Methods for UE reporting performance monitoring results for CSI prediction with periodic or semi-persistent CSI-rs

Abstract

Systems and methods related to User Equipment (UE) reporting of performance monitoring results for Channel State Information (CSI) prediction are disclosed. In one embodiment, a method performed by a UE comprises performing first channel measurements on a single periodic or semi-persistent (P / SP) CSI Reference Signal (CSI-RS) during a first measurement time window and generating predicted CSI for a prediction time window based on the first channel measurements. The method further comprises performing second channel measurements on the single P / SP CSI-RS during a second measurement time window that comprises the prediction time window and generating a performance monitoring output or metric based on the second channel measurements. The method further comprises transmitting, to the network node, a report comprising the performance monitoring output or metric.

Description

METHODS FOR UE REPORTING PERFORMANCE MONITORING RESULTS FOR CSI PREDICTION WITH PERIODIC OR SEMI-PERSISTENT CSI-RSRELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Patent Application No.63 / 755,743, filed February 7, 2025, the disclosure of which is hereby incorporated herein by reference in its entirety.TECHNICAL FIELD

[0002] The present disclosure related to a mobile communications network and, more specifically, Channel State Information (CSI) prediction in a mobile communications network.BACKGROUND

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

[0004] In 3rd Generation Partnership Project (3GPP) New Radio (NR) standardization work, a release 18 study item on AI / ML for the NR air interface started in May 2022 and completed in Dec 2023. This study item explored the benefits of augmenting the air-interface with features enabling improved support of AI / ML based algorithms for enhanced performance and / or reduced complexity / overhead. Through studying a few selected use cases (CSI feedback, beam management, and positioning), this study item aims at laying the foundation for future air-interface use cases leveraging AI / ML techniques.1 General aspects for NR Rel-18 AI / ML for NR air interface1.1 LCM operations for AI / ML for NR air interface

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

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

[0007] Two types of LCM operations were studied in NR Rel-18, functionality -based LCM and model-ID based LCM. In regard to functionality-based LCM, functionality refers to an AI / ML-enabled Feature / FG enabled by configuration(s), where configuration(s) is(are) supported based on conditions indicated by UE capability. Correspondingly, functionality-based LCM operates based on, at least, one configuration of an AI / ML-enabled Feature / FG or specific configurations of an AI / ML-enabled Feature / FG. In functionality -based LCM, the network indicates activation / deactivation / fallback / switching of AI / ML functionality via 3GPP signaling (e.g., RRC, MAC-CE, DCI). Models may not be identified at the network, and UE may perform model-level LCM. Whether and how much awareness / interaction the network should have about model-level LCM requires further study. For functionality identification, there may be either one or more than one functionalities defined within an AI / ML-enabled feature, whereby an AI / ML-enabled feature refers to a feature where AI / ML may be used. In model-ID-based LCM, models are identified at the network, and the network / UE may activate / deactivate / select / switch individual AI / ML models via model ID. A model may be associated with specific configurations / conditions associated with UE capability of an AI / ML-enabled feature / feature group (FG) and additional conditions (e.g., scenarios, sites, and datasets) as determined / identified between the UE-side and network-side. An AI / ML model identified by a model ID may be logical, and how it maps to physical AI / ML model(s) may be up to implementation.1.2 Functional framework for AI / ML for NR air interface

[0008] Figure 1 shows a functional framework that can be used for studying model LCM aspects for different Al for PHY use cases. The general framework consists of the following: Data Collection, Model Training, Management, Inference, and Model Storage.

[0009] Data Collection is a function that provides input data to the Model Training, Management, and Inference functions. Training Data is data needed as input for the AI / ML ModelTraining function. Monitoring Data is data needed as input for the Management of AI / ML models or AI / ML functionalities. Inference Data is data needed as input for the AI / ML Inference function.

[0010] Model Training is a function that performs AI / ML model training, validation, and testing which may generate model performance metrics which can be used as part of the model testing procedure. The Model Training function is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on Training Data delivered by a Data Collection function, if required. In case of having a Model Storage function, the Trained / Updated Model function is used to deliver trained, validated, and tested AI / ML models to the Model Storage function, or to deliver an updated version of a model to the Model Storage function.

[0011] Management is a function that oversees the operation (e.g., selection / (de)activation / switching / fallback) and monitoring (e.g., performance) of AI / ML models or AI / ML functionalities. This function is also responsible for making decisions to ensure the proper inference operation based on data received from the Data Collection function and the Inference function. Management Instruction is information needed as input to manage the Inference function. Concerning information may include selection / (de)activation / switching of AI / ML models or AI / ML-based functionalities, fallback to non-AI / ML operation (i.e., not relying on inference process), etc. A Model Transfer / Delivery Request is Used to request model(s) to the Model Storage function. Performance Feedback / Retraining Request is information needed as input for the Model Training function, e.g., for model (re)training or updating purposes.

[0012] Inference is a function that provides outputs from the process of applying AI / ML models or AI / ML functionalities, using the data that is provided by the Data Collection function (i.e., Inference Data) as an input. The Inference function is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on Inference Data delivered by a Data Collection function, if required. Inference Output is data used by the Management function to monitor the performance of AI / ML models or AI / ML functionalities.

[0013] Model Storage is a function responsible for storing trained / updated models that can be used to perform the Inference function. Note that the Model Storage function in Figure 1 is only intended as a reference point (if any) when applicable for protocol terminations, model transfer / delivery, and related processes. It should be stressed that its purpose does not encompass restricting the actual storage locations of models. Therefore, the specification impact of all data / information / instruction flows (i.e., the arrows in Figure 1) to / from this function should be studied case by case. Model Transfer / Delivery is used to deliver an AI / ML model to the Inference function.2 Time domain CSI prediction at UE2.1 3GPP NR Rel-18 time domain Type II CSI prediction at UE

[0014] In 3GPP NR Rel-18, Channel Measurement Resource (CMR) enhancement for Type II CSI prediction at UE (a.k.a. Enhanced Type II predicted Precoding Matrix Indicator (PMI)) has been introduced, see the measurement part in Figure 2, where Kpsamples of a periodic or semi-persistent Channel State Information Reference Signal (CSI-RS) resource are used by the UE for channel measurement. The Kpsamples of the periodic or semi-persistent CSI-RS resource are used by the UE to extract time domain channel properties of the channel, based on which a future CSI can be predicted (where PMI for N4prediction time occasions are predicted). The periodicity of the periodic or semi-persistent CSI-RS resource is denoted as P slots. The gap between adjacent prediction occasions is d = P slots as shown in Figure 2.

[0015] For the Rel-18 Type II predicted PMI enhancement, a UE can be configured by a next generation NodeB (gNB) to report predicted PMIs for N4E {1, 2, 4, 8} time slots, see the Rel-18 Type II PMI part in Figure 2. The predicted N4PMIs are supposed to reflect the channels with d = P slots separation, starting from 8 E {—nCSIre j ,0,1,2} slots into the future relative to the uplink (UL) slot n in which the predicted CSI is reported. The offset 6 relative shown in Figure 2 can be configured by the gNB via RRC signaling. The N4PMIs are compressed in a beam-frequency-Doppler domain, and the compressed PMI is reported to the gNB in a single CSI report in slot n.2.2 3GPP NR Rel-19 time-domain CSI prediction using UE-side AI / ML model

[0016] In NR Rel-18, Al-based UE-side CSI prediction was introduced as an Al for PHY use case and being specified in NR Rel-19 work item on AI / ML for the NR air interface.

[0017] One or more AI / ML models can be trained and deployed at a UE for the Al-based CSL prediction feature. During model inference, a UE is configured by the gNB to measure a set of historical CSI-RS occasions (e.g., the Kpoccasions of the periodic or semi-persistent CSI-RS measurements in the observation window shown in Figure 2) and then report a predicted CSI in the scheduled UL slot for one or multiple future time instances (e.g., the N4future tine instances in Figure 2 ) using its AI / ML model(s).3 Performance monitoring

[0018] For CSI prediction using UE side AI / ML model use case studied in Rel-18 AI / ML for NR air interface study item, at least the following aspects have been proposed by companies on performance monitoring for functionality -based LCM:Type 1:o UE calculates the performance metric(s)o UE reports performance monitoring output that facilitates functionality fallback decision at the network■ Performance monitoring output details can be further defined■ Network (NW) may configure threshold criterion to facilitate UE side performance monitoring (if needed).o NW makes decision(s) of functionality fallback operation (fallback mechanism to legacy CSI reporting).Type 2:o UE reports predicted CSI and / or the corresponding ground-trutho NW calculates the performance metrics.o NW makes decision(s) of functionality fallback operation (fallback mechanism to legacy CSI reporting).Type 3:o UE calculates the performance metric(s)o UE reports performance metric(s) to the NWo NW makes decision(s) of functionality fallback operation (fallback mechanism to legacy CSI reporting).- Functionality selection / activation / deactivation / switching as defined for other UE side use cases can be reused, if applicable.Configuration and procedure for performance monitoringCSI-RS configuration for performance monitoring- Performance metric including at least intermediate Key Performance Indicator (KPI) (e.g., Normalized Mean Square Error (NMSE) or Square Generalized Cosine Similarity (SGCS)) - UE report, including periodic / semi-persistent / aperiodic reporting, and event driven report - Note: UE may make decision within the same functionality on model selection, activation, deactivation, switching operation transparent to the NW.

