Channel state information prediction scheme for CSI reporting

Abstract

According to some embodiments, a method is performed by a wireless device (200) for indicating a channel state information (CSI) prediction scheme used by the wireless device for CSI reporting The method comprises obtaining (514) a configuration related to CSI prediction for at least one of a first CSI prediction scheme and a second CSI prediction scheme and reporting (522) to a network node (300) an indication of a selected one of the first CSI prediction scheme and the second CSI prediction scheme used by the wireless device for generating a CSI report.

Description

[0001] CHANNEL STATE INFORMATION PREDICTION SCHEME FOR CSI REPORTING

[0002] TECHNICAL FIELD

[0003] The present disclosure generally relates to communication networks, and more specifically to channel state information (CSI) prediction scheme for CSI reporting.

[0004] BACKGROUND

[0005] Artificial intelligence (Al) and machine learning (ML) have been investigated, both in academia and industry, as promising tools to optimize the design of the air interface in wireless communication networks. Example use cases include using autoencoders for channel state information (CSI) compression to reduce the feedback overhead and improve channel prediction accuracy; using deep neural networks for classifying line-of-sight (LOS) and non-LOS (NLOS) conditions to enhance positioning accuracy; and using reinforcement learning for beam selection at the network side and / or the user equipment (UE) side to reduce the signaling overhead and beam alignment latency; and using deep reinforcement learning to learn an optimal precoding policy for complex multiple input multiple output (MIMO) precoding problems.

[0006] The Third Generation Partnership Project (3 GPP) New Radio (NR) standardization work for release 18 (Rel. 18) included a study item (SI) on AI / ML for the NR air interface in TR 38.843 V2.0.1 and will continue in release 19 with work item description RP -243244. The works will explore 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 and specifying a few selected use cases (CSI feedback, beam management, and positioning), the works aim to design the mechanisms to accommodate AI / ML into the 3GPP standard.

[0007] An important part of Al development and operation is the lifecycle management (LCM) of the AI / ML model (e.g., model training, model deployment, model inference, model monitoring, model updating) and AI / ML functionality.

[0008] In NR Rel-18 AI / ML for NR air interface study item, the LCM procedure is studied where an AI / ML model has a model ID with associated information and / or where a given functionality is provided by AI / ML operations.

[0009] Two types of LCM operations were studied in NR Rel-18, functionality -based LCM, and model-ID-based LCM.

[0010] Functionality refers to an AI / ML-enabled feature / feature group enabled by configuration(s), where configuration(s) is(are) supported based on conditions indicated by UE capability. Correspondingly, functionality-based LCM operates based on, at least, one configuration of an AI / ML-enabled feature / feature group or specific configurations of an AI / ML-enabled feature / feature group. In functionality-based LCM, the network indicates activation / deactivation / fallback / switching of AI / ML functionality via 3GPP signaling (e.g., Radio Resource Control (RRC), medium access control (MAC) control element (CE), and / or downlink control information (DCI)). Models may not be identified at the network, and the UE may perform model -level LCM. For functionality identification, there may be either one or more than one functionalities defined within an AI / ML-enabled feature, whereby AI / ML-enabled feature refers to a feature where AI / ML may be used.

[0011] In model-ID-based LCM, models are identified at the network, and the network / UE may activate / deactivate / select / switch individual AI / ML models via model ID. A model may be associated with specific configurations / conditions associated with the UE capability of an AI / ML- enabled feature / feature group and additional conditions (e.g., scenarios, sites, and datasets) as determined / identified between the UE side and network side. An AI / ML model identified by a model ID may be logical, and how it maps to physical AI / ML model(s) may be up to implementation.

[0012] Figure 1 is a block diagram illustrating a functional framework for AI / ML for NR air interface. The illustrated example shows a functional framework that can be used for studying model LCM aspects for different Al for physical layer (PHY) use cases. The general framework consists of the following.

[0013] Data Collection is a function that provides input data to the Model Training, Management, and Inference functions.

[0014] • Training Data: Data needed as input for the AI / ML Model Training function.

[0015] • Monitoring Data: Data needed as input for the Management of AI / ML models or AI / ML functionalities.

[0016] • Inference Data: Data needed as input for the AI / ML Inference function.

[0017] Model Training is a function that performs AI / ML model training, validation, and testing, which may generate model performance metrics that can be used as part of the model testing procedure. The Model Training function is also responsible for data preparation (e.g., data preprocessing and cleaning, formatting, and transformation) based on Training Data delivered by a Data Collection function if required.

[0018] • Trained / Updated Model: for a Model Storage function, this is used to deliver trained, validated, and tested AI / ML models to the Model Storage function, or to deliver an updated version of a model to the Model Storage function. Management is a function that oversees the operation (e.g., selection, (de)activation, switching, fallback) and monitoring (e.g., performance) of AI / ML models or AI / ML functionalities. This function is also responsible for making decisions to ensure the proper inference operation based on data received from the Data Collection function and the Inference function.

[0019] • Management Instruction: Information needed as input to manage the Inference function. Concerning information may include selection / (de)activation / switching of AI / ML models or AI / ML-based functionalities, fallback to non-AI / ML operation (i.e., not relying on inference process), etc.

[0020] • Model Transfer / Delivery Request: Used to request model(s) to the Model Storage function.

[0021] • Performance Feedback / Retraining Request: Information needed as input for the Model Training function, e.g., for model (re)training or updating purposes.

[0022] Inference is a function that provides outputs from the process of applying AI / ML models or AI / ML functionalities, using the data that is provided by the Data Collection function (i.e., Inference Data) as an input. The Inference function is also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on Inference Data delivered by a Data Collection function, if required.

[0023] • Inference Output: Data used by the Management function to monitor the performance of AI / ML models or AI / ML functionalities.

[0024] Model Storage is a function responsible for storing trained / updated models that can be used to perform the Inference function.

[0025] • The Model Storage function in Figure 1 is only intended as a reference point (if any) when applicable for protocol terminations, model transfer / delivery, and related processes. It should be stressed that its purpose does not encompass restricting the actual storage locations of models.

[0026] • Model Transfer / Delivery: Used to deliver an AI / ML model to the Inference function.

[0027] 3GPP NR Rel. 18 also includes time domain Type II channel state information (CSI) prediction at the UE. 3 GPP NR Rel- 18 introduces channel measurement resource (CMR) enhancement for Type II CSI prediction at UE (also referred to as Rel-18 Type II CSI). To enable CSI prediction, channel measurements for a sufficient number of time instances are required for extracting the time domain channel property, based on which a future CSI can be predicted. To obtain such measurements, a gNB can either configure a legacy periodic (P) or semi-persistent (SP) CSI reference signal (CSLRS) resource, or an aperiodic (AP) CSLRS burst according to Rel- 18. To be more specific, for AP CSLRS burst based CSI prediction at a UE, a single burst of K G {4, 8, 12} CSI-RS resources can be configured to the UE within a single CSI-RS resource set, which can be aperiodically triggered using a single downlink control information (DCI). The CSI- RS resources are uniformly spaced in time, separated by m G {1, 2} slots, within the resource set.

