Method and apparatus for reporting channel state information

Abstract

A method according to an embodiment of the present specification comprises the steps of: receiving, from a base station, configuration information related to channel state information (CSI); and reporting the CSI to the base station. The CSI includes information related to one or more transmission occasions among multiple transmission occasions. The one or more transmission occasions are related to measurements performed prior to the reporting of the CSI.

Description

Method and apparatus for reporting channel status information

[0001] This specification relates to a method and apparatus for reporting channel status information.

[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 necessitate the capacity 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 this end, various technologies are being researched, including dual connectivity, Massive Multiple Input Multiple Output (MIMO), in-band full duplex, Non-Orthogonal Multiple Access (NOMA), super wideband support, and device networking. Meanwhile, beam prediction operations based on network-side models for beam management are being defined. For example, data collection methods for training network-side models and for inference of network-side models are being discussed. In particular, for data collection for inference of the NW-side model, measurement results for multiple measurement instances capable of reflecting temporal changes in the downlink transmission beam (DL Tx beam) may be reported to ensure the inference performance of the network-side model.

[0004] According to beam prediction operations, in relation to data collection for inference of the base station-side model (NW-side model), the terminal may be required to report measurement results for multiple measurement instances. In an aperioditic or semi-persistent CSI reporting structure, when the reporting of measurement results for multiple measurement instances is reported as a CSI report, the terminal's reporting operation is performed based on a specific point in time when the CSI report is activated or triggered. In this case, if the terminal fails to receive all of the multiple measurement instances set by the base station prior to the specific reporting point in time, a discrepancy may arise between the input data structure required by the NW-side model and the reported CSI report. The purpose of this specification is to propose a method to solve the aforementioned problem.

[0005] 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 invention belongs from the description below.

[0006] To solve the aforementioned technical problem, a method according to one embodiment of the present specification includes the steps of receiving configuration information related to Channel State Information (CSI) from a base station and reporting the CSI to the base station.

[0007] The above CSI includes information related to one or more transmission occasions among multiple transmission occasions.

[0008] The above one or more transmission opportunities relate to measurements performed prior to the above report of the CSI.

[0009] The above setting information may include information related to the reporting time of the above CSI and information related to the number of the above multiple transmission opportunities.

[0010] The above CSI may be reported through a Physical Uplink Control Channel (PUCCH). In this case, the reporting of the CSI may be activated based on a MAC Control Element (MAC-CE).

[0011] The method may further include the step of receiving Downlink Control Information (DCI) from the base station. In this case, the report of the CSI may be triggered based on the DCI.

[0012] The information related to the above one or more transmission opportunities may be related to inference of the base station side model (NW-side model).

[0013] The information associated with one or more of the above transmission opportunities may include i) Reference Signal Received Power (RSRP) and / or ii) Channel State Information Reference Signal Resource Indicator (CSI-RS Resource Indicator, CRI).

[0014] The above CSI may further include information related to the remaining transmission opportunities other than one or more of the plurality of transmission opportunities.

[0015] The information related to the remaining transmission opportunities mentioned above may be based on pre-set or defined information.

[0016] The above-mentioned preset or defined information may include i) a zero padding value, ii) a preset or defined RSRP value, or iii) a preset or defined CRI.

[0017] The above CSI may be reported based on the number of the plurality of transmission opportunities and the number of one or more transmission opportunities being the same.

[0018] The above report of the above CSI may be skipped based on the fact that the number of one or more transmission opportunities is less than the number of the plurality of transmission opportunities.

[0019] The information related to the one or more transmission opportunities may further include information for identifying the one or more transmission opportunities.

[0020] As described above, by specifically defining a method for the terminal to report the CSI when the terminal has not received all of the plurality of transmission opportunities set by the base station at the time of reporting the CSI, the base station can consistently interpret the terminal's report, thereby eliminating ambiguity in interpretation between the base station and the terminal.

[0021] A terminal according to another embodiment of the present specification comprises one or more transceivers, one or more processors, and one or more memories connected to the one or more processors and storing instructions. The instructions are characterized by causing the terminal to perform all steps of any one of the methods based on execution by the one or more processors.

[0022] An apparatus according to another embodiment of the present specification comprises one or more memories and one or more processors connected to the one or more memories. The one or more memories are characterized by storing instructions that cause the apparatus to perform all steps of any one of the methods based on execution by the one or more processors.

[0023] A computer-readable storage medium according to another embodiment of the present specification stores instructions. The instructions, executable by one or more processors, are characterized by enabling a terminal to perform all steps of any one of the methods.

[0024] A method according to another embodiment of the present specification includes the steps of transmitting configuration information related to Channel State Information (CSI) to a terminal and receiving the CSI from the terminal.

[0025] The above CSI includes information related to one or more transmission occasions among multiple transmission occasions.

[0026] The above one or more transmission opportunities relate to measurements performed prior to the report of the above CSI.

[0027] A base station according to another embodiment of the present specification includes one or more transceivers, one or more processors, and one or more memories connected to the one or more processors and storing instructions. The instructions are characterized by causing the base station to perform all steps of any one of the methods based on execution by the one or more processors.

[0028] According to the operation of the prior art, in the reporting of Channel State Information (CSI) for beam prediction based on a network-side model, a problem exists in that the terminal's reporting method is not clearly defined for cases where the terminal has not received all of the multiple measurement instances required for inference of the network-side model by a specific reporting point, resulting in ambiguity regarding how the base station should interpret the CSI reported by the terminal.