[0019] For intermediate KPI based performance monitoring of an AI / ML-based CSI prediction feature, a monitoring data sample should consist of both the channel measurementswithin an observation window and the channel measurements within the associated prediction window. The channel measurements within the observation window are used for creating model input, which is then fed to the AI / ML model for generating a model output (predicted CSI). The measurements within the prediction window are used for creating the ground truth label.

[0020] An intermediate KPI (e.g., NMSE or SGCS) per monitoring data sample can be derived by comparing a CSI prediction model output (i.e., predicted CSI for the one or more future time instances) with the corresponding ground truth label (i.e., the channel measurements(s) corresponding to the one or more future time instances). Sufficient monitoring data samples may be needed to ensure reliable and accurate model performance monitoring results, based on which LCM operation decisions will be made.SUMMARY

[0021] Systems and methods related to User Equipment (UE) reporting of performance monitoring results for Channel State Information (CSI) prediction are disclosed. In one embodiment, a method performed by a UE comprises performing first channel measurements on a single periodic or semi-persistent (P / SP) CSI Reference Signal (CSI-RS) during a first measurement time window and generating predicted CSI for a prediction time window based on the first channel measurements. The method further comprises performing second channel measurements on the single P / SP CSI-RS during a second measurement time window that comprises the prediction time window and generating a performance monitoring output or metric based on the second channel measurements. The method further comprises transmitting, to the network node, a report comprising the performance monitoring output or metric. In this manner, the network is enabled to know the quality of the CSI predicted by the UE based channel measurements performed on a single P / SP CSI-RS.

[0022] In one embodiment, generating the predicted CSI comprises generating the predicted CSI for the prediction time window based on the first channel measurements using an Artificial Intelligence (Al) / Machine Learning (ML) based model or functionality or a non-AI / ML based model or functionality. In one embodiment, the performance monitoring output or metric is for the AI / ML based model or functionality or non-AI / ML based functionality.

[0023] In one embodiment, the method further comprises receiving, from a network node, a CSI report configuration for performance monitoring output report. In one embodiment, the report is in accordance with the CSI report configuration for performance monitoring output report. In one embodiment, the CSI report configuration for performance monitoring output report is a standalone CSI report configuration.

[0024] In one embodiment, the CSI report configuration for performance monitoring output report is linked to a CSI-inference report configuration. In one embodiment, a linkage between the CSI report configuration for performance monitoring output report and the CSI-inference report configuration is indicated via a CSI-inference report configuration identifier (ID) comprised in the CSI report configuration for performance monitoring output report. In one embodiment, the method further comprises receiving, from the network node, configuration information that configures the UE with the single P / SP CSI-RS as a channel measurement resource. In one embodiment, the report configured by the CSI report configuration for performance monitoring output report can only be triggered after a CSI-inference reported configured by the linked CSI-inference report configuration is triggered. In another embodiment, the linkage is indicated via at least one of the following: inclusion of information on a CSI-inference report configuration ID in the report configuration for performance monitoring output report, inclusion of information on a report configuration for performance monitoring output report ID in the CSI-inference report configuration, and inclusion of information on a same single P / SP CSI-RS resource for channel measurement in both the CSI-inference report configuration and the report configuration for performance monitoring output report.

[0025] In one embodiment, the method further comprises receiving, from the network node, information that configures one or more parameters associated to the CSI report configuration.

[0026] In one embodiment, values for one or more associated to the CSI report configuration are predefined.

[0027] In one embodiment, the one or more parameters comprise any one or more of a gap between a last P / SP CSI-RS occasion in the first measurement time window and a first P / SP CSI-RS occasion in the second measurement time window, a number of P / SP CSI-RS occasions within the first measurement time window, and a number of time slots within the prediction time window.

[0028] In one embodiment, the method further comprises receiving, from the network node, configuration information that configures the UE with the single P / SP CSI-RS as a channel measurement resource.

[0029] In one embodiment, generating the performance monitoring metric comprises, for one or more predicted CSI values, generating corresponding measured CSI values based on the second channel measurements. The method further comprises generating the performance monitoring output or metric based on the one or more predicted CSI values and the one or more corresponding measured CSI values.

[0030] In one embodiment, a time gap (xgap) between a last P / SP CSI-RS occasion used for the first channel measurements and a first P / SP CSI-RS occasion used for the second channelmeasurements is an integer multiple of a periodicity of the single P / SP CSI-RS resource. In one embodiment, xgapis configured by the network node. In one embodiment, xgapis configured as part of the CSI report configuration for performance monitoring output report. In one embodiment, xgapis configured as part of a CSI resource configuration corresponding to the single P / SP CSI-RS resource. In one embodiment, xgapis predefined.

[0031] In one embodiment, the first measurement time window and the second measurement time window are defined with respect to a CSI reference resource.

[0032] Corresponding embodiments of a UE are also disclosed. In on embodiment, a UE comprises a communication interface comprising a transmitter and receiver. The UE further comprises processing circuitry associated with the communication interface. The processing circuitry configured to cause the UE to perform first channel measurements on a single P / SP CSI-RS during a first measurement time window, generate predicted CSI for a prediction time window based on the first channel measurements, perform second channel measurements on the single P / SP CSI-RS during a second measurement time window that comprises the prediction time window, generate a performance monitoring output or metric based on the second channel measurements, and transmit, to the network node, a report comprising the performance monitoring output or metric.

[0033] Embodiments of a method performed by a network node are also disclosed. In one embodiment, a method performed by a network node comprises transmitting, to a UE, a CSI report configuration for performance monitoring output report and receiving, from the UE, a report comprising a performance monitoring output or metric related to generation of predicted CSI, in accordance with the CSI report configuration for performance monitoring output report.

[0034] In one embodiment, the CSI report configuration for performance monitoring output report is a standalone CSI report configuration.

[0035] In one embodiment, the CSI report configuration for performance monitoring output report is linked to a CSI-inference report configuration. In one embodiment, a linkage between the CSI report configuration for performance monitoring output report and the CSI-inference report configuration is indicated via a CSI-inference report configuration ID comprised in the CSI report configuration for performance monitoring output report. In one embodiment, the method further comprises transmitting, to the UE, configuration information that configures the UE with the single P / SP CSI-RS as a channel measurement resource. In one embodiment, the report configured by the CSI report configuration for performance monitoring output report can only be triggered after a CSI-inference reported configured by the linked CSI-inference report configuration is triggered.

[0036] Corresponding embodiments of a network node are also disclosed. In one embodiment, a network node comprises processing circuitry configured to cause the network node to transmit, to a UE, a CSI report configuration for performance monitoring output report and receive, from the UE, a report comprising a performance monitoring output or metric related to generation of predicted CSI, in accordance with the CSI report configuration for performance monitoring output report.BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the disclosure, and together with the description serve to explain the principles of the disclosure.

[0038] Figure 1 shows a functional framework that can be used for studying model Lifecycle Management (LCM) aspects for different Artificial Intelligence (Al) for physical (PHY) layer use cases.

[0039] Figure 2 illustrates Channel Measurement Resource (CMR) enhancement for 3rdGeneration Partnership Project (3GPP) Release 18 Type II Channel State Information (CSI) prediction at a User Equipment (UE).

[0040] Figure 3 illustrates an example of aperiodic CSI for Performance Monitoring (CSI-PM) reporting with a periodic or semi-periodic Channel State Information Reference Signal (CSI-RS) resource, in accordance with embodiments of the present disclosure.

