Wireless device, network node, and methods performed thereby for handling channel state information

Abstract

A method, performed by a wireless device (130) operating in a wireless communications network (100). The wireless device (130) obtains (503) first measurements (K) on first channel state information, CSI, reference signals, RSs, and one or more second measurements (n) on one or more second CSI RSs. The first measurements do not overlap with the one or more second measurements. The wireless device (130) obtains (504) one or more predictions, (N), of CSI based on the first measurements. The wireless device (130) obtains (505), based on the one or more second measurements, one or more ground truth labels corresponding to the obtained one or more predictions of CSI. The wireless device (130) then reports (507), after having obtained the one or more ground truth labels, an indication indicating one or more of: the one or more predictions, and the one or more ground truth labels.

Description

[0001] WIRELESS DEVICE, NETWORK NODE, AND METHODS PERFORMED THEREBY FOR HANDLING CHANNEL STATE INFORMATION

[0002] TECHNICAL FIELD

[0003] The present disclosure relates generally to a wireless device and methods performed thereby for handling channel state information (CSI). The present disclosure also generally relates to a network node and methods performed thereby for handling CSI.

[0004] BACKGROUND

[0005] Wireless devices within a wireless communications network may be e.g., User Equipments (UEs), stations (STAs), mobile terminals, wireless terminals, terminals, and / or Mobile Stations (MS). Wireless devices are enabled to communicate wirelessly in a cellular communications network or wireless communication network, sometimes also referred to as a cellular radio system, cellular system, or cellular network. The communication may be performed e.g., between two wireless devices, between a wireless device and a regular telephone and / or between a wireless device and a server via a Radio Access Network (RAN) and possibly one or more core networks, comprised within the wireless communications network. Wireless devices may further be referred to as mobile telephones, cellular telephones, laptops, or tablets with wireless capability, just to mention some further examples. The wireless devices in the present context may be, for example, portable, pocket-storable, hand-held, computer-comprised, or vehicle-mounted mobile devices, enabled to communicate voice and / or data, via the RAN, with another entity, such as another terminal or a server.

[0006] The wireless communications network covers a geographical area which may be divided into cell areas, each cell area being served by a network node, which may be an access node such as a radio network node, radio node or a base station, e.g., a Radio Base Station (RBS), which sometimes may be referred to as e.g., gNB, evolved Node B (“eNB”), “eNodeB”, “NodeB”, “B node”, Transmission Point (TP), or Base Transceiver Station (BTS), depending on the technology and terminology used. The base stations may be of different classes such as e.g., Wide Area Base Stations, Medium Range Base Stations, Local Area Base Stations, Home Base Stations, pico base stations, etc-, based on transmission power and thereby also cell size. A cell is the geographical area where radio coverage is provided by the base station or radio node at a base station site, or radio node site, respectively. One base station, situated on the base station site, may serve one or several cells. Further, each base station may support one or several communication technologies. The base stations communicate over the air interface operating on radio frequencies with the terminals within range of the base stations. The wireless communications network may also be a non-cellular system, comprising network nodes which may serve receiving nodes, such as wireless devices, with serving beams. In 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE), base stations, which may be referred to as eNodeBs or even eNBs, may be directly connected to one or more core networks. In the context of this disclosure, the expression Downlink (DL) may be used for the transmission path from the base station to the wireless device. The expression Uplink (UL) may be used for the transmission path in the opposite direction i.e., from the wireless device to the base station.

[0007] The standardization organization 3GPP is currently in the process of specifying a New Radio Interface called NR or 5G-Universal Terrestrial Radio Access (UTRA), as well as a Fifth Generation (5G) Packet Core Network (CN), which may be referred to as Next Generation (NG) Core Network, abbreviated as NG-CN, NGC, 5G CN or 5G Core (5GC). NG may be understood to refer to the interface / reference point between the Radio Access Network (RAN) and the CN in 5G / NR. In a 5G System (5GS), a radio base station in NR may be referred to as a gNB or 5G Node B. An NR User Equipment (UE) may be referred to as an nUE.

[0008] Machine Learning

[0009] Machine learning (ML) may be understood as the study of computer algorithms that may improve automatically through experience. It is seen as a part of Artificial Intelligence (Al). ML algorithms may build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. ML algorithms may be used in a wide variety of applications, such as email filtering and computer vision, where it may be difficult or unfeasible to develop conventional algorithms to perform the needed tasks.

[0010] There may be basically three types of ML Algorithms: Supervised Learning, Unsupervised Learning, and Reinforcement Learning (RL).

[0011] Supervised Learning algorithms may comprise a target / outcome variable, or dependent variable, which may have to be predicted from a given set of predictors, that is, independent variables. Using this set of variables, a function may be generated that may map inputs to desired outputs. The training process may continue until the model may achieve a desired level of accuracy on the training data. Once an ML model may have been trained, an inference process may begin, whereby new data may be run through the ML model to calculate an output. Examples of Supervised Learning may be Regression, Decision Tree, Random Forest, K- nearest neighbors (KNN), Logistic Regression etc.

[0012] Time domain Channel State Information (CSI) prediction at UE

[0013] 3GPP NR Rel-18 time-domain Type II CSI prediction at UE

[0014] In 3GPP NR Rel-18, channel measurement resource (CMR) enhancement for Type II CSI prediction at UE, a.k.a. Enhanced Type II predicted Precoding Matrix Indicator (PMI), has been introduced. Figure 1 is a schematic diagram illustrating an example of a CMR enhancement for Rel-18 Type II CSI prediction at UE. As illustrated by the measurement part of Figure 1 , that is the part of the diagram marked as Observation Window, a burst of K e {4, 8, 12} CSI-Reference Signal (RS) resources, depicted as dotted rectangles, may be configured to the UE in a single CSI-RS resource set. The burst of CSI-RS resources may be aperiodically (AP) triggered using a single downlink control information (DCI). The K CSI-RS resources may be used for the UE to extract time domain channel properties of the channel, based on which a future CSI may be predicted. The burst of CSI-RS resources may be uniformly spaced in time, separated by m e {1, 2} slots, within the resource set. Alternatively, the network (NW) may also configure a legacy periodic (P) or semi-persistent (SP) CSI-RS resource.

[0015] For the Rel-18 Type II predicted PMI enhancement, a UE may be configured by a gNB to report predicted PMIs for N4E {1, 2, 4, 8} time slots, see the Rel-18 Type II PMI part in Figure 1 , that is the part of the diagram marked as Prediction Window. Note that the prediction herein may be understood to be relative to the slot, depicted as a black rectangle, in which the predicted PMIs may be reported as part of a single CSI report. The predicted N4PMIs, depicted as striped rectangles, may be supposed to reflect the channels with d e {1, m} slots separation, starting from S e {-nCSIref, 0,1,2} slots into the future relative to the slot in which the predicted PMIs may be reported. For AP CSI-RS burst, m e {1,2}, while for P / SP CSI-RS, m may be understood to be the CSI-RS periodicity. The spacing d between the N4PMIs and offset 6 relative to the slot in which the predicted PMIs may be reported may be configured by the gNB via Radio Resource Control (RRC) signalling. The N4PMIs may be compressed in a beam- frequency-Doppler domain, and the compressed PMI may be reported to the gNB in a single CSI report.

[0016] Rel-18 / Rel-19 AI / ML based CSI prediction

[0017] Al and ML have been investigated, both in academia and industry, as promising tools to optimize the design of the air-interface in wireless communication networks. Examples may 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; using reinforcement learning for beam selection at the network side and / or the UE side to reduce the signalling overhead and beam alignment latency; and using deep reinforcement learning to learn an optimal precoding policy for complex Multiple Input Multiple Output (MIMO) preceding problems.

[0018] In 3GPP NR standardization work, a release 18 study item on AI / ML for the NR air interface started in May 2022 and completed in December 2023. This study item explored the benefits of augmenting the air-interface with features enabling improved support of AI / ML based algorithms for enhanced performance and / or reduced complexity / overhead. Through studying a few selected examples, e.g., CSI feedback, beam management, and positioning, this study item aims at laying the foundation for future air-interface examples leveraging AI / ML techniques.

[0019] Terminologies such as AI / ML model, AI / ML model inference, which may be henceforth referred to as inference, AI / ML model training, which may be henceforth referred to as training, data collection, and model monitoring may be used as defined in Section 3.1 of 3GPP TR 38.843 v. 18.0.0.

[0020] The Al based CSI prediction example was studied in 3GPP Rel-18, and this example is under continued study in 3GPP Rel-19:

[0021] The CSI prediction example relates to using one or more one-sided UE-sided models, where the model inference may be performed entirely at the UE. One or more AI / ML models may be trained and deployed at a UE for the Al-based CSI-prediction feature. During model inference, a UE may be configured by the gNB to measure a set of historical CSI-RSs in an observation window and then report a predicted CSI for one or multiple future time instances in a prediction window using its AI / ML model(s). Figure 2 is a schematic diagram illustrating an example of the CSI prediction using UE-sided Al model(s). Particularly, Figure 2 provides an example for the inference procedure for CSI prediction. For generating the input of the CSI prediction model, it may need some further pre-processing on the measured channel; for the output of the CSI prediction model, some further post-processing may also be applied.

[0022] LifeCycle Management (LCM) operations for AI / ML for NR air interface

[0023] An important part in Al development and operation is the 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 identifier / identity (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, functionality-based LCM and model-ID based LCM, were studied in NR Rel-18, functionality-based LCM and model-ID based LCM. Functionality may be understood to referto an AI / ML-enabled Feature / Feature Group (FG) enabled by configuration(s), where configuration(s) may be supported based on conditions indicated by UE capability. Correspondingly, functionality-based LCM may operate 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 may indicate activation / deactivation / fallback / switching of AI / ML functionality via 3GPP signalling, e.g., RRC, Medium Access Control (MAC)-Control Element (CE), DCI. Models may not be identified at the Network, and the UE may perform modellevel LCM. Whether and how much awareness / interaction the NW may be required to have about model-level LCM may require further study. For functionality identification, there may be either one, or more than one, functionalities defined within an AI / ML-enabled feature, whereby AI / ML- enabled Feature may be understood to refer to a Feature where AI / ML may be used.

[0026] In model-ID-based LCM, models may be 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 NW-side. An AI / ML model identified by a model ID may be logical, and how it may map to physical AI / ML model(s) may be up to implementation.

[0027] Performance monitoring for Al based CSI prediction

[0028] There may be several methods for model monitoring. Monitoring based on intermediate Key Performance Indicators (KPIs), e.g., inference accuracy, may require collecting new ground-truth data similar and / or identical to the training data, which may be understood to be very accurate but may be understood to have a high cost due to the potentially large measurement and / or reporting overhead. Monitoring based on data distribution of input and / or output data may be understood to not require any additional signalling overhead, but may be understood to be less accurate than monitoring based on inference accuracy, since one may be understood to not retrieve the ground truth. Similarly, monitoring based on system performance may not require any additional signalling overhead, however, it may be challenging to detect that the root-cause for bad system performance is due to an inaccurate model, and not due to some other malfunctioning procedure or hardware. Monitoring based on data distribution may, in contrast, identify a potential problem in the model by detecting that the dataset observed during inference is not same as during training. However, it may be understood to be not-trivial to define conditions and measurable data-distribution based KPIs for sounding a model failure alarm with a good trade-off between model failure detection reliability and accuracy, e.g., low false alarm rate, low missed detection rate and low latency.

[0029] To ensure reliable / accurate model performance monitoring results, sufficient monitoring data samples may be collected and used to derive the performance monitoring results. Examples of model output accuracy based performance monitoring results may include intermediate KPI per monitoring data sample, intermediate KPI statistics associated to a monitoring data set, the percentage of monitoring data samples within a monitoring dataset for which the intermediate KPI may fulfil a certain condition, a flag indicating whether the model is functioning ok or not. Examples of data drift based performance monitoring results may include monitoring data statistics, the difference between the monitoring data statistics and the data statistics obtained in the model training stage, a flag indicating whether a data drift is detected or not. An intermediate KPI may be understood as a performance metric that may indicate the accuracy and / or quality of the inference output.

[0030] When implementing model monitoring, the monitoring method may be selected based on UE service requirements. For example, a UE with Mobile Broadband (MBB) may start with a low- cost approach, e.g., system performance based, if problems are observed / predicted, then the UE may activate an inference accuracy based monitoring method associated with a higher complexity. High-complexity and signalling overhead monitoring may be required for certain UE service requirements, such as for emergency localization examples or UEs with Ultra Reliable Low Latency Communications (URLLC) connection.

[0031] For CSI prediction using a UE side AI / ML model example, at least the following aspects have been proposed in Rel-18 on performance monitoring for functionality-based LCM, and these aspects will be continue studied in Rel-19. According to a Type 1 , a UE may calculate the performance metric(s). The UE may report performance monitoring output that may facilitate functionality fallback decision at the network. Performance monitoring output details may be further defined. The NW may configure a threshold criterion to facilitate UE side performance monitoring, if needed. The NW may make decision(s) of functionality fallback operation, fallback mechanism to legacy CSI reporting. According to a Type 2, a UE may report predicted CSI and / or the corresponding ground-truth. The NW may calculate the performance metrics. The NW may make decision(s) of functionality fallback operation, fallback mechanism to legacy CSI reporting. According to a Type 3, a UE may calculate the performance metric(s). The UE may report performance metric(s) to the NW. The NW may make decision(s) of functionality fallback operation, fallback mechanism to legacy CSI reporting.

[0032] Functionality selection / activation / deactivation / switching as defined for other UE side examples may be reused, if applicable. Configuration and procedure for performance monitoring.

[0033] Other aspects that will be continue studied in Rel-19 may be CSI-RS configuration for performance monitoring, performance metric including at least intermediate KPI, e.g., Normalized Mean Square Error analysis (NMSE) or Symplectic Geometry Components (SGCS), UE report, including periodic / semi-persistent / aperiodic reporting, and event driven report. It may be noted that a UE may make a decision within the same functionality on model selection, activation, deactivation, and / or switching operation transparent to the NW.

[0034] Existing methods to monitor performance of AI / L models for CSI prediction may lead to loss of accuracy of the predictions, hence resulting in both UE and NW taking decisions based on inaccurate CSI, which may impact the performance of the network.

[0035] SUMMARY

[0036] Certain aspects of the present disclosure and their embodiments address one or more of the challenges identified with the existing methods and provide solutions to these challenges or other challenges.

[0037] According to a first aspect of embodiments herein, the object is achieved by a method, performed by a wireless device. The wireless device operates in a wireless communications network. The wireless device obtains first measurements (K) on first CSI RSs and one or more second measurements (n) on one or more second CSI RSs. The first measurements do not overlap with the one or more second measurements. The wireless device obtains one or more predictions (N) of CSI based on the first measurements. The wireless device obtains, based on 1 the one or more second measurements, one or more ground truth labels corresponding to the obtained one or more predictions of CSI. The wireless device then reports, after having obtained the one or more ground truth labels, an indication. The indication indicates one or more of: the one or more predictions, and the one or more ground truth labels.

[0038] According to a second aspect of embodiments herein, the object is achieved by a method, performed by a network node. The network node operates in the wireless communications network. The network node receives the indication from the wireless device operating in the wireless communications network. The indication indicates one or more of: i) the one or more predictions (N) of CSI based on the first measurements, and ii) the one or more ground truth labels corresponding to the obtained one or more predictions. The one or more ground truth labels are based on the one or more second measurements (n), obtained by the wireless device, on the one or more second CSI RSs. The one or more second measurements do not overlap with the first measurements K, obtained by the wireless device, on the first CSI RSs.

