Method and apparatus for request related to associated id in wireless communication system

Abstract

A method according to one embodiment of the present specification comprises the steps of: receiving, from a base station, configuration information related to channel state information; and transmitting, to the base station, a request related to an associated ID. The request related to the associated ID is related to the configuration and / or change of the associated ID.

Description

Method and apparatus for a request related to an associated identifier in a wireless communication system

[0001] This specification relates to a method and apparatus for a request related to an associated identifier in a wireless communication system.

[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] Meanwhile, beam management, CSI prediction / compression, and positioning operations are defined in relation to AI / ML. In beam management, CSI prediction / compression, and / or positioning operations based on the UE-side model, the data attributes for training and inference may differ, which can lead to degradation of the terminal's inference performance. Therefore, an associated ID has been introduced to maintain consistency between the training and inference data and to align network-side additional conditions during training and inference.

[0005] When an associated ID is not assigned or configured in a report setting for learning and / or a report setting for inference, there is a problem of ambiguity in determining whether a specific functionality is applicable to the terminal. The purpose of this specification is to solve the aforementioned problem by proposing a method in which the terminal requests the assignment and / or modification of an associated ID from the base station when the associated ID is not assigned or configured.

[0006] If an associated ID is not assigned or configured to the reporting settings related to training and / or inference for the UE-side model, the data attributes for training purposes and the data attributes for inference purposes may differ from each other, which may lead to a problem where the inference performance of the terminal is degraded. Another objective of this specification is to propose a method for performing performance monitoring and / or reporting the results of performance monitoring when an associated ID is not assigned or configured to the reporting settings related to training and / or inference in order to solve the aforementioned problem.

[0007] 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.

[0008] 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 from a base station and transmitting a request related to an associated ID to the base station.

[0009] The above request related to the above association identifier relates to the setting and / or change of the above association identifier.

[0010] Through this, ambiguity on the terminal side can be resolved in that, when an associated identifier is not assigned or set for a report setting for learning and / or a report setting for inference, the terminal requests the base station to assign and / or change an associated identifier, thereby enabling the terminal to more reliably determine whether its inference and / or the functionality it intends to perform is applicable, and the accuracy of inference can be improved by enabling the execution of specific inference and / or functions based on a more appropriate report setting.

[0011] The above request related to the above association identifier may be transmitted based on the fact that the association identifier is not included in the above configuration information.

[0012] The above request related to the above association identifier may be included in the CSI.

[0013] The above CSI may further include a prediction accuracy indicator (PAI).

[0014] The above CSI may be reported according to a preset or defined cycle based on the fact that the associated identifier is not included in the above setting information.

[0015] The above request related to the above association identifier may be transmitted through a report related to functionality applicability.

[0016] The above setting information may include CSI reporting settings.

[0017] The above CSI reporting settings may include information related to the association identifier that is set and / or changed based on the request related to the association identifier.

[0018] The above-mentioned associated identifier may be set or changed based on i) RRC Reconfiguration, ii) MAC control element (MAC-CE) and / or iii) downlink control information (DCI).

[0019] The above request related to the above association identifier may further include a request for data collection configuration.

[0020] The above data collection settings may be associated with an associated identifier that is set and / or changed based on the request related to the above associated identifier.

[0021] The above request related to the above association identifier may further include a request related to a time interval for changing the UE-side model.

[0022] A terminal (user equipment, UE) 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.

[0023] 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.

[0024] A non-transitory 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.

[0025] A method according to another embodiment of the present specification comprises the steps of: transmitting configuration information related to channel state information to a terminal (user equipment, UE); and receiving a request related to an associated ID from the terminal.

[0026] The above request related to the above association identifier relates to the setting and / or change of the above association identifier.

[0027] A base station 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 in that the base station performs all steps of any one of the methods based on execution by the one or more processors.

