Method for monitoring prediction and apparatus therefor

Abstract

A method, according to one embodiment of the present specification, comprises the steps of: receiving configuration information related to channel state information (CSI); transmitting a first CSI report related to prediction; and transmitting a second CSI report related to prediction accuracy.

Description

Method and device for monitoring predictions

[0001] The present specification relates to a method and device for monitoring predictions.

[0002] Mobile communication systems were developed to provide voice services while ensuring user activity. However, they have expanded beyond voice to include data services. Currently, explosive growth in traffic is leading to resource shortages and users are demanding faster services, necessitating a more advanced mobile communication system.

[0003] Next-generation mobile communication systems must support explosive data traffic growth, dramatically increasing data rates per user, a vastly increased number of connected devices, ultra-low end-to-end latency, and high energy efficiency. To achieve these goals, various technologies are being studied, including dual connectivity, massive multiple input multiple output (MIMO), in-band full duplex, non-orthogonal multiple access (NOMA), super wideband support, and device networking.

[0004] In the beam management field of Rel-19 AI / ML WI, standardization discussions were conducted on performance monitoring operations of UE-side AI / ML. Specifically, performance monitoring may be performed for CSI prediction (e.g., CSI including at least one of predicted RS (predicted CRI, predicted SSBRI), predicted L1-RSRP, and / or predicted PMI). For example, the UE may transmit a report (e.g., a CSI report) related to monitoring the accuracy of predicted information (e.g., predicted CRI, predicted SSBRI, and / or predicted PMI, etc.). For example, the report may include a prediction accuracy indicator (e.g., a Prediction Accuracy Indicator).

[0005] Meanwhile, it may be considered to utilize a resource set (e.g., Set A) set for prediction for the above monitoring. For example, the terminal may perform measurement and monitoring based on resources (e.g., CSI-RS resources / SSB resources) within Set A. The terminal may transmit the report related to the monitoring based on the report configuration (e.g., CSI-ReportConfig) related to Set A.

[0006] The information included in a CSI report related to Set A (e.g., a CSI report related to prediction) may differ from the information included in a CSI report related to monitoring (e.g., a CSI report related to prediction accuracy). Therefore, the parameters related to the reporting settings need to be set / defined differently.

[0007] When a report configuration related to Set A (e.g., a report configuration related to resource configuration for prediction) is used for a CSI report related to the above monitoring, ambiguity may arise in terminal operation. Specifically, when a terminal transmits a CSI report related to prediction accuracy, it may be unclear whether the report should contain the same information as the CSI report related to prediction.

[0008] The purpose of this specification is to propose a method to solve the above-mentioned problems.

[0009] The technical problems to be achieved in this specification are not limited to the technical problems mentioned above, and other technical problems not mentioned can be clearly understood by a person having ordinary skill in the technical field to which this specification pertains from the description below.

[0010] According to one embodiment of the present disclosure for solving the above-described problem, a method includes the steps of receiving configuration information related to Channel State Information (CSI), transmitting a first CSI report related to prediction, and transmitting a second CSI report related to prediction accuracy. The configuration information includes i) one or more report configurations and ii) one or more resource configurations. The one or more report configurations include i) a first report configuration related to the prediction, and ii) a second report configuration related to the prediction accuracy. The one or more resource configurations include i) a first resource configuration and a second resource configuration related to the first report configuration, and ii) a third resource configuration related to the second report configuration. The first resource configuration includes information about a first resource set for measurement, the second resource configuration includes information about a second resource set for prediction, and the third resource configuration includes information about a third resource set for measurement. The resources of the third resource set are characterized in that they are mapped to the resources of the second resource set.

[0011] As described above, since the settings related to prediction accuracy (report settings for the second CSI report, resource settings) are set separately in the terminal from the settings related to prediction (report settings for the first CSI report, resource settings), ambiguity in terminal operation when performing reporting for monitoring prediction can be resolved.

[0012] According to an embodiment of the present specification, transmission of a CSI report related to prediction accuracy is performed based on a separate reporting configuration from the prediction-related reporting configuration. Accordingly, the CSI report can be configured / transmitted based on parameters suitable for reporting related to monitoring of the prediction (e.g., time domain behavior configuration (e.g., higher layer parameter reportConfigType), information to be reported configuration (e.g., higher layer parameter reportQuantity)). Accordingly, the ambiguity of terminal operability described above can be resolved. In addition, RS overhead can be reduced compared to the case where all beams (RS resources) within Set A for prediction are always measured for monitoring of the prediction.

[0013] The effects that can be obtained from this specification are not limited to the effects mentioned above, and other effects that are not mentioned can be clearly understood by a person having ordinary skill in the technical field to which this specification belongs from the description below.

[0014] Figure 1 is a diagram to explain overall functions from an AI / ML model perspective.

[0015] Figure 2 illustrates a general form of AI / ML related procedures performed between a network and a terminal.

[0016] Figure 3 illustrates an example of AI / ML-based beam management operations.

[0017] Figure 4 illustrates an example of AI / ML-based CSI measurement / reporting operations.

[0018] Figure 5 illustrates an example of AI / ML-based positioning operation.

[0019] Figure 6 is a flowchart showing an example of a CSI-related procedure.

[0020] FIG. 7 illustrates an example of a CSI reporting setting according to an embodiment of the present specification.

[0021] FIG. 8 illustrates another example of a CSI reporting configuration according to an embodiment of the present specification.

[0022] FIG. 9 is a flowchart illustrating a method according to one embodiment of the present specification.

[0023] FIG. 10 is a flowchart illustrating a method according to another embodiment of the present specification.

[0024] FIG. 11 is a drawing showing the configuration of a first device and a second device according to an embodiment of the present specification.

[0025] As used herein, "A or B" can mean "only A," "only B," or "both A and B." In other words, as used herein, "A or B" can be interpreted as "A and / or B." For example, as used herein, "A, B or C" can mean "only A," "only B," "only C," or "any combination of A, B and C."

[0026] As used herein, a slash ( / ) or a comma can mean "and / or." For example, "A / B" can mean "A and / or B." Accordingly, "A / B" can mean "only A," "only B," or "both A and B." For example, "A, B, C" can mean "A, B, or C."

[0027] In this specification, "at least one of A and B" may mean "only A", "only B" or "both A and B". Additionally, in this specification, the expressions "at least one of A or B" or "at least one of A and / or B" may be interpreted identically to "at least one of A and B".

[0028] Additionally, in this specification, “at least one of A, B and C” can mean “only A,” “only B,” “only C,” or “any combination of A, B and C.” Additionally, “at least one of A, B or C” or “at least one of A, B and / or C” can mean “at least one of A, B and C.”

[0029] Additionally, parentheses used herein may mean "for example." Specifically, when indicated as "control information (PDCCH)", "PDCCH" may be proposed as an example of "control information." In other words, "control information" in this specification is not limited to "PDCCH," and "PDCCH" may be proposed as an example of "control information." Furthermore, even when indicated as "control information (i.e., PDCCH)", "PDCCH" may be proposed as an example of "control information."

[0030] In the following explanation, ‘when, if, in case of’ can be replaced with ‘based on’.

[0031] Technical features individually described in a single drawing in this specification may be implemented individually or simultaneously.

[0032] Hereinafter, preferred embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. The detailed description set forth below, together with the accompanying drawings, is intended to illustrate exemplary embodiments of the present disclosure and is not intended to represent the only embodiments in which the present disclosure may be implemented. The following detailed description includes specific details to provide a thorough understanding of the present disclosure.

[0033] In this specification, a terminal is a user equipment (UE) or a consumer-side device, and may also be referred to as a base station / second node / IAB node / first node that receives / transmits signals from / to a Transmission-Reception Point (TRP). A terminal may correspond to a physical node or a logical node. A terminal may correspond to an endpoint on the user side, or may correspond to an intermediate point between other endpoints. In communication between two points that are not limited to endpoints (including one-to-one / many-to-one / one-to-many / many-to-many communication), a terminal may correspond to a served node. A terminal may be a node with a fixed location, or a node with an unfixed location (or mobile).

[0034] In this specification, a base station (BS) is a device on the network side, and may also be called a second node / IAB node / x-NodeB (x-NodeB, x may be an abbreviation related to radio access technology (RAT)) / Transmission-Reception Point (TRP). A BS may correspond to a physical node or a logical node. A BS may correspond to an endpoint on the network side, or may correspond to an intermediate point between other endpoints. In communication between two points that are not limited to endpoints (including one-to-one / many-to-one / one-to-many / many-to-many communication), a BS may correspond to a serving node. A BS may be a node with a fixed location, or a node with an unfixed location.

[0035] In this specification, higher layer parameters may be set for the terminal, preset, or predefined. For example, the base station may transmit higher layer parameters to the terminal. For example, the terminal may transmit parameters such as capabilities to the base station as higher layer parameters. For example, the higher layer parameters may be transmitted via radio resource control (RRC) signaling or medium access control (MAC) signaling.

[0036] In this specification, the information / state / parameter being “configured or pre-configured” can be interpreted as the information / state / parameter being provided / pre-provided to the terminal through pre-defined signaling (e.g., SIB, MAC, RRC) from the base station. In this specification, the information / state / parameter being “defined or pre-defined” can be interpreted as the information / state / parameter being known in advance or pre-stored at the base station and the terminal without signaling between the base station and the terminal.

[0037] < AI / ML for Wireless Communication >

[0038] With the advancement of computing technology, artificial intelligence (AI) and machine learning (ML) are being adopted in various industries and technical fields. In the field of wireless communications, various discussions are underway to apply AI models trained based on ML, and the 3GPP standardization process refers to this as AI / ML. This specification describes "AI / ML" according to the terminology used in the 3GPP standardization process. However, "AI / ML" may be referred to by various other terms depending on the progress and implementation of future standards. For example, it may be referred to as "transmission / reception mode" or "signal / channel / operation / transmission / reception configuration" configured for AI / ML, but is not limited thereto. The meanings of the terms currently used in the 3GPP standardization process are briefly summarized as follows.

[0039] - AI / ML model: A data-driven algorithm that applies AI / ML technology to generate a set of outputs containing prediction information and / or decision parameters based on a set of inputs.

[0040] - Data collection: The process of collecting data required for AI / ML model training, data analysis, and inference from network nodes, management entities, or terminals.

[0041] - AI / ML Training: An online or offline process of training an AI model by learning features and patterns that best represent the data and obtain a trained AI / ML model for inference.

[0042] - Offline training: The process of training a model based on a previously collected data set, and the trained model is used or provided for future inference.

[0043] - Online training: This is a method in which the model is trained in real time when new training sample data is acquired and used for inference.

[0044] - AI / ML Inference: This is the process of making predictions or inducing decisions based on collected data and the AI ​​model using a trained AI model. Meanwhile, depending on whether the AI / ML model is set up on both the transmitting and receiving devices or only on one of them, it can be divided into (i) a two-sided model and (ii) a one-sided model. (i) In the case of the two-sided model, collaborative inference is performed through paired AI / ML models. Collaborative inference refers to cooperation between the network and the UE, in which one party performs part of the inference and the other party performs the rest of the inference. (ii) The one-sided model is divided into a UE-side model and a network-side model. In the one-sided model, inference is performed entirely by the UE / network-side model.

[0045] 1. Life Cycle Management (LCM) for AI / ML models

[0046] LCM for AI / ML models is a concept that encompasses all the overall procedures for AI / ML models, including data collection, model training, model deployment, model inference, model monitoring, and model updates.

[0047] LCM for AI / ML models can be broadly categorized into functionality-based LCM and model ID-based LCM. In functionality-based LCM, the network can instruct the activation / deactivation / fallback / switching of specific functions, even if the target AI / ML model may not be identified by the network. In model ID-based LCM, the network can instruct the activation / deactivation / selection / switching of AI / ML models identified by their AI / ML model ID.

[0048] Figure 1 is a diagram to explain overall functions from an AI / ML model perspective.

[0049] Referring to Fig. 1, a general AI / ML functional framework can be configured to include a data collection function (10), a model training function (20), a management function (30), an inference function (40), and a model storage function (50).

[0050] The Data Collection function (10) is a function that provides input data to the Model Training function (20), Management function (30), and Inference function (40). The Data Collection function (10) can perform data preparation and provide input data processed through data preparation.

[0051] Here, training data (11) refers to data required as input for the AI / ML Model Training function (20). Monitoring data (12) refers to data required as input for the Management (30) of the AI / ML model or AI / ML function. Inference data (13) refers to data required as input for the AI / ML Inference function (30).

[0052] The Model Training function (20) is a function that performs AI / ML model training, validation, and testing, and can generate model performance metrics that can be used as part of the AI / ML model testing procedure. If necessary, the Model Training function (20) can perform data preparation (e.g., data pre-processing and cleaning, forming, and transformation) based on the Training Data (11) transmitted from the Data Collection function (10).

[0053] Trained / Updated Model (21): If there is a Model Storage function (50), it is used to pass a trained, validated and tested AI / ML model to the Model Storage function (50) or to pass an updated version of the model to the Model Storage function (50).

[0054] The Management function (30) is a function that monitors the operation of the AI / ML model or AI / ML function. In addition, the Management function (30) may perform a decision to ensure appropriate inference operation based on data received from the Data Collection function (10) (i.e., Monitoring Data (12)) and / or data received from the Inference function (40) (i.e., Inference Output (41)).

[0055] Management Instruction (32) is information required as input to manage the Inference function (40). The relevant information may include selection / (de)activation / switching of an AI / ML model or AI / ML-based function, and may also include fallback to non-AI / ML operations (i.e., not relying on the inference process).

[0056] A Model Transfer / Delivery Request (33) can be used to request model(s) from Model Storage (50).

[0057] A Performance Feedback / Retraining Request (31) refers to information required as input to the Model Training function (20) (e.g., for the purpose of (re)training or updating the model).

[0058] The Inference function (40) is a function that provides output from the process of applying an AI / ML model or AI / ML function using data (i.e., Inference Data (13)) provided by Data Collection (10) as input. Data preparation (e.g., data preprocessing and cleaning, formatting, and transformation) may also be performed based on the Inference Data (13) delivered by Data Collection (10). If necessary, the Inference function (40) may also perform data preparation (e.g., data preprocessing and cleaning, forming, and transformation) based on the Inference Data (13) provided by Data Collection function (10).

[0059] Inference Output (41) is data used in the Management function (30) to monitor the performance of an AI / ML model or AI / ML function. Inference Output (41) may include the inference output of the AI / ML model generated by the Inference function (30), and the details of the inference output may vary depending on the use case.

[0060] The Model Storage function (50) stores a learned / updated model that can be used to perform the Inference function (40). The Model Storage function (50) illustrated in Fig. 1 can be used as a reference point (if any) when applicable to protocol termination, model transmission / delivery, and related processes. Furthermore, the Model Storage function (50) is merely an example and is not intended to limit the storage location of actual AI / ML models, and may be omitted.

[0061] Model Transfer / Delivery (51) is used to transfer AI / ML models to inference functions.

[0062] 2. General AI / ML-related procedures between networks and terminals

[0063] Figure 2 illustrates the general form of AI / ML-related procedures performed between a network and a terminal. While Figure 1 examined LCM from an AI / ML model perspective, Figure 2 describes the general form of procedures performed from a signaling / protocol perspective between a terminal and the network.

[0064] (1) Setting procedures related to AI / ML

[0065] Referring to FIG. 2, an AI / ML-related configuration procedure may be performed between the network and the terminal (B05). The AI / ML-related configuration procedure may include information exchange between the terminal and the network via at least one upper-layer signaling, and / or preparatory / follow-up operations at the terminal / network, respectively, before / after the upper-layer signaling.

[0066] Specifically, the AI / ML-related configuration procedure may include, but is not limited to, at least one of (i) AI / ML-related terminal capability reporting, (ii) data collection, (iii) model training, (iv) model transfer / transfer, (v) AI / ML function / model selection, and (vi) configuration for various operations performed based on AI / ML models (e.g., AI / ML-based CSI / Positioning / Beam Management).