[0039] According to a third aspect of embodiments herein, the object is achieved by the wireless device, configured to perform the method. The wireless device is configured to operate in the wireless communications network. The wireless device is configured to obtain the first measurements (K) on the first CSI RSs, and the one or more second measurements (n) on the one or more second CSI RSs. The first measurements are configured to not overlap with the one or more second measurements. The wireless device is configured to, obtain the one or more predictions (N) of CSI, based on the first measurements. The wireless device is configured to obtain, based on the one or more second measurements, the one or more ground truth labels corresponding to the one or more predictions of CSI configured to be obtained. The wireless device is configured to report, after having obtained the one or more ground truth labels, the indication configured to indicate the one or more of: the one or more predictions, and the one or more ground truth labels.

[0040] According to a fourth aspect of embodiments herein, the object is achieved by the network node, configured to perform the method. The network node is configured to operate in the wireless communications network. The network node is configured to receive the indication from the wireless device configured to operate in the wireless communications network. The indication is configured to indicate one or more of: i) the one or more predictions (N) of CSI based on first measurements, and ii) the one or more ground truth labels corresponding to the one or more predictions configured to be obtained. The one or more ground truth labels are configured to be based on the one or more second measurements (n) configured to be obtained by the wireless device, on the one or more second CSI RSs. The one or more second measurements are configured to not overlap with the first measurements (K), configured to be obtained by the wireless device, on the first CSI RSs. By obtaining the first measurements and the one or more second measurements, the wireless device may be enabled to use part of the measurements to generate the predicted CSI, and part of the measurements to create ground-truth label.

[0041] By obtaining the one or more predictions of CSI, the wireless device may avoid having to use time-frequency and energy resources performing measurements in the one or more instances of prediction of CSI.

[0042] By obtaining the one or more ground truth labels corresponding to the obtained one or more predictions of CSI, the wireless device may then be enabled to determine, using the obtained one or more ground truth labels, an intermediate key performance indicator (KPI)- based metric for the obtained one or more predictions of CSI when reporting the one or more predictions of CSI, e.g., to the network node.

[0043] By reporting the indication, the wireless device may enable, e.g., the network node, to determine the an intermediate key performance indicator (KPI)-based metric for the obtained one or more predictions of CSI, and thereby enable, e.g., the network node to know if the predicted CSI is with good quality or not when receiving such CSI report and, if necessary, take appropriate action, such as for example, refrain from using the received predicted CSI for making proceeding DL data scheduling decisions if the intermediated KPI indicates that the prediction accuracy and / or quality is poor; or configure the wireless device to fallback to a non- Al based CSI reporting feature, if, for example, a number of consecutive intermediated KPI reports indicate that the corresponding prediction accuracy and / or quality of the predicted CSI is poor; or use the received predicted CSI for assisting the proceeding data scheduling if the prediction quality is good.

[0044] BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Examples of embodiments herein are described in more detail with reference to the accompanying drawings, according to the following description.

[0046] Figure 1 is a schematic diagram illustrating an example of an CMR enhancement for Rel-18 Type II CSI prediction at UE, according to existing methods.

[0047] Figure 2 is a schematic diagram depicting an example of the CSI prediction using UE-sided Al model(s), according to existing methods.

[0048] Figure 3 is a schematic diagram depicting another example of the CSI prediction using UE- sided Al model(s), according to existing methods.

[0049] Figure 4 is a schematic diagram depicting two examples, in panel a) and panel b), respectively, of a wireless communications network, according to embodiments herein.

[0050] Figure 5 is a flowchart depicting a method in a wireless device, according to embodiments herein.

[0051] Figure 6 is a flowchart depicting a method in a network node, according to embodiments herein. Figure 7 is a flowchart depicting a non-limiting example of a method performed by a wireless device, according to embodiments herein.

[0052] Figure 8 is a schematic diagram depicting a non-limiting example of aspects of a method according to embodiments herein.

[0053] Figure 9 is a schematic diagram depicting another non-limiting example of aspects of a method according to embodiments herein.

[0054] Figure 10 is a schematic diagram depicting a further non-limiting example of aspects of a method according to embodiments herein.

[0055] Figure 11 is a schematic diagram depicting another non-limiting example of aspects of a method according to embodiments herein.

[0056] Figure 12 is a flowchart depicting another non-limiting example of a method performed by a wireless device, according to embodiments herein.

[0057] Figure 13 is a schematic block diagram illustrating an embodiments of a wireless device, according to embodiments herein.

[0058] Figure 14 is a schematic block diagram illustrating an embodiment of a network node, according to embodiments herein.

[0059] Figure 15 is a schematic block diagram illustrating an example of a communication system 1500 in accordance with some embodiments.

[0060] Figure 16 is a schematic block diagram illustrating an example of a UE 1600 in accordance with some embodiments.

[0061] Figure 17 is a schematic block diagram illustrating an example of a network node 1700 in accordance with some embodiments.

[0062] Figure 18 is a block diagram illustrating an example of a virtualization environment 1800 in which functions implemented by some embodiments may be virtualized.

[0063] DETAILED DESCRIPTION

[0064] As part of the development of embodiments herein, one or more challenges with the existing technology will first be identified and discussed.

[0065] For the CSI prediction example using one or more UE side AI / ML models, during model inference, a UE may be configured to measure a set of historical CSI-RSs within an observation window and then report a predicted CSI for one or multiple future time instances in a prediction window using its AI / ML model(s).

[0066] For intermediate KPI based performance monitoring, an intermediate KPI, e g., NMSE or SGCS, per monitoring data sample may be derived by comparing a CSI prediction model output, e.g., predicted CSI for the one or more future time instances, with a corresponding ground truth label, e.g., channel measurements(s) corresponding to one or more future time instances, as shown in Figure 3. Figure 3 is a schematic diagram depicting an example of the CSI prediction using UE-sided Al model(s).

[0067] However, before the UE feeds back the predicted CSI to the gNB on the UL slot scheduled for carrying the CSI report, the UE has not obtained the ground truth label yet, that is, the UE has not done the channel measurements corresponding to the one or more future time instances yet. Without the ground-truth label, the UE cannot calculate the intermediated KPI for a predicted CSI when reporting the predicted CSI to the NW. This may be understood to imply that if the intermediate KPI based performance monitoring is performed at the UE-side, then, the UE cannot take actions on optimizing the CSI report, e.g., whether to drop, e.g., part of, the CSI report, carrying the predicted CSI based on the corresponding intermediate-KPI based performance metric(s). If the intermediate KPI based performance monitoring is performed at the NW-side, the NW may be understood to not be able to obtain the intermediate-KPI based performance metric(s) of the predicted CSI from the received CSI report, that is, the NW does not know if the predicted CSI is with good quality or not when receiving such CSI report.

[0068] Hence, it is a problem how to enable an Al CSI prediction capable UE to obtain the intermediate KPI of a predicted CSI report while generating a CSI report carrying the predicted CSI, and / or report the intermediated KPI of a predicted CSI in the same CSI report that carries the predicted CSI.

[0069] Certain aspects of the present disclosure and their embodiments may provide solutions to these or other challenges. Embodiments herein may be generally understood relate to measurement and report configuration for Al CSI prediction. Particularly, embodiments herein may relate to an approach where a UE may receive configuration from a network node (NW) of an observation, e.g., measurement, window that may be partly overlapping with a prediction window for UE-sided Al-based CSI prediction. The configuration of the observation, e.g., measurement, window and the prediction window may be implicit as described later.

[0070] The UE may perform channel measurements on the CSI-RS resource(s) within the measurement window.

[0071] The channel measurements associated to the CSI-RS occasion(s) within the nonoverlapping part of the observation, e.g., measurement, window, may be used by the UE to create a model input, which may be fed to a UE-side Al CSI prediction model to generate predicted CSI for the one or more future time instances in the prediction window.

[0072] The channel measurements associated to the CSI-RS occasion(s) within the overlapping part of the observation, e.g., measurement, window may be used for creating ground-truth label, which may be used for deriving an intermediate KPI based performance metric associated to the predicted CSI.

[0073] The UE may report the predicted CSI related information and / or the intermediate KPI based performance metric related information to the NW. Some of the embodiments contemplated will now be described more fully hereinafter with reference to the accompanying drawings, in which examples are shown. In this section, the embodiments herein will be illustrated in more detail by a number of example embodiments. Other embodiments, however, are contained within the scope of the subject matter disclosed herein. The disclosed subject matter should not be construed as limited to only the embodiments set forth herein; rather, these embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art. It should be noted that the exemplary embodiments herein are not mutually exclusive. Components from one embodiment may be tacitly assumed to be present in another embodiment and it will be obvious to a person skilled in the art how those components may be used in the other exemplary embodiments.

[0074] Figure 4 depicts two non-limiting examples, in panel a) and panel b), respectively, of a wireless network or wireless communications network 100, sometimes also referred to as a wireless communications system, cellular radio system, or cellular network, in which embodiments herein may be implemented. The wireless communications network 100 may be a 5G system, 5G network, or Next Gen System. In other non-limiting examples, the wireless communications network 100 may be a newer system, e.g., Sixth Generation (6G), with similar functionality. In other examples, the wireless communications network 100 may, e.g., alternatively or additionally, support other technologies such as, for example, Long-Term Evolution (LTE), e.g., LTE for Machines (LTE-M), LTE Frequency Division Duplex (FDD), LTE Time Division Duplex (TDD), LTE Half-Duplex Frequency Division Duplex (HD-FDD), LTE operating in an unlicensed band, such as LTE Licensed-Assisted Access (LAA), enhanced eLAA (eLAA), further enhanced LAA (feLAA) and / or MulteFire. Yet in other examples, the wireless communications network 100 may further support other technologies such as, for example Wideband Code Division Multiple Access (WCDMA), UTRA TDD, Global System for Mobile communications (GSM) network, GSM / Enhanced Data Rates for GSM Evolution (EDGE) Radio Access Network (GERAN) network, Ultra-Mobile Broadband (UMB), EDGE network, network comprising any combination of Radio Access Technologies (RATs) such as e.g. MultiStandard Radio (MSR) base stations, multi-RAT base stations etc., any 3rd Generation Partnership Project (3GPP) cellular network, WiFi networks, Worldwide Interoperability for Microwave Access (WiMax), or any cellular network or system. The wireless communications network 100 may support Machine Type Communication (MTC), enhanced MTC (eMTC), Internet of Things (loT) and / or NarrowBand loT (NB-loT). Thus, although terminology from 5G / NR and LTE may be used in this disclosure to exemplify embodiments herein, this should not be seen as limiting the scope of the embodiments herein to only the aforementioned system.

[0075] The wireless communications network 100 may comprise a plurality of network nodes, whereof a network node 110 is depicted in the non-limiting examples depicted in panel a) and panel b), of Figure 4. The network node 110 may be a radio network node. That is, a transmission point such as a radio base station, for example a gNB, or any other network node with similar features capable of serving a user equipment, such as a wireless device or a machine type communication device, in the wireless communications network 100. In some examples, such as that depicted in panel b) of Figure 4, the network node 110 may be a distributed node, and may partially perform its functions in collaboration with a virtual node 114 in a cloud 115. The network node 110 may be directly connected to one or more core networks, e.g., to one or more network nodes in the one or more core networks.

[0076] The wireless communications network 100 may cover a geographical area, which in some embodiments may be divided into cell areas, wherein each cell area may be served by a radio network node, although, one radio network node may serve one or several cells. In the examples of Figure 4, the network node 110 serves a cell 120. The network node 110 may be of different classes, such as, e.g., macro base station, home base station or pico base station, based on transmission power and thereby also cell size. In some examples, the network node 110 may serve receiving nodes with serving beams. The network node 100 may support one or several communication technologies, and its name may depend on the technology and terminology used.

[0077] A plurality of wireless devices may be located in the wireless communication network 100, whereof a wireless device 130, is depicted in the non-limiting examples of Figure 4. The wireless device 130 comprised in the wireless communications network 100 may be a wireless communication device such as a User Equipment (UE), e.g., 5G UE or nil E, which may also be known as e.g., mobile terminal, wireless terminal and / or mobile station, a mobile telephone, cellular telephone, or laptop with wireless capability, just to mention some further examples. The wireless device 130 may be, for example, portable, pocket-storable, hand-held, computer- comprised, or a vehicle-mounted mobile device, enabled to communicate voice and / or data, via the RAN, with another entity, such as a server, a laptop, a Personal Digital Assistant (PDA), or a tablet, Machine-to-Machine (M2M) device, goggles, a sensor, loT device, NB-loT device, device equipped with a wireless interface, such as a printer or a file storage device, modem, or any other radio network unit capable of communicating over a radio link in a communications system. The wireless device 130 comprised in the wireless communications network 100 may be enabled to communicate wirelessly in the wireless communications network 100. The communication may be performed e.g., via a RAN, and possibly the one or more core networks, which may be comprised within the wireless communications network 100.

[0078] The wireless device 130 may be configured to communicate within the wireless communications network 100 with the network node 110 over a first link 141 , e.g., a radio link. The network node 110 may be configured to communicate within the wireless communications network 100 with the virtual network node 144 over a second link 142, e.g., a radio link or a wired link.

[0079] Generally, all terms used herein are to be interpreted according to their ordinary meaning in the relevant technical field, unless a different meaning is clearly given and / or is implied from the context in which it is used. All references to a / an / the element, apparatus, component, means, step, etc. are to be interpreted openly as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any methods disclosed herein do not have to be performed in the exact order disclosed, unless a step is explicitly described as following or preceding another step and / or where it is implicit that a step must follow or precede another step. Any feature of any of the embodiments disclosed herein may be applied to any other embodiment, wherever appropriate. Likewise, any advantage of any of the embodiments may apply to any other embodiments, and vice versa. Other objectives, features and advantages of the enclosed embodiments will be apparent from the following description.

[0080] In general, the usage of “first”, “second” and / or “third” herein may be understood to be an arbitrary way to denote different elements or entities, and may be understood to not confer a cumulative or chronological character to the nouns they modify, unless otherwise noted, based on context.

[0081] Several embodiments are comprised herein. It should be noted that the examples herein are not mutually exclusive. Components from one embodiment may be tacitly assumed to be present in another embodiment and it will be obvious to a person skilled in the art how those components may be used in the other exemplary embodiments.

[0082] More specifically, the following are embodiments related to a wireless device, such as the wireless device 130, e.g., a 5G UE, nllE or a UE, and embodiments related to a network node, such as the network node 110, e.g., a gNB.

[0083] Some embodiments herein will now be further described with some non-limiting examples, which may be combined with the embodiments described.

[0084] In the following description, any reference to a / the UE, or simply “UE” may be understood to equally refer the wireless device 130; any reference to a / the gNB, a / the NW, a / the network node and / or a / the network may be understood to equally refer to the network node 110; any reference to a / the “cell” may be understood to equally refer to the cell 120.

[0085] Embodiments of a method, performed by the wireless device 130 will now be described with reference to the flowchart depicted in Figure 5. The wireless device 130 operates in the wireless communications network 100. The method may be understood to be for handling CSI. The method may be understood to be computer-implemented. In some embodiments, the wireless communications network 100 may support, or operate in, New Radio (NR).

[0086] The method may comprise one or more of the following actions. In some embodiments, all the actions may be performed. In some embodiments, one or more actions may be performed. In particular examples, the method may comprise Action 503, Action 504, Action 505 and Action 507. One or more embodiments may be combined, where applicable. All possible combinations are not described to simplify the description. Components from one embodiment may be tacitly assumed to be present in another embodiment and it will be obvious to a person skilled in the art how those components may be used in the other exemplary embodiments. All possible combinations are not described to simplify the description. A nonlimiting example of the method performed by the wireless device 130 is depicted in Figure 5. In Figure 5 optional actions in some embodiments may be represented with dashed lines. In some embodiments, the actions may be performed in a different order than that depicted Figure 5.

[0087] Action 501

[0088] In this Action 501, the wireless device 130 may obtain a first indication indicating a configuration. Obtaining in this Action 501 may comprise receiving, e.g., via the first link 141 , retrieving, fetching or similar.

