Channel state information prediction processing criteria

Abstract

The invention concerns a method performed by a wireless device. The method comprises receiving, from a network node, a request to report performance monitoring results of a channel state information (CSI) prediction feature and calculating a number of CSI processing units (CPUs) and an occupancy time of the of CPUSs for computing the performance monitoring results. Upon determining the calculated occupancy time of the CPUs is available to the wireless device, the method further comprises computing the performance monitoring results and transmitting a CSI report comprising the performance monitoring results to the network node. The invention also concerns a related wireless device, method performed by a network node and network node.

Description

P112980W001 PCT APPLICATION 1 of 48Channel State Information Prediction Processing Criteria TECHNICAL FIELD

[0001] The present disclosure generally relates to communication networks, and more specifically to channel state information (CSI) prediction processing criteria.BACKGROUND

[0002] In Third Generation Partnership Project (3GPP) New Radio (NR), a user equipment (UE) can be configured with one or multiple channel state information (CSI) report settings, each configured by a higher layer parameter CSI-ReportConfig. Each CSI-ReportConfig is associated with a bandwidth part (BWP) and contains one or more of the following: a CSI resource configuration for channel measurement; a CSI interference measurement (CSI-IM) resource configuration for interference measurement; reporting configuration type, i.e., aperiodic CSI (on physical uplink shared channel (PUSCH)), periodic CSI (on physical uplink control channel (PUCCH)), or semi-persistent CSI on PUCCH or PUSCH; report quantity specifying what to be reported, such as rank indicator (RI), precoding matrix indicator (PMI), and channel quality indicator(CQI); codebook configuration such as type I or type II CSI; frequency domain configuration, i.e., subband vs. wideband CQI or PMI and subband size; and CQI table to be used.

[0003] A UE can be configured with one or multiple CSI resource configurations for channel measurement and one or more CSI-IM resources for interference measurement. Each CSI resource configuration for channel measurement can contain one or more non-zero power (NZP) CSI reference signal (CSI-RS) resource sets. Each NZP CSI-RS resource set contains one or more NZP CSI-RS resources. A NZP CSI-RS resource can be periodic, semi -persistent, or aperiodic.

[0004] Similarly, each CSI-IM resource configuration for interference measurement can contain one or more CSI-IM resource sets. Each CSI-IM resource set contains one or more CSI-IM resources. A CSI-IM resource can be periodic, semi-persistent, or aperiodic.CSI reporting types and CSI-RS configuration types

[0005] Table 1 below provides a summary for the CSI reporting types and CSI-RS configuration types supported in NR.Table 1: The CSI reporting types and CSI-RS configuration types supported in NRP112980W001 PCT APPLICATION3GPP NR Rel-18 time domain Type II CSI prediction at UE

[0006] 3GPP NR Rel-18 introduces channel measurement resource (CMR) enhancement for Type II CSI prediction at UE (also referred to as Enhanced Type II predicted PMI). This is illustrated in the measurement part of Figure 1, where a burst of K E {4, 8, 12} same CSI-RS resources are configured to the UE in a single CSI-RS resource set. The burst of CSI-RS resources is aperiodically (AP) triggered using a single downlink control information (DCI). The K CSI-RS resources are used for the UE to extract time domain channel properties of the channel, based on which a future CSI can be predicted. Alternatively, the network may also configure a legacy periodic (P) or semi-persistent (SP) CSI-RS resource. The CSI-RS resources are uniformly spaced in time, separated by m E {1, 2} slots, within the resource set.

[0007] For the Rel-18 Type II predicted PMI enhancement, a UE can be configured by gNB to report predicted PMIs for / V4e {1, 2, 4, 8} time slots. An example is illustrated in the Rel-18 Type II PMI part in Figure 1. The predicted N4PMIs are supposed to reflect the channels with d EP112980W001 PCT APPLICATION 3 of 48{l,m} slots separation, starting from 8 E {—nCSIr, 0,1,2} slots into the future relative to the uplink slot in which the predicted CSI is reported. For AP CSI-RS burst, m E {1,2}, while for P / SP CSI-RS, m is the CSI-RS periodicity. The spacing d between the N4PMIs and offset 8 relative to the uplink (UL) slot for CSI reporting can be configured by the gNB via Radio Resource Control (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. The codebookType for this Type II CSI prediction is set to 'typeII-Doppler-rl8' or 'typeII-Doppler-PortSelection-rl8'.CSI processing unit (CPU) for computing CSI report

[0008] NR introduces the concept of CPU, where the number of CPUs, denoted as NCPU, is equal to the number of simultaneous CSI calculations supported by the UE. The UE indicates NCPUto the network as part of the UE capability. When the UE is triggered for a CSI report, a certain number of CPUs, denoted as OCPU, will be allocated to the UE from the available CPU pool, which will be occupied for a period of time (measured in symbols). If there are not enough CPUs for a given time instance, the newly triggered CSI report does not need to be calculated by the UE.

[0009] The number of occupied CPUs for a given CSI report depends on the content (configured by higher layer parameter ‘ reporlQtianlily'). actually the complexity, for calculating it. The following options are based on the current 3GPP NR specification 38.214 vl8.5.0.

[0010] When 'reporlQtianlily' is set to ‘none’ and aperiodic tracking reference signal (TRS) is configured, then the TRS is mainly used for time and / or frequency synchronization at the UE, and nothing needs to be reported. In addition, the UE is assumed to have dedicated resources for TRS processing. Therefore, for this case, OCPU= 0.

[0011] When 'reporlQtianlily' is set to beam related parameters, such as ‘cri-RSRP’, ‘ssb-Index-RSRP’, etc., OCPU= 1, because beam related processing is usually not complex.

[0012] When ' reporlQuaniily' is set to non-beam related parameters, such as ‘cri-RI-PMI-CQI’, ‘cri-RI-il’, etc., the CSI report typically occupies as many CPUs as the number of CSI-RS resources in the CSI-RS resource set for channel measurement.

[0013] When 'reporlQiiantity' is set to 'cri-RLPMI-CQI' and with codebookType set to 'typeII-Doppler-rl8' or 'typeII-Doppler-PortSelection-rl8', if the corresponding CSI-RS Resource Set for channel measurement is aperiodic and configured with K CSI-RS resources, OCPU= 8 for K = 12 and OCPU= Y4• K for K < 12 , where Y4E {1, 2, 3} is reported by UE capability indication. If the corresponding CSI-RS Resource Set for channel measurement is periodic or semi-persistent and configured with a single CSI-RS resource, OCPU= 4 for N4= 1 and OCPU= max( Y2• N4, 4) for N4> 1 , where the value of N4is configured by the higher layer parameter N4, and Y2E {1, 2, 3} is reported by UE capability indication.P112980W001 PCT APPLICATION 4 of 48

[0014] The period of time (measured by the number of symbols) for which the CPU is occupied for a given CSI report depends on the time domain behavior of the CSI report.

[0015] For periodic or semi-persistent CSI report (excluding an initial semi-persistent CSI report on PUSCH after the physical downlink control channel (PDCCH) triggering the report and a semi-persistent CSI report on PUSCH configured with the higher layer parameter codebookType set to 'typeII-Doppler-rl8' or 'typeII-Doppler-PortSelection-rl8'), the CPU is occupied from the first symbol of the earliest CSI-RS / CSI-IM / synchronization signal block (SSB) resource for channel or interference measurement, no later than the CSI-RS reference resource, until the last symbol of the configured PUSCH / PUCCH carrying the report. For the example in Figure 2, one CSI-RS resource is configured to the UE for channel measurement (denoted by the first bar), then T' is the CPU occupancy period for periodic or semi-persistent CSI report.

[0016] For aperiodic CSI report, the CPU is occupied from the first symbol after the PDCCH triggering the CSI report, until the last symbol of the scheduled PUSCH carrying the report. For the example in Figure 2, T" is the CPU occupancy period for aperiodic CSI report.

[0017] An initial semi-persistent CSI report on PUSCH after the PDCCH trigger occupies CPU(s) from the first symbol after the PDCCH until the last symbol of the scheduled PUSCH carrying the report. For the example in Figure 2, T" is the CPU occupancy period for an initial semi-persistent CSI report.

[0018] A semi-persistent CSI report on PUSCH configured with the higher layer parameter codebookType set to 'typeII-Doppler-rl8' or 'typeII-Doppler-PortSelection-rl8' occupies CPU(s) from the first symbol of 7-th latest consecutive periodic / semi-persistent CSI-RS occasions no later than CSI reference resource, until the last symbol of the PUSCH carrying the report, where the value of KPG {1,2,4} is indicated by UE capability.

[0019] If a CSI-RS resource is referred to N times by one or more CSI Reporting Settings not configured with higher layer parameter csi-ReportSubConfigToAddModList, the CSI-RS resource and the CSI-RS ports within the CSI-RS resource are counted N times.

[0020] For a periodic or semi-persistent CSI-RS resource in a CSI-RS resource set for channel measurement linked to a CSI-ReportConfig configured with the higher layer parameter codebookType set to 'typeII-Doppler-rl8' or 'typeII-Doppler-PortSelection-rl8', the CSI-RS resource and the CSI-RS ports within the CSI-RS resource are counted KPtimes, where the value of KPe {1,2,4} is indicated by UE capability.P112980W001 PCT APPLICATION 5 of 48General aspects for NR Rel-18 artificial intelligence (AI) / machine learning (ML) for NR air interface

[0021] 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 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 UE side to reduce the signaling overhead and beam alignment latency; and using deep reinforcement learning to learn an optimal precoding policy for complex multiple input multiple output (MIMO) precoding problems.