[0067] (i) A terminal can report to the network its capabilities, such as models / functionalities supported by the terminal in relation to AI / ML, through UE Capability Reporting. The network can provide AI / ML-related settings to the terminal based on the AI / ML-related capabilities reported by the terminal.

[0068] (ii) AI / ML-related configuration procedures may include data collection and / or provision of configuration information related to AI / ML model training / inference, etc. Configuration information related to data collection may relate to how to configure the method / action of data collection, etc.

[0069] (iii) AI / ML-related configuration procedures may include online or offline AI / ML model training and / or providing configuration information for AI / ML model training. Configuration information for AI / ML model training may relate to how to configure the method / operation of training the AI / ML model, etc.

[0070] (iv) The AI / ML-related configuration procedure may include transmitting / delivering configuration information for the model. The configuration information for the model may include parameters configuring the AI / ML model and / or an identifier (ID) for the AI / ML model.

[0071] The AI / ML model provided can be either a network-trained model or a model that requires self-training on the terminal. Even if a network-trained model is provided, the terminal can perform fine-tuning / retraining processes as needed. Meanwhile, if a network-trained model is provided, the terminal can provide training data to the network.

[0072] Meanwhile, AI / ML models can be categorized into Type A models, which can be identified without over-the-air (OTA) signaling, and Type B models, which are identified through OTA signaling. A model ID can be assigned during the model identification process, which can be further subdivided into terminal-initiated and network-initiated methods.

[0073] (v) The AI / ML-related configuration procedure may include a configuration of how to select an AI / ML Functionality / model and / or a selection process for the AI / ML Functionality / model. Selection of the UE part in a UE-side AI / ML model or a two-sided AI / ML model may be performed through instructions / signaling from the network or may be performed by the UE itself. Selection of the AI / ML Functionality / model may be performed when multiple AI / ML Functionality / models are configured / provided.

[0074] (vi) The AI / ML-related setup procedure may include setup information for various inference operations performed based on AI / ML models, for example, AI / ML-based CSI measurement / reporting, AI / ML-based Positioning, and / or AI / ML-based Beam Management.

[0075] (2) Actions based on inference from AI / ML models

[0076] Referring back to FIG. 2, the network and / or the terminal may perform inference of the AI / ML model through the trained AI / ML model, and perform various operations based on the inference of the AI / ML model (B10). If the AI / ML model is a one-sided model, the inference of the AI / ML model may be performed on either the network or the terminal where the AI / ML model is configured. If the AI / ML model is a two-sided model, each part of the inference of the AI / ML model may be performed on the network and the terminal, and such inference may be performed cooperatively between the network and the terminal depending on the implementation.

[0077] (i) Actions performed based on the inference of the AI / ML model may include AI / ML-based CSI measurement / reporting. The AI / ML-based CSI measurement / reporting may be for improving CSI feedback, and may be related to overhead reduction / CSI compression, accuracy improvement, and / or CSI prediction.

[0078] (ii) The actions performed based on the inference of the AI / ML model may include AI / ML-based beam management. The AI / ML-based beam management may be related to beam prediction in the time domain, reducing overhead / delay in the spatial domain, and / or improving beam selection accuracy.

[0079] (iii) Actions performed based on the inference of the AI / ML model may include AI / ML-based positioning. AI / ML-based positioning may be relevant to improving positioning accuracy in various scenarios, such as non-line-of-sight environments.

[0080] (3) Procedures for AI / ML management

[0081] The network and / or terminal can perform procedures for managing AI / ML Functionality / model or its settings (B15).

[0082] The network and / or terminal may perform monitoring of AI / ML Functionality / model during the process of AI / ML model inference or operation based thereon (B10) for management procedures (B15).

[0083] Management procedures may include, for example, at least one of activation / deactivation, switching, model update, and / or fallback operations for AI / ML Functionality / models. Signaling for management procedures may use various 3GPP signaling methods, such as RRC, MAC-CE, and DCI.

[0084] As an example of model switching, multiple model groups are formed, and switching between them, groups can be performed based on models having a common model structure or partially common sub-structures, and models within the same group can be related to different input / output formats or processing.

[0085] Model updating is the process of changing the parameters used by the model to adapt them to changing channel conditions over time, and fine-tuning is an example of model updating.

[0086] fallback: In a wireless communication system using an AI / ML model, when the reliability of the AI / ML model is reduced due to internal / external environmental factors, it can mean not using the AI / ML model or operating in a default operation mode that is set / defined in advance.

[0087] For example, the decision to perform a management procedure may be made by the network. For example, the network may decide to perform a management procedure upon network initiation, or upon terminal initiation and request.

[0088] As another example, the decision to perform a management procedure can be made by the terminal. For example, the terminal's decision to perform a management procedure can be triggered by the satisfaction of an event condition set by the network, by reporting the terminal's decision to the network, or by the terminal performing the decision autonomously.

[0089] 3. Specific examples of actions based on AI / ML model inference

[0090] (1) Beam management

[0091] Figure 3 illustrates an example of AI / ML-based beam management operations.

[0092] Referring to FIG. 3, the network / terminal can perform a configuration procedure related to AI / ML-based beam management (C05). The network / terminal can exchange configuration information for upper layer signaling for AI / ML-based beam management and perform a configuration procedure for an AI / ML model to be used for AI / ML-based beam management. For example, at least one of information related to model inference, configuration for the first set / second set of beams, monitoring performance, data collection, and assistance information for beam measurement can be signaled.

[0093] The network / terminal can perform measurements on the first set of beams (C10). The beam measurements can be related to RSRP measurements.

[0094] The network / terminal can obtain information about the second set of beams based on the measurement results for the first set of beams (C15). For example, the network / terminal can perform AI / ML inference using the measurement results for the first set of beams as AI / ML input data. Beam ID information may be additionally provided as AI / ML input data. The information about the second set of beams may correspond to AI / ML output data. The AI / ML output data may be related to prediction of future beam quality, such as, but not limited to, the probability that each beam will become a top-N beam, and the predicted RSRP.

[0095] In some embodiments, the network / terminal may transmit and receive information about the acquired second set of beams.

[0096] Specifically, the AI / ML-based beam management operation may include at least one of the following BM-Case 1 and BM-Case 2.

[0097] - BM-Case 1: Prediction of the second set of DL beams in the spatial domain using the first set of beam measurements.

[0098] - BM-Case 2: Prediction of the second set of DL beams in the time domain using the first set of beam measurements.

[0099] In BM-Case 1 and / or 2, both AI / ML model training and inference can be performed in the network or at the terminal. The first set of beams and the second set of beams can be different beams. Alternatively, the first set of beams can be a subset of the second set of beams. Alternatively, particularly in BM-Case 2, the first set of beams and the second set of beams can be the same beams.

[0100] The report corresponding to the inference of the UE-side model for BM-Case 1 may relate to the predicted RSRP for the upper N beams. The report may include, for example, the predicted RSRP value, and for example, the predicted RSRP value may be reported together with the actually measured RSRP.

[0101] UE-side AI / ML model inference for BM-Case 2 can report inference results for N future time points in a single report. Each time point report can correspond to the report for BM-Case 1.

[0102] For performance monitoring of the UE-side model for BM-Case 1 / 2, (i) network side performance monitoring and / or (ii) UE-assisted performance monitoring may be supported. (i) For network side performance monitoring, the UE may report information necessary for the network to calculate performance metrics, for example, measurement results (e.g., RSRP) and / or RS index for a resource set for monitoring. (ii) For UE-assisted performance monitoring, the UE may also calculate performance metrics.

[0103] Regarding the NW-side model for BM-Case 1 / 2, quantization of reported RSRPs may be supported, e.g., differential RSRP reporting may be supported along with existing quantization steps and ranges. The reported content may include information about the RSRP and the corresponding upper N beams, where N may be configured by the network.

[0104] Regarding the configuration of the first set of beams and the second set of beams in the UE-side model of BM Case-1, two resource sets may be separately configured for each of the first set and the second set, and the corresponding resource sets may be provided through the CSI reporting configuration. The terminal may perform inference / measurement for the resource set of the first set of beams. The terminal may not be expected to perform measurement / inference for the resource set of the second set of beams. The beam information of the inference report may include resource set information for the first set.

[0105] In relation to the UE-side model, relevant IDs can be provided via the CSI framework. UEs can assume identical / similar characteristics for DL ​​transmission beams / sets (lists) with the same relevant ID.

[0106] Regarding UE-assisted performance monitoring for the UE-side models of BM-Case 1 and 2, the following methods can be considered.

[0107] i) Compare prediction results based on resources for monitoring and use the top 1 or top K beam prediction accuracy.

[0108] ii) Use RSRP difference information based on RSRP measurements of resources for monitoring and actual RSRP measurements for at least one of the top N predicted beams.

[0109] iii) Use the difference information between the measured RSRP and the predicted RSRP for the corresponding beam of the resources for monitoring.

[0110] iv) Probability information about the predicted beam being one of the top 1 or N beams.

[0111] Quantization of RSRP can be supported for reporting inference results for UE-side models, and differential RSRP with existing quantization steps can be supported. The scope of RSRP reporting is that differential RSRP can be supported among multiple beams in the case of BM-case 1, and differential RSRP can be supported among multiple beams at multiple viewpoints in the case of BM-case 2.

[0112] For BM-Case 2 of the UE-side model, the network can be configured to report inferences for N future time points to the terminal.

[0113] (2) CSI prediction and / or compression (CSI prediction / compression)

[0114] Figure 4 illustrates an example of AI / ML-based CSI measurement / reporting operations.

[0115] Referring to FIG. 4, the network / terminal may perform a configuration procedure related to AI / ML-based CSI (D05). The network / terminal may exchange configuration information for upper-layer signaling for AI / ML-based CSI measurement / reporting, and perform a configuration procedure for an AI / ML model to be used for AI / ML-based CSI. For example, at least one of information related to model inference, configuration of RS / resources to be used for CSI measurement, monitoring performance, data collection, and conditions / resources for CSI reporting may be signaled.

[0116] The terminal can perform CSI measurements based on AI / ML model inference (D10). The AI / ML model used by the terminal for CSI measurements may be a UE-side AI / ML model corresponding to a one-side AI / ML model, or an AI / ML model corresponding to the terminal portion of a two-side AI / ML model.

[0117] A terminal may report CSI to the network based on the CSI measurement results (D15). CSI reporting may be performed periodically or aperiodically depending on the configuration, and in the case of aperiodic CSI reporting, a network instruction (not shown) such as DCI that triggers it may be additionally signaled. CSI reporting may include AI / ML-based CSI content, and additionally (depending on the configuration / scheduling) may further include legacy CSI content (e.g., non-AI / ML-based RI, PMI, CQI, etc.). AI / ML-based CSI content may be related to at least one of 1) CSI compression to reduce the overhead of CSI reporting, and 2) CSI prediction for future time points in the time domain.

[0118] The network can obtain CSI based on the CSI report of the terminal.

[0119] If a two-sided AI / ML model is configured, the network can reconstruct CSI using the terminal's CSI report as input data to the network-configured AI / ML model (D20). The inference (output) of the network-configured AI / ML model can be the reconstructed CSI. In such a two-sided AI / ML model, the terminal-side AI / ML model can be understood as a CSI encoder, while the network-side AI / ML model can be understood as a concept similar to a CSI decoder.

[0120] CSI compression is a spatial-frequency domain CSI compression, which can be primarily based on two-sided AI / ML models. CSI prediction can primarily be based on one-sided, specifically, UE-side AI / ML models.

[0121] In CSI compression based on two-side AI / ML models, AI / ML model training may include at least one of (i) Type 1, in which either the terminal or the network jointly trains two-side AI / ML models, (ii) Type 2, in which the terminal and the network each jointly train their respective two-side AI / ML model parts, and (iii) Type 3, in which the terminal and the network each separately train their respective two-side AI / ML model parts, with the terminal training being primarily related to CSI generation and the network training being primarily related to CSI reconstruction. Joint training means that the CSI generation / reconstruction model is trained in the same loop for forward / backward delays, and separate training may mean a sequential method in which either the terminal or the network starts training first and then the other performs training.

[0122] (3) Positioning

[0123] Figure 5 illustrates an example of AI / ML-based positioning operation.

[0124] Referring to FIG. 5, the network / terminal may perform a configuration procedure related to AI / ML-based positioning (E05). The network / terminal may exchange configuration information for upper-layer signaling for AI / ML-based positioning and perform a configuration procedure for an AI / ML model to be used for AI / ML-based positioning. For example, at least one of information related to model inference, positioning configuration for RS, monitoring performance, data collection, and assistance information for positioning measurement may be signaled.

[0125] The network / terminal can perform measurements for positioning (E10). The measurements for positioning may be related to PRS and / or SRS measurements.

[0126] Based on the measurement results, the network / terminal can obtain information about terminal positioning (E15). For example, the network / terminal can use the PRS / SRS measurement results as AI / ML input data to perform AI / ML inference. Information about terminal positioning may correspond to AI / ML output data. The AI / ML output data may be, for example, terminal location or assistance information that serves as the basis for determining terminal location, but is not limited thereto.

[0127] Depending on the embodiment, the network / terminal may transmit and receive information about the acquired terminal positioning.

[0128] Specifically, the AI / ML-based positioning may include at least one of (i) direct AI / ML positioning and / or (ii) AI / ML-assisted positioning. (i) In direct AI / ML positioning, the output of the AI / ML model includes the UE location. (ii) In AI / ML-assisted positioning, the output of the AI / ML model may be a new measurement result and / or an improvement to an existing measurement (e.g., LoS / NLoS identification, timing and / or angle of the measurement, likelihood of the measurement, etc.).

[0129] The data sample collected for training data collection may include at least one of the following Parts:

[0130] - Part A: Channel measurements, quality indicators of channel measurements, timestamps of channel measurements

[0131] - Part B: ground truth label (or its approximation), label quality indicator, label timestamp

[0132] The training data samples for Part A and Part B may be for the same terminal (e.g., PRU or Non-PRU UE) and for the same location associated with Part B.

[0133] Measurements for positioning can be divided into sample-based measurements and path-based measurements.

[0134] - In sample-based measurements, the measurement consists of Nt' samples of the estimated channel response in the time domain. Timing information for the Nt' samples is reported with a timing granularity T, where T = 2k x Tc. k denotes a timing reporting granularity factor, and Tc denotes the fundamental time unit of the wireless communication system. Nt' and k can be signaled parameters. Timing information can be defined as a value relative to a reference time.

[0135] - Path-based measurement refers to the measurement defined in existing wireless communication systems.

[0136] AI / ML-based positioning may relate to at least one of the following specific cases:

[0137] - (i) Case 1: UE-based positioning using a UE-side model, in the case of direct AI / ML or AI / ML assisted positioning.

[0138] - (ii) Case 2a: UE-assisted / LMF-based positioning using the UE-side model, in the case of AI / ML assisted positioning

[0139] - (iii) Case 2b: UE-assisted / LMF-based positioning using the LMF-side model, in the case of direct AI / ML positioning

[0140] - (iv) Case 3a: NG-RAN node assisted positioning using the gNB-side model, in the case of AI / ML assisted positioning

[0141] - (v) Case 3b: NG-RAN node assisted positioning using LMF-side model, in case of direct AI / ML positioning

[0142] (i) In relation to model performance monitoring in Case 1, the following options may be considered for calculating model performance metrics in label-based model monitoring.

[0143] 1) Option A. Calculate monitoring metrics on the target terminal side.

[0144] - Option A-1: ​​At least some of the information about the target terminal's ground truth label is generated by the LMF and provided to the target terminal. For example, the target terminal and / or base station may send measurement results to the LMF, which may then derive information about the ground truth label.