[0089] The configuration may be of one or more of the following.

[0090] According to a first option, the configuration may be of a first window of time. The first window of time may be configured to be for performing first measurements (K) and one or more second measurements. The first window of time may be an observation window T 1 .

[0091] The first measurements (K) may be on first CSI reference signals (RSs). The one or more second measurements (n) may be on one or more second CSI RSs. The first measurements do not overlap with the one or more second measurements. That is, they may not overlap in time.

[0092] According to a second option, the configuration may be of a second window of time.

[0093] The second window of time may be configured to be for the wireless device 130 to make one or more predictions on the CSI based on the performed measurements. The predictions may be based on an ML model run by the wireless device 130. The first window of time may partially overlap with the second window of time. The second window of time may be a prediction window T2.

[0094] The first measurements may be performed on a non-overlapping part of the first window and may be used as input to the ML model.

[0095] The one or more second measurements may be performed on an overlapping part of the first window and are used to obtain the one or more ground truth labels for the ML model.

[0096] The one or more predictions may be one or more instances of prediction of CSI. The first measurements and the one or more second measurements may be performed on resources allocated for transmission of CSI RSs. The resources may be comprised in the first window of time.

[0097] According to a third option, the configuration may be of the one or more instances of prediction (N).

[0098] In some embodiments, the one or more predictions may be one or more instances of prediction of CSI, and one of the following may apply. According to a first option, the one or more second measurements may be overlapping in time with one or more of the one or more instances of prediction of CSI. According to a second option, one or more of the one or more second measurements may be partially overlapping in time with the one or more instances of prediction of CSI. According to a third option, one or more of the one or more second measurements may be non-overlapping with the one or more instances of prediction of CSI, and a distance (d) between one of the one or more second measurements and a nearest instance in time may be within a threshold.

[0099] The first measurements may be performed on first measurement occasions and the one or more second measurements may be performed on second measurement occasions. The first measurement occasions may be non-overlapping with the second measurement occasions.

[0100] According to a fourth option, the configuration may be of the first measurement occasions (K).

[0101] The one or more instances of prediction of CSI may at least partially overlap in time with the second measurement occasions.

[0102] According to a fifth option, the configuration may be of the second measurement occasions (n).

[0103] The K measurement occasions and the n measurement occasion(s) may be configured using two different resource sets or the same resource set.

[0104] In some embodiments, one of the following may apply. According to one option, the first measurements and the one or more second measurements may be performed with one of: a same time-domain behavior and different time-domain behavior. In some examples, the K measurement occasions and the n measurement occasion(s) may be configured with same time-domain behaviors. As an example, the K measurement occasions and the n measurement occasion(s) may be both configured with aperiodic CSI-RS resources as shown in Figure 8 and Figure 9, which will be described later.

[0105] The K measurement occasions and the n measurement occasions may be configured with different time-domain behaviors. As an example, the K measurement occasions may be configured via aperiodic CSI-RSs for high speed UEs, and the n measurement occasion(s) may be configured via periodic CSI-RS(s) with large periodicity for periodic performance monitoring or semi-persistence CSI-RS(s) for event-triggered / on-demand performance monitoring. As another example, the K measurement occasions may be configured via periodic CSI-RSs and the network node 110 may occasionally trigger performance monitoring via configured aperiodic / semi-persistent CSI-RS(s) for the n measurement occasion(s).

[0106] According to another option, one or more of the one or more predictions, the first measurements and the one or more second measurements may be performed periodically. As another example shown in Figure 10, one periodic CSI-RS resource may be used for configuring both the K measurement occasions and the n measurement occasion(s). As another example, two periodic CSI-RS resources may be configured, where one periodic CSI-RS resource may be used for configuring the K measurement occasions, and the other periodic CSI-RS resource may be used for configuring the n measurement occasion(s). The periodic CSI-RS resource associated to the n measurement occasion(s) may be configured with a larger periodicity than the CSI-RS resource associated to the K measurement occasions.

[0107] In some embodiments, the first indication may be obtained from the network node 110.

[0108] In some embodiments, the configuration may be one of: explicitly indicated and implicitly indicated. In particular examples, the observation window and the prediction window may be configured implicitly.

[0109] Although the terms observation window and prediction window are used in this disclosure, such windows may not be explicitly configured by the network node 110 to the wireless device 130. Instead, the observation window may be implicitly given by the first sample of the set of historical CSI-RSs and the last sample of the set of historical CSI-RSs. In other words, the first window may be implicitly given by the initial first measurement on the first CSI RSs and the last first measurement of the first CSI RSs. Similarly, the prediction window may be implicitly given by the first predicted CSI or PMI instance and the last predicted CSI or PMI instance. In otherwords, the second window may be implicitly given by the initial instance of prediction and the last instance of prediction. How an observation window and prediction window may be implicitly given is illustrated later, in Figure 8, which will be described later.

[0110] The network node 110 may be understood to be a radio network node e.g., a gNB, serving the wireless device 130.

[0111] In one particular example of this Action 501 , the wireless device 130 may receive a configuration from the network node 110, e.g., a gNB, for CSI reporting, e.g., an Al-based CSI reporting configuration, for reporting predicted CSIs / PMIs for / V4time instances in a prediction window T2, and CSI channel measurement occasions, e.g., K+n CSI-RS measurement occasions defined via either K+n CSI-RS resources or a single CSI-RS resource, in an observation window T 1.

[0112] In an example, the CSI channel measurement resource(s), e.g., Non-Zero-Power (NZP) CSI-RS resources, may be configured to perform channel measurements on K + n CSI measurement occasions, where the K CSI measurement occasions may be in the part of the observation window that may be non-overlapping with the prediction window, and the n measurement occasion(s) may be in the other part of the observation window that may be overlapping with the prediction window. That is, the wireless device 130 may be configured, e.g., implicitly, with a measurement / observation window that may be partly overlapping with the prediction window for Al-based CSI prediction. Henceforth a ‘CSI measurement occasion’ may be referred to as ‘measurement occasion’ for brevity.

[0113] The association between measurement occasions and the observation / prediction window may be configured by the network node 110. Different non-limiting examples of the configuration are shown in Figure 8, Figure 9, and Figure 10, which will be described later.

[0114] By obtaining the first indication in this Action 501 , the wireless device 130 may then be enabled to perform the first measurements and the one or more second measurements and obtain the one or more predictions on the CSI based on the performed measurements. By the first indication configuring the wireless device 130 to perform the first measurements on the non-overlapping part of the first window, they may be used as input to the ML model. By the one or more second measurements being configured to be performed on the overlapping part of the first window, they may be used to obtain the one or more ground truth labels for the ML model.

[0115] Action 502

[0116] Always configuring / activating / triggering the n measurement occasion(s) for every model inference for Al-based predicted CSI report may result in unnecessary RS overhead.

[0117] Obtaining in this Action 502 may comprise receiving, e.g., via the first link 141 , retrieving, fetching or similar.

[0118] Hence, in this Action 502, the wireless device 130 may obtain a second indication indicating a request to report an indication. The indication that may be requested to be reported may indicate one or more of: the one or more predictions, and the one or more ground truth labels. The reported indication may be understood to be, or may be referred to herein as, a third indication.

[0119] In some embodiments, one or more of the following may apply. According to one option, the third indication may be a report. In this Action 502, the wireless device 130 may receive a request from the network node 110 for CSI report using an Al-based CSI prediction feature / functionality / model.

[0120] In an example, the presence of the n measurement occasion(s) and / or the value of n may be configured / indicated by the network node 110 to the wireless device 130. As an example, the network node 110 may only configure / indicate / activate / trigger the n measurement occasion(s) when it may detect a need of performance monitoring for the wireless device 130- sided CSI prediction model / functionality, or when it may receive requests from wireless device(s), e g., the wireless device 130, to provide NW assistance for UE-sided Al-based CSI prediction performance monitoring.

[0121] The request, that is, the second indication, may be received via an UL DCI that may request the Al-based CSI report. In an optional example, the UL DCI may also indicate the associations between one or more of measurement occasions and the observation / prediction window.

[0122] According to another option, the third indication may comprise one of CSI and a preceding matrix indicator (PM I).

[0123] In some examples, the wireless device 130 may report the third indication only responsive to obtaining the second indication and not otherwise. By obtaining the second indication, the wireless device 130 may be enabled to report the third indication when triggered by the network node 110 and thereby save RS overhead by refraining to send the third indication otherwise, that is, when not prompted.

[0124] Action 503

[0125] In this Action 503, the wireless device 130 obtains the first measurements (K) on the first CSI RSs and the one or more second measurements (n) on the one or more second CSI RSs. The first measurements do not overlap with the one or more second measurements.

[0126] The obtaining in this Action 503 may be, e.g., performing the measurements, that is, the first measurements and the one or more second measurements. In this Action 503, the wireless device 130 may perform channel measurements on the configured K + n measurement occasions.

[0127] In some embodiments, the obtaining in this Action 503 of the first measurements and the one or more second measurements may be performed according to the obtained first indication.

[0128] In some embodiments, the obtaining in this Action 503 of the first measurements and the one or more second measurements may be based on the obtained second indication. That is, the measurements may be performed when triggered by the second indication.

[0129] By obtaining the first measurements and the one or more second measurements in this Action 503, the wireless device 130 may then be enabled to use part of the measurements to create model input for generating the predicted CSI, and part of the measurements to create ground-truth label to compute an intermediate KPI based performance metric, which will be described later. This may be understood to imply that if the intermediate KPI based performance monitoring is performed at the side of the wireless device 130, then, the wireless device 130 may be enabled to take actions on optimizing the CSI report, e.g., whether to drop, e.g., part of, the CSI report, carrying the predicted CSI based on the corresponding intermediate-KPI based performance metric(s), and the wireless device 130 may be enabled to report the calculated intermediate KPI based performance metric(s) of the predicted CSI to the network node 110. If the intermediate KPI based performance monitoring is performed at the side of the network node 110, the network node 110 may then be enabled to obtain the intermediate-KPI based performance metric(s) of the predicted CSI from the received CSI report, that is, the network node 110 may be able to know if the predicted CSI is with good quality or not when receiving such CSI report. The intermediated KPI based performance metric(s) may indicate the quality / accuracy of the predicted CSI. For instance, if the performance metric is defined as SGCS, a larger SGCS value may be understood to indicate a better quality of the predicted CSI.

[0130] Action 504

[0131] In this Action 504, the wireless device 130 obtains the one or more predictions (N) of CSI based on the first measurements.

[0132] Obtaining in this Action 504 may comprise determining, deriving, calculating or similar.

[0133] The one or more predictions are based on the first measurements.

[0134] In some examples, the obtaining in this Action 504 of the one or more predictions, e.g., one or more instances of prediction, of CSI may be by the wireless device 130 running a machine-leaning (ML) model. The ML model may use the first measurements as input. The ML model, may be for example, a transformer based model, a convolutional neural networks (CNN) based model, a long short term memory (LSTM) based model, or an autoregressive (AR) based model. The ML model may have been trained by a wireless device 130-side training entity, e.g., a server of the vendor of the wireless device 130 / chipset used for training the AI / ML models. This may be understood to be an example of UE-side offline model training, referred to as OTT server in the TR 38.843, v. 18.0.0. The model may also be trained on the wireless device 130, which may be referred to online training in TR 38.843, v. 18.0.0.

[0135] In some examples, the one or more predictions may be one or more instances of prediction of CSI, and one of the following may apply: i) the one or more second measurements may be overlapping in time with the one or more of the one or more instances of prediction of CSI, ii) the one or more of the one or more second measurements may be partially overlapping in time with the one or more instances of prediction of CSI, and iii) the one or more of the one or more second measurements may be non-overlapping with the one or more instances of prediction of CSI, and the distance between the one of the one or more second measurements and the nearest instance in time may be within the threshold.

[0136] In some embodiments, the one or more predictions may be the one or more instances of prediction, e.g., N, of CSI, and one or more of the following may apply.

[0137] According to one option, the first measurements may be performed on the first measurement occasions and the one or more second measurements may be performed on the second measurement occasions. The first measurement occasions may be non-overlapping with the second measurement occasions.

[0138] According to another option, the one or more instances of prediction of CSI may at least partially overlap in time with the second measurement occasions.

[0139] According to yet another option, the one or more predictions of CSI may be based on the ML model run by the wireless device 130. In other words, the obtaining in this Action 504 may comprise executing the ML model in an inference phase to obtain the one or more predictions of CSI as output.

[0140] According to another option, the first measurements and the one or more second measurements may be performed on resources allocated for transmission of CSI RSs. The resources may be comprised in the first window of time configured to be for performing the first measurements and the one or more second measurements. The first window of time may partially overlap with the second window of time. The second window of time may be configured to be for the wireless device 130 to make the one or more predictions on the CSI based on the performed measurements. The predictions may be based on the ML model run by the wireless device 130. The first measurements may be performed on the non-overlapping part of the first window and may be used as input to the ML model. The one or more second measurements may be performed on the overlapping part of the first window and may be used to obtain the one or more ground truth labels for the ML model.

[0141] In an example, the channel measurements associated to the K measurement occasion(s) within the non-overlapping part of the observation, e.g., measurement, window may be used by the wireless device 130 to create a model input, which may be fed to the ML model, a UE-side Al CSI prediction model, to generate predicted CSI for the one or more future time instances in the prediction window. Alternatively stated, the channel measurements associated to the K measurement occasion(s) that may be understood to not overlap with any time instances with a predicted CSI may be used by the wireless device 130 to create a model input, which may be fed to the ML model, the UE-side Al CSI prediction model, to generate predicted CSI for the one or more future time instances.

[0142] By obtaining the one or more predictions of CSI in this Action 504, the wireless device 130 may avoid having to use time-frequency and energy resources performing measurements in the one or more instances of prediction of CSI.

[0143] Action 505

[0144] In this Action 505, the wireless device 130 obtains, based on the one or more second measurements, the one or more ground truth labels corresponding to the obtained one or more predictions of CSI.

[0145] Obtaining in this Action 504 may comprise determining, deriving, calculating or similar. The obtaining in this Action 505 of the one or more ground truth labels are based on the one or more second measurements.

[0146] By obtaining the one or more ground truth labels corresponding to the obtained one or more predictions of CSI, the wireless device 130 may then be enabled to determine, using the obtained one or more ground truth labels, an intermediate key performance indicator (KPI)- based metric for the obtained one or more predictions of CSI when reporting the one or more predictions of CSI to the network node 110.

[0147] Action 506

[0148] In this Action 506, wireless device 130 may determine, using the obtained one or more ground truth labels, an intermediate KPI-based metric of performance, that is, the intermediate KPI based performance metric associated to the predicted CSI.

[0149] Determining may be understood as calculating, selecting or deriving.

[0150] Examples of the intermediate KPI based performance metric related information may include the quantized value of the intermediate KPI, e.g., SGCS or NMSE, associated to each of the n measurement occasion(s), the quantized value of the mean intermediate KPI, e.g., SGCS or NMSE, associated to the n measurement occasion(s), and / or a flag indicating if the intermediated KPI based performance metric is larger or smaller than a configured threshold.

[0151] In an example, the channel measurements associated to the n measurement occasion(s) within the overlapping part of the observation, e.g., measurement, window may be used for creating ground-truth label, which may be used for deriving an intermediate KPI based performance metric associated to the predicted CSI. Alternatively stated, the channel measurements associated to the n measurement occasion(s) that may overlap with any one of the time instances with a predicted CSI may be used by the wireless device 130 for creating ground-truth label, which may be used for deriving an intermediate KPI based performance metric associated to the predicted CSI.