[0029] According to an embodiment of the present specification, by specifically defining a method for reporting CSI in cases where a terminal has not received all measurement instances necessary for inference of the NW-side model, the base station can consistently interpret the terminal's report, thereby eliminating ambiguity in interpretation between the base station and the terminal.

[0030] 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 from the description below.

[0031] The drawings attached below are intended to aid in understanding the present disclosure and may provide embodiments of the present disclosure together with the detailed description. However, the technical features of the present disclosure are not limited to specific drawings, and the features disclosed in each drawing may be combined with one another to form new embodiments. Reference numerals in each drawing may denote structural elements.

[0032] Figure 1 shows an example of beam forming using SSB and CSI-RS.

[0033] Figure 2 is a flowchart showing an example of a DL BM procedure.

[0034] FIG. 3 shows an example of a CSI reporting setting according to an embodiment of the present specification.

[0035] Figure 4 is a diagram illustrating the overall functions from the perspective of an AI / ML model.

[0036] Figure 5 illustrates a general form of an AI / ML-related procedure performed between a network and a terminal.

[0037] Figure 6 illustrates an example of AI / ML-based beam management operation.

[0038] FIG. 7 is a flowchart illustrating a method according to one embodiment of the present specification.

[0039] FIG. 8 is a flowchart illustrating a method according to another embodiment of the present specification.

[0040] FIG. 9 is a drawing showing the configuration of a first device and a second device according to an embodiment of the present specification.

[0041] 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 invention according to the present specification can be practiced. The following detailed description includes specific details to provide a complete understanding of the present specification.

[0042] In some cases, to avoid obscuring the concept of the invention according to the embodiments of this specification, known structures and devices may be omitted or illustrated in the form of a block diagram focusing on the core functions of each structure and device.

[0043] In the following, the downlink (DL) refers to communication from a base station to a terminal, and the uplink (UL) refers to communication from a terminal to a base station. In the downlink, the transmitter may be part of the base station and the receiver may be part of the terminal. In the uplink, the transmitter may be part of the terminal and the receiver may be part of the base station. The base station may be referred to as the first communication device and the terminal as the second communication device. The base station (BS) may be replaced by terms such as fixed station, Node B, eNB (evolved-NodeB), gNB (Next Generation NodeB), BTS (base transceiver system), Access Point (AP), network (5G network), AI system, RSU (road side unit), vehicle, robot, drone (Unmanned Aerial Vehicle, UAV), AR (Augmented Reality) device, VR (Virtual Reality) device, etc. In addition, the terminal may be fixed or mobile and may be replaced with terms such as UE (User Equipment), MS (Mobile Station), UT (user terminal), MSS (Mobile Subscriber Station), SS (Subscriber Station), AMS (Advanced Mobile Station), WT (Wireless terminal), MTC (Machine-Type Communication) device, M2M (Machine-to-Machine) device, D2D (Device-to-Device) device, vehicle, robot, AI module, drone (Unmanned Aerial Vehicle, UAV), AR (Augmented Reality) device, VR (Virtual Reality) device.

[0044] Beam Management (BM)

[0045] 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.

[0046] - Beam measurement: An operation in which a base station or UE measures the characteristics of a received beamforming signal.

[0047] - Beam determination: The operation in which a base station or UE selects its transmit beam (Tx beam) / receive beam (Rx beam).

[0048] - Beam sweeping: An operation that covers a spatial area using transmitting and / or receiving beams for a set time interval in a predetermined manner.

[0049] - Beam report: An operation in which the UE reports information about the beam-formed signal based on beam measurements.

[0050] 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).

[0051] In addition, each BM procedure may include Tx beam sweeping to determine the Tx beam and Rx beam sweeping to determine the Rx beam.

[0052] DL BM

[0053] 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.

[0054] Here, beam reporting may include preferred DL RS ID(identifier)(s) and the corresponding L1-RSRP(Reference Signal Received Power).

[0055] The above DL RS ID may be SSBRI (SSB Resource Indicator) or CRI (CSI-RS Resource Indicator).

[0056] Figure 1 shows an example of beam forming using SSB and CSI-RS.

[0057] As shown in Fig. 1, the SSB beam and CSI-RS beam can be used for beam measurement. The measurement metric is L1-RSRP per resource / block. The SSB is used for coarse beam measurement, while the CSI-RS can be used for fine beam measurement. The SSB can be used for both Tx beam sweeping and Rx beam sweeping.

[0058] 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.

[0059] Figure 2 is a flowchart showing an example of a DL BM procedure.

[0060] Configuration for beam reporting using SSB is performed during CSI / beam configuration in the RRC connected state (or RRC connected mode).

[0061] - 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 (S210).

[0062] 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.

[0063]

[0064] 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.

[0065] - The terminal receives a DownLink Reference Signal (DL RS) from the base station. As a specific example, the terminal receives an SSB from the base station based on the CSI-SSB-ResourceSetList (S220).

[0066] - 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 (S230).

[0067] 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.

[0068] 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'.

[0069] 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.

[0070] < CSI Related Actions >

[0071] In NR (New Radio) systems, CSI-RS (channel state information-reference signal) is used for time and / or frequency tracking, CSI computation, L1 (layer 1)-RSRP (reference signal received power) computation, and mobility. Here, CSI computation is related to CSI acquisition, and L1-RSRP computation is related to beam management (BM).

[0072] Figure 3 is a flowchart showing an example of a CSI-related procedure.