[0145] - Option A-2: At least part of the information for position calculation assistance data is provided from the LMF to the target UE.

[0146] - Option A-3: This is a method of reusing assistance data previously provided from the LMF to the target terminal, in which PRU measurement results and corresponding PRU location information are provided from the LMF to the target terminal.

[0147] - Option A-4: PRU measurements and PRU locations are provided from the PRU to the target UE.

[0148] 2) Option B. LMF calculates monitoring metrics

[0149] - Option B-1: At least the inference results of the target terminal (i.e., the model output corresponding to the channel measurements of the target terminal) can be transmitted to the LMF by the target terminal.

[0150] - Option B-2: The channel measurement of the PRU is provided to the target terminal through the LMF, and the inference result (i.e., the model output corresponding to the channel measurement of the PRU) can be transmitted from the target terminal to the LMF.

[0151] (iv) With regard to generating training data in Case 3a, at least LMF can generate labels and data related to them (e.g., timestamps).

[0152] (iv) For calculating model performance monitoring metrics in label-based model monitoring in Case 3a, the following options A and B may be considered.

[0153] - Option A: NG-RAN nodes perform monitoring metric calculations for their own models.

[0154] - Option B: LMF performs monitoring metric calculations for models located on NG-RAN nodes.

[0155] (iv) For generating training data for Case 3a and (v) Case 3b, measurements and related data (e.g., timestamp) can be generated at the TRP / base station.

[0156] (v) For base station channel measurements reported to the LMF (location management server) in Case 3b, timing information can be expressed as a relative value to the UL RTOA reference time T0+tSRS.

[0157] (iii) Time domain channel measurements supported for reporting in Case 2b and (v) Case 3b may include timing information and / or power information corresponding to timing information.

[0158] For the definition of a sample-based measurement, Nt' samples can be selected from a list of consecutive Nt samples, and the Nt samples can have a time granularity of T.

[0159] In relation to sample-based measurements, the Nt' samples selected for measurement of the network may be those having the highest power.

[0160] For positioning based on Case 3b, for sample-based measurements, LMF can signal parameter values ​​such as Nt, Nt', k, etc. to the base station.

[0161] < CSI-related actions >

[0162] The channel state information (CSI) may include at least one of a channel quality indicator (CQI), a precoding matrix indicator (PMI), a CSI-RS resource indicator (CRI), an SS / PBCH block resource indicator (SSBRI), a layer indicator (LI), a rank indicator (RI), a Layer 1-Reference Signal Received Power (L1-RSRP), and / or a Layer 1-Signal-to-Interference-plus-Noise Ratio (L1-SINR).

[0163] In the case of the CSI prediction described below, the CSI related to the prediction may include at least one of predicted CQI (predicted CQI, P-CQI), predicted PMI (predicted PMI, P-PMI), predicted CRI (predicted CRI, P-CRI), predicted SSBRI (predicted SSBRI, P-SSBRI), predicted LI (predicted LI, P-LI), predicted RI (predicted RI, P-RI), predicted L1-RSRP (predicted L1-RSRP, P-L1-RSRP), and / or predicted L1-SINR (predicted L1-SINR, P-L1-SINR).

[0164] When monitoring the performance / accuracy of the CSI prediction described below, the CSI related to the prediction accuracy may include a Prediction Accuracy Indicator (PAI). For example, the PAI may indicate the accuracy of predicted downlink reference signal(s) (e.g., predicted CRI(s) and / or predicted SSBRI(s)), and thus the PAI may be interpreted / replaced with a Reference Signal-Prediction Accuracy Indicator (RS-PAI). For example, the PAI may indicate the accuracy of predicted CSI (e.g., predicted PMI), and thus the PAI may be interpreted / replaced with a Channel State Information-Prediction Accuracy Indicator (CSI-PAI).

[0165] Figure 6 is a flowchart showing an example of a CSI-related procedure.

[0166] Referring to FIG. 6, in order to perform one of the purposes of CSI-RS, a terminal (e.g., user equipment, UE) receives configuration information related to CSI from a base station (e.g., general Node B, gNB) through RRC (radio resource control) signaling (S610).

[0167] The configuration information related to the CSI may include at least one of CSI-IM (interference management) resource related information, CSI measurement configuration related information, CSI resource configuration related information, CSI-RS resource related information (e.g., M≥1 CSI-ResourceConfig resource setting), or CSI report configuration related information (e.g., N≥1 CSI-ReportConfig reporting setting). For example, the configuration information may include at least one of one or more CSI resource settings and / or one or more CSI reporting settings.

[0168] For example, the configuration information may include a first CSI resource configuration for measurement and a second CSI resource configuration for prediction. As a specific example, a measurement related to prediction of CSI, which will be described later, may be performed based on the first CSI resource configuration. The terminal may perform L1-RSRP measurements on CSI-RS resources or SS / PBCH block resources associated with the first CSI resource configuration. As a specific example, the prediction of CSI, which will be described later, may be performed based on the second CSI resource configuration. The terminal may perform prediction on CSI-RS resources or SS / PBCH block resources associated with the second CSI resource configuration based on the L1-RSRP measurements. In other words, best CRI / best SSBRI (e.g., P-CRI(s), P-SSBRI(s)) may be predicted using L1-RSRPs as measurement metrics.

[0169] For example, the configuration information may include a first CSI reporting setting related to prediction and a second CSI reporting setting related to prediction accuracy.

[0170] CSI resource configuration related information can be expressed as CSI-ResourceConfig IE. The CSI resource configuration related information defines a group including at least one of a non-zero power (NZP) CSI-RS resource set, a CSI-IM resource set, or a CSI-SSB resource set. That is, the CSI resource configuration related information includes a CSI-RS resource set list, and the CSI-RS resource set list can include at least one of an NZP CSI-RS resource set list, a CSI-IM resource set list, or a CSI-SSB resource set list. A CSI-RS resource set is identified by a CSI-RS resource set ID, and one resource set includes at least one CSI-RS resource. Each CSI-RS resource is identified by a CSI-RS resource ID.

[0171] Information related to CSI report configuration (e.g., CSI-ReportConfig IE) includes a report configuration type (reportConfigType) parameter indicating time domain behavior and a report quantity (reportQuantity) parameter indicating the CSI-related quantity to be reported. The time domain behavior may be periodic, aperiodic, or semi-persistent.

[0172] The above reportQuantity parameter is a channel quality indicator (CQI), a precoding matrix indicator (PMI), a CSI-RS resource indicator (CRI), an SS / PBCH block resource indicator (SSBRI), a layer indicator (LI), a rank indicator (RI), a Layer 1-Reference Signal Received Power (L1-RSRP), a Layer 1-Signal-to-Interference-plus-Noise Ratio (L1-SINR), a predicted CQI (predicted CQI, P-CQI), a predicted PMI (predicted PMI, P-PMI), a predicted CRI (predicted CRI, P-CRI), a predicted SSBRI (predicted SSBRI, P-SSBRI), a predicted LI (predicted LI, P-LI), a predicted RI (predicted RI, P-RI), a predicted L1-RSRP (predicted L1-RSRP, P-L1-RSRP) and / or a predicted It can be set to a value representing at least one of L1-SINR (predicted L1-SINR, P-L1-SINR) and / or Prediction Accuracy Indicator (PAI) (or RS-PAI).

[0173] For example, the reportQuantity parameter can be set to cri, ssb-Index, cri-RSRP, or ssb-Index-RSRP. cri represents the CSI-RS resource indicator (CRI). RSRP represents the Layer 1-Reference Signal Received Power (L1-RSRP). ssb-Index represents the SS / PBCH block resource indicator (SSBRI).

[0174] For example, the reportQuantity parameter can be set to p-cri, p-ssb-index, p-cri-RSRP, or p-ssb-index-RSRP. p-cri represents predicted CRI (P-CRI). p-ssb-index represents predicted SSBRI (P-SSBRI). p-cri-RSRP represents predicted CRI (P-CRI) and predicted L1-RSRP (P-L1-RSRP). p-ssb-index-RSRP represents predicted SSBRI (P-SSBRI) and predicted L1-RSRP (P-L1-RSRP).

[0175] For example, the reportQuantity parameter can be set to pai (or rs-pai), which represents PA (or RS-PAI).

[0176] Measurement resources may include configurations for downlink signals and / or downlink resources on which a terminal will perform measurements to determine feedback information. Measurement resources may be configured as ZP and / or NZP CSI-RS resource sets associated with CSI reporting configurations. The NZP CSI-RS resource set may include a CSI-RS set or an SSB set. For example, L1-RSRP may be measured for a CSI-RS set or an SSB set.

[0177] The terminal measures CSI based on configuration information related to the CSI (S620). The CSI measurement may include (1) a process of receiving a CSI-RS by the terminal (S621) and (2) a process of computing CSI using the received CSI-RS (S622). The terminal reports the CSI to the base station (S630).

[0178] Resource setting

[0179] Each CSI resource setting 'CSI-ResourceConfig' contains a configuration for S≥1 CSI resource sets (given by the higher layer parameter csi-RS-ResourceSetList). A CSI resource setting corresponds to a CSI-RS-resourcesetlist, where S represents the number of configured CSI-RS resource sets. Wherein, the list of S≥1 CSI resource sets contains either or both of NZP CSI-RS resource set(s) and SS / PBCH block (SSB) set(s) used for L1-RSRP computation, or contains CSI-IM resource set(s).

[0180] One or more CSI resource settings for channel measurement (CM) and interference measurement (IM) are configured via higher layer signaling.

[0181] - CSI-IM resource for interference measurement.

[0182] - NZP CSI-RS resources for interference measurement.

[0183] - NZP CSI-RS resources for channel measurement.

[0184] That is, the CMR (channel measurement resource) can be NZP CSI-RS for CSI acquisition, and the IMR (Interference measurement resource) can be NZP CSI-RS for CSI-IM and IM.

[0185] Here, CSI-IM (or ZP CSI-RS for IM) is mainly used for inter-cell interference measurement.

[0186] And, NZP CSI-RS for IM is mainly used for intra-cell interference measurement from multi-user.

[0187] A UE may assume that the CSI-RS resource(s) configured for channel measurement for one CSI reporting and the CSI-IM / NZP CSI-RS resource(s) for interference measurement (when NZP CSI-RS resource(s) are used for interference measurement) are in a QCL relationship with respect to 'QCL-TypeD' per resource.

[0188] As we have seen, resource setting can mean a resource set list.

[0189] For aperiodic CSI, each trigger state set using the higher layer parameter CSI-AperiodicTriggerState is associated with one or more CSI-ReportConfigs, and each CSI-ReportConfig is linked to a periodic or semi-persistent or aperiodic resource setting.

[0190] A single reporting setting (e.g., CSI-ReportConfig) can be associated with up to three resource settings (e.g., CSI-ResourceConfig). For example, a single CSI reporting setting can include the ID of at least one CSI resource setting (e.g., CSI-ResourceConfigId). The at least one CSI resource setting can include a CSI resource setting related to a measurement.

[0191] < Beam Management (BM) >

[0192] BM procedures are L1 (layer 1) / L2 (layer 2) procedures for acquiring and maintaining a set of base station (e.g., gNB, TRP, etc.) and / or terminal (e.g., UE) beams that can be used for downlink (DL) and uplink (UL) transmission / reception, and may include the following procedures and terminology.

[0193] - Beam measurement: An operation in which a base station or UE measures the characteristics of a received beam-forming signal.

[0194] - Beam determination: An operation in which a base station or UE selects its own transmit beam (Tx beam) / receive beam (Rx beam).

[0195] - Beam sweeping: The operation of covering a spatial area using a transmit and / or receive beam over a predetermined time interval in a predetermined manner.

[0196] - Beam report: An operation in which a UE reports information about a beam-formed signal based on beam measurement.

[0197] The BM procedure can be divided into (1) a DL BM procedure using SS (synchronization signal) / PBCH (physical broadcast channel) Block or CSI-RS, and (2) a UL BM procedure using SRS (sounding reference signal).

[0198] Additionally, each BM procedure may include Tx beam sweeping to determine the Tx beam and Rx beam sweeping to determine the Rx beam.

[0199] DL BM

[0200] The DL BM procedure may include (1) transmission of beamformed DL RSs (reference signals) (e.g., CSI-RS or SS Block (SSB)) of the base station and (2) beam reporting of the terminal.

[0201] Here, beam reporting may include preferred DL RS ID(identifier)(s) and corresponding L1-RSRP (Reference Signal Received Power).

[0202] The above DL RS ID may be an SSBRI (SSB Resource Indicator) or a CRI (CSI-RS Resource Indicator).

[0203] An example of beamforming using SSB and CSI-RS is described in detail below.

[0204] Both SSB and CSI-RS beams can be used for beam measurement. The measurement metric is L1-RSRP per resource / block. SSB is used for coarse beam measurement, and CSI-RS can be used for fine beam measurement. SSB can be used for both Tx beam sweeping and Rx beam sweeping.

[0205] Rx beam sweeping using SSB can be performed by the UE changing the Rx beam for the same SSBRI across multiple SSB bursts, where one SS burst contains one or more SSBs, and one SS burst set contains one or more SSB bursts.

[0206] Below we will look at the DL BM procedure.

[0207] The configuration for beam report using SSB is performed during CSI / beam configuration in RRC connected state (or RRC connected mode).

[0208] - The terminal receives configuration information from the base station. As a specific example, the terminal receives a CSI-ResourceConfig IE containing a CSI-SSB-ResourceSetList containing SSB resources used for BM from the base station.

[0209] Table 1 shows an example of the CSI-ResourceConfig IE. As shown in Table 1, BM configuration using SSB is not defined separately, and SSB is configured as a CSI-RS resource.

[0210]

[0211] In Table 1, the csi-SSB-ResourceSetList parameter indicates a list of SSB resources used for beam management and reporting in a single CSI-RS resource set. Here, the SSB resource set can be set to {SSBx1, SSBx2, SSBx3, SSBx4, …}. For example, the SSB index can be defined from 0 to 63.

[0212] - The terminal receives a downlink reference signal (DL RS) from the base station. As a specific example, the terminal receives an SSB resource from the base station based on the CSI-SSB-ResourceSetList.

[0213] - The terminal transmits a beam report to the base station. For example, if CSI-ReportConfig related to reporting on SSBRI (SSB Resource Indicator) and L1-RSRP is set, the terminal reports the best SSBRI and its corresponding L1-RSRP to the base station.

[0214] That is, when the reportQuantity of the above CSI-ReportConfig IE is set to 'ssb-Index-RSRP', the terminal reports the best SSBRI and the corresponding L1-RSRP to the base station.

[0215] And, if the terminal sets the CSI-RS resource in the same OFDM symbol(s) as the SSB (SS / PBCH Block) and 'QCL-TypeD' is applicable, the terminal can assume that the CSI-RS and SSB are quasi co-located from the 'QCL-TypeD' perspective.

[0216] Here, the QCL TypeD may mean that the antenna ports are QCL-connected from a spatial Rx parameter perspective. When a terminal receives multiple DL antenna ports in a QCL Type D relationship, the same reception beam may be applied. In addition, the terminal does not expect the CSI-RS to be configured in an RE that overlaps with the SSB RE.

[0217] The configuration for beam reporting using CSI-RS is performed in the same manner as the configuration for beam reporting using SSB described above, and thus, redundant description is omitted. The operation of the beam reporting procedure using CSI is described below.

[0218] - The terminal receives configuration information from the base station. As a specific example, the terminal receives a CSI-ResourceConfig IE containing a CSI-SSB-ResourceSetList containing CSI resources used for BM (e.g., NZP CSI-RS resource set IE) from the base station.

[0219] - The terminal receives CSI-RS resources within the NZP CSI-RS resource set through different Tx beams (DL spatial domain transmission filters) of the base station.

[0220] - The terminal selects (or decides) the best beam.