[0041] Figure 4 illustrates an example of aperiodic CSI-PM reporting with a periodic or semiperiodic CSLRS resource, in accordance with some other embodiments of the present disclosure.

[0042] Figure 5 illustrates the operation of a UE and a network node, in accordance with at least some of the embodiments described herein.

[0043] Figure 6 shows an example of a communication system in accordance with some embodiments.

[0044] Figure 7 is another example of a communication system according to some embodiments.

[0045] Figure 8 shows a wireless device, which may be configured to operate in communication system of Figure 6 or in communication system of Figure 7.

[0046] Figure 9 shows a network node in accordance with some embodiments.

[0047] Figure 10 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized.DETAILED DESCRIPTION

[0048] 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.

[0049] 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. Additional information may also be found in the document(s) provided in the Appendix.

[0050] There currently exist certain challenge(s). For monitoring the performance of a User Equipment (UE)-sided Artificial Intelligence (AI) / Machine Learning (ML) model, if the performance monitoring is performed at the UE-side (e.g., type 1 or type 3 monitoring mentioned in Section 3 of the Background above), then a UE can report the model performance monitoring results to the network (NW) so that the NW takes the UE reported model performance information into account when making model level or functionality level Life Cyle Management (LCM) decisions (e.g., fallback to non-AL / ML algorithm, functionality / model switching, etc.).

[0051] Different report configuration methods have been proposed to enable a UE to report performance monitoring results for a UE-sided Channel State Information (CSI) prediction AI / ML model / algorithm by reusing the CSI reporting framework. More specifically, new value(s) for parameter reportQuantity were proposed for introduction in the CSLReportConfig Information Element (IE) as defined in 3rdGeneration Partnership Project (3GPP) Technical Specification (TS) 38.331 V18.4.0 to support the NW configuring a UE to report a performance monitoring result in a CSI report. The performance monitoring result can be reported together with the predicted CSI generated by the CSI prediction AI / ML model / algorithm in the same CSI report, or a dedicated CSI report is configured for the UE to report the performance monitoring result.

[0052] An open problem is how to configure measurement resources for determining the performance monitoring result when periodic or semi-persistent CSI Reference Signal (CSLRS) is used as channel measurement resource for deriving the predicted CSI.

[0053] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. Exemplary embodiments of the present disclosure may include any one or more of the following:• UE receiving configuration of a standalone CSI report configuration for the performance monitoring output report (denoted herein as a CSI-PM report)• UE receiving a single periodic / semi-persistent (P / SP) CSI-RS as channel measurement resource whose information is provided in the standalone CSI report configuration• UE receiving information on one or more parameters such as xgap, Kp, and N4• UE performing first channel measurements on the single P / SP CSI-RS (e.g., during a first window) and generating the predicted CSI• UE performing second channel measurements on the single P / SP CSI-RS (e.g., in a second window) and generating a performance monitoring output or metric• UE reporting the performance monitoring output or metric as part of the CSI-PM report.

[0054] Certain embodiments may provide one or more of the following technical advantages. Some embodiments proposed in this disclosure allow a single P / SP CSI-RS resource to be used for both measurements related to predicting the CSI and measurements related to generating the ground truth label. The predicted CSI and the ground truth label can be used to generate the performance monitoring metric which the UE reports to the NW. This enables NW to know the quality of the received predicted CSI and therefore enables the network to make better decisions on scheduling and downlink transmission for the future time slots.

[0055] In addition, using a single P / SP CSI-RS resource can result in energy savings and CSI-RS transmission overhead savings at the network node.

[0056] The teachings of certain embodiments may improve, e.g., data rate, latency, and / or power consumption.

[0057] Now, a more detailed description of exemplary embodiments of the solution(s) disclosed herein will be provided.

[0058] In accordance with embodiments of the present disclosure, a UE receives signaling of a CSI prediction performance report configuration (e.g., for CSI-PM report) for an AI / ML based or a non-AI / ML based CSI prediction model / algorithm from a network node. Based on the received CSI prediction performance report configuration, the UE derives one or more performance monitoring result(s) and reports the derived performance monitoring result(s) to the network. The AI / ML based or a non-AI / ML based CSI prediction model / algorithm is implemented at the UE, and it can be an AI / ML-based functionality (e.g., a neural network model trained by using a large dataset and then deployed at the UE) or non-AI / ML based functionality (e.g., an Auto-Regression based algorithm, or a Kalman-filer based algorithm).

[0059] The network node is any network node of a cellular (i.e., mobile) communications system such as, e.g., a Radio Access Network (RAN) node in a RAN, Operations, Administration,and Maintenance (OAM) node, or core network node of a cellular communications system such as, e.g., a 3GPP 5thGeneration (5G) or 6thGeneration (6G) system. A network node may be, for example, a base station (e.g., a next-generation NodeB (gNB) in the case of New Radio (NR), a network node that provides some of the functionality of a base station (e.g. a gNB-Central Unit (CU) or the gNB -Distributed Unit (DU) in the case of a gNB distributed architecture), a OAM node, or a core network node, e.g. a Network Data Analytics Function (NWDAF) in a 5G or 6G core network. In the following, the term “network node” or “network” can refer to any of the aforementioned entities.

[0060] The CSI prediction model / algorithm is not limited to predicting the Precoding Matrix Indicators (PMIs) or raw channels for one or multiple future time instances. Examples of predicted CSI include predicted PMI(s), predicted raw channel(s), predicted Rank Indicator(s) (RI(s)), predicted Reference Signal Received Power(s) (RSRP(s)), predicted Channel Quality Indicator(s) (CQI(s)), predicted CSI-RS Resource Indicator(s) (CRI(s)), predicted top-K strongest beams, predicted top-K cells, or the like.

[0061] A model input sample for CSI prediction comprises measurement of multiple occasions of a periodic or semi-persistent CSI-RS (e.g., the Kpmeasured periodic or semi-persistent CSI-RS occasions within the ‘Measurement’ window as shown in Figure 2).

[0062] A model output sample for CSI prediction comprises predicted CSI for the N4prediction time instances (e.g., the predicted CSIs on the N4time slots within the prediction window as shown in Figure 2).

[0063] For CSI prediction using UE-sided AI / ML models, the UE reports predicted PMI in a CSI report (denoted as CSI-inference report in this disclosure) to the network in the format of the Rel-18 “typeII-Doppler-rl8” codebook as defined in 3GPP TS 38.214 V18.4.0. The model output format is up to UE implementation. Different AI / ML model designs can result in different model output formats, e.g.,a) The model output is predicted raw channel per prediction time instance for all the N4prediction time instances. The predicted raw channels are then used for calculating predicted PMIs according to the Rel-18 “typeII-Doppler-rl8” codebook as defined in 3GPP TS 38.214.b) The model output is predicted PMI following the Rel-16 type II CSI codebook per prediction time instance for all the N4prediction time instances. The predicted Rel-16 type II PMIs are then compressed into a PMI following the Rel-18 “typeII-Doppler-rl8” codebook as defined in 3GPP TS 38.214.c) The model directly outputs a predicted PMIs following the Rel-18 “typeII-Doppler-rl8” codebook for the N4prediction time instances.

[0064] A ground truth label for performance monitoring can be obtained using measured CSI(s) for the corresponding one or multiple prediction time instances (e.g., the measured CSIs on the N4time slots as shown in Figure 2.)

[0065] Note that for the UE-side CSI prediction functionality, the predicted CSI(s) for N4prediction time instances are reported in the format of “typeII-Doppler-rl8” codebook from the UE to the network. Hence, to acquire accurate performance metrics for the reported CSI, it can be beneficial to define the ground truth label format as “typeII-Doppler-rl8” codebook for calculating the intermediate Key Performance Indicator (KPI), regardless of which model output format is used for the AI / ML model. The ground truth label in the format of “typell-Doppler-rl 8” codebook can be obtained by compressing the measured CSIs on the N4prediction time instances according to section 5.2.2.2.10 of 3GPP TS 38.214 (see, e.g., V18.4.0 or V18.5.0). The intermediate KPI is calculated using the UE predicted CSI and the ground-truth label, both represented in the format of “typell-Doppler-rl 8” codebook.

[0066] Note that the proposed method for signaling and configuration of the performance monitoring output report is not limited to the case that the ground truth format must be “typell-Doppler-rl8” codebook.

[0067] To support intermediate KPI calculation, a monitoring data sample can be represented as {predicted CSI, ground-truth label}, where the predicted CSI is generated based on the model output and the associated ground truth label is generated based on the measurements.