[0028] For the Rel-18 Type II precoding matrix indicator (PMI) enhancement, a UE can be configured by gNB to report predicted PMIs for N4G {1, 2, 4} time slots. The prediction herein is relative to the CSI-RS reference resource. The predicted N4PMIs are supposed to reflect the channels with d G {1, 2} slots separation, starting from 8 G {0, 1, 2} slots into the future relative to the CSI-RS reference resource. The spacing d between the N4PMIs and offset 6 relative to the CSI-RS reference resource can be configured by the gNB via RRC signaling. The N4PMIs are compressed in a beam-frequency -Doppler domain, and the compressed PMI is reported to the gNB in a single CSI report and then transmitted to the network in the CSI-RS reference resource slot.

[0029] 3GPP NR Rel-18 also includes time-domain CSI prediction using UE-side AI / ML model. Rel-18 introduces Al-based UE-side CSI prediction as an Al for PHY use case and was being studied in the study item on AI / ML for the NR air interface. One or more AI / ML models can be trained and deployed at a UE for the Al-based CSLprediction feature. During model inference, a UE is configured by the gNB to measure a set of historical CSLRSs and then report a predicted CSI for one or multiple future time instances using its AI / ML model(s).

[0030] There currently exist certain challenges. For example, because CSI prediction is conducted on the UE side, the CSI prediction mechanism may be transparent to the network (UE proprietary), i.e., the same CSI report configurations and CSI report format are used for both AI / ML-based and non-AI / ML-based CSI prediction. Because the network cannot differentiate the mechanism that is currently applied by the UE, the network may not be able to, for example, determine whether insufficient prediction performance (e.g., too many negative acknowledgements (NACK) are received by the network) is caused by one of the mechanism (and thus, another mechanism could give sufficient prediction performance) or both mechanisms.

[0031] This circumstance may hinder the network to take the appropriate action. For example, when only one of the prediction mechanisms does not perform well, the network may configure the UE with the other prediction mechanism. If both prediction mechanisms do not perform well, the network may take another action, for example, by reconfiguring the CSI-RS measurement resources (giving a denser or longer duration of CSI-RS measurement resources) or deactivating the CSI prediction features / functionalities and reconfiguring the CSI report.

[0032] SUMMARY As described above, certain challenges currently exist with artificial intelligence (Al)-based channel state information (CSI). Considering this, a mechanism to facilitate the same understanding between a user equipment (UE) and the network on the CSI prediction mechanism currently in use by the UE is needed.

[0033] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. For example, particular embodiments include UEs that are capable of both non- Al / machine learning (ML)-based and AI / ML-based CSI prediction. Particular embodiments include a separate CSI-report configuration for the non-AI / ML-based and AI / ML-based CSI prediction, a joint CSI-report configuration, and / or an indication from the UE on the CSI prediction mechanism the UE is currently using. Particular embodiments identify the CSI prediction mechanism / scheme used by the UE for calculating a CSI report with CSI prediction.

[0034] In general, particular embodiments include a method for identifying the CSI prediction scheme used by a UE for calculating a CSI report with CSI prediction, the method comprising one or more of the following steps.

[0035] In particular embodiments, a UE receives network configuration related to CSI prediction for at least one of non-AI / ML-based CSI prediction and AI / ML-based CSI prediction. The UE reports to the network the selected CSI prediction scheme.

[0036] In particular embodiments, the UE transmits a UE capability report indicating support for at least the AI / ML-based CSI prediction.

[0037] In particular embodiments, the UE receives and measures CSLRS and performs CSI prediction according to the measured CSLRS and the configured CSI prediction configurations.

[0038] In particular embodiments, the received configuration contains a parameter that indicates whether the UE should predict the CSI using non-AI / ML-based or AI / ML-based CSI prediction. In particular embodiments, the received configuration contains a parameter that indicates whether the UE should predict the CSI using non-AI / ML-based or at least one AI / ML-based CSI prediction model / functionality / feature. In particular embodiments, the parameter contains a maximum of one value or may contain one or more values. In particular embodiments, the parameter is included in the prediction configuration or the CSI report configurations.

[0039] In particular embodiments, the UE transmits the predicted CSI according to a predefined / configured CSI-report format. The CSI-report format contains an indication of which mechanism the UE uses for its CSI prediction. In particular embodiments, the indication exists when the UE is configured with both non-AI / ML-based and AI / ML-based CSI prediction. In particular embodiments, the indication exists when the UE is configured with non-AI / ML-based and at least one AI / ML-based CSI prediction model / functionality / feature or when the UE is configured with at least two AI / ML-based CSI prediction models / functionalities / features.

[0040] In particular embodiments, the indication is included in the CSI report Part 1.

[0041] In particular embodiments, the size of the indication depends on the number of CSI prediction mechanisms configured for the UE.

[0042] In particular embodiments, the non-AI / ML based CSI prediction is associated to the Rel-18 Type II CSI feature (eType2Doppler-r 18, eType2DopplerN4-r 18).

[0043] According to some embodiments, a method is performed by a wireless device (e.g., UE) for indicating a CSI prediction scheme used by the wireless device for CSI reporting. The method comprises obtaining a configuration related to CSI prediction for at least one of a first CSI prediction scheme and a second CSI prediction scheme and reporting to a network node an indication of a selected one of the first CSI prediction scheme and the second CSI prediction scheme used by the wireless device for generating a CSI report.

[0044] In particular embodiments, the method further comprises transmitting a capability report to the network node. The capability report includes at least an indication of a capability of the wireless device to support at least one CSI prediction scheme.

[0045] In particular embodiments, the method further comprises receiving an indication from the network node of a CSI prediction scheme to use for CSI prediction.

[0046] In particular embodiments, the method further comprises measuring a CSI reference signal and performing CSI prediction according to the obtained configuration related to CSI prediction.

[0047] In particular embodiments, the indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme is included in the CSI report, such as the CSI report Part 1.

[0048] In particular embodiments, the indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme comprises a bit field and a size of the bit field is based on a number of CSI prediction schemes configured for the wireless device.

[0049] In particular embodiments, the indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme comprises an indication of CSI performance-related information.

[0050] In particular embodiments, the first CSI prediction scheme comprise an artificial intelligence / machine learning-based CSI prediction scheme and the second CSI prediction scheme comprises a non-artificial intelligence / machine learning-based CSI prediction scheme.

[0051] According to some embodiments, a wireless device comprises processing circuitry operable to perform any of the wireless device methods described above. Also disclosed is a computer program product comprising a non-transitory computer readable medium storing computer readable program code, the computer readable program code operable, when executed by processing circuitry to perform any of the methods performed by the wireless device described above.