[0221] - The terminal reports the ID and related quality information (e.g., L1-RSRP) for the selected beam to the base station. In this case, the reportQuantity of the CSI report config can be set to 'cri-RSRP'.

[0222] < Description of Rel-17 / 18 beam management >

[0223] In Rel-17, both the DL TCI state and the UL TCI state can be indicated through DL DCI (e.g., DCI format 1-1 or 1-2), or only the UL TCI state can be indicated without indicating the DL TCI state. Therefore, the methods used for UL beam and power control (PC) configuration in the existing R15 / R16 are replaced in Rel-17 with the above UL TCI state indication method. More specifically, in R17, one UL TCI state can be indicated through the TCI field of DL DCI, and the UL TCI state is applied to all PUSCHs and all PUCCHs after a certain time called the beam application time, and can be applied to some or all of the indicated SRS resource sets. In addition, the base station can perform a terminal common beam update by using DCI and / or MAC-CE to perform indication / update with one beam in common (using a joint or separate TCI state) for specific DL / UL channel / RS combinations of multiple terminals. The target channels / RS of common beam update include UE-dedicated CORESET, UE-dedicated reception on PDSCH for DL, DG / CG-PUSCH, all or a subset of dedicated PUCCH for UL, and additionally, AP CSI-RS for tracking / BM, SRS can be set as target channels / RS.In Rel-18, considering the M-TRP environment, the method of indicating multiple UL TCI states (and / or DL ​​TCI states) through the TCI field of DL DCI has been standardized, and the uplink / downlink resources to which multiple indicated TCIs are applied can be defined / configured depending on the S-DCI based M-TRP environment and the M-DCI based M-TRP environment.

[0224] In this document, ' / ' means 'and', 'or', or 'and / or' depending on the context.

[0225] In this specification, 'beam' may mean a source RS for a 'spatial filter' or a 'spatial relation', and may be interpreted as a QCL (type-D) RS or a TCI state or (in the case of uplink) a spatial relation RS.

[0226] For example, in this specification, 'beam' may mean a spatial filter determined based on the reference RS or the source RS. The spatial filter may include a spatial domain filter, a spatial domain transmission filter, and a spatial domain receive filter. For example, in this specification, 'beam' may be interpreted / replaced with a reference signal index (RS index), a reference signal resource index (RS resource index), and / or a resource indicator (e.g., RS index, SSB index, CSI-RS resource index, SRS resource index, SSB Resource Indicator (SSBRI), CSI-RS Resource Indicator (CRI), etc.).

[0227] For example, a beam associated with UL may be referred to as i) a spatial filter (for uplink transmission or uplink reception), ii) a spatial domain filter (for uplink transmission or uplink reception), iii) an uplink spatial domain transmission filter, iv) an uplink spatial domain receive filter, v) an uplink transmit spatial filter (UL Tx spatial filter), or vi) an uplink receive spatial filter (UL Rx spatial filter).

[0228] For example, a beam associated with DL may be referred to as i) a spatial filter (for downlink transmission or downlink reception), ii) a spatial domain filter (for downlink transmission or downlink reception), iii) a downlink spatial domain transmission filter, iv) a downlink spatial domain receive filter, v) a downlink transmit spatial filter (DL Tx spatial filter) or vi) a downlink receive spatial filter (DL Rx spatial filter).

[0229] In the NR standard, QCL setting and spatialRelation setting by TCI state setting are utilized to set the UL / DL transmission / reception beam of the terminal. In the Rel-15 NR standard, RRC and MAC CE signaling are mainly used for UL / DL number / transmission beam. Dynamic signaling was allowed only for the reception beam of the PDSCH using the TCI state field of the DL grant DCI. The Rel-17 / 18 NR standard introduced a unified TCI framework. Specifically, a method was introduced to dynamically manage the common beam by indicating the reception / transmission beam using the indicated TCI using DCI. Meanwhile, in the Rel-18 AI / ML study item, a study was conducted on performance evaluation and specification impact in the spatial beam prediction and temporal beam prediction sub-use cases in the beam management field. The study discussed the NW / UE-side AI / ML operation that predicts the best beam of Set A based on Set B measurements. For UE-side AI / ML, the operation in which the terminal measures Set B and reports the predicted Set A beam can be discussed in the Rel-19 AI / ML work item. In this case, if the beam prediction performance of the terminal is poor, it is necessary to switch the AI / ML model / functionality on the terminal side or fallback to non-AI / ML-based conventional beam management rather than AI / ML-based beam management (measurement / reporting).

[0230] This specification proposes a performance monitoring method for a terminal-side AI / ML model, and proposes an operation in which the terminal reports performance monitoring results to the base station when a specific event occurs.

[0231] < UE initiated BM related background >

[0232] In existing LTE / NR systems, the reporting of CSI / beam information from a UE is determined / controlled by the base station / network (except in the case of BFR). These NW (network)-initiated / triggered reports have limitations in that they require UEs to be configured / instructed to frequently send CSI / beam information in environments where the wireless channel is likely to change rapidly. In such environments, the UL resource overhead for CSI / beam reporting and the related DL measurement RS overhead increase, and the UE's power consumption also increases due to frequent uplink transmission. Furthermore, the more UEs within cell / TRP coverage, the greater the UL resource overhead, as each UE must be allocated UL resources. To overcome these limitations of NW-initiated / triggered reports, recently emerging approaches are UE-initiated / triggered reports or event-based / triggered reports.

[0233] In the UE-initiated / triggered report method or event-based / triggered report method, the UE determines whether and when to report. By performing the report only when necessary (e.g., when a specific event occurs), UL resource overhead and UE power consumption can be reduced. With the above motivation, standardization of UE-initiated / triggered beam reports is expected in NR Rel-19. Furthermore, in 6G communication systems, UE-initiated / triggered or event-based transmission methods can be more actively expanded and adopted to efficiently manage uplink resources.

[0234] In the NR system, there are two representative reporting methods for event-based or UE-initiated / triggered information: SR (scheduling request) and BFR (beam failure recovery). SR reports whether PUSCH allocation is required for UL-SCH transmission, and BFR reports whether BF occurs and new beam-related information. This information is conveyed / transmitted to the base station in an explicit or implicit manner (e.g., conveying a new beam index as PRACH resource selection information). The above-mentioned SR / BFR-related information is conveyed simultaneously or separately through one or two UL resources (e.g., BFRQ on PUCCH + beam information via MAC-CE on PUSCH).

[0235] In this specification, information transmitted to the network based on a terminal event and / or via a UE-initiated / triggered transmission method (e.g., SR, BFRQ, new beam information, etc.) as described above is referred to as “event information” for convenience of explanation. Event information is composed of one or more information parts / blocks, and encoding / rate matching / RE mapping can be performed for each part / block unit. Each information part / unit can also be transmitted via different transmission methods (e.g., BFRQ via UCI as an L1 message, new beam information via MAC-CE as an L2 message).

[0236] < Background related to AI / ML beam management >

[0237] In the Rel-18 AI / ML study item, we conducted a study on performance analysis and potential specification impact through evaluation when NW and / or UE-side AI / ML models operate in three use cases: CSI compression / prediction, beam management, and positioning. In particular, in the beam management use case, we divided the sub-use cases into BM-case1 and BM-case2, and studied performance analysis and potential specification impact for spatial domain beam prediction and temporal beam prediction. The WID goals of AI / ML BM, BM-case1, and BM-case2 are summarized in Tables 2 to 4 below.

[0238] - AI / ML BM's WID goals

[0239]

[0240] - BM-case1: Spatial domain downlink beam prediction for beam set A based on measurement results for beam set B.

[0241]

[0242] - BM-case2: Temporal downlink beam prediction for beam set A based on past measurement results for beam set B.

[0243]

[0244] Additionally, an example of the operation for data collection of AI / ML models in the Beam management use case is shown in Table 5 below.

[0245]

[0246] Additionally, an example of the operation for inference of AI / ML model in Beam management use case is as shown in Table 6 below.

[0247]

[0248] < Method for beam prediction >

[0249] As cited above, the NW / UE-side AI / ML operation that predicts the best beam of Set A based on Set B measurements was discussed. For UE-side AI / ML, the UE is required to measure Set B and report the predicted Set A beam. For NW-side AI / ML, the UE is required to report the Set B measurements.

[0250] Based on the above background, the following examines the beam measurement / reporting setup method for base station-side AI / ML and terminal-side AI / ML, as well as the subsequent base station / terminal operations.

[0251] In this specification, ' / ' can be interpreted as 'and', 'or', or 'and / or' depending on the context.

[0252] Proposal 1

[0253] For beam prediction operation of the NW-side AI / ML model and / or the UE-side AI / ML model, a method of configuring one or more combinations associated with a specific CSI-ReportConfig may be considered. Each combination may include i) one or more Set As and ii) one or more Set Bs associated with each of the one or more Set As. The base station may configure the one or more combinations associated with the specific CSI-ReportConfig to the terminal.

[0254] For example, a specific CSI-ReportConfig may be related / connected to multiple CSI-ResourceConfigs. The multiple CSI-ResourceConfigs may include a CSI-ResourceConfig related to Set A and a CSI-ResourceConfig related to Set B. As a specific example, the specific CSI-ReportConfig may include information about the multiple CSI-ResourceConfigs (e.g., an ID of each of the multiple CSI-ResourceConfigs; CSI-ResourceConfigId). As a specific example, multiple CSI-ResourceConfigs related to the specific CSI-ReportConfig may be set.

[0255] For example, multiple CSI resource sets may be configured / connected to a CSI-ResourceConfig for a specific CSI-ReportConfig. The multiple CSI resource sets may include a CSI resource set related to Set A and a CSI resource set related to Set B. As a specific example, the specific CSI-ReportConfig may include information about the CSI-ResourceConfig (e.g., CSI-ResourceConfigId). The CSI-ResourceConfig may include multiple CSI resource sets.

[0256] In the above examples, CSI-ResourceConfig may be related to channel measurement.

[0257] The methods for setting / connecting one or more of the above combinations (Set A / Set B combinations) are described in more detail in the examples below.

[0258] Example 1 of Proposal 1)

[0259] A base station can configure / connect one Set A and one or more Set Bs for a specific CSI-ReportConfig for beam prediction purposes. CSI-ReportConfig based on Embodiment 1) below is described with reference to FIG. 7.

[0260] Fig. 7 illustrates an example of a CSI reporting configuration according to an embodiment of the present specification. Specifically, Fig. 7 illustrates Set A / Set B based on CSI-ReportConfig. Referring to Fig. 7, CSI-ReportConfig#1 may be associated with Set A and three Set Bs. The three Set Bs include i) Set B configured based on 1 / 8 of the beams (e.g., RS indices or RS resources) in Set A, ii) Set B configured based on 1 / 16 of the beams in Set A, and iii) Set B configured based on beams other than the beams in Set A.

[0261] Subsequently, information may be indicated as to which Set B the terminal will utilize for beam measurement / reporting purposes for the specific CSI-ReportConfig. For example, one of the Set Bs associated with the specific CSI-ReportConfig may be activated. As a specific example, the base station may transmit information (e.g., an activation message) to the terminal for activating one of the Set Bs associated with the specific CSI-ReportConfig. The information may be transmitted based on MAC CE or DCI.

[0262] Example 2 of Proposal 1)

[0263] A base station can set / connect i) multiple Set As and ii) multiple Set Bs associated / related with each of the multiple Set As for a specific CSI-ReportConfig for beam prediction purposes. CSI-ReportConfig based on embodiment 2) is described below with reference to FIG. 8.

[0264] Figure 8 illustrates another example of a CSI reporting configuration according to an embodiment of the present specification. Specifically, Figure 8 illustrates combinations of Set A / Set B based on CSI-ReportConfig. Referring to Figure 8, CSI-ReportConfig#1 may be associated with three combinations.

[0265] Set A / B combination #1 may include Set A (Set A #1) and two Set Bs. The two Set Bs include i) Set B configured based on 1 / 4 of the beams (e.g., RS indices or RS resources) in Set A #1, and ii) Set B configured based on 1 / 8 of the beams in Set A #1.

[0266] Set A / B combination #2 may include Set A (Set A #2) and three Set Bs. The three Set Bs include i) Set B configured based on 1 / 8 of the beams (e.g., RS indices or RS resources) in Set A #2, ii) Set B configured based on 1 / 16 of the beams in Set A #2, and iii) Set B configured based on beams other than the beams in Set A #2.

[0267] Set A / B combination #3 may include Set A (Set A #3) and two Set Bs. The two Set Bs include i) Set B configured based on 1 / 4 of the beams (e.g., RS indices or RS resources) in Set A #3, and ii) Set B configured based on beams other than the beams in Set A #3.

[0268] Subsequently, information may be indicated as to which Set A and which Set B (associated / related with the Set A) for the specific CSI-ReportConfig is to be utilized by the terminal for beam measurement / reporting purposes. For example, i) a specific Set A among a plurality of Set As associated with the specific CSI-ReportConfig and ii) a specific Set B among a plurality of Set Bs associated with the specific Set A may be activated. As a specific example, the base station may transmit information (e.g., an activation message) for activating the specific Set A and the specific Set B associated with the specific CSI-ReportConfig to the terminal. The information may be transmitted based on MAC CE or DCI.

[0269] For example, in the embodiment of the above proposal 1, Set B may be set / linked to a specific CSI-ReportConfig in the form of a separate CSI resource set from Set A.

[0270] For example, in the embodiment of the proposal 1, Set B may be configured to include some resources among a plurality of CSI resources in a CSI resource set set as Set A. Base station signaling related to the configuration of Set B may be performed. For example, beams corresponding to 64 CSI resources may exist in Set A. In this case, a specific Set B may be configured based on a 64-bitmap. Specifically, the 64-bitmap indicates CSI resources belonging to the specific Set B among the 64 CSI resources in Set A.

[0271] Additionally, for environments where Set A and Set B do not intersect (e.g., when Set A and Set B are different), Set B can be defined / set as follows.

[0272] For example, Set B may be defined / configured based on i) CSI resource(s) within Set A and ii) coefficient value(s) applied to the CSI resource(s). As a specific example, one or more beams of Set B may be configured based on a linear combination. The linear combination may be based on CSI resources and coefficient values ​​associated with the resources.

[0273] For example, one or more beams of Set B can be set based on a 2D-bitmap for a beamforming range (e.g., horizontal angle, vertical angle).

[0274] Specifically, when the embodiment of Proposal 1 is utilized for UE-side AI / ML, the terminal can report information indicating preferred Set A and / or Set B for terminal-side beam prediction operation to the base station. As a specific example, among the Set As and Set Bs based on Embodiments 1 and 2 described above, the terminal can report preferred Set A and / or preferred Set B to the base station. Subsequently, the base station can activate (using MAC CE signaling, etc.) the combination of Set A and Set B preferred by the terminal for the specific CSI-ReportConfig.

[0275] Effect of the above suggestion 1

[0276] When the terminal performs a report for the Set B beam through the Set A / B beam set setting operation of the above proposal 1, the base station can perform a beam prediction operation for Set A using the NW sided AI / ML, and the terminal can derive the predicted Set A beam using the Set B beam measurement (as input data of the UE sided AI / ML).

[0277] In addition, NW / UE-side AI / ML models / functionality may vary, and even the size of input data may vary for a specific model / functionality. In this case, based on the present embodiment, various combinations of Set A and Set B may be preset in the terminal by the base station. The combination of Set A and Set B suitable for the AI / ML model / functionality of the NW / UE may be adaptively activated / indicated (via MAC CE or DCI) in response to changes in the input data size. This operation may result in effects such as delay reduction and improved beam prediction performance.

[0278] Proposal 2

[0279] The base station may set reportQuantity related to reports of Alt 1) to Alt 5) below for beam measurement / reporting of the terminal for the CSI-ReportConfig of the above proposal 1. For example, reportQuantity may be set to a value indicating a report based on at least one of Alt 1) to Alt 5) / information included in the report.

