Method and apparatus for CSI reporting

Abstract

A method according to an embodiment of the present specification comprises the steps of: receiving configuration information related to CSI; receiving DCI including a CSI request field; and transmitting a CSI report including predicted information for each of one or more time instances. The configuration information comprises a list of the one or more trigger states. A trigger state among the one or more trigger states is initiated on the basis of the CSI request field. The trigger state is associated with a report configuration related to prediction, and the report configuration is linked to two resource configurations. A first resource configuration for measurement among the two resource configurations is related to an aperiodic CSI-RS resource set.

Description

Method and apparatus for CSI reporting

[0001] This specification relates to a method and apparatus for CSI reporting.

[0002] Mobile communication systems were developed to provide voice services while ensuring user mobility. However, mobile communication systems have expanded their scope to include data services as well as voice. Currently, due to the explosive increase in traffic leading to resource shortages and users demanding higher-speed services, more advanced mobile communication systems are required.

[0003] The requirements for next-generation mobile communication systems largely include the ability to accommodate explosive data traffic, a dramatic increase in transmission rates per user, a significantly increased number of connected devices, very low end-to-end latency, and high energy efficiency. To achieve this, various technologies are being researched, such as dual connectivity, massive multiple input multiple output (MMIMO), in-band full duplex, non-orthogonal multiple access (NOMA), super wideband support, and device networking.

[0004] Discussions are underway regarding the operation of reporting predicted Set A beam information based on Set B measurements in the inference operation of UE-side AI / ML (e.g., CSI reporting containing information related to the prediction). As an example, an operation based on BM-case2 (temporal DL Tx beam prediction) can be considered. Specifically, the terminal can transmit a CSI report containing predicted information for each of one or more time instances to the base station.

[0005] According to the existing method, in BM-case2, periodic resource configuration (e.g., CSI-ResourceConfig with resourceType set to periodic) or semi-static resource configuration (e.g., CSI-ResourceConfig with resourceType set to semipersistent) is supported as a resource configuration for measurement (e.g., Set B configuration). In other words, in the case of BM-case2 (e.g., when nrofTimeInstance is configured), the terminal does not expect a non-periodic resource configuration (e.g., CSI-ResourceConfig with resourceType set to aperiodic) to be configured. Therefore, even if the CSI associated with BM-case2 is a non-periodic CSI, the terminal performs the measurement based on periodic / semi-static resource configuration. In this case, even after the CSI is transmitted by the terminal, Reference Signals (RS) based on the periodic / semi-static resource configuration can be continuously transmitted by the base station.

[0006] As described above, according to the existing method related to BM-case2, more RS overhead is caused than the RS overhead required for non-periodic CSI, so it may be inefficient in terms of downlink resource utilization.

[0007] The purpose of this specification is to propose a method for solving the aforementioned problems.

[0008] The technical problems to be solved in this specification are not limited to those mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art to which this specification belongs from the description below.

[0009] A method according to one embodiment of the present specification for solving the aforementioned problem comprises the steps of receiving configuration information related to Channel State Information (CSI), receiving Downlink Control Information (DCI) including a CSI request field, and transmitting a CSI report including predicted information for each of one or more time instances. The configuration information includes a list of one or more trigger states. Based on the CSI request field, a trigger state among the one or more trigger states is initiated. The trigger state is associated with a report configuration related to the prediction, and the report configuration is connected to two resource configurations. Among the two resource configurations, the first resource configuration for measurement is characterized as being associated with an aperiodic CSI-RS (aperiodic CSI-Reference Signal, RS) resource set. Accordingly, RS overhead can be reduced compared to the existing method.

[0010] According to an embodiment of the present specification, the RS overhead required for a non-periodic CSI containing predicted information for each of one or more time instances can be reduced compared to the case where periodic / semistatic RS (periodic / semistatic resource setting) is utilized as in conventional methods.

[0011] In addition, compared to cases where periodic / semi-static resource settings are utilized, measurements can be performed selectively on RSs based on specific resources. Therefore, measurement efficiency can be improved.

[0012] In addition, when periodic / semi-static resource settings are utilized, measurement / CSI reporting cannot be performed even if a non-periodic CSI is triggered, unless the period of the RS(s) based on said resource settings has arrived. In other words, optimal resources may not be utilized for measurement based on the time when the CSI is triggered. As a specific example, due to the aforementioned period, the measurement timing / CSI reporting timing may be excessively delayed compared to the time when the CSI is triggered. Consequently, the efficiency of beam management through CSI reporting may decrease. However, according to the embodiments of this specification, the decrease in beam management efficiency caused by the measurement timing / CSI reporting timing can be minimized.

[0013] The effects obtainable in this specification are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art to which this specification belongs from the description below.

[0014] Figure 1 is a diagram illustrating the overall functions from the perspective of an AI / ML model.

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

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

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

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

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

[0020] FIG. 7 is a flowchart illustrating a method according to one embodiment of the present specification.

[0021] FIG. 8 is a flowchart illustrating a method according to another embodiment of the present specification.

[0022] FIG. 9 is a drawing showing the configuration of a first device and a second device according to an embodiment of the present specification.

[0023] In this specification, "A or B" may mean "only A," "only B," or "both A and B." Alternatively, in this specification, "A or B" may be interpreted as "A and / or B." For example, in this specification, "A, B or C" may mean "only A," "only B," "only C," or "any combination of A, B and C."

[0024] A slash ( / ) or a comma used in this specification may mean "and / or." For example, "A / B" may mean "A and / or B." Accordingly, "A / B" may mean "only A," "only B," or "both A and B." For example, "A, B, C" may mean "A, B or C."

[0025] 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 as synonymous with "at least one of A and B."

[0026] Additionally, in this specification, "at least one of A, B and C" may mean "only A," "only B," "only C," or "any combination of A, B and C." Also, "at least one of A, B or C" or "at least one of A, B and / or C" may mean "at least one of A, B and C."

[0027] Additionally, parentheses used in this specification 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."

[0028] In the following explanation, 'when, if, in case of' can be replaced with 'based on'.

[0029] Technical features described individually within a single drawing in this specification may be implemented individually or simultaneously.

[0030] Hereinafter, preferred embodiments according to the present specification will be described in detail with reference to the accompanying drawings. The detailed description disclosed below, together with the accompanying drawings, is intended to describe exemplary embodiments of the present specification and is not intended to represent the only embodiment in which the present specification may be practiced. The following detailed description includes specific details to provide a complete understanding of the present specification.

[0031] In this specification, a terminal is a user-side device (user equipment, UE) or a consumer-side device, and may also be referred to as a first node that receives / transmits signals from / to a base station / second node / IAB node / Transmission-Reception Point (TRP). A terminal may correspond to a physical node or a logical node. A terminal may correspond to a user-side endpoint or an intermediate point between other endpoints. In communication between two points 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 fixed-location node or a non-fixed-location (or mobile) node.

[0032] In this specification, a Base Station (BS) is a device on the network side and may also be referred to as a second node / IAB node / x-NodeB (x-NodeB, where x may be an abbreviation related to Radio Access Technology (RAT)) / Transmission-Reception Point (TRP). A Base Station may correspond to a physical node or a logical node. A Base Station may correspond to an endpoint on the network side or an intermediate point between other endpoints. In communication between two points not limited to endpoints (including one-to-one / many-to-one / one-to-many / many-to-many communication), a Base Station may correspond to a serving node. A Base Station may be a node with a fixed location or a node with an indefinite location.

[0033] In this specification, higher layer parameters may be set for the terminal, pre-set, or pre-defined. For example, a base station may transmit higher layer parameters to the terminal. For example, the terminal may transmit parameters such as capability to the base station as higher layer parameters. For example, higher layer parameters may be transmitted via RRC (radio resource control) signaling or MAC (medium access control) signaling.

[0034] In this specification, information / state / parameters being "configured" or "pre-configured" may be interpreted as the information / state / parameters being provided / pre-provided to the terminal through pre-defined signaling (e.g., SIB, MAC, RRC) from the base station. In this specification, information / state / parameters being "defined" or "pre-defined" may be interpreted as being known or stored in advance by the base station and the terminal without signaling between the base station and the terminal.

[0035] < AI / ML for Wireless Communication >

[0036] With the advancement of computing technology, artificial intelligence (AI) and machine learning (ML) are being adopted across various industries and technological fields. In the field of wireless communication, various discussions are underway regarding the application of AI models trained on ML; notably, the 3GPP standardization process refers to this as AI / ML. In this specification, we use the term "AI / ML" following the terminology currently in use during the 3GPP standardization discussions; however, "AI / ML" may be referred to by various other terms depending on the progress of standardization and implementation in the future. For example, it may be referred to as a "transmission / reception mode" or a "signal / channel / operation / transmission / reception configuration" set 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.

[0037] - AI / ML Model: Refers to a data-driven algorithm that applies AI / ML technology to generate a set of outputs containing predictive information and / or decision parameters based on a set of inputs.

[0038] - Data collection: This is the process of collecting data necessary for AI / ML model training, data analysis, and inference from network nodes, management entities, or terminals.

[0039] - AI / ML Training: An online or offline process of training an AI model by learning features and patterns that best represent data and acquire an AI / ML model trained for inference.

[0040] - AI / ML Inference: This is the process of making predictions or deriving decisions based on collected data and AI models using trained AI models. Meanwhile, depending on whether the AI / ML model is configured on both the transmitting and receiving devices or on only one, it can be classified into (i) two-sided models and (ii) one-sided models. In the case of (i) two-sided models, cooperative inference is performed through paired AI / ML models. Cooperative inference refers to cooperation between the network and the UE, where one side performs part of the inference and the other performs the remainder. (ii) One-sided models are divided into UE-side models and network-side models. In the case of one-sided models, inference is performed entirely by the UE / network-side models.

[0041] 1. Life Cycle Management (LCM) for AI / ML models

[0042] For AI / ML models, LCM is a concept that encompasses all overall procedures for the model, such as data collection, model training, model deployment, model inference, model monitoring, and model updates.

[0043] Figure 1 is a diagram illustrating the overall functions from the perspective of an AI / ML model.

[0044] 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).

[0045] 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) performs data preparation and can provide input data processed through data preparation.

[0046] 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).

[0047] 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) delivered from the Data Collection function (10).

