the identification of the serving cell configuration (i.e. the serving cell configuration identifier e.g. servCellIndex, ServCellIndex, sCellIndex, or IE SCellIndex) corresponds to a serving cell index (per cell group) which is unique within a given cell group the UE 500 may be configured with e.g. MCG or SCG. In such an option, the inclusion of the serving cell index per cell group indicates a serving cell. Then, in addition, the UE 500 also includes in the report the cell group identifier of the cell group in which the serving cell is configured, so that both the serving cell and its associated cell group are indicated.

[0091] In one option, the identification of the serving cell configuration (i.e. the serving cell configuration identifier e.g. servCellIndex, ServCellIndex, sCellIndex, or IE SCellIndex) corresponds to a serving cell index (per cell group) which is unique across multiple cell groups the UE 500 may be configured with e.g. MCG or SCG. In such an option, the inclusion of the serving cell index indicates both the serving cell and its associated cell group.

[0092] In one option, the identification of the cell group corresponds to a cell group identifier, which is included in the report.Applicability information

[0093] In one set of embodiments, the applicability information (or indication) of an AI / ML functionality (or AI / ML functionality configuration, and / or inference configuration) associated to CSI reporting configuration and / or serving cell configuration and / or cell group configuration comprises one or more of the following:An indication indicating that the AI / ML functionality (or AI / ML functionality configuration, and / or inference configuration) is 'applicable'In one option this indication corresponds to a field and / or IE and / or parameter;In one option this indication corresponds to a field which is set to 'true' to indicate that the AI / ML functionality (or inference configuration) is applicable;Ο An indication indicating that the AI / ML functionality (or AI / ML functionality configuration, and / or inference configuration) is 'not applicable' (or 'non- applicable')In one option this indication corresponds to a field and / or IE and / or parameter;In one option this indication corresponds to the absence of a field and / or the absence of an IE and / or the absence of a parameter;Ο An indication of an applicability status, which may take one or more values, such as 'applicable' or 'not applicable';A recommended (or preferred) AI / ML functionality configuration (or AI / ML functionality configuration, and / or inference configuration) for which the AI / ML functionality becomes applicable i.e. the UE 500 indicates that it may not be applicable for a configuration(x) but it may be applicable for a configuration(y), wherein the applicability indication corresponds to an indication of configuration(y), wherein the configuration (x) corresponds to one or more of the included identifiers; CSI reporting configuration ID, serving cell configuration ID, cell group configuration ID.- In one option that is included only when the UE 500 includes the indication indicating that the AI / ML functionality is 'not applicable';In one option the recommended or preferred AI / ML functionality configuration is provided via an identifier associated to the configuration.An identifier associated to a one or more NW-side additional conditions associated to the AI / ML model e.g. the identifier of the NW configuration(s) (settings) in which the AI / ML model was trained.

[0094] In a set of embodiments, before the UE 500 transmits the applicability information of an AI / ML functionality (or AI / ML functionality configuration, and / or inference configuration) (e.g., in step 508A or 506B), the UE 500 determines whether the AI / ML functionality (or AI / ML functionality configuration, and / or inference configuration) 'applicable' or 'not applicable' (e.g., in step 506A or 506B). In one option, the UE 500 is configured to include the applicability information (or indication) of an AI / ML, and when the UE 500 is configured, the UE 500 determines whether the AI / ML functionality is 'applicable' or 'not applicable'.

[0095] Initial report of applicability informationIn one set of embodiments, the UE 500 receives (e.g., in step 504A or 504B) a message from the network (e.g. RRC Reconfiguration, RRC Resume) including one or more AI / ML functionality configuration, wherein the AI / ML functionality configuration comprises an inference configuration(s) for an AI / ML functionality within an instance of a Channel State Information (CSI) reporting configuration information element (e.g. in an instance of the IE CSI- ReportConfig), wherein each of the instances has an associated identification of the CSI reporting configuration, such as a CSI reporting configuration identifier (e.g. reportConfigId of IE CSI- ReportConfigId), and an associated serving cell configuration identifier for the servi9ng cell in which each CSI reporting configuration is include. In other words, the message is configuring the UE 500 with an AI / ML functionality, such as AI / ML for Beam Management (BM), e.g., reporting of time-domain beam prediction(s) and / or reporting of spatial-domain beam prediction. In response to receiving the message including the one or more inference configuration(s) for an AI / ML functionality, the UE 500 transmits a report (e.g. in an RRC Reconfiguration Complete, or UE Assistance Information message, or RRC Resume Complete) (e.g., in step 508A or 508B) including applicability information (e.g. applicability indication) one or more of the received inference configurations, wherein for each applicability information to be reported the UE 500 includes one or more of: i) identification of a CSI reporting configuration in which the inference configuration (e.g. set A, set B, etc.) was received by the UE 500; ii) the identification of a serving cell configuration (e.g. Serving cell index) of the serving cell in which the UE 500 is meant to report one or more inference / predicted values (e.g. predicted RSRP) according to the AI / ML functionality configuration (e.g. inference configuration); and iii) the identification of a cell group in which the inference configuration is associated with.

[0096] In one option, the message is the first RRC message to be transmitted after the UE 500 has received the inference configuration and corresponds to an RRC Reconfiguration Complete (or an RRC Resume Complete).

[0097] In another option, the message is the second RRC message to be transmitted after the UE 500 has received the inference configuration and corresponds to a UE Assistance Information message. In other words, the UE 500 transmits the RRC Reconfiguration Complete and after that it transmits a UE Assistance information message.

[0098] This may be called the initial report of applicability information since it is transmitted in response to an initial determination at the UE 500 of a configured AI / ML functionality (and / or inference configuration).

[0099] Report of applicability information to be updatedIn one set of embodiments, the UE 500 receives (e.g., in step 504A or 504B) a message from the network (e.g. RRC Reconfiguration, RRC Resume) including one or more AI / ML functionality configuration, wherein the AI / ML functionality configuration comprises an inference configuration(s) for an AI / ML functionality within an instance of a Channel State Information (CSI) reporting configuration information element (e.g. in an instance of the IE CSI- ReportConfig), wherein each of the instances has an associated identification of the CSI reporting configuration, such as a CSI reporting configuration identifier (e.g. reportConfigId of IE CSI- ReportConfigId), and an associated serving cell configuration identifier for the servi9ng cell in which each CSI reporting configuration is include.

[0100] In response to receiving the message including the one or more inference configuration(s) for an AI / ML functionality, the UE 500 transmits a first report (e.g., in step 504A or 504B) including applicability information (e.g. applicability indication) of a received inference configuration, the UE 500 including in the report for the inference configuration one or more of: i) identification of a CSI reporting configuration in which the inference configuration (e.g. set A, set B, etc.) was received by the UE 500; ii) the identification of a serving cell configuration (e.g. Serving cell index) of the serving cell in which the UE 500 is meant to report one or more inference / predicted values (e.g. predicted RSRP) according to the AI / ML functionality configuration (e.g. inference configuration); and iii) the identification of a cell group in which the inference configuration is associated with. And, in addition the UE 500 further transmits a second report including an update of the applicability information (e.g. applicability indication) of that same received inference configuration e.g. when that changes from applicable to non-applicable, or from non-applicable to applicable, wherein the UE 500 also includes in the second report, for the inference configuration for which the updated applicability is being transmitted, one or more of: i) identification of a CSI reporting configuration in which the inference configuration (e.g. set A, set B, etc.) was received by the UE 500; ii) the identification of a serving cell configuration (e.g. Serving cell index) of the serving cell in which the UE 500 is meant to report one or more inference / predicted values (e.g. predicted RSRP) according to the AI / ML functionality configuration (e.g. inference configuration); and iii)the identification of a cell group in which the inference configuration is associated with.