[0152] In another example, the wireless device 130 may derive intermediate KPI approximation if the n measurement window overlaps with the prediction window, even if the measurement occasions, e.g., ground truth label, may not be exactly overlapping with, e.g., overlap in the same slot, any of the N4CSI prediction time instances.

[0153] Hence, in an example, the network node 110 may configure (K + n) CSI channel measurement occasions with n = N4. Accordingly, the first K CSI measurements may be fed as input to the Al model to generate predicted CSI for JV4time instances, which together with the last / V4CSI measurements, e.g., ground truth, may be used to compute the performance metrics associated with the predicted channel. One example is shown in Figure 11 , where the network node 110 may configure periodic CSI reports, such that K CSI occasions configured by a periodic CSI-RS resource may be used to estimate CSI measurements, which may be fed to the Al model to predict CSI for the N4future time instances. Further, the network node 110 may trigger an aperiodic CSI report, which may also trigger the wireless device 130 to measure n = N4aperiodic CSI-RS resources to obtain ground truth for the predicted channels generated by the Al model from K CSI resources. The predicted channels along with the corresponding ground truth may be used to compute the performance metrics associated with the Al model, which may be reported to the network node 110.

[0154] In one example, the performance metrics with n < N4may be configured to be computed and reported for more than one feature / functionality / model. For example, the n predicted channels along with the corresponding ground truth may be used to compute performance metrics for the active Al model, together with any other alternative Al model(s) stored within the wireless device 130 and with a non-AI fallback algorithm. The wireless device 130 may either feedback all or a subset of the performance metrics obtained with the available feature / functionality / model. The network node 110 may use the performance metric(s) to determine whether to switch to a different feature / functionality / model available at the wireless device 130.

[0155] By determining the intermediate KPI-based metric of performance in this Action 506, the wireless device 130 may be enabled to determine if a predicted CSI fulfils a certain condition, e.g., based on a configured threshold, and / or the quality / accuracy of the predicted CSI, and / or if the activated Al model is better or worse than another alternative stored Al model, and / or if the activated Al model is better or worse than a non-AI fallback algorithm. This may be understood to mean that if the intermediate KPI based performance monitoring is performed at the side of the wireless device 130, then, the wireless device 130 may be enabled to take actions on optimizing the CSI report, e.g., whether to drop, e.g., part of, the CSI report, carrying the predicted CSI based on the corresponding intermediate-KPI based performance metric(s) and the wireless device 130 may be enabled to report the calculated intermediate KPI based performance metric(s) of the predicted CSI to the network node 110. If the intermediate KPI based performance monitoring is performed at the side of the network node 110, the network node 110 may then be enabled to obtain the intermediate-KPI based performance metric(s) of the predicted CSI from the received CSI report, that is, the network node 110 may be able to know if the predicted CSI is with good quality or not when receiving such CSI report.

[0156] One example of how it may be determined in the activated Al model may be better or worse may be that if the intermediate KPI performance metric may be defined as SGCS, the activated Al model may be considered to be better than a non-AI fallback algorithm if the SGCS of the activated Al model is larger than the non-AI fallback algorithm with a certain margin Action 507

[0157] In this Action 507, the wireless device 130 reports, after having obtained the one or more ground truth labels, the indication, that is, the third indication. The third indication indicates one or more of: the one or more predictions, e.g., one or more CSI predictions, and the one or more ground truth labels.

[0158] Reporting in this Action 507 may comprise sending or similar.

[0159] The reporting in this Action 507 is performed after having obtained the one or more ground truth labels.

[0160] In some embodiments, one or more of the following may apply. According to one option, the first indication may be obtained from the network node 110. According to another option, the configuration may be one of: explicitly indicated and implicitly indicated. According to yet another option, the third indication may be a report. According to a further option, the third indication may comprise one of channel state information and a pre-coding matrix indicator. According to yet another option, the third indication may further indicate one or more associations between one or more of the K first measurement occasions and one or more of the: the second window of time and the overlapping part. According to another option, the resources may be NZP CSI-RS resources.

[0161] In some embodiments, one of the following may apply. According to one option, the reported indication may indicate the determined intermediate KPI-based metric of performance.

[0162] According to another option, the reported indication may further indicate the determined KPI-based metric of performance.

[0163] That is, in this Action 507, the wireless device 130 may report the predicted CSI related information and / or the intermediate KPI based performance metric related information to the network node 110.

[0164] In an example, the wireless device 130 may report the predicted CSI associated to all the configured N4time instances to the network node 110, together with the intermediate KPI based performance metric related information. Examples of the intermediate KPI based performance metric related information may include the quantized value of the intermediate KPI, e.g., SGCS or NMSE, associated to each of the n measurement occasion(s), the quantized value of the mean intermediate KPI, e.g., SGCS or NMSE, associated to the n measurement occasion(s), and / or a flag indicating if the intermediated KPI based performance metric is larger or smaller than a configured threshold. Using the first example, the wireless device 130 may report performance metric related information with a size that may be proportional to n. For example, the wireless device 130 may report nxB information, where B may be the size of one performance metric related information. It may be noted that the term proportional may not necessarily be linearly proportional, e.g., one of n KPI related information may serve as a reference while other information may be derived as a relative value toward the first information. In one example, the earliest measurement of the n measurements may serve as the reference information. The reference information may have a size that may be larger than other information.

[0165] In some embodiments, one of the following may apply. According to one option, the first measurements and the one or more second measurements may be performed with one of: a same time-domain behavior and different time-domain behavior.

[0166] According to another option, the reporting in this Action 507 of the indication may be performed periodically or aperiodically.

[0167] According to yet another option, one or more of the one or more predictions, e.g., one or more instances of prediction, the first measurements and the one or more second measurements may be performed periodically.

[0168] According to yet another option, the one or more predictions, e.g., one or more instances of prediction, and the first measurements may be performed periodically, and the one or more second measurements and the reporting in this Action 507 of the indication may be performed aperiodically. The reported indication may be based on the aperiodic one or more second measurements.

[0169] In another example, the wireless device 130 may report only the performance metric associated to the predicted CSI based on the NW configuration. For example, a network, e.g., the network node 110, may configure an aperiodic CSI report, such that the wireless device 130 may feedback only the performance metric associated to predicted CSI generated with the Al model before subsequent CSI reports, carrying predicted channel measurements. In other examples, this CSI report with only performance metrics may be aperiodic / periodic / semi- persistence. The network node 110 may use the performance metrics received through the CSI report to determine the accuracy of the prediction and / or weather to switch to a different feature / functionality / model.

[0170] In some embodiments, the one or more predictions may be the one or more instances of prediction of CSI, and one or more of the following may apply. According to one option, the first measurements may be performed on the first measurement occasions and the one or more second measurements may be performed on the second measurement occasions. The first measurement occasions may be non-overlapping with the second measurement occasions. According to another option, the one or more instances of prediction of CSI may at least partially overlap in time with the second measurement occasions. According to yet another option, the one or more predictions of CSI may be based on the ML model run by the wireless device 130. According to another option, the reporting in this Action 507 of the indication, that is, the third indication, may comprise sending the (third) indication to the network node 110 operating in the wireless communications network 100. According to another option, the first measurements and the one or more second measurements may be performed on resources allocated for transmission of CSI RSs. The resources may be comprised in the first window of time configured to be for performing the first measurements and the one or more second measurements. The first window of time may partially overlap with the second window of time. The second window of time may be configured to be for the wireless device 130 to make the one or more predictions on the CSI based on the performed measurements. The predictions may be based on the ML model run by the wireless device 130. The first measurements may be performed on the non-overlapping part of the first window and may be used as input to the ML model. The one or more second measurements may be performed on the overlapping part of the first window and may be used to obtain the one or more ground truth labels for the ML model.

[0171] It may be noted that the network node 110 may configure an aperiodic CSI report with n < N4aperiodic Sounding Reference Signal (SRS) resources, similar to above examples, to obtain additional predicted channel measurements while obtaining performance monitoring outcome corresponding to a subset of predicted channel.

[0172] By reporting the third indication, the wireless device 130 may enable the network node 110 to know if the predicted CSI is with good quality or not when receiving such CSI report and, if necessary, take appropriate action, such as for example, refrain from using the received predicted CSI for making proceeding DL data scheduling decisions if the intermediated KPI indicates that the prediction accuracy / quality is poor; or configure the wireless device to fallback to a non-AI based CSI reporting feature if a number of consecutive intermediated KPI reports indicate that the corresponding prediction accuracy / quality of the predicted CSI is poor; or use the received predicted CSI for assisting the proceeding data scheduling if the prediction quality is good.

[0173] Action 508

[0174] In this Action 508, wireless device 130 may perform a first action based on the determined KPI-based metric of performance. The first action may be to optimize one or more of: a future indication to be reported, and the one or more predictions, e.g., one or more instances of prediction, of CSI.

[0175] In another example, based on the derived intermediated KPI based performance metric, the wireless device 130 may only report predicted CSI associated to part of the configured N4time instances. For instance, the wireless device 130 may report only the predicted CSI for the first future time instance, if the SGCS is below a certain configured threshold.

[0176] By performing the first action, the wireless device 130 may be enabled to, for example, refrain from using resources to send a report to the network node 110 that may have insufficient or poor quality, based on a criterion, hence enabling more efficient usage of the resources of the wireless communications network 100. Embodiments of a method, performed by the network node 110 will now be described with reference to the flowchart depicted in Figure 6. The method may be understood to be for handling CSI. The network node 110 operates in the wireless communications network 100. The method may be understood to be computer-implemented.

[0177] In some examples, the wireless communications network 100 may support at least one of: NR and NB-loT.

[0178] Several embodiments are comprised herein. The method may comprise one or more of the following actions. In a particular non-limiting example, Action 603 may be performed, in other non-limiting examples, Action 603 and Action 605 may be performed. In some embodiments, all the actions may be performed. One or more embodiments may be combined, where applicable. It should be noted that the examples herein may be not mutually exclusive. Components from one embodiment may be tacitly assumed to be present in another embodiment and it will be obvious to a person skilled in the art how those components may be used in the other exemplary embodiments. All possible combinations are not described to simplify the description. A non-limiting example of the method performed by the network node 110 is depicted in Figure 6. In Figure 6, optional actions in some embodiments may be represented with dashed lines.

[0179] The detailed description of some of the following corresponds to the same references provided above, in relation to the actions described for the wireless device 130 and will thus not be repeated here to simplify the description. For example, the third indication may be a CSI report.

[0180] Action 601

[0181] In this Action 601, the network node 110 may send the first indication to the wireless device 130.

[0182] The sending in this Action 601 may be performed, e.g., via the first link 141 .

[0183] The first indication may indicate the configuration of the one or more of: i) the first window of time, ii) the second window of time, iii) the one or more instances of prediction N, iv) the first measurement occasions K, and v) the second measurement occasions n.

[0184] Action 602

[0185] In this Action 602, the network node 110 may send the second indication to the wireless device 130.

[0186] The sending in this Action 602 may be performed, e.g., via the first link 141 .

[0187] The second indication may indicate the request to report the indication. The reported indication may be the third indication. Action 603

[0188] In this Action 603, the network node 110 receives the indication, that is, the third indication, from the wireless device 130 operating in the wireless communications network 100.

[0189] The receiving in this Action 603 may be performed, e.g., via the first link 141.

[0190] The indication indicates one or more of: i) the one or more predictions N of CSI based on the first measurements, such as e.g., one or more CSI predictions, and ii) the one or more ground truth labels corresponding to the obtained one or more predictions. The one or more ground truth labels are based on the one or more second measurements n, obtained by the wireless device 130, on the one or more second channel state information, CSI, reference signals, RSs. The one or more second measurements do not overlap with the first measurements K, obtained by the wireless device 130, on the first CSI RSs.

[0191] The received (third) indication may be based on the sent first indication.

[0192] The receiving in Action 603 of the third indication may be based on the sent second indication.

[0193] In some examples, the one or more predictions may be the one or more instances of prediction of CSI, and one of the following may apply: i) the one or more second measurements may be overlapping in time with the one or more of the one or more instances of prediction of CSI, ii) the one or more of the one or more second measurements may be partially overlapping in time with the one or more instances of prediction of CSI, and iii) the one or more of the one or more second measurements may be non-overlapping with the one or more instances of prediction of CSI; and the distance between the one of the one or more second measurements and the nearest instance in time may be within the threshold.

[0194] In some examples, one or more of the following may apply: a) the first measurements may be performed on the first measurement occasions and the one or more second measurements may be performed on the second measurement occasions; the first measurement occasions may be non-overlapping with the second measurement occasions, b) the one or more instances of prediction, of CSI may at least partially overlap in time with the second measurement occasions, c) the one or more predictions of CSI may be based on the ML model run by the wireless device 130, d) the first measurements and the one or more second measurements may be performed on resources allocated for transmission of CSI RSs; the resources may be comprised in the first window of time configured to be for performing the first measurements and the one or more second measurements; the first window of time may partially overlap with the second window of time; the second window of time may be configured to be for the wireless device 130 to make the one or more predictions on the CSI based on the performed measurements; the predictions may be based on the L model run by the wireless device 130; the following may apply: the first measurements may be performed on the non-overlapping part of the first window and may be used as input to the ML model, and the one or more second measurements may be performed on the overlapping part of the first window and may be used to obtain the one or more ground truth labels for the ML model.

[0195] In some examples, one or more of the following may apply: i) the configuration may be one of: explicitly indicated and implicitly indicated, the third indication may be the report, ii) the third indication may comprise one of the channel state information and the pre-coding matrix indicator, iii) the third indication may further indicate the one or more associations between the one or more of the K first measurement occasions and the one or more of the: the second window of time and the overlapping part, and iv) the resources may be NZP CSI-RS resources.

[0196] Action 604

[0197] In this Action 604, the network node 110 may determine, using the indicated one or more ground truth labels, the intermediate KPI-based metric of performance.

[0198] Determining may be understood as calculating, selecting or deriving.

[0199] In some examples, one of the following may apply. According to one option, the reported indication may indicate the determined intermediate KPI-based metric of performance. According to another option, the received indication may further indicate the determined KPIbased metric of performance. The KPI-based metric of performance may be based on the one or more ground truth labels. In such examples, the network node 110 may refrain from performing the determining of Action 604.

[0200] Action 605

[0201] In this Action 605, the network node 110 may perform a second action.

[0202] The performing in this Action 605 of the second action may be based on the KPI-based metric of performance. For example, the second action may be to optimize one or more of: the future indication to be received, and the one or more predictions.