[0073] Referring to FIG. 3, 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 (S310).

[0074] The configuration information related to the above CSI may include at least one of information related to CSI-IM (interference management) resources, information related to CSI measurement configurations, information related to CSI resource configurations, information related to CSI-RS resources, or information related to CSI report configurations.

[0075] 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.

[0076] Information related to CSI report configuration 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.

[0077] The above reportQuantity parameter may be associated with at least one of the Channel Quality Indicator (CQI), Precoding Matrix Indicator (PMI), CRI (CSI-RS Resource Indicator), SSBRI (SSB Resource block Indicator), LI (Layer Indicator), Rank Indicator (RI), and Layer 1-Reference Signal Received Strength (RSRP).

[0078] 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.

[0079] The terminal measures the CSI based on configuration information related to the above CSI (S320). The CSI measurement may include (1) a process of receiving the terminal's CSI-RS (S321) and (2) a process of computing the CSI through the received CSI-RS (S322). The terminal reports the CSI to the base station (S330).

[0080] resource setting

[0081] 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).

[0082] Next, one or more CSI resource settings for channel measurement (CM) and interference measurement (IM) are established through higher layer signaling.

[0083] - CSI-IM resource for interference measurement.

[0084] - NZP CSI-RS resources for interference measurement.

[0085] - NZP CSI-RS resources for channel measurement.

[0086] 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.

[0087] Here, CSI-IM (or ZP CSI-RS for IM) is primarily used for inter-cell interference measurements.

[0088] Also, the NZP CSI-RS for IM is mainly used for intra-cell interference measurement from multi-users.

[0089] 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.

[0090] As examined, resource setting can refer to a resource set list.

[0091] 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.

[0092] One reporting setting can be linked to up to three resource settings.

[0093] Explanation regarding Rel-17 / 18 Beam Management >

[0094] In Rel-17, DL DCI (e.g., DCI format 1-1 or 1-2) can be used to specify both the DL TCI state and the UL TCI state, or to specify only the UL TCI state without specifying the DL TCI state. Consequently, the methods used in Rel-15 / Rel-16 for configuring UL beam and power control (PC) are replaced in Rel-17 by the aforementioned method of specifying the UL TCI state. More specifically, in Rel-17, a single UL TCI state can be specified through the TCI field of the DL DCI; this UL TCI state is applied to all PUSCH and all PUCCH after a certain period known as the beam application time, and can be applied to some or all of the specified SRS resource sets. In addition, the base station can perform a terminal common beam update by utilizing DCI and / or MAC-CE to perform indication / updates for multiple specific DL / UL channel / RS combinations in common with one beam (using joint or separate TCI states). For the common beam update, the target channel / RS includes UE-dedicated CORESET and UE-dedicated reception on PDSCH for DL, and DG / CG-PUSCH, all or subset of dedicated PUCCH for UL, and additionally, AP CSI-RS for tracking / BM and SRS can be set as target channel / RS.In Rel-18, standardization was carried out on the method of indicating multiple UL TCI states (and / or DL ​​TCI states) through the TCI field of DL DCI in consideration of the M-TRP environment, and uplink / downlink resources to which each of the multiple indicated TCIs will be applied can be defined / configured depending on the S-DCI based M-TRP environment and the M-DCI based M-TRP environment.

[0095] AI / ML for Wireless Communication

[0096] 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 "transmission / reception mode" or "signal / channel / operation / transmission / reception configuration" configured for AI / ML, but is not limited thereto. The meanings of the terms currently used in the 3GPP standardization process are briefly summarized as follows.

[0097] - 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.

[0098] - 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.

[0099] - 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.

[0100] - Offline training: A process of training a model based on previously collected data sets, where the trained model is used or provided for future inference.

[0101] - Online Training: A method in which the model is trained in real-time upon the acquisition of new training sample data and used for inference.

[0102] - 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.

[0103] 1. Life Cycle Management (LCM) for AI / ML models

[0104] 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.

[0105] LCMs for AI / ML models can be broadly classified into functionality-based LCMs and model-ID-based LCMs. In functionality-based LCMs, the network can direct activation, deactivation, fallback, or switching for specific functions; in this case, the target AI / ML model may not be identified by the network. In model-ID (identifier)-based LCMs, the network can direct activation, deactivation, selection, or switching for AI / ML models identified based on their AI / ML model IDs.

[0106] Figure 4 is a diagram illustrating the overall functions from the perspective of an AI / ML model.

[0107] Referring to FIG. 4, 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).

[0108] 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.

[0109] 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).

[0110] 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).

[0111] 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).

[0112] The Management function (30) is a function that monitors the operation of an AI / ML model or an AI / ML function. Additionally, the Management function (30) may make decisions to ensure appropriate inference operations based on data received from the Data Collection function (10) (i.e., Monitoring Data (12)) and / or data received from the Inference function (40) (i.e., Inference Output (41)).

[0113] 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).

[0114] A Model Transfer / Delivery Request (33) can be used to request model(s) from Model Storage (50).

[0115] 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).

[0116] 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).

[0117] 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.

[0118] The Model Storage function (50) is a function that stores a trained / updated model that can be used to perform the Inference function (40). The Model Storage function (50) exemplified in FIG. 4 can be used as a reference point (if any) applicable to protocol termination, model transmission / delivery, and related processes. Additionally, the Model Storage function (50) is an example and is not intended to restrict the storage location of the actual AI / ML model, and may be omitted.