[0101] In other words, the UE 500 has indicated that an AI / ML functionality configuration (e.g. inference configuration), identified by a CSI reporting configuration, serving cell configuration identification and / or cell group configuration identification, e.g., not applicable'. However, while the UE 500 is connected the conditions have changed so that such AI / ML configuration becomes 'applicable', so the updated applicability information is reported with the associated identification of the CSI reporting configuration in which the inference configuration was included, the serving cell configuration identification and cell group configuration identification.

[0102] Report of applicability information in response to applicability reporting configuration (i.e. not full inference)In a set of embodiments, the UE 500 receives (e.g., in step 504A or 504B) an applicability reporting configuration (e.g. in the IE OtherConfig, within the RRC Reconfiguration message) which includes an identification of an inference configuration, by including: i) an indication of a CSI reporting configuration; and / or ii) an indication of a serving cell configuration in which the CSI reporting configuration is included; and / or iii) an indication of a cell group configuration in which both the serving cell configuration and the CSI reporting configuration are included. In other words, the applicability reporting configuration points to an inference configuration in the CSI reporting configuration. That CSI reporting configuration includes and / or indicates one or more parameters which are necessary for the UE 500 to determine whether the inference configuration of the AI / ML functionality is applicable or not. Then, when the UE 500 reports the applicability indication (e.g., in step 508A or 508B), the UE 500 includes the identification of the inference configuration for which the UE 500 is indicating the applicability indication, by including: i) an indication of a CSI reporting configuration; and / or ii) an indication of a serving cell configuration in which the CSI reporting configuration is included; and / or iii) an indication of a cell group configuration in which both the serving cell configuration and the CSI reporting configuration are included.

[0103] Further examplesIn a first example illustrated in Figure 6, the UE 500 receives an RRC Reconfiguration message including a Cell Group configuration for a Master Cell Group (MCG) with Cell Group ID = 0 (e.g. CellGroupId=0), the IE CellGroupConfig for the MCG configuration. The UE 500 receives in that message K+1 inference configurations, one per serving cell, and each having a CSI reporting configuration (with a CSI reporting configuration Id) for its inference configuration. As can be seen in the example, the same CSI reporting identifier (e.g. CSI-ReportConfigId='0') is used in the different CSI reporting configuration(s) for the inference configurations, since they are anyways in different serving cell configuration(s). Notice that the UE 500 does not have to have an inference configuration per serving cell, just for the serving cells for which the UE 500 is meant to report inferences.

[0104] Still in that example, as shown in Figure 7, the UE 500 receives, in the same or in a different RRC Reconfiguration message, a Cell Group configuration for a Secondary Cell Group (SCG) with Cell Group ID = 1 (e.g. CellGroupId=1). The UE 500 receives in that message other M +1 inference configurations, one per serving cell of the SCG, and each having its own CSI reporting configuration. As can be seen in the example, the same CSI reporting configuration identifier is used in the different CSI reporting configuration(s), since they are anyways in different serving cell configuration(s).

[0105] According to the example, in response to receiving the RRC Reconfiguration message including one or more inference configurations for an AI / ML functionality, the UE 500 transmits to the network applicability information (e.g. applicability indication set to 'applicable' or 'not applicable') for each of the received inference configuration(s), by including the identification of each inference configuration to be reported. In this case, the UE 500 transmits the RRC message including the applicability information per inference configuration (e.g. indication of 'applicable' or 'non applicable') and the respective identification of the cell group (e.g. Cell group ID), the identification of the serving cell (e.g. serving cell identifier) and the identification of the CSI reporting configuration (e.g. CSI-ReportConfigID). Each applicability information per inference configuration may be included as an information element in a list (e.g. SEQUENCE OF), as shown in Figure 8.

[0106] In a variant of this example, related to the same inference configurations the UE 500 receives, the UE 500 transmits the RRC message (e.g. RRC Reconfiguration Complete or UE Assistance Information) including the applicability information per inference configuration and only the indication(s) of the applicable inference configuration(s) are included i.e. the absence of an indication of an inference configuration indicates that such inference configuration is not applicable. In that case, the UE 500 might not even need to include a string and / or flag indicating that the inference configuration is applicable, since its inclusion already implies that. Then, the UE 500 includes the respective identification of the cell group (e.g. Cell group ID), the identification of the serving cell (e.g. serving cell identifier) and the identification of the CSI reporting configuration (e.g. CSI-ReportConfigID), to indicate that the inference configuration for the identifiers is applicable. Each applicability information per inference configuration may be included as an information element in a list (e.g. SEQUENCE OF) which may be named 'applicableConfig'. The benefit of such variant is the reduction of the overhead in terms of bits to be reported back.

[0107] This variant is shown in Figure 9.

[0108] In another variant of this example, related to the same inference configurations the UE 500 receives, the UE 500 transmits the RRC message (e.g. RRC Reconfiguration Complete or UE Assistance Information) including the applicability information per inference configuration and only the indication(s) of the not applicable inference configuration(s) are included i.e. the absence of an indication of an inference configuration indicates that such inference configuration is applicable. In that case, the UE 500 might not even need to include a string and / or flag indicating that the inference configuration is not applicable, since its inclusion already implies that. Then, the UE 500 includes the respective identification of the cell group (e.g. Cell group ID), the identification of the serving cell (e.g. serving cell identifier) and the identification of the CSI reporting configuration (e.g. CSI-ReportConfigID), to indicate that the inference configuration for the identifiers is not applicable. Each applicability information per inference configuration may be included as an information element in a list (e.g. SEQUENCE OF) which may be named 'non- applicableConfig'. The benefit of such variant is the reduction of the overhead in terms of bits to be reported back.

[0109] In another variant of this example, related to the same inference configurations the UE 500 receives, the UE 500 transmits the RRC message including the applicability information per inference configuration (e.g. indication of 'applicable' or 'non applicable') and the identification of the serving cell (e.g. serving cell identifier) and the identification of the CSI reporting configuration (e.g. CSI-ReportConfigID). In this option, the respective identification of the cell group is not necessarily a Cell group ID which is included, but instead the message has different lists (or filed names), one for each cell group. Each applicability information per inference configuration may be included as an information element in a list (e.g. SEQUENCE OF) for a given cell group, e.g. one list for the MCG, another list for the SCG. In Figure 10, a case in which both applicable and non-applicable inference configurations are indicated is illustrated.

[0110] Figure 11 shows a case in which only applicable inference configurations are indicated.

[0111] In another example, as shown in Figure 12, the UE 500 receives an RRC Reconfiguration message including a Cell Group configuration for an MCG in the IE CellGroupConfig for the MCG configuration. The UE 500 receives in that message K+1 inference configurations, one per serving cell of the MCG and each having a CSI reporting configuration (with a CSI reporting configuration Id) for its inference configuration. As can be seen in the example, the same CSI reporting identifier (e.g. CSI-ReportConfigId='0') is used in the different CSI reporting configuration(s) for the inference configurations, since they are anyways in different serving cell configuration(s). Notice that the UE 500 does not have to have an inference configuration per serving cell, just for the serving cells for which the UE 500 is meant to report inferences.

[0112] Still in that example, the UE 500 receives, in the same or in a different RRC Reconfiguration message, a Cell Group configuration for an SCG with Cell Group ID = 1 (e.g. CellGroupId=1), as shown in Figure 13. The UE 500 receives in that message other M inference configurations, one per serving cell of the SCG, and each having its own CSI reporting configuration. As can be seen in the example, the same CSI reporting configuration identifier is used in the different CSI reporting configuration(s), since they are anyways in different serving cell configuration(s).

[0113] According to the example, the values of serving cell identifiers (e.g. serving cell indexes) are shared across both cell groups when the UE 500 is configured. Thus, in response to receiving the RRC Reconfiguration message including one or more inference configurations for an AI / ML functionality, the UE 500 transmits to the network applicability information (e.g. applicability indication set to 'applicable' or 'not applicable') for each of the received inference configuration(s), by including the identification of each inference configuration to be reported and the associated identifier of the serving cell configuration in which the inference configuration was included, as shown in Figure 14. In this case, the UE 500 transmits the RRC message including the applicability information per inference configuration (e.g. indication of 'applicable' or 'non applicable') and the respective identification of the serving cell without the need to include an explicit identification of the cell group (e.g. Cell group ID) and the identification of the CSI reporting configuration (e.g. CSI-ReportConfigID). Notice that the value of the serving cell configuration identifier implicitly indicates the associated cell group.