[0022] In 3GPP NR standardization work, a release 18 study item on AI / ML for the NR air interface 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.Life cycle management (LCM) operations for AI / ML for NR air interface

[0023] 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.

[0024] 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.

[0025] Two types of LCM operations were studied in NR Rel-18, functionality-based LCM and model-ID based LCM.

[0026] Functionality refers to an AI / ML-enabled feature / feature group (FG) enabled by configuration(s), where configuration(s) is(are) supported based on conditions indicated by 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, medium access control (MAC) control element (CE), DCI). Models may not be identified at the network, and UE may perform model-level LCM. For functionality identification, there may beP112980W001 PCT APPLICATION 6 of 48either one or more than one functionalities defined within an AI / ML-enabled feature, whereby AI / ML-enabled feature refers to a feature where AI / ML may be used.

[0027] In model-ID-based LCM, models are identified at the network, and network / UE may activate / deactivate / select / switch individual AI / ML models via model ID. A model may be associated with specific configurations / conditions associated with UE capability of an AI / ML-enabled feature / FG and additional conditions (e.g., scenarios, sites, and datasets) as determined / identified between the UE-side and network-side. An AI / ML model identified by a model ID may be logical, and how it maps to physical AI / ML model(s) may be up to implementation.Functional framework for AI / ML for NR air interface

[0028] Figure 3 illustrates a functional framework for AI / ML for the NR air interface . Figure 3 shows a functional framework that can be used for studying model LCM aspects for different Al for physical layer (PHY) use cases. The general framework consists of the following.

[0029] Data Collection is a function that provides input data to the Model Training, Management, and Inference functions.

[0030] Training Data is data needed as input for the AI / ML Model Training function.

[0031] Monitoring Data is data needed as input for the management of AI / ML models or AI / ML functionalities.

[0032] Inference Data is data needed as input for the AI / ML Inference function.

[0033] Model Training is a function that performs AI / ML model training, validation, and testing that may generate model performance metrics that can be used as part of the model testing procedure. The Model Training function is also responsible for data preparation (e.g., data preprocessing and cleaning, formatting, and transformation) based on Training Data delivered by a Data Collection function, if required.

[0034] Trained / Updated Model, when using a Model Storage 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.

[0035] 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.

[0036] Management Instruction is information needed as input to manage the Inference function. The information may include selection / (de)activation / switching of AI / ML models orP112980W001 PCT APPLICATION 7 of 48AI / ML-based functionalities, fallback to non-AI / ML operation (i.e., not relying on inference process), etc.

[0037] Model Transfer / Delivery Request is used to request model(s) to the Model Storage function.

[0038] Performance Feedback / Retraining Request is information needed as input for the Model Training function, e.g., for model (re)training or updating purposes.

[0039] 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.

[0040] Inference Output is data used by the Management function to monitor the performance of AI / ML models or AI / ML functionalities.

[0041] Model Storage is a function responsible for storing trained / updated models that can be used to perform the Inference function.

[0042] The Model Storage function in Figure 3 is only intended as a reference point (if any) when applicable for protocol terminations, model transfer / delivery, and related processes. The Model Storage function 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 3) to / from this function should be studied case by case.

[0043] Model Transfer / Delivery is used to deliver an AI / ML model to the Inference function.3GPP NR Rel-19 time-domain CSI prediction using UE-side AI / ML model

[0044] NR Rel-18 introduced Al-based UE-side CSI prediction as an Al for PHY use case and being specified in NR Rel-19 work item on AI / ML for the NR air interface.

[0045] One or more AI / ML models can be trained and deployed at a UE for the Al-based CSI-prediction feature. During model inference, a UE is configured by the gNB to measure a set of historical CSI-RSs (e.g., the K CSI-RS measurements in the observation window shown in Figure 1) 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 1) using its AI / ML model(s). The same codebookType (e.g., 'typeII-Doppler-rl8') that is defined in Rel-18 for time domain Type II CSI prediction at UE can be reused for configuring the UE to send the predicted CSI using a UE-side AI / ML model. The CSI report carrying the predicted CSI based on the inference output of a UE-sided AI / ML model is referred to as the CSI-inference report.P112980W001 PCT APPLICATION 8 of 48Performance monitoring for CSI prediction

[0046] For the 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 for 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. The network may configure threshold criterion to facilitate UE side performance monitoring (if needed).o Network 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-truth.o Network calculates the performance metrics.o Network 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 network.o Network 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 monitoring.CSI-RS configuration for performance monitoring.Performance metric including at least intermediate key performance indicators (KPI) (e.g., normalized mean square error (NMSE) or squared generalized cosine similarity (SGCS)).UE report, including periodic / semi-persistent / aperiodic reporting, and event driven report.

[0047] The UE may make decision within the same functionality on model selection, activation, deactivation, switching operation transparent to the network.

[0048] For intermediate KPI based performance monitoring of an AI / ML-bascd CSI prediction feature, a monitoring data sample should consist of both the channel measurementsP112980W001 PCT APPLICATION 9 of 48within 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.

[0049] 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.

[0050] For monitoring the performance of the AI / ML-based CSI prediction feature, if the performance monitoring is performed at the UE-side (e.g., type 1 or type 3 monitoring described above), then a UE can report the performance monitoring result(s) (e.g., the performance metric(s) and / or the performance monitoring output) to the network, so that the network accounts for the UE reported model performance information when making model level or functionality level LCM decisions (e.g., fallback to non-AL / ML algorithm, functionality / model switching, etc.).

[0051] There currently exist certain challenges. For example, in the current 3GPP NR specification 38.214 v 18.5.0, the CPU for the Type II CSI prediction (i.e., when the codebookType is set to 'typeII-Doppler-rl8' or 'typeII-Doppler-PortSelection-rl8') is defined only for the case when a UE is triggered to report a legacy Rel-18 predicted CSI to the network.

[0052] The CSI-inference reporting for CSI prediction using UE side AI / ML model use case is similar to the legacy Rel-18 predicted CSI reporting when the codebookType is set to 'type II-Doppler-rl8', except forthat dedicated hardware may be used by the UE to derive a CSI-inference report carrying the predicted PMI obtained based on the AI / ML model inference output. This can result in a different CSI processing capability based on the dedicated AI / ML hardware. Methods are proposed to handle the CSI processing timeline for AI / ML based CSI report by defining new types of CPU (e.g., AI-CPU) for UEs that are capable of AI / ML-based processing.

[0053] For CSI prediction using UE side AI / ML model, a UE may be configured by the network to compute the performance monitoring result(s) of the CSI prediction feature, and the performance monitoring result(s) can be sent to the network in a CSI report (referred to as CSI-PM report). A solution is needed to define the CPU for CSI-PM report for the Al based CSI prediction use case to enable the network to efficiently trigger and configure the CSI-PM report.P112980W001 PCT APPLICATION 10 of 48SUMMARY

[0054] As described above, certain challenges currently exist with channel state information (CSI) prediction processing criteria. Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. For example, particular embodiments define the CSI processing timeline for a CSI report carrying a performance monitoring result(s) of a CSI prediction feature using user equipment (UE) side artificial intelligence (AI) / machine learning (ML) model.

[0055] In general, particular embodiments define the CSI processing timeline for a CSI report carrying a performance monitoring result(s), referred to as CSI-PM report, of a CSI prediction feature using UE side AI / ML model. The specification impact aspects include methods for determining the number of SCI processing units (CPUs) that are required for a CSI-PM report; methods for determining the CPU occupancy time for a CSI-PM report; and / or methods for handling CPUs when both CSI-inference reporting and CSI-PM reporting are triggered by the network.

[0056] According to some embodiments, a method is performed by a wireless device (e.g., UE). The method comprises receiving, from a network node, a request to report performance monitoring results of a CSI prediction feature and calculating a number of CPUs and an occupancy time of the of CPUSs for computing the performance monitoring results. Upon determining the calculated occupancy time of the CPUs is available to the wireless device, the method further comprises computing the performance monitoring results and transmitting a CSI report comprising the performance monitoring results to the network node.

[0057] In particular embodiments, computing the performance monitoring results comprises performing CSI inference based on a first burst of CSI-RSs received in a first time period. The inference predicts CSI for a second burst of CSI-RSs to be received a second time period. The method further comprises measuring the second burst of CSI-RSs in the second time period to generate ground truth measurement results and generating a performance metric by comparing the ground truth measurement results from the second time period to the inference results for the second time period.

[0058] In particular embodiments, transmitting the CSI report comprises transmitting a single report to the network node, the single report comprising a CSI performance monitoring report, and wherein the calculated number of CPUs is occupied from a first symbol of a downlink control channel in which the wireless device received the request to report performance monitoring results until a last symbol of an uplink channel carrying the CSI report.P112980W001 PCT APPLICATION 11 of 48

[0059] In particular embodiments, transmitting the CSI report comprises transmitting a single report to the network node, the single report comprising a CSI performance monitoring report, and wherein a first calculated number of CPUs is occupied from a first symbol of a downlink control channel in which the wireless device received the request to report performance monitoring results until the performing CSI inference is complete and a second calculated number of CPUs is occupied from a start of measuring the second burst of CSI-RSs in the second time period until a last symbol of an uplink channel carrying the CSI report.