[0119] Model Transfer / Delivery (51) is used to transfer an AI / ML model to an inference function.

[0120] 2. General AI / ML related procedures between the network and the terminal

[0121] Figure 5 illustrates the general form of AI / ML-related procedures performed between a network and a terminal. While Figure 4 examined the LCM from the perspective of an AI / ML model, Figure 5 describes the general form of procedures performed between a terminal and a network from the perspective of signaling / protocols.

[0122] (1) AI / ML related setup procedure

[0123] Referring to FIG. 5, an AI / ML-related configuration procedure may be performed between the network and the terminal (S510). 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.

[0124] 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).

[0125] (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.

[0126] (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.

[0127] (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.

[0128] (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.

[0129] 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.

[0130] Meanwhile, AI / ML models can be classified into Type A models, which can be identified without OTA (over-the-air) signaling, and Type B models, which are identified through OTA signaling. A model ID may be assigned during the model identification process, and this process can be subdivided into methods initiated by the terminal and methods identified by the network.

[0131] (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.

[0132] (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.

[0133] (2) Operation based on inference by AI / ML models

[0134] Referring again to FIG. 5, 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 (S520). 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 may be performed cooperatively between the network and the terminal.

[0135] (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.

[0136] (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.

[0137] (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.

[0138] (3) Procedures for AI / ML management

[0139] The network and / or terminal can perform a procedure for managing AI / ML Functionality / model or settings therefor (S530).

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 3. Specific operation examples based on AI / ML model inference

[0148] Beam management

[0149] Figure 6 illustrates an example of AI / ML-based beam management operation.

[0150] Referring to FIG. 6, the network / terminal can perform a configuration procedure related to AI / ML-based beam management (S610). 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.

[0151] The network / terminal can perform measurements on the first set of beams (S620). The beam measurements may be related to RSRP measurements.

[0152] The network / terminal can obtain information about the second set of beams based on the measurement results for the first set of beams (S630). 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. The information about the second set of beams may correspond to AI / ML output data. The AI / ML output data may be related to the prediction of future beam quality, such as the probability that each beam will become a top-N beam and the predicted RSRP, but is not limited thereto.

[0153] According to an embodiment, the network / terminal can transmit and receive information about the acquired second set of beams.

[0154] Specifically, AI / ML-based beam management operations may include at least one of the following BM-Case 1 and BM-Case 2.

[0155] - BM-Case 1: Prediction of the second set of DL beams in the spatial domain through the first set of beam measurements

[0156] - BM-Case 2: Prediction of the second set of DL beams in the time domain through the first set of beam measurements

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Regarding UE-assisted performance monitoring for the UE-side models of BM-Case 1 and 2, the following methods may be considered.

[0165] i) Compare prediction results based on resources for monitoring and use the top 1 or top K beam prediction accuracy.

[0166] 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.

[0167] iii) Use the difference information between the measured RSRP and the predicted RSRP for the corresponding beam of the resources for monitoring.

[0168] iv) Probability information that the predicted beam will become one of the top 1 or N beams

[0169] 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.

[0170] For BM-Case 2 of the UE-side model, the network can be configured to report inferences about N future times to the terminal.

[0171] < Background related to AI / ML beam management >

[0172] In Release 18, a study was conducted on performance analysis and potential specification impact through evaluation when network (NW) and / or user equipment (UE) side AI / ML models were operating for 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 the performance and potential specification impact regarding spatial domain beam prediction and temporal domain beam prediction. A summary of BM-case1 and BM-case2 is presented in Tables 2 through 4 below.

[0173] - WID goals for AI / ML BM

[0174]

[0175] - BM-case1: Spatial domain downlink beam prediction for beam set A based on measurement results of beam set B

[0176]

[0177] - BM-case2: Temporal downlink beam prediction for Beam Set A based on historical measurement results of Beam Set B

[0178]

[0179] 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.

[0180]

[0181] 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.

[0182]

[0183] As cited above, in the BM-case2 scenario of NW-side AI / ML operation, for inference of the NW-side AI / ML model, the terminal needs to report beam measurement results for multiple past instances to prepare input data in the NW, and discussions on standardization for terminal reporting for these multiple measurement instances are underway.

[0184] Tables 7 and 8 below summarize the standardization agreements to date.

[0185]

[0186]

[0187] As referenced above, enhancements regarding L1 reporting were discussed for the inference operation of NW-side AI / ML. As a result of the discussions, an agreement was reached to introduce beam measurement / reporting related to a larger number of beam indices compared to the conventional beam measurement / reporting related to a maximum of four beam indices. In addition, for temporal DL Tx beam prediction in NW-side AI / ML (as shown in the last proposal captured in Table 8 above), standardization discussions are underway regarding an operation in which reporting for multiple measurement instances for a specific NZP CSI-RS resource set is performed through a single report. Furthermore, it was agreed that the time domain behavior of CSI-RS utilized for L1 reporting of the aforementioned multiple measurement instances would support periodic CSI-RS and semi-persistent CSI-RS.

[0188] Meanwhile, as shown in Table 9 below, the time domain behavior of the CSI report that can be combined for each P / SP / AP CSI-RS is defined. In the case of SP CSI reporting, reporting on PUCCH and reporting on PUSCH are supported. In this case, CSI reporting on PUCCH can be turned on / off via activation / deactivation MAC CE messages. CSI reporting on PUSCH can be turned on / off via DCI. In this case, if the base station is configured to report on N measurement instances of a specific NZP CSI-RS resource set for SP CSI reporting, but N measurement instances have not arrived at the time of the first report after the SP CSI-RS reporting is activated / triggered, ambiguity may occur in the terminal reporting the report contents at that time. This is referred to as Problem 1.