[0068] An intermediate KPI per monitoring data sample {predicted CSI, ground-truth label} can be defined as the Square Generalized Cosine Similarity (SGCS) or Normalized Mean Squared Error (NMSE) between the predicted CSI and the ground-truth label.

[0069] In this disclosure, the performance monitoring metrics for UE-sided CSI prediction use case is defined as intermediate KPI per monitoring data sample. With this approach, the UE can feedback the predicted CSI and / or the corresponding intermediate KPI to the network in the same CSI report. This enables the network to know the quality of the received predicted CSI; thus, the network is enabled to make better decisions on scheduling and downlink transmission for the future time slots.

[0070] Examples of signaling and configurations for CSI-PM report:

[0071] In one embodiment, the CSI-PM report configuration is configured as a standalone CSI reporting configuration that is separate from the CSI-inference report configuration. An example of CSI-PM reporting is shown in Figure 3 for the case when CSI-PM report is triggeredaperiodically. In this embodiment, a single periodic or semi-persistent (P / SP) CSI-RS resource is assumed for channel measurements. The CSI-PM report configuration in this embodiment refers to one CSI resource configuration (e.g., refers to one CSI-ResourceConfig where CSI-ResourceConfig is according is defined in 3GPP TS 38.331 V18.4.0).

[0072] In Figure 3, the CSI-PM report is triggered via a Downlink Control Information (DCI) signal such that the CSI-PM report is reported by the UE in slot n2. The CSI reference resource for the CSI-PM report in slot n2is denoted in Figure 3 where the time location of the CSI reference resource is as per the definition in Section 5.2.2.5 of 3GPP TS 38.214 V18.4.0.

[0073] In the example of Figure 3, the KpP / SP CSI-RS occasions within Measurement window 1 (e.g., the KpP / SP CSI-RS occasions in diagonally hashed slots) are used for channel measurement for the purpose of generating the predicted CSI. The measurements on the KpP / SP CSI-RS occasions are used for creating a model input. The UE uses the model input and the AI / ML CSI prediction model / algorithm to generate a predicted CSI. The predicted CSI comprises predicted CSI for the N4prediction time instances within the Prediction window shown in Figure 3 (e.g., the predicted CSIs on the N4time slots shown with square-block hashing in Figure 3).

[0074] Then, the N4P / SP CSI-RS occasions within Measurement window 2 are used for channel measurement for the purpose of creating a ground-truth label associated to the predicted CSI The UE uses the predicted CSI and the ground-truth label to obtain a performance monitoring output and reports the performance monitoring output (denoted as CSI-PM report in Figure 3) on the scheduled PUSCH at the UL slot n2.

[0075] It should be noted that, in this embodiment, all occasions of P / SP CSI-RS shown in Figure 3 belong to P / SP CSI-RS transmitted in a single P / SP CSI-RS resource. The benefit of this embodiment is that a single P / SP CSI-RS resource is sufficient for measurements performed in Measurement window 1 and Measurement window 2 which reduces the CSI-RS overhead as opposed to having separate P / SP CSI-RS resources for the two measurement windows.

[0076] In this embodiment, it is assumed that the gap between the last P / SP CSI-RS occasion in Measurement window 1 and the first P / SP CSI-RS occasion in Measurement window 2 is an integer multiple of the periodicity of the single P / SP CSI-RS resource. In Figure 3, this gap is denoted as xgap* P where xgapis a positive integer multiple and P is the periodicity of the single P / SP CSI-RS resource. In some embodiments, the UE receives configuration of xgapfrom the network (e.g., gNB). In one variant of this embodiment, xgapis configured as part of a CSI report configuration corresponding to the CSI-PM report. In another variant of the embodiment, xgapis configured as part of a CSI resource configuration corresponding to the single P / SP CSI-RSresource. In yet another embodiment, information on xgapis reported by the UE to the network (e.g., gNB) as part of UE capability reporting. In another embodiment, information on xgapis prespecified as part of 3GPP specifications.

[0077] It should be noted that the terms Measurement window 1 and Measurement window 2 may not necessarily be captured in 3GPP specification. Instead, the two measurement windows may be defined with respect to the CSI reference resource.

[0078] In one embodiment, Measurement window 1 may be defined by Kpconsecutive P / SP CSI-RS occasions:• starting from thelatest P / SP CSI-RS occasion no later than the CSI reference resource, andz >. th• ending at the N4+ xgap) latest P / SP CSI-RS occasion no later than the CSI reference resource.

[0079] In another embodiment, Measurement window 2 may be defined by N4consecutive P / SP CSI-RS occasions:• starting from the ( V4)t / llatest P / SP CSI-RS occasion no later than the CSI reference resource, and• ending at the 1stlatest P / SP CSI-RS occasion no later than the CSI reference resource.

[0080] Similarly, the Prediction window may not necessarily be captured in 3GPP specification. Instead, the Prediction window may be defined similar to Measurement window 2.

[0081] In some embodiments, information on N4and / or Kpare received by the UE from the network. In some embodiments, the UE receives configuration of N4and / or Kpfrom the gNB. In one variant of this embodiment, N4and / or Kpis / are configured as part of a CSI report configuration corresponding to the CSI-PM report. In another variant of the embodiment, N4and / or Kpis / are configured as part of a CSI resource configuration corresponding to the single P / SP CSI-RS resource. In yet another embodiment, information on N4and / or Kpis / are reported by the UE to the gNB as part of UE capability reporting. In another embodiment, information on N4and / or Kpis / are pre-specified as part of 3GPP specifications.

[0082] In an alternative embodiment, the CSI-PM report configuration is linked to a CSI-inference report configuration. An example of CSI-PM reporting according to this alternative embodiment is shown in Figure 4 for the case where CSI-inference report and CSI-PM report are triggered aperiodically via different DCI signals (e.g., 1stDCI signaling triggers aperiodic CSI-inference report in slot n, and 2ndDCI signaling triggers aperiodic CSI-PM report in slot n2). In this alternative embodiment, both the CSI-inference report configuration and the CSI-PM reportconfiguration refer to the same single periodic or semi-persistent (P / SP) CSI-RS resource is assumed for channel measurements. That is, the CSI-Inference report configuration contains information on one periodic or semi-persistent CSI-RS resource to be used for channel measurement; and the CSI-PM report configuration contains information on the same one periodic or semi -persistent CSI-RS resource to be used for channel measurement.

[0083] In Figure 4, the CSI-Inference report is triggered via a 1stDCI signal such that the CSI-Inference report is reported by the UE in slot n . The KpP / SP CSI-RS occasions within Measurement window 1 (e.g., the KpP / SP CSI-RS occasions in slots with diagonal hashing) are used for channel measurement for the purpose of generating the predicted CSI. The measurements on the KpP / SP CSI-RS occasions are used for creating a model input. The UE uses the model input and the AI / ML CSI prediction model / algorithm to generate a predicted CSI. The predicted CSI comprises predicted CSI for the N4prediction time instances within the Prediction window shown in Figure 4 (e.g., the predicted CSIs on the N4time slots shown in blue in Figure 4). Information on the predicted CSI is reported as part of the CSI-Inference report in slot n.

[0084] In Figure 4, the CSI-PM report is triggered via a 2ndDCI signal such that the CSI-PM report is reported by the UE in slot n2. The CSI reference resource for the CSI-PM report in slot n2is denoted in in the figure with the indicated hashing where the time location of the CSI reference resource is as per the definition in Section 5.2.2.5 of 3GPP TS38.214 V18.4.0. The N4P / SP CSI-RS occasions within Measurement window 2 are used for channel measurement for the purpose of creating a ground-truth label associated to the predicted CSI that was reported as part of the CSI-Inference report in slot n. The UE uses the predicted CSI that was reported as part of the CSI-Inference report in slot n and the ground-truth label to obtain a performance monitoring output and reports the performance monitoring output (denoted as CSI-PM report in Figure 3) on the scheduled PUSCH at the UL slot n2.

[0085] In an embodiment, the linking between the CSI-PM report and the CSI-inference report is indicated via at least one of the following methods:• include information on the CSI-inference report configuration ID in the CSI-PM report configuration• include information on the CSI-PM report configuration ID in the CSI-inference configuration• include information on the same single P / SP CSI-RS resource for channel measurement on both CSI-inference report configuration and in the CSI-PM report configuration

[0086] For this option, there is a coupling between a CSI-inference report configuration and a CSI-PM report configuration. Hence, for this option, a CSI-PM report can only be triggered after the linked CSI-inference report is triggered.