[0052] According to some embodiments, a method is performed by a network node for determining a CSI prediction scheme used by a wireless device for CSI reporting. The method comprises transmitting a configuration related to CSI prediction for at least one of a first CSI prediction scheme and a second CSI prediction scheme to the wireless device and receiving from the wireless device an indication of a selected one of the first CSI prediction scheme and the second CSI prediction scheme used by the wireless device for generating a CSI report.

[0053] In particular embodiments, the method further comprises receiving a capability report from the wireless device. The capability report includes at least an indication of a capability of the wireless device to support at least one CSI prediction scheme.

[0054] In particular embodiments, the method further comprises transmitting an indication to the wireless device of a CSI prediction scheme to use for CSI prediction.

[0055] In particular embodiments, the method further comprises modifying a configuration related to CSI prediction based on the received indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme.

[0056] According to some embodiments, a network node comprises processing circuitry operable to perform any of the network node methods described above.

[0057] Another computer program product comprises a non-transitory computer readable medium storing computer readable program code, the computer readable program code operable, when executed by processing circuitry to perform any of the methods performed by the network node described above.

[0058] Certain embodiments may provide one or more of the following technical advantages. For example, particular embodiments facilitate an aligned understanding between the network and UE (e.g., wireless device) on which mechanism the UE is currently using for CSI prediction. Having such alignment, the network may then take appropriate action should the prediction accuracy be lower than the expected accuracy. In addition, the network may also configure or indicate to the UE with a more proper configuration depending on the current or expected condition. The proper configuration may include whether to use non-AI / ML-based or AI / ML-based CSI prediction, the prediction horizon, the number of CSI-RS measurement occasions, etc. The current or expected condition may be, for example, the UE speed, deployment scenario, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The present disclosure may be best understood by way of example with reference to the following description and accompanying drawings that are used to illustrate embodiments of the present disclosure. In the drawings:

[0060] Figure 1 is a block diagram illustrating a functional framework for artificial intelligence (AI) / machine learning (ML) for New Radio (NR) air interface;

[0061] Figure 2 shows an example of a communication system, according to certain embodiments;

[0062] Figure 3 shows a user equipment (UE), according to certain embodiments;

[0063] Figure 4 shows a network node, according to certain embodiments;

[0064] Figure 5 is a flowchart illustrating an example method in a wireless device, according to certain embodiments; and

[0065] Figure 6 is a flowchart illustrating an example method in a network node, according to certain embodiments.

[0066] DETAILED DESCRIPTION

[0067] As described above, certain challenges currently exist with artificial intelligence (Al)-based channel state information (CSI). Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges. For example, particular embodiments identify the CSI prediction mechanism / scheme used by the UE for calculating a CSI report with CSI prediction.

[0068] Particular embodiments are described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0069] As used herein, the concept of network (NW) and / or a gNB may be understood as a generic network node, gNB, base station, unit within the base station, relay node, core network node, a core network node, or a device supporting device-to-device (D2D) communication. The node may be deployed in a fifth generation (5G) network, or a sixth generation (6G) network. Moreover, although the term Al / machine learning (ML) model uses a single form, it should be well understood that it should not limit the implementation to more than one AI / ML model. The UE may either be configured or autonomously switch between the models depending on certain conditions and / or proprietary implementations.

[0070] Particular examples described herein refer to the AI / ML-based CSI prediction feature because it has both non-AI / ML-based and AI / ML-based mechanisms to support its implementation. Particular embodiments may also be extended to different features that have non- AI / ML-based and AI / ML-based mechanisms to support its implementation.

[0071] In addition, the term “model ID” may be a logical ID that may refer to similar ID, e.g., pairing ID, dataset ID, etc. Further, the term “training” may also be understood as a generic term and may represent training, retraining, or fine-tuning.

[0072] With respect to a UE, particular embodiments may be summarized in the following steps.

[0073] Step 100 comprises transmitting a UE capability report indicating a UE’s capability in supporting, at least, AI / ML-based CSI prediction.

[0074] Step 110 comprises receiving configurations related to the CSI prediction.

[0075] Step 120 comprises receiving and measuring the channel measurement resources and conducting CSI prediction according to the measurement results and the configured CSI prediction configurations.

[0076] Step 130 comprises transmitting the predicted CSI according to a predefined CSI-report format.

[0077] The steps on the network side may comprise the mirror of the above steps.

[0078] A more detailed description of the general steps follows.

[0079] Step 100 comprises transmitting a UE capability report. In Step 100, the UE transmits its capability report, indicating its capability in supporting, at least, AI / ML-based CSI prediction. If the UE also supports non- AI / ML-based CSI prediction, the UE may indicate, either explicitly (i.e., the UE reports the capability for both non-AI / ML- and AI / ML-based CSI prediction or implicitly (e.g., supporting non-AI / ML-based CSI prediction is a prerequisite for the AI / ML-based CSI prediction capability) to the network.

[0080] Step 110 comprises receiving configurations related to CSI prediction. In Step 110, the UE receives configurations related to CSI prediction. The configurations may include the following.

[0081] Particular embodiments include channel measurement resource configurations, for example, the CSLRS resource(s) / resource set(s) for channel measurements, the measurement window, resource type (aperiodic, periodic, semi-persistent), number of measurement occasions, etc.

[0082] Particular embodiments include CSI prediction configurations, for example, the number of predicted slots / instances, the distance between each predicted slot / instance, the distance between the first predicted slot / instance and the last CSLRS measurement occasion / instance, etc.

[0083] Particular embodiments include CSI report configuration, for example, CSI report quantity (e g., PMI, CQI, RI, CRI, SSBRI, Ll-RSRP, Ll-SINR, etc ), CSI report time domain behavior type (periodic, semi-persistent, aperiodic), CSI report periodicity, time restriction for channel measurements, codebook type, etc. Although being described separately in the above description, the channel measurement resource configurations, and / or CSI prediction configurations, and / or CSI report configurations may be described under one configuration umbrella, e.g., using the CSI-ReportConfig information element (IE).

[0084] In one approach, the CSI prediction that will be used by the UE may be left to UE implementation, i.e., no parameter in the configuration (e.g., CSI prediction configuration or CSI report configuration) that configures the UE on which CSI prediction mechanism / feature the UE should use for its CSI prediction. In this case, the alignment may be obtained by including the information on the CSI prediction mechanism used by the UE in the CSI report.

[0085] In a certain condition, the network may be aware that the UE is better to be configured with a certain mechanism. For example, the network may be located indoors, and using the non-AI / ML- based CSI prediction may give sufficient performance while giving benefit to the UE in terms of reduced computational complexity. In another example, the network may be (e.g., based on the performance monitoring) aware that the AI / ML-based CSI prediction gives substantial performance gain compared to the non-AI / ML-based CSI prediction.

[0086] Therefore, in one embodiment, the UE may be configured by the network with only one CSI prediction mechanism. Configured with one CSI prediction mechanism, the UE needs to follow this configuration and conduct CSI prediction accordingly. For example, if the UE is configured to conduct CSI prediction with AI / ML-based CSI prediction, the UE should only use AI / ML-based CSI prediction and not use the non-AI / ML-based CSI prediction.