[0203] In some examples, one or more of the following may apply: i) the first measurements and the one or more second measurements may be performed with one of: the same time-domain behavior and different time-domain behavior, ii) the receiving in Action 603 of the indication may be performed periodically or aperiodically, iii) the one or more of the one or more predictions, the first measurements and the one or more second measurements may be performed periodically, and iv) the one or more predictions, and the first measurements may be performed periodically, and the one or more second measurements and the receiving in Action 603 of the indication may be performed aperiodically; the reported indication may be based on the aperiodic one or more second measurements. Figure 7 is a flowchart depicting a non-limiting example of a method performed by a wireless device, according to embodiments herein. Some actions shown in the example flowchart may be optional, and the actions shown in the example flowchart may in some cases be performed in different orders. In Action 701 , the wireless device 130 may, according to Action 501 and Action 601 , receive configuration from the network node 110, e.g., a gNB, for CSI reporting, e.g., an Al-based CSI reporting configuration for reporting predicted CSIs / PMIs for / 4time instances in a prediction window T2, and CSI channel measurement occasions, e.g., K+n CSI-RS measurement occasions defined via either K+n CSI-RS resources or a single CSI- RS resource, in an observation window T1. It may be noted that the observation window and the prediction window may be configured implicitly. In an example, the CSI channel measurement resource(s), e.g., Non-Zero-Power (NZP) CSI-RS resources, may be configured to perform channel measurements on K + n CSI measurement occasions, where the K CSI measurement occasions may be in the part of the observation window that may be nonoverlapping with the prediction window, and the n , e.g., the last n, measurement occasion(s) may be in the other part of the observation window that may be overlapping with the prediction window. That is, the UE may be configured, e.g., implicitly, with a measurement / observation window that may be partly overlapping with the prediction window for Al-based CSI prediction. Henceforth a ‘CSI measurement occasion’ may be referred to as ‘measurement occasion for brevity. In Action 702, the wireless device 130 may, according to Action 502 and Action 602, receive a request from the network node for CSI report using an Al-based CSI prediction feature / functionality / model. The request may be received via an UL DCI that may request the Al-based CSI report. In an optional example, the UL DCI may also indicate the associations between one or more of measurement occasions and the observation / prediction window. In Action 703, the wireless device 130 may, according to Action 503, perform channel measurements on the configured K + n measurement occasions. The wireless device 130 may use part of the channel measurements / estimates on the K CSI-RS measurement occasions to create model input for, according to Action 504, generating the predicted CSI, feed the model input to an Al CSI prediction model to generate predicted CSI for the N4time instances. The wireless device 130 may, according to Action 505, use part of the channel measurements / estimates on the n CSI-RS measurement occasion(s) to create ground-truth label to, according to Action 506, compute the intermediate KPI based performance metric associated to the predicted CSI using the ground-truth label. In an example, the channel measurements associated to the K measurement occasion(s) within the non-overlapping part of the observation, e.g., measurement, window may be used by the wireless device 130 to create a model input, which may be fed to a UE-side Al CSI prediction model to, according to Action 504, generate predicted CSI for the one or more future time instances in the prediction window. Alternatively stated, the channel measurements associated to the K measurement occasion(s) that may be understood to not overlap with any time instances with a predicted CSI may be used by the wireless device 130 to create a model input, which may be fed to the UE-side Al CSI prediction model to, according to Action 504, generate predicted CSI for the one or more future time instances. In an example, the channel measurements associated to the n measurement occasion(s) within the overlapping part of the observation, e.g., measurement, window may be used for, according to Action 505, creating ground-truth label, which may be used for, according to Action 506, deriving an intermediate KPI based performance metric associated to the predicted CSI. Alternatively stated, the channel measurements associated to the n measurement occasion(s) that may overlap with any one of the time instances with a predicted CSI may be used by the wireless device 130 for creating ground-truth label, which may be used for deriving, according to Action 506, an intermediate KPI based performance metric associated to the predicted CSI. In Action 704, the wireless device 130 may, according to Action 507 and Action 603, report the predicted CSI related information and / or the intermediate KPI based performance metric related information to the network node 110, e.g., based on the reporting configuration. In an example, the wireless device 130 may report, according to Action 507, the predicted CSI associated to all the configured N4time instances to the network node 110, together with the intermediate KPI based performance metric related information.

[0204] Examples of the intermediate KPI based performance metric related information may include the quantized value of the intermediate KPI, e.g., SGCS or NMSE, associated to each of the n measurement occasion(s), the quantized value of the mean intermediate KPI, e.g., SGCS or NMSE, associated to the n measurement occasion(s), and / or a flag indicating if the intermediated KPI based performance metric is larger or smaller than a configured threshold. Using the first example, the wireless device 130 may, according to Action 507, report performance metric related information with a size that may be proportional to n. For example, the wireless device 130 may report nxB information, where B may be the size of one performance metric related information. Note that the term proportional may not necessarily be linearly proportional, e.g., one of n KPI related information may serve as a reference while other information may be derived as a relative value toward the first information. In one example, the earliest measurement of the n measurements may serve as the reference information. The reference information may have a size that may be larger than other information.

[0205] As stated earlier, the association between measurement occasions and the observation / prediction window may be configured by the network node 110. Different nonlimiting examples of the configuration are shown in Figure 8, Figure 9, and Figure 10. In any of Figure 8, Figure 9 and Figure 10, the K measurement occasions and the n measurement occasion(s) may be configured using two different resource sets or the same resource set. The K measurement occasions and the n measurement occasion(s) may be configured with same time-domain behaviors. As an example, the K measurement occasions and the n measurement occasion(s) may be both configured with aperiodic CSI-RS resources as shown in Figure 8 and Figure 9.

[0206] Figure 8 is a schematic diagram depicting a non-limiting example of the observation window and prediction window configuration(s) for Al CSI prediction using aperiodic CSI-RS resources, where one CSI-RS resource (n = 1) may be within the overlapping part of the observation window. The wireless device 130 performs channel measurements on the K+n CSI-RS measurement occasions. Then the wireless device 130 uses the channel measurements / estimates on the K CSI-RS measurement occasions to create a model input, feed the model input to an Al CSI prediction model to generate predicted CSI for the N4 time instances. Striped slots indicated predicted PM I for one time instance. This CSI-RS resource n - 1 may be used as ground truth label to then, compute the intermediate KPI-based performance metric associated to the predicted CSI using the ground-truth label. In the indicated UL slot for CSI reporting, the wireless device 130 may report the third indication. Four different slots 1 to N4. Also indicated in Figure 8 are m, 6, and d, as described in Figure 1. How an observation window and prediction window may be implicitly given is illustrated, in Figure 8. As noted earlier, although the terms observation window and prediction window are used in this disclosure, such windows may not be explicitly configured by the network node 110 to the wireless device 130. Instead, the observation window may be implicitly given by the first sample of the set of historical CSI-RSs and the last sample of the set of historical CSI-RSs. In other words, the first window may be implicitly given by the initial first measurement on the first CSI RSs and the last first measurement of the first CSI RSs. Similarly, the prediction window may be implicitly given by the first predicted CSI or PMI instance and the last predicted CSI or PMI instance. In other words, the second window may be implicitly given by the initial instance of prediction and the last instance of prediction.

[0207] Figure 9 is a schematic diagram depicting a non-limiting example of the observation window and prediction window configuration(s) for Al CSI prediction using aperiodic CSI-RS resources, where more than one CSI-RS resources (n = 2) may be within the overlapping part of the observation window. These CSI-RS resources n = 2 may be used as ground truth labels to then, compute the intermediate KPI-based performance metric associated to the predicted CSI using the ground-truth label. The other representations depicted in Figure 9 correspond to those already described in Figure 8. Figure 10 is a schematic diagram depicting a non-limiting example of the observation window and prediction window configuration(s) for Al CSI prediction using periodic CSI-RS resources, where one CSI-RS resource (n = 1) may be within the overlapping part of the observation window. As another example shown in Figure 10, one periodic CSI-RS resource may be used for configuring both the K measurement occasions and the n measurement occasion(s). The other representations depicted in Figure 10 correspond to those already described in Figure 8.

[0208] Figure 11 is a schematic diagram illustrating a non-limiting example of the aperiodic CSI report, where n = N4, such that the CSI report may be used to compute and feedback performance monitoring outcome to the network node 110. The aperiodic CSI report may be triggered between the configured periodic CSI report i and (i + 1) to monitor the performance of the Al model. In an example, the network node 110 may configure (K + n) CSI channel measurement occasions with n = N4. Accordingly, the first K CSI measurements may be fed as input to the Al model to generate predicted CSI for JV4time instances, which together with the last N4CSI measurements, e.g., ground truth, may be used to compute the performance metrics associated with the predicted channel. One example is shown in Figure 11, where the network node 110 may configure periodic CSI reports, such that K CSI occasions configured by a periodic CSI-RS resource may be used to estimate CSI measurements, which may be fed to the Al model to predict CSI for the N4future time instances. Further, the network node 110 may trigger an aperiodic CSI report, which may also trigger the wireless device 130 to measure n = N4aperiodic CSI-RS resources to obtain ground truth for the predicted channels generated by the Al model from K CSI resources. The predicted channels along with the corresponding ground truth may be used to compute the performance metrics associated with the Al model, which may be reported to the network node 110. The other representations depicted in Figure 11 correspond to those already described in Figure 8.

[0209] Figure 12 is a flowchart depicting another non-limiting example of a method performed by the wireless device 130, according to embodiments herein. Action 1201 may be understood to correspond to Action 701. Action 1202 may be understood to correspond to Action 702. Action 1203 may be understood to correspond to Action 703. Action 1204 may be understood to correspond to Action 704. Different from the flowchart shown in Figure 7, in this example, the wireless device 130 may be configured to report both the predicted CSI and the ground-truth label to the network node 110. Using the received information, the network node 110 may compute the intermediate KPI based performance metric related information if needed. It may be noted that the network node 110 may configure an aperiodic CSI report with n < N4aperiodic Sounding Reference Signal (SRS) resources, similar to above examples, to obtain additional predicted channel measurements while obtaining performance monitoring outcome corresponding to a subset of predicted channel.

[0210] Certain embodiments disclosed herein may provide one or more of the following technical advantage(s), which may be summarized as follows.

[0211] Embodiments herein, may be understood to enable that the wireless device 130, e.g., a UE, may take actions on optimizing the CSI report, e.g., whether to drop, e.g., part of, the predicted CSI, based on the computed intermediate-KPI based performance metric(s) for the current predicted CSI, and the network node 110 may obtain / compute the intermediate-KPI based performance metric(s) of a predicted CSI from the same CSI report.

[0212] Figure 13 depicts an example of the arrangement that the wireless device 130 may comprise to perform the method actions described above in relation to Figure 5, and / or any of Figures 7 12. The wireless device 130 may be configured to handle CSI. The wireless device 130 is configured to operate in the wireless communications network 100.

[0213] In some embodiments, the wireless communications network 100 may be configured to support, or operate in, New Radio (NR).

[0214] Several embodiments are comprised herein. Components from one embodiment may be tacitly assumed to be present in another embodiment and it will be obvious to a person skilled in the art how those components may be used in the other exemplary embodiments. The detailed description of some of the following corresponds to the same references provided above, in relation to the actions described for the wireless device 130 and will thus not be repeated here. For example, the third indication may be a CSI report.

[0215] The wireless device 130 is configured and / or operable to perform the sending in Action

[0216] 503, e.g., by means of a processing circuitry 1301 within the wireless device 130 configured to, obtain the first measurements K on the first CSI RSs, and the one or more second measurements n on the one or more second CSI RSs. The first measurements are configured to not overlap with the one or more second measurements.

[0217] The wireless device 130 is configured and / or operable to perform the obtaining in Action

[0218] 504, e.g., by means of the processing circuitry 1301 within the wireless device 130 configured to, obtain the one or more predictions, N, of CSI, based on the first measurements.

[0219] The wireless device 130 is configured and / or operable to perform the obtaining in Action

[0220] 505, e.g., by means of the processing circuitry 1301 within the wireless device 130 configured to, obtain, based on the one or more second measurements, the one or more ground truth labels corresponding to the one or more predictions of CSI configured to be obtained. The wireless device 130 is configured and / or operable to perform the reporting in Action 507, e.g., by means of the processing circuitry 1301 within the wireless device 130 configured to, report, after having obtained the one or more ground truth labels, the indication configured to indicate the one or more of: the one or more predictions, and the one or more ground truth labels.

[0221] In some embodiments, the one or more predictions may be configured to be one or more instances of prediction of CSI, and one of the following may apply: a) the one or more second measurements may be configured to be overlapping in time with one or more of the one or more instances of prediction of CSI, b) one or more of the one or more second measurements may be configured to be partially overlapping in time with the one or more instances of prediction of CSI, and c) one or more of the one or more second measurements may be configured to be non-overlapping with the one or more instances of prediction of CSI, and the distance between one of the one or more second measurements and the nearest instance in time may be configured to be within the threshold.

[0222] In some embodiments, the one or more predictions may be configured to be one or more instances of prediction of CSI, and one or more of the following may apply: a) the first measurements may be configured to be performed on first measurement occasions and the one or more second measurements may be configured to be performed on second measurement occasions, the first measurement occasions may be configured to be non-overlapping with the second measurement occasions, b) the one or more instances of prediction of CSI may be configured to at least partially overlap in time with the second measurement occasions, c) the one or more predictions of CSI may be configured to be based on a machine-leaning, ML, model configured to be run by the wireless device 130, d) the reporting of the indication may be configured to comprise sending the indication to the network node 110 configured to operate in the wireless communications network 100, and d) the first measurements and the one or more second measurements may be configured to be performed on the resources configured to be allocated for transmission of CSI RS. The resources may be configured to be comprised in the first window of time configured to be for performing the first measurements and the one or more second measurements. The first window of time may be configured to partially overlap with the second window of time, the second window of time may be configured to be for the wireless device 130 to make the one or more predictions on the CSI based on the measurements configured to be performed, the predictions may be configured to be based on the ML model configured to be run by the wireless device 130, wherein: i) the first measurements may be configured to be performed on the non-overlapping part of the first window and may be configured to be used as input to the ML model, and ii) the one or more second measurements may be configured to be performed on the overlapping part of the first window and may be configured to be used to obtain the one or more ground truth labels for the ML model. In some embodiments, the wireless device 130 may be configured with one or more of the following two configurations.

[0223] The wireless device 130 may be configured and / or operable to perform the obtaining in Action 501 , e.g., by means of the processing circuitry 1301 within the wireless device 130 configured to, obtain the first indication configured to indicate the configuration of the one or more of: i) the first window of time, ii) the second window of time, iii) the one or more instances of prediction N, iv) the first measurement occasions K, and v) the second measurement occasions n. The obtaining of the first measurements and the one or more second measurements may be configured to be performed according to the obtained first indication.

[0224] The wireless device 130 may be configured and / or operable to perform the obtaining in Action 502, e.g., by means of the processing circuitry 1301 within the wireless device 130 configured to, obtain the second indication configured to indicate the request to report the indication. The indication configured to be reported may be the third indication. The obtaining of the first measurements and the one or more second measurements may be configured to be based on the second indication configured to be obtained.

[0225] In some embodiments, one or more of the following may apply: i) the first indication may be configured to be obtained from the network node 110, ii) the configuration may be configured to be one of: explicitly indicated and implicitly indicated, iii) the third indication may be configured to be the report, iv) the third indication may be configured to comprise one of the CSI and the PMI, v) the third indication may be further configured to indicate the one or more associations between the one or more of the K first measurement occasions and the one or more of the: the second window of time and the overlapping part, and vi) the resources may be configured to be NZP CSI-RS resources.

[0226] In some embodiments, the wireless device 130 may be configured with the following configuration.

[0227] The wireless device 130 may be configured and / or operable to perform the determining in Action 506, e.g., by means of the processing circuitry 1301 within the wireless device 130 configured to, determine, using the one or more ground truth labels configured to be obtained, the intermediate KPI-based metric of performance. One of the following may apply: i) the indication configured to be reported may be configured to indicate the intermediate KPI-based metric of performance configured to be determined, and ii) the indication configured to be reported may be further configured to indicate the KPI-based metric of performance configured to be determined.

[0228] In some embodiments, the wireless device 130 may be configured with the following configuration.

[0229] The wireless device 130 may be configured and / or operable to perform the performing in Action 508, e.g., by means of the processing circuitry 1301 within the wireless device 130 configured to, perform the first action based on the KPI-based metric of performance configured to be determined. The first action may be configured to be optimize the one or more of: the future indication to be reported, and the one or more predictions.

[0230] In some embodiments, one of the following may apply: a) the first measurements and the one or more second measurements may be configured to be performed with one of: the same time-domain behavior and different time-domain behavior, b) the reporting of the indication may be configured to be performed periodically or aperiodically, c) the one or more of the one or more predictions, the first measurements and the one or more second measurements may be configured to be performed periodically, and d) the one or more predictions, and the first measurements may be configured to be performed periodically, and the one or more second measurements and the reporting of the indication may be configured to be performed aperiodically. The indication configured to be reported may be configured to be based on the aperiodic one or more second measurements.