[0087] Figure 5 illustrates the operation of a UE 500 and a network node 502, in accordance with at least some of the embodiments described above. Optional steps are represented by dashed lines / boxes. Note that while not all details provided above are repeated here in the description of Figure 5, it is to be understood that those details are equally applicable to the corresponding step(s) / aspect(s) of the procedure of Figure 5.

[0088] As illustrated in Figure 5, the UE 500 receives, from the network node 502, a standalone CSI report configuration for performance monitoring output report, which is denoted herein as a CSI-IM report (step 504). The UE 500 further receives, from the network node 502, configuration information that configures the UE 500 with a single S / SP CSI-RS as a channel measurement resource whose information is provided in the standalone CSI report configuration (step 506). Optionally, the UE 500 receives, from the network node 502, information on (e.g., that defines value(s) for) one or more parameters such as, for example, any one or more of the following parameters: xgap, Kp, and N4(step 508). Numerous embodiments and variations for how the UE 500 is configured with or otherwise obtains these parameters are described above.

[0089] As described above, the UE 500 performs first channel measurements on the single P / SP CSI-RS (e.g., during a first time measurement window) and, based on the first channel measurements, generates predicted CSI (e.g., for a prediction time window) using an AI / ML-based or non-AI / ML based model or functionality (step 510). The UE 500 also performs second channel measurements on the single S / SP CSI-RS (e.g., during a second measurement window) and, based thereon, generates a performance monitoring output or metric of the AI / ML-based or non-AI / ML based model or functionality used to generate the CSI predictions (step 512). Details regarding the channel measurements, time and prediction windows, and the performance monitoring output or metric are provided above. The UE 500 reports (or sends a report containing) the performance monitoring output or metric to the network node 502 (step 514). Details regarding this reporting are also provided above. The network node 502 may then perform one or more actions based on the reported performance monitoring output or metric (step 516).

[0090] Figure 6 shows an example of a communication system 600 in accordance with some embodiments.

[0091] In the example, the communication system 600 includes a telecommunications network 602 that includes an access network 604, such as a radio access network (RAN), and a core network 606, which includes one or more core network nodes 608. The access network 604includes one or more access network nodes or base stations of various types, access network nodes 610A and 61 OB are depicted (which may be collectively referred to as network nodes 610), or any other similar 3rdGeneration Partnership Project (3GPP) access nodes or non-3GPP access points (APs). Some embodiments of the access network 604 may include more than one access network technology. The network nodes 610 of access network 604 facilitate direct or indirect connection of wireless devices, also referred to as user equipments (UEs), such as by connecting UEs 612A, 612B, 612C, and 612D (one or more of which may be generally referred to as UEs 612) to the core network 606 over one or more wireless connections.

[0092] 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 602 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a network node in the telecommunications network 602 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 602, including one or more access network nodes 610 and / or core network nodes 608.

[0093] Examples of an ORAN network node include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O-CU-CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). An ORAN network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an Al, Fl, Wl, El, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN network node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an O-2 interface defined by the O-RAN Alliance or comparable technologies.

[0094] The network nodes 610 facilitate direct or indirect connection of one or more UEs 612 to the core network 606 over one or more wireless connections. Example wireless communicationsover 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 600 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 600 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0095] The UEs 612 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 610 and other communication devices. Similarly, the network nodes 608, 610 are arranged, capable, configured, and / or operable to communicate directly or indirectly (e.g., via other devices of telecommunications network 602) with the UEs 612 and / or with other network nodes or equipment in the telecommunications network 602 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 602. More specifically, UEs 612 may send messages, data, and / or other signals to network nodes 608, 610 or other elements of the telecommunications network 602 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 608, 610 may send messages, data, and other signals to UEs 612, other network nodes 608, 610, and other devices in telecommunications network 602 directly or indirectly. As one specific example, a core network node 608 may transmit a particular message to a UE 612 by transmitting the message to an access network node 610 that will then transmit the message to the intended UE 612. Similarly, a core network node 608 may receive a particular message from a UE 612 by receiving the message from an access network node 610 that itself received the message from the UE 612.

[0096] In the depicted example, the core network 606 connects elements of the access network 604 (e.g., one or more of the network nodes 610) to one or more host computing systems, such as host 616. 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 606 includes one or more core network nodes (e.g., core network node 608) of various types, one or more of which may be generally referred to as network nodes 608. Network nodes 608 arestructured 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 608. 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).

[0097] The host 616 may be under the ownership or control of a service provider other than an operator or provider of the access network 604 and / or the telecommunications network 602. The host 616 may be operated by the service provider or on behalf of the service provider. The host 616 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0098] As a whole, the communication system 600 of Figure 6 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system 600 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 600 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 600 supporting different standards, protocols, or rule sets.

[0099] As one example, in certain embodiments, access network 604 may contain some access network nodes 610 that support 3 GPP radio access technologies (RAT), such as LTE or NR, whileother access network nodes 610 support (or the same access network nodes 610 additionally support) non-3GPP RATs, such as Wi-Fi or a proprietary RAT. As another example, telecommunications network 602 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 604 and / or a core network 606 that supports multiple different standard generations or may include multiple access networks 604 and / or multiple core networks 606 with individual networks 604, 606 supporting different standard generations.

[0100] Telecommunications network 602 may support network slicing to provide different logical networks to different devices that are connected to the telecommunications network 602. For example, the telecommunications network 602 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC) / Massive loT services to yet further UEs.

[0101] In some examples, one or more of the UEs 612 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 604 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 604. 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).

[0102] In the example, the hub 614 communicates with the access network 604 to facilitate indirect communication between one or more UEs (e.g., UE 612C and / or 612D) and network nodes (e.g., network node 610B). In some examples, the hub 614 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 614 may be a broadband router enabling access to the core network 606 for the UEs. As another example, the hub 614 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 610, or by executable code, script, process, or other instructions in the hub 614.

[0103] As another example, the hub 614 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 614 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 614 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub614 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 614 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0104] The hub 614 may have a constant / persistent or intermittent connection to the network node 610B. The hub 614 may also allow for a different communication scheme and / or schedule between the hub 614 and UEs (e.g., UE 612C and / or 612D), and between the hub 614 and the core network 606. In other examples, the hub 614 is connected to the core network 606 and / or one or more UEs via a wired connection. Moreover, the hub 614 may be configured to connect to an M2M service provider over the access network 604 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 610 while still connected via the hub 614 via a wired or wireless connection. In some embodiments, the hub 614 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 610B. In other embodiments, the hub 614 may be a nondedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 610B, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0105] Figure 7 is another example of a communication system 700 according to some embodiments. As used herein, the communication system 700 includes multiple access points (APs) 710 (with four exemplary APs 710A, 710B, 710C, and 710D being depicted) and multiple wireless devices, referred to in the context of communication system 700 as stations (STAs) 712 (referred to individually as ST A 712A, ST A 712B, STA 712C, ST A 712D, and ST A 712E). ST A 712A is served by AP 710A in a first basic service set (BSS) 720A. STA 712B and STA 712C are served by AP 710B in a second BSS, BSS 720B. STA 712D is served by AP 710C in a third BSS, BSS 720C. STA 712E is served by AP 710D in a fourth BSS, BSS 720D. Stations 712 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 712 could, for example, correspond to other kinds of equipment like smart home devices, printers, multimedia devices, data storage devices, or the like.

[0106] Each of STAs 712 may connect through a radio link to one of APs 710. For example, depending on location or channel conditions experienced by a given STA 712, 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 spectrumthat 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.

[0107] Each AP 710 may provide data connectivity to STAs 712 connected to a particular AP 710. As illustrated, APs 710 may be connected to a data network 730. In this way, APs 710 may also provide data connectivity between STAs 712 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 712 and its serving AP 710 may be used for providing various kinds of services to STA 712, e.g., a voice service, a multimedia service, or other data service. Such services may be based on applications that are executed on STA 712 and / or on a device linked to STA 712. By way of example, Figure 7 illustrates an application service platform 732 provided in data network 730. The application(s) executed on STA 712 and / or on one or more other devices linked to STA 712 may use the radio link for data communication with one or more other STA 712 and / or the application service platform 732, thereby enabling utilization of the corresponding service(s) at STA 712.

[0108] Figure 8 shows a wireless device 800, which may be configured to operate in communication system 600 of Figure 6 or in communication system 700 of Figure 7. The wireless device 800 may be alternatively referred to as a UE 800, like a UE 612 within the context of communication system 600, or as a station (STA) 800 or as a non-access-point station (non-AP STA) 800, like a STA 712 within the context of the communication system 700, 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.