[0087] In one embodiment, the one of CSI prediction mechanisms may be one of non-AI / ML-based CSI prediction or AI / ML-based CSI prediction.

[0088] In some cases, the UE may have more than one AI / ML model that can be used for CSI prediction. Therefore, in some embodiments, the one CSI prediction mechanism may be one of non-AI / ML-based CSI prediction or one AI / ML-based CSI prediction model.

[0089] Under another condition, the UE may have more than one CSI prediction mechanism that may give sufficient performance. In this case, a more flexible method in selecting the CSI prediction mechanism may be beneficial for the UE. For example, the UE may use a reduced complexity mechanism as long as it gives sufficient performance.

[0090] In some embodiments, the UE may be configured with one or more CSI prediction mechanisms. When the network configures the UE with one CSI prediction mechanism, the UE follows and uses the CSI prediction mechanism configured by the network. When the network configures the UE with more than one CSI prediction mechanism, the UE may proprietarily select one CSI prediction mechanism according to its preference. Information on the CSI prediction mechanism currently being used by the UE may be included in the CSI report.

[0091] In an example embodiment, the UE is configured with a new reporting quantity reportQuantity-rl9. The new reporting quantity has the value ‘cri-RI-PMI-CQI-CPSI’ which indicates to the UE that the UE is configured to report the CSI prediction scheme indicator (CPSI) along with one or more of CSI-RS resource indicator (CRI), rank indicator (RI), precoder matrix indicator (PMI), and channel quality indicator (CQI). The CPSI, for example, may indicate if the CSI is based on AI / ML-based CSI prediction or non-AI / ML-based CSI prediction. Alternatively, the new reporting quantity may have a value ‘cri-RI-LI-PMI-CQI-CPSI’ which indicates to the UE that the UE is configured to report the CPSI along with one or more of CRI, RI, layer indicator (LI which indicates the strongest layer), PMI, and CQI.

[0092] Alternatively, the UE may be configured in the CSI-ReportConfig IE with a separate parameter (i.e., different from ‘reportQuantity') that configures the UE to report the CPSI.

[0093] The following is an example of CSLreport configuration to accommodate non-AI / ML-based and AI / ML-based CSI prediction.

[0094] CSI-ReportConfig information element

[0095] — ASN1START

[0096] — TAG-CSI-REPORTCONFIG-START

[0097] CSI-ReportConfig : : = SEQUENCE { reportConf igld CSI-

[0098] ReportConf igl , carrier ServCell Index

[0099] OPTIONAL, — Need S resources ForChannelMeasurement CSI-

[0100] ResourceConf igld, cs i- IM- Res our ces For Interference CSI-

[0101] ResourceConf igld OPTIONAL, -- Need R nzp-CS I -RS -Res ounces For Interference CSI-

[0102] ResourceConf igld OPTIONAL, -- Need R reportConf igType CHOICE { periodic SEQUENCE { reportSlotConf ig CSI-

[0103] ReportPeriodicityAndOf f set , pucch-CSI-ResourceList SEQUENCE

[0104] ( SI ZE ( 1 . . maxNrof BWPs ) ) OF PUCCH-CSI-Resource

[0105] } , semiPersistentOnPUCCH SEQUENCE { reportSlotConf ig CSI-

[0106] ReportPeriodicityAndOf f set , pucch-CSI-ResourceList SEQUENCE

[0107] ( SI ZE ( 1 . . maxNrof BWPs ) ) OF PUCCH-CSI-Resource } , semiPersistentOnPUSCH SEQUENCE { reportSlotConf ig

[0108] ENUMERATED { sl5 , sl i d , sl20 , sl40 , sl 80 , sl ! 60 , sl320 } , reportSlotOf f setList SEQUENCE

[0109] ( SI ZE ( 1 . . maxNrofUL-Allocations ) ) OF INTEGER ( 0 . . 32 ) , pOalpha PO-PUSCH-

[0110] AlphaSetld } , aperiodic SEQUENCE { reportSlotOf f setList SEQUENCE

[0111] ( SI ZE ( 1 . . maxNrofUL-Allocations ) ) OF INTEGER ( 0 . . 32 ) }

[0112] } , reportQuantity CHOICE { none NULL, cri-RI-PMI-CQI NULL, cri-RI-i l NULL, cri-RI-i l-CQI SEQUENCE { pds ch-BundleSi zeForCSI ENUMERATED { n2 , n4 } OPTIONAL — Need S

[0113] } , cri-RI-CQI NULL, cri-RSRP NULL, s sb-Index-RSRP NULL, cri-RI-LI-PMI-CQI NULL

[0114] } ,

[0115] [ [ reportQuantity-r! 9 cri-RI-PMI-CQI-CPSI OPTIONAL, — Need R

[0116] ] ]

[0117] }

[0118] — TAG-CSI-REPORTCONFIG-STOP

[0119] Step 120 comprises receiving and measuring the channel measurement resources and performing CSI prediction according to the measurement results and the configured CSI prediction configuration. In Step 120, the UE receives and measures the channel measurement resources and conducts the CSI prediction according to the measurement results and the configured CSI prediction configuration. Depending on the received configuration, the UE may use the configured CSI prediction mechanism or select one of the available CSI prediction mechanisms in conducting CSI prediction.

[0120] Step 130 comprises transmitting the predicted CSI according to a predefined CSI-report format. After conducting the CSI prediction operation, the UE transmits the CSI report to the network. When the UE is configured with one CSI prediction mechanism, the CSI report may not contain information on the CSI prediction mechanism used by the UE.

[0121] When the UE may be configured with more than one CSI prediction mechanism, the UE informs the network of the CSI prediction mechanism used by the UE for the report. Therefore, in some embodiments, the CSI report may contain a bitfield that provides information on which CSI prediction mechanism the UE currently uses. In the example of embodiment described above, the bit field corresponds to the CPSI.

[0122] In some embodiments, the bitfield may be one bit representing either non-AI / ML-based CSI prediction or AI / ML-based CSI prediction. For example, 0 and 1 may represent non-AI / ML-based CSI prediction and AI / ML-based CSI prediction, respectively.

[0123] In some embodiments, the size of the bitfield may depend on the number of configured mechanisms. For example, when the UE is configured with two CSI prediction mechanisms (e.g., non-AI / ML-based CSI prediction and AI / ML-based CSI prediction or two models of AI / ML-based CSI prediction), the bitfield may have a size of 1 bit. Here, 0 may represent the CSI prediction mechanism configured with an index 0, while 1 may represent the CSI prediction mechanism configured with an index 1.