[0060] In particular embodiments, transmitting the CSI report comprises transmitting a CSI inference report to the network node and transmitting a CSI performance monitoring report to the network node, and wherein the calculated number of CPUs for the CSI performance monitoring report is occupied from a start of measuring the second burst of CSI-RSs in the second time period until a last symbol of an uplink channel carrying the CSI performance monitoring report.

[0061] In particular embodiments, transmitting the CSI report comprises transmitting a CSI inference report to the network node and transmitting a CSI performance monitoring report to the network node, and wherein the calculated number of CPUs for the CSI performance monitoring report is occupied from a last symbol of an uplink channel carrying the CSI inference report until a last symbol of an uplink channel carrying the CSI performance monitoring report.

[0062] In particular embodiments, transmitting the CSI report comprises transmitting a CSI inference report to the network node and transmitting a CSI performance monitoring report to the network node, and wherein the calculated number of CPUs for the CSI performance monitoring report is occupied from a first symbol of a downlink control channel in which the wireless device received the request to report CSI prediction performance monitoring results until a last symbol of an uplink channel carrying the CSI report. The request to report CSI prediction performance monitoring results may be received before the first time period. The request to report CSI prediction performance monitoring results may be received after the first time period and before the second time period.

[0063] In particular embodiments, transmitting the CSI report comprises transmitting a CSI inference report to the network node and transmitting a CSI performance monitoring report to the network node, and wherein a first calculated number of CPUs for the CSI report is occupied from a first symbol of a downlink control channel in which the wireless device received the request to report CSI prediction performance monitoring results until a last symbol of an uplink channel carrying the CSI inference report, and a second calculated number of CPUs for the CSI report is occupied from a last symbol of an uplink channel carrying the CSI-inference report until a last symbol of an uplink channel carrying the CSI performance monitoring report.P112980W001 PCT APPLICATION 12 of 48

[0064] According to some embodiments, a wireless device comprises processing circuitry operable to perform any of the wireless device methods described above.

[0065] Also disclosed is a computer program product comprising a non-transitory computer readable medium storing computer readable program code, the computer readable program code operable, when executed by processing circuitry to perform any of the methods performed by the wireless device described above.

[0066] According to some embodiments, a method is performed by a network node (e.g., gNB). The method comprises obtaining a CSI prediction performance monitoring configuration for a wireless device and calculating a number of CPUs and an occupancy time of the CPUs the wireless device uses for computing performance monitoring results based on the CSI prediction performance monitoring configuration. Upon determining the calculated occupancy time of the CPUs is available to the wireless device, the method further comprises transmitting a request to the wireless device to report performance monitoring results.

[0067] In particular embodiments, the method further comprises receiving a wireless device capability indication from the wireless device. The capability indication indicates a number of CPUs for performing CSI inference and prediction.

[0068] According to some embodiments, a network node comprises processing circuitry operable to perform any of the network node methods described above.

[0069] Another computer program product comprises a non-transitory computer readable medium storing computer readable program code, the computer readable program code operable, when executed by processing circuitry to perform any of the methods performed by the network node described above.

[0070] Certain embodiments may provide one or more of the following technical advantages. For example, particular embodiments provide ways to quantify, measure and monitor the CSI processing timeline for CSI reports, which may help the gNB configure CSI-PM report efficiently.BRIEF DESCRIPTION OF THE DRAWINGS

[0071] The present disclosure may be best understood by way of example with reference to the following description and accompanying drawings that are used to illustrate embodiments of the present disclosure. In the drawings:Figure 1 illustrates channel measurement resource (CMR) enhancement for Rel-18 Type II channel state information (CSI) prediction at user equipment (UE);Figure 2 illustrates an example of CSI processing unit (CPU) occupancy period;P112980W001 PCT APPLICATION 13 of 48Figure 3 illustrates a functional framework for artificial intelligence (Al)Zmachine learning (ML) for the New Radio (NR) air interface;Figure 4 illustrates example signaling for CSI-inference reporting with aperiodic (AP) CSI-RS resource;Figure 5 illustrates example signaling for CSI performance monitoring (CSI-PM) reporting with two bursts of AP CSI-RS resources, where PM is calculated per sample;Figure 6 illustrates an example using two separate downlink control information (DCI) signaling for CSI-inference reporting and CSI-PM reporting with AP CSI-RS resource, where PM is calculated per sample;Figure 7 illustrates another example signaling two separate DCI signaling for CSI-inference reporting and CSI-PM reporting with AP CSI-RS resource, where PM is calculated per sample;Figure 8 illustrates an example using a single DCI signaling for CSI-inference reporting and CSI-PM reporting with AP CSI-RS resource, where PM is calculated per sample;Figure 9 illustrates another example using a single DCI signaling for CSI-inference reporting and CSI-PM reporting with AP CSI-RS resource, where PM is calculated per sample;Figure 10 illustrates an example using a single DCI signaling for CSI-inference report(s) and CSI-PM report with AP CSI-RS resource, where PM is calculated over multiple samples;Figure 11 shows an example of a communication system, according to certain embodiments; Figure 12 shows a UE, according to certain embodiments;Figure 13 shows a network node, according to certain embodiments;Figure 14 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized;Figure 15 is a flowchart illustrating an example method in a wireless device, according to certain embodiments; andFigure 16 is a flowchart illustrating an example method in a network node, according to certain embodiments.DETAILED DESCRIPTION

[0072] As described above, certain challenges currently exist with channel state information (CSI) prediction processing criteria. Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. For example, particular embodiments define the CSI processing timeline for a CSI report carrying a performance monitoring result(s) of a CSI prediction feature using user equipment (UE) side artificial intelligence (Al)Zmachine learning (ML) model.P112980W001 PCT APPLICATION 14 of 48

[0073] Particular embodiments are 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.

[0074] The network signals CSI prediction performance report configuration for a CSI prediction feature to a UE, based on which the UE derives one or more performance monitoring result(s), and reports the derived performance monitoring result(s) to the network. The Al-based CSI prediction feature model / algorithm is implemented at the UE using UE side AI / ML model(s) (e.g., one or more neural network models trained by using a large dataset and then deployed at the UE).

[0075] The network may refer to the gNB, e.g. the gNB central unit (gNB-CU) or the gNB distributed unit (gNB-DU), the operations, administration and maintenance (0AM) node, or a core network node, e.g. the network data analytics function (NWDAF). In the following, the term network may refer to any of the aforementioned entities.

[0076] The CSI prediction feature is not limited to predicting the precoding matric 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, and predicted top K cells.

[0077] A model input sample for CSI prediction comprises multiple CSI-RS measurements spread overtime on a set of T aperiodic CSI-RS resources (e.g., the measured CSIs on the aperiodic K CSI-RS resources within the observation window as shown in Figure 1).

[0078] 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 1).

[0079] 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 may result in different model output formats.

[0080] For example, in one format 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 3 GPP TS 38.214.P112980W001 PCT APPLICATION 15 of 48

[0081] In another format, 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 3 GPP TS 38.214.

[0082] In another format, the model directly outputs predicted PMIs following the Rel-18 “typeII-Doppler-rl8” codebook for the N4prediction time instances.

[0083] A ground truth label for performance monitoring may 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 1).

[0084] 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. Thus, 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 “typeII-Doppler-rl8” codebook may 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. The intermediate KPI is calculated using the UE reported predicted CSI and the ground-truth label, both represented in the format of “typeII-Doppler-rl8” codebook.

[0085] To reduce the CSI-RS overhead for performance monitoring, in some cases, it is sufficient for the network to check the quality of the predicted CSI for only part of the prediction time instances (e.g., the predicted CSI for the first prediction time instance). More specifically, the network may configure a UE with a pair of CSI-RS resource sets, where the first resource set consists of K CSI-RS resources and the second resource set consists of n CSI-RS resources, with K > 1 and 1 < n < N4. The measurements on the K CSI-RS resources in the first resource set are used by the UE to create a model input, which is then fed to the CSI prediction model to generate a model output. The measurements on the n CSI-RS resources in the second resource set are used by the UE to create a ground-truth label in the format of “typeII-Doppler-rl8” codebook. Based on the model output, the UE generates a predicted CSI for the n CSI-RS resources in the format of “typeII-Doppler-rl8” codebook. The intermediate KPI is calculated using the predicted CSI and the ground-truth label for the n CSI-RS resources.

[0086] The embodiments described herein for signaling and configuration of the performance monitoring output report are not limited to the case where the ground truth format must be “typell-Doppler-rl8” codebook.P112980W001 PCT APPLICATION 16 of 48

[0087] To support intermediate KPI calculation, a monitoring data sample may 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.

[0088] An intermediate KPI per monitoring data sample {predicted CSI, ground-truth label} may be defined as the squared generalized cosine similarity (SGCS) or normalized mean square error (NMSE) between the predicted CSI and the ground-truth label.Examples of performance monitoring metric(s) for UE-sided CSI prediction use case include: Option 1, intermediate KPI per monitoring data sample; and Option 2, statistics of the intermediate KPI over Ml monitoring data samples, e.g., the mean and variance ofthe intermediate KPI associated to the collected monitoring data samples within a time window, and a percentage of monitoring data samples that fulfill certain condition(s).