[0109] A wireless device 800 may support device-to-device (D2D) communication, for example by implementing a 3 GPP standard for sidelink communication, Dedicated Short-RangeCommunication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to-everything (V2X). In other examples, wireless device 800 may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, wireless device 800 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 800 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).

[0110] In particular embodiments, wireless device 800 includes processing circuitry 802 that is operatively coupled via a bus 804 to an input / output interface 806, a power source 808, a memory 810, a communication interface 812, and / or any other component, or any combination thereof. Certain embodiments of wireless device 800 may include all or a subset of the components shown in Figure 8. The level of integration between the components may vary from one embodiment of wireless device 800 to another. In general, in a particular embodiment of wireless device 800, processing circuitry 802, input / output interface 806, power source 808, memory 810, and communication interface 812 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 800. Further, certain embodiments of wireless devices 800 may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.[OHl] The processing circuitry 802 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 810. The processing circuitry 802 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 802 may include multiple central processing units (CPUs).

[0112] In the example, the input / output interface 806 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into wireless device 800. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digitalcamera, 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.

[0113] In some embodiments, the power source 808 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used to supply power to circuitry or to charge an associated battery. The power source 808 may further include power circuitry for delivering power from the power source 808 itself, and / or an external power source, to the various parts of wireless device 800 via input circuitry or an interface such as an electrical power cable. Power source 808 may perform any formatting, converting, or other modification to make accessible power suitable for the respective components of the wireless device 800 to which power is supplied.

[0114] The memory 810 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 810 includes one or more programs 814, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 816. The memory 810 may store, for use by wireless device 800, any of a variety of various operating systems or combinations of operating systems.

[0115] The memory 810 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 810 may allow wireless device 800 to access instructions, programs, and the like, stored on transitory ornon-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 810, which may be or comprise a device-readable storage medium.

[0116] The processing circuitry 802 may be configured to communicate with an access network or other network via or using the communication interface 812. The communication interface 812 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 822. The communication interface 812 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another wireless device or a network node in an access network). Each transceiver may include a transmitter 818 and / or a receiver 820 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 818 and receiver 820 may be coupled to one or more antennas (e.g., antenna 822) and may share circuit components, software, or firmware, or alternatively be implemented separately.

[0117] In the illustrated embodiment, communication functions of the communication interface 812 may include cellular communication, Wi-Fi communication (e.g., according to an IEEE 802.11 family standard), LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / intemet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0118] In particular embodiments, wireless device 800 may provide an output of data captured via a sensor, through its communication interface 812, via a wireless connection to a network node, and / or in any appropriate manner. Data captured by sensors of a wireless device 800 can be communicated through a wireless connection to a network node via another wireless device 800. 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).

[0119] As another example, wireless device 800 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 800 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.

[0120] Wireless device 800, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. In particular embodiments, wireless device 800 represents an loT device that comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the example embodiment of wireless device 800 shown in Figure 8.

[0121] As yet another specific example, in an loT scenario, wireless device 800 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 800 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 800 may implement the 3GPP NB-IoT standard. In other scenarios, wireless device 800 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.

[0122] In practice, any number of wireless devices 800 may be used together with respect to a single use case. For example, a first wireless device 800 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 800 that is a remote controller operating the drone. When a user makes changes from theremote controller, the first wireless device 800 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 800 can also include more than one of the functionalities described above. For example, wireless device 800 might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0123] Figure 9 shows a network node 900 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunications network. In accordance with respective embodiments, network node 900 may be configured to operate in communication system 600 of Figure 6, like network nodes 608 or 610, or in communication system 700 of Figure 7, like an AP 710 or a station 712. 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 0-RAN node (e.g., O-RU, O-DU, O-CU).

[0124] Network nodes 900 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 900 may be a relay node or a relay donor node controlling a relay. Network nodes 900 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).

[0125] Other examples of network nodes 900 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).

[0126] In particular embodiments, network node 900 includes a processing circuitry 902, a memory 904, a communication interface 906, and a power source 908. In general, in a particular embodiment of network node 900, processing circuitry 902, memory 904, communicationinterface 906, and power source 908 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 900.

[0127] The network node 900 may be composed of multiple distinct network entities (e.g., a NodeB entity and an RNC entity, or a BTS entity and a BSC entity, etc.), which may each have or utilize their own respective physical components. In certain scenarios in which the network node 900 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 900 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memories 904 or portions of memory 904 for different RATs) and some components may be reused (e.g., a same antenna 910 may be shared by different RATs). The network node 900 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 900, for example GSM, WCDMA, LTE, NR, 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 900.

[0128] The processing circuitry 902 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other components, such as the memory 904, to provide network node 900 functionality.

[0129] In some embodiments, the processing circuitry 902 includes a system on a chip (SOC).In some embodiments, the processing circuitry 902 includes one or more of radio frequency (RF) transceiver circuitry 912 and baseband processing circuitry 914. In some embodiments, the RF transceiver circuitry 912 and the baseband processing circuitry 914 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 912 and baseband processing circuitry 914 may be on the same chip or set of chips, boards, or units.

[0130] The memory 904 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory(ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 902. The memory 904 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 902 and utilized by the network node 900. The memory 904 may be used to store any calculations made by the processing circuitry 902 and / or any data received via the communication interface 906. In some embodiments, the processing circuitry 902 and memory 904 is integrated.

[0131] The communication interface 906 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 906 comprises port(s) / terminal(s) 916 to send and receive data, for example to and from a network over a wired connection. In particular embodiments, network node 900 may be capable of wireless communication and communication interface 906 may also include radio front-end circuitry 918 that may be coupled to, or in certain embodiments a part of, an antenna 910. Particular embodiments of radio front-end circuitry 918 include filter(s) 920 and amplifier(s) 922. The radio front-end circuitry 918 may be connected to an antenna 910 and processing circuitry 902. The radio front-end circuitry may be configured to condition signals communicated between antenna 910 and processing circuitry 902. The radio front-end circuitry 918 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 918 may convert the digital data into a radio signal(s) having the appropriate channel and bandwidth parameters using a combination of filters 920 and / or amplifiers 922. The radio signal(s) may then be transmitted via the antenna 910. Similarly, when receiving data, the antenna 910 may collect radio signals which are then converted into digital data by the radio front-end circuitry 918. The digital data may be passed to the processing circuitry 902. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0132] In certain alternative embodiments, network node 900 may be capable of wireless communication but does not include separate radio front-end circuitry 918, instead, the processing circuitry 902 includes radio front-end circuitry and is connected to the antenna 910. Similarly, in some embodiments, all or some of the RF transceiver circuitry 912 is part of the communication interface 906. In still other embodiments, the communication interface 906 includes one or more ports or terminals 916, the radio front-end circuitry 918, and the RF transceiver circuitry 912, aspart of a radio unit (not shown), and the communication interface 906 communicates with the baseband processing circuitry 914, which is part of a digital unit (not shown).

[0133] The antenna 910 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 910 may be coupled to the radio front-end circuitry 918 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 910 is separate from the network node 900 and connectable to the network node 900 through one or more interfaces or ports.

[0134] The antenna 910, communication interface 906, and / or the processing circuitry 902 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 900. Any information, data, and / or signals may be received from a UE, another network node, and / or any other network equipment. Similarly, the antenna 910, the communication interface 906, and / or the processing circuitry 902 may be configured to perform some or all of the transmitting or sending operations described herein as being performed by the network node 900. Any information, data and / or signals may be transmitted to a UE, another network node, and / or any other network equipment.

[0135] The power source 908 provides power to the various components of network node 900 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 908 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 900 with power for performing the functionality described herein. For example, the network node 900 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 908. As a further example, the power source 908 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0136] Embodiments of the network node 900 may include additional components beyond those shown in Figure 9 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node 900 may include user interface equipment to allow input of information into the network node 900 and to allow output of information from the network node 900. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 900.

[0137] Figure 10 is a block diagram illustrating a virtualization environment 1000 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 1000 hosted by one or more of hardware nodes, such as a hardware computing device that operates as 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 1000 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an O-2 interface.

[0138] Applications 1002 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 1000 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0139] Hardware 1004 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 1006 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VM 1008 A and VM 1008B (which may be collectively referred to as VMs 1008), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 1006 may present a virtual operating platform that appears like networking hardware to one or more of the VMs 1008.