[0114] Some exemplary embodiments of the present disclosure are as follows:A1. A method at a UE for reporting applicability of an AI / ML functionality, comprising:• Transmitting to a network node a report including an applicability information for an AI / ML functionality configuration and one or more identifications each associated to a CSI reporting configuration in which the AI / ML functionality configuration is configured.Alb. A method of A1, wherein the UE transmits in the report applicability information for multiple AI / ML functionality configuration(s), and for each of the multiple one or more associated identification each associated to a CSI reporting configuration in which the AI / ML functionality configuration is configured.Alc. The method of Alb, wherein out of the multiple AI / ML functionality configuration(s) the UE only includes in the report the applicability information associated to those AI / ML functionality configuration(s) for which the one or more AI / ML functionalities are applicable.Ald. The method of Alb, wherein out of the one or more CSI reporting configurations associated to an AIML functionality configuration, the UE only includes in the report the one or more CSI reporting configurations associated to the AIML functionality configuration according to which the AI / ML functionality is applicable.A2. A of Al, wherein the report corresponds to one or more of: an RRC Reconfiguration Complete, a UE Assistance Information, or an RRC Resume Complete, or an RRC measurement report.A3. A method of A1, all, further comprising including in the report an identification of a serving cell configuration in which the AI / ML functionality configuration is configured.A3b. A method of A3, wherein the identification of the serving cell configuration in which the AI / ML functionality configuration is configured also indicates a cell group in which the serving cell is configured.A3c. A method of A3, wherein the identification of the serving cell configuration in which the AI / ML functionality configuration is configured is an index within a range of indexes shared for multiple cell groups the UE is configured with.A4. A method of Al, all, further comprising including in the report an identification of a cell group in which the AI / ML functionality configuration is configured.A4b. A method of A4 and all, wherein the identification of the cell group comprises the identification of a serving cell configuration in which the AI / ML functionality configuration is configured.A5. A method of Al and all, wherein transmitting to the network node the report is in response to receiving an AI / ML functionality configuration comprising an inference configuration, associated to a CSI reporting configuration, and / or a serving cell configuration and / or a cell group configuration.A6. A method of A1, all wherein transmitting to the network node the report is in response to receiving an AI / ML functionality configuration comprising an applicability reporting configuration, associated to a CSI reporting configuration, and / or a serving cell configuration and / or a cell group configuration.A7. A method of A1, wherein the report includes the applicability information for the AI / ML functionality configuration and the identification of a CSI reporting configuration in which the AI / ML functionality configuration is configured, is the first or second message transmitted after reception of the AI / ML functionality configuration (initial applicability report).A8. A method of Al, wherein the report includes the applicability information for the AI / ML functionality configuration and the identification of a CSI reporting configuration in which the AI / ML functionality configuration is configured, is for updating a previously reported applicability information which has changed for the indicated AI / ML configuration.A9. A method of A1, wherein receiving the AI / ML functionality configuration comprising receiving an inference configuration, associated to a CSI reporting configuration, and / or a serving cell configuration and / or a cell group configuration in an RRC Reconfiguration message, or an RRC Resume message.B1. A method at a network node for receiving applicability information of an AI / ML functionality, comprising:• Receiving from a UE a report including an applicability information for an AI / ML functionality configuration and an identification of a CSI reporting configuration in which the AI / ML functionality configuration is configured at the UE.B2. A method of B1 and all, wherein the report corresponds to or is comprised within one or more of: an RRC Reconfiguration Complete, a UE Assistance Information, an RRC Resume Complete, or an RRC Measurement Report.B3. A method of B1 and all, further comprising receiving in the report an identification of a serving cell configuration in which the AI / ML functionality configuration is configured at the UE.B4. A method of B1 and all, further comprising receiving in the report an identification of a cell group in which the AI / ML functionality configuration is configured in the UE.B5. A method of B1, all wherein receiving from the UE the report is in response to transmitting an AI / ML functionality configuration comprising an inference configuration, associated to a CSI reporting configuration, and / or a serving cell configuration and / or a cell group configuration.B6. A method of B1, all wherein receiving from the UE the report is in response to transmitting to the UE an AI / ML functionality configuration comprising an applicability reporting configuration, associated to a CSI reporting configuration, and / or a serving cell configuration and / or a cell group configuration.B7. A method of B1, wherein the report includes the applicability information for the AI / ML functionality configuration and the identification of a CSI reporting configuration in which the AI / ML functionality configuration is configured, is the first or second message received after transmission of the AI / ML functionality configuration (initial applicability report).B8. A method of B1, wherein the report includes the applicability information for the AI / ML functionality configuration and the identification of a CSI reporting configuration in which the AI / ML functionality configuration is configured, is for updating a previously reported applicability information which has changed for the indicated AI / ML configuration.B9. A of Al, wherein transmitting the AI / ML functionality configuration comprising transmitting an inference configuration, associated to a CSI reporting configuration, and / or a serving cell configuration and / or a cell group configuration in an RRC Reconfiguration message, or an RRC Resume message.

[0115] Figure 15 shows an example of a communication system 1500 in accordance with some embodiments.

[0116] In the example, the communication system 1500 includes a telecommunications network 1502 that includes an access network 1504, such as a radio access network (RAN), and a core network 1506, which includes one or more core network nodes 1508. The access network 1504 includes one or more access network nodes or base stations of various types, access network nodes 1510A and 1510B are depicted (which may be collectively referred to as network nodes 1510), or any other similar 3rd Generation Partnership Project (3GPP) access nodes or non-3GPP access points (APs). Some embodiments of the access network 1504 may include more than one access network technology. The network nodes 1510 of access network 1504 facilitate direct or indirect connection of wireless devices, also referred to as user equipments (UEs), such as by connecting UEs 1512A, 1512B, 1512C, and 1512D (one or more of which may be generally referred to as UEs 1512) to the core network 1506 over one or more wireless connections.

[0117] 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 1502 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a network node in the telecommunications network 1502 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other network nodes to implement one or more functionalities of any network node in the telecommunications network 1502, including one or more access network nodes 1510 and / or core network nodes 1508.

[0118] 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 A1, F1, W1, E1, 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 O-RAN Alliance or comparable technologies.

[0119] The network nodes 1510 facilitate direct or indirect connection of one or more UEs 1512 to the core network 1506 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 1500 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 1500 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0120] The UEs 1512 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 1510 and other communication devices. Similarly, the network nodes 1508, 1510 are arranged, capable, configured, and / or operable to communicate directly or indirectly (e.g., via other devices of telecommunications network 1502) with the UEs 1512 and / or with other network nodes or equipment in the telecommunications network 1502 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunications network 1502. More specifically, UEs 1512 may send messages, data, and / or other signals to network nodes 1508, 1510 or other elements of the telecommunications network 1502 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 1508, 1510 may send messages, data, and other signals to UEs 15122, other network nodes 1508, 1510, and other devices in telecommunications network 1502 directly or indirectly. As one specific example, a core network node 108 may transmit a particular message to a UE 1512 by transmitting the message to an access network node 1510 that will then transmit the message to the intended UE 1512. Similarly, a core network node 108 may receive a particular message from a UE 1512 by receiving the message from an access network node 1510 that itself received the message from the UE 1512.

[0121] In the depicted example, the core network 1506 connects elements of the access network 1504 (e.g., one or more of the network nodes 1510) to one or more host computing systems, such as host 1516. 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 1506 includes one or more core network nodes (e.g., core network node 1508) of various types, one or more of which may be generally referred to as network nodes 1508. Network nodes 1508 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 1508. 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).