[0124] In another example, the UE may be configured with more than two CSI prediction mechanisms (e.g., non-AI / ML-based CSI prediction and two models of AI / ML-based CSI prediction or three models of AI / ML-based CSI prediction). In this case, the bitfield may have a size of 2 bits. Here, 00, 01, and 10 may represent the CSI prediction mechanism configured with an index of 0, 1, and 2, respectively. The unused codepoint (e.g., 11 in this example) may be set as a reserve codepoint or simply considered as not valid.

[0125] In general, the size of the bitfield may be formulated as [log2M], where M is the number of the CSI prediction mechanisms configured for the UE. When the UE is configured with only one CSI prediction mechanism, the bitfield may be absent (has a size of 0 bit).

[0126] As described in Step 110, the UE may not be explicitly configured by the network with which CSI prediction to use. The determination on which CSI prediction the UE uses may be fully proprietary. Therefore, in some embodiments, the bitfield size and / or the bitfield’s codepoint representation may be derived from the UE capability report.

[0127] For example, when the UE is only capable of supporting one CSI prediction mechanism, the bitfield will be absent (has a size of 0 bit). In another example, when the UE is capable of supporting two CSI prediction mechanisms (e.g., non-AI / ML-based CSI prediction and AL ML / based CSI prediction or two models of AI / ML-based CSI prediction), the bitfield may have a size of 1 bit, where one codepoint (e.g., 0) represents the first CSI prediction mechanism and another codepoint (e.g., 1) represents the second CSI prediction mechanism.

[0128] In yet another example, when the UE is capable of supporting more than two CSI prediction mechanisms (e.g., non-AI / ML-based CSI prediction and two models of AI / ML-based CSI prediction or three models of AI / ML-based CSI prediction), the bitfield may have a size of 2 bits, where the first codepoint (e.g., 00), the second codepoint (e.g., 01) and the third codepoint (e.g., 10) may represent the first CSI prediction mechanism, the second CSI prediction mechanism, and the third CSI prediction mechanism, respectively. Here, the fourth codepoint may be set as a reserve or simply considered as not valid.

[0129] In general, the size of the bitfield may be formulated as [log2C], where C is the number of the CSI prediction mechanisms reported by the UE in its UE capability report. In the above, ordering of the CSI prediction mechanisms may be according to the UE capability reporting.

[0130] Information on which CSI prediction mechanism is currently used by the UE may be considered important information. Therefore, in some embodiments, the bitfield containing such information may be included in the CSI report Part 1.

[0131] In one embodiment, which CSI prediction mechanism is used for generating the CSI prediction report is implicitly indicated by the UE to the network via the CSI prediction performance reporting. As an example, the CSI report generated by using an AI / ML-based CSI prediction mechanism always includes CSI prediction performance-related information (e.g., prediction accuracy), while the CSI report generated by using a non-AI / ML-based CSI prediction mechanism does not contain CSI prediction performance related information.

[0132] Figure 2 shows an example of a communication system 100 in accordance with some embodiments. In the example, the communication system 100 includes a telecommunication network 102 that includes an access network 104, such as a radio access network (RAN), and a core network 106, which includes one or more core network nodes 108. The access network 104 includes one or more access network nodes, such as network nodes 110a and 110b (one or more of which may be generally referred to as network nodes 110), or any other similar 3rd Generation Partnership Project (3 GPP) access node or non-3GPP access point. The network nodes 110 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 112a, 112b, 112c, and 112d (one or more of which may be generally referred to as UEs 112) to the core network 106 over one or more wireless connections.

[0133] Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 100 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system 100 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system. The UEs 112 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes 110 and other communication devices. Similarly, the network nodes 110 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 112 and / or with other network nodes or equipment in the telecommunication network 102 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network 102.

[0134] In the depicted example, the core network 106 connects the network nodes 110 to one or more hosts, such as host 116. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 106 includes one more core network nodes (e.g., core network node 108) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 108. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0135] The host 116 may be under the ownership or control of a service provider other than an operator or provider of the access network 104 and / or the telecommunication network 102 and may be operated by the service provider or on behalf of the service provider. The host 116 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving 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.

[0136] As a whole, the communication system 100 of Figure 2 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.

[0137] In some examples, the telecommunication network 102 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 102. For example, the telecommunications network 102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC) / Massive loT services to yet further UEs.

[0138] In some examples, the UEs 112 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 104. Additionally, a UE may be configured for operating in single- or multi -RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi -radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0139] In the example, the hub 114 communicates with the access network 104 to facilitate indirect communication between one or more UEs (e.g., UE 112c and / or 112d) and network nodes (e.g., network node 110b). In some examples, the hub 114 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 114 may be a broadband router enabling access to the core network 106 for the UEs. As another example, the hub 114 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 110, or by executable code, script, process, or other instructions in the hub 114. As another example, the hub 114 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 114 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 114 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 114 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy loT devices.

[0140] The hub 114 may have a constant / persistent or intermittent connection to the network node 110b. The hub 114 may also allow for a different communication scheme and / or schedule between the hub 114 and UEs (e.g., UE 112c and / or 112d), and between the hub 114 and the core network 106. In other examples, the hub 114 is connected to the core network 106 and / or one or more UEs via a wired connection. Moreover, the hub 114 may be configured to connect to an M2M service provider over the access network 104 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 110 while still connected via the hub 114 via a wired or wireless connection. In some embodiments, the hub 114 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 110b. In other embodiments, the hub 114 may be a nondedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 110b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0141] Figure 3 shows a UE 200 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3 GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0142] 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).

[0143] The UE 200 includes processing circuitry 202 that is operatively coupled via a bus 204 to an input / output interface 206, a power source 208, a memory 210, a communication interface 212, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 2. 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.

[0144] The processing circuitry 202 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 210. The processing circuitry 202 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field- programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 202 may include multiple central processing units (CPUs).

[0145] In the example, the input / output interface 206 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE 200. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive 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.

[0146] In some embodiments, the power source 208 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source 208 may further include power circuitry for delivering power from the power source 208 itself, and / or an external power source, to the various parts of the UE 200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 208. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 208 to make the power suitable for the respective components of the UE 200 to which power is supplied.

[0147] The memory 210 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 210 includes one or more application programs 214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 216. The memory 210 may store, for use by the UE 200, any of a variety of various operating systems or combinations of operating systems.

[0148] The memory 210 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 210 may allow the UE 200 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 210, which may be or comprise a device-readable storage medium.

[0149] The processing circuitry 202 may be configured to communicate with an access network or other network using the communication interface 212. The communication interface 212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 222. The communication interface 212 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter 218 and / or a receiver 220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 218 and receiver 220 may be coupled to one or more antennas (e.g., antenna 222) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0150] In the illustrated embodiment, communication functions of the communication interface 212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0151] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 212, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0152] 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.

[0153] A UE, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or itemtracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the UE 200 shown in Figure 2.

[0154] 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 3 GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3 GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0155] In practice, any number of UEs may be used together with respect to a single use case. For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed. The first and / or the second UE can also include more than one of the functionalities 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.

[0156] Figure 4 shows a network node 300 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NRNodeBs (gNBs)).