[0048] Trained / Updated Model (21): If there is a Model Storage function (50), it is used to transfer trained, validated, and tested AI / ML models to the Model Storage function (50) or to transfer updated versions of the models to the Model Storage function (50).

[0049] The Management function (30) is a function that monitors the operation of an AI / ML model or an AI / ML function.

[0050] Management Instruction (32) is information required as input to manage the Inference function (40). The relevant information may include the selection / (de)activation / switching of an AI / ML model or an AI / ML-based function, and may also include a fallback to a non-AI / ML operation (i.e., not relying on the inference process).

[0051] A Model Transfer / Delivery Request (33) can be used to request model(s) from Model Storage (50).

[0052] Performance Feedback / Retraining Request (31) refers to information required as input to Model Training function (20) (e.g., for the purpose of retraining or updating the model).

[0053] 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 the 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 the data collection (10). If necessary, the inference function (40) may also perform data preparation (e.g., data pre-processing and cleaning, forming and transformation) based on the inference data (13) provided by the data collection function (10).

[0054] 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 an AI / ML model generated by the Inference function (30), and the details of the inference output may vary depending on the use case.

[0055] The Model Storage function (50) is a function that stores a trained / updated model that can be used to perform the Inference function (40).

[0056] Model Transfer / Delivery (51) is used to transfer an AI / ML model to an inference function.

[0057] 2. General AI / ML related procedures between the network and the terminal

[0058] Figure 2 illustrates the general form of AI / ML-related procedures performed between a network and a terminal. While Figure 1 examined the LCM from the perspective of an AI / ML model, Figure 2 describes the general form of procedures performed between a terminal and a network from the perspective of signaling / protocols.

[0059] (1) AI / ML related setup procedure

[0060] 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 through at least one upper-layer signaling between the terminal and the network, and / or prior preparation / subsequent operations at the terminal / network respectively before / after the upper-layer signaling.

[0061] Specifically, the configuration procedure related to AI / ML may include, but is not limited to, at least one of the following: (i) reporting the capability of the AI / ML-related terminal, (ii) data collection, (iii) model training, (iv) model delivery / transmission, (v) selection of AI / ML functions / models, and (vi) configuration of various operations performed based on the AI / ML model (e.g., AI / ML-based CSI / Positioning / Beam Management).

[0062] (i) The terminal can inform the network of its capabilities, such as models and functions related to AI / ML, that it supports through UE Capability reporting. The network can provide AI / ML-related settings to the terminal based on the terminal's capabilities related to AI / ML reported by the terminal.

[0063] (ii) AI / ML-related configuration procedures may include data collection related to the training / inference of AI / ML models and / or the provision of configuration information regarding data collection. The configuration information regarding data collection may relate to how to configure the method / operation of data collection.

[0064] (iii) AI / ML-related configuration procedures may include training AI / ML models online or offline and / or providing configuration information for AI / ML model training. The configuration information for AI / ML model training may relate to how to configure the method / behavior, etc., of training the AI / ML model.

[0065] (iv) AI / ML-related configuration procedures may include transmitting / transmitting configuration information for a model. The configuration information for a model may include parameters that constitute the AI / ML model and / or an identifier (ID) for the AI / ML model.

[0066] The provided AI / ML model may be a model trained by the network or a model that requires self-training at the terminal. Even when a model trained by the network is provided, the terminal may perform fine-tuning or retraining as necessary. Meanwhile, if a model trained by the network is provided, the terminal may provide data for training to the network.

[0067] (v) The configuration procedure related to AI / ML may include the configuration of how to select AI / ML Functionality / models and / or the selection process for AI / ML Functionality / models. In UE-side AI / ML models or two-sided AI / ML models, the selection of the UE part may be performed through instructions / signaling from the network or the terminal may select it itself. The selection of AI / ML Functionality / models may be performed when multiple AI / ML Functionality / models are configured / provided.

[0068] (vi) The configuration procedure related to AI / ML may include configuration information for various inference operations performed based on AI / ML models, e.g., AI / ML-based CSI measurement / reporting, AI / ML-based positioning, and / or AI / ML-based beam management.

[0069] (2) Operation based on inference by AI / ML models

[0070] Referring again to FIG. 2, the network and / or terminal can 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 can be performed at 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 can be performed at the network and the terminal, and depending on the implementation, such inference can be performed cooperatively between the network and the terminal.

[0071] (i) Actions performed based on the inference of an AI / ML model may include AI / ML-based CSI measurement / reporting. AI / ML-based CSI measurement / reporting is intended to improve CSI feedback and may be related to overhead reduction / CSI compression, accuracy improvement, and / or CSI prediction.

[0072] (ii) Actions performed based on the inference of an AI / ML model may include AI / ML-based beam management. AI / ML-based beam management may be related to beam prediction in the time domain, reduction of overhead / latency in the spatial domain, and / or improvement of beam selection accuracy.

[0073] (iii) Actions performed based on the inference of an AI / ML model may include AI / ML-based positioning. AI / ML-based positioning may be relevant to improving positioning accuracy in various scenarios, for example, in non-line-of-sight environments.

[0074] (3) Procedures for AI / ML management

[0075] The network and / or terminal can perform procedures for the management of AI / ML Functionality / model or the settings therefor (B15).

[0076] The network and / or terminal may perform monitoring of AI / ML Functionality / model during the AI / ML model inference or operation based thereon (B10) for the management procedure (B15).

[0077] The management procedure may include, for example, at least one of activation / deactivation, switching, model update, and / or fallback operation for AI / ML Functionality / model. For the signaling of the management procedure, various 3GPP signaling schemes, such as RRC, MAC-CE, DCI, etc., may be used.

[0078] As an example of model switching, multiple model groups are configured, and switching between them can be performed based on models having a common model structure or partially common substructures, and models within the same group may be associated with different input / output formats or processing.

[0079] Model updating involves modifying the parameters used by the model to suit channel conditions that change over time, and fine-tuning is an example of model updating.

[0080] Fallback: In a wireless communication system using an AI / ML model, this may refer to the operation of not using the AI / ML model or operating in a pre-configured / defined default mode when the reliability of the AI / ML model decreases due to internal or external environmental factors.

[0081] For example, the decision on whether to perform a management procedure can be made by the network. For instance, the network may decide to perform the management procedure upon network initiation, or the network may decide to perform the management procedure upon terminal initiation and request.

[0082] As another example, the decision on whether to perform a management procedure can be made by the terminal. For instance, the terminal's decision on the management procedure may be triggered when an event condition set by the network is satisfied, performed by reporting the terminal's decision to the network, or performed autonomously by the terminal.

[0083] 3. Specific operation examples based on AI / ML model inference

[0084] (1) Beam management

[0085] Figure 3 illustrates an example of AI / ML-based beam management operation.

[0086] Referring to FIG. 3, the network / terminal can perform a configuration procedure related to AI / ML-based beam management (C05). The network / terminal can perform a configuration procedure for an AI / ML model to be used for AI / ML-based beam management, and an exchange of configuration information for upper-layer signaling for AI / ML-based beam management. For example, at least one of information related to model inference, configuration for a first set / second set beam, monitoring performance, and assistance information for data collection and beam measurement may be signaled.

[0087] The network / terminal can perform measurements on the first set of beams (C10). The beam measurements may be related to RSRP measurements.

[0088] A network / terminal can obtain information about a second set of beams based on measurement results for a first set of beams (C15). For example, the network / terminal can perform AI / ML inference by using the measurement results for the first set of beams as AI / ML input data. Beam ID information may also be additionally provided as AI / ML input data. Information about the second set of beams may correspond to AI / ML output data. The AI / ML output data may be related to, for example, the probability that each beam will become a top-N beam, the predicted RSRP, etc., for predicting future beam quality, but is not limited thereto.

[0089] According to an embodiment, the network / terminal can transmit and receive information about the acquired second set of beams.

[0090] Specifically, AI / ML-based beam management operations may include at least one of the following BM-Case 1 and BM-Case 2.

[0091] - BM-Case 1: Prediction of the second set of DL beams in the spatial domain through the first set of beam measurements

[0092] - BM-Case 2: Prediction of the second set of DL beams in the time domain through the first set of beam measurements

[0093] In BM-Case 1 and / or 2, both AI / ML model training and inference may be performed on the network or on the terminal. The first set of beams and the second set of beams may be different beams. Or the first set of beams may be a subset of the second set of beams. Or, particularly in BM-Case 2, the first set of beams and the second set of beams may be the same beam.

[0094] The report corresponding to the inference of the UE-side model for BM-Case 1 may relate to the RSRP for the predicted top N beams. The report may include, for example, the predicted RSRP values, and as an example, the predicted RSRP values ​​may be reported together with the actual measured RSRP.

[0095] UE-side AI / ML model inference for BM-Case 2 can report inference results for N future time points through a single report. The report for each time point can correspond to the report in BM-Case 1.

[0096] 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 terminal may report information necessary for the network to calculate performance metrics, for example, by reporting measurement results (e.g., RSRP) and / or RS index for a set of resources for monitoring. (ii) For UE-assisted performance monitoring, the terminal may calculate performance metrics.

[0097] With respect to the NW-side model for BM-Case 1 / 2, quantization of the reported RSRP may be supported, for example, differential RSRP reporting may be supported along existing quantization steps and ranges. The reported content may include information on the RSRP and the corresponding upper N beam, where N can be set by the network.

[0098] With respect to the configuration of the first set of beams and the second set of beams of the UE-side model of BM Case-1, two resource sets may be configured separately for each of the first set and the second set, and the resource sets may be provided through CSI reporting settings. The terminal may perform inference / measurement on the resource set of the first set of beams. The terminal may not be expected to perform measurement / inference on the resource set of the second set of beams. The beam information in the inference report may include resource set information for the first set.

[0099] In relation to the UE-side model, the associated ID may be provided through the CSI framework. The terminal may assume identical / similar characteristics for DL ​​transmit beams / sets (lists) for the same associated ID.

[0100] Regarding UE-assisted performance monitoring for the UE-side models of BM-Case 1 and 2, the following methods may be considered.

[0101] i) Compare prediction results based on resources for monitoring and use the top 1 or top K beam prediction accuracy.

[0102] 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 prediction beams.

[0103] iii) Use the difference information between the measured RSRP and the predicted RSRP for the corresponding beam of the resources for monitoring.