[0122] The host 1516 may be under the ownership or control of a service provider other than an operator or provider of the access network 1504 and / or the telecommunications network 1502. The host 1516 may be operated by the service provider or on behalf of the service provider. The host 1516 may host a variety of applications to provide one or more services. 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.

[0123] As a whole, the communication system 1500 of Figure 15 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system 1500 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 1500 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 1500 supporting different standards, protocols, or rule sets.

[0124] As one example, in certain embodiments, access network 1504 may contain some access network nodes 1510 that support 3GPP radio access technologies (RAT), such as LTE or NR, while other access network nodes 1510 support (or the same access network nodes 1510 additionally support) non-3GPP RATs, such as Wi-Fi or a proprietary RAT. As another example, telecommunications network 1502 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.

[0125] Telecommunications network 1502 may support network slicing to provide different logical networks to different devices that are connected to the telecommunications network 1502. For example, the telecommunications network 1502 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 IoT services to yet further UEs.

[0126] In some examples, one or more of the UEs 1512 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 1504 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 1504. 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).

[0127] In the example, the hub 1514 communicates with the access network 1504 to facilitate indirect communication between one or more UEs (e.g., UE 1512C and / or 1512D) and network nodes (e.g., network node 1510B). In some examples, the hub 1514 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 1514 may be a broadband router enabling access to the core network 1506 for the UEs. As another example, the hub 1514 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 1510, or by executable code, script, process, or other instructions in the hub 1514.

[0128] As another example, the hub 1514 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 1514 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 1514 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 1514 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 1514 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy IoT devices.

[0129] The hub 1514 may have a constant / persistent or intermittent connection to the network node 1510B. The hub 1514 may also allow for a different communication scheme and / or schedule between the hub 1514 and UEs (e.g., UE 1512C and / or 1512D), and between the hub 1514 and the core network 1506. In other examples, the hub 1514 is connected to the core network 1506 and / or one or more UEs via a wired connection. Moreover, the hub 1514 may be configured to connect to an M2M service provider over the access network 1504 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 1510 while still connected via the hub 1514 via a wired or wireless connection. In some embodiments, the hub 1514 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 1510B. In other embodiments, the hub 1514 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 1510B, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0130] Figure 16 is another example of a communication system 1600 according to some embodiments. As used herein, the communication system 1600 includes multiple access points (APs) 1610 (with four exemplary APs 1610A, 1610B, 1610C, and 1610D being depicted) and multiple wireless devices, referred to in the context of communication system 1600 as stations (STAs) 1612 (referred to individually as STA 1612A, STA 1612B, STA 1612C, STA 1612D, and STA 1612E). STA 1612A is served by AP 1610A in a first basic service set (BSS) 1620A. STA 1610B and STA 1610C are served by AP 1610B in a second BSS, BSS 1620B. STA 1612D is served by AP 1610C in a third BSS, BSS 1620C. STA 1612E is served by AP 1610D in a fourth BSS, BSS 1620D. Stations 1612 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, head- mounted displays (HMDs) for Augmented Reality (AR) or Virtual Reality (VR), or the like. Further, stations 1612 could, for example, correspond to other kinds of equipment like smart home devices, printers, multimedia devices, data storage devices, or the like.

[0131] Each of STAs 1612 may connect through a radio link to one of APs 1610. For example, depending on location or channel conditions experienced by a given STA 1612, 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.

[0132] Each AP 1610 may provide data connectivity to STAs 1612 connected to a particular AP 1610. As illustrated, APs 1610 may be connected to a data network 1630. In this way, APs 1610 may also provide data connectivity between STAs 1612 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 1612 and its serving AP 1610 may be used for providing various kinds of services to STA 1612, e.g., a voice service, a multimedia service, or other data service. Such services may be based on applications that are executed on STA 1612 and / or on a device linked to STA 1612. By way of example, Figure 16 illustrates an application service platform 1632 provided in data network 1630. The application(s) executed on STA 1612 and / or on one or more other devices linked to STA 1612 may use the radio link for data communication with one or more other STA 1612 and / or the application service platform 1632, thereby enabling utilization of the corresponding service(s) at STA 1612.

[0133] Figure 17 shows a wireless device 1700, which may be configured to operate in communication system 1500 of Figure 15 or in communication system 1600 of Figure 16. The wireless device 1700 may be alternatively referred to as a UE 1700, like a UE 1512 within the context of communication system 1500, or as a station (STA) 1700 or as a non-access-point station (non-AP STA) 1700, like a STA 1612 within the context of the communication system 1600, 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.

[0134] A wireless device 1700 may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to- everything (V2X). In other examples, wireless device 1700 may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, wireless device 1700 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 1700 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).

[0135] In particular embodiments, wireless device 1700 includes processing circuitry 1702 that is operatively coupled via a bus 1704 to an input / output interface 1706, a power source 1708, a memory 1710, a communication interface 1712, and / or any other component, or any combination thereof. Certain embodiments of wireless device 1700 may include all or a subset of the components shown in Figure 17. The level of integration between the components may vary from one embodiment of wireless device 1700 to another. In general, in a particular embodiment of wireless device 1700, processing circuitry 1702, input / output interface 1706, power source 1708, memory 1710, and communication interface 1712 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 1700. Further, certain embodiments of wireless devices 1700 may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0136] The processing circuitry 1702 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 1710. The processing circuitry 1702 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 1702 may include multiple central processing units (CPUs).

[0137] In the example, the input / output interface 1706 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 1700. 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.

[0138] In some embodiments, the power source 1708 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 1708 may further include power circuitry for delivering power from the power source 1708 itself, and / or an external power source, to the various parts of wireless device 1700 via input circuitry or an interface such as an electrical power cable. Power source 1708 may perform any formatting, converting, or other modification to make accessible power suitable for the respective components of the wireless device 1700 to which power is supplied.

[0139] The memory 1710 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 1710 includes one or more programs 1714, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 1716. The memory 1710 may store, for use by wireless device 1700, any of a variety of various operating systems or combinations of operating systems.

[0140] The memory 1710 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 (¡UICC) or a removable UICC commonly known as 'SIM card.' The memory 1710 may allow wireless device 1700 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 1710, which may be or comprise a device-readable storage medium.

[0141] The processing circuitry 1702 may be configured to communicate with an access network or other network via or using the communication interface 1712. The communication interface 1712 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 1722. The communication interface 1712 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 1718 and / or a receiver 1720 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 1718 and receiver 1720 may be coupled to one or more antennas (e.g., antenna 1722) and may share circuit components, software, or firmware, or alternatively be implemented separately.

[0142] In the illustrated embodiment, communication functions of the communication interface 1712 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 / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0143] In particular embodiments, wireless device 1700 may provide an output of data captured via a sensor, through its communication interface 1712, via a wireless connection to a network node, and / or in any appropriate manner. Data captured by sensors of a wireless device 1700 can be communicated through a wireless connection to a network node via another wireless device 1700. 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).

[0144] As another example, wireless device 1700 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 1700 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.

[0145] Wireless device 1700, when in the form of an Internet of Things (IoT) 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 IoT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. In particular embodiments, wireless device 1700 represents an IoT device that comprises circuitry and / or software in dependence of the intended application of the IoT device in addition to other components as described in relation to the example embodiment of wireless device 1700 shown in Figure 17.

[0146] As yet another specific example, in an IoT scenario, wireless device 1700 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 1700 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 1700 may implement the 3GPP NB-IoT standard. In other scenarios, wireless device 1700 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.

[0147] In practice, any number of wireless devices 1700 may be used together with respect to a single use case. For example, a first wireless device 1700 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 1700 that is a remote controller operating the drone. When a user makes changes from the remote controller, the first wireless device 1700 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 1700 can also include more than one of the functionalities described above. For example, wireless device 1700 might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0148] Figure 18 shows a network node 1800 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 1800 may be configured to operate in communication system 1500 of Figure 15, like network nodes 1508 or 1510, or in communication system 1600 of Figure 16, like an AP 1610 or a station 1612. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)), O-RAN nodes or components of an O-RAN node (e.g., O-RU, O-DU, O-CU).