[0231] The embodiments herein in the wireless device 130 may be implemented through one or more processors, such as a processing circuitry 1301 in the wireless device 130 depicted in Figure 13, together with computer program code for performing the functions and actions of the embodiments herein. A processor, as used herein, may be understood to be a hardware component. The program code mentioned above may also be provided as a computer program product, for instance in the form of a data carrier carrying computer program code for performing the embodiments herein when being loaded into the wireless device 130. One such carrier may be in the form of a CD ROM disc. It is however feasible with other data carriers such as a memory stick. The computer program code may furthermore be provided as pure program code on a server and downloaded to the wireless device 130.

[0232] The processing circuitry 1301 may be configured to, or operable to, perform the method actions according to Figure 5, and / or any of Figures 712.

[0233] The wireless device 130 may further comprise a memory 1302 comprising one or more memory units. The memory 1302 is arranged to be used to store obtained information, store data, configurations, schedulings, and applications etc. to perform the methods herein when being executed in the wireless device 130.

[0234] In some embodiments, the wireless device 130 may receive information from, e.g., the network node 110 or another structure in the wireless communications network 100, through a receiving port 1303. In some embodiments, the receiving port 1303 may be, for example, connected to one or more antennas in the wireless device 130. In other embodiments, the wireless device 130 may receive information from another structure in the wireless communications network 100 through the receiving port 1303. Since the receiving port 1303 may be in communication with the processing circuitry 1301 , the receiving port 1303 may then send the received information to the processing circuitry 1301. The receiving port 1303 may also be configured to receive other information.

[0235] The processing circuitry 1301 in the wireless device 130 may be further configured to transmit or send information to e g., the network node 110 or another structure in the wireless communications network 100, through a sending port 1304, which may be in communication with the processing circuitry 1301 , and the memory 1302.

[0236] Those skilled in the art will also appreciate that the processing circuitry 1301 described above may comprise a combination of analog and digital modules, and / or one or more processors configured with software and / or firmware, e.g., stored in memory, that, when executed by the one or more processors such as the processing circuitry 1301, perform as described above. One or more of these processors, as well as the other digital hardware, may be included in a single Application-Specific Integrated Circuit (ASIC), or several processors and various digital hardware may be distributed among several separate components, whether individually packaged or assembled into a System-on-a-Chip (SoC).

[0237] Also, in some embodiments, the wireless device 130 may be configured to perform the actions of Figure 5 with respective units that may be implemented as one or more applications running on one or more processors such as the processing circuitry 1301.

[0238] Thus, the methods according to the embodiments described herein for the wireless device 130 may be respectively implemented by means of a computer program 1305 product, comprising instructions, i.e., software code portions, which, when executed on at least one processing circuitry 1301 , cause the at least one processing circuitry 1301 to carry out the actions described herein, as performed by the wireless device 130. The computer program 1305 product may be stored on a computer-readable storage medium 1306. The computer- readable storage medium 1306, having stored thereon the computer program 1305, may comprise instructions which, when executed on at least one processing circuitry 1301, cause the at least one processing circuitry 1301 to carry out the actions described herein, as performed by the wireless device 130. In some embodiments, the computer-readable storage medium 1306 may be a non-transitory computer-readable storage medium, such as a CD ROM disc, or a memory stick. In other embodiments, the computer program 1305 product may be stored on a carrier containing the computer program 1305 just described, wherein the carrier is one of an electronic signal, optical signal, radio signal, or the computer-readable storage medium 1306, as described above.

[0239] The wireless device 130 may comprise a communication interface configured to facilitate communications between the wireless device 130 and other nodes or devices, e.g., the network node 110 or another structure in the wireless communications network 100. The interface may, for example, include a transceiver configured to transmit and receive radio signals over an air interface in accordance with a suitable standard. In other embodiments, the wireless device 130 may also comprise a radio circuitry 1307, which may comprise e.g., the receiving port 1303 and the sending port 1304. The radio circuitry 1307 may be configured to set up and maintain at least a wireless connection with the network node 110 or another structure in the wireless communications network 100. Circuitry may be understood herein as a hardware component.

[0240] Hence, embodiments herein also relate to the wireless device 130 comprising the processing circuitry 1301 and the memory 1302, said memory 1302 containing instructions executable by said processing circuitry 1301 , whereby the wireless device 130 is operative to perform the actions described herein in relation to the wireless device 130, e.g., in Figure 5, and / or any of Figures 7-12.

[0241] Figure 14 depicts an example of the arrangement that the network node 110 may comprise to perform the method actions described above in relation to Figure 6. The network node 110 may be understood to be configured to handle CSI. The network node 110 is configured to operate in the wireless communications network 100.

[0242] In some embodiments, the wireless communications network 100 may be configured to support, or operate in, New Radio (NR).

[0243] Several embodiments are comprised herein. Components from one embodiment may be tacitly assumed to be present in another embodiment and it will be obvious to a person skilled in the art how those components may be used in the other exemplary embodiments. The detailed description of some of the following corresponds to the same references provided above, in relation to the actions described for the network node 110 and will thus not be repeated here. For example, the third indication may be a CSI report.

[0244] The network node 110 may be configured and / or operable to perform the receiving in Action 603, e.g., by means of a processing circuitry 1401 within the network node 110 configured to, receive the indication from the wireless device 130 configured to operate in the wireless communications network 100. The indication is configured to indicate one or more of: i) the one or more predictions N of CSI based on first measurements, and ii) the one or more ground truth labels corresponding to the one or more predictions configured to be obtained. The one or more ground truth labels are configured to be based on the one or more second measurements n, configured to be obtained by the wireless device 130, on the one or more second CSI RSs. The one or more second measurements are configured to not overlap with the first measurements K, configured to be obtained by the wireless device 130, on the first CSI RSs.

[0245] In some embodiments, the one or more predictions may be configured to be one or more instances of prediction of CSI, and one of the following may apply: a) the one or more second measurements may be configured to be overlapping in time with one or more of the one or more instances of prediction of CSI, b) one or more of the one or more second measurements may be configured to be partially overlapping in time with the one or more instances of prediction of CSI, and c) one or more of the one or more second measurements may be configured to be non-overlapping with the one or more instances of prediction of CSI, and the distance between one of the one or more second measurements and the nearest instance in time may be configured to be within the threshold.

[0246] In some embodiments, one or more of the following may apply: a) the first measurements may be configured to be performed on the first measurement occasions and the one or more second measurements may be configured to be performed on the second measurement occasions, the first measurement occasions may be configured to be non-overlapping with the second measurement occasions, b) the one or more instances of prediction of CSI may be configured to at least partially overlap in time with the second measurement occasions, c) the one or more predictions of CSI may be configured to be based on the ML model configured to be run by the wireless device 130, d) the reporting of the indication may be configured to comprise sending the indication to the network node 110 configured to operate in the wireless communications network 100, and d) the first measurements and the one or more second measurements may be configured to be performed on the resources configured to be allocated for transmission of CSI RS; the resources may be configured to be comprised in the first window of time configured to be for performing the first measurements and the one or more second measurements; the first window of time may be configured to partially overlap with the second window of time, the second window of time may be configured to be for the wireless device 130 to make the one or more predictions on the CSI based on the measurements configured to be performed, the predictions may be configured to be based on the ML model configured to be run by the wireless device 130, wherein: i) the first measurements may be configured to be performed on the non-overlapping part of the first window and may be configured to be used as input to the ML model, and ii) the one or more second measurements may be configured to be performed on the overlapping part of the first window and may be configured to be used to obtain the one or more ground truth labels for the ML model.

[0247] In some embodiments, the network node 110 may be configured with one or more of the following configurations.

[0248] The network node 110 may be configured and / or operable to perform the sending in Action 601 , e.g., by means of the processing circuitry 1401 within the network node 110 configured to, send the first indication to the wireless device 130. The first indication may be configured to indicate the configuration of one or more of: i) the first window of time, ii) the second window of time, iii) the one or more instances of prediction N, iv) the first measurement occasions K, and v) the second measurement occasions n. The indication configured to be received may be configured to be based on the first indication configured to be sent. The network node 110 may be configured and / or operable to perform the sending in Action 602, e.g., by means of the processing circuitry 1401 within the network node 110 configured to, send the second indication to the wireless device 130. The second indication may be configured to indicate the request to report the indication. The indication configured to be reported may be configured to be the third indication. The receiving of the third indication may be configured to be based on the second indication configured to be sent.

[0249] In some embodiments, one or more of the following may apply: i) the configuration may be configured to be one of: explicitly indicated and implicitly indicated, ii) the third indication may be configured to be the report, iii) the third indication may be configured to comprise one of the CSI and the PM I, iv) the third indication may be further configured to further indicate the one or more associations between the one or more of the K first measurement occasions and the one or more of the: the second window of time and the overlapping part, and v) the resources may be configured to be NZP CSI-RS resources.

[0250] In some embodiments, the network node 110 may be configured with the following configuration.

[0251] The network node 110 may be configured and / or operable to perform the determining in Action 604, e.g., by means of the processing circuitry 1401 within the network node 110 configured to, determine, using the one or more ground truth labels configured to be indicated, the intermediate KPI-based metric of performance.

[0252] In some embodiments, one of the following may apply: i) the indication configured to be reported may be configured to indicate the intermediate KPI-based metric of performance configured to be determined, and ii) the indication configured to be received may be configured to further indicate the KPI-based metric of performance based on the one or more ground truth labels

[0253] The network node 110 may be configured and / or operable to perform the performing in Action 605, e.g., by means of the processing circuitry 1401 within the network node 110 configured to, perform the second action based on the KPI-based metric of performance. The second action may be configured to be to optimize one or more of: the future indication configured to indicate configured to be received, and the one or more predictions.

[0254] In some embodiments, one or more of the following may apply: a) the first measurements and the one or more second measurements may be configured to be performed with one of: the same time-domain behavior and different time-domain behavior, b) the receiving of the indication may be configured to be performed periodically or aperiodically, c) the one or more of the one or more predictions, the first measurements and the one or more second measurements may be configured to be performed periodically, and d) the one or more predictions, and the first measurements may be configured to be performed periodically, and the one or more second measurements and the receiving of the indication may be configured to be performed aperiodically. The indication configured to be received may be configured to be based on the aperiodic one or more second measurements.

[0255] The embodiments herein in the network node 110 may be implemented through one or more processors, such as a processing circuitry 1401 in the network node 110 depicted in Figure 14, together with computer program code for performing the functions and actions of the embodiments herein. A processor, as used herein, may be understood to be a hardware component. The program code mentioned above may also be provided as a computer program product, for instance in the form of a data carrier carrying computer program code for performing the embodiments herein when being loaded into the network node 110. One such carrier may be in the form of a CD ROM disc. It is however feasible with other data carriers such as a memory stick. The computer program code may furthermore be provided as pure program code on a server and downloaded to the network node 110.

[0256] The processing circuitry 1401 may be configured to, or operable to, perform the method actions according to Figure 6.

[0257] The network node 110 may further comprise a memory 1402 comprising one or more memory units. The memory 1402 is arranged to be used to store obtained information, store data, configurations, schedulings, and applications etc. to perform the methods herein when being executed in the network node 110.

[0258] In some embodiments, the network node 110 may receive information from, e.g., the wireless device 130 and / or another structure in the wireless communications network 100, through a receiving port 1403. In some embodiments, the receiving port 1403 may be, for example, connected to one or more antennas in the network node 110. In other embodiments, the network node 110 may receive information from another structure in the wireless communications network 100 through the receiving port 1403. Since the receiving port 1403 may be in communication with the processing circuitry 1401 , the receiving port 1403 may then send the received information to the processing circuitry 1401. The receiving port 1403 may also be configured to receive other information.

[0259] The processing circuitry 1401 in the network node 110 may be further configured to transmit or send information to e.g., the wireless device 130 and / or another structure in the wireless communications network 100, through a sending port 1404, which may be in communication with the processing circuitry 1401 , and the memory 1402.

[0260] Those skilled in the art will also appreciate that the processing circuitry 1401 described above may comprise a combination of analog and digital modules, and / or one or more processors configured with software and / or firmware, e.g., stored in memory, that, when executed by the one or more processors such as the processing circuitry 1401, perform as described above. One or more of these processors, as well as the other digital hardware, may be included in a single Application-Specific Integrated Circuit (ASIC), or several processors and various digital hardware may be distributed among several separate components, whether individually packaged or assembled into a System-on-a-Chip (SoC).

[0261] Also, in some embodiments, the network node 110 may be configured to perform the actions of Figure 6 with respective units that may be implemented as one or more applications running on one or more processors such as the processing circuitry 1401.

[0262] Thus, the methods according to the embodiments described herein for the network node 110 may be respectively implemented by means of a computer program 1405 product, comprising instructions, i.e., software code portions, which, when executed on at least one processing circuitry 1401 , cause the at least one processing circuitry 1401 to carry out the actions described herein, as performed by the network node 110. The computer program 1405 product may be stored on a computer-readable storage medium 1406. The computer- readable storage medium 1406, having stored thereon the computer program 1405, may comprise instructions which, when executed on at least one processing circuitry 1401, cause the at least one processing circuitry 1401 to carry out the actions described herein, as performed by the network node 110. In some embodiments, the computer-readable storage medium 1406 may be a non-transitory computer-readable storage medium, such as a CD ROM disc, or a memory stick. In other embodiments, the computer program 1405 product may be stored on a carrier containing the computer program 1405 just described, wherein the carrier is one of an electronic signal, optical signal, radio signal, or the computer-readable storage medium 1406, as described above.

[0263] The network node 110 may comprise a communication interface configured to facilitate communications between the network node 110 and other nodes or devices, e.g., the wireless device 130 and / or another structure in the wireless communications network 100. The interface may, for example, include a transceiver configured to transmit and receive radio signals over an air interface in accordance with a suitable standard.

[0264] In other embodiments, the network node 110 may also comprise a radio circuitry 1407, which may comprise e.g., the receiving port 1403 and the sending port 1404. The radio circuitry 1407 may be configured to set up and maintain at least a wireless connection with the wireless device 130 and / or another structure in the wireless communications network 100. Circuitry may be understood herein as a hardware component.

[0265] Hence, embodiments herein also relate to the network node 110 comprising the processing circuitry 1401 and the memory 1402, said memory 1402 containing instructions executable by said processing circuitry 1401 , whereby the network node 110 is operative to perform the actions described herein in relation to the network node 110, e.g., in Figure 6.

[0266] Generally, all terms used herein are to be interpreted according to their ordinary meaning in the relevant technical field, unless a different meaning is clearly given and / or is implied from the context in which it is used. All references to a / an / the element, apparatus, component, means, step, etc. are to be interpreted openly as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any methods disclosed herein do not have to be performed in the exact order disclosed, unless a step is explicitly described as following or preceding another step and / or where it is implicit that a step must follow or precede another step. Any feature of any of the embodiments disclosed herein may be applied to any other embodiment, wherever appropriate. Likewise, any advantage of any of the embodiments may apply to any other embodiments, and vice versa. Other objectives, features and advantages of the enclosed embodiments will be apparent from the following description.

[0267] As used herein, the expression “at least one of:” followed by a list of alternatives separated by commas, and wherein the last alternative is preceded by the “and” term, may be understood to mean that only one of the list of alternatives may apply, more than one of the list of alternatives may apply or all of the list of alternatives may apply. This expression may be understood to be equivalent to the expression “at least one of:” followed by a list of alternatives separated by commas, and wherein the last alternative is preceded by the “or” term.

[0268] In particular examples related to embodiments herein, the method performed by the wireless device 130 may comprise Action 507.

[0269] Further Extensions And Variations

[0270] Figure 15 shows an example of a communication system 1500 in accordance with some embodiments.