[0104] iv) Probability information that the predicted beam will become one of the top 1 or N beams

[0105] For reporting inference results for the UE-side model, quantization of RSRP may be supported, and differential RSRP with existing quantization steps may be supported. The scope of RSRP reporting is such that differential RSRP among multiple beams is supported in the case of BM-case 1, and differential RSRP among multiple beams at multiple time points is supported in the case of BM-case 2.

[0106] For BM-Case 2 of the UE-side model, the network can be configured to report inferences about N future times to the terminal.

[0107] (2) CSI prediction and / or compression

[0108] Figure 4 illustrates an example of an AI / ML-based CSI measurement / reporting operation.

[0109] Referring to FIG. 4, the network / terminal can perform a configuration procedure related to AI / ML-based CSI (D05). The network / terminal can perform a configuration procedure for an AI / ML model to be used for AI / ML-based CSI, and for the exchange of configuration information for upper-layer signaling for AI / ML-based CSI measurement / reporting. For example, at least one of information related to model inference, settings for RS / resources to be used for CSI measurement, monitoring performance, data collection, and conditions / resources for CSI reporting may be signaled.

[0110] 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 part of a two-side AI / ML model.

[0111] The terminal may report CSI to the network based on the results of CSI measurements (D15). CSI reporting may be performed periodically or non-periodically depending on the configuration, and in the case of non-period CSI reporting, network instructions (not shown), such as DCI, that trigger it may be additionally signaled. CSI reporting may include AI / ML-based CSI content and may additionally include legacy CSI content (e.g., non-AI / ML-based RI, PMI, CQI, etc.) (depending on the configuration / scheduling). 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.

[0112] The network can acquire CSI based on the terminal's CSI report.

[0113] If a two-sided AI / ML model is configured, the network can reconstruct the CSI by using the terminal's CSI report as input data to the AI / ML model configured in the network (D20). The inference (output) of the AI / ML model configured in the network may be the reconstructed CSI. In such a two-sided AI / ML model, the terminal-side AI / ML model part can be understood as a CSI encoder, and the network-side AI / ML model part can be understood as a concept similar to a CSI decoder.

[0114] CSI compression is CSI compression in the spatial-frequency domain and can primarily be based on two-sided AI / ML models. CSI prediction can primarily be based on one-sided, specifically UE-side AI / ML models.

[0115] In CSI compression based on a two-sided AI / ML model, AI / ML model training may include at least one of the following: (i) Type 1, in which the two-sided AI / ML model is jointly trained at either the terminal or the network; (ii) Type 2, in which the terminal and the network each jointly train the corresponding parts of the two-sided AI / ML model; and (iii) Type 3, in which the terminal and the network each separately train the corresponding parts of the two-sided AI / ML model, wherein the training of the terminal is mainly related to CSI generation and the training of the network is mainly related to CSI reconstruction. Joint training means that the CSI generation / reconstruction models are trained in the same loop for forward / backward delays, and separate training may mean a sequential method in which one of the terminals or the network starts training first, and then the other performs training.

[0116] (3) Positioning

[0117] Figure 5 illustrates an example of an AI / ML-based positioning operation.

[0118] Referring to FIG. 5, the network / terminal can perform a setup procedure related to AI / ML-based positioning (E05). The network / terminal can perform measurements for positioning (E10). The measurements for positioning may be related to PRS and / or SRS measurements. Based on the measurement results, the network / terminal can obtain information regarding terminal positioning (E15). For example, the network / terminal can perform AI / ML inference by using the measurement results for PRS / SRS as AI / ML input data. The information regarding 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.

[0119] < CSI Related Actions >

[0120] 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), Layer 1-RSRP (Layer 1-Reference Signal Received Power), and / or Layer 1-SINR (Layer 1-Signal-to-Interference-plus-Noise Ratio).

[0121] In the case of the CSI prediction described below, the CSI related to the prediction may include at least one of the 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).

[0122] When monitoring the performance / accuracy of the CSI prediction described below, the CSI related to 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 the PAI may be interpreted / replaced as a Reference Signal-Prediction Accuracy Indicator (RS-PAI). For example, the PAI may indicate the accuracy of predicted CSI (e.g., predicted PMI), and the PAI may be interpreted / replaced as a Channel State Information-Prediction Accuracy Indicator (CSI-PAI).

[0123] Figure 6 is a flowchart showing an example of a CSI-related procedure.

[0124] Referring to FIG. 6, to perform one of the uses 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) via RRC (radio resource control) signaling (S610).

[0125] The configuration information related to the above CSI may include at least one of CSI-IM (interference management) resource information, CSI measurement configuration information, CSI resource configuration information, CSI-RS resource information (e.g., M≥1 CSI-ResourceConfig resource setting), or CSI report configuration information (e.g., N≥1 CSI-ReportConfig reporting setting). As an example, the configuration information may include at least one of one or more CSI resource settings and / or one or more CSI reporting settings.

[0126] For example, the configuration information may include a first CSI resource setting for measurement and a second CSI resource setting for prediction. As a specific example, the measurement related to the prediction of CSI described below may be performed based on the first CSI resource setting. The terminal may perform L1-RSRP measurements on CSI-RS resources or SS / PBCH block resources associated with the first CSI resource setting. As a specific example, the prediction of CSI described below may be performed based on the second CSI resource setting. Based on the L1-RSRP measurements, the terminal may perform predictions on CSI-RS resources or SS / PBCH block resources associated with the second CSI resource setting. In other words, using L1-RSRPs as measurement metrics, the best CRI / best SSBRI (e.g., P-CRI(s), P-SSBRI(s)) may be predicted.

[0127] For example, the above configuration information may include a first CSI reporting setting related to prediction and a second CSI reporting setting related to prediction accuracy.

[0128] Information related to CSI resource configuration can be expressed as CSI-ResourceConfig IE. Information related to CSI resource configuration defines a group including at least one of an NZP (non-zero power) CSI-RS resource set, a CSI-IM resource set, or a CSI-SSB resource set. That is, the information related to CSI resource configuration includes a CSI-RS resource set list, and the CSI-RS resource set list may 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.

[0129] Information related to CSI report configuration (e.g., CSI-ReportConfig IE) includes a reportConfigType parameter representing time domain behavior and a reportQuantity parameter representing the CSI-related quantity to be reported. The time domain behavior may be periodic, aperiodic, or semi-persistent.

[0130] The above reportQuantity parameter includes the channel quality indicator (CQI), precoding matrix indicator (PMI), CSI-RS resource indicator (CRI), SS / PBCH block resource indicator (SSBRI), layer indicator (LI), rank indicator (RI), L1-RSRP (Layer 1-Reference Signal Received Power), L1-SINR (Layer 1-Signal-to-Interference-plus-Noise Ratio), predicted CQI (P-CQI), predicted PMI (P-PMI), predicted CRI (P-CRI), predicted SSBRI (P-SSBRI), predicted LI (P-LI), predicted RI (P-RI), predicted L1-RSRP (P-L1-RSRP), and / or predicted L1-SINR (predicted It can be set to a value representing at least one of L1-SINR, P-L1-SINR) and / or Prediction Accuracy Indicator (PAI) (or RS-PAI).

[0131] 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 L1-RSRP (Layer 1-Reference Signal Received Power). ssb-Index represents the SS / PBCH block resource indicator (SSBRI).

[0132] 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 the predicted CRI (predicted CRI, P-CRI). p-ssb-index represents the predicted SSBRI (predicted SSBRI, P-SSBRI). p-cri-RSRP represents the predicted CRI (predicted CRI, P-CRI) and predicted L1-RSRP (predicted L1-RSRP, P-L1-RSRP). p-ssb-index-RSRP represents the predicted SSBRI (predicted SSBRI, P-SSBRI) and predicted L1-RSRP (predicted L1-RSRP, P-L1-RSRP).

[0133] For example, the reportQuantity parameter can be set to pai (or rs-pai). pai (or rs-pai) represents PA (or RS-PAI).

[0134] The measurement resource may include settings for downlink signals and / or downlink resources for which the terminal will perform measurements to determine feedback information. The measurement resource may be set as a set of ZP and / or NZP CSI-RS resources associated with a CSI reporting setting. The NZP CSI-RS resource set may include a CSI-RS set or an SSB set. For example, L1-RSRP may be measured against a CSI-RS set or against an SSB set.

[0135] The terminal measures the CSI based on configuration information related to the above CSI (S620). The CSI measurement may include (1) a process of receiving the terminal's CSI-RS (S621) and (2) a process of computing the CSI through the received CSI-RS (S622). The terminal reports the CSI to the base station (S630).

[0136] resource setting

[0137] Each CSI resource setting 'CSI-ResourceConfig' contains a configuration for S≥1 CSI resource sets (given by the higher layer parameter csi-RS-ResourceSetList). The CSI resource setting corresponds to the CSI-RS-resourcesetlist, where S represents the number of configured CSI-RS resource sets. Here, the list of S≥1 CSI resource sets includes either or both of the NZP CSI-RS resource set(s) and the SS / PBCH block (SSB) set(s) used for L1-RSRP computation, or includes CSI-IM resource set(s).

[0138] Next, one or more CSI resource settings for channel measurement (CM) and interference measurement (IM) are established through higher layer signaling.

[0139] - CSI-IM resource for interference measurement.

[0140] - NZP CSI-RS resources for interference measurement.

[0141] - NZP CSI-RS resources for channel measurement.

[0142] That is, the CMR (channel measurement resource) may be an NZP CSI-RS for CSI acquisition, and the IMR (Interference measurement resource) may be an NZP CSI-RS for CSI-IM and IM.

[0143] Here, CSI-IM (or ZP CSI-RS for IM) is primarily used for inter-cell interference measurements.

[0144] Also, the NZP CSI-RS for IM is mainly used for intra-cell interference measurement from multi-users.

[0145] A UE can assume that the CSI-RS resource(s) for channel measurement set 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) have a QCL relationship with respect to 'QCL-TypeD' on a resource-by-resource basis.

[0146] As examined, resource setting can refer to a resource set list.

[0147] 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, semi-persistent, or aperiodic resource setting.

[0148] One reporting setting (e.g., CSI-ReportConfig) can be associated with up to three resource settings (e.g., CSI-ResourceConfig). For example, one CSI reporting setting may include the ID (e.g., CSI-ResourceConfigId) of at least one CSI resource setting. The at least one CSI resource setting may include a CSI resource setting associated with a measurement.