[0149] Network nodes 1800 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 1800 may be a relay node or a relay donor node controlling a relay. Network nodes 1800 may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an O-RAN access node) and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0150] Other examples of network nodes 1800 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).

[0151] In particular embodiments, network node 1800 includes a processing circuitry 1802, a memory 1804, a communication interface 1806, and a power source 1808. In general, in a particular embodiment of network node 1800, processing circuitry 1802, memory 1804, communication interface 1806, and power source 1808 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 1800.

[0152] The network node 1800 may be composed of multiple distinct network entities (e.g., a NodeB entity and a RNC entity, or a BTS entity and a BSC entity, etc.), which may each have or utilize their own respective physical components. In certain scenarios in which the network node 1800 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 1800 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memories 1804 or portions of memory 1804 for different RATs) and some components may be reused (e.g., a same antenna 1810 may be shared by different RATs). The network node 1800 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1800, 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 1800.

[0153] The processing circuitry 1802 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 1804, to provide network node 1800 functionality.

[0154] In some embodiments, the processing circuitry 1802 includes a system on a chip (SOC). In some embodiments, the processing circuitry 1802 includes one or more of radio frequency (RF) transceiver circuitry 1812 and baseband processing circuitry 1814. In some embodiments, the RF transceiver circuitry 1812 and the baseband processing circuitry 1814 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 1812 and baseband processing circuitry 1814 may be on the same chip or set of chips, boards, or units.

[0155] The memory 1804 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 1802. The memory 1804 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 1802 and utilized by the network node 1800. The memory 1804 may be used to store any calculations made by the processing circuitry 1802 and / or any data received via the communication interface 1806. In some embodiments, the processing circuitry 1802 and memory 1804 are integrated.

[0156] The communication interface 1806 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 1806 comprises port(s) / terminal(s) 1816 to send and receive data, for example to and from a network over a wired connection. In particular embodiments, network node 1700 may be capable of wireless communication and communication interface 1806 may also include radio front-end circuitry 1818 that may be coupled to, or in certain embodiments a part of, an antenna 1810. Particular embodiments of radio front-end circuitry 1818 include filter(s) 1820 and amplifier(s) 1822. The radio front-end circuitry 1818 may be connected to an antenna 1810 and processing circuitry 1802. The radio front-end circuitry may be configured to condition signals communicated between antenna 1810 and processing circuitry 1802. The radio front-end circuitry 1818 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 1818 may convert the digital data into a radio signal(s) having the appropriate channel and bandwidth parameters using a combination of filters 1820 and / or amplifiers 1822. The radio signal(s) may then be transmitted via the antenna 1810. Similarly, when receiving data, the antenna 1810 may collect radio signals which are then converted into digital data by the radio front-end circuitry 1818. The digital data may be passed to the processing circuitry 1802. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0157] In certain alternative embodiments, network node 1800 may be capable of wireless communication but does not include separate radio front-end circuitry 1818, instead, the processing circuitry 1802 includes radio front-end circuitry and is connected to the antenna 1810. Similarly, in some embodiments, all or some of the RF transceiver circuitry 1812 is part of the communication interface 1806. In still other embodiments, the communication interface 1806 includes one or more ports or terminals 1816, the radio front-end circuitry 1818, and the RF transceiver circuitry 1812, as part of a radio unit (not shown), and the communication interface 1806 communicates with the baseband processing circuitry 1814, which is part of a digital unit (not shown).

[0158] The antenna 1810 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 1810 may be coupled to the radio front-end circuitry 1818 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 1810 is separate from the network node 1800 and connectable to the network node 1800 through one or more interfaces or ports.

[0159] The antenna 1810, communication interface 1806, and / or the processing circuitry 1802 may be configured to perform some or all of the receiving operations and / or obtaining operations described herein as being performed by the network node 1800. Any information, data, and / or signals may be received from a UE, another network node, and / or any other network equipment. Similarly, the antenna 1810, the communication interface 1806, and / or the processing circuitry 1802 may be configured to perform some or all of the transmitting or sending operations described herein as being performed by the network node 1800. Any information, data and / or signals may be transmitted to a UE, another network node, and / or any other network equipment.

[0160] The power source 1808 provides power to the various components of network node 1800 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 1808 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 1800 with power for performing the functionality described herein. For example, the network node 1800 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 1808. As a further example, the power source 1808 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.

[0161] Embodiments of the network node 1800 may include additional components beyond those shown in Figure 18 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 1800 may include user interface equipment to allow input of information into the network node 1800 and to allow output of information from the network node 1800. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 1800.

[0162] Figure 19 is a block diagram illustrating a virtualization environment 1900 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 1900 hosted by one or more of hardware nodes, such as a hardware computing device that operates as an access network node, UE, core network node, or host. Further, in embodiments in which a virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment 1900 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an O-2 interface.

[0163] Applications 1902 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 1800 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0164] Hardware 1904 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 1906 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VM 1908A and VM 1908B (which may be collectively referred to as VMs 1908), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 1906 may present a virtual operating platform that appears like networking hardware to one or more of the VMs 1908.

[0165] The VMs 1908 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by virtualization layer 1906. Different embodiments of the instance of a virtual appliance 1902 may be implemented on one or more of VMs 1908, 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.

[0166] In the context of NFV, each of the VMs 1908 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 1908, and that part of hardware 1904 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 1908 on top of the hardware 1904 and corresponds to an application 1902.

[0167] Hardware 1904 may be implemented in a standalone network node with generic or specific components. Hardware 1904 may implement some functions via virtualization. Alternatively, hardware 1904 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 1910, which, among others, oversees lifecycle management of applications 1902. In some embodiments, hardware 1904 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 1912 which may alternatively be used for communication between hardware nodes and radio units.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Some exemplary embodiments of the present disclosure are as follows:

[0172] Group A Embodiments

[0173] Embodiment 1: A method performed by a UE for reporting applicability of an AI / ML functionality, the method comprising: transmitting (508A; 508B) to a network node a report including an applicability information for an AI / ML functionality configuration and identification information associated to the AI / ML functionality configuration.

[0174] Embodiment 2: The method of embodiment 1, wherein the identification information associated to the AI / ML functionality configuration comprises one or more identifications each associated to a CSI reporting configuration in which the AI / ML functionality configuration is configured.

[0175] Embodiment 3: The method of embodiment 1, wherein the identification information associated to the AI / ML functionality configuration comprises any one or any combination of two or more of the following identifications: (a) an identification of a CSI reporting configuration in which the AI / ML functionality configuration is configured for the UE, (b) an identification of a serving cell configuration of a serving cell in which the UE is to report one or more inferred (e.g., predicted) values according to the AI / ML functionality configuration, and (c) an identification of a cell group to which the AI / ML functionality configuration is associated.

[0176] Embodiment 4: The method of embodiment 1, wherein the report comprises: applicability information for two or more AI / ML functionality configurations (i.e., the AI / ML functionality configuration and one or more additional AI / ML functionality configurations); and for each of the two or more AI / ML functionality configurations, one or more associated identifications each associated to a CSI reporting configuration in which the AI / ML functionality configuration is configured.

[0177] Embodiment 5: The method of embodiment 4, wherein out of the one or more CSI reporting configurations associated to an AI / ML functionality configuration, the UE only includes in the report the one or more CSI reporting configurations associated to the AI / ML functionality configuration according to which the AI / ML functionality is applicable.

[0178] Embodiment 6: The method of embodiment 1, wherein the report comprises: applicability information for two or more AI / ML functionality configurations (i.e., the AI / ML functionality configuration and one or more additional AI / ML functionality configurations); and for each of the two or more AI / ML functionality configurations, identification information associated to that AI / ML functionality configuration.