[0157] Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0158] 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).

[0159] The network node 300 includes a processing circuitry 302, a memory 304, a communication interface 306, and a power source 308. The network node 300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 300 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 304 for different RATs) and some components may be reused (e.g., a same antenna 310 may be shared by different RATs). The network node 300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 300, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 300.

[0160] The processing circuitry 302 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other network node 300 components, such as the memory 304, to provide network node 300 functionality.

[0161] In some embodiments, the processing circuitry 302 includes a system on a chip (SOC). In some embodiments, the processing circuitry 302 includes one or more of radio frequency (RF) transceiver circuitry 312 and baseband processing circuitry 314. In some embodiments, the radio frequency (RF) transceiver circuitry 312 and the baseband processing circuitry 314 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 312 and baseband processing circuitry 314 may be on the same chip or set of chips, boards, or units.

[0162] The memory 304 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 302. The memory 304 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 302 and utilized by the network node 300. The memory 304 may be used to store any calculations made by the processing circuitry 302 and / or any data received via the communication interface 306. In some embodiments, the processing circuitry 302 and memory 304 is integrated.

[0163] The communication interface 306 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, the communication interface 306 comprises port(s) / terminal(s) 316 to send and receive data, for example to and from a network over a wired connection. The communication interface 306 also includes radio frontend circuitry 318 that may be coupled to, or in certain embodiments a part of, the antenna 310. Radio front-end circuitry 318 comprises filters 320 and amplifiers 322. The radio front-end circuitry 318 may be connected to an antenna 310 and processing circuitry 302. The radio frontend circuitry may be configured to condition signals communicated between antenna 310 and processing circuitry 302. The radio front-end circuitry 318 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 320 and / or amplifiers 322. The radio signal may then be transmitted via the antenna 310. Similarly, when receiving data, the antenna 310 may collect radio signals which are then converted into digital data by the radio front-end circuitry 318. The digital data may be passed to the processing circuitry 302. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0164] In certain alternative embodiments, the network node 300 does not include separate radio front-end circuitry 318, instead, the processing circuitry 302 includes radio front-end circuitry and is connected to the antenna 310. Similarly, in some embodiments, all or some of the RF transceiver circuitry 312 is part of the communication interface 306. In still other embodiments, the communication interface 306 includes one or more ports or terminals 316, the radio front-end circuitry 318, and the RF transceiver circuitry 312, as part of a radio unit (not shown), and the communication interface 306 communicates with the baseband processing circuitry 314, which is part of a digital unit (not shown).

[0165] The antenna 310 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 310 may be coupled to the radio front-end circuitry 318 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 310 is separate from the network node 300 and connectable to the network node 300 through an interface or port.

[0166] The antenna 310, communication interface 306, and / or the processing circuitry 302 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna 310, the communication interface 306, and / or the processing circuitry 302 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.

[0167] The power source 308 provides power to the various components of network node 300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 300 with power for performing the functionality described herein. For example, the network node 300 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 308. As a further example, the power source 308 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0168] Embodiments of the network node 300 may include additional components beyond those shown in Figure 4 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node 300 may include user interface equipment to allow input of information into the network node 300 and to allow output of information from the network node 300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 300.

[0169] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer-readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer-readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and / or by end users and a wireless network generally.

[0170] FIGURE 5 is a flowchart illustrating an example method 500 in a wireless device, according to certain embodiments. In particular embodiments, one or more steps of FIGURE 5 may be performed by UE 200 described with respect to FIGURE 3.

[0171] The method 500 may begin at step 512, where the wireless device (e.g., UE 200) transmits a capability report to the network node (e.g., network node 300). The capability report includes at least an indication of a capability of the wireless device to support at least one CSI prediction scheme. For example, the capability report may indicate that the wireless device is capable of using one or more artificial intelligence / machine learning-based CSI prediction schemes and / or one or more non-artificial intelligence / machine learning-based CSI prediction schemes. In particular embodiments, the capability report may include any of the capabilities described with respect to the embodiments and examples described herein (e.g., step 100 described above).

[0172] At step 514, the wireless device obtains a configuration related to CSI prediction for at least one of a first CSI prediction scheme and a second CSI prediction scheme. The wireless device may obtain the configuration from the network node. In particular embodiments, the first CSI prediction scheme comprise an artificial intelligence / machine learning-based CSI prediction scheme and the second CSI prediction scheme comprises a non-artificial intelligence / machine learning-based CSI prediction scheme.

[0173] The configuration may include channel measurement resource configurations, CSI prediction configurations, and / or CSI report configurations.

[0174] In particular embodiments, the configuration related to CSI prediction may include any of the configurations described with respect to the embodiments and examples described herein (e.g., step 110 described above).

[0175] At step 516, the wireless device may receive an indication from the network node of a CSI prediction scheme to use for CSI prediction. For example, the network node may request that the wireless device use a particular CSI prediction scheme. In some embodiments, the indication may be part of the configuration obtained in step 514. In other embodiments, the wireless device may receive a separate indication.

[0176] In some embodiments, the indication may indicate a class of prediction schemes (e.g., either AI / ML-based or non-AI / ML-based) and the wireless device may choose a particular prediction scheme from the indicated class of prediction schemes.

[0177] In some embodiments, the indication may comprise any of the indications described with respect to the embodiments and examples described herein.

[0178] At step 518, the wireless device may measure a CSI reference signal, and at step 520 the wireless device may perform CSI prediction according to the obtained configuration related to CSI prediction. In particular embodiments, the wireless device may receive, measure, and conduct the CSI prediction according to any of the embodiments and examples described herein (e.g., step 120 described above).

[0179] At step 522, the wireless device reports to a network node an indication of a selected one of the first CSI prediction scheme and the second CSI prediction scheme used by the wireless device for generating a CSI report.

[0180] In particular embodiments, the indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme is included in the CSI report. In some embodiments, the indication may be included the CSI report Part 1.

[0181] In particular embodiments, the indication may comprise an explicit indication. For example, the indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme comprises a bit field and a size of the bit field is based on a number of CSI prediction schemes configured for the wireless device. In particular embodiments, the indication may comprise an implicit indication. For example, the indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme comprises an indication of CSI performance-related information. The indication of performance-related information may be used to determine the accuracy of an AI / ML-based prediction scheme, and thus the inclusion of the performance-related information may indicate than an AI / ML-based prediction scheme was used by the wireless device.

[0182] Although in some embodiments the network node may indicate to the wireless device which CSI prediction scheme to use, the wireless device may or may not be able to use the prediction scheme requested by the network node. Thus, the network node can determine which CSI prediction scheme the wireless device actually used based on the indication.

[0183] In particular embodiments, the wireless device may report the indication according to any of the embodiments and examples described herein (e.g., step 130 described above).

[0184] Modifications, additions, or omissions may be made to method 500 of FIGURE 5. Additionally, one or more steps in the method of FIGURE 5 may be performed in parallel or in any suitable order.