[0149] Beam Management (BM)

[0150] 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 terms.

[0151] - Beam measurement: An operation in which a base station or UE measures the characteristics of a received beamforming signal.

[0152] - Beam determination: The operation in which a base station or UE selects its transmit beam (Tx beam) / receive beam (Rx beam).

[0153] - Beam sweeping: An operation that covers a spatial area using transmitting and / or receiving beams for a set time interval in a predetermined manner.

[0154] - Beam report: An operation in which the UE reports information about the beam-formed signal based on beam measurements.

[0155] The BM procedure can be divided into (1) a DL BM procedure using an SS (synchronization signal) / PBCH (physical broadcast channel) Block or CSI-RS, and (2) a UL BM procedure using an SRS (sounding reference signal).

[0156] In addition, each BM procedure may include Tx beam sweeping to determine the Tx beam and Rx beam sweeping to determine the Rx beam.

[0157] DL BM

[0158] The DL BM procedure may include (1) transmission to beamformed DL RS (reference signals) of the base station (e.g., CSI-RS or SS Block (SSB)) and (2) beam reporting of the terminal.

[0159] Here, beam reporting may include preferred DL RS ID(identifier)(s) and the corresponding L1-RSRP(Reference Signal Received Power).

[0160] The above DL RS ID may be SSBRI (SSB Resource Indicator) or CRI (CSI-RS Resource Indicator).

[0161] An example of beam forming using SSB and CSI-RS will be examined in detail below.

[0162] SSB beams 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, while CSI-RS can be used for fine beam measurement. SSB can be used for both Tx beam sweeping and Rx beam sweeping.

[0163] Rx beam sweeping using SSBs can be performed as the UE changes the Rx beam across multiple SSB bursts for the same SSBRI. Here, one SS burst includes one or more SSBs, and one set of SS bursts includes one or more SSB bursts.

[0164] The DL BM procedure is examined below.

[0165] Configuration for beam reporting using SSB is performed during CSI / beam configuration in the RRC connected state (or RRC connected mode).

[0166] - The terminal receives configuration information from the base station. As a specific example, the terminal receives from the base station a CSI-ResourceConfig IE containing a CSI-SSB-ResourceSetList containing SSB resources used for BM.

[0167] Table 1 shows an example of CSI-ResourceConfig IE. As shown in Table 1, BM configuration using SSB is not defined separately, and SSB is configured like a CSI-RS resource.

[0168]

[0169] In Table 1, the csi-SSB-ResourceSetList parameter represents 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.

[0170] - 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.

[0171] - The terminal transmits a beam report to the base station. As a specific example, if a CSI-ReportConfig related to reporting on SSBRI (SSB Resource Indicator) and L1-RSRP is configured, the terminal reports the best SSBRI and the corresponding L1-RSRP to the base station.

[0172] That is, if 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.

[0173] And, if the terminal has a CSI-RS resource configured 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 in terms of 'QCL-TypeD'.

[0174] Here, the above QCL Type D may mean that the antenna ports are QCL-connected in terms of spatial Rx parameters. When a terminal receives multiple DL antenna ports that are in a QCL Type D relationship, it is acceptable to apply the same receiving beam. Additionally, the terminal does not expect CSI-RS to be established in an RE that overlaps with the RE of the SSB.

[0175] The configuration for beam reporting using CSI-RS is performed in the same manner as the configuration for beam reporting using SSB described above, so a redundant explanation is omitted. The operation of the beam reporting procedure using CSI is described below.

[0176] - The terminal receives configuration information from the base station. As a specific example, the terminal receives from the base station a CSI-ResourceConfig IE containing a CSI-SSB-ResourceSetList containing CSI resources used for BM (e.g., NZP CSI-RS resource set IE).

[0177] - 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.

[0178] - The terminal selects (or determines) the best beam.

[0179] - The terminal reports the ID and associated 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'.

[0180] Explanation regarding Rel-17 / 18 beam management >

[0181] In Rel-17, DL DCI (e.g., DCI format 1-1 or 1-2) can indicate both the DL TCI state and the UL TCI state, or it can indicate only the UL TCI state without specifying the DL TCI state. Consequently, the methods used in the existing R15 / R16 for configuring UL beam and power control (PC) are replaced in Rel-17 by the aforementioned method of indicating the UL TCI state. More specifically, in R17, a single UL TCI state can be indicated through the TCI field of the DL DCI; this UL TCI state is applied to all PUSCHs and all PUCCHs after a certain period known as the beam application time, and can be applied to some or all of the indicated SRS resource sets. Additionally, the base station can utilize DCI and / or MAC-CE to perform a terminal common beam update, which performs indication / updates for multiple specific DL / UL channel / RS combinations using a single beam (utilizing joint or separate TCI states). For the target channel / RS of the common beam update, UE-dedicated CORESET and UE-dedicated reception on PDSCH are available for DL, and DG / CG-PUSCH and all or subset of dedicated PUCCH are available for UL, and additionally, AP CSI-RS for tracking / BM and SRS can be set as target channel / 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 was standardized, and depending on the S-DCI based M-TRP environment and the M-DCI based M-TRP environment, uplink and downlink resources to which each indicated TCI is applied can be defined / configured.

[0182] In this document, ' / ' means 'and', 'or', or 'and / or' depending on the context.

[0183] In this specification, 'beam' may refer to a source RS for a 'spatial filter' or 'spatial relation', and may be interpreted as a QCL (type-D) RS, a TCI state, or (in the case of an uplink) a spatial relation RS.

[0184] For example, in this specification, 'beam' may refer to 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 or substituted 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.).

[0185] For example, a beam associated with a 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 transmission spatial filter (UL Tx spatial filter) or vi) an uplink receive spatial filter (UL Rx spatial filter).

[0186] 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 transmission spatial filter (DL Tx spatial filter), or vi) a downlink receive spatial filter (DL Rx spatial filter).

[0187] In NR standards, QCL configuration via TCI state settings and spatial relation configuration are utilized to configure the UL / DL transmit / receive beams of a terminal. In the Rel-15 NR standard, RRC and MAC CE signaling are primarily used for uplink and downlink transmit / receive beams. Dynamic signaling has been permitted only for the PDSCH receive beam by utilizing the TCI state field of the DL grant DCI. A unified TCI framework was introduced through the Rel-17 / 18 NR standards. Specifically, a method was introduced to dynamically manage the common beam by using DCI to indicate the indicated TCI for the receive / transmit beams. Meanwhile, in the Rel-18 AI / ML study item, a study was conducted on performance evaluation and specification impact regarding spatial beam prediction and temporal beam prediction sub-use cases in the field of beam management. This study discussed NW / UE-side AI / ML operations that predict the best beam of Set A based on Set B measurements. In the case of UE-side AI / ML, the behavior of the terminal measuring Set B and reporting the predicted Set A beam can be discussed in the Rel-19 AI / ML work item. In this case, if the terminal's beam prediction performance is poor, actions such as switching the terminal-side AI / ML model / functionality or falling back to non-AI / ML-based conventional beam management instead of AI / ML-based beam management (measurement / reporting) are necessary.

[0188] 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 a base station when a specific event occurs.

[0189] < Background related to AI / ML beam management >

[0190] In the Rel-18 AI / ML study item, performance analysis and potential specification impact were studied through evaluation when NW and / or UE-side AI / ML models were operating in three use cases: CSI compression / prediction, beam management, and positioning. In particular, for the beam management use case, the study was conducted by dividing the sub-use cases into BM-case1 and BM-case2 to analyze performance and potential specification impact regarding spatial domain beam prediction and temporal beam prediction. The WID objectives of the AI / ML BM, as well as BM-case1 and BM-case2, are summarized in Tables 2 through 4 below.

[0191] - WID goals for AI / ML BM

[0192]

[0193] - BM-case1: Spatial domain downlink beam prediction for beam set A based on measurement results of beam set B

[0194]

[0195] - BM-case2: Temporal downlink beam prediction for Beam Set A based on historical measurement results of Beam Set B

[0196]

[0197] In addition, an example of the operation for data collection of an AI / ML model in a beam management use case is shown in Table 5 below.

[0198]

[0199] In addition, an example of the operation for inference of an AI / ML model in a beam management use case is shown in Table 6 below.

[0200]

[0201] Meanwhile, the Rel-18 AI / ML study discussed NW / UE-sided AI / ML operations that predict the best beam of Set A based on Set B measurements. In the case of UE-sided AI / ML, the terminal needs to measure Set B and report the predicted Set A beam, whereas in the case of NW-sided AI / ML, the terminal needs to report the Set B measurements.

[0202] The standardization agreements for UE-sided AI / ML to date are as follows.

[0203] Agreement

[0204] For UE-side models, at least for BM-Case1, the following is supported regarding the content of the inference result report.

[0205] Option 1: Beam information for the predicted Top K beams within the beam set

[0206] Option 2: Beam information for the predicted Top K beams within the beam set, and the RSRP of the predicted Top K beams

[0207] At least K=1, and for the maximum value, use FFS

[0208] For beam information, FFS

[0209] For the definition of the predicted Top K beam, FFS

[0210] For the definition of the reported RSRP where applicable, FFS

[0211] For other information within the report along with potential down selections among the following options, FFS

[0212] Option 3: Beam information for the predicted Top K beams within the beam set, and probability information for the predicted Top K beams

[0213] Regarding the quantization method of probability information, FFS

[0214] The probability information is the probability that the beam will become the Top 1 or Top K beam.

[0215] Option 4: Beam information for the predicted Top K beams within the beam set, the RSRP of the predicted Top K beams, and the reliability information of the corresponding RSRPs

[0216] Regarding the definition of the reported RSRP, FFS

[0217] Regarding the definition of reliability information and quantization methods, FFS

[0218] Other options are not excluded either.

[0219] Here, the beam set is Set A, which refers to the beams for UE prediction.

[0220] Conclusion

[0221] For the UE-side model, at least during inference, the configuration of Set B (Set B) for measurement is taken from the current CSI framework.

[0222] Agreement

[0223] Regarding UE-side AI / ML model inference, in the case of BM-Case2, one report supports reporting inference results for N (N≥1, FFS for N) future time points.

[0224] The inference result information for a given point in time is identical to one report in BM-Case1.