[0089] Option 1 may be useful if the UE can feedback the predicted CSI and 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, making better decisions on scheduling and downlink transmission for the future time slots.

[0090] An intermediate KPI of a single monitoring data sample cannot indicate whether the CSI prediction model is functioning properly or not. A low SGCS value of a monitoring data sample may be because the channel is hard to predict (i.e., the SGCS value is expected to be low) or the Al model is not working well (i.e., the SGCS value should be high). Thus, to enable the network to make reliable functionality-based LCM operation decisions, e.g., fallback to legacy CSI reporting and root cause analysis if the UE performance drops, statistics of the intermediate KPI over multiple monitoring data samples, i.e., Option 2, is needed.

[0091] The performance monitoring output is determined based on performance metric, and additionally, baseline and / or threshold criterion if configured. Examples of performance monitoring output include:• An intermediate KPI range indicator, indicating whether the intermediate KPI of a monitoring data sample fulfills a certain condition. For example, two SGCS threshold values, thr l and thr_2, are configured per multiple input multiple output (MIMO) layer or common for all MIMO layers for performance monitoring. The intermediate KPI range indicator is set to 00, 01 or 10, if the SGCS of this layer for the monitoring data sample is within in the range of [0, thr_l], [thr l , thr_2] or [thr_2, 1], respectively.• An intermediate KPI statistics range indicator, indicating whether the statistics of the intermediate KPI over multiple samples in a monitoring window fulfills a certain condition. For example, two SGCS threshold values, mean thr l and mean_thr_2, are configured perP112980W001 PCT APPLICATION 17 of 48MIMO layer or common for all MIMO layers for performance monitoring. The intermediate KPI statistics range indicator is set to 00, 01 or 10, if the mean SGCS of this layer for the monitoring data sample is within in the range of [0, thr l], [thr l, thr_2] or [thr_2, 1], respectively.

[0092] A performance monitoring result(s) report (denoted by CSI-PM report) may include one or more performance metric(s) and / or one or more performance monitoring output(s).CPU for computing a CSI-inference report

[0093] Figure 4 illustrates signaling for CSI-inference reporting with aperiodic (AP) CSI-RS resource. As shown in Figure 4, a CSI-inference report configuration points to a channel measurement resource (CMR) configuration that contains a set of aperiodic CSI-RS burst consisting of K CSI-RS resources, with K>1. The measurements on the K CSI-RS resources are used for creating a model input. The UE uses the model input and the AI / ML CSI prediction model / algorithm to generate a CSI-inference report.

[0094] Thus, the CSI-inference reporting for a CSI prediction feature using UE side AI / ML model use case is similar to the legacy Rel-18 predicted CSI reporting when the codebookType is set to 'typeII-Doppler-rl8', except that dedicated hardware may be used at UE for model inference. In this case, a new CPU type, e.g., AI-CPU, may be defined for a CSI-inference report. Otherwise, the legacy CPU type may be reused for the CSI-inference report.CPU for computing a CSI-PM report

[0095] Depending on how the CMR for a CSI-PM report is defined or configured, and whether the CSI-PM report is triggered together with or linked to a CSI-inference report, different methods may be used for handling the CSI processing timeline for the CSI-PM report.Case 1: A CSI-PM report is computed based on a single monitoring data sample.Case la: A CSI-inference report is configured with aperiodic CSI-RS resources for channel measurements, and the CSI-PM report is triggered without triggering the corresponding CSI-inference report.

[0096] Figure 5 illustrates signaling for CSI-PM reporting with two bursts of AP CSI-RS resources, where PM is calculated per sample. In the example shown in Figure 5, the CSI-PM report configuration points to a CMR configuration that contains two sets of aperiodic CSI-RS bursts. The first aperiodic CSI-RS burst (e.g., burst 1 shown in Figure 5) consists of K CSI-RS resources, with K>1, where the measurements on the K CSI-RS resources 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 second aperiodic CSI-RS burst (e.g., burst 2 shown in Figure 5) consists of N4 CSI-RS resources, with N4>=1, where the measurements on the N4 CSI-RSP112980W001 PCT APPLICATION 18 of 48resources are used for 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 5) on the scheduled PUS CH at the uplink slot n2.

[0097] Compared to the CSI processing for generating a CSI-inference report shown in Figure 4, the additional processing steps required for generating a CSI-PM report include channel measurements on the second aperiodic CSI-RS burst consisting of N4 CSI-RS resources and computing the performance monitoring output.

[0098] In an embodiment, for CSI prediction using UE-side AI / ML model, when the UE is triggered for a CSI-PM report without the corresponding CSI-inference report, the number of occupied CPUs for this CSI-PM report, denoted as OCPU-CSI-PM, is defined as OCPU-CSI-PM=Ocpu-csi-inference + OCPU-deita, where OCPU-CSi-inferenceis the number of occupied CPUs when the UE is triggered for the corresponding CSI-inference report, and Ocpu-deita is associated to the CSI processing complexity for measuring the N4 CSI-RS resources and calculating the performance monitoring output. In some embodiments, OCPU-deitais a function of N4. In some embodiments, for an aperiodic CSI-PM report, the OCPU-CSI-PMCPU(s) are occupied from the first symbol after the PDCCH triggering the CSI-PM report, until the last symbol of the scheduled PUSCH carrying the report.

[0099] In some embodiments, for CSI predicting using UE-side AI / ML model, when the UE is triggered for a CSI-PM report without the corresponding CSI-inference report, the number of occupied CPUs for the CSI-PM report, denoted as OCPU-CSI-PM, is defined as °CPU-CSI-PM=ocpu-csi-inference-111some embodiments, the OCPU-CSI-PMCPU(s) are occupied from the first symbol after the PDCCH triggering the CSI-PM report, until the last symbol of the slot that is 80ffSetslots before the first prediction time instance. Here, OCPU-CSI-inferenceis the number of occupied CPUs when the UE is triggered for the CSI-inference report only. The value of 80ffSetis specified in 3GPP specifications. The value of 80ffSetmay depend on UE capability.

[0100] In one example, 80^set= 8. In this case, the OCPU-CSI-PMCPU(s) are occupied until the last symbol in slot nl in Figure 5.

[0101] In some embodiments, for CSI predicting using UE-side AI / ML model, when the UE is triggered for a CSI-PM report without the corresponding CSI-inference report, the number of occupied CPUs for the CSI-PM report, denoted as OCPU-CSI-PM. is defined as OCPU-CSI-PM= 0Cpu-deita- In some embodiments, the OCPU-CSI-PMCPU(s) are occupied from a symbol (e.g., the last symbol, or the first symbol) of the slot that is 80^setslots before the first prediction timeP112980W001 PCT APPLICATION 19 of 48instance until the last symbol of the scheduled PUSCH carrying the CSI-PM report, where Ocpu-deita is associated to the CSI processing complexity for measuring the N4 CSI-RS resources and calculating the performance monitoring output using the N4 measurements and the predicted CSI used for generating the CSI-inference report. The value of 80ffSetis specified in 3GPP specifications.

[0102] In one example, 80^set= 8. In this case the OCPU-CSI-PMCPU(s) are occupied from a symbol (e.g., the last symbol) of slotnl in Figure 5, until the last symbol of the scheduled PUSCH carrying the CSI-PM report.

[0103] In one embodiment, OCPU-deita= S ■ N4wherein S is a positive scaling factor (e.g., a positive integer) value that the UE reports as part of UE capability reporting. In some embodiments, OCPU-CSI-inference= U ■ K where U is a second positive scaling factor (e.g., a second positive integer) value the UE reports as part of UE capability reporting. In some embodiments, S is different from U . In yet another embodiment, S = U.Case lb: A CSI-inference report is configured with aperiodic CSI-RS resources for channel measurements, and the CSI-PM report is triggered together with the corresponding CSI-inference report.

[0104] Figure 6 illustrates using two separate DCI signaling for CSI-inference reporting and CSI-PM reporting with AP CSI-RS resource, where PM is calculated per sample. Figure 6 shows an example, where a CSI-PM report is triggered together with a CSI-inference report using two separate DCI signaling. Both DCIs are sent before the first CSI-RS resource in the aperiodic CSI-RS burst 1.

[0105] Figure 7 illustrates signaling two separate DCI signaling for CSI-inference reporting and CSI-PM reporting with AP CSI-RS resource, where PM is calculated per sample. Figure 7 shows another example where a CSI-PM report is triggered together with a CSI-inference report using two separate DCI signaling. Different from the example shown in Figure 6, the DCI for triggering the CSI-PM report is sent after the last CSI-RS resource in the aperiodic CSI-RS burst 1.

[0106] In both examples of Figure 6 and Figure 7, the UE computes the predicted CSI for generating the CSI-inference report. Thus, the UE may store the computed predicted CSI and reuse it for computing the CSI-PM report. Because the occupied CPU for computing the predicted CSI is already counted for the CSI-inference report, there is no need to count it again for the CSI-PM report.