[0185] FIGURE 6 is a flowchart illustrating an example method 600 in a network node, according to certain embodiments. In particular embodiments, one or more steps of FIGURE 6 may be performed by network node 300 described with respect to FIGURE 4.

[0186] The method 600 may begin at step 612, where the network node (e.g., network node 300) receives a capability report from the wireless device. The capability report includes at least an indication of a capability of the wireless device to support at least one CSI prediction scheme. The capability report is described in more detail with respect to step 512 of FIGURE 5 and the embodiments and examples described herein (e.g., step 100 described above).

[0187] At step 614, the network node transmits a configuration related to CSI prediction for at least one of a first CSI prediction scheme and a second CSI prediction scheme to the wireless device. The configuration is described in more detail with respect to step 514 of FIGURE 5 and the embodiments and examples described herein (e.g., step 110 described above).

[0188] At step 616, the network node may transmit an indication to the wireless device of a CSI prediction scheme to use for CSI prediction. In some embodiments, the indication may be included with the configuration received at step 614, and in other embodiments the indication may comprise a separate indication. The indication is described in more detail with respect to step 516 of FIGURE 5 and the embodiments and examples described herein.

[0189] At step 618, the network node receives from the wireless device an indication of a selected one of the first CSI prediction scheme and the second CSI prediction scheme used by the wireless device for generating a CSI report. The indication is described in more detail with respect to step 522 of FIGURE 5 and the embodiments and examples described herein (e.g., step 130 described above).

[0190] At step 620, the network node may modify a configuration related to CSI prediction based on the received indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme. For example, the network node may determine the CSI prediction received from the wireless device does not meet a particular accuracy threshold and may modify the configuration for CSI prediction to improve the accuracy of the prediction.

[0191] In particular embodiments, the network node may modify the configuration according to any of the embodiments and examples described herein.

[0192] Modifications, additions, or omissions may be made to method 600 of FIGURE 6. Additionally, one or more steps in the method of FIGURE 6 may be performed in parallel or in any suitable order.

[0193] The foregoing description sets forth numerous specific details. It is understood, however, that embodiments may be practiced without these specific details. In other instances, well-known circuits, structures and techniques have not been shown in detail in order not to obscure the understanding of this description. Those of ordinary skill in the art, with the included descriptions, will be able to implement appropriate functionality without undue experimentation.

[0194] References in the specification to “one embodiment,” “an embodiment,” “an example embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to implement such feature, structure, or characteristic in connection with other embodiments, whether or not explicitly described.

[0195] Although this disclosure has been described in terms of certain embodiments, alterations and permutations of the embodiments will be apparent to those skilled in the art. Accordingly, the above description of the embodiments does not constrain this disclosure. Other changes, substitutions, and alterations are possible without departing from the scope of this disclosure, as defined by the claims below.

[0196] Some example embodiments are described below.

[0197] Group A Embodiments

[0198] 1. A method performed by a wireless device for indicating a channel state information (CSI) prediction scheme used by the wireless device for CSI reporting, the method comprising:

[0199] - obtaining configuration related to CSI prediction for at least one of a first CSI prediction scheme and a second CSI prediction scheme; and

[0200] - reporting to a network node an indication of a selected one of the first CSI prediction scheme and the second CSI prediction scheme used by the wireless device for generating a CSI report.

[0201] 2. The method of the previous embodiment, further comprising transmitting a capability report to the network node, wherein the capability report includes an indication of a capability of the wireless device for reporting the indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme.

[0202] 3. The method of any one of the previous embodiments, further comprising measuring a CSI reference signal according to the obtained configuration related to CSI prediction.

[0203] 4. The method of any one of the previous embodiments, wherein the indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme is included in the CSI report Part 1.

[0204] 5. The method of any one of the previous embodiments, wherein the first CSI prediction scheme comprise an artificial intelligence / machine learning-based CSI prediction scheme and the second CSI prediction scheme comprises a non-artificial intelligence / machine learning-based CSI prediction scheme.

[0205] 6. A method performed by a wireless device, the method comprising:

[0206] - any of the wireless device steps, features, or functions described above, either alone or in combination with other steps, features, or functions described above.

[0207] 7. The method of the previous embodiment, further comprising one or more additional wireless device steps, features or functions described above.

[0208] 8. The method of any of the previous two embodiments, further comprising:

[0209] - providing user data; and

[0210] - forwarding the user data to a host computer via the transmission to the base station.

[0211] 9. A method performed by a base station for identifying a channel state information (CSI) prediction scheme used by a wireless device for CSI reporting, the method comprising:

[0212] - transmitting configuration related to CSI prediction for at least one of a first CSI prediction scheme and a second CSI prediction scheme to the wireless device; and

[0213] - receiving from the wireless device an indication of a selected one of the first CSI prediction scheme and the second CSI prediction scheme used by the wireless device for generating a CSI report.

[0214] 10. The method of the previous embodiment, further comprising receiving a capability report from the wireless device, wherein the capability report includes an indication of a capability of the wireless device for reporting the indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme.

[0215] 11. The method of any one of the previous embodiments, wherein the indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme is included in the CSI report Part 1.

[0216] 12. The method of any one of the previous embodiments, wherein the first CSI prediction scheme comprise an artificial intelligence / machine learning-based CSI prediction scheme and the second CSI prediction scheme comprises a non-artificial intelligence / machine learning-based CSI prediction scheme.

[0217] 13. A method performed by a base station, the method comprising:

[0218] - any of the steps, features, or functions described above with respect to base stations, either alone or in combination with other steps, features, or functions described above.

[0219] 14. The method of the previous embodiment, further comprising one or more additional base station steps, features or functions described above.

[0220] 15. The method of any of the previous embodiments, further comprising:

[0221] - obtaining user data; and forwarding the user data to a host computer or a wireless device. bodiments obile terminal comprising:

[0222] - processing circuitry configured to perform any of the steps of any of the Group A embodiments; and

[0223] - power supply circuitry configured to supply power to the wireless device. ase station comprising:

[0224] - processing circuitry configured to perform any of the steps of any of the Group B embodiments;

[0225] - power supply circuitry configured to supply power to the wireless device. ser equipment (UE) comprising:

[0226] - an antenna configured to send and receive wireless signals;

[0227] - radio front-end circuitry connected to the antenna and to processing circuitry, and configured to condition signals communicated between the antenna and the processing circuitry;

[0228] - the processing circuitry being configured to perform any of the steps of any of the Group A embodiments;

[0229] - an input interface connected to the processing circuitry and configured to allow input of information into the UE to be processed by the processing circuitry;

[0230] - an output interface connected to the processing circuitry and configured to output information from the UE that has been processed by the processing circuitry; and

[0231] - a battery connected to the processing circuitry and configured to supply power to the UE. mmunication system including a host computer comprising:

[0232] - processing circuitry configured to provide user data; and

[0233] - a communication interface configured to forward the user data to a cellular network for transmission to a user equipment (UE),

[0234] - wherein the cellular network comprises a base station having a radio interface and processing circuitry, the base station’s processing circuitry configured to perform any of the steps of any of the Group B embodiments. The communication system of the pervious embodiment further including the base station. The communication system of the previous 2 embodiments, further including the UE, wherein the UE is configured to communicate with the base station.