[0280] Alt 1)

[0281] Based on the reportQuantity set by the base station, the terminal can report the L1-RSRP value for one or more CMRs (Channel Measurement Resources) related to Set B for the CSI-ReportConfig of Proposal 1. The one or more CMRs related to Set B can be i) all CMRs in Set B, ii) N CMRs (where N is a natural number) having the highest L1-RSRP value in Set B, or iii) specific N CMRs in Set B set / instructed by the base station.

[0282] Alt 2)

[0283] Based on the reportQuantity set by the base station, the terminal can perform reporting as follows. For the CSI-ReportConfig of Proposal 1, the terminal can report i) L1-RSRP value for one or more CMRs associated with Set B and ii) L1-RSRP value for one or more CMRs associated with Set A.

[0284] One or more CMRs associated with the above Set A may be i) specific N CMRs in Set A that have a connection relationship (by base station presetting) with CMRs in Set B reported by the terminal, ii) N CMRs with the highest L1-RSRP value in Set A, or iii) specific N CMRs in Set A configured / instructed by the base station.

[0285] Specifically, in addition to the report related to Set B, the report related to Set A can be performed only for a portion of the total reporting instances of the CSI-ReportConfig (e.g., 1 / X of the total reporting instances, where X is a natural number). In order to match the reporting payload when performing reports related to both Set B and Set A and when performing reports related to only Set B, the following embodiments may be considered when Set A is also reported.

[0286] i) A step size of 2 dB or more may be applied when reporting L1-RSRP of the best CMR in Set B. For example, the legacy step size may be 1 dB.

[0287] ii) A larger step size may be applied for differential reporting when reporting L1-RSRP of non-best CMRs within Set B (while utilizing a step size of 2 dB or more when reporting L1-RSRP of best CMRs). For example, according to the legacy standard, the step size for differential reporting is 2 dB, but according to this embodiment, a step size such as 4 dB, 6 dB, or 8 dB may be applied.

[0288] iii) The number of CMRs reported in Set B may be reduced or some CMRs in Set B may be omitted from reporting (e.g., N CMRs with the lowest L1-RSRP values ​​may be omitted).

[0289] Additionally, the reporting granularity for expressing L1-RSRP may be different for reports on Set B and reports on Set A.

[0290] Alt 3)

[0291] Based on the reportQuantity set by the base station, the terminal may report one or more predicted best CMRs related to Set A (based on Set B beam measurement) and the predicted L1-RSRP values ​​for the same for the CSI-ReportConfig of Proposal 1.

[0292] One or more predicted best CMRs associated with the above Set A may be specific K CMRs (where K is a natural number) set / instructed by the base station (corresponding to the highest predicted L1-RSRP value of Top-K).

[0293] Alt 4)

[0294] Based on the reportQuantity set by the base station, the terminal can perform reporting as follows. For the CSI-ReportConfig of the above proposal 1, the terminal can report i) one or more predicted best CMRs related to Set A (based on Set B beam measurement) and their predicted L1-RSRP values, and ii) (actually measured) L1-RSRP values ​​for one or more CMRs related to Set A.

[0295] The one or more CMRs associated with the above Set A may be i) specific N CMRs within Set A that have a connection relationship (by base station presetting) with the predicted best CMR associated with Set A reported by the terminal, ii) N CMRs with the highest L1-RSRP value within Set A, or iii) specific N CMRs within Set A configured / instructed by the base station.

[0296] Characteristically, in addition to the predicted CMR-related report related to Set A, the report related to Set A can be performed only for a portion of the total reporting instances of the CSI-ReportConfig (e.g., 1 / X of the total reporting instances, where X is a natural number). In order to match the reporting payload to be identical / similar when performing reports related to both the predicted CMR of Set A and Set A, and when performing reports related only to the predicted CMR of Set A, the following embodiments may be considered when Set A is also reported.

[0297] i) A step size of 2 dB or more may be applied when reporting the predicted L1-RSRP of the predicted best CMR within Set A. For example, the legacy step size may be 1 dB.

[0298] ii) A larger step size may be applied for differential reporting when reporting predicted L1-RSRP of predicted non-best CMR (while utilizing a step size of 2 dB or more when reporting predicted L1-RSRP of predicted best CMR within Set A). For example, the step size for differential reporting is 2 dB according to the legacy standard, but a step size such as 4 dB, 6 dB, or 8 dB may be applied according to this embodiment.

[0299] iii) The number of predicted CMRs reported in Set A may be reduced, or some CMRs in Set A may be omitted from reporting (e.g., the N CMRs with the lowest L1-RSRP values ​​may be omitted).

[0300] Additionally, the reporting granularity for expressing the (predicted) L1-RSRP in the report on Set A may be different from that in the report on Set A for predicted CMR.

[0301] Alt 5)

[0302] Based on the reportQuantity set by the base station, the terminal can perform reporting as follows. The terminal reports based on Alt 3 to Alt 4, but can also report based on Alt 1 if certain conditions are met. This will be described in detail below.

[0303] Based on the UE-side AI / ML, the predicted best beam performance of Set A is determined / judged to be below the reference value, and the UE may revert to Alt 1 and perform reporting. The UE may report the L1-RSRP value for one or more CMRs associated with Set B.

[0304] The reporting of the above proposal 2 can be performed using P / SP / A CSI on PUSCH / PUCCH.

[0305] The effect of Proposal 2

[0306] In Proposal 2 above, Alt 1 and Alt 2 can be utilized to secure input data for NW-side AI / ML (by having the terminal report the actually measured results of Set A / B). In particular, Alt 2 can be helpful in performing AI / ML performance monitoring by comparing the quality of Set A beam predicted by the base station with the actual quality of Set A beam.

[0307] Additionally, Alt 3 to Alt 5 in Proposal 2 can be utilized for UE-side AI / ML. In particular, Alt 4 can be helpful for the base station to perform performance monitoring of UE-side AI / ML, as the terminal compares and reports the predicted Set A beam with the measured Set A beam. Alt 5 can have the effect of returning to legacy beam management (based on the terminal's decision) by having the terminal perform performance monitoring of UE-side AI / ML.

[0308] The operations of Proposals 1 and 2 above are applicable to both spatial domain beam prediction and temporal domain beam prediction. For example, if the operations of Alt 1 to Alt 5 of Proposal 2 are applied to temporal domain beam prediction, when performing reports for specific CMRs of Set A and Set B, reports / information for multiple time instances can be included in the report of Proposal 2.

[0309] The above embodiments may be operated by a combination of specific embodiments.

[0310] < Method for UE report on AI / ML model performance monitoring>

[0311] As described above, the Rel-18 AI / ML study item discussed the NW / UE sided AI / ML operation that predicts the best beam of Set A based on Set B measurements. In the case of UE sided AI / ML, the operation in which the terminal measures Set B and reports the predicted Set A beam can be discussed in the Rel-19 AI / ML work item. Methods for such operations are proposed in Proposals 1 and 2. In the UE sided AI / ML beam prediction operation including the above examples, if the beam prediction performance of the terminal is poor, operations such as switching the AI / ML model / functionality on the terminal side or falling back to non-AI / ML based conventional beam management rather than AI / ML based beam management may be required.

[0312] Performance monitoring for these AI / ML models is also required for NW-side AI / ML. In this case, the terminal measures and reports the Set A beam. The actual Set A best beam is then compared to the predicted Set A best beam on the NW side. If performance falls below a certain standard, UE-transparent model / functionality switching or fallback to conventional beam management is performed.

[0313] For UE-side AI / ML, the base station simply configures the terminal to measure Set A beam, allowing the terminal to monitor model performance. However, considering the traditional slave node role of the terminal, hybrid monitoring, in which the terminal reports performance monitoring results to the base station and the base station (taking into account system-level / cell-level circumstances) makes decisions such as model / functionality switching or fallback to conventional beam management, can be considered to improve wireless communication reliability. For example, for hybrid monitoring, the terminal can periodically report performance monitoring results to the base station. In addition to the periodic reporting described above, event-triggered reporting can be considered to save resource overhead. For example, the terminal can report only when an event occurs that affects the prediction performance of the UE-side AI / ML model.

[0314] Based on the above background, the following examines the performance monitoring method of the terminal-side AI / ML model and the operation of the terminal reporting performance monitoring results to the base station when a specific event occurs.

[0315] Proposal 3

[0316] When the terminal performs beam prediction operation using UE-side AI / ML, the terminal can report to the base station the performance monitoring results indicating that the performance of the AI / ML model has fallen below the standard when an event such as the options below occurs for performance monitoring of the AI / ML model.

[0317] For example, actions and agreements related to performance monitoring can be based on Table 7 below.

[0318]

[0319] Option 1)

[0320] An event can be defined based on the difference in RSRP values.

[0321] For example, the condition related to the event may be defined as being satisfied based on the difference between the RSRP value of the actually measured Top-K best beam related to Set A and the RSRP value of the predicted Top-K best beam (based on the output result of the UE-sided AI / ML model) related to Set A exceeding a certain threshold. In this specification, the satisfaction of the condition related to the event may be interpreted / replaced as the occurrence of the event.

[0322] For example, if the difference in RSRP value between the Top-1 beam among the actually measured Top-K best beams and the Top-1 beam among the predicted Top-K best beams exceeds a specific threshold, the event may be defined to occur (a condition related to the event may be defined to be satisfied).

[0323] For example, if the sum of the RSRP value differences (K RSRP value differences) (absolute value) between the actually measured Top-K best beams and the predicted Top-K best beams exceeds a specific threshold, the event may be defined as occurring (a condition related to the event may be defined as being satisfied).

[0324] For example, if the sum of all K differences (absolute values ​​of) between the predicted RSRP of the Kth best beam among the predicted Top-K best beams and the actual measured L1-RSRP value of the Kth best beam exceeds a specific threshold, the event may be defined as occurring (the condition related to the event may be defined as being satisfied).

[0325] The sum of all K difference values ​​above can mean sum_(k=1 to K) |predicted RSRP - actual measured L1-RSRP| when k=1,2,…,K. (|predicted RSRP - actual measured L1-RSRP| is the absolute value of the RSRP value difference.)

[0326] In the above, the base station can set an error (RSRP) margin value to compensate for temporary measurement errors and prediction errors. Even if the RSRP difference value (or the sum of the RSRP difference values) exceeds the specific threshold, the terminal may not determine it as a prediction error if the exceeding value is within the margin value. As a specific example, it may be assumed that the RSRP difference value (or the sum of the RSRP difference values) is greater than the specific threshold by X. If X is less than or equal to the margin value, the terminal may determine that the event according to the present embodiment has not occurred.

[0327] Option 2)

[0328] An event can be defined based on a mismatch of the best beam (Top-1 beam).

[0329] For example, when comparing the actual measured Top-K best beam related to Set A with the predicted Top-K best beam (based on the output result of the UE sided AI / ML model) related to Set A, the condition related to the event can be defined as being satisfied based on the occurrence of a mismatch in the best beam (Top-1 beam).

[0330] As a concrete example, the event may be defined as having occurred (or a condition related to the event may be defined as being satisfied) based on the fact that the actual measured Top-1 best beam associated with Set A is different from the predicted Top-1 best beam (based on the output result of the UE-sided AI / ML model).

[0331] Option 3)

[0332] Events can be defined based on prediction accuracy.

[0333] For example, when comparing the actual measured Top-K best beam related to Set A and the predicted Top-K best beam (based on the output of the UE sided AI / ML model) related to Set A, the condition related to the event can be defined as being satisfied based on the presence of more than K_threshold incorrect answers.

[0334] As a concrete example, it can be assumed that among predicted Top-K best beams, M predicted best beams match M actually measured best beams (corresponding to RSRP order), and KM predicted best beams are different from KM actually measured best beams. (When KM exceeds K_threshold), whether an event occurs can be determined based on the prediction accuracy set by the base station. The base station can transmit to the terminal information about specific beam combinations for which the prediction accuracy must be guaranteed (e.g., the match between the measured Top-X best beam and the predicted Top-X best beam).

[0335] For example, the information may indicate that the measured Top-1 best beam and the predicted Top-1 best beam should match. In other words, the predicted accuracy indicated based on the information may indicate the match of one beam (the Top-1 best beam).

[0336] For example, the information may indicate that the measured Top-2 best beams and the predicted Top-2 best beams should match. In other words, the predicted accuracy indicated based on the information may indicate the agreement between the two beams (the Top-2 best beams).

[0337] For example, the information may indicate that the measured Top-3 best beams and the predicted Top-3 best beams should be consistent. In other words, the predicted accuracy indicated based on the information may indicate the consistency of the three beams (Top-3 best beams).

[0338] It can be assumed that the base station sets K_threshold to 2, and the terminal compares the predicted Top-K best beams with the measured Top-K best beams and detects three mismatches (e.g., KM (3) > K_threshold (2)). In this case, whether an event based on this embodiment occurs can be determined as follows.

[0339] For example, if the predicted Top-2 best beams match the measured Top-2 best beams, the terminal may not judge it as a prediction error (it may judge that the conditions related to the event are not met).

[0340] For example, if at least one of the predicted Top-2 best beams does not match at least one of the measured Top-2 best beams, the terminal may determine that a prediction error has occurred (a condition related to the event may be determined to be satisfied). As a specific example, if a first predicted beam among the predicted Top-2 best beams does not match a first measured beam among the measured Top-2 best beams, the condition related to the event may be determined to be satisfied. As a specific example, if a second predicted beam among the predicted Top-2 best beams does not match a second measured beam among the measured Top-2 best beams, the condition related to the event may be determined to be satisfied. As a specific example, if the predicted Top-2 best beams do not match the measured Top-2 best beams, the condition related to the event may be determined to be satisfied.

[0341] The operation of option 3 above can also be utilized when comparing the actual measured best beam and the predicted best beam for N instances during temporal domain beam prediction. For example, whether an event of option 3 occurs can be determined based on a (set / defined) N_threshold.

[0342] Option 4)

[0343] Events can be defined based on confidence level and / or probability.

[0344] For example, a condition related to an event may be defined as being satisfied based on the confidence level or / and probability of the terminal AI / ML model for the predicted best beam (based on the output result of the UE-side AI / ML model) related to Set A being below a certain threshold.

[0345] An event may be defined based on a combination of one or more of the options in Proposal 3 above. For example, an event may be defined as occurring based on the conditions associated with Option 1 and Option 2 being met together.

[0346] In addition, for each of the above options, the terminal can report the performance monitoring result when an event occurs instantaneously in a single instance performing Set A actual measured beam measurement / reporting (which is shorter than the period for reporting Set A predicted beam). Considering the occurrence of temporary errors, the terminal can perform the performance monitoring result report based on a counter. Specifically, a counter (=I) related to the number of event occurrences can be defined. The terminal can report the performance monitoring result based on the occurrence of each of the above events I or more times (within a specific time window). This operation can ensure the reliability of the performance monitoring result report. Separately from or in addition to the above counter operation, a (terminal AI / ML model / functionality-specific) timer (equivalent to the event occurrence condition) that triggers a performance monitoring result report for a terminal-specific AI / ML model / functionality is set / defined in the terminal, and when the timer expires, the terminal reports the performance monitoring result for the specific AI / ML model / functionality to the base station and resets / restarts the timer. If the timer operation is performed in the terminal in addition to the counter operation, the running timer can be reset / restarted when the performance monitoring result report for the specific AI / ML model / functionality of the terminal is performed by the occurrence of the event(s) of the proposal 1.

[0347] As an alternative solution to the event definition / setting of the above proposal 1, the following actions may be performed when 'the terminal determines as a result of performance monitoring that there is an abnormality in the beam prediction function for a specific AI / ML model / functionality' and / or 'the terminal wants to request a fallback to a non-predicted beam-based operation (i.e., non-AI / ML based beam management)'. For the terminal-specific AI / ML model / functionality, the terminal may operate based on at least one of the following i) to iii).