[0179] Embodiment 7: The method of embodiment 6, wherein, for each of the two or more AI / ML functionality configurations, the identification information associated to that AI / ML functionality configuration comprises any one or any combination of two or more of the following identifications: (a) an identification of a CSI reporting configuration in which that AI / ML functionality configuration is configured for the UE, (b) an identification of a serving cell configuration of a serving cell in which the UE is to report one or more inferred (e.g., predicted) values according to that AI / ML functionality configuration, and (c) an identification of a cell group to which that AI / ML functionality configuration is associated.

[0180] Embodiment 8: The method of embodiment 1, wherein the report comprises: applicability information for each of at least one of two or more AI / ML functionality configurations (e.g., at least one of two or more AI / ML functionality configurations configured for the UE) for which one or more associated AI / ML functionalities (e.g., the one or AI / ML functionalities associated to the two or more AI / ML functionality configurations) are applicable; and for each of the at least one of the two or more AI / ML functionality configurations, one or more associated identifications each associated to a CSI reporting configuration in which that AI / ML functionality configuration is configured.

[0181] Embodiment 9: The method of embodiment 1, wherein the report comprises: applicability information for each of at least one of two or more AI / ML functionality configurations (e.g., at least one of two or more AI / ML functionality configurations configured for the UE) for which one or more associated AI / ML functionalities (e.g., the one or AI / ML functionalities associated to the two or more AI / ML functionality configurations) are applicable; and for each of the at least one of the two or more AI / ML functionality configurations, identification information for that AI / ML functionality configuration.

[0182] Embodiment 10: The method of embodiment 9, wherein, for each of the at least one of the two or more AI / ML functionality configurations, the identification information associated to that AI / ML functionality configuration comprises any one or any combination of two or more of the following identifications: (a) an identification of a CSI reporting configuration in which that AI / ML functionality configuration is configured for the UE, (b) an identification of a serving cell configuration of a serving cell in which the UE is to report one or more inferred (e.g., predicted) values according to that AI / ML functionality configuration, and (c) an identification of a cell group to which that AI / ML functionality configuration is associated.

[0183] Embodiment 11: The method of any of embodiments 1 to 10, wherein the report corresponds to one or more of: an RRC Reconfiguration Complete, a UE Assistance Information, an RRC Resume Complete, or an RRC measurement report.

[0184] Embodiment 12: The method of any of embodiments 1 to 11, wherein the identification information associated to the AI / ML functionality configuration comprises one or more identifications each associated to a CSI reporting configuration in which the AI / ML functionality configuration is configured and an identification of a serving cell configuration in which the AI / ML functionality configuration is configured.

[0185] Embodiment 13: The method of embodiment 12, wherein the identification of the serving cell configuration in which the AI / ML functionality configuration is configured also indicates a cell group in which the serving cell is configured.

[0186] Embodiment 14: The method of embodiment 12, wherein the identification of the serving cell configuration in which the AI / ML functionality configuration is configured is an index within a range of indexes shared for multiple cell groups the UE is configured with.

[0187] Embodiment 15: The method of any of embodiments 1 to 11, wherein the identification information associated to the AI / ML functionality configuration comprises one or more identifications each associated to a CSI reporting configuration in which the AI / ML functionality configuration is configured and an identification of a cell group in which the AI / ML functionality configuration is configured.

[0188] Embodiment 16: The method of embodiment 15, wherein the identification of the cell group comprises the identification of a serving cell configuration in which the AI / ML functionality configuration is configured.

[0189] Embodiment 17: The method of any of embodiments 1 to 16, further comprising: receiving the AI / ML functionality configuration from a network node; wherein transmitting (508A; 508B) to the network node the report is in response to receiving the AI / ML functionality configuration.

[0190] Embodiment 18: The method of embodiment 17, wherein the AI / ML functionality configuration or a messaging containing the AI / ML functionality configuration received from the network node comprises the identification information associated to the AI / ML functionality configuration.

[0191] Embodiment 19: The method of embodiment 18, wherein the identification information associated to the AI / ML functionality configuration comprises any one or any combination of two or more of the following identifications: (a) an identification of a CSI reporting configuration in which the AI / ML functionality configuration is configured for the UE, (b) an identification of a serving cell configuration of a serving cell in which the UE is to report one or more inferred (e.g., predicted) values according to the AI / ML functionality configuration, and (c) an identification of a cell group to which the AI / ML functionality configuration is associated.

[0192] Embodiment 20: The method of embodiment 18 or 19, wherein the AI / ML functionality configuration comprises an inference configuration associated to the identification information.

[0193] Embodiment 21: The method of embodiment 18 or 19, wherein the AI / ML functionality configuration comprises an inference configuration associated to a CSI reporting configuration, and / or a serving cell configuration and / or a cell group configuration.

[0194] Embodiment 22: The method of embodiment 21, wherein the identification information associated to the AI / ML functionality configuration comprises an identifier of the CSI reporting configuration, and / or an identifier of the serving cell configuration, and / or an identifier of the cell group configuration.

[0195] Embodiment 23: The method of embodiment 18 or 19, wherein the AI / ML functionality configuration comprises an applicability reporting configuration.

[0196] Embodiment 24: The method of embodiment 23, wherein the applicability reporting configuration is associated to a CSI reporting configuration, and / or a serving cell configuration, and / or a cell group configuration.

[0197] Embodiment 25: The method of any of embodiments 1 to 24, wherein the report including the applicability information for the AI / ML functionality configuration and the associated identification information is either a first or second message transmitted after reception of the AI / ML functionality configuration (e.g., an initial applicability report).

[0198] Embodiment 26: The method of any of embodiments 1 to 24, wherein the report includes the applicability information for the AI / ML functionality configuration and the identification information is for updating a previously reported applicability information which has changed for the indicated AI / ML functionality configuration.

[0199] Embodiment 27: The method of any of embodiments 1 to 26, further comprising receiving the AI / ML functionality configuration comprising an inference configuration associated to a CSI reporting configuration, and / or a serving cell configuration, and / or a cell group configuration in an RRC Reconfiguration message, or an RRC Resume message.

[0200] Embodiment 28: 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.

[0201] Group B Embodiments

[0202] Embodiment 29: A method performed by a network node for receiving applicability information of an AI / ML functionality, the comprising: receiving (508A; 508B) from a UE a report including an applicability information for an AI / ML functionality configuration and identification information associated to the AI / ML functionality configuration.

[0203] Embodiment 30: The method of embodiment 29, wherein the identification information associated to the AI / ML functionality configuration comprises an identification of a CSI reporting configuration in which the AI / ML functionality configuration is configured at the UE.

[0204] Embodiment 31: The method of embodiment 30, wherein the identification information associated to the AI / ML functionality configuration further comprises an identification of a serving cell configuration in which the AI / ML functionality configuration is configured at the UE.

[0205] Embodiment 32: The method of embodiment 30 or 31, wherein the identification information associated to the AI / ML functionality configuration further comprises an identification of a cell group in which the AI / ML functionality configuration is configured in the UE.

[0206] Embodiment 33: The method of embodiment 29, wherein the identification information associated to the AI / ML functionality configuration comprises any one or any combination of two or more of the following identifications: (a) an identification of a CSI reporting configuration in which the AI / ML functionality configuration is configured for the UE, (b) an identification of a serving cell configuration of a serving cell in which the UE is to report one or more inferred (e.g., predicted) values according to the AI / ML functionality configuration, and (c) an identification of a cell group to which the AI / ML functionality configuration is associated.

[0207] Embodiment 34: The method of any of embodiments 29 to 33, wherein the report corresponds to or is comprised within one or more of: an RRC Reconfiguration Complete, a UE Assistance Information, an RRC Resume Complete, or an RRC Measurement Report.

[0208] Embodiment 35: The method of any of embodiments 29 to 34, further comprising: transmitting (504A; 504B) to the UE the AI / ML functionality configuration comprising an inference configuration, associated to a CSI reporting configuration, and / or a serving cell configuration, and / or a cell group configuration; wherein receiving from the UE the report is in response to transmitting the AI / ML functionality configuration to the UE.