[0140] The VMs 1008 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by virtualization layer 1006. Different embodiments of the instance of a virtual appliance 1002 may be implemented on one or more of VMs 1008, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physicalswitches, and physical storage, which can be located in data centers, and customer premise equipment.

[0141] In the context of NFV, each of the VMs 1008 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 1008, and that part of hardware 1004 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more of the VMs 1008 on top of the hardware 1004 and corresponds to an application 1002.

[0142] Hardware 1004 may be implemented in a standalone network node with generic or specific components. Hardware 1004 may implement some functions via virtualization. Alternatively, hardware 1004 may be part of a larger cluster of hardware (e.g., such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 1010, which, among others, oversees lifecycle management of applications 1002. In some embodiments, hardware 1004 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 1012 which may alternatively be used for communication between hardware nodes and radio units.

[0143] 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 maybe 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.

[0144] 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.

[0145] 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.

[0146] Some exemplary embodiments of the present disclosure are as follows:Group A Embodiments

[0147] Embodiment 1: A method performed by a User Equipment, UE, (500), the method comprising any one or more of the following: performing (510) first channel measurements on a single periodic or semi-persistent, P / SP, Channel State Information, CSI, Reference Signal, CSI-RS, (e.g., during a first measurement time window); generating (510) predicted CSI (e.g., for a prediction time window) based on the first channel measurements using an Artificial Intelligence, Al, / Machine Learning, ML, based model or functionality or a non-AI / ML based model or functionality; performing (512) second channel measurements on the single P / SP CSLRS (e.g., during a second measurement time window); generating (512) a performance monitoring output or metric for the AI / ML based model or functionality or non-AI / ML based functionality based on the second channel measurements; and transmitting (514), to the network node (502), a report(e.g., a CSI-PM report or UE capability report) comprising the performance monitoring output or metric.

[0148] Embodiment 2: The method of embodiment 1, further comprising receiving (504), from a network node (502), a CSI report configuration (e.g., a standalone CSI report configuration) for performance monitoring output report.

[0149] Embodiment 3 : The method of embodiment 2, wherein the report is in accordance with the CSI report configuration for performance monitoring output report.

[0150] Embodiment 4: The method of embodiment 2 or 3, wherein the CSI report configuration for performance monitoring output report is a standalone CSI report configuration.

[0151] Embodiment 5: The method of embodiment 2 or 3, wherein the CSI report configuration for performance monitoring output report (denoted as CSI-PM report configuration) is linked to a CSI-inference report configuration.

[0152] Embodiment 6: The method of embodiment 5, wherein the linkage is indicated via at least one of the following:• include information on the CSI-inference report configuration ID in the CSI-PM report configuration;• include information on the CSI-PM report configuration ID in the CSI-inference configuration;• include information on the same single P / SP CSI-RS resource for channel measurement on both CSI-inference report configuration and in the CSI-PM report configuration.

[0153] Embodiment 7: The method of any of embodiments 2 to 6, further comprising receiving (508), from the network node (502), one or more parameters (e.g., values for one or more parameters) associated to the CSI report configuration.

[0154] Embodiment 8: The method of any of embodiments 2 to 6, wherein one or more parameters (e.g., values for one or more parameters) associated to the CSI report configuration are predefined (e.g., by a 3 GPP specification).

[0155] Embodiment 9: The method of embodiment 7 or 8, wherein the one or more parameters comprise any one or more of xgap, Kp, and N4.

[0156] Embodiment 10: The method of any of embodiments 1 to 9, further comprising receiving (504), from the network node (502), configuration information that configures the UE (500) with the single P / SP CSI-RS as a channel measurement resource.

[0157] Embodiment 11: The method of any of embodiments 1 to 10, wherein generating the performance monitoring metric comprises: for one or more predicted CSI values (e.g., for one or more respective prediction time instances), generating corresponding measured CSI values (e.g.,for the respective prediction time instances) based on the second channel measurements; and generating the performance monitoring output or metric (e.g., SGCS or NMSE) based on the one or more predicted CSI values and the one or more corresponding measured CSI values.

[0158] Embodiment 12: The method of any of embodiments 1 to 11, wherein a time gap between a last P / SP CSI-RS occasion used for the first channel measurements and a first P / SP CSI-RS occasion used for the second channel measurements is an integer multiple (denoted herein as “xgap”) of a periodicity of the single P / SP CSI-RS resource.

[0159] Embodiment 13: The method of embodiment 12, wherein xgap is either configured by the network node.

[0160] Embodiment 14: The method of embodiment 13, wherein xgapis configured as part of a CSI report configuration corresponding to the CSI-PM report.

[0161] Embodiment 15: The method of embodiment 13, wherein xgapis configured as part of a CSI resource configuration corresponding to the single P / SP CSI-RS resource.

[0162] Embodiment 16: The method of embodiment 12, wherein xgapis predefined.

[0163] Embodiment 17: The method of any of embodiments 1 to 16, wherein the first measurement time window and the second measurement time window are defined with respect to a CSI reference resource.

[0164] Embodiment 18: The method of any of embodiments 1 to 17, wherein the second measurement window comprises the prediction time window.

[0165] Embodiment 19: The method of any of the previous embodiments, further comprising: providing user data; and forwarding the user data to a host via the transmission to the network node.Group B Embodiments

[0166] Embodiment 20: A method performed by a network node (502), the method comprising any one or more of the following: transmitting (504), to a User Equipment, UE, (500), a CSI report configuration (referred to herein as a CSI-PM report configuration) for performance monitoring output report; and receiving (514), from the UE (500), a report (e.g., a CSI-PM report or UE capability report) comprising a performance monitoring output or metric for an AI / ML based or non-AI / ML based model or functionality utilized at the UE to generate predicted CSI.

[0001] Embodiment 21 : The method of embodiment 20, wherein the report is in accordance with the CSI report configuration for performance monitoring output report.

[0002] Embodiment 22: The method of embodiment 20 or 21, wherein the CSI report configuration for performance monitoring output report is a standalone CSI report configuration.

[0003] Embodiment 23: The method of embodiment 20 or 21, wherein the CSI report configuration for performance monitoring output report is linked to a CSI-inference report configuration.

[0004] Embodiment 24: The method of embodiment 23, wherein the linkage is indicated via at least one of the following:• include information on the CSI-inference report configuration ID in the CSI-PM report configuration;• include information on the CSI-PM report configuration ID in the CSI-inference configuration;• include information on the same single P / SP CSI-RS resource for channel measurement on both CSI-inference report configuration and in the CSI-PM report configuration.

[0167] Embodiment 25: The method of any of embodiments 20 to 24, further comprising transmitting (508), to the UE (500), one or more parameters (e.g., values for one or more parameters) associated to the CSI-PM report configuration.

[0168] Embodiment 26: The method of embodiment 25, wherein the one or more parameters comprise any one or more of xgap, Kp, and N4.

[0169] Embodiment 27: The method of any of embodiments 20 to 26, further comprising transmitting (504), to the UE (500), configuration information that configures the UE (500) with a single P / SP CSI-RS as a channel measurement resource for the configured performance reporting.

[0170] Embodiment 28: The method of any of the previous embodiments, further comprising: obtaining user data; and forwarding the user data to a host or a user equipment.Group C Embodiments

[0171] Embodiment 29: 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.

[0172] Embodiment 30: A network node comprising: processing circuitry configured to perform any of the operations of any of the Group B embodiments; a power source circuitry configured to supply power to the processing circuitry.

[0173] Embodiment 31 : A wireless device comprising: one or more antennas; communication interface connected to the one or more antennas and to processing circuitry; the processing circuitry being configured to perform any of the operations of any of the Group A embodiments; an input interface connected to the processing circuitry and configured to allow input ofinformation 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), the method comprising:performing (510) first channel measurements on a single periodic or semi-persistent, P / SP, Channel State Information, CSI, Reference Signal, CSLRS, during a first measurement time window;generating (510) predicted CSI for a prediction time window based on the first channel measurements;performing (512) second channel measurements on the single P / SP CSLRS during a second measurement time window that comprises the prediction time window;generating (512) a performance monitoring output or metric based on the second channel measurements; andtransmitting (514), to the network node (502), a report comprising the performance monitoring output or metric.

2. The method of claim 1, wherein generating (510) the predicted CSI comprises generating (510) the predicted CSI for the prediction time window based on the first channel measurements using an Artificial Intelligence, Al, / Machine Learning, ML, based model or functionality or a non-AI / ML based model or functionality.