[0348] i) The terminal can report performance monitoring results to the base station.

[0349] ii) The terminal can perform the operations of suggestions 2 and / or 3 below.

[0350] iii) The terminal can request the base station to fallback to non-predicted beam-based operation (i.e., non-AI / ML based beam management).

[0351] Additionally, with respect to the method of reporting the predicted beam associated with Set A and the actual measured beam associated with Set A together, as in Alt 4 of Proposal 2, a method of reporting the number of incorrect answers (the number of mismatched beams) rather than reporting the actual measured beam associated with Set A beams together may be considered. As a specific example, the terminal may report i) the predicted Top-K best beam associated with Set A, and ii) the number of predicted beams that are mismatched when comparing the actual measured Top-K best beam with the predicted Top-K best beam.

[0352] For example, in relation to reporting the number of incorrect answers, the number of incorrect answers itself may be reported based on ceil(log2(K)) bits.

[0353] For example, in relation to the reporting of the number of incorrect answers, whether each beam among the Top-K predicted beams is incorrect / mismatched may be reported based on the K-bitmap. As a specific example, if the bit value (the 3rd bit value) of the K-bitmap is 1, it may mean that the predicted beam and the measured beam according to the corresponding order (3) match. As a specific example, if the bit value (the 3rd bit value) of the K-bitmap is 0, it may mean that the predicted beam and the measured beam according to the corresponding order (3) do not match.

[0354] In one embodiment, the base station can perform performance monitoring of UE-sided AI / ML based on these terminal reports and determine terminal-side AI / ML model / functionality switching or fallback to conventional beam management.

[0355] In one embodiment, the operation of Alt 5 of Proposal 2 may be performed in combination with Proposal 3. For example, based on the satisfaction of at least one of the options of Proposal 3, the terminal may revert to the reporting operation of Alt 1. If at least one event of the options of Proposal 3 occurs, the terminal may revert to the operation according to conventional beam management.

[0356] Proposal 4

[0357] When an event described in Proposal 3 occurs, the terminal can report the terminal AI / ML performance monitoring results to the base station using the signaling method / medium below.

[0358] Option 1) dedicated SR-PUCCH

[0359] In one embodiment, an environment may be assumed in which the NW controls UE-side AI / ML model / functionality switching / refinement. The UE may report one of the following i) to iv) to the base station based on the 2-bit codepoint (e.g., 00, 01, 10, 11) of the SR-PUCCH.

[0360] i) Set A / B beam set change request

[0361] ii) model / functionality switching request

[0362] iii) model / functionality stop / hold request

[0363] iv) Fallback request (to conventional beam management)

[0364] In one embodiment, an environment may be assumed in which the UE independently performs switching / refinement of the UE-side AI / ML model / functionality. The UE may independently switch / stop the model / functionality or stop AI / ML-based beam prediction.

[0365] The terminal may report one of the following i) to iv) to the base station based on the 2-bit codepoint of the SR-PUCCH (e.g., 00, 01, 10, 11).

[0366] i) model / functionality switching report

[0367] ii) model / functionality stop report

[0368] iii) Hold request / report on prediction or model / functionality

[0369] iv) Report hold notification

[0370] The above "model / functionality stop report" may mean that the terminal will no longer perform prediction and will not perform reports (related to the prediction) due to beam prediction performance issues.

[0371] In the case of the above "hold request / report for prediction or model / functionality" or "report hold notification", it can be interpreted that the terminal reports to the base station that "prediction will not be performed for the time being." After a certain period of time (as agreed upon / agreed upon between the base station and the terminal), the terminal can resume prediction operations (after applying a new model or performing maintenance). In addition, the above "hold request / report for prediction or model / functionality" or "report hold notification" can also be interpreted that the terminal reports to the base station that "report will not be performed for the time being." The terminal and the base station can perform actions such as releasing the corresponding CSI reporting resources (e.g., PUCCH, PUSCH) for a certain period of time (as agreed upon / agreed upon between the base station and the terminal). During this period of time, the base station can flexibly utilize the corresponding time / frequency resources (e.g., allocate the corresponding time / frequency resources to other terminals). After the certain period of time, the terminal can resume beam prediction reporting again.

[0372] The terminal can report monitoring results only by transmitting the SR-PUCCH of option 1. Therefore, the base station may not allocate the UL grant to the terminal after receiving the SR-PUCCH.

[0373] Option 2) dedicated SR-PUCCH + PUSCH MAC CE (via subsequent UL grant)

[0374] The terminal can transmit a PUSCH based on the UL grant allocated via SR-PUCCH. The terminal can perform performance monitoring reporting using the MAC CE message associated with the PUSCH. Specifically, the MAC CE message can include performance monitoring results of options 1 to 4 of Proposal 1 (e.g., which event occurred and which threshold value was exceeded by the event).

[0375] For example, an environment in which the NW controls UE-side AI / ML model / functionality switching / refine, similar to the above option 1, may be assumed. The terminal may transmit the MAC CE message including information indicating one or more of the following i) to v) to the base station.

[0376] i) Set A / B beam set change request

[0377] ii) model / functionality switching / stop request

[0378] iii) fallback request (to conventional beam management)

[0379] iv) Preferred combination of Set A / B

[0380] v) Preferred model / functionality

[0381] For example, it may be assumed that the UE independently performs switching / refinement of the UE-side AI / ML model / functionality. (Similar to option 1 above), the UE may independently switch / stop the model / functionality or stop AI / ML-based beam prediction. The UE may transmit the MAC CE message containing information indicating one of the following i) to iv) to the base station.

[0382] i) model / functionality switching report

[0383] ii) model / functionality stop report

[0384] iii) Hold request / report on prediction or model / functionality

[0385] iv) Report hold notification

[0386] Option 3) UE-initiated CFRA

[0387] Similar to the above option 1, the SSB indices related to the following i) to vii) can be defined / set in advance by the base station to the terminal.

[0388] i) Set A / B beam set change request

[0389] ii) model / functionality switching / stop request

[0390] iii) fallback request (to conventional beam management)

[0391] iv) model / functionality switching report

[0392] v) model / functionality stop report

[0393] vi) Hold request / report on prediction or model / functionality

[0394] vii) Report hold notification

[0395] When a terminal transmits a PRACH based on a specific SSB index among the above SSB indices, a performance monitoring report content (e.g., one of i) to vii) defined between the base station and the terminal may be transmitted to the base station. A base station that receives a PRACH based on a specific SSB index among the above SSB indices may determine that a report (e.g., one of i) to vii) related to the specific SSB index has been performed, and may perform subsequent operations related thereto.

[0396] A terminal can report monitoring results solely through PRACH transmission. For example, the base station may not transmit an RAR to the terminal after receiving the corresponding PRACH (omitting RAR transmission). For example, the base station may transmit a RAR scheduling DCI and an RAR PDSCH. The RAR MAC CE of the RAR PDSCH may not include a UL grant.

[0397] Option 4) PRACH + Msg 3 PUSCH MAC CE (by UL grant of subsequent RAR MAC CE)

[0398] Similar to option 2, the UE can transmit a PUSCH based on the UL grant allocated via (CBRA / CFRA) PRACH transmission. The UE can perform performance monitoring reporting using the MAC CE message associated with the PUSCH. Specific embodiments of the MAC CE message configuration method may be identical to option 2.

[0399] In the above options, by utilizing the PUSCH scheduled after SR-PUCCH or CFRA, the terminal can report the event content that occurred in a specific AI / ML model / functionality (e.g., the event of the above proposal 1) or / and the corresponding performance monitoring result (e.g., the correct / incorrect answer rate of the predicted beam in the above proposal 1, the difference in RSRP value between the predicted beam and the actual measured beam, and the confidence level or / and probability, etc.).

[0400] The effects of the above suggestions 3 and 4

[0401] The terminal can report the performance monitoring results of the terminal AI / ML model / functionality of Proposal 4 to the base station only when the event of Proposal 3 occurs. This can reduce resource overhead. Based on the AI / ML monitoring results of the terminal, the base station can decide to stop / switch the model / functionality or fallback to the legacy BM (if the base station manages the terminal's AI / ML model / functionality). In particular, if there is a problem with beam prediction performance in the case where the terminal manages its own AI / ML model / functionality as in Option 1 of Proposal 4, the terminal can temporarily suspend prediction and reorganize. While the terminal's prediction is suspended, reporting resources can be released and allocated to other terminals, which can increase the flexibility of resource utilization.

[0402] The performance monitoring reporting method of the above proposal 4 can also be used for performance monitoring reporting using periodic / static resources rather than event-triggered operations.

[0403] Proposal 5

[0404] (When an event described in Proposal 3 occurs), the terminal may transmit a report (e.g., a monitoring result report) to the base station. At this time, based on the report, the base station may be requested to switch the relevant Set A configuration for a specific AI / ML model / functionality (related to beam management). In other words, the report may include information indicating the switch of the Set A configuration.

[0405] More specifically, the base station can configure one or more candidate values ​​for Set A. The terminal can perform model inference based on the Set B configuration associated with a specific Set A. If performance is abnormal, the terminal can provide feedback (based on an event) on the preferred Set A index among the candidate values ​​for Set A.

[0406] Among the candidate values ​​of the above Set A, a default Set A (e.g., Set A index #0) (related to non-AI / ML based beam management) can be set / defined. The terminal can request a fallback to non-AI / ML based beam management by feeding back this default Set A to the base station.

[0407] For example, when there are 2 to 4 Set A candidate values, the terminal's Set A configuration switch / fallback request or feedback (e.g., monitoring report) can be transmitted to the base station in the form of a new UCI type based on PUCCH format 0 / 1.

[0408] For example, a request for a switch / fallback of the Set A configuration of a terminal or such feedback (e.g., a monitoring report) can be transmitted to the base station using SR-PUCCH, similar to Proposal 2 above. Each index of the candidate values ​​of Set A can be encoded in 2 bits of SR-PUCCH. As described above, when the terminal requests the base station to switch the Set A configuration in the form of a new UCI type or requests the base station to switch the Set A configuration using SR-PUCCH, among the candidate values ​​of Set A configured by the base station, a candidate Set A that is identical to Set B can be indicated.

[0409] Specifically, the Set A indices encoded in SR-PUCCH 2 bits may include a Set A index related to non-AI / ML based beam management or / and a Set A index having the property of Set A = Set B. When the terminal requests / feeds back the Set A index to the base station through the method of Proposal 5, the feedback (monitoring report) may mean a fallback request to non-AI / ML based beam management. In other words, the base station may interpret and operate as if a fallback to non-AI / ML based beam management is requested by the feedback (monitoring report).

[0410] For example, i) multiple Set A candidate values ​​set by the base station may be related / associated with different CSI-reportConfigs. For example, ii) multiple Set A candidate values ​​set by the base station may be related / associated with a single CSI-reportConfig simultaneously.

[0411] In the case of method ii, the number of beams / CMRs (e.g., number of CRIs / SSBRIs) set / existing in each of the plurality of Sets A may be different. Therefore, when the base station changes and configures Set A in response to a request for a change in the Set A setting of the terminal, a problem may arise in that the number of bits for expressing the CRI / SSBRI reported through the single CSI-reportConfig may be different. For example, in the case of Set A having the property of Set A = Set B, the number of beams / CMRs may be smaller than that of other Sets As. To prevent this problem, the number of bits for expressing the CRI / SSBRI may be determined based on the Set A that includes the maximum number of beams among the plurality of Sets As (Set A having the largest number of beams).

[0412] For example, the number of bits of log2 (the number of beams / CMRs in Set A that includes the maximum number of beams) may be required to express the CRI / SSBRI. In this case, Set A candidates other than Set A that include the beam with the maximum value may express the CRI / SSBRI using the number of bits suggested above, and the following embodiments may be considered.

[0413] CRI / SSBRI can be expressed by using only as many codepoints as the number of beams included in each Set A, starting from the LSB. Let's explain specifically with an example where Set A #0~2 has 16 beams and Set A #3 has 4 beams. In the case of Set A #0~2, all 4 bits can be utilized to express CRI / SSBRI. In the case of Set A #3, only 4 codepoints of 0000, 0001, 0010, 0011 from the LSB can be utilized out of the 4 bits to express CRI / SSBRI.

[0414] In the above example, if the terminal uses a codepoint other than the four codepoints 0000, 0001, 0010, and 0011 for beam reporting to express the CRI / SSBRI of Set A #3, the base station may recognize / interpret this as an error case. Specifically, the base station may ignore the beam report for the corresponding CRI / SSBRI local index of Set A #3.

[0415] When a switch / fallback request or feedback of the Set A configuration of the above proposal 3 is reported based on a new UCI type, an additional priority may be set / defined based on at least one of the following embodiments in comparison with the terminal operation related to CSI priority described in Table 8 below.

[0416]

[0417] Example 1) beam related CSI = report (monitoring report) of the above proposal 5 = / > predicted Set A beam related CSI (inference report) > non-beam related CSI

[0418] Example 2) beam related CSI > report of the above proposal 5 (monitoring report) = / > predicted Set A beam related CSI (inference report) > non-beam related CSI

[0419] Example 3) Report of the above proposal 5 (monitoring report) = / > predicted Set A beam related CSI (inference report) > beam related CSI > non-beam related CSI

[0420] Example 4) Report of the above proposal 5 (monitoring report) = / > predicted Set A beam related CSI (inference report) > beam related CSI = measured Set A / B beam related CSI > non-beam related CSI

[0421] Example 5) Measured Set A / B beam-related CSI = beam-related CSI > Report of the above proposal 5 (monitoring report) = / > predicted Set A beam-related CSI related CSI (inference report) > non-beam-related CSI

[0422] Example 6) In terms of priority at the CSI-reporConfig level, in the case of the report of the proposal 5, if it conflicts with a CSI report indicated based on the existing legacy CSI report config, it may have a higher priority. If the report of the proposal 5 conflicts with the LTM-CSI report configg, the report of the proposal 5 has a lower priority than the report based on the LTM-CSI report configg. Alternatively, the report of the proposal 5 may have a lower priority than the legacy CSI report config and the LTM-CSI report configg.

[0423] In the above-described embodiments, 'beam related CSI' means a CSI report carrying L1-RSRP or L1-SINR of Table 8. 'Non-beam related CSI' means a CSI report not carrying L1-RSRP or L1-SINR of Table 8. For example, the value (k) for determining the priority value may vary based on Embodiments 1 to 6. This will be described in detail below with reference to Embodiment 1.

[0424] For example, referring to Embodiment 1, the monitoring report and / or the predicted Set A beam related CSI (inference report) of the proposal 5 may have a higher priority than the CSI report that does not include L1-RSRP or L1-SINR. Specifically, a value (e.g., k=0) for determining a priority value related to the monitoring report and / or the predicted Set A beam related CSI (inference report) of the proposal 5 may be lower than a value (e.g., k=1) for determining a priority value related to the CSI report that does not include L1-RSRP or L1-SINR.

[0425] For example, referring to Embodiment 1, the monitoring report and / or the predicted Set A beam related CSI (inference report) of the proposal 5 may have the same priority as the CSI report including L1-RSRP or L1-SINR. Specifically, the value (e.g., k=0) for determining the priority value related to the monitoring report and / or the predicted Set A beam related CSI (inference report) of the proposal 5 may be the same as the value (e.g., k=0) for determining the priority value related to the CSI report including L1-RSRP or L1-SINR.

[0426] When a UE utilizes SR-PUCCH to report / request performance monitoring results for UE-specific AI / ML functionality, as in Proposals 4 and 5, the following considerations may be made. The base station may anticipate that the UE will require UL-SCH allocation. However, the UE may not require UL-SCH if there is no more information to transmit. The following embodiments may be considered to enable the base station to determine whether the UE requires UL-SCH. A specific dedicated SR PUCCH resource may be allocated for SR-PUCCH transmission, as in Proposals 4 and 5.