[0189]

[0190] In this specification, when a base station performs DL Tx beam prediction for (BM-case1) / BM-case2 using NW-side AI / ML, a method for setting up a report related to the beam measurement of a terminal for inference of the NW-side AI / ML model is proposed, and subsequent terminal operations are proposed. In particular, a method for solving Problem 1 described above is proposed.

[0191] According to one embodiment, in the CSI reporting of a terminal, the reporting of multiple measurement instances for a specific NZP CSI-RS resource set may include i) instance information (or time information) by an explicit time stamp method and / or ii) instance information by an implicit time stamp method.

[0192] For example, if a base station is configured to report K beams for each of the N measurement instances for an NZP CSI-RS resource set related to a specific CSI report of a terminal, the terminal can perform reporting by explicitly including information for each time instance in the report according to the Explicit time stamp method as follows.

[0193] - Time information 1, K beam indices, K L1-RSRP values

[0194] - Time information 2, K beam indices, K L1-RSRP values

[0195] …

[0196] - Time information N, K beam indices, K L1-RSRP values

[0197] As another example, if a base station is configured to report K beams for each of N measurement instances for an NZP CSI-RS resource set related to a specific CSI report of a terminal, the terminal may perform the report by implicitly including information for each time instance in the report as follows, according to the Implicit time stamp method. In other words, the terminal's report may not explicitly include time stamp information. In this case, i) the first K beam indices and K L1-RSRP values ​​may be reports for the first (or latest) instance among the (past) N measurement instances, and ii) the subsequent K beam indices and K L1-RSRP values ​​may be reports for measurement instances in chronological order (or reverse order) among the N-1 measurement instances.

[0198] - K beam indices, K L1-RSRP values

[0199] - K beam indices, K L1-RSRP values

[0200] …

[0201] - K beam indices, K L1-RSRP values

[0202] In the above two methods (explicit time stamp method and / or implicit time stamp), to express the L1-RSRP value, the beam index having the strongest RSRP can be expressed as an absolute L1-RSRP value (7 bits), and the remaining beam indices can be expressed as differential L1-RSRP values ​​(4 bits).

[0203] At this time, i) a method in which only the beam index having the strongest RSRP across all measurement instances is expressed as an absolute L1-RSRP, and ii) a method in which the beam indices having the strongest RSRP within each measurement instance are expressed as an absolute L1-RSRP can be considered.

[0204] For example, to apply the above method i) (a method in which only the beam index having the strongest RSRP across all measurement instances is expressed as an absolute L1-RSRP), the strongest time instance information may be further included in the report regarding the reporting method for multiple measurement instances (e.g., an explicit time stamp method and / or an implicit time stamp method). In this case, the strongest time instance information may refer to information related to the time instance to which the beam index having the strongest RSRP belongs.

[0205] As another example, for the application of method ii) (a method expressed as absolute L1-RSRP for beam indices having the strongest RSRP within each measurement instance), information on the strongest time instance (e.g., the time instance to which the beam index having the strongest RSRP belongs) may not be included in the report in the reporting method for multiple measurement instances (e.g., the Explicit time stamp method and / or the Implicit time stamp method).

[0206] In this specification, ' / ' may be interpreted as 'and', 'or', or 'and / or' depending on the context.

[0207] In this specification, a CSI report may include reporting on a PUCCH and / or reporting on a PUSCH. For convenience of explanation in this specification, proposals (Proposals 1 to 5) are described with reference to SP CSI reporting, which supports reporting on a PUCCH and / or reporting on a PUSCH. However, reporting on a PUCCH described in this specification may include periodic CSI reporting (P CSI reporting) and / or semi-persistent CSI reporting (SP CSI reporting), and reporting on a PUSCH may include SP CSI reporting and / or aperiodic CSI reporting (AP CSI reporting).

[0208] In this specification, N measurement instances of a specific NZP CSI-RS resource set may be replaced / interpreted as N measurement instances associated with the specific NZP CSI-RS resource set.

[0209] Proposal 1

[0210] For a (SP) CSI report that reports on N measurement instances of a specific NZP CSI-RS resource set, the terminal can expect the base station to be configured so that N measurement instances of the NZP CSI-RS resource set (related to the said CSI report) exist / are received by the time of the first report after the said CSI report is activated / triggered.

[0211] In other words, the terminal may receive configuration information from the base station regarding a (SP) CSI report that performs reporting on N measurement instances of a specific NZP CSI-RS resource set. The terminal may expect that N measurement instances of the NZP CSI-RS resource set associated with the CSI report will be received (or exist) by the time of the first report after the CSI report is activated / triggered. In this case, when the CSI report is activated, the CSI report may be associated with (SP) CSI reporting on the PUCCH, and the activation may be based on MAC-CE. Additionally, when the CSI report is triggered, the CSI report may be associated with (SP / AP) CSI reporting on the PUSCH, and the activation may be based on DCI.

[0212] In other words, the base station may configure the terminal (SP) with configuration information related to the CSI report to report beam measurements for N measurement instances of a specific NZP CSI-RS resource set. The base station may configure the terminal to receive N measurement instances of the NZP CSI-RS resource set related to the CSI report from the time the terminal first reports after the CSI report is activated / triggered.