[0271] In the example, the communication system 1500, such as the wireless communications network 100, includes a telecommunication network 1502 that includes an access network 1504, such as a radio access network (RAN), and a core network 1506, which includes one or more core network nodes 1508. The access network 1504 includes one or more access network nodes, such as the network node 110, such as network nodes 1510a and 1510b (one or more of which may be generally referred to as network nodes 1510), or any other similar 3rdGeneration Partnership Project (3GPP) access nodes or non-3GPP access points. Moreover, as will be appreciated by those of skill in the art, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunication network 1502 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a node in the telecommunication network 1502 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other nodes to implement one or more functionalities of any node in the telecommunication network 1502, including one or more network nodes 1510 and / or core network nodes 1508.

[0272] Examples of an ORAN network node include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O-CU-CP) or an O- CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non- real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an A1 , F1 , W1 , E1 , E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN access node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an 0-2 interface defined by the O- RAN Alliance or comparable technologies. The network nodes 1510 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 1512a, 1512b, 1512c, and 1512d (one or more of which may be generally referred to as UEs 1512) to the core network 1506 over one or more wireless connections. Any of the UEs 1512a, 1512b, 1512c, and 1512d are examples of the wireless device 130.

[0273] Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 1500 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system 1500 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0274] The wireless device 130, exemplified in Figure 15 as the UEs 1512 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network node 110, exemplified in Figure 15 as network nodes 1510 and other communication devices. Similarly, the network nodes 1510 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 1512 and / or with other network nodes or equipment in the telecommunication network 1502 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network 1502.

[0275] In the depicted example, the core network 1506 connects the network nodes 1510 to one or more host computing systems, such as host 1516. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 1506 includes one more core network nodes (e.g., core network node 1508) 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 1508. 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 Deconcealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0276] The host 1516 may be under the ownership or control of a service provider other than an operator or provider of the access network 1504 and / or the telecommunication network 1502. The host 1516 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0277] As a whole, the communication system 1500 of Figure 15 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system 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.

[0278] In some examples, the telecommunication network 1502 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 1502 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 1502. For example, the telecommunications network 1502 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC)ZMassive loT services to yet further UEs.

[0279] In some examples, the UEs 1512 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 1504 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 1504. Additionally, a UE may be configured for operating in single- or multi-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0280] In the example, the hub 1514 communicates with the access network 1504 to facilitate indirect communication between one or more UEs (e.g., UE 1512c and / or 1512d) and network nodes (e.g., network node 1510b). In some examples, the hub 1514 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 1514 may be a broadband router enabling access to the core network 1506 for the UEs. As another example, the hub 1514 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 1510, or by executable code, script, process, or other instructions in the hub 1514. As another example, the hub 1514 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 1514 may be a content source. For example, for a UE that is a VR device, display, loudspeaker, or other media delivery device, the hub 1514 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 1514 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 1514 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0281] The hub 1514 may have a constant / persistent or intermittent connection to the network node 1510b. The hub 1514 may also allow for a different communication scheme and / or schedule between the hub 1514 and UEs (e.g., UE 1512c and / or 1512d), and between the hub 1514 and the core network 1506. In other examples, the hub 1514 is connected to the core network 1506 and / or one or more UEs via a wired connection. Moreover, the hub 1514 may be configured to connect to an M2M service provider over the access network 1504 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 1510 while still connected via the hub 1514 via a wired or wireless connection. In some embodiments, the hub 1514 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 1510b. In other embodiments, the hub 1514 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 1510b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0282] Figure 16 shows a UE 1600 in accordance with some embodiments. The UE 1600 presents additional details of some embodiments of the UE 1512 of Figure 1 . 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 / playback device, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), an Augmented Reality (AR) or Virtual Reality (VR) device, wireless customer-premise equipment (CPE), vehicle, vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-loT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0283] A UE may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to-everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0284] The UE 1600 includes processing circuitry 1602 that is operatively coupled via a bus 1604 to an input / output interface 1606, a power source 1608, a memory 1610, a communication interface 1612, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 16. 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.

[0285] The processing circuitry 1602 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 1610. The processing circuitry 1602 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 1602 may include multiple central processing units (CPUs).

[0286] In the example, the input / output interface 1606 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 1600. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0287] In some embodiments, the power source 1608 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 1608 may further include power circuitry for delivering power from the power source 1608 itself, and / or an external power source, to the various parts of the UE 1600 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 1608. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 1608 to make the power suitable for the respective components of the UE 1600 to which power is supplied.

[0288] The memory 1610 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 1610 includes one or more application programs 1614, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 1616. The memory 1610 may store, for use by the UE 1600, any of a variety of various operating systems or combinations of operating systems. The memory 1610 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 (eUlCC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 1610 may allow the UE 1600 to access instructions, application programs and the like, stored on transitory or non- transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 1610, which may be or comprise a device-readable storage medium.

[0289] The processing circuitry 1602 may be configured to communicate with an access network or other network using the communication interface 1612. The communication interface 1612 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 1622. The communication interface 1612 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 1618 and / or a receiver 1620 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 1618 and receiver 1620 may be coupled to one or more antennas (e.g., antenna 1622) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0290] In the illustrated embodiment, communication functions of the communication interface 1612 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 / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth. Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 1612, 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).

[0291] As another example, a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

[0292] 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 wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the UE 1600 shown in Figure 16.

[0293] As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements, and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-loT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation. In practice, any number of UEs may be used together with respect to a single example. For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed. The first and / or the second UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0294] Figure 17 shows a network node 1700 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)), O-RAN nodes or components of an O-RAN node (e.g., O-RU, O-DU, O-CU).

[0295] 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, distributed units (e.g., in an O-RAN access node) and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0296] 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).

[0297] The network node 1700 includes a processing circuitry 1702, a memory 1704, a communication interface 1706, and a power source 1708. The network node 1700 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 1700 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 1700 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 1704 for different RATs) and some components may be reused (e.g., a same antenna 1710 may be shared by different RATs). The network node 1700 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1700, 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 1700.

[0298] The processing circuitry 1702 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 1700 components, such as the memory 1704, to provide network node 1700 functionality.

[0299] In some embodiments, the processing circuitry 1702 includes a system on a chip (SOC). In some embodiments, the processing circuitry 1702 includes one or more of radio frequency (RF) transceiver circuitry 1712 and baseband processing circuitry 1714. In some embodiments, the radio frequency (RF) transceiver circuitry 1712 and the baseband processing circuitry 1714 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 1712 and baseband processing circuitry 1714 may be on the same chip or set of chips, boards, or units.

[0300] The memory 1704 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 1702. The memory 1704 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 1702 and utilized by the network node 1700. The memory 1704 may be used to store any calculations made by the processing circuitry 1702 and / or any data received via the communication interface 1706. In some embodiments, the processing circuitry 1702 and memory 1704 is integrated.

[0301] The communication interface 1706 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 1706 comprises port(s) / terminal(s) 1716 to send and receive data, for example to and from a network over a wired connection. The communication interface 1706 also includes radio front-end circuitry 1718 that may be coupled to, or in certain embodiments a part of, the antenna 1710. Radio front-end circuitry 1718 comprises filters 1720 and amplifiers 1722. The radio front-end circuitry 1718 may be connected to an antenna 1710 and processing circuitry 1702. The radio front-end circuitry may be configured to condition signals communicated between antenna 1710 and processing circuitry 1702. The radio front-end circuitry 1718 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 1718 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 1720 and / or amplifiers 1722. The radio signal may then be transmitted via the antenna 1710. Similarly, when receiving data, the antenna 1710 may collect radio signals which are then converted into digital data by the radio front-end circuitry 1718. The digital data may be passed to the processing circuitry 1702. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0302] In certain alternative embodiments, the network node 1700 does not include separate radio front-end circuitry 1718, instead, the processing circuitry 1702 includes radio front-end circuitry and is connected to the antenna 1710. Similarly, in some embodiments, all or some of the RF transceiver circuitry 1712 is part of the communication interface 1706. In still other embodiments, the communication interface 1706 includes one or more ports or terminals 1716, the radio frontend circuitry 1718, and the RF transceiver circuitry 1712, as part of a radio unit (not shown), and the communication interface 1706 communicates with the baseband processing circuitry 1714, which is part of a digital unit (not shown).

[0303] The antenna 1710 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 1710 may be coupled to the radio front-end circuitry 1718 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 1710 is separate from the network node 1700 and connectable to the network node 1700 through an interface or port.

[0304] The antenna 1710, communication interface 1706, and / or the processing circuitry 1702 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna 1710, the communication interface 1706, and / or the processing circuitry 1702 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.

[0305] The power source 1708 provides power to the various components of network node 1700 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 1708 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 1700 with power for performing the functionality described herein. For example, the network node 1700 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 1708. As a further example, the power source 1708 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.

[0306] Embodiments of the network node 1700 may include additional components beyond those shown in Figure 17 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 1700 may include user interface equipment to allow input of information into the network node 1700 and to allow output of information from the network node 1700. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 1700. In some embodiments providing a core network node, such as core network node 108 of FIG. 15, some components, such as the radio front-end circuitry 1718 and the RF transceiver circuitry 1712 may be omitted.

[0307] Figure 18 is a block diagram illustrating a virtualization environment 1800 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 1800 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment 1800 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an 0-2 interface. Virtualization may facilitate distributed implementations of a network node, UE, core network node, or host.

[0308] Applications 1802 (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.

[0309] Hardware 1804 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 1806 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 1808a and 1808b (one or more of which may be generally referred to as VMs 1808), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 1806 may present a virtual operating platform that appears like networking hardware to the VMs 1808.

[0310] The VMs 1808 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 1806. Different embodiments of the instance of a virtual appliance 1802 may be implemented on one or more of VMs 1808, 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.

[0311] In the context of NFV, a VM 1808 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 1808, and that part of hardware 1804 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 1808 on top of the hardware 1804 and corresponds to the application 1802.

[0312] Hardware 1804 may be implemented in a standalone network node with generic or specific components. Hardware 1804 may implement some functions via virtualization. Alternatively, hardware 1804 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 1810, which, among others, oversees lifecycle management of applications 1802. In some embodiments, hardware 1804 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 1812 which may alternatively be used for communication between hardware nodes and radio units.

[0313] Although the computing devices described herein (e.g., UEs, network nodes) 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.

[0314] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer-readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device- readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer-readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and / or by end users and a wireless network generally. The wireless device 130 embodiments relate to any of Figure 5, Figures 7-12 and

[0315] Figures 15-18.

[0316] The wireless device 130 may comprise an arrangement as shown in Figure 13 or in Figure 16.

[0317] The network node 110 embodiments relate to any of Figure 6 and Figures 15-18.

[0318] The network node 110 may comprise an arrangement as shown in Figure 14 or in Figure 17.

[0319] Optionally, in the methods presented above each action may be optional

[0320] Additional examples

[0321] Some embodiments herein will now be further described with some additional non-limiting examples, which may be combined with any of the embodiments and / or examples described herein.

[0322] Additional Example 1. A method performed by a wireless device (130), the wireless device (130) operating in a wireless communications network (100), and the method comprising:

[0323] - performing (503) K+n measurements on channel state information, CSI, reference signals, RSs, the K measurements not overlapping the n measurement(s),

[0324] - obtaining (504) a CSI prediction based on the K measurements,

[0325] - obtaining (505) one or more ground truth label(s) associated with the obtained CSI prediction, the one or more ground truth label(s) based on the n measurement(s),

[0326] - reporting (507) an CSI information indicating one or more of: the CSI prediction, and the ground truth label.

[0327] Additional Example 2. The method according to additional example 1, wherein at least one of:

[0328] - the reporting (507) of the CSI information comprises sending the information to a network node (110) operating in the wireless communications network (100),

[0329] - the predictions are performed for N time instances.

[0330] Additional Example 3. The method according to additional example 2, further comprising at least one of:

[0331] - obtaining (501) a first indication indicating information on at least one of: i) the N time instances, ii) the K measurements, and iii) the n measurements, iv) a first window of time associated to the K+n measurements, v) a second window of time associated to the N time instances, and wherein the performing (503) of the measurements is according to the obtained first indication, and

[0332] - obtaining (502) a second indication indicating a request to report the CSI information, and wherein the performing (507) of the CSI information reporting is based on the obtained second indication.

[0333] Additional Example 4. The method according to additional example 3, wherein at least one of:

[0334] - the first indication is obtained from the network node (110),

[0335] - the first indication is one of: explicit indication and implicit indication,

[0336] - the CSI information comprises at least one of a pre-coding matrix indicator, rank indicator, L1-RSRP, CQI , and CRI.

[0337] - the first indication further indicates one or more associations between one or more of the K+n measurements and at least one of the: the first time window, the N time instances, and the second time window

[0338] Additional Example 5. The method according to any of additional examples 1-4, wherein at least one of:

[0339] - the n measurement(s) is overlapping in time with one or more of the N time instances.

[0340] - at least one of the n measurement(s) is partially overlapping in time with the one of the N time instances.

[0341] - at least one of the n measurement(s) is non-overlapping with the N time instances, and the distance between the one of the n measurement(s) and the nearest instance in time is within a threshold.

[0342] Additional Example 6. The method according to any of additional examples 1-5 further comprising:

[0343] - determining (506), using the n measurement(s) as ground truth label(s), an intermediate key performance indicator, KPI , -based metric of performance, and wherein the CSI information further indicates the determined KPI-based metric of performance.

[0344] Additional Example 7. The method according to additional example 6, further comprising:

[0345] - performing (507) an action to optimize the CSI information based on the derived KPI-based metric of performance. Additional Example 8. The method according to any of additional examples 1-7, wherein one of:

[0346] - the K measurements and the n measurement(s) are performed on CSI-RSs with one of: the same time-domain behavior and different time-domain behavior, - the reporting (505) of the CSI information is performed periodically or aperiodically or semi-persistently,

[0347] - at least one of the predictions, the K measurements and the n measurement(s) are performed periodically, and

[0348] - the predictions and the K measurements are performed periodically, and the n measurement(s) and the reporting (507) of the CSI information is performed aperiodically, wherein the KPI-based metric of performance is based on the aperiodic n measurements.

[0349] Additional Example 9. The method according to additional example 1, wherein the CSI prediction based on a machine-leaning, ML, model run by the wireless device (130).

Claims

CLAIMS:

1. A method performed by a wireless device (130), the wireless device (130) operating in a wireless communications network (100), and the method comprising:- obtaining (503) first measurements (K) on first channel state information, CSI, reference signals, RSs, and one or more second measurements (n) on one or more second CSI RSs, the first measurements not overlapping with the one or more second measurements,- obtaining (504) one or more predictions, (N), of CSI based on the first measurements,- obtaining (505), based on the one or more second measurements, one or more ground truth labels corresponding to the obtained one or more predictions of CSI,- reporting (507) after having obtained the one or more ground truth labels, an indication indicating one or more of: the one or more predictions, and the one or more ground truth labels.

2. The method according to claim 1 , wherein the one or more predictions are one or more instances of prediction of CSI, and wherein one of:- the one or more second measurements are overlapping in time with one or more of the one or more instances of prediction of CSI,- one or more of the one or more second measurements is partially overlapping in time with the one or more instances of prediction of CSI, and- one or more of the one or more second measurements is non-overlapping with the one or more instances of prediction of CSI, and a distance between one of the one or more second measurements and a nearest instance in time is within a threshold.