[0427] On the other hand, after transmitting SR-PUCCH as in Proposals 4 and 5, the UE can transmit subsequent information (e.g., details about performance monitoring output, etc.) to the base station based on MAC-CE. In this case, SR-PUCCH as in Proposals 4 and 5 can share SR PUCCH resources of a general logical channel (e.g., SR PUCCH resource requesting UL-SCH resources for new transmission) similar to BFR.

[0428] For example, when a terminal reports as in Proposals 4 and 5 above using SR-PUCCH, the terminal may report an indicator to the base station via the SR-PUCCH indicating whether a subsequent UL-SCH is required. The base station may or may not schedule the UL-SCH based on the indication of the terminal (requiring / not requiring the UL-SCH).

[0429] In the latter case in the above paragraph (an operation in which SR-PUCCHs such as Proposals 4 and 5 share SR PUCCH resources of a general logical channel), additional priority-related operations can be set / defined in relation to the legacy operation in which SR for BFR has priority over other SRs as shown in Table 9 below.

[0430]

[0431] For example, a pending SR related to SR-PUCCH, such as in Proposals 4 and 5, may have a higher priority than other SRs. A pending SR related to SR-PUCCH, such as in Proposals 4 and 5, may have a lower priority than a pending SR related to BFR (SR for SCell beam failure recovery and / or SR for beam failure recovery of a BFD-RS set of Serving Cell).

[0432] Effect of Proposal 5 above: If a performance abnormality occurs during performance monitoring for a specific AI / ML model / functionality of a terminal, the terminal can request a change to (preferred) Set A from the base station. In other words, the terminal can request the base station to utilize a model / functionality with better inference performance. In addition, it can request a fallback to conventional beam management operation. More specifically, in situations where the performance of AI / ML-based beam management is very poor, the beam management performance can be maintained above a certain level through fallback.

[0433] Proposal 6

[0434] In order to perform performance monitoring for a terminal-specific AI / ML model / functionality, the base station may set Set X for the purpose of performance monitoring in the terminal. For example, the Set X may be set in a separate CSI-reportConfig (e.g., a separate reporting configuration for monitoring). In other words, a reporting configuration (CSI-reportConfig) related to performance monitoring (prediction accuracy) may include information about the Set X. For example, the Set X may be set in a reporting configuration (CSI-reportConfig) related to Set A / Set B. In other words, a reporting configuration (CSI-reportConfig) related to measurement / prediction may include information about the Set X.

[0435] The above Set X can have the property of being a subset of Set A. This is described in detail below.

[0436] For example, the Set X can be mapped to a subset whose size is equal to that of Set A (e.g., X) (e.g., if the size of Set A is X1, the size of Set X is X2=X1). In this case, each resource of Set X can correspond one-to-one with each resource of Set A. Specifically, the nth resource in Set X can be mapped to the nth resource in Set A.

[0437] For example, the Set X can be mapped to a subset smaller than the size of Set A (e.g., if the size of Set A is X1, the size of Set X is Y). <X1). 이러한 경우, 상기 Set X의 Y개의 자원들이 Set A의 X1개의 자원들(예측을 위한 X1개의 자원들)에 매핑될 수 있다. 구체적인 예로, 상기 Set X에 대한 정보는 비트맵에 기초하여 설정될 수 있다(예: bitmap with Y non-zero bits among X1 bits).

[0438] For example, the number of beams (number of resources) of Set X may be greater than the number of beams (number of resources set for measurement) of Set B.

[0439] For example, one or more Set Xs related to a specific Set A may be set. The base station may instruct the terminal to select a specific Set X among the one or more Set Xs for terminal AI / ML model / functionality performance monitoring.

[0440] Effect of the above proposal 6: In comparison to configuring a full set for Set A for performance monitoring of terminal AI / ML model / functionality, overhead between base station and terminal can be reduced through Set X configuration. In addition, overhead for DL ​​RS transmitted by the base station for actual performance monitoring can also be reduced. In addition, in the above Set X configuration, the base station can perform Set X configuration based on / based on the tendency of the terminal to report the predicted Set A beam based on AI / ML before the present point in time, depending on the base station implementation.

[0441] The above embodiments may be operated by a combination of specific embodiments.

[0442] An example of a terminal (or base station) operation based on at least one of the embodiments described above (e.g., at least one of the embodiments of Proposals 1 to 6) is as follows.

[0443] 1) The terminal (base station) receives (transmits) settings related to event-triggered AI / ML model / functionality performance monitoring reports. The settings may include information related to at least one of Proposals 1 to 6.

[0444] 2) The terminal (base station) receives (transmits) a message scheduling the transmission of the performance monitoring report. The transmission of the performance monitoring report may be performed based on a UE-triggered operation. In this case, this step (step 2)) may be omitted.

[0445] 3) The terminal (base station) transmits (receives) the above performance monitoring report.

[0446] The transmission of the above report may be performed based on the occurrence of the event of Proposal 3. Specifically, the report may be transmitted based on the fulfillment of a condition related to the event of Proposal 3. The report may be transmitted (received) based on the embodiments of Proposals 4 to 6.

[0447] The above terminal / base station operations are only an example, and each operation (or step) is not necessarily required, and depending on the terminal / base station implementation method, the terminal's AI / ML model / functionality performance monitoring reporting operation according to the above-described embodiments may be omitted or added.

[0448] In terms of implementation, the operations of the base station / terminal according to the embodiments described above (e.g., operations based on at least one of Proposals 1 to 6) can be processed by the device of FIG. 11 (e.g., the processor (110, 210) of FIG. 11).

[0449] In addition, the operations of the base station / terminal according to the above-described embodiment (e.g., operations based on at least one of proposals 1 to 6) may be stored in a memory (e.g., 140, 240 of FIG. 11) in the form of commands / programs (e.g., instructions, executable codes) for driving at least one processor (e.g., 110, 210 of FIG. 11).

[0450] The embodiments described below are specifically described with reference to FIGS. 9 and 10 in terms of the operation of the terminal and base station. The methods described below are distinguished for convenience of explanation, and it is understood that some components of one method may be substituted for or combined with some components of another method.

[0451] FIG. 9 is a flowchart illustrating a method according to one embodiment of the present specification.

[0452] Referring to FIG. 9, a method according to one embodiment of the present specification includes a step of receiving setup information (S910), a step of transmitting a first CSI report related to prediction (S920), and a step of transmitting a second CSI report related to prediction accuracy (S930).

[0453] In S910, the terminal receives configuration information related to channel state information (CSI) from the base station.

[0454] For example, the configuration information may include information based on the above-described CSI-related operation and at least one of Proposals 1 to 6. As a specific example, the configuration information may include i) one or more reporting settings (e.g., N≥1 CSI-ReportConfig reporting setting) and ii) one or more resource settings (e.g., M≥1 CSI-ResourceConfig resource setting).

[0455] Each of the above one or more reporting configurations may be associated with up to three resource configurations. In other words, each reporting configuration may include IDs (e.g., CSI-ResourceConfigId) of up to three resource configurations.

[0456] In one embodiment, the one or more reporting settings may include a separate reporting setting for a monitoring report in addition to a reporting setting for an inference report (e.g., a report related to Set A). Based on the reporting setting, a separate resource set (Set X) may be set / indicated. This embodiment may be based on Proposal 6, which will be described in detail below.

[0457] The one or more reporting settings may include i) a first reporting setting related to prediction and ii) a second reporting setting related to prediction accuracy.

[0458] For example, the report quantity of the first report configuration may be set to p-cri, p-cri-RSRP, p-ssb-index or p-ssb-index-RSRP. p-cri represents a predicted channel state information-reference signal resource indicator (P-CRI). p-ssb-index represents a predicted SSB resource indicator (P-SSBRI). In p-cri-RSRP or p-ssb-index-RSRP, RSRP represents a predicted Layer 1-Reference Signal Received Power (P-L1-RSRP).

[0459] For example, the prediction may be performed based on a measurement. The measurement may be performed based on a first resource configuration (e.g., CSI-ResourceConfig) associated with the first reporting configuration. For example, the first reporting configuration may be associated with a first resource configuration associated with measurement and a second resource configuration associated with prediction. The first reporting configuration may include an ID of the first resource configuration and an ID of the second resource configuration. Each ID may be based on CSI-ResourceConfigId.

[0460] For example, the measurement may include Layer 1-Reference Signal Received Power (L1-RSRP) measurements. Based on the L1-RSRP measurements, i) at least one predicted channel state information-reference signal resource indicator (CSI-RS Resource Indicator, P-CRI), ii) at least one predicted SSB Resource Indicator (P-SSBRI), and / or iii) at least one predicted Layer 1-Reference Signal Received Power (P-L1-RSRP) may be determined.

[0461] More specifically, predictions can be made on CSI-RS resources or SSB resources associated with the second resource configuration based on the L1-RSRP measurements. Specifically, predicted L1-RSRPs of the CSI-RS resources or SSB resources associated with the second resource configuration can be determined. For example, best CRI(s) or best SSBRI(s) can be determined based on an order or ranking of the predicted L1-RSRPs. For example, at least one P-CRI or at least one P-SSBRI included in the first CSI report described below can be based on the best CRI(s) or the best SSBRI(s).

[0462] For example, the report quantity of the second report setting may be set to pai (or rs-pai).

[0463] The one or more resource settings may include i) a first resource setting and a second resource setting related to the first report setting, and ii) a third resource setting related to the second report setting.

[0464] Each resource configuration (e.g., CSI-ResourceConfig) may contain information about a resource set (e.g., csi-SSB-ResourceSetList or nzp-CSI-RS-ResourceSetList based on csi-RS-ResourceSetList). For example, resources in the resource set may be SSB resources or CSI-RS resources based on csi-RS-ResourceSetList. csi-SSB-ResourceSetList may contain information for referencing the SSB resources (e.g., CSI-SSB-ResourceSetIds). nzp-CSI-RS-ResourceSetList may contain information for referencing CSI-RS resources (e.g., NZP-CSI-RS-ResourceSetIds).

[0465] For example, the first resource configuration may include information about a first resource set for measurement (e.g., Set B described above). As a specific example, the first resource configuration may include a list of SSB resources or CSI-RS resources for measurement.

[0466] For example, the second resource configuration may include information about a second resource set for prediction (e.g., Set A described above). As a specific example, the second resource configuration may include a list of SSB resources or CSI-RS resources for prediction.

[0467] For example, the third resource configuration may include information about a third resource set for measurement (e.g., Set X described above). As a specific example, the third resource configuration may include a list of SSB resources or CSI-RS resources for measurement. In this case, the resources of the third resource set may be mapped to the resources of the second resource set.

[0468] As explained in Proposal 6, Set X, which is related to performance monitoring (prediction accuracy), can be based on a subset of Set A.

[0469] In one embodiment, the resources based on the third resource set may be mapped to a subset of the resources of the second resource set.

[0470] In one embodiment, the size of the third resource set may be less than or equal to the size of the second resource set. For example, if the size of the second resource set is X and the size of the third resource set is Y, then Y=X or Y <X일 수 있다. 상기 제3 자원 세트와 관련된 설정 / 지시의 구체적인 예로, 상기 제2 자원 세트의 서브세트(subset)에 매핑된 상기 제3 자원 세트는 비트맵에 기초하여 지시될 수 있다(예: X개의 bit들 중 Y개의 bit들은 non-zero인 비트맵). 다시 말하면, 상기 제3 자원 설정은 Y개의 자원들을 나타내는 정보(예: csi-RS-ResourceSetList) 및 매핑 정보(상기 비트맵)(예: RSMappingtoSetA)를 포함할 수 있다.

[0471] In S920, the terminal transmits a first CSI report related to prediction to the base station.

[0472] In S930, the terminal transmits a second CSI report related to prediction accuracy to the base station.

[0473] In one embodiment, inference reports / monitoring reports may have a higher priority than non-beam related CSI reports, as described in detail below.

[0474] For example, the first CSI report or the second CSI report may have a higher priority than the third CSI report that does not carry L1-RSRP (Layer1-Reference Signal Received Power) or L1-SINR (Layer1-Signal to Interference plus Noise Ratio). This embodiment may be based on Embodiment 1 of Proposal 5. Specifically, the priority value (e.g., Table 8) associated with the first CSI report or the second CSI report ) may be smaller than the value (e.g., k=1) for determining the priority value associated with the third CSI report. The above-described priority rule may be applied based on whether two CSI reports collide based on Table 8. As a specific example, based on whether the first CSI report or the second CSI report collides with the third CSI report, the first CSI report or the second CSI report may be transmitted. The third CSI report may not be transmitted or may be dropped.

[0475] In one embodiment, the inference report / monitoring report may have the same priority as the beam related CSI report. Here, the same priority may mean that the value (k) for determining the priority value is the same. This embodiment may be based on Embodiment 1) of Proposal 5. Specifically, the value (e.g., k=0) for determining the priority value related to the first CSI report or the second CSI report may be the same as the value (e.g., k=0) for determining the priority value related to the fourth CSI report carrying the L1-RSRP or the L1-SINR. The above-described priority rule may be applied based on whether two CSI reports collide based on Table 8. As a specific example, based on whether the first CSI report or the second CSI report collides with the fourth CSI report, the first CSI report (or the second CSI report) may be transmitted, or the fourth CSI report may be transmitted, based on another value (e.g., c=serving cell index, s=CSI-ReportConfigId) for determining a priority value.

[0476] For example, if the serving cell index (c) associated with the first CSI report (or the second CSI report) is smaller than the serving cell index (c) associated with the fourth CSI report, the first CSI report (or the second CSI report) may be transmitted.

[0477] For example, assuming that the serving cell index (c) is the same, if the ID (CSI-ReportConfigId) of the report configuration (the first report configuration or the second report configuration) related to the first CSI report or the second CSI report is smaller than the ID (CSI-ReportConfigId) of the report configuration related to the fourth CSI report, the first CSI report (or the second CSI report) may be transmitted.

[0478] In one embodiment, the second CSI report may be transmitted based on an event. This embodiment may be based on Proposals 5 and 3. Specifically, the second CSI report may be transmitted based on an event in Proposal 3.

[0479] In one embodiment, a switching of Set A may be requested based on a monitoring report. Specifically, the second CSI report may include information related to a switching of the second resource configuration. Based on the information related to the switching of the second resource configuration, one of a plurality of resource configurations related to the prediction (e.g., the plurality of Set A candidate values ​​described above) may be indicated. This embodiment may be based on Proposal 5.

[0480] For example, the plurality of resource settings may include resource settings related to fallback.

[0481] For example, the resource settings associated with the fallback may include default resource settings defined for non-model based operation.

[0482] For example, the fallback-related candidate Set A may have the same properties as Set B. Specifically, the resource setting related to the fallback may be the same as the first resource setting.

[0483] In one embodiment, the second CSI report may be transmitted based on a physical uplink control channel (PUCCH). This embodiment may be based on Proposal 5.

[0484] For example, the PUCCH may be associated with an uplink control information (UCI) type for the report. In this case, the UCI type may be based on a newly defined UCI type or an existing UCI type (e.g., SR).

[0485] For example, the PUCCH may be associated with a Scheduling Request (SR). The plurality of resource configurations (e.g., indices of the plurality of resource configurations) may be encoded in bits of a PUCCH format associated with the SR.

[0486] In one embodiment, the plurality of resource configurations may be associated with different reporting configurations. As a specific example, each of the different reporting configurations may include an ID (e.g., CSI-ResourceConfigId) of each of the plurality of resource configurations.

[0487] In one embodiment, the plurality of resource settings may be associated with the same reporting setting.

[0488] Meanwhile, if the number of resources differs for each of the multiple resource settings, it may be unclear how the number of bits in the CSI field (CRI / SSBRI) should be determined. To address this issue, the embodiment of Proposal 5 may be considered. This will be described in detail below.