[0225] Note: Overhead reduction is not excluded.

[0226] Details are on FFS.

[0227] Agreement

[0228] Regarding the RSRP of the predicted Top K beam among the inference result reports for the UE-side model of BM-Case1, if applicable, the following options are additionally investigated.

[0229] Option A: Predicted RSRP

[0230] Option B: Predicted RSRP if the beam is not configured for the corresponding measurement, measured L1-RSRP if the beam is configured for the corresponding measurement

[0231] The predicted RSRP is based on AI / ML output.

[0232] Note: Supporting both Option A and Option B is not excluded.

[0233] Agreement

[0234] For the UE-side model, at least for BM Case-1, CSI-ReportConfig is used for the inference result reporting configuration.

[0235] For details within CSI-ReportConfig, consider FFS, at a minimum, the following:

[0236] Alternative 1: One CSI-ResourceConfigId is configured for Set B.

[0237] FFS: How can the UE determine information about Set A?

[0238] Alternative 2: A single CSI-ResourceConfigId is configured for both Set A and Set B

[0239] FFS: How to configure resource sets of Set A and Set B in CSI-ResourceConfig

[0240] Alternative 3: Separate CSI-ResourceConfigIds are configured for Set A and Set B, respectively.

[0241] Alternative 4: A single CSI-ResourceConfigId is configured for Set B, and Set A is configured using a separate set of resources not represented by the CSI-ResourceConfigId.

[0242] FFS: How to configure / direct a separate set of resources for Set A

[0243] Note: Using separate CSI-ReportConfigs for Set A and Set B is also not excluded.

[0244] Note: Measurements are not performed on Set A, and are performed only on Set B according to CSI-ReportConfig.

[0245] Regarding the association between Set A and Set B, regardless of the presence or absence of additional IE, FFS.

[0246] Other necessary components are not excluded.

[0247] Agreement

[0248] The following working assumptions have been established.

[0249] Working Assumption

[0250] In the inference result report for the UE-side model of BM-Case 2, the predicted RSRP of the beam is the predicted RSRP, and this predicted RSRP is based on the AI / ML output.

[0251] Agreement

[0252] For UE-side models, at least for the quantization of RSRP values ​​in inference result reports, the following is supported:

[0253] Support for differential RSRP reporting using existing quantization steps and ranges for L1-RSRP reporting

[0254] For BM-Case1, differential RSRP reporting between multiple beams is supported.

[0255] For BM-Case2, support for differential RSRP reporting between multiple beams across multiple time points.

[0256] Details are FFS

[0257] Agreement

[0258] For the UE-side model, at least in the case of BM Case-1, in the inference result report

[0259] In the CSI report configuration, two resource sets can be configured separately for Set A and Set B.

[0260] Whether to support configuring a resource set solely for Set B is FFS.

[0261] The UE performs measurements on the resource set of Set B for inference, and the UE is not expected to measure the resource set of Set A for inference.

[0262] The beam information in the inference report refers to the resource set of Set A.

[0263] Agreement

[0264] With respect to the UE-side AI / ML models in BM-Case1 and BM-Case2, **Option 2 (UE-supported performance monitoring)** shall be further reviewed, including at least the following alternatives:

[0265] Alternative 1: Compare the Top 1 or Top K beams based on prediction results and measurements from resource sets / resources for monitoring, along with the presence or absence of margins for Top 1 or Top K beam prediction accuracy.

[0266] Alternative 2: Resource set for actual L1-RSRP measurements of one or more predicted Top K beams and monitoring / L1-RSRP difference information based on L1-RSRP measurements from resources

[0267] Alternative 3: RSRP difference information between the predicted RSRP and the measured L1-RSRP of the resource set / corresponding beam(s) of the resource for monitoring

[0268] Note: Resources of Set B for monitoring are not excluded and can be studied.

[0269] Note: This applies only if the model can predict RSRP.

[0270] Alternative 4: Probability information that the predicted beam(s) will become the Top 1 or Top K beams

[0271] Note: This applies only when the model can generate probability information.

[0272] FFS: For Alternatives 1 / 2 / 3, further review the details regarding how to configure resource sets / resources for monitoring.

[0273] Example: Whether / method to use the entire set of Set A for measurement. If not used, how to acquire measurements from the predicted Top 1 or Top K beams to calculate prediction accuracy or RSRP difference.

[0274] For all alternatives, we investigate whether performance information is calculated on a sample-by-sample basis (one-shot) or on a sample set basis (window).

[0275] Agreement

[0276] For the UE-side model of BM-Case 2, to report inference results, NW supports configuring the UE for N future points in time where applicable.

[0277] FFS: How to determine the reference time for those points in time

[0278] FFS: Predictable duration value at time N

[0279] Agreement

[0280] For UE-side AI / ML models in BM-Case1 and BM-Case2, in the case of Option 2 (UE-supported performance monitoring),

[0281] Support at least the following alternatives: Determine Top 1 or Top K beam prediction accuracy (with or without margins) by comparing the prediction results with the Top 1 or Top K beams based on measurements from the resource set / resources.

[0282] FFS: Detailed definition of the metric, including whether to configure or define a window for calculation

[0283] FFS: Includes other details related to how to configure resource sets / resources for monitoring, e.g.

[0284] Example: Whether / how to use the entire set of Set A for measurement. If the entire Set A is not configured, whether / how to define a metric.

[0285] FFS: Other Alternatives

[0286] Agreement

[0287] In BM-Case 2 of the UE-side model, the reference time of the earliest time instance for the prediction result considers at least the following potential down-selection alternatives:

[0288] Option 1: Based on the UL slot for reporting

[0289] Option 2: Based on CSI reference resources corresponding to the report

[0290] Option 3: Based on the latest transmission time of the CSI-RS / SSB resource within Set B for measurement for reporting, and this transmission time is not later than the CSI reference resource

[0291] Agreement

[0292] For at least UE-side model monitoring of Monitoring Type 1 Option 2 (if applicable), consider at least the following options and include potential down-selection for monitoring configuration:

[0293] Option 1: Resource set(s) for monitoring and reporting configuration are configured within the CSI reporting configuration used for inference (if applicable).

[0294] FFS: Resource set(s) for monitoring

[0295] The UE measures resource set(s) for monitoring

[0296] FFS: When / how to report monitoring results

[0297] Option 2: Dedicated resource set(s) and reporting configuration for monitoring are configured within the dedicated CSI reporting configuration used for monitoring.

[0298] The dedicated reporting configuration used for monitoring is linked to the inference reporting configuration.

[0299] FFS: A method to check connectivity between RSs within resource set(s) for monitoring and Set A beams.

[0300] The UE measures resource set(s) for monitoring

[0301] FFS: When to report monitoring results

[0302] In this specification, a method for setting aperiodic CSI-RS for Set B measurement, which can be used as input data for an AI / ML model during BM-case2 (temporal DL Tx beam prediction) operation utilizing AI / ML on the terminal side, is proposed, and a subsequent terminal operation is proposed.

[0303] As mentioned above, in NR standards, QCL configuration via TCI state settings and spatialRelation configuration are utilized to configure the uplink and downlink receiver and transmitter beams of a terminal. In the Rel-15 NR standard, RRC and MAC CE signaling were primarily used for uplink and downlink receiver and transmitter beams, and dynamic signaling was permitted only for the PDSCH receiver beam by utilizing the TCI state field of the DL grant DCI. With the introduction of the unified TCI framework through the Rel-17 / 18 NR standards, a method was introduced to dynamically manage the common beam by indicating the TCI using DCI for receiver and transmitter beams. Meanwhile, in the Rel-18 AI / ML study item, a study was conducted on performance evaluation and specification impact regarding spatial beam prediction and temporal beam prediction sub-use cases in the field of beam management.

[0304] This study discussed NW / UE-sided AI / ML operations that predict the best beam of Set A based on Set B measurements. In the case of UE-sided AI / ML, the terminal needs to measure Set B and report the predicted Set A beam, while in the case of NW-sided AI / ML, the terminal needs to report the Set B measurements.

[0305] Additionally, BM-case1 and BM-case2 are supported for UE-sided AI / ML operations. BM-case1 is an operation for spatial domain DL Tx beam prediction (e.g., operation when the upper layer parameter nroftimeinstance is not set), and BM-case2 is an operation for temporal DL Tx beam prediction (e.g., operation when the upper layer parameter nroftimeinstance is set). Meanwhile, in the BM-case1 and BM-case2 scenarios, Set A and Set B can be configured for the CSI report configuration for the inference result report (e.g., a CSI report containing predicted information (P-CRI, P-SSBRI, P-L1-RSRP)). In other words, two resource configurations connected to the CSI report configuration can be configured. The two resource configurations may include a first resource configuration for measurement (e.g., Set B configuration) and a second resource configuration for prediction (e.g., Set A configuration).

[0306] A consensus on the time domain behavior of CSI-RS that can be set as Set B was reached as shown in Table 7 below.

[0307]

[0308] Referring to the above agreement, P / SP / AP CSI-RS are all supported for the Set B configuration for inference in BM-case 1. For the Set B configuration for inference in BM-case 2, only P / SP CSI-RS is supported, and further discussion is required regarding AP CSI-RS. The reason why support for AP CSI-RS in BM-case 2 was not agreed upon is as follows.

[0309] In the case of BM-case2, multiple measurement instances (for the Set B beam) are required for DL ​​Tx beam prediction for one or more future prediction instances. Legacy aperiodic CSI-RS triggering up to Rel-17, which allows the terminal to measure AP CSI-RS for a single instance and perform CSI reporting, may not be suitable for Set B beam measurement in BM-case2.

[0310] In the Rel-18 CSI enhancement, an operation was introduced for CSI TD prediction in the aperiodic CSI-RS triggering operation to transmit K AP CSI-RS (based on K AP CSI-RS resources) based on a single triggering. However, this operation was introduced based on limited enhancement. Specifically, K resources must be occupied for burst AP CSI-RS, the number of resources is limited to K={4, 8, 12}, and each of the K AP CSI-RS is configured to be transmitted contiguously or with a 1-slot interval (see Table 8 below).

[0311]

[0312] Based on this background, the present specification proposes a method for setting aperiodic CSI-RS for Set B measurements that can be used as input data for an AI / ML model during BM-case2 (temporal DL Tx beam prediction) operation using AI / ML on the terminal side, and proposes a subsequent terminal operation.