[0213] According to the above-described proposal 1, as the base station ensures that the terminal receives the N measurement instances by the time of the terminal's first report, the base station / terminal can always receive / transmit reports on the N measurement instances, thereby resolving the ambiguity of the terminal in problem 1.

[0214] Proposal 2

[0215] Regarding a (SP) CSI report that reports on N measurement instances of a specific NZP CSI-RS resource set, if the terminal has not filled N measurement instances at the time of the first report after the CSI report is activated / triggered (or has not received N measurement instances), it may report on M measurement instances received up to the time of the first report. In this case, for NM measurement instances (or UCI payloads associated with NM measurement instances), instead of beam + L1-RSRP information associated with NM measurement instances, specific / agreed information (or pre-set or defined information) may be reported so that the reported UCI payload remains the same for every reporting cycle. In other words, the terminal may i) report beam and L1-RSRP information associated with the M measurement instances for the M measurement instances, and ii) report pre-set or defined information for the NM measurement instances to the base station.

[0216] In the above proposal 2, an embodiment for reporting specific / promised information regarding the NM measurement instances (or the UCI payload associated with the NM measurement instances) may be considered as follows.

[0217] Example 1) The terminal may report the NM measurement instances (and associated payloads) by performing zero padding. In other words, the report on the NM measurement instances (or the payloads associated with the NM measurement instances) may include zero padding values.

[0218] Example 2) For the NM measurement instances (and associated payloads), the terminal may report the L1-RSRP value of a predefined beam index(es) as i) the lowest (differential) L1-RSRP value or ii) zero padding. For example, the beam index(es) reported for the NM measurement instances may be i) K predefined beam index(es) and / or ii) K beam index(es) having the lowest / highest ID.

[0219] Example 3) The terminal can inform the base station that i) no measurement was performed for the NM measurement instance (and the payload associated with it) and ii) that the L1-RSRP value reported in relation to the beam index is a fake reported value not based on measurement by expressing the reported beam index as a specific default value (e.g., expressing the 7-bit CRI as only “0000000”).

[0220] According to the aforementioned proposal 2, the terminal can always maintain a constant UCI payload at each reporting cycle without ambiguity, and the base station can also receive the terminal's CSI report based on a constant UCI payload.

[0221] Proposal 3

[0222] Regarding a (SP) CSI report that reports on N measurement instances of a specific NZP CSI-RS resource set, the terminal may only report on the received M measurement instances, even if N measurement instances are not filled at the time of the first report after the CSI report is activated / triggered. For example, although the terminal CSI report setting configured by the base station is configured to report on N measurement instances, the terminal may construct a UCI payload using only the information (or bits) corresponding to the M measurement instances received up to the time of the first report and report this to the base station. In this case, it may be based on the assumption that the base station and the terminal have a common understanding of the activation / triggering time of the report and the period of the CMR set associated with the report. For example, the base station may know in advance the M measurement instances to be reported and / or the UCI payload size.

[0223] According to the above-described proposal 3, if the base station and the terminal know in advance the activation / triggering time of the report and the period of the CMR set associated with the report, the terminal can transmit reports for M measurement instances, which are fewer than N, to the base station, and the base station has the advantage of being able to receive reports for M measurement instances without blind detection.

[0224] Proposal 4

[0225] For a (SP) CSI report that reports on N measurement instances of a specific NZP CSI-RS resource set, after the CSI report is activated / triggered, the terminal may i) skip / omit the report if N measurement instances are not filled at the time of the first report, and ii) perform the CSI report from the time when N measurement instances are filled. This operation is a method of skipping the reporting cycle when N measurement instances are not sufficiently filled, and can be applied not only at the time of the first start of the report but also in cases where the period of the CSI report is not sufficiently long compared to the transmission period of the NZP CSI-RS resource set, so that N instance measurements are not received whenever the reporting cycle arrives.

[0226] According to the aforementioned proposal 4, there is an effect that a terminal that has received a report setting for N measurement instances can always perform a report for N measurement instances by performing a CSI report in the report cycle in which all N measurement instances have been received.

[0227] Proposal 5

[0228] A terminal may perform two-part encoding on a (SP) CSI report that reports on N measurement instances of a specific NZP CSI-RS resource set. For example, i) the first part may include information on the measurement instance (or the time stamp of the measurement instance) to be reported in the second part, and ii) the second part may include K beam index(es) and / or K L1-RSRP value information for the measurement instances of the first part (or the measurement instances indicated in the first part). In this case, the information reported by the terminal through the first part may include i) the number of measurement instances included in the second part and / or ii) information related to the time of the measurement instances included in the second part (e.g., (explicit / implicit) time stamp information for M measurement instances reported out of N measurement instances).

[0229] According to the above-described proposal 5, even though the base station is configured to report on N measurement instances in terminal CSI reporting, if the terminal does not receive all of the N measurement instances at a specific report time that is activated / triggered on the terminal, the terminal reports information about the measurement instances actually received and reported to the base station through two-part encoding, so the terminal (or base station) can transmit (or receive) reports on multiple measurement instances without ambiguity between the terminal and the base station.

[0230] In the above-described proposals (Proposals 1 to 5), N, M, and K may be natural numbers. Also, in the above-described proposals, instance may be replaced / interpreted as occasion, sample, etc.

[0231] The operation according to the above-described proposals can be applied not only to beam reports for multiple measurement instances via semi-persistent CSI reports, but also to periodic CSI reports and aperiodic CSI reports.