[0209] Embodiment 36: The method of any of embodiments 29 to 34, further comprising: transmitting (504A; 504B) to the UE an AI / ML functionality configuration comprising an applicability reporting configuration, associated to a CSI reporting configuration, and / or a serving cell configuration, and / or a cell group configuration; wherein receiving from the UE the report is in response to transmitting to the UE the AI / ML functionality configuration.

[0210] Embodiment 37: The method of any of embodiments 29 to 36, wherein the report includes the applicability information for the AI / ML functionality configuration and the identification information associated to the AI / ML functionality configuration, is a first or second message received after transmission of an AI / ML functionality configuration (initial applicability report) to the UE.

[0211] Embodiment 38: The method of any of embodiments 29 to 36, wherein the report includes the applicability information for the AI / ML functionality configuration and the identification information associated to the AI / ML functionality configuration, is for updating a previously reported applicability information which has changed for the indicated AI / ML functionality configuration.

[0212] Embodiment 39: The method of any of embodiments 29 to 36, further comprising transmitting (504A; 504B) to the UE the AI / ML functionality configuration, wherein transmitting the AI / ML functionality configuration to the UE comprises transmitting an inference configuration, associated to a CSI reporting configuration, and / or a serving cell configuration, and / or a cell group configuration in an RRC Reconfiguration message, or an RRC Resume message.

[0213] Embodiment 40: 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.

[0214] Group C Embodiments

[0215] Embodiment 41: 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.

[0216] Embodiment 42: A network node, the 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.

[0217] Embodiment 43: A wireless device, the 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, (500) for reporting applicability of an Artificial Intelligence, AI, or Machine Learning, ML, (AI / ML) functionality, the method comprising:transmitting (508A; 508B), to a network node (502), a report comprising an applicability information for an inference configuration of an AI / ML functionality and identification information associated to the inference configuration of the AI / ML functionality.

2. The method of claim 1, wherein the report corresponds to a Radio Resource Control, RRC, message.

3. The method of claim 2, wherein the identification information associated to the inference configuration of the AI / ML functionality comprises an identification of a channel state information configuration and an identification of a serving cell configuration.

4. The method of claim 2, wherein the identification information associated to the inference configuration of the AI / ML functionality comprises an identification of a measurement configuration and an identification of a serving cell configuration.

5. The method of claim 1, wherein transmitting (508A, 508B) the report comprises transmitting (508A; 508B) an RRC reconfiguration complete message to the network node (502), the RRC reconfiguration complete message corresponding to the report comprising the applicability information for the inference configuration of the AI / ML functionality and the identification information associated to the inference configuration of the AI / ML functionality.

6. The method of claim 5, wherein the identification information associated to the inference configuration of the AI / ML functionality comprises an identification of a channel state information configuration and an identification of a serving cell configuration.

7. The method of claim 5, wherein the identification information associated to the inference configuration of the AI / ML functionality comprises an identification of a measurement configuration and an identification of a serving cell configuration.

8. The method of claim 5, further comprising, prior to transmitting (508A; 508B) the RRC reconfiguration complete message, receiving (504A; 504B) an RRC reconfiguration message from the network node (502), the RRC reconfiguration message comprising one or more inference configurations of one or more AI / ML functionalities and, for each inference configuration, associated identification information.

9. The method of claim 8, wherein, for each inference configuration, the associated identification information comprises an identification of a channel state information configuration and an identification of a serving cell configuration.

10. The method of claim 8, wherein, for each inference configuration, the associated identification information comprises an identification of a measurement configuration and an identification of a serving cell configuration.

11. The method of claim 1, wherein the identification information associated to the inference configuration of the AI / ML functionality comprises one or more identifications each associated to a channel state information reporting configuration in which the inference configuration of the AI / ML functionality is configured.

12. The method of claim 1, wherein the identification information associated to the inference configuration of the AI / ML functionality comprises one or more identifications each associated to a measurement configuration in which the inference configuration of the AI / ML functionality is configured.

13. The method of claim 1, wherein the identification information associated to the inference configuration of the AI / ML functionality comprises either or both of the following identifications: (a) an identification of a channel state information reporting configuration in which the inference configuration of the AI / ML functionality is configured for the UE and (b) an identification of a serving cell configuration of a serving cell.

14. The method of claim 1, wherein the identification information associated to the inference configuration of the AI / ML functionality comprises any one or any combination of two or more of the following identifications: (a) an identification of a channel state information reporting configuration in which the inference configuration of the AI / ML functionality is configured for the UE, (b) an identification of a serving cell configuration of a serving cell, and (c) an identification of a cell group to which the inference configuration of the AI / ML functionality is associated.

15. The method of claim 1, wherein the identification information associated to the inference configuration of the AI / ML functionality comprises either or both of the following identifications: (a) an identification of a measurement configuration in which the inference configuration of the AI / ML functionality is configured for the UE and (b) an identification of a serving cell configuration of a serving cell.

16. The method of claim 1, wherein the report comprises:applicability information for two or more inference configurations of the AI / ML functionality; andfor each of the two or more inference configurations of the AI / ML functionality, one or more associated identifications each associated to a channel state information reporting configuration in which the inference configuration of the AI / ML functionality is configured.

17. The method of claim 1, wherein the report comprises:applicability information for two or more inference configurations of the AI / ML functionality; andfor each of the two or more inference configurations of the AI / ML functionality, one or more associated identifications each associated to a measurement configuration in which the inference configuration of the AI / ML functionality is configured.

18. The method of claim 17, wherein out of the one or more measurement configurations associated to an AI / ML functionality configuration, the UE only includes in the report the one or more measurement configurations associated to the AI / ML functionality configuration according to which the AI / ML functionality is applicable.

19. The method of claim 17, wherein out of the one or more measurement configurations associated to an AI / ML functionality configuration, the UE includes in the report both the one or more measurement configurations associated to the AI / ML functionality configuration according to which the AI / ML functionality is applicable and the one or more measurement configurations associated to the AI / ML functionality configuration according to which the AI / ML functionality is not applicable.

20. The method of claim 1, wherein the report comprises:applicability information for each of at least one of two or more inference configurations of the AI / ML functionality configured for the UE and for which the AI / ML functionality is applicable; andfor each of the at least one of the two or more inference configurations of the AI / ML functionality, one or more associated identifications each associated to a channel state information reporting configuration in which that inference configuration of the AI / ML functionality is configured.

21. The method of claim 1, wherein the report comprises:applicability information for each of at least one of two or more inference configurations of the AI / ML functionality configured for the UE and for which the AI / ML functionality is applicable; andfor each of the at least one of the two or more inference configurations of the AI / ML functionality, identification information for that inference configuration of the AI / ML functionality.

22. The method of claim 21, wherein, for each of the at least one of the two or more inference configurations of the AI / ML functionality, the identification information associated to that inference configuration of the AI / ML functionality comprises any either or both of the following identifications: (a) an identification of a channel state information reporting configuration in which that inference configuration of the AI / ML functionality is configured for the UE and (b) an identification of a serving cell configuration.

23. The method of claim 21, wherein, for each of the at least one of the two or more inference configurations of the AI / ML functionality, the identification information associated to that inference configuration of the AI / ML functionality comprises any one or any combination of two or more of the following identifications: (a) an identification of a channel state information reporting configuration in which that inference configuration of the AI / ML functionality is configured for the UE, (b) an identification of a serving cell configuration, and (c) an identification of a cell group to which that inference configuration of the AI / ML functionality is associated.

24. The method of any of claims 11 to 23, wherein the report corresponds to one or more of: an Radio Resource Control, RRC, Reconfiguration Complete, a UE Assistance Information, an RRC Resume Complete, or an RRC measurement report.

25. The method of any of claims 11 to 24, wherein the identification information associated to the inference configuration of the AI / ML functionality comprises one or more identifications each associated to a channel state information reporting configuration in which the inference configuration of the AI / ML functionality is configured and an identification of a serving cell configuration in which the inference configuration of the AI / ML functionality is configured.