[0313] In this specification, ' / ' may be interpreted as 'and', 'or', or 'and / or' depending on the context.

[0314] Proposal 1

[0315] In the BM-case2 scenario, when configuring a CSI report configuration for inference reporting of terminal UE-side AI / ML, the base station can trigger the Set B configuration by configuring AP CSI-RS. For example, a CSI report configuration related to prediction (e.g., CSI-ReportConfig) can be linked to a resource configuration for measurement (e.g., CSI-ResourceConfig). More specifically, the CSI report configuration may include the ID of the resource configuration for measurement (e.g., CSI-ResourceConfigId). The resource configuration may be a non-periodic resource configuration. As a specific example, the resource type (e.g., time domain behavior of the resource configuration) of the resource configuration may be set to aperiodic.

[0316] When the base station triggers the above AP CSI-RS to the terminal, the following operations may be performed.

[0317] A specific aperiodic trigger state among one or more aperiodic trigger states may be indicated / initiated by a base station trigger (e.g., a CSI request field within the DCI). The base station may be configured to transmit X consecutively (repeatedly) at an N-slot period for AP CSI-RS resource(s) within an aperiodic CSI-RS resource set associated with an aperiodic CSI-ReportConfig (e.g., CSI-ReportConfig with reportConfig set to aperiodic) connected to the specific aperiodic trigger state. N and X may be natural numbers.

[0318] In other words, the terminal may receive a DCI containing a CSI request field from the base station. Based on the CSI request field, a specific aperiodic trigger state among one or more aperiodic trigger states may be indicated / initiated. The terminal may receive X consecutive (repeatedly) AP CSI-RS resource(s) within the aperiodic CSI-RS resource set associated with the aperiodic CSI-ReportConfig connected to the specific aperiodic trigger state in an N-slot period.

[0319] In this specification, the expression 'CSI-RS resource(s) are transmitted / received' may be interpreted / replaced with 'CSI-RS(s) are transmitted / received based on CSI-RS resource(s)'.

[0320] The above-described operation may be performed based on at least one of the base station setting conditions (or / and terminal-side expectation) of conditions 1 to 3 below.

[0321] The statement above that AP CSI-RS resource(s) within the aperiodic CSI-RS resource set are configured to transmit X times in succession (repeatedly) in an N slot period may be based on at least one of the two embodiments below.

[0322] i) A method in which A AP CSI-RS resource(s) (where A is a natural number) are configured within an Aperiodic CSI-RS resource set and the A AP CSI-RS resource(s) are transmitted X times in succession (repeatedly).

[0323] ii) A method in which A * X AP CSI-RS resource(s) are configured within an Aperiodic CSI-RS resource set (X groups of A resources are configured), and A resources are transmitted X times consecutively (repeatedly) based on different slot offsets.

[0324] In the above method ii, each of the X groups composed of A resources may include A resources having the same properties. Specifically, A * X resources may be CSI-RS resources having different global IDs. The A * X resources may be composed of A * X resources obtained by replicating A resources having A DL Tx beam information X times.

[0325] For example, assume that CSI-RS resources associated with 8 DL Tx beams are configured in Set B (e.g., A=8). Each of the X groups may contain CSI-RS resources (with separate global IDs) that have the properties of 8 DL Tx beams within the corresponding Set B.

[0326] As a specific embodiment of the above method ii, if CSI-RS resource IDs #0 to #31 are configured in the AP CSI-RS resource set (e.g., A=8, X=4), the corresponding CSI-RS resources can be implicitly grouped into groups of 8 in ascending / descending order according to the order of the CSI-RS resource IDs (e.g., grouping into CSI-RS resource IDs #0 to #7, IDs #8 to #15, #16 to #23, #24 to #31). Resources within each group may have the same slot offset value and be transmitted at the same slot index. For example, the nth resource within each group may be associated with the same DL Tx beam. In this case, it may mean that X groups are associated with the same beam pattern / group within Set B. As another example, each of the X groups may be associated with a different beam pattern / group within Set B by base station configuration.

[0327] Through the operation of methods i and ii, the terminal can acquire Set B beam measurement results for multiple instances and utilize them as input data for the terminal AI / ML model. The terminal can report inference results through the triggered CSI-ReportConfig setting.

[0328] Condition 1 of Proposal 1

[0329] The number of AP CSI-RS resources in the aperiodic CSI-RS resource set for the above Set B configuration may be M or less. For example, the aperiodic CSI-RS resource set may be configured to be transmitted X times consecutively at an N slot period only when the number of AP CSI-RS resources in the aperiodic CSI-RS resource set for the above Set B configuration is M or less. For example, M may be the number of symbols in one slot (i.e., 14).

[0330] Effect of Condition 1 of Proposal 1

[0331] Condition 1 above takes into account the following technical considerations. Under the current 38.331 standard, the number of configurable AP CSI-RS resources within an aperiodic CSI-RS resource set is 16. On the other hand, the number of symbols that can be located within a single slot for aperiodic CSI-RS resources intended for Business Management (BM) that must be scheduled in TDM form is 14. Condition 1 above takes into account that CSI-RS resources exceeding 14 within an aperiodic CSI-RS resource set cannot be located within a single slot. If aperiodic CSI-RS resources for Business Management are located across two or more slots, triggering them X times consecutively increases the probability of collision with other DL channels / RS, which can result in a loss of base station scheduling flexibility. Through this Condition 1, the base station can transmit AP CSI-RS resource(s) scheduled within a specific single slot X times consecutively at N slot intervals without collisions between each repeated transmission, while reducing DL resource overhead. For example, a base station may configure / instruct a terminal that AP CSI-RS resource(s) will be transmitted as described above. For example, based on the configuration / instruction of the base station, the terminal may expect AP CSI-RS resource(s) to be transmitted from the base station to the terminal as described above.

[0332] Condition 2 of Proposal 1

[0333] The AP CSI-RS resource(s) within the aperiodic CSI-RS resource set for the above Set B configuration can be scheduled to be located within S slots (S is a natural number). Specifically, the aperiodic CSI-RS resource set can be configured / defined to be transmitted X times consecutively in an N-slot period only when the AP CSI-RS resource(s) within the aperiodic CSI-RS resource set for the above Set B configuration are scheduled to be located within S slots (S is a natural number). For example, S can be 1.

[0334] Effect of Condition 2 of Proposal 1

[0335] Condition 2 of Proposal 1 above has an effect similar to that of Condition 1 of Proposal 1 above. Specifically, when triggering a burst AP CSI-RS, it can prevent the problem of each AP CSI-RS transmission overlapping in the time domain when multiple slots are associated with a transmission instance for a single AP CSI-RS resource set. In addition, it can prevent overhead such as the burst AP CSI-RS excessively occupying downlink resources.

[0336] Condition 3 of Proposal 1

[0337] For the AP CSI-RS resource(s) within the aperiodic CSI-RS resource set for the above Set B configuration, the frequency domain RE density may be 1 / 2 or less. Specifically, the aperiodic CSI-RS resource set may be configured / defined to be transmitted X times consecutively with an N slot period only when the frequency domain RE density for the AP CSI-RS resource(s) within the aperiodic CSI-RS resource set for the above Set B configuration is 1 / 2 (or less). For this operation, 1 / 4 and 1 / 6, which are coarse than 1 / 2, may be supported for the frequency domain RE density of the CSI-RS resource.

[0338] Effect of Condition 3 of Proposal 1

[0339] According to Condition 3, in terms of downlink resources, frequency domain overhead can be reduced and energy saving on the base station side can be achieved.

[0340] In the above proposal 1, A, N, and X are associated with the capability of the AI / ML model equipped by the terminal (e.g., observation window / instance(s) or / and prediction window / instance(s)). As an example, at least one value of A, N, and / or X may be determined / defined / set by the base station based on UE capability reporting. As a specific example, at least one value of A, N, and / or X may be determined / defined / set by the base station based on a maximum value supported by the terminal (e.g., at least one maximum value of A, N, and / or X based on UE capability reporting).

[0341] In addition, based on an applicable functionality report regarding a specific AI / ML-related functionality of the terminal, the base station may determine / define / set at least one value among A, N, and / or X corresponding to the AI / ML model functionality available to the terminal.

[0342] As another example, if Condition 1 of Proposal 1 is not satisfied, the base station may trigger by configuring AP CSI-RS for Set B configuration based on the following embodiment. For example, the M symbols are M symbols within consecutive S slot(s), and the M symbols may be consecutive symbols or symbols configured by a pre-defined rule. As an example of the pre-defined rule, M may be divided into specific sub-groups, and a slot offset value may be set for each sub-group. Within a sub-group, scheduling may be performed on consecutive symbols. In this case, since S > 1 is considered, the repeat transmission period N of AP CSI-RS may need to be greater than or equal to S (e.g., if S = 2, N = > 2 slots must be satisfied).

[0343] Proposal 2

[0344] The base station may perform the operation of Proposal 1 without conditions 1 / 2 / 3 of Proposal 1. In this case, an operation based on the following embodiments may be performed. Specifically, when setting the AP CSI-RS resource within the aperiodic CSI-RS resource set for the Set B setting, an operation according to Embodiment 1 or Embodiment 2 may be applied.

[0345] Example 1 of Proposal 2

[0346] When configuring an aperiodic CSI-RS resource set to be transmitted X times consecutively with an N-slot period, a value greater than 2 for N may be supported. This embodiment is intended to relax the limitations on K AP CSI-RS(s) (e.g., K AP CSI-RS resource(s)) triggered by the existing Rel-18 CSI prediction enhancement. Specifically, when the Rel-18 CSI prediction enhancement triggers the transmission of K AP CSI-RS(s) with a single aperiodic CSI triggering, the K AP CSI-RS(s) are restricted to being transmitted contiguously or with a 1-slot interval. Since a value greater than 2 for N is supported, the above limitations may be relaxed.

[0347] Effects of Example 1 of Proposal 2

[0348] According to Example 1, an aperiodic CSI-RS resource set is transmitted X times in succession based on the number of slots in a period greater than 2. Therefore, when aperiodic CSI-RS resources within a specific aperiodic CSI-RS resource set scheduled over 2 slots or more are repeatedly transmitted, they have the advantage of being able to be transmitted X times in succession without overlap in the time domain.