3. The method according to any of claims 1-2, wherein the one or more predictions are one or more instances of prediction of CSI, and wherein one or more of:- the first measurements are performed on first measurement occasions and the one or more second measurements are performed on second measurement occasions, wherein the first measurement occasions are non-overlapping with the second measurement occasions,- the one or more instances of prediction of CSI at least partially overlap in time with the second measurement occasions,- the one or more predictions of CSI are based on a machine-leaning, ML, model run by the wireless device (130),- the reporting (507) of the indication comprises sending the indication to a network node (110) operating in the wireless communications network (100), and- the first measurements and the one or more second measurements are performed on resources allocated for transmission of CSI RSs, wherein the resources are comprised in a first window of time configured to be for performing the first measurements and the one or more second measurements, wherein the first window of time partially overlaps with a second window of time, the second window of time being configured to be for the wireless device (130) to make the one or more predictions on the CSI based on the performed measurements, the predictions being based on the ML model run by the wireless device (130), wherein: i. the first measurements are performed on a non-overlapping part of the first window and are used as input to the ML model, and ii. the one or more second measurements are performed on an overlapping part of the first window and are used to obtain the one or more ground truth labels for the ML model.

4. The method according to claim 3, further comprising one or more of:- obtaining (501) a first indication indicating a configuration of one or more of: i) the first window of time, ii) the second window of time, iii) the one or more instances of prediction (N), iv) the first measurement occasions (K), and v) the second measurement occasions (n), and wherein the obtaining (503) of the first measurements and the one or more second measurements is performed according to the obtained first indication, and- obtaining (502) a second indication indicating a request to report the indication, wherein the reported indication is a third indication, and wherein the obtaining (503) of the first measurements and the one or more second measurements is based on the obtained second indication.

5. The method according to claim 4, wherein one or more of:- the first indication is obtained from the network node (110),- the configuration is one of: explicitly indicated and implicitly indicated,- the third indication is a report,- the third indication comprises one of channel state information and a pre-coding matrix indicator,- the third indication further indicates one or more associations between one or more of the (K) first measurement occasions and one or more of the: the second window of time and the overlapping part, and- the resources are NZP CSI-RS resources.

6. The method according to any of claims 1-5 further comprising:- determining (506), using the obtained one or more ground truth labels, an intermediate key performance indicator, KPI , -based metric of performance, and wherein one of: i. the reported indication indicates the determined intermediate KPI-based metric of performance, and ii. the reported indication further indicates the determined KPI-based metric of performance.

7. The method according to claim 6, further comprising:- performing (508) a first action based on the determined KPI-based metric of performance, the first action being to optimize one or more of: a future indication to be reported, and the one or more predictions.

8. The method according to any of claims 1-7, wherein one of:- the first measurements and the one or more second measurements are performed with one of: a same time-domain behavior and different time-domain behavior,- the reporting (507) of the indication is performed periodically or aperiodically,- one or more of the one or more predictions, the first measurements and the one or more second measurements are performed periodically, and- the one or more predictions, and the first measurements are performed periodically, and the one or more second measurements and the reporting (507) of the indication is performed aperiodically, wherein the reported indication is based on the aperiodic one or more second measurements.

9. A method performed by a network node (110), the network node (110) operating in a wireless communications network (100), and the method comprising:- receiving (603) an indication from a wireless device (130) operating in the wireless communications network (100), the indication indicating one or more of: i) one or more predictions (N) of CSI based on first measurements, andii) one or more ground truth labels corresponding to the obtained one or more predictions, wherein the one or more ground truth labels are based on one or more second measurements (n), obtained by the wireless device (130), on one or more second channel state information, CSI, reference signals, RSs, the one or more second measurements not overlapping with the first measurements (K), obtained by the wireless device (130), on first CSI RSs.

10. The method according to claim 9, wherein the one or more predictions are one or more instances of prediction of CSI, and wherein one of:- the one or more second measurements are overlapping in time with one or more of the one or more instances of prediction of CSI,- one or more of the one or more second measurements is partially overlapping in time with the one or more instances of prediction of CSI, and- one or more of the one or more second measurements is non-overlapping with the one or more instances of prediction of CSI, and a distance between the one of the one or more second measurements and a nearest instance in time is within a threshold.

11. The method according to any of claims 9-10, wherein one or more of:- the first measurements are performed on first measurement occasions and the one or more second measurements are performed on second measurement occasions, wherein the first measurement occasions are non-overlapping with the second measurement occasions,- the one or more instances of prediction, of CSI at least partially overlap in time with the second measurement occasions,- the one or more predictions of CSI are based on a machine-leaning, ML, model run by the wireless device (130),- the first measurements and the one or more second measurements are performed on resources allocated for transmission of CSI RSs, wherein the resources are comprised in a first window of time configured to be for performing the first measurements and the one or more second measurements, wherein the first window of time partially overlaps with a second window of time, the second window of time being configured to be for the wireless device (130) to make the one or more predictions on the CSI based on the performed measurements, the predictions being based on the ML model run by the wireless device (130), wherein:i. the first measurements are performed on a non-overlapping part of the first window and are used as input to the ML model, and ii. the one or more second measurements are performed on an overlapping part of the first window and are used to obtain the one or more ground truth labels for the ML model.

12. The method according to claim 11 , further comprising one or more of:- sending (601) a first indication to the wireless device (130), the first indication indicating a configuration of one or more of: i) the first window of time, ii) the second window of time, iii) the one or more instances of prediction (N), iv) the first measurement occasions (K), and v) the second measurement occasions (n), and wherein the received indication is based on the sent first indication, and- sending (602) a second indication to the wireless device (130), the second indication indicating a request to report the indication, wherein the reported indication is a third indication, and wherein the receiving (603) of the third indication is based on the sent second indication.

13. The method according to claim 12, wherein one or more of:- the configuration is one of: explicitly indicated and implicitly indicated,- the third indication is a report,- the third indication comprises one of channel state information and a pre-coding matrix indicator,- the third indication further indicates one or more associations between one or more of the (K) first measurement occasions and one or more of the: the second window of time and the overlapping part, and- the resources are NZP CSI-RS resources.

14. The method according to any of claims 9-13, further comprising:- determining (604), using the indicated one or more ground truth labels, an intermediate key performance indicator, KPI , -based metric of performance.

15. The method according to claim 14, wherein one of: i. the reported indication indicates the determined intermediate KPI-based metric of performance, and ii. the received indication further indicates a KPI-based metric of performance based on the one or more ground truth labels.

16. The method according to any of claims 14-15, further comprising:- performing (605) a second action based on the KPI-based metric of performance, the second action being to optimize one or more of: a future indication to be received, and the one or more predictions.

17. The method according to any of claims 9-16, wherein one or more of:- the first measurements and the one or more second measurements are performed with one of: a same time-domain behavior and different time-domain behavior,- the receiving (603) of the indication is performed periodically or aperiodically,- one or more of the one or more predictions, the first measurements and the one or more second measurements are performed periodically, and- the one or more predictions, and the first measurements are performed periodically, and the one or more second measurements and the receiving (603) of the indication is performed aperiodically, wherein the reported indication is based on the aperiodic one or more second measurements.

18. A wireless device (130) configured to operate in a wireless communications network (100), and the wireless device (130) being further configured to:- obtain first measurements (K) on first channel state information, CSI, reference signals, RSs, and one or more second measurements (n) on one or more second CSI RSs, the first measurements being configured to not overlap with the one or more second measurements,- obtain one or more predictions, (N), of CSI based on the first measurements,- obtain, based on the one or more second measurements, one or more ground truth labels corresponding to the one or more predictions of CSI configured to be obtained,- report, after having obtained the one or more ground truth labels, an indication configured to indicate one or more of: the one or more predictions, and the one or more ground truth labels.

19. The wireless device (130) according to claim 18, wherein the one or more predictions are configured to be one or more instances of prediction of CSI, and wherein one of:- the one or more second measurements are configured to be overlapping in time with one or more of the one or more instances of prediction of CSI,- one or more of the one or more second measurements is configured to be partially overlapping in time with the one or more instances of prediction of CSI, and- one or more of the one or more second measurements is configured to be nonoverlapping with the one or more instances of prediction of CSI, and a distance between one of the one or more second measurements and a nearest instance in time is configured to be within a threshold.

20. The wireless device (130) according to any of claims 18-19, wherein the one or more predictions are configured to be one or more instances of prediction of CSI, and wherein one or more of:- the first measurements are configured to be performed on first measurement occasions and the one or more second measurements are configured to be performed on second measurement occasions, wherein the first measurement occasions are configured to be non-overlapping with the second measurement occasions,- the one or more instances of prediction of CSI are configured to at least partially overlap in time with the second measurement occasions,- the one or more predictions of CSI are configured to be based on a machineleaning, ML, model configured to be run by the wireless device (130),- the reporting of the indication is configured to comprise sending the indication to a network node (110) configured to operate in the wireless communications network (100), and- the first measurements and the one or more second measurements are configured to be performed on resources configured to be allocated for transmission of CSI RSs, wherein the resources are configured to be comprised in a first window of time configured to be for performing the first measurements and the one or more second measurements, wherein the first window of time is configured to partially overlap with a second window of time, the second window of time being configured to be for the wireless device (130) to make the one or more predictions on the CSI based on the measurements configured to be performed, the predictions being configured to be based on the ML model configured to be run by the wireless device (130), wherein: i. the first measurements are configured to be performed on a nonoverlapping part of the first window and are configured to be used as input to the ML model, andii. the one or more second measurements are configured to be performed on an overlapping part of the first window and are configured to be used to obtain the one or more ground truth labels for the ML model.

21. The wireless device (130) according to claim 20, further configured to one or more of:- obtain a first indication configured to indicate a configuration of one or more of: i) the first window of time, ii) the second window of time, iii) the one or more instances of prediction (N), iv) the first measurement occasions (K), and v) the second measurement occasions (n), and wherein the obtaining of the first measurements and the one or more second measurements is configured to be performed according to the obtained first indication, and- obtain a second indication configured to indicate a request to report the indication, wherein the indication configured to be reported is a third indication, and wherein the obtaining of the first measurements and the one or more second measurements is configured to be based on the second indication configured to be obtained.

22. The wireless device (130) according to claim 21 , wherein one or more of:- the first indication is configured to be obtained from the network node (110),- the configuration is configured to be one of: explicitly indicated and implicitly indicated,- the third indication is configured to be a report,- the third indication is configured to comprise one of channel state information and a pre-coding matrix indicator,- the third indication is further configured to indicate one or more associations between one or more of the (K) first measurement occasions and one or more of the: the second window of time and the overlapping part, and- the resources are configured to be NZP CSI-RS resources.

23. The wireless device (130) according to any of claims 18-22, being further configured to:- determine, using the one or more ground truth labels configured to be obtained, an intermediate key performance indicator, KPI , -based metric of performance, and wherein one of: i. the indication configured to be reported is configured to indicate the intermediate KPI-based metric of performance configured to be determined, andii. the indication configured to be reported is further configured to indicate the KPI-based metric of performance configured to be determined.

24. The wireless device (130) according to claim 23, being further configured to:- perform a first action based on the KPI-based metric of performance configured to be determined, the first action being configured to be to optimize one or more of: a future indication to be reported, and the one or more predictions.

25. The wireless device (130) according to any of claims 18-24, wherein one of:- the first measurements and the one or more second measurements are configured to be performed with one of: a same time-domain behavior and different time-domain behavior,- the reporting of the indication is configured to be performed periodically or aperiodically,- one or more of the one or more predictions, the first measurements and the one or more second measurements are configured to be performed periodically, and- the one or more predictions, and the first measurements are configured to be performed periodically, and the one or more second measurements and the reporting of the indication is configured to be performed aperiodically, wherein the indication configured to be reported is configured to be based on the aperiodic one or more second measurements.

26. A network node (110) configured to operate in a wireless communications network (100), and the network node (110) being further configured to:- receive an indication from a wireless device (130) configured to operate in the wireless communications network (100), the indication being configured to indicate one or more of: i) one or more predictions (N) of CSI based on first measurements, and ii) one or more ground truth labels corresponding to the one or more predictions configured to be obtained, wherein the one or more ground truth labels are configured to be based on one or more second measurements (n), configured to be obtained by the wireless device (130), on one or more second channel state information, CSI, reference signals, RSs, the one or more second measurements being configured to not overlap with the first measurements (K), configured to be obtained by the wireless device (130), on first CSI RSs.

27. The network node (110) according to claim 26, wherein the one or more predictions are configured to be one or more instances of prediction of CSI, and wherein one of:- the one or more second measurements are configured to overlap in time with one or more of the one or more instances of prediction of CSI,- one or more of the one or more second measurements is configured to be partially overlapping in time with the one or more instances of prediction of CSI, and- one or more of the one or more second measurements is configured to be nonoverlapping with the one or more instances of prediction of CSI, and a distance between the one of the one or more second measurements and a nearest instance in time is configured to be within a threshold.

28. The network node (110) according to any of claims 26-27, wherein one or more of:- the first measurements are configured to be performed on first measurement occasions and the one or more second measurements are configured to be performed on second measurement occasions, wherein the first measurement occasions are configured to be non-overlapping with the second measurement occasions,- the one or more instances of prediction of CSI are configured to at least partially overlap in time with the second measurement occasions,- the one or more predictions of CSI are configured to be based on a machineleaning, ML, model configured to be run by the wireless device (130),- the first measurements and the one or more second measurements are configured to be performed on resources configured to be allocated for transmission of CSI RSs, wherein the resources are configured to be comprised in a first window of time configured to be for performing the first measurements and the one or more second measurements, wherein the first window of time is configured to partially overlap with a second window of time, the second window of time being configured to be for the wireless device (130) to make the one or more predictions on the CSI based on the measurements configured to be performed, the predictions being configured to be based on the ML model configured to be run by the wireless device (130), wherein: i. the first measurements are configured to be performed on a nonoverlapping part of the first window and are configured to be used as input to the ML model, andii. the one or more second measurements are configured to be performed on an overlapping part of the first window and are configured to be used to obtain the one or more ground truth labels for the ML model.

29. The network node (110) according to claim 28, further configured to one or more of:- send a first indication to the wireless device (130), the first indication being configured to indicate a configuration of one or more of: i) the first window of time, ii) the second window of time, iii) the one or more instances of prediction (N), iv) the first measurement occasions (K), and v) the second measurement occasions (n), and wherein the indication configured to be received is configured to be based on the first indication configured to be sent, and- send a second indication to the wireless device (130), the second indication being configured to indicate a request to report the indication, wherein the indication configured to be reported is configured to be a third indication, and wherein the receiving of the third indication is configured to be based on the second indication configured to be sent.

30. The network node (110) according to claim 29, wherein one or more of:- the configuration is configured to be one of: explicitly indicated and implicitly indicated,- the third indication is configured to be a report,- the third indication is configured to comprise one of channel state information and a pre-coding matrix indicator,- the third indication is configured to further indicate one or more associations between one or more of the (K) first measurement occasions and one or more of the: the second window of time and the overlapping part, and- the resources are configured to be NZP CSI-RS resources.

31. The network node (110) according to any of claims 26-30, being further configured to:- determine, using the one or more ground truth labels configured to be indicated, an intermediate key performance indicator, KPI , -based metric of performance.

32. The network node (110) according to claim 31 , wherein one of: i. the indication configured to be reported is configured to indicate the intermediate KPI-based metric of performance configured to be determined, andii. the indication configured to be received is configured to further indicate a KPI-based metric of performance based on the one or more ground truth labels.

33. The network node (110) according to any of claims 31-32, being further configured to:- perform a second action based on the KPI-based metric of performance, the second action being configured to be to optimize one or more of: a future indication configured to indicate configured to be received, and the one or more predictions.

34. The network node (110) according to any of claims 26-33, wherein one or more of:- the first measurements and the one or more second measurements are configured to be performed with one of: a same time-domain behavior and different time-domain behavior,- the receiving of the indication is configured to be performed periodically or aperiodically,- one or more of the one or more predictions, the first measurements and the one or more second measurements are configured to be performed periodically, and- the one or more predictions, and the first measurements are configured to be performed periodically, and the one or more second measurements and the receiving of the indication is configured to be performed aperiodically, wherein the indication configured to be received is configured to be based on the aperiodic one or more second measurements.

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