Claims

CLAIMS1. A method performed by a wireless device (200) for indicating a channel state information (CSI) prediction scheme used by the wireless device for CSI reporting, the method comprising: obtaining (514) a configuration related to CSI prediction for at least one of a first CSI prediction scheme and a second CSI prediction scheme; and reporting (522) to a network node (300) an indication of a selected one of the first CSI prediction scheme and the second CSI prediction scheme used by the wireless device for generating a CSI report.

2. The method of claim 1, further comprising transmitting (512) a capability report to the network node (300), wherein the capability report includes at least an indication of a capability of the wireless device to support at least one CSI prediction scheme.

3. The method of any one of claims 1-2, further comprising receiving (516) an indication from the network node (300) of a CSI prediction scheme to use for CSI prediction.

4. The method of any one of claims 1-3, further comprising: measuring (518) a CSI reference signal; and performing CSI prediction (520) according to the obtained configuration related to CSI prediction.

5. The method of any one of claims 1-4, wherein the indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme is included in the CSI report.

6. The method of claim 5, wherein the indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme is included in the CSI report Part 1.

7. The method of any one of claims 1-6, wherein the indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme comprises a bit field and a size of the bit field is based on a number of CSI prediction schemes configured for the wireless device.

8. The method of any one of claims 1-6, wherein the indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme comprises an indication of CSI performance-related information.

9. The method of any one of claims 1-8, wherein the first CSI prediction scheme comprises an artificial intelligence / machine learning-based CSI prediction scheme and the second CSI prediction scheme comprises a non-artificial intelligence / machine learning-based CSI prediction scheme.

10. A wireless device (200) capable of indicating a channel state information (CSI) prediction scheme used by the wireless device for CSI reporting, the wireless device comprising processing circuitry (202) operable to: obtain a configuration related to CSI prediction for at least one of a first CSI prediction scheme and a second CSI prediction scheme; and report to a network node (300) an indication of a selected one of the first CSI prediction scheme and the second CSI prediction scheme used by the wireless device for generating a CSI report.

11. The wireless device (200) of claim 10, the processing circuitry further operable to transmit a capability report to the network node, wherein the capability report includes at least an indication of a capability of the wireless device to support at least one CSI prediction scheme.

12. The wireless device (200) of any one of claims 10-11, the processing circuitry further operable to receive an indication from the network node of a CSI prediction scheme to use for CSI prediction.

13. The wireless device (200) of any one of claims 10-12, the processing circuitry further operable to: measure a CSI reference signal; and perform CSI prediction according to the obtained configuration related to CSI prediction.

14. The wireless device (200) of any one of claims 10-13, wherein the indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme is included in the CSI report.

15. The wireless device (200) of claim 14, wherein the indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme is included in the CSI report Part 1.

16. The wireless device (200) of any one of claims 10-15, wherein the indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme comprises a bit field and a size of the bit field is based on a number of CSI prediction schemes configured for the wireless device.

17. The wireless device (200) of any one of claims 10-15, wherein the indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme comprises an indication of CSI performance-related information.

18. The wireless device (200) of any one of claims 10-17, wherein the first CSI prediction scheme comprises an artificial intelligence / machine learning-based CSI prediction scheme and the second CSI prediction scheme comprises a non-artificial intelligence / machine learning-based CSI prediction scheme.

19. A method performed by a network node (300) for determining a channel state information (CSI) prediction scheme used by a wireless device for CSI reporting, the method comprising: transmitting (614) a configuration related to CSI prediction for at least one of a first CSI prediction scheme and a second CSI prediction scheme to the wireless device; and receiving (618) from the wireless device an indication of a selected one of the first CSI prediction scheme and the second CSI prediction scheme used by the wireless device for generating a CSI report.

20. The method of claim 19, further comprising receiving (612) a capability report from the wireless device, wherein the capability report includes at least an indication of a capability of the wireless device to support at least one CSI prediction scheme.

21. The method of any one of claims 19-20, further comprising transmitting (616) an indication to the wireless device of a CSI prediction scheme to use for CSI prediction.

22. The method of any one of claims 19-21, wherein the indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme is included in the CSI report.

23. The method of claim 22, wherein the indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme is included in the CSI report Part 1.

24. The method of any one of claims 19-23, wherein the indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme comprises a bit field and a size of the bit field is based on a number of CSI prediction schemes configured for the wireless device.

25. The method of any one of claims 19-23, wherein the indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme comprises an indication of CSI performance-related information.

26. The method of any one of claims 19-25, wherein the first CSI prediction scheme comprises an artificial intelligence / machine learning-based CSI prediction scheme and the second CSI prediction scheme comprises a non-artificial intelligence / machine learning-based CSI prediction scheme.

27. The method of any one of claims 19-26, further comprising modifying (620) a configuration related to CSI prediction based on the received indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme.

28. A network node (300) operable to determine a channel state information (CSI) prediction scheme used by a wireless device (200) for CSI reporting, the network node comprising processing circuitry (302) operable to: transmit a configuration related to CSI prediction for at least one of a first CSI prediction scheme and a second CSI prediction scheme to the wireless device; and receive from the wireless device an indication of a selected one of the first CSI prediction scheme and the second CSI prediction scheme used by the wireless device for generating a CSI report.

29. The network node (300) of claim 28, the processing circuitry further operable to receive a capability report from the wireless device, wherein the capability report includes at least an indication of a capability of the wireless device to support at least one CSI prediction scheme.

30. The network node (300) of any one of claims 28-29, the processing circuitry further operable to transmit an indication to the wireless device of a CSI prediction scheme to use for CSI prediction.

31. The network node (300) of any one of claims 28-30, wherein the indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme is included in the CSI report.

32. The network node (300) of claim 31, wherein the indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme is included in the CSI report Part 1.

33. The network node (300) of any one of claims 28-32, wherein the indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme comprises a bit field and a size of the bit field is based on a number of CSI prediction schemes configured for the wireless device.

34. The network node (300) of any one of claims 28-32, wherein the indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme comprises an indication of CSI performance-related information.

35. The network node (300) of any one of claims 28-34, wherein the first CSI prediction scheme comprises an artificial intelligence / machine learning-based CSI prediction scheme and the second CSI prediction scheme comprises a non-artificial intelligence / machine learning-based CSI prediction scheme.

36. The network node (300) of any one of claims 28-35, the processing circuitry further operable to modify a configuration related to CSI prediction based on the received indication of the selected one of the first CSI prediction scheme and the second CSI prediction scheme.

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