[0349] Example 2 of Proposal 2

[0350] For example, the value of N can be determined in proportion to the number of AP CSI-RS resources within the set. For example, the value of N can be determined in proportion to the number of (contiguous) slots where AP CSI-RS resources are located within the set.

[0351] Effects of Example 2 of Proposal 2

[0352] In Example 2, similar to the effect of Example 1, a larger value of period N is utilized as the number of AP CSI-RS resources in the set or the number of (contiguous) slots where the AP CSI-RS resources in the set are located increases. When aperiodic CSI-RS resources within a specific aperiodic CSI-RS resource set scheduled over 2 slots or more are repeatedly transmitted, they have the advantage of being able to be transmitted X times consecutively without overlap in the time domain.

[0353] In the case of the N and X values ​​in Proposal 2 above, the maximum and / or minimum values ​​of N and / or X can be determined / defined by the number of resources (range) (e.g., A) within the AP CSI-RS resource set. For example, when the number of resources within the AP CSI-RS resource set exceeds A1, the terminal does not expect the number of repetitions X to exceed the maximum value X1. Through this operation, the maximum value of A * X, which is the total number of transmissions of beams within Set B, can be limited. This prevents waste of base station DL resources and collisions between DL channels / RS, and can also help save power for the terminal.

[0354] For example, the maximum and minimum values ​​of A, N, and X can be determined or defined by considering terminal memory, computational load, latency, etc. Specifically, the maximum and minimum values ​​of A, N, and X can be determined or defined based on the size of Set A or Set B. This is intended to prevent terminal computational load and latency by limiting the number of measurements for the Set B beam used as input data for the AI / ML model, as a large number of beams in Set A or Set B causes a load on the terminal's computations based on AI / ML.

[0355] Proposal 1 and Proposal 2 above may be applied together.

[0356] The embodiments of the above proposals 1 and 2 may be operated by a combination of specific embodiments.

[0357] The above AP CSI-RS related settings can also be used to configure multiple CSI measurement instances that serve as inference inputs for AI / ML-based CSI prediction.

[0358] An example of a terminal (or base station) operation based on at least one of the aforementioned embodiments (e.g., at least one of the embodiments of Proposal 1 to 2) is as follows.

[0359] 1) The terminal (base station) receives (transmits) settings related to beam measurement / reporting.

[0360] The above settings may include reporting settings related to Set A and Set B. Based on the above settings, AP CSI-RS related to Set B may be configured. For example, the above settings may include resource settings for measurements. The resource settings may be associated with a non-periodic CSI-RS resource set.

[0361] 2) The terminal (base station) receives (transmits) a message scheduling the transmission of an inference result report based on beam measurement. For example, the message may be a DCI to trigger AP CSI. As a specific example, the DCI may include a CSI request field.

[0362] 3) The terminal (base station) transmits (receives) an inference result report based on beam measurement based on the above message. The UE assumption and UE behavior for the report may be based on the embodiments of Proposal 1 and 2.

[0363] The above terminal / base station operation is merely an example, and each operation (or step) is not necessarily essential; depending on the terminal / base station implementation method, the beam measurement / reporting operation of the terminal according to the aforementioned embodiments may be omitted or added.

[0364] 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 2) can be processed by the device of FIG. 9 (e.g., the processor (110, 210) of FIG. 9).

[0365] 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 2) may be stored in memory (e.g., 140, 240 of FIG. 9) in the form of instructions / programs (e.g., instruction, executable code) for driving at least one processor (e.g., 110, 210 of FIG. 9).

[0366] The embodiments described above will be explained in detail below with reference to FIGS. 7 and FIGS. 8 regarding the operation of the terminal and base station. The methods described below are distinguished only for convenience of explanation, and it is understood that a part of one method may be substituted with a part of another method or combined with one another and applied.

[0367] FIG. 7 is a flowchart illustrating a method according to one embodiment of the present specification.

[0368] Referring to FIG. 7, a method according to one embodiment of the present specification includes a step of receiving configuration information related to CSI (S710), a step of receiving DCI including a CSI request field (S720), and a step of transmitting a CSI report (S730).

[0369] In S710, the terminal receives configuration information related to Channel State Information (CSI) from the base station.

[0370] For example, the above configuration information may include information based on at least one of the above-described CSI-related operations and proposals 1 to 2. As a specific example, the above configuration information may include at least one of i) one or more reporting settings (e.g., N≥1 CSI-ReportConfig reporting setting), ii) one or more resource settings (e.g., M≥1 CSI-ResourceConfig resource setting), and / or iii) a list of one or more trigger states.

[0371] 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 the IDs (e.g., CSI-ResourceConfigId) of up to three resource configurations.

[0372] The above one or more reporting settings may include reporting settings related to prediction.

[0373] For example, the report quantity of the above report setting can be set to p-cri, p-cri-RSRP, p-ssb-index, or p-ssb-index-RSRP. p-cri represents the predicted CSI-RS Resource Indicator (P-CRI). p-ssb-index represents the predicted SSB Resource Indicator (P-SSBRI). In p-cri-RSRP or p-ssb-index-RSRP, RSRP represents the predicted Layer1-Reference Signal Received Power (P-L1-RSRP).

[0374] For example, the above 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 above reporting configuration. For example, the above reporting configuration may be linked to two resource configurations (e.g., a first resource configuration for measurement and a second resource configuration for prediction). The above reporting configuration may include the ID of the first resource configuration and the ID of the second resource configuration. Each ID may be based on the CSI-ResourceConfigId.

[0375] For example, the above measurements may include Layer1-Reference Signal Received Power (L1-RSRP) measurements. Based on the L1-RSRP measurements, 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 Layer1-Reference Signal Received Power (P-L1-RSRP) may be determined.

[0376] More specifically, predictions for CSI-RS resources or SSB resources associated with the second resource configuration may be performed based on the L1-RSRP measurements. Specifically, predicted L1-RSRPs of CSI-RS resources or SSB resources associated with the second resource configuration may be determined. For example, best CRI(s) or best SSBRI(s) may be determined based on the 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 may be based on the best CRI(s) or best SSBRI(s).

[0377] The above one or more resource settings may include a first resource setting and a second resource setting related to the above reporting setting.

[0378] Each resource configuration (e.g., CSI-ResourceConfig) may include information about a resource set (e.g., csi-SSB-ResourceSetList or nzp-CSI-RS-ResourceSetList based on csi-RS-ResourceSetList). For example, the resources within the resource set may be SSB resources or CSI-RS resources based on csi-RS-ResourceSetList. csi-SSB-ResourceSetList may include information for referencing the SSB resources (e.g., CSI-SSB-ResourceSetIds). nzp-CSI-RS-ResourceSetList may include information for referencing the CSI-RS resources (e.g., NZP-CSI-RS-ResourceSetIds).

[0379] For example, the first resource setting may include information about a first resource set for measurement (e.g., Set B described above). As a specific example, the first resource setting may include a list of SSB resources or CSI-RS resources for measurement.

[0380] For example, the second resource setting may include information regarding a second resource set for the prediction (e.g., Set A described above). As a specific example, the second resource setting may include a list of SSB resources or CSI-RS resources for the prediction.

[0381] For example, the above configuration information may include a list of one or more trigger states (e.g., CSI-AperiodicTriggerStateList).

[0382] In S720, the terminal receives Downlink Control Information (DCI) from the base station, which includes a CSI request field.

[0383] For example, the above DCI can be interpreted / replaced with a DCI format. The above DCI format may be DCI format 0_1, DCI format 0_2, or DCI format 0_3.

[0384] In S730, the terminal transmits a CSI report to the base station containing predicted information for each of one or more time instances.

[0385] For example, the above CSI report may refer to a report related to the BM-case2 described above. In other words, the above CSI report may be related to temporal DL Tx beam prediction. Specifically, the above CSI report may be related to temporal-domain downlink transmission beam prediction for one set of beams based on historic measurement results of another set of beams.

[0386] According to one embodiment, a trigger state among the one or more trigger states may be initiated / indicated based on the CSI request field. The trigger state may be associated with a reporting setting related to prediction. The reporting setting may be connected to two resource settings (e.g., a first resource setting for measurement and a second resource setting for prediction). Among the two resource settings, the first resource setting for measurement may be associated with an aperioditic CSI-RS (aperiodic CSI-Reference Signal, RS) resource set. This embodiment may be based on Proposal 1 and / or Proposal 2. For example, the resourceType of the first resource setting may be set to aperioditic.

[0387] According to one embodiment, for the measurement based on the set of non-periodic CSI-RS resources, CSI-RSs based on A non-periodic CSI-RS resources may be received repeatedly X times. This embodiment may be based on i) of Proposal 1. This embodiment may be combined with at least one of conditions 1 to 3 of Proposal 1 and embodiments 1 to 2 of Proposal 2 described below.

[0388] According to one embodiment, for the measurement based on the set of non-periodic CSI-RS resources, CSI-RSs based on A*X non-periodic CSI-RS resources may be received based on different slot offsets. This embodiment may be based on ii) of Proposal 1. This embodiment may be combined with at least one of conditions 1 to 3 of Proposal 1 and embodiments 1 to 2 of Proposal 2 described below.

[0389] According to one embodiment, the number of non-periodic CSI-RS resources in the set of non-periodic CSI-RS resources may be less than or equal to the number of symbols per slot. This embodiment may be based on Condition 1 of Proposal 1.

[0390] According to one embodiment, the non-periodic CSI-RS resources within the set of non-periodic CSI-RS resources may be configured within a single slot. This embodiment may be based on condition 2 of Proposal 1.

[0391] According to one embodiment, the CSI-RS frequency density (e.g., density parameter) associated with each of the aperiodic CSI-RS resources within the set of aperiodic CSI-RS resources may be less than or equal to 1 / 2. This embodiment may be based on Condition 3 of Proposal 1. The CSI-RS frequency density may refer to the number of resource elements (RE) allocated per CSI-RS port within a physical resource block (PRB) (e.g., CSI-RS frequency density of each CSI-RS port per PRB).

[0392] According to one embodiment, the non-periodic CSI-RS resources within the set of non-periodic CSI-RS resources may be configured within N slots. This embodiment may be based on Embodiment 1 or Embodiment 2 of Proposal 2.