[0107] For the CSI-inference report, OCPU-CSI-inferenceCP Js are occupied from the first symbol after the PDCCH triggering the CSI-inference report, until the last symbol of the scheduled PUSCH carrying the CSI-inference report.P112980W001 PCT APPLICATION 20 of 48

[0108] In some embodiments, for CSI predicting using UE-side AI / ML model, when the UE is triggered for a CSI-PM report together with a corresponding CSI-inference report, the number of occupied CPUs for the CSI-PM report, denoted as OCPU-CSI-PM, is defined as OCPU- CSI- PM=0CPU- delta, where OCPU-deitais associated to the CSI processing complexity for measuring the N4 CSI-RS resources and calculating the performance monitoring output using the N4 measurements and the predicted CSI used for generating the CSI-inference report. In some embodiments, OCPU-deitais a function of N4.

[0109] In some embodiments, if the CSI-PM report is an aperiodic CSI report, the OCPU-CSI-PM CPU(s) are occupied from the last symbol of the scheduled PUSCH carrying the CSI-inference report, until the last symbol of the scheduled PUSCH carrying the CSI-PM report, e.g., as for the example shown in Figure 6.

[0110] In some embodiments, if the CSI-PM report is an aperiodic CSI report, the OCPU-CSI-PM CPU(s) are occupied from the first symbol after the PDCCH triggering the CSI-PM report, until the last symbol of the scheduled PUSCH carrying the CSI-PM report, e.g., as for the example shown in Figure 7.

[0111] Figure 8 illustrates using a single DCI signaling for CSI-inference reporting and CSI-PM reporting with AP CSI-RS resource, where PM is calculated per sample. Figure 8 shows an example where a CSI-PM report is triggered together with a CSI-inference report using a single DCI. In this example, the CSI-inference report and the CSI-PM report are scheduled to be fed back on different uplink slots.

[0112] In some embodiments, for CSI predicting using UE-side AI / ML model, when the UE is triggered for a CSI-PM report together with a corresponding CSI-inference report by a single DCI, and when the CSI-inference report and the CSI-PM report are scheduled on two different PUSCHs, the number of occupied CPUs for the CSI-inference and CSI-PM report, denoted as OcPU-CSI-PM-inf rence IS defined aS OcPy_c i_P-in rence— OcPU—CSI—inferenCe. In Some embodiments, the OCPU-CSI-PMCPU(s) are occupied from the first symbol after the PDCCH triggering the CSI-inference and CSI-PM report until the last symbol of the scheduled PUSCH carrying the CSI-inference report. Here, OCPU-CSI-inj-erenceis the number of occupied CPUs when the UE is triggered for the CSI-inference report only.

[0113] In some embodiments, for CSI predicting using UE-side AI / ML model, when the UE is triggered for a CSI-PM report together with a corresponding CSI-inference report by a single DCI, and when the CSI-inference report and the CSI-PM report are scheduled on two different PUSCHs, the number of occupied CPUs for the CSI-inference and CSI-PM report, denoted as Ocpu-csi-PM-inference is defined sOCPj_CSi-PM_tnfrence=OCPj_deita. In some embodiments,P112980W001 PCT APPLICATION 21 of 48the OCPU-CSI-PMCPU(s) are occupied from the last symbol of the scheduled PUSCH carrying the CSI-inference report until the last symbol of the scheduled PUSCH carrying the CSI-PM report. Here, OCPU-deitais associated to the CSI processing complexity for measuring the N4 CSI-RS resources and calculating the performance monitoring output using the N4 measurements and the predicted CSI used for generating the CSI-inference report. In some embodiments, OCPU-deitais a function of N4.

[0114] Figure 9 illustrates using a single DCI signaling for CSI-inference reporting and CSI-PM reporting with AP CSI-RS resource, where PM is calculated per sample. Figure 9 shows another example where a CSI-PM report is triggered together with a CSI-inference report using a single DCI. In this example, the CSI-inference report and the CSI-PM report are scheduled to be sent over the same uplink slot / PUSCH. More specifically, the CSI-inference report and the CSI-PM report are sent on an uplink slot that is after the last CSI-RS resource in the aperiodic CSI-RS burst 2, because the UE needs to measure the CSI-RS resources in burst 2 to create ground truth label for calculating the performance monitoring output.

[0115] In some embodiments, for CSI predicting using UE-side AI / ML model, when the UE is triggered for a CSI-PM report together with a corresponding CSI-inference report by a single DCI, and when the CSI-inference report and the CSI-PM report are scheduled on the same PUSCH, the number of occupied CPUs for the CSI-inference and CSI-PM report, denoted as cpu-csi-PM-inference is defined as Ocpu—csi—PM—infrenCe — OcPu—csi—tnferenCe . In some embodiments, the OCPU-CSI-PMCPU(s) are occupied from the first symbol after the PDCCH triggering the CSI-PM report, until the last symbol of the slot that is 80ffSetslots before the first prediction time instance. Here, OCPU-CSI-inferenceis the number of occupied CPUs when the UE is triggered for the CSI-inference report only. The value of 80ffSetis specified in 3GPP specifications. The value of 80ffSetmay depend on UE capability.

[0116] In one example, 80^set= 8. In this case, the OCPU-CSI-PMCPU(s) are occupied until the last symbol in slot nl in Figure 9.

[0117] In some embodiments, for CSI predicting using UE-side AI / ML model, when the UE is triggered for a CSI-PM report together with a corresponding CSI-inference report by a single DCI, and when the CSI-inference report and the CSI-PM report are scheduled on the same PUSCH, the number of occupied CPUs for the CSI-inference and CSI-PM report, denoted as Ocpu-csi-PM-inference is defined as OCPy _CSi_Pinfrence=OCPj_deita. In some embodiments, the OCPU-CSI-PMCPU(s) are occupied from a symbol (e.g., the last symbol, orthe first symbol) of the slot that is 80^setslots before the first prediction time instance until the last symbol of theP112980W001 PCT APPLICATION 22 of 48scheduled PUSCH carrying the CSI-PM report. Here, OCPU-deitais associated to the CSI processing complexity for measuring the N4 CSI-RS resources and calculating the performance monitoring output using the N4 measurements and the predicted CSI used for generating the CSI-inference report. The value of 80ffSetis specified in 3GPP specifications.

[0118] In one example, 80^set= 8. In this case the OCPU-CSI-PMCPU(s) are occupied from a symbol (e.g., the last symbol) of slotnl in Figure 9, until the last symbol of the scheduled PUSCH carrying the CSI-PM report.Case 2: A CSI-PM report is computed based on multiple monitoring data samples

[0119] A UE may be configured to derive a performance monitoring output based on multiple monitoring data samples.

[0120] Figure 10 illustrates using a single DCI signaling for CSI-inference report(s) and CSI-PM report with AP CSI-RS resource, where PM is calculated over multiple samples. Figure 10 shows an example of using different DCI signaling for triggering CSI-inference report and CSI-PM report, where the CSI-PM report is derived based on multiple monitoring data samples. In this example, the CSI-PM report may be triggered regardless of whether there is a CSI-inference report configured associated to its monitoring data samples.

[0121] The methodology discussed for Case 1 may be extended to apply for this case, and used for defining the number of occupied CPUs for the CSI-PM report and the period of time that the CPUs are occupied.

[0122] For the monitoring data sample 1 shown in Figure 10, a corresponding CSI-inference report is triggered. This is similar to the Case lb shown in Figure 6. The UE computes the predicted CSI for generating the CSI-inference report. The UE may store the computed predicted CSI and reuse it for computing the CSI-PM report. Because the occupied CPU for computing the predicted CSI for monitoring data sample 1 is already counted for the CSI-inference report for a certain time window (e.g., from the first symbol after the PDCCH triggering the CSI-inference report, until the last symbol of the scheduled PUSCH carrying the CSI-inference report), there is no need to count it again for the CSI-PM report in this time period, thus, OCPU-csl-PM=0 for this time period. And OCPu-csi-PM=Ocpu-deita fromthe last symbol of the scheduled PUSCH carrying the CSI-inference report, until a reference time, e.g., the last symbol of the last CSI-RS resource in the corresponding CSI-RS burst 2.

[0123] For monitoring data sample 2 shown in Figure 10, the corresponding CSI-inference report is not triggered.

[0124] If the UE shall not consider this monitoring data sample as a valid data sample for calculating the performance monitoring results, then the OCPU-CS[-PM-infrence=0 for the timeP112980W001 PCT APPLICATION 23 of 48period associated to this monitoring data sample, e.g., from the first symbol of the first CSI-RS resource in the corresponding CSI-RS burst 1, until the last symbol of the last CSI-RS resource in the corresponding CSI-RS burst 2.

[0125] If the UE shall consider this monitoring data sample as a valid data sample for calculating the performance monitoring results, then, this is similar to the Case la shown in Figure 5.

[0126] As an example, define an invalid monitoring data sample as a monitoring data sample that shall not be used by the UE for performance monitoring results calculation, and define a time period for a monitoring data sample as the time from the first symbol of the first CSI-RS resource in the corresponding CSI-RS burst 1, until the last symbol of the last CSI-RS resource in the corresponding CSI-RS burst 2. For each valid monitoring data sample, the UE may calculate a corresponding performance monitoring matric, which is used for calculating the performance monitoring results to be reported in the CSI-PM report. Alternatively, the UE may store the monitoring data sample and compute the performance monitoring results at the end of a monitoring window and then send the computed results in the CSI-PM report.