3. The method of claim 2, wherein the performance monitoring output or metric is for the AI / ML based model or functionality or non-AI / ML based functionality.

4. The method of any of claims 1 to 3, further comprising receiving (504), from a network node (502), a CSI report configuration for performance monitoring output report.

5. The method of claim 4, wherein the report is in accordance with the CSI report configuration for performance monitoring output report.

6. The method of claim 4 or 5, wherein the CSI report configuration for performance monitoring output report is a standalone CSI report configuration.

7. The method of any of claims 4 to 6, wherein the CSI report configuration for performance monitoring output report is linked to a CSI-inference report configuration.

8. The method of claim 7, wherein a linkage between the CSI report configuration for performance monitoring output report and the CSI-inference report configuration is indicated via a CSI-inference report configuration identifier, ID, comprised in the CSI report configuration for performance monitoring output report.

9. The method of claim 7 or 8, further comprising receiving (504), from the network node (502), configuration information that configures the UE (500) with the single P / SP CSI-RS as a channel measurement resource.

10. The method of any of claims 7 to 9, wherein the report configured by the CSI report configuration for performance monitoring output report can only be triggered after a CSI-inference reported configured by the linked CSI-inference report configuration is triggered.

11. The method of claim 7, wherein the linkage is indicated via at least one of the following:• inclusion of information on a CSI-inference report configuration identifier, ID, in the report configuration for performance monitoring output report;• inclusion of information on a report configuration for performance monitoring output report ID in the CSI-inference report configuration;• inclusion of information on a same single P / SP CSI-RS resource for channel measurement in both the CSI-inference report configuration and the report configuration for performance monitoring output report .

12. The method of any of claims 4 to 11, further comprising receiving (508), from the network node (502), information that configures one or more parameters associated to the CSI report configuration.

13. The method of any of claims 4 to 11, wherein values for one or more associated to the CSI report configuration are predefined.

14. The method of claim 12 or 13, wherein the one or more parameters comprise any one or more of a gap between a last P / SP CSI-RS occasion in the first measurement time window and a first P / SP CSI-RS occasion in the second measurement time window, a number of P / SP CSI-RS occasions within the first measurement time window, and a number of time slots within the prediction time window.

15. The method of any of claims 1 to 14, further comprising receiving (504), from the network node (502), configuration information that configures the UE (500) with the single P / SP CSI-RS as a channel measurement resource.

16. The method of any of claims 1 to 15, wherein generating (512) the performance monitoring metric comprises:for one or more predicted CSI values, generating corresponding measured CSI values based on the second channel measurements; andgenerating the performance monitoring output or metric based on the one or more predicted CSI values and the one or more corresponding measured CSI values.

17. The method of any of claims 1 to 16, wherein a time gap (xgap) between a last P / SP CSI-RS occasion used for the first channel measurements and a first P / SP CSI-RS occasion used for the second channel measurements is an integer multiple of a periodicity of the single P / SP CSI-RS resource.

18. The method of claim 17, wherein xgapis configured by the network node.

19. The method of claim 18, wherein xgapis configured as part of the CSI report configuration for performance monitoring output report.

20. The method of claim 18, wherein xgapis configured as part of a CSI resource configuration corresponding to the single P / SP CSI-RS resource.

21. The method of claim 17, wherein xgapis predefined.

22. The method of any of claims 1 to 21, wherein the first measurement time window and the second measurement time window are defined with respect to a CSI reference resource.

23. A User Equipment, UE, (500; 800), comprising:a communication interface (812) comprising a transmitter (818) and receiver (820); and processing circuitry (802) associated with the communication interface (812), the processing circuitry (802) configured to cause the UE (500; 800) to:perform (510) first channel measurements on a single periodic or semi-persistent, P / SP, Channel State Information, CSI, Reference Signal, CSI-RS, during a first measurement time window;generate (510) predicted CSI for a prediction time window based on the first channel measurements;perform (512) second channel measurements on the single P / SP CSI-RS during a second measurement time window that comprises the prediction time window;generate (512) a performance monitoring output or metric based on the second channel measurements; andtransmit (514), to the network node (502), a report comprising the performance monitoring output or metric.

24. The UE (500; 800) of claim 23, wherein generating (510) the predicted CSI comprises generating (510) the predicted CSI for the prediction time window based on the first channel measurements using an Artificial Intelligence, Al, / Machine Learning, ML, based model or functionality or a non-AI / ML based model or functionality.

25. The UE (500; 800) of claim 24, wherein the performance monitoring output or metric is for the AI / ML based model or functionality or non-AI / ML based functionality.

26. The UE (500; 800) of any of claims 23 to 25, wherein the processing circuitry (802) is further configured to cause the UE (500; 800) to receive (504), from a network node (502), a CSI report configuration for performance monitoring output report.

27. The UE (500; 800) of claim 26, wherein the report is in accordance with the CSI report configuration for performance monitoring output report.

28. The UE (500; 800) of claim 26 or 27, wherein the CSI report configuration for performance monitoring output report is a standalone CSI report configuration.

29. The UE (500; 800) of any of claims 26 to 28, wherein the CSI report configuration for performance monitoring output report is linked to a CSI-inference report configuration.

30. The UE (500; 800) of claim 29, wherein a linkage between the CSI report configurationfor performance monitoring output report and the CSI-inference report configuration is indicated via a CSI-inference report configuration identifier, ID, comprised in the CSI report configuration for performance monitoring output report.

31. The UE (500; 800) of claim 29 or 30, wherein the processing circuitry (802) is further configured to cause the UE (500; 800) to receive (504), from the network node (502), configuration information that configures the UE (500) with the single P / SP CSI-RS as a channel measurement resource.

32. The UE (500; 800) of any of claims 29 to 31, wherein the report configured by the CSI report configuration for performance monitoring output report can only be triggered after a CSI-inference reported configured by the linked CSI-inference report configuration is triggered.

33. A method performed by a network node (502), the method comprising:transmitting (504), to a User Equipment, UE, (500), a Channel State Information, CSI, report configuration for performance monitoring output report; andreceiving (514), from the UE (500), a report comprising a performance monitoring output or metric related to generation of predicted CSI, in accordance with the CSI report configuration for performance monitoring output report.

34. The method of claim 33, wherein the CSI report configuration for performance monitoring output report is a standalone CSI report configuration.

35. The method of claim 33, wherein the CSI report configuration for performance monitoring output report is linked to a CSI-inference report configuration.

36. The method of claim 35, wherein a linkage between the CSI report configuration for performance monitoring output report and the CSI-inference report configuration is indicated via a CSI-inference report configuration identifier, ID, comprised in the CSI report configuration for performance monitoring output report.

37. The method of claim 35 or 36, further comprising transmitting (504), to the UE (500), configuration information that configures the UE (500) with the single P / SP CSI-RS as a channel measurement resource.

38. The method of any of claims 35 to 37, wherein the report configured by the CSI report configuration for performance monitoring output report can only be triggered after a CSI-inference reported configured by the linked CSI-inference report configuration is triggered.

39. The method of claim 35, wherein the linkage is indicated via at least one of the following:• inclusion of information on a CSI-inference report configuration identifier, ID, in the report configuration for performance monitoring output report;• inclusion of information on a report configuration for performance monitoring output report configuration ID in the CSI-inference configuration;• inclusion of information on a same single P / SP CSI-RS resource for channel measurement in both the CSI-inference report configuration and the report configuration for performance monitoring output report .

40. The method of any of claims 33 to 39, further comprising transmitting (508), to the UE (500), information that configures values for one or more parameters (associated to the CSI-PM report configuration.

41. The method of claim 40, wherein the one or more parameters comprise any one or more of a gap between a last P / SP CSI-RS occasion in the first measurement time window and a first P / SP CSI-RS occasion in the second measurement time window, a number of P / SP CSI-RS occasions within the first measurement time window, and a number of time slots within the prediction time window.

42. The method of any of claims 33 to 41, further comprising transmitting (504), to the UE (500), configuration information that configures the UE (500) with a single P / SP CSI-RS as a channel measurement resource for the configured performance reporting.

43. A network node (502; 900), comprising processing circuitry (902) configured to cause the network node (502; 900) to:transmit (504), to a User Equipment, UE, (500), a Channel State Information, CSI, report configuration for performance monitoring output report; andreceive (514), from the UE (500), a report comprising a performance monitoring output or metric related to generation of predicted CSI, in accordance with the CSI report configuration for performance monitoring output report.

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