26. The method of claim 25, wherein the identification of the serving cell configuration in which the inference configuration of the AI / ML functionality is configured also indicates a cell group in which the serving cell is configured.

27. The method of claim 25, wherein the identification of the serving cell configuration in which the inference configuration of the AI / ML functionality is configured is an index within a range of indices shared for multiple cell groups with which the UE is configured.

28. The method of any of claims 1 to 24, wherein the identification information associated to the inference configuration of the AI / ML functionality comprises one or more identifications each associated to a channel state information reporting configuration in which the inference configuration of the AI / ML functionality is configured and an identification of a cell group in which the inference configuration of the AI / ML functionality is configured.

29. The method of claim 28, wherein the identification of the cell group comprises the identification of a serving cell configuration in which the inference configuration of the AI / ML functionality is configured.

30. The method of any of claims 11 to 29, further comprising:receiving (504A; 504B) the inference configuration of the AI / ML functionality from the network node (502);wherein transmitting (508A; 508B) the report comprises transmitting (508A; 508B) the report to the network node (502) is in response to receiving (504A; 504B) the inference configuration of the AI / ML functionality from the network node (502).

31. The method of claim 30, wherein the inference configuration of the AI / ML functionality or a message containing the inference configuration of the AI / ML functionality configuration received from the network node (502) comprises the identification information associated to the inference configuration of the AI / ML functionality.

32. The method of claim 1, wherein the applicability information for an inference configuration of the AI / ML functionality is applicability information for an inference configuration for an AI / ML model used for the AI / ML functionality.

33. The method of claim 1, further comprising transmitting a second report including an update of the applicability information of the same inference configuration.

34. A User Equipment, UE, (500; 1700) for reporting applicability of an Artificial Intelligence, AI, or Machine Learning, ML, (AI / ML) functionality, the UE (500; 1700) comprising:a communication interface (1712) comprising a transmitter (1718) and a receiver (1720); andprocessing circuitry (1702) associated with the communication interface (1712), the processing circuitry (1702) configured to cause the UE (500; 1700) to transmit (508A; 508B), to a network node (502), a report comprising an applicability information for an inference configuration of an AI / ML functionality and identification information associated to the inference configuration of the AI / ML functionality.

35. The UE (500; 1700) of claim 34, wherein the report corresponds to a Radio Resource Control, RRC, message.

36. The UE (500; 1700) of claim 35, wherein the identification information associated to the inference configuration of the AI / ML functionality comprises an identification of a channel state information configuration and an identification of a serving cell configuration.

37. The UE (500; 1700) of claim 35, wherein the identification information associated to the inference configuration of the AI / ML functionality comprises an identification of a measurement configuration and an identification of a serving cell configuration.

38. The UE (500; 1700) of claim 34, wherein the report corresponds to an RRC reconfiguration complete message.

39. The UE (500; 1700) of claim 38, wherein the identification information associated to the inference configuration of the AI / ML functionality comprises an identification of a channel state information configuration and an identification of a serving cell configuration.

40. The UE (500; 1700) of claim 38, wherein the identification information associated to the inference configuration of the AI / ML functionality comprises an identification of a measurement configuration and an identification of a serving cell configuration.

41. The UE (500; 1700) of claim 38, wherein the processing circuitry (1702) is further configured to cause the UE (500; 1700) to, prior to transmitting (508A; 508B) the RRC reconfiguration complete message that corresponds to the report, receive (504A; 504B) an RRC reconfiguration message from the network node (502), the RRC reconfiguration message comprising one or more inference configurations of the AI / ML functionality and, for each inference configuration, associated identification information.

42. The UE (500; 1700) of claim 41, wherein, for each inference configuration, the associated identification information comprises an identification of a channel state information configuration and an identification of a serving cell configuration43. The UE (500; 1700) of claim 41, wherein, for each inference configuration, the associated identification information comprises an identification of a measurement configuration and an identification of a serving cell configuration44. A method performed by a network node (502) for receiving applicability information of an Artificial Intelligence, AI, or Machine Learning, ML, (AI / ML) functionality, the method comprising:receiving (508A; 508B), from a User Equipment, UE, (502), a report comprising applicability information for an inference configuration of an AI / ML functionality and identification information associated to the inference configuration of the AI / ML functionality.

45. The method of claim 44, wherein the report corresponds to a Radio Resource Control, RRC, message.

46. The method of claim 45, wherein the identification information associated to the inference configuration of the AI / ML functionality comprises an identification of a channel state information configuration and an identification of a serving cell configuration.

47. The method of claim 45, wherein the identification information associated to the inference configuration of the AI / ML functionality comprises an identification of a measurement configuration and an identification of a serving cell configuration.

48. The method of claim 44, wherein receiving (508A, 508B) the report comprises receiving (508A; 508B) an RRC reconfiguration complete message from the UE (500), the RRC reconfiguration complete message corresponding to the report comprising the applicability information for the inference configuration of the AI / ML functionality and the identification information associated to the inference configuration of the AI / ML functionality.

49. The method of claim 48, wherein the identification information associated to the inference configuration of the AI / ML functionality comprises an identification of a channel state information configuration and an identification of a serving cell configuration.

50. The method of claim 48, wherein the identification information associated to the inference configuration of the AI / ML functionality comprises an identification of a measurement configuration and an identification of a serving cell configuration.

51. The method of claim 48, further comprising, prior to receiving (508A; 508B) the RRC reconfiguration complete message, transmitting (504A; 504B) an RRC reconfiguration message to the UE (500), the RRC reconfiguration message comprising one or more inference configurations of the AI / ML functionality and, for each inference configuration, associated identification information.

52. The method of claim 51, wherein, for each inference configuration, the associated identification information comprises an identification of a channel state information configuration and an identification of a serving cell configuration.

53. The method of claim 51, wherein, for each inference configuration, the associated identification information comprises an identification of a measurement configuration and an identification of a serving cell configuration.

54. A network node (502; 1800) for receiving applicability information of an Artificial Intelligence, AI, or Machine Learning, ML, (AI / ML) functionality, the network node (502; 1800) comprising:processing circuitry (1802) configured to cause the network node (502; 1800) to receive (508A; 508B), from a User Equipment, UE, (502), a report comprising applicability information for an inference configuration of an AI / ML functionality and identification information associated to the inference configuration of the AI / ML functionality.

55. The network node (502; 1800) of claim 54, wherein the report corresponds to a Radio Resource Control, RRC, message.

56. The network node (502; 1800) of claim 55, wherein the identification information associated to the inference configuration of the AI / ML functionality comprises an identification of a channel state information configuration and an identification of a serving cell configuration.

57. The network node (502; 1800) of claim 55, wherein the identification information associated to the inference configuration of the AI / ML functionality comprises an identification of a measurement configuration and an identification of a serving cell configuration.

58. The network node (502; 1800) of claim 54, wherein the report corresponds to an RRC reconfiguration complete message.

59. The network node (502; 1800) of claim 58, wherein the identification information associated to the inference configuration of the AI / ML functionality comprises an identification of a channel state information configuration and an identification of a serving cell configuration.

60. The network node (502; 1800) of claim 58, wherein the identification information associated to the inference configuration of the AI / ML functionality comprises an identification of a measurement configuration and an identification of a serving cell configuration.

61. The network node (502; 1800) of claim 58, wherein the processing circuitry (1802) is further configured to cause the network node (502; 1800) to, prior to receiving (508A; 508B) the RRC reconfiguration complete message that corresponds to the report, transmit (504A; 504B) an RRC reconfiguration message to the UE (500), the RRC reconfiguration message comprising one or more inference configurations of the AI / ML functionality and, for each inference configuration, associated identification information.

62. The network node (502; 1800) of claim 61, wherein, for each inference configuration, the associated identification information comprises an identification of a channel state information configuration and an identification of a serving cell configuration.

63. The network node (502; 1800) of claim 61, wherein, for each inference configuration, the associated identification information comprises an identification of a measurement configuration and an identification of a serving cell configuration.

Images