[0127] In some embodiments, for CSI predicting using UE-side AI / ML model, when the UE is triggered for a CSI-PM report, which is computed based on multiple monitoring data samples, the number of occupied CPUs for the CSI-PM report, denoted as OCPU-CSI-PM, is defined as OCPU-CSI-PM=^- Insome embodiments, the OCPU-CSI-PMCPU(s) are occupied for the time period associated to an invalid monitoring data sample.

[0128] For a valid monitoring data sample, and if the corresponding CSI-inference report is triggered, OCPU-csl-PM=0. In some embodiments, the OCPU-CSI-PMCPU(s) are occupied from the first symbol of the first CSI-RS resource in the corresponding CSI-RS burst 1 (or the first symbol after PDCCH triggering the CSI-PM report if it is the first valid monitoring data sample), until the last symbol of the scheduled PUS CH carrying the CSI-inference report.

[0129] For a valid monitoring data sample, and if the corresponding CSI-inference report is triggered, OCPU-CSI-PM= OCPU-deita. In some embodiments, the OCPU-CSI-PMCPU(s) are occupied from the last symbol of the scheduled PUSCH carrying the CSI-inference report until the last symbol of the last CSI-RS resource in the corresponding CSI-RS burst 2 (or the last symbol of the scheduled PUSCH carrying the CSI-PM report if it is the last monitoring data sample before CSI-PM reporting), where OCPU-deitais associated to the CSI processing complexity for measuring the N4 CSI-RS resources and optionally including the computing of a performance metric for this monitoring data sample. In some embodiments, OCPU-deitais a function of N4.P112980W001 PCT APPLICATION 24 of 48

[0130] For a valid monitoring data sample, if the corresponding CSI-inference report is not triggered, OCPU-CSI-PM= OCPU-CSI-inference. In some embodiments, the OCPU-CSI-PMCPU(s) are occupied from the first symbol of the first CSI-RS resource in the corresponding CSI-RS burst 1 (or the first symbol after PDCCH triggering the CSI-PM report if it is the first valid monitoring data sample), until the last symbol of the slot that is 80ffSetslots before the first prediction time instance. Here, OCPU-CSI-inferenceis the number of occupied CPUs when the UE is triggered for the CSI-inference report only. The value of 80ffSetis specified in 3GPP specifications. The value of 80ffSetmay depend on UE capability.

[0131] In one example, 80^set= 8 . In this case, for monitoring data sample 2, the OCPU-CSI-PM CPU(s) are occupied from the first symbol of the first CSI-RS resource in the corresponding CSI-RS burst 1 until the last symbol in slot n2 in Figure 10.

[0132] For a valid monitoring data sample, if the corresponding CSI-inference report is not triggered, OCPU-CSI-PM= OCPU-deita. In some embodiments, the OCPU-CSI-PMCPU(s) are occupied from the last symbol of the slot that is 80^setslots before the first prediction time instance until the last symbol of the last CSI-RS resource in the corresponding CSI-RS burst 2 (or the last symbol of the scheduled PUSCH carrying the CSI-PM report if it is the last monitoring data sample before CSI-PM reporting), where OCPU-deitais associated to the CSI processing complexity for measuring the N4 CSI-RS resources and optionally including the computing of a performance metric for this monitoring data sample. In some embodiments, OCPU-deitais a function of N4. The value of 80^setis specified in 3 GPP specifications.

[0133] In one example, 80^set= 8 . In this case, for monitoring data sample 2, the OCPU-CSI-PM CPU(s) are occupied from a symbol (e.g., the last symbol) of slot n2 in Figure 9 until the last symbol of the scheduled PUSCH carrying the CSI-PM report.

[0134] Figure 11 shows an example of a communication system 100 in accordance with some embodiments. In the example, the communication system 100 includes a telecommunication network 102 that includes an access network 104, such as a radio access network (RAN), and a core network 106, which includes one or more core network nodes 108. The access network 104 includes one or more access network nodes, such as network nodes 110a and 110b (one or more of which may be generally referred to as network nodes 110), or any other similar 3rd Generation Partnership Project (3GPP) access node or non-3GPP access point. The network nodes 110 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 112a, 112b, 112c, and 112d (one or more of which may be generally referred to as UEs 112) to the core network 106 over one or more wireless connections.P112980W001 PCT APPLICATION 25 of 48

[0135] Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 100 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 100 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0136] The UEs 112 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 110 and other communication devices. Similarly, the network nodes 110 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 112 and / or with other network nodes or equipment in the telecommunication network 102 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network 102.

[0137] In the depicted example, the core network 106 connects the network nodes 110 to one or more hosts, such as host 116. 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 106 includes one more core network nodes (e.g., core network node 108) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 108. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0138] The host 116 may be under the ownership or control of a service provider other than an operator or provider of the access network 104 and / or the telecommunication network 102 and may be operated by the service provider or on behalf of the service provider. The host 116 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 andP112980W001 PCT APPLICATION 26 of 48compiling 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.

[0139] As a whole, the communication system 100 of Figure 11 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox.

[0140] In some examples, the telecommunication network 102 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 102. For example, the telecommunications network 102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC)ZMassive loT services to yet further UEs.

[0141] In some examples, the UEs 112 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 104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 104. 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).

[0142] In the example, the hub 114 communicates with the access network 104 to facilitate indirect communication between one or more UEs (e.g., UE 112c and / or 112d) and network nodes (e.g., network node 110b). In some examples, the hub 114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 114 may be a broadband router enabling access to the core network 106 forP112980W001 PCT APPLICATION 27 of 48the UEs. As another example, the hub 114 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 110, or by executable code, script, process, or other instructions in the hub 114. As another example, the hub 114 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 114 may be a content source . For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 114 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 114 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy loT devices.

[0143] The hub 114 may have a constant / persistent or intermittent connection to the network node 110b. The hub 114 may also allow for a different communication scheme and / or schedule between the hub 114 and UEs (e.g., UE 112c and / or 112d), and between the hub 114 and the core network 106. In other examples, the hub 114 is connected to the core network 106 and / or one or more UEs via a wired connection. Moreover, the hub 114 may be configured to connect to an M2M service provider over the access network 104 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 110 while still connected via the hub 114 via a wired or wireless connection. In some embodiments, the hub 114 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 110b. In other embodiments, the hub 114 may be a nondedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 110b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0144] Figure 12 shows a UE 200 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless 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-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rdP112980W001 PCT APPLICATION 28 of 48Generation 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.

[0145] A UE may support device-to-device (D2D) communication, for example by implementing a 3 GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to-everything (V2X) . In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0146] The UE 200 includes processing circuitry 202 that is operatively coupled via a bus 204 to an input / output interface 206, a power source 208, a memory 210, a communication interface 212, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 11. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0147] The processing circuitry 202 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 210. The processing circuitry 202 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 202 may include multiple central processing units (CPUs).

[0148] In the example, the input / output interface 206 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE 200. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive displayP112980W001 PCT APPLICATION 29 of 48may 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.

[0149] In some embodiments, the power source 208 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source 208 may further include power circuitry for delivering power from the power source 208 itself, and / or an external power source, to the various parts of the UE 200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 208. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 208 to make the power suitable for the respective components of the UE 200 to which power is supplied.

[0150] The memory 210 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 210 includes one or more application programs 214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 216. The memory 210 may store, for use by the UE 200, any of a variety of various operating systems or combinations of operating systems.

[0151] The memory 210 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 210 may allow the UE 200 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, suchP112980W001 PCT APPLICATION 30 of 48as one utilizing a communication system may be tangibly embodied as or in the memory 210, which may be or comprise a device-readable storage medium.

[0152] The processing circuitry 202 may be configured to communicate with an access network or other network using the communication interface 212. The communication interface 212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 222. The communication interface 212 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter 218 and / or a receiver 220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 218 and receiver 220 may be coupled to one or more antennas (e.g., antenna 222) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0153] In the illustrated embodiment, communication functions of the communication interface 212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / intemet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0154] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 212, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0155] As another example, a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or theP112980W001 PCT APPLICATION 31 of 48switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

[0156] A UE, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non -limiting examples of such an loT device are 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 head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or itemtracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the UE 200 shown in Figure 11.

[0157] As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0158] In practice, any number of UEs may be used together with respect to a single use case. For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed. The first and / or the second UE can also include more than one of the functionalities describedP112980W001 PCT APPLICATION 32 of 48above. For example, a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0159] Figure 13 shows a network node 300 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NRNodeBs (gNBs)).

[0160] Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units 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).

[0161] Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0162] The network node 300 includes a processing circuitry 302, a memory 304, a communication interface 306, and a power source 308. The network node 300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 300 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components mayP112980W001 PCT APPLICATION 33 of 48be duplicated (e.g., separate memory 304 for different RATs) and some components may be reused (e.g., a same antenna 310 may be shared by different RATs). The network node 300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 300, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 300.

[0163] The processing circuitry 302 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other network node 300 components, such as the memory 304, to provide network node 300 functionality.