[0393] For example, the above N may be greater than 2. This embodiment may be based on Embodiment 1 of Proposal 2.

[0394] For example, the above N may be determined based on the number of non-periodic CSI-RS resources within the set of non-periodic CSI-RS resources. This embodiment may be based on Embodiment 2 of Proposal 2.

[0395] For example, the above N may be determined based on the number of consecutive slots in which the non-periodic CSI-RS resources are located within the set of non-periodic CSI-RS resources. This embodiment may be based on Embodiment 2 of Proposal 2.

[0396] According to one embodiment, the CSI report may be based on an inference report / predicted Set A beam-related CSI. The predicted information may include predicted CSI parameter(s). The predicted CSI parameter(s) may include predicted CSI parameter(s) based on the report quantity of the report set (e.g., P-CRI(s), P-SSBRI(s), and / or P-L1-RSRP(s)). Specifically, the predicted information 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 Layer1-Reference Signal Received Power (P-L1-RSRP).

[0397] For example, the above CSI report can be interpreted / replaced with CSI.

[0398] According to one embodiment, the setting information may include the reporting setting.

[0399] Operations based on S710 to S730 described above can be implemented by the device of FIG. 9. For example, referring to FIG. 9, the terminal (200) can control one or more transceivers (230) and / or one or more memories (240) to perform operations based on S710 to S730.

[0400] The embodiments described above will be explained in detail below in terms of base station operation.

[0401] S810 to S830 described below correspond to operations based on S710 to S730 described in FIG. 7. Considering the above correspondence, redundant descriptions are omitted. That is, the specific description of the base station operation described below can be replaced by the description / embodiment of FIG. 7 corresponding to the operation.

[0402] FIG. 8 is a flowchart illustrating a method according to another embodiment of the present specification.

[0403] Referring to FIG. 8, a method according to another embodiment of the present specification includes a step of transmitting configuration information related to CSI (S810), a step of transmitting DCI including a CSI request field (S820), and a step of receiving a CSI report (S830).

[0404] In S810, the base station transmits configuration information related to Channel State Information (CSI) to the terminal. For example, the configuration information may include a list of one or more trigger states.

[0405] In S820, the base station transmits Downlink Control Information (DCI) containing a CSI request field to the terminal.

[0406] In S830, the base station receives a CSI report from the terminal containing predicted information for each of one or more time instances.

[0407] According to one embodiment, a trigger state among the one or more trigger states may be initiated / indicated based on the CSI request field. The trigger state may be associated with a reporting setting related to prediction. The reporting setting may be connected to two resource settings (e.g., a first resource setting for measurement and a second resource setting for prediction). Among the two resource settings, the first resource setting for measurement may be associated with an aperiodic CSI-RS (aperiodic CSI-Reference Signal, RS) resource set.

[0408] Operations based on S810 to S830 described above can be implemented by the device of FIG. 9. For example, referring to FIG. 9, a base station (100) can control one or more transceivers (130) and / or one or more memories (140) to perform operations based on S810 to S830.

[0409] The operations / terms based on the embodiments described above are described under the assumption of an existing system (e.g., a 5G system). However, this is for the convenience of explanation and is not intended to limit the scope of application of the technical problems and means for solving problems that are to be solved by this specification to a specific system. That is, the technical problems / technical issues / problems mentioned in this specification may exist in other systems (e.g., a 6G system). It is evident that the embodiments of this specification can be extended to solve problems that exist in other systems as well. Therefore, for the extended application of the embodiments of this specification to other systems, terms defined / described based on a 5G system may be replaced / changed with terms defined in other systems (or generalized terms not specific to one system). For example, PRACH, PUSCH, PUCCH, or SRS may be replaced / changed to uplink signals (or uplink channels). For example, SSB, CSI-RS, PDSCH, and PDCCH may be replaced / changed to downlink signals (or downlink channels).

[0410] Hereinafter, an apparatus to which the embodiments of the present specification can be applied (an apparatus implementing the method / operation according to the embodiments of the present specification) will be described with reference to FIG. 9.

[0411] FIG. 9 is a drawing showing the configuration of a first device and a second device according to an embodiment of the present specification.

[0412] The first device (100) may include a processor (110), an antenna unit (120), a transceiver (130), and a memory (140).

[0413] The processor (110) performs baseband-related signal processing and may include an upper layer processing unit (111) and a physical layer processing unit (115). The upper layer processing unit (111) may process operations of the MAC layer, RRC layer, or higher upper layers. The physical layer processing unit (115) may process operations of the PHY layer. For example, if 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, if the first device (100) is a first terminal device in terminal-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).

[0414] The antenna section (120) may include one or more physical antennas, and if 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, operating systems, applications, etc. related to the operation of the first device (100), and may include components such as a buffer.

[0415] The processor (110) of the first device (100) may be configured to implement the operation of the base station in base station-terminal communication (or the operation of the first terminal device in terminal-terminal communication) in the embodiments described in this disclosure.

[0416] The second device (200) may include a processor (210), an antenna unit (220), a transceiver (230), and a memory (240).

[0417] The processor (210) performs baseband-related signal processing and may include an upper layer processing unit (211) and a physical layer processing unit (215). The upper layer processing unit (211) may process operations of the MAC layer, RRC layer, or higher upper layers. The physical layer processing unit (215) may process operations of the PHY layer. For example, if 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, if the second device (200) is a second terminal device in terminal-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).

[0418] The antenna section (220) may include one or more physical antennas, and may support MIMO transmission and reception if it includes multiple antennas. The transceiver (230) may include an RF transmitter and an RF receiver. The memory (240) may store information processed by the processor (210) and software, operating systems, applications, etc. related to the operation of the second device (200), and may include components such as a buffer.

[0419] The processor (210) of the second device (200) may be configured to implement the operation of the terminal in base station-terminal communication (or the operation of the second terminal device in terminal-terminal communication) in the embodiments described in this disclosure.

[0420] In the operation of the first device (100) and the second device (200), the details described in the examples of the present disclosure regarding the base station and terminal (or the first terminal and the second terminal in terminal-to-terminal communication) in base station-to-terminal communication may be applied in the same way, and redundant descriptions are omitted.

[0421] 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 Low Power Wide Area Network (LPWAN) technology and may be implemented according to standards such as LTE Cat NB1 and / or LTE Cat NB2, but is not limited to the names mentioned above.

[0422] 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 referred to by various names such as eMTC (enhanced Machine Type Communication). For example, LTE-M technology may be implemented in 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 names mentioned above.

[0423] Additionally or generally, the wireless communication technology implemented in the device of the present disclosure may include at least one of ZigBee, Bluetooth, and a Low Power Wide Area Network (LPWAN) for low-power communication, but is not limited to the names mentioned above. 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 referred to by various names.

Claims

1. Regarding the method, A step of receiving configuration information related to Channel State Information (CSI), said configuration information including a list of one or more trigger states; A step of receiving Downlink Control Information (DCI) including a CSI request field; and The method includes the step of transmitting a CSI report containing predicted information for each of one or more time instances, wherein Based on the above CSI request field, one or more of the trigger states is initiated, and The above trigger state is associated with a reporting setting related to a prediction, and the above reporting setting is connected to two resource settings, and A method characterized in that, among the two resource settings above, the first resource setting for measurement is associated with a non-periodic CSI-RS (aperiodic CSI-Reference Signal, RS) resource set.

2. In Paragraph 1, A method characterized by receiving CSI-RS based on A non-periodic CSI-RS resources repeatedly X times for the measurement based on the above non-periodic CSI-RS resource set.

3. In Paragraph 1, A method characterized by receiving CSI-RS based on A*X non-periodic CSI-RS resources based on different slot offsets for the measurement based on the above non-periodic CSI-RS resource set.

4. In Paragraph 1, A method characterized in that the number of non-periodic CSI-RS resources in the above non-periodic CSI-RS resource set is less than or equal to the number of symbols per slot.

5. In Paragraph 1, A method characterized in that the non-periodic CSI-RS resources within the above non-periodic CSI-RS resource set are configured within a single slot.

6. In Paragraph 1, A method characterized in that the CSI-RS frequency density associated with each of the aperiodic CSI-RS resources within the above set of aperiodic CSI-RS resources is less than or equal to 1 / 2.

7. In Paragraph 1, A method characterized in that the non-periodic CSI-RS resources within the above non-periodic CSI-RS resource set are configured within N slots.

8. In Paragraph 7, A method characterized in that the above N is greater than 2.

9. In Paragraph 7, A method characterized in that the above N is determined based on the number of non-periodic CSI-RS resources within the set of non-periodic CSI-RS resources.

10. In Paragraph 7, A method characterized in that the above N is determined based on the number of consecutive slots in which non-periodic CSI-RS resources are located within the set of non-periodic CSI-RS resources.

11. In the terminal, One or more transmitters / receivers; One or more processors; and It includes one or more memories connected to the above one or more processors and storing instructions, A terminal characterized by the above instructions enabling the terminal to perform all steps of the method according to any one of claims 1 to 10, based on execution by the one or more processors.

12. A device comprising one or more memories and one or more processors connected to the one or more memories, An apparatus characterized in that the above one or more memories store instructions that cause the apparatus to perform all steps of the method according to any one of claims 1 to 10, based on execution by the above one or more processors.

13. In a non-transitory computer-readable storage medium for storing instructions, A non-transitory computer-readable storage medium characterized by instructions executable by one or more processors that cause a terminal to perform all steps of the method according to any one of claims 1 to 10.

14. Regarding the method, A step of transmitting configuration information related to Channel State Information (CSI), wherein the configuration information includes a list of one or more trigger states; A step of transmitting Downlink Control Information (DCI) including a CSI request field; and The method includes the step of receiving a CSI report containing predicted information for each of one or more time instances, wherein Based on the above CSI request field, one or more of the trigger states is initiated, and The above trigger state is associated with a reporting setting related to a prediction, and the above reporting setting is connected to two resource settings, and A method characterized in that, among the two resource settings above, the first resource setting for measurement is associated with a non-periodic CSI-RS (aperiodic CSI-Reference Signal, RS) resource set.

15. Regarding base stations, One or more transmitters / receivers; One or more processors; and It includes one or more memories connected to the above one or more processors and storing instructions, A base station characterized by the above instructions, based on execution by one or more processors, having the base station perform all steps of the method according to claim 14.

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