[0232] Embodiments of the above-described proposals (proposals 1 to 5) may be operated by a combination of specific embodiments.

[0233] An example of a terminal (or base station) operation based on at least one of the above-described proposals (e.g., at least one of the embodiments of proposals 1 to 5) is as follows.

[0234] 1) The terminal (or base station) receives (transmits) settings related to beam measurement / reporting

[0235] At this time, the above settings may include settings for beam measurements for multiple measurement instances and / or reports thereon.

[0236] 2) The terminal (or base station) receives (transmits) a message scheduling the transmission of the beam measurement report.

[0237] In this case, the transmission of reports scheduled by the base station can have periodic, semi-persistent, or dynamic (aperiodic) time domain behavior.

[0238] 3) The terminal (or base station) transmits (receives) a beam measurement report based on the above message.

[0239] At this time, the above report may include beam reporting for multiple measurement instances. The beam reporting for multiple measurement instances may be based on at least one of the embodiments of the above-described proposals (proposals 1 to 5).

[0240] 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.

[0241] In terms of implementation, the operations of the base station / terminal according to the embodiments described above (e.g., beam measurement / reporting operations of the terminal based on at least one of the embodiments of Proposals 1 to 5) can be processed by the device of FIG. 9 described later (e.g., the processor (102, 202) of FIG. 9).

[0242] In addition, the operations of the base station / terminal according to the above-described embodiment (e.g., beam measurement / reporting operations of the terminal based on at least one of the embodiments of Proposal 1 to 5) may be stored in memory (e.g., 104, 204 in FIG. 9) in the form of instructions / programs (e.g., instruction, executable code) for driving at least one processor (e.g., 102, 202 in FIG. 9).

[0243] 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.

[0244] FIG. 7 is a flowchart illustrating a method according to one embodiment of the present specification.

[0245] Referring to FIG. 7, the method according to an embodiment of the present specification includes a step of receiving configuration information related to channel state information (CSI) (S710) and a step of reporting CSI (S730).

[0246] In S710, the terminal receives configuration information related to Channel State Information (CSI) from the base station.

[0247] In S730, the terminal reports the CSI to the base station.

[0248] The above CSI includes information related to one or more transmission occasions among multiple transmission occasions.

[0249] The above one or more transmission opportunities relate to measurements performed prior to the above report of the CSI.

[0250] For example, the above setting information may include information related to the reporting time of the CSI and information related to the number of the plurality of transmission opportunities.

[0251] For example, information related to the reporting time of the above CSI may include information related to the reporting period of the above CSI and / or information related to the offset of the above CSI reporting. In this case, the information related to the offset may include information related to the slot offset.

[0252] For example, the above CSI may be reported through a Physical Uplink Control Channel (PUCCH). In this case, the reporting of the CSI may be activated based on a MAC Control Element (MAC-CE).

[0253] For example, a method according to one embodiment of the present specification may further include the step of receiving Downlink Control Information (DCI) from the base station. In this case, the report of the CSI may be triggered based on the DCI.

[0254] As a specific example, if the report of the above CSI is a periodic CSI report or a semi-persistent CSI report on PUCCH, the information related to the reporting time of the above CSI may include information related to the period and information related to the offset. The information related to the period and the information related to the offset may be set by the reportSlotConfig, which is a higher layer parameter.

[0255] As another specific example, if the report of the CSI is a semi-persistent CSI report on PUSCH, the information related to the reporting time of the CSI may include information related to the period and / or information related to the offset. i) The information related to the period may be set by reportSlotConfig, and ii) The information related to the offset may be selected from the activation / triggering DCI among the offsets set by the higher layer parameter.

[0256] As another specific example, if the above CSI report is an aperioditic CSI report on PUSCH, the information related to the reporting time of the CSI may include information related to the offset. The information related to the offset may be selected from among the offsets set by the higher layer parameter in the activation / triggering DCI.

[0257] For example, information related to the number of the aforementioned multiple transmission opportunities may be related to N measurement instances of the specific NZP CSI-RS resource set described above.

[0258] For example, the information related to the one or more transmission opportunities described above may be related to M measurement instances actually received by the terminal up to the time of reporting the terminal's CSI, as described in the proposals described above (Proposals 1 to 5).

[0259] For example, the information related to the one or more transmission opportunities mentioned above may be related to inference in a base station side model (NW-side model).

[0260] For example, the information associated with one or more of the above transmission opportunities may include i) Reference Signal Received Power (RSRP) and / or ii) Channel State Information Reference Signal Resource Indicator (CSI-RS Resource Indicator, CRI).

[0261] According to one embodiment, the CSI may further include information related to the remaining transmission opportunities other than one or more of the plurality of transmission opportunities.

[0262] For example, information related to the remaining transmission opportunities described above may be related to NM measurement instances that the terminal has not yet received by the time of reporting the terminal's CSI, as described in the proposals described above (Proposals 1 to 5).

[0263] For example, the information related to the remaining transmission opportunities mentioned above may be based on pre-set or defined information.

[0264] As a specific example, the above-mentioned preset or defined information may include i) a zero padding value, ii) a preset or defined RSRP value, or iii) a preset or defined CRI.

[0265] This embodiment may be based on the aforementioned Proposal 2.

[0266] According to one embodiment, the CSI may be reported based on the number of the plurality of transmission opportunities and the number of the one or more transmission opportunities being the same.