[0164] In some embodiments, the processing circuitry 302 includes a system on a chip (SOC).In some embodiments, the processing circuitry 302 includes one or more of radio frequency (RF) transceiver circuitry 312 and baseband processing circuitry 314. In some embodiments, the radio frequency (RF) transceiver circuitry 312 and the baseband processing circuitry 314 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 312 and baseband processing circuitry 314 may be on the same chip or set of chips, boards, or units.

[0165] The memory 304 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 302. The memory 304 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 302 and utilized by the network node 300. The memory 304 may be used to store any calculations made by the processing circuitry 302 and / or any data received via the communication interface 306. In some embodiments, the processing circuitry 302 and memory 304 is integrated.P112980W001 PCT APPLICATION 34 of 48

[0166] The communication interface 306 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, the communication interface 306 comprises port(s) / terminal(s) 316 to send and receive data, for example to and from a network over a wired connection. The communication interface 306 also includes radio front-end circuitry 318 that may be coupled to, or in certain embodiments a part of, the antenna 310. Radio front-end circuitry 318 comprises filters 320 and amplifiers 322. The radio front-end circuitry 318 may be connected to an antenna 310 and processing circuitry 302. The radio front-end circuitry may be configured to condition signals communicated between antenna 310 and processing circuitry 302. The radio front-end circuitry 318 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 318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 320 and / or amplifiers 322. The radio signal may then be transmitted via the antenna 310. Similarly, when receiving data, the antenna 310 may collect radio signals which are then converted into digital data by the radio front-end circuitry 318. The digital data may be passed to the processing circuitry 302. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0167] In certain alternative embodiments, the network node 300 does not include separate radio front-end circuitry 318, instead, the processing circuitry 302 includes radio front-end circuitry and is connected to the antenna 310. Similarly, in some embodiments, all or some of the RF transceiver circuitry 312 is part of the communication interface 306. In still other embodiments, the communication interface 306 includes one or more ports or terminals 316, the radio front-end circuitry 318, and the RF transceiver circuitry 312, as part of a radio unit (not shown), and the communication interface 306 communicates with the baseband processing circuitry 314, which is part of a digital unit (not shown).

[0168] The antenna 310 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 310 may be coupled to the radio front-end circuitry 318 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 310 is separate from the network node 300 and connectable to the network node 300 through an interface or port.

[0169] The antenna 310, communication interface 306, and / or the processing circuitry 302 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly,P112980W001 PCT APPLICATION 35 of 48the antenna 310, the communication interface 306, and / or the processing circuitry 302 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.

[0170] The power source 308 provides power to the various components of network node 300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 300 with power for performing the functionality described herein. For example, the network node 300 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 308. As a further example, the power source 308 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.

[0171] Embodiments of the network node 300 may include additional components beyond those shown in Figure 13 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 300 may include user interface equipment to allow input of information into the network node 300 and to allow output of information from the network node 300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 300.

[0172] Figure 14 is a block diagram illustrating a virtualization environment 500 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 500 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized.P112980W001 PCT APPLICATION 36 of 48

[0173] Applications 502 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0174] Hardware 504 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 506 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 508a and 508b (one or more of which may be generally referred to as VMs 508), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 506 may present a virtual operating platform that appears like networking hardware to the VMs 508.

[0175] The VMs 508 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 506. Different embodiments of the instance of a virtual appliance 502 may be implemented on one or more of VMs 508, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

[0176] In the context of NFV, a VM 508 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 508, and that part of hardware 504 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs 508 on top of the hardware 504 and corresponds to the application 502.

[0177] Hardware 504 may be implemented in a standalone network node with generic or specific components. Hardware 504 may implement some functions via virtualization. Alternatively, hardware 504 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 510, which, among others, oversees lifecycle management of applications 502. In some embodiments, hardware 504 is coupled to one or more radio units that each include one orP112980W001 PCT APPLICATION 37 of 48more transmiters 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 512 which may alternatively be used for communication between hardware nodes and radio units.

[0178] Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

[0179] 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 processingP112980W001 PCT APPLICATION 38 of 48circuitry 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.

[0180] FIGURE 15 is a flowchart illustrating an example method 1500 in a wireless device, according to certain embodiments. In particular embodiments, one or more steps of FIGURE 15 may be performed by UE 200 described with respect to FIGURE 12.

[0181] The method 1500 begins at step 1512, where the wireless device (e.g., UE 200) receives, from a network node (e.g., network node 300), a request to report performance monitoring results of a CSI prediction feature.

[0182] The request to report performance monitoring results may be received at different points in time with respect to when the wireless device performs CSI inference and when the wireless device computes performance monitoring results. In particular embodiments, the request to report CSI prediction performance monitoring results may be received before a first time period, wherein the first time period is a time period for performing CSI inference. In some embodiments, the request to report CSI prediction performance monitoring results may be received after the first time period and before a second time period, wherein the second time period is for computing performance monitoring results. In some embodiments, the request comprises a request for performing CSI inference and for reporting performance monitoring results. In some embodiments, the request comprises only a request for reporting performance monitoring results.

[0183] In particular embodiments, the wireless device may receive the request to report performance monitoring results according to any of the embodiments and examples described herein. Particular examples are illustrated with respect to the DCI signaling arrows illustrated in FIGURES 5-10.

[0184] At step 1514, the wireless device calculates a number of CPUs and an occupancy time of the of CPUSs for computing the performance monitoring results. The calculations are based at least in part on when the request to report performance monitoring results is received by the wireless (e.g., before inference and performance monitoring, between inference and performance monitoring, etc.) and / or the contents of the performance monitoring report (e.g., reporting both inference and performance monitoring, only reporting performance monitoring, etc.).

[0185] In particular embodiments, the calculated number of CPUs is occupied from a first symbol of a downlink control channel in which the wireless device received the request to report performance monitoring results until a last symbol of an uplink channel carrying the CSI report. An example is illustrated in FIGURE 5.

[0186] In particular embodiments, a first calculated number of CPUs is occupied from a first symbol of a downlink control channel in which the wireless device received the request to reportP112980W001 PCT APPLICATION 39 of 48performance monitoring results until the performing CSI inference is complete and a second calculated number of CPUs is occupied from a start of measuring the second burst of CSI-RSs in the second time period until a last symbol of an uplink channel carrying the CSI report.

[0187] In particular embodiments, the calculated number of CPUs for the CSI performance monitoring report is occupied from a start of measuring the second burst of CSI-RSs in the second time period until a last symbol of an uplink channel carrying the CSI performance monitoring report.

[0188] In particular embodiments, the calculated number of CPUs for the CSI performance monitoring report is occupied from a last symbol of an uplink channel carrying the CSI inference report until a last symbol of an uplink channel carrying the CSI performance monitoring report.

[0189] In particular embodiments, the calculated number of CPUs for the CSI performance monitoring report is occupied from a first symbol of a downlink control channel in which the wireless device received the request to report CSI prediction performance monitoring results until a last symbol of an uplink channel carrying the CSI report. The request to report CSI prediction performance monitoring results may be received before the first time period (e.g., FIGURE 8). The request to report CSI prediction performance monitoring results may be received after the first time period and before the second time period (e.g., FIGURE 7).

[0190] In particular embodiments, a first calculated number of CPUs for the CSI report is occupied from a first symbol of a downlink control channel in which the wireless device received the request to report CSI prediction performance monitoring results until a last symbol of an uplink channel carrying the CSI inference report, and a second calculated number of CPUs for the CSI report is occupied from a last symbol of an uplink channel carrying the CSI-inference report until a last symbol of an uplink channel carrying the CSI performance monitoring report.

[0191] Examples are illustrated with respect to FIGURES 5-10.

[0192] Upon determining that the calculated occupancy time of the CPUs is available to the wireless device, the method continues to step 1516, where the wireless device computes the performance monitoring results. In particular embodiments, computing the performance monitoring results comprises performing CSI inference based on a first burst of CSI-RSs received in a first time period. The inference predicts CSI for a second burst of CSI-RSs to be received a second time period. The step further comprises measuring the second burst of CSI-RSs in the second time period to generate ground truth measurement results and generating a performance metric by comparing the ground truth measurement results from the second time period to the inference results for the second time period.P112980W001 PCT APPLICATION 40 of 48

[0193] At step 1518, the wireless device transmits a CSI report comprising the performance monitoring results to the network node. In particular embodiments, transmitting the CSI report comprises transmitting a single report to the network node, the single report comprising a CSI performance monitoring report. In particular embodiment, the single report comprises both a CSI inference report and a CSI performance monitoring report. In some embodiments, transmitting the CSI report comprises transmitting two reports, e.g., transmitting a CSI inference report to the network node and transmitting a CSI performance monitoring report to the network node.

[0194] Modifications, additions, or omissions may be made to method 1500 of FIGURE 15. Additionally, one or more steps in the method of FIGURE 15 may be performed in parallel or in any suitable order.

[0195] FIGURE 16 is a flowchart illustrating an example method 1600 in a network node, according to certain embodiments. In particular embodiments, one or more steps of FIGURE 16 may be performed by network node 300 described with respect to FIGURE 13.

[0196] The method 1600 may begin at step 1612, where the network node (e.g., network node 300) receives a wireless device capability indication from the wireless device. The capability indication indicates a number of CPUs for performing CSI inference and prediction.

[0197] At step 1614, the network node obtains a CSI prediction performance monitoring configuration for a wireless device. For example, the configuration may indicate any one or more of the CSI-RS resources for the inference phase and performance monitoring phase described above.