[0489] In one embodiment, the bitwidth of a CSI field (e.g., CRI / SSBRI) for indicating a resource based on one of the plurality of resource configurations may be determined based on a number. The number may be based on a specific resource configuration (e.g., Set A #0~2 in the example of Proposal 5) associated with the most resources among the plurality of resource configurations. For example, the number may be the number of resources based on the specific resource configuration (e.g., the number of resources of Set A #0 (=16)). The bitwidth may be determined as log2(the number of resources based on the specific resource configuration) (=4).

[0490] In one embodiment, resources based on the remaining resource settings (e.g., Set A #3 in the example of Proposal 5) other than the specific resource setting among the plurality of resource settings may be indicated based on specific code points (e.g., 0000, 0001, 0010, 0011) among the code points (e.g., 0000 to 1111) based on the bit width. The specific code points may be determined based on the number of resources (e.g., 4) related to the remaining resource settings.

[0491] For example, the specific code points may be determined based on the order of bit values ​​of each of the code points. As a specific example, when the bit width is 4 and the number of resources related to the remaining resource setting is 4, the specific code points may be determined in ascending or descending order of bit values. As a specific example, according to the ascending order of bit values, the specific code points may be determined as 0000, 0001, 0010, 0011. As a specific example, according to the descending order of bit values, the specific code points may be determined as 1111, 1110, 1101, 1100.

[0492] In one embodiment, the first CSI report may be based on an inferred report / predicted Set A beam related CSI. The first CSI report may include predicted information (predicted CSI parameter(s)). The first CSI report may include predicted CSI parameter(s) (e.g., P-CRI(s), P-SSBRI(s) and / or P-L1-RSRP(s)) based on a report quantity of the first report configuration. Specifically, the first CSI report may include at least one of i) at least one predicted CSI-RS Resource Indicator (P-CRI), ii) at least one predicted SSB Resource Indicator (P-SSBRI) and / or iii) at least one predicted Layer 1-Reference Signal Received Power (L1-RSRP).

[0493] In one embodiment, the second CSI report may be based on a monitoring report. The second CSI report may include information related to monitoring the performance / accuracy of CSI prediction. The second CSI report may include a CSI parameter (e.g., PAI or RS-PAI) based on a report quantity (e.g., pai or rs-pai) of the second report configuration. Specifically, the second CSI report may include a Prediction Accuracy Indicator (PAI). The PAI may be interpreted / replaced with a Reference Signal-Prediction Accuracy Indicator (RS-PAI).

[0494] For example, the PAI may be based on the accuracy of the proposal 4 described above. The accuracy may be determined based on whether the actual measured best beam based on the measurement matches the top K beam(s) based on the prediction. Specifically, the PAI may be determined based on whether at least one of the resources determined based on the measurement associated with the third resource set is mapped to the at least one P-CRI or the at least one P-SSBRI.

[0495] For example, the 1st / 2nd CSI report can be interpreted / replaced with the 1st / 2nd CSI.

[0496] The operations based on S910 to S930 described above can be implemented by the device of FIG. 11. For example, referring to FIG. 11, the terminal (200) can control one or more transceivers (230) and / or one or more memories (240) to perform the operations based on S910 to S930.

[0497] The embodiments described below are specifically described in terms of base station operation.

[0498] S1010 to S1030 described below correspond to S910 to S930 described in FIG. 9. Considering the above correspondence, redundant descriptions are omitted. That is, the specific description of the base station operation described below may be replaced with the description / example of FIG. 9 corresponding to the corresponding operation.

[0499] FIG. 10 is a flowchart illustrating a method according to another embodiment of the present specification.

[0500] Referring to FIG. 10, a method according to another embodiment of the present specification includes a setting information transmission step (S1010), a first CSI report reception step related to prediction (S1020), and a second CSI report reception step related to prediction accuracy (S1030).

[0501] In S1010, the base station transmits configuration information related to channel state information (CSI) to the terminal. For example, the configuration information may include i) one or more reporting configurations (e.g., N≥1 CSI-ReportConfig reporting settings) and ii) one or more resource configurations (e.g., M≥1 CSI-ResourceConfig resource settings).

[0502] In S1020, the base station receives a first CSI report related to prediction from the terminal.

[0503] At S1030, the base station receives a second CSI report related to prediction accuracy from the terminal.

[0504] In one embodiment, the one or more report settings may include i) a first report setting related to the prediction, and ii) a second report setting related to the prediction accuracy. The one or more resource settings may include i) a first resource setting and a second resource setting related to the first report setting, and ii) a third resource setting related to the second report setting.

[0505] For example, the first resource configuration may include information about a first set of resources for measurement. The second resource configuration may include information about a second set of resources for prediction.

[0506] For example, the third resource set may include information about a third resource set for measurement. Resources of the third resource set may be mapped to resources of the second resource set.

[0507] The operations based on S1010 to S1030 described above can be implemented by the device of FIG. 11. For example, referring to FIG. 11, the base station (100) can control one or more transceivers (130) and / or one or more memories (140) to perform the operations based on S1010 to S1030.

[0508] The operations / terms based on the embodiments described above have been described assuming an existing system (e.g., a 5G system). However, this is for convenience of explanation and is not intended to limit the scope of application of the technical problems and problem-solving means to be solved by this specification to a specific system. That is, the technical problems / technical issues / problems mentioned in this specification may equally exist in other systems (e.g., a 6G system). It is clear that the embodiments of this specification can be expanded and applied to solve problems that equally exist in the other systems. Therefore, for the expanded application of the embodiments of this specification to other systems, the terms defined / described based on the 5G system may be replaced / changed with terms defined in the other systems (or generalized terms that are not specific to one system). For example, PRACH, PUSCH, PUCCH, or SRS may be replaced / changed with uplink signals (or uplink channels). For example, SSB, CSI-RS, PDSCH, and PDCCH may be replaced / changed with downlink signals (or downlink channels).

[0509] Hereinafter, a device to which an embodiment of the present specification can be applied (a device that implements a method / operation according to an embodiment of the present specification) is described with reference to FIG. 11.

[0510] FIG. 11 is a drawing showing the configuration of a first device and a second device according to an embodiment of the present specification.

[0511] The first device (100) may include a processor (110), an antenna unit (120), a transceiver (130), and a memory (140).

[0512] The processor (110) performs baseband-related signal processing and may include a higher layer processing unit (111) and a physical layer processing unit (115). The higher layer processing unit (111) may process operations of a MAC layer, an RRC layer, or higher layers. The physical layer processing unit (115) may process operations of a PHY layer. For example, when the first device (100) is a base station device in base station-terminal communication, the physical layer processing unit (115) may perform uplink reception signal processing, downlink transmission signal processing, etc. For example, when the first device (100) is a first terminal device in terminal-to-terminal communication, the physical layer processing unit (115) may perform downlink reception signal processing, uplink transmission signal processing, sidelink transmission signal processing, etc. In addition to performing baseband-related signal processing, the processor (110) may also control the overall operation of the first device (100).

[0513] The antenna unit (120) may include one or more physical antennas, and when it includes multiple antennas, it may support MIMO transmission and reception. The transceiver (130) may include an RF (Radio Frequency) transmitter and an RF receiver. The memory (140) may store information processed by the processor (110), and software, an operating system, applications, etc. related to the operation of the first device (100), and may also include components such as a buffer.

[0514] The processor (110) of the first device (100) may be configured to implement the operation of the base station in the base station-to-terminal communication (or the operation of the first terminal device in the terminal-to-terminal communication) in the embodiments described in the present disclosure.

[0515] The second device (200) may include a processor (210), an antenna unit (220), a transceiver (230), and a memory (240).

[0516] The processor (210) performs baseband-related signal processing and may include a higher layer processing unit (211) and a physical layer processing unit (215). The higher layer processing unit (211) may process operations of a MAC layer, an RRC layer, or higher layers. The physical layer processing unit (215) may process operations of a PHY layer. For example, when the second device (200) is a terminal device in base station-terminal communication, the physical layer processing unit (215) may perform downlink reception signal processing, uplink transmission signal processing, etc. For example, when the second device (200) is a second terminal device in terminal-to-terminal communication, the physical layer processing unit (215) may perform downlink reception signal processing, uplink transmission signal processing, sidelink reception signal processing, etc. In addition to performing baseband-related signal processing, the processor (210) may also control the overall operation of the second device (210).

[0517] The antenna unit (220) may include one or more physical antennas, and when it includes multiple antennas, it may support MIMO transmission and reception. The transceiver (230) may include an RF transmitter and an RF receiver. The memory (240) may store information processed by the processor (210), software, an operating system, applications, etc. related to the operation of the second device (200), and may also include components such as a buffer.

[0518] The processor (210) of the second device (200) may be configured to implement operations of the terminal in base station-to-terminal communication (or operations of the second terminal device in terminal-to-terminal communication) in the embodiments described in the present disclosure.

[0519] In the operation of the first device (100) and the second device (200), the same explanations given for the base station and the terminal (or the first terminal and the second terminal in the terminal-to-terminal communication) in the examples of the present disclosure may be applied, and redundant explanations are omitted.

[0520] Here, the wireless communication technology implemented in the device of the present disclosure may include LTE, NR, and 6G, as well as Narrowband Internet of Things (NB-IoT) for low-power communication. For example, NB-IoT technology may be an example of LPWAN (Low Power Wide Area Network) technology and may be implemented in standards such as LTE Cat NB1 and / or LTE Cat NB2, and is not limited to the above-described names.

[0521] Additionally or alternatively, the wireless communication technology implemented in the device of the present disclosure may perform communication based on LTE-M technology. For example, LTE-M technology may be an example of LPWAN technology and may be called by various names such as eMTC (enhanced Machine Type Communication). For example, LTE-M technology may be implemented by at least one of various standards such as 1) LTE CAT 0, 2) LTE Cat M1, 3) LTE Cat M2, 4) LTE non-BL (non-Bandwidth Limited), 5) LTE-MTC, 6) LTE Machine Type Communication, and / or 7) LTE M, and is not limited to the above-described names.

[0522] Additionally or alternatively, the wireless communication technology implemented in the device of the present disclosure may include at least one of ZigBee, Bluetooth, and Low Power Wide Area Network (LPWAN), which take low-power communication into account, and is not limited to the above-described names. For example, ZigBee technology can create personal area networks (PANs) related to small / low-power digital communication based on various standards such as IEEE 802.15.4, and may be called by various names.

Claims

1. In the method, A step of receiving configuration information related to channel state information (CSI), wherein the configuration information includes i) one or more reporting configurations and ii) one or more resource configurations; A step of transmitting a first CSI report related to prediction; and A step of transmitting a second CSI report related to prediction accuracy; comprising: The one or more reporting settings include i) a first reporting setting related to the prediction and ii) a second reporting setting related to the prediction accuracy, The one or more resource settings include i) a first resource setting and a second resource setting related to the first report setting, and ii) a third resource setting related to the second report setting, The first resource setting includes information about a first resource set for measurement, the second resource setting includes information about a second resource set for prediction, and the third resource setting includes information about a third resource set for measurement. A method characterized in that the resources of the third resource set are mapped to the resources of the second resource set.

2. In paragraph 1, A method characterized in that the resources based on the third resource set are mapped to a subset of the resources of the second resource set.

3. In paragraph 1, A method characterized in that the size of the third resource set is smaller than or equal to the size of the second resource set.

4. In paragraph 1, A method characterized in that the first CSI report or the second CSI report has a higher priority than a third CSI report that does not carry L1-RSRP (Layer1-Reference Signal Received Power) or L1-SINR (Layer1-Signal to Interference plus Noise Ratio).

5. In paragraph 4, A method characterized in that a value for determining a priority value related to the first CSI report or the second CSI report is smaller than a value for determining a priority value related to the third CSI report.

6. In paragraph 4, A method characterized in that the value for determining the priority value associated with the first CSI report or the second CSI report is the same as the value for determining the priority value associated with the fourth CSI report carrying the L1-RSRP or the L1-SINR.

7. In paragraph 1, A method characterized in that the second CSI report is transmitted based on an event.

8. In paragraph 1, The above second CSI report includes information related to the switching of the second resource settings, A method characterized in that one of a plurality of resource settings related to the prediction is indicated based on the information related to the change in the second resource setting.

9. In paragraph 8, A method characterized in that the above plurality of resource settings include resource settings related to fallback.

10. In paragraph 9, A method characterized in that the resource settings related to the fallback include default resource settings defined for non-model based operation.

11. In paragraph 9, A method characterized in that the resource setting related to the fallback is the same as the first resource setting.

12. In paragraph 8, A method characterized in that the second CSI report is transmitted based on a physical uplink control channel (PUCCH).

13. In paragraph 12, A method characterized in that the above PUCCH is related to an uplink control information type (Uplink Control Information, UCI, type) for the above report.

14. In paragraph 12, The above PUCCH is related to a scheduling request (SR), A method characterized in that the above plurality of resource settings are encoded in bits of a PUCCH format associated with the SR.

15. In paragraph 8, A method characterized in that the above multiple resource settings are related to different reporting settings.

16. In paragraph 8, A method characterized in that the above multiple resource settings are related to the same reporting setting.

17. In paragraph 16, The bitwidth of the CSI field for indicating a resource based on one of the above multiple resource settings is determined based on the number, A method characterized in that the above number is based on a specific resource setting associated with the most resources among the plurality of resource settings.

18. In paragraph 17, Among the above multiple resource settings, resources based on the remaining resource settings other than the specific resource setting are indicated based on specific code points among the code points based on the bit width, A method characterized in that the above specific code points are determined based on the number of resources related to the remaining resource settings.

19. In paragraph 18, A method characterized in that the above specific code points are determined based on the order of bit values ​​by each of the above code points.

20. In paragraph 1, A method characterized in that the first CSI report comprises at least one of i) at least one predicted channel state information-reference signal resource indicator (P-CRI), ii) at least one predicted SSB resource indicator (P-SSBRI), and / or iii) at least one predicted Layer 1-Reference Signal Received Power (L1-RSRP).

21. In paragraph 20, A method characterized in that the second CSI report includes a Prediction Accuracy Indicator (PAI).

22. In paragraph 21, A method characterized in that the PAI is determined based on whether at least one of the resources determined based on measurements related to the third resource set is mapped to the at least one P-CRI or the at least one P-SSBRI.

23. At the terminal, One or more transmitters and receivers; one or more processors; and One or more memories connected to said one or more processors and storing instructions, A terminal characterized in that the instructions, based on being executed by the one or more processors, cause the terminal to perform all steps of the method according to any one of claims 1 to 22.

24. In a device comprising one or more memories and one or more processors connected to the one or more memories, A device characterized in that said one or more memories store instructions that cause said device to perform all steps of a method according to any one of claims 1 to 22, based on being executed by said one or more processors.

25. In a non-transitory computer-readable storage medium storing instructions, A non-transitory computer-readable storage medium characterized in that the instructions executable by one or more processors cause a terminal to perform all steps of a method according to any one of claims 1 to 22.

26. In the method, A step of transmitting configuration information related to channel state information (CSI), wherein the configuration information includes i) one or more reporting configurations and ii) one or more resource configurations; A step of receiving a first CSI report related to a prediction; and A step of receiving a second CSI report related to prediction accuracy; comprising: The one or more reporting settings include i) a first reporting setting related to the prediction and ii) a second reporting setting related to the prediction accuracy, The one or more resource settings include i) a first resource setting and a second resource setting related to the first report setting, and ii) a third resource setting related to the second report setting, The first resource setting includes information about a first resource set for measurement, the second resource setting includes information about a second resource set for prediction, and the third resource setting includes information about a third resource set for measurement. A method characterized in that the resources of the third resource set are mapped to the resources of the second resource set.

27. At the base station, One or more transmitters and receivers; one or more processors; and One or more memories connected to said one or more processors and storing instructions, A base station characterized in that the instructions, based on being executed by the one or more processors, cause the base station to perform all steps of the method according to claim 26.

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