[0267] For example, the terminal may expect to receive all of the plurality of transmission opportunities from the base station prior to the reporting time of the CSI. This example may be based on Proposal 1 described above.

[0268] For example, the above report of the CSI may be skipped based on the fact that the number of one or more transmission opportunities is less than the number of multiple transmission opportunities. This example may be based on the above-described proposal 4.

[0269] According to one embodiment, the information related to the one or more transmission opportunities may further include information for identifying the one or more transmission opportunities.

[0270] This embodiment may be based on the above-described proposal 5.

[0271] 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.

[0272] The embodiments described above will be explained in detail below in terms of base station operation.

[0273] S810 to S830 described below correspond to S710 to S730 described in FIG. 7. Considering the above correspondence, redundant descriptions are omitted. The specific description of the base station operation described below may be replaced by the description / embodiment of FIG. 7 corresponding to the operation.

[0274] FIG. 8 is a flowchart illustrating a method according to another embodiment of the present specification.

[0275] Referring to FIG. 8, a method according to another embodiment of the present specification includes a step of transmitting configuration information (S810) and a step of receiving CSI (S830) related to channel state information (CSI).

[0276] In S810, the base station transmits configuration information related to Channel State Information (CSI) to the terminal.

[0277] In S830, the base station receives the CSI from the terminal.

[0278] The above CSI includes information related to one or more transmission occasions among multiple transmission occasions.

[0279] The above one or more transmission opportunities relate to measurements performed prior to the report of the above CSI.

[0280] 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.

[0281] The operations / terms based on the embodiments described above are described assuming 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 to be solved by this specification to a specific system. The technical problems / technical issues / problems mentioned in this specification may exist in other systems (e.g., 6G systems). 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 said 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).

[0282] 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.

[0283] FIG. 9 is a drawing showing the configuration of a first device and a second device according to an embodiment of the present specification.

[0284] The first device (100) may include a processor (110), an antenna unit (120), a transceiver (130), and a memory (140).

[0285] 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).

[0286] 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.

[0287] 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.

[0288] The second device (200) may include a processor (210), an antenna unit (220), a transceiver (230), and a memory (240).

[0289] 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).

[0290] 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.

[0291] 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.

[0292] 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.

[0293] 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.

[0294] 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.

[0295] 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

In terms of method, A step of receiving configuration information related to Channel State Information (CSI) from a base station; and The step of reporting the CSI to the base station; including, The above CSI includes information related to one or more transmission occasions among multiple transmission occasions, and A method characterized in that the above one or more transmission opportunities are related to measurements performed prior to the above report of the CSI. In Article 1, A method characterized in that the above setting information includes information related to the reporting time of the above CSI and information related to the number of the above multiple transmission opportunities. In Article 1, The above CSI is reported via the Physical Uplink Control Channel (PUCCH), and A method characterized in that the above report of the above CSI is activated based on a MAC Control Element (MAC-CE). In Article 1, The method further includes the step of receiving Downlink Control Information (DCI) from the base station. A method characterized in that the above report of the above CSI is triggered based on the above DCI. In Article 1, A method characterized in that the information related to the above one or more transmission opportunities is related to the inference of a base station side model (NW-side model). In Article 1, A method characterized in that the information associated with one or more of the above transmission opportunities includes i) Reference Signal Received Power (RSRP) and / or ii) Channel State Information Reference Signal Resource Indicator (CSI-RS Resource Indicator, CRI). In Article 1, The above CSI further includes information related to the remaining transmission opportunities other than one or more of the plurality of transmission opportunities, and A method characterized in that the information related to the remaining transmission opportunities is based on pre-set or defined information. In Article 7, A method characterized in that the above-mentioned preset or defined information includes i) a zero padding value, ii) a preset or defined RSRP value, or iii) a preset or defined CRI. In Article 1, A method characterized in that the above CSI is reported based on the number of the plurality of transmission opportunities and the number of the one or more transmission opportunities being the same. In Article 9, A method characterized in that the above report of the above CSI is skipped based on the fact that the number of one or more transmission opportunities is less than the number of the plurality of transmission opportunities. In Article 1, A method characterized in that the information related to the one or more transmission opportunities further includes information for identifying the one or more transmission opportunities. In the terminal, One or more transmitters / receivers; One or more processors; and One or more memories connected to the above one or more processors and storing instructions; comprising, A terminal characterized in that the above instructions, based on execution by the one or more processors, cause the terminal to perform all steps of the method according to any one of claims 1 to 11. In a device comprising one or more memories and one or more processors connected to said one or more memories, A device characterized in that the one or more of the above memories store instructions that cause the device to perform all steps of the method according to any one of claims 1 to 11, based on execution by the one or more processors. In a non-transitory computer-readable storage medium for storing instructions, A non-transient computer-readable storage medium characterized in that the instructions executable by one or more processors enable the terminal to perform all steps of the method according to any one of claims 1 to 11. In terms of method, A step of transmitting configuration information related to Channel State Information (CSI) to a terminal; and The method includes the step of receiving CSI from the terminal; The above CSI includes information related to one or more transmission occasions among multiple transmission occasions, and A method characterized in that one or more of the above transmission opportunities are related to measurements performed prior to the report of the CSI. In the case of a base station, One or more transmitters / receivers; One or more processors; and One or more memories connected to the above one or more processors and storing instructions; comprising, A base station characterized by the above instructions, based on execution by one or more processors, causing the base station to perform all steps of the method according to claim 15.

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