[0198] At step 1616, the network node calculating a number of CPUs and an occupancy time of the CPUs the wireless device uses for computing performance monitoring results based on the CSI prediction performance monitoring configuration. The calculations may be based on any of the examples and embodiments described herein, such as those described with respect to FIGURES 5-10.

[0199] At step 1618, upon determining the calculated occupancy time of the CPUs is available to the wireless device, the method further comprises transmitting a request to the wireless device to report performance monitoring results.

[0200] Modifications, additions, or omissions may be made to method 1600 of FIGURE 16. Additionally, one or more steps in the method of FIGURE 16 may be performed in parallel or in any suitable order.

[0201] Some example embodiments are described below.P112980W001 PCT APPLICATION 41 of 48Group A Embodiments1. A method performed by a user equipment for cell selection or reselection in a non-terrestrial network (NTN), the method comprising:receiving, from a network node, a request to perform channel state information (CSI) prediction and performance monitoring;calculating a number of CSI processing units (CPUs) used for performing the CSI prediction and performance monitoring; andtransmitting a CSI report to the network node, the CSI report comprising an indication of the calculated number of CPUs.2. The method of the previous embodiment, wherein calculating a number of CSI processing units (CPUs) used for performing the CSI prediction and performance monitoring is performed according to any of the embodiments and examples described above.3. A method performed by a wireless device, the method comprising:any of the wireless device steps, features, or functions described above, either alone or in combination with other steps, features, or functions described above.4. The method of the previous embodiment, further comprising one or more additional wireless device steps, features or functions described above.Group B Embodiments5. A method performed by a network node, the method comprising:transmitting, to a wireless device, a request to perform channel state information (CSI) prediction and performance monitoring;receiving, from the wireless device, a CSI report comprising an indication of a number of CPUs used by the wireless device for performing the CSI prediction and performance monitoring.6. A method performed by a network node, the method comprising:any of the steps, features, or functions described above with respect to a network node, either alone or in combination with other steps, features, or functions described above.7. The method of the previous embodiment, further comprising one or more additional networkP112980W001 PCT APPLICATION 42 of 48node steps, features or functions described above.Group C Embodiments8. A user equipment comprising:processing circuitry configured to perform any of the steps of any of the Group A embodiments; andpower supply circuitry configured to supply power to the processing circuitry.9. A network node comprising:processing circuitry configured to perform any of the steps of any of the Group B embodiments;power supply circuitry configured to supply power to the processing circuitry.10. A user equipment (UE) comprising:an antenna configured to send and receive wireless signals;radio front-end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry;the processing circuitry being configured to perform any of the steps of any of the Group A embodiments;an input interface connected to the processing circuitry and configured to allow input of information into the UE to be processed by the processing circuitry;an output interface connected to the processing circuitry and configured to output information from the UE that has been processed by the processing circuitry; anda battery connected to the processing circuitry and configured to supply power to the UE.

[0202] The foregoing description sets forth numerous specific details. It is understood, however, that embodiments may be practiced without these specific details. In other instances, well-known circuits, structures and techniques have not been shown in detail in order not to obscure the understanding of this description. Those of ordinary skill in the art, with the included descriptions, will be able to implement appropriate functionality without undue experimentation.

[0203] References in the specification to “one embodiment,” “an embodiment,” “an example embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particularP112980W001 PCT APPLICATION 43 of 48feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to implement such feature, structure, or characteristic in connection with other embodiments, whether or not explicitly described.

[0204] Although this disclosure has been described in terms of certain embodiments, alterations and permutations of the embodiments will be apparent to those skilled in the art. Accordingly, the above description of the embodiments does not constrain this disclosure. Other changes, substitutions, and alterations are possible without departing from the scope of this disclosure, as defined by the claims below.

Claims

1. P112980W001 PCT APPLICATION 44 of 48Claims1. A method performed by a wireless device, the method comprising:receiving (1512), from a network node, a request to report performance monitoring results of a channel state information (CSI) prediction feature;calculating (1514) a number of CSI processing units (CPUs) and an occupancy time of the of CPUSs for computing the performance monitoring results; andupon determining the calculated occupancy time of the CPUs is available to the wireless device:computing (1516) the performance monitoring results; andtransmitting (1518) a CSI report comprising the performance monitoring results to the network node.

2. The method of claim 1, wherein computing the performance monitoring results comprises:performing CSI inference based on a first burst of CSI reference signals (CSI-RSs) received in a first time period, wherein the inference predicts CSI for a second burst of CSI-RSs to be received a second time period;measuring the second burst of CSI-RSs in the second time period to generate ground truth measurement results; andgenerating a performance metric by comparing the ground truth measurement results from the second time period to the inference results for the second time period.

3. The method of claim 2, wherein transmitting the CSI report comprises transmitting a single report to the network node, the single report comprising a CSI performance monitoring report, and wherein the calculated number of CPUs is occupied from a first symbol of a downlink control channel in which the wireless device received the request to report performance monitoring results until a last symbol of an uplink channel carrying the CSI report.

4. The method of claim 2, wherein transmitting the CSI report comprises transmitting a single report to the network node, the single report comprising a CSI performance monitoring report, and wherein a first calculated number of CPUs is occupied from a first symbol of a downlink control channel in which the wireless device received the request to report performanceP112980W001 PCT APPLICATION 45 of 48monitoring results until the performing CSI inference is complete and a second calculated number of CPUs is occupied from a start of measuring the second burst of CSI-RSs in the second time period until a last symbol of an uplink channel carrying the CSI report.

5. The method of claim 2, wherein transmitting the CSI report comprises transmitting a CSI inference report to the network node and transmitting a CSI performance monitoring report to the network node, and wherein the calculated number of CPUs for the CSI performance monitoring report is occupied from a start of measuring the second burst of CSI-RSs in the second time period until a last symbol of an uplink channel carrying the CSI performance monitoring report.

6. The method of claim 2, wherein transmitting the CSI report comprises transmitting a CSI inference report to the network node and transmitting a CSI performance monitoring report to the network node, and wherein the calculated number of CPUs for the CSI performance monitoring report is occupied from a last symbol of an uplink channel carrying the CSI inference report until a last symbol of an uplink channel carrying the CSI performance monitoring report.

7. The method of claim 2, wherein transmitting the CSI report comprises transmitting a CSI inference report to the network node and transmitting a CSI performance monitoring report to the network node, and wherein the calculated number of CPUs for the CSI performance monitoring report is occupied from a first symbol of a downlink control channel in which the wireless device received the request to report CSI prediction performance monitoring results until a last symbol of an uplink channel carrying the CSI report.

8. The method of claim 7, wherein the request to report CSI prediction performance monitoring results is received before the first time period.

9. The method of claim 7, wherein the request to report CSI prediction performance monitoring results is received after the first time period and before the second time period.

10. The method of claim 2, wherein transmitting the CSI report comprises transmitting a CSI inference report to the network node and transmitting a CSI performance monitoring report to the network node, and wherein a first calculated number of CPUs for the CSI report is occupied from a first symbol of a downlink control channel in which the wireless device received the request to report CSI prediction performance monitoring results until a last symbol of an uplink channelP112980W001 PCT APPLICATION 46 of 48carrying the CSI inference report, and a second calculated number of CPUs for the CSI report is occupied from a last symbol of an uplink channel carrying the CSI-inference report until a last symbol of an uplink channel carrying the CSI performance monitoring report.

11. A wireless device (200) comprising processing circuitry (202) operable to: receive, from a network node (300), a request to report performance monitoring results of a channel state information (CSI) prediction feature;calculate a number of CSI processing units (CPUs) and an occupancy time of the of CPUSs for computing the performance monitoring results; andupon determining the calculated occupancy time of the CPUs is available to the wireless device:compute the performance monitoring results; andtransmit a CSI report comprising the performance monitoring results to the network node.

12. The wireless device of claim 11, the processing circuitry operable to perform the steps of any one of claims 2-10.

13. A method performed by a network node, the method comprising:obtaining (1614) a channel state information (CSI) prediction performance monitoring configuration for a wireless device;calculating (1616) a number of CSI processing units (CPUs) and an occupancy time of the CPUs the wireless device uses for computing performance monitoring results based on the CSI prediction performance monitoring configuration; andupon determining the calculated occupancy time of the CPUs is available to the wireless device, transmitting (1618) a request to the wireless device to report performance monitoring results;14. The method of claim 13, further comprising receiving (1612) a wireless device capability indication from the wireless device, the capability indication indicating a number of CPUs for performing CSI inference and prediction.

15. A network node (300) comprising processing circuitry (302) operable to: obtain a channel state information (CSI) prediction performance monitoring configurationP112980W001 PCT APPLICATION 47 of 48for a wireless device (200);calculate a number of CSI processing units (CPUs) and an occupancy time of the CPUs the wireless device uses for computing performance monitoring results based on the CSI prediction performance monitoring configuration; andupon determining the calculated occupancy time of the CPUs is available to the wireless device, transmit a request to the wireless device to report performance monitoring results;16. The network node of claim 15, the processing circuitry further operable to receive a wireless device capability indication from the wireless device, the capability indication indicating a number of CPUs for performing CSI inference and prediction.

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