[0028] According to the prior art, since an associated ID is not assigned to the reporting settings for learning and / or the reporting settings for inference, ambiguity may exist in determining whether a specific functionality is applicable from the perspective of the terminal. According to the embodiments of this specification, when an associated ID is not assigned, a specific method is proposed in which the terminal requests the base station to assign and / or change an associated ID for specific inference (e.g., beam management, CSI prediction / compression, positioning, etc.) and / or specific functionality. By doing so, such ambiguity can be eliminated, and the reliability of the inference results of the terminal-side model (UE-side model) can be improved.

[0029] In addition, when an associated ID is not assigned to the aforementioned reporting settings for learning and / or reporting settings for inference, by specifically defining a method for performing performance monitoring and / or a method for reporting the results of said performance monitoring of the terminal, the base station can more agilely perform changes, activations, or deactivations of specific functions of the terminal when it recognizes performance degradation of the terminal-side model, thereby improving the inference performance of the terminal-side model.

[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] Figure 7 shows an example of an applicability report procedure.

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

[0040] FIG. 9 is a flowchart illustrating a method according to another embodiment of the present specification.

[0041] FIG. 10 is a drawing showing the configuration of a first device and a second device according to an embodiment of the present specification.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] Beam Management (BM)

[0046] 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.

[0047] - Beam measurement: An operation in which a base station or UE measures the characteristics of a received beamforming signal.

[0048] - Beam determination: The operation in which a base station or UE selects its transmit beam (Tx beam) / receive beam (Rx beam).

[0049] - Beam sweeping: An operation that covers a spatial area using transmitting and / or receiving beams for a set time interval in a predetermined manner.

[0050] - Beam report: An operation in which the UE reports information about the beam-formed signal based on beam measurements.

[0051] 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).

[0052] In addition, each BM procedure may include Tx beam sweeping to determine the Tx beam and Rx beam sweeping to determine the Rx beam.

[0053] DL BM

[0054] 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.

[0055] Here, beam reporting may include preferred DL RS ID(identifier)(s) and the corresponding L1-RSRP(Reference Signal Received Power).

[0056] The above DL RS ID may be SSBRI (SSB Resource Indicator) or CRI (CSI-RS Resource Indicator).

[0057] Figure 1 shows an example of beam forming using SSB and CSI-RS.

[0058] 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.

[0059] 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.

[0060] Figure 2 is a flowchart showing an example of a DL BM procedure.

[0061] Configuration for beam reporting using SSB is performed during CSI / beam configuration in the RRC connected state (or RRC connected mode).

[0062] - 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).

[0063] 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.

[0064]

[0065] 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.

[0066] - 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).

[0067] - 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).

[0068] 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.

[0069] 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'.

[0070] 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.

[0071] < CSI Related Operations >

[0072] 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).

[0073] Figure 3 is a flowchart showing an example of a CSI-related procedure.

[0074] 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).

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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).

[0079] 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.

[0080] 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).

[0081] resource setting

[0082] 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).

[0083] Next, one or more CSI resource settings for channel measurement (CM) and interference measurement (IM) are established through higher layer signaling.

[0084] - CSI-IM resource for interference measurement.

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

[0086] - NZP CSI-RS resources for channel measurement.

[0087] 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.

[0088] Here, CSI-IM (or ZP CSI-RS for IM) is primarily used for inter-cell interference measurements.

[0089] Also, the NZP CSI-RS for IM is mainly used for intra-cell interference measurement from multi-users.

[0090] 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 the 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.

[0091] As examined, resource setting can refer to a resource set list.

[0092] 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.

[0093] One reporting setting can be linked to up to three resource settings.

[0094] Explanation regarding Rel-17 / 18 Beam Management >

[0095] 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.

[0096] AI / ML for Wireless Communication

[0097] 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.

[0098] - 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.

[0099] - 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.

[0100] - 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.

[0101] - 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.

[0102] - 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.

[0103] - 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.

[0104] 1. Life Cycle Management (LCM) for AI / ML models

[0105] 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.

[0106] 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.

[0107] Figure 4 is a diagram illustrating the overall functions from the perspective of an AI / ML model.

[0108] 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).

[0109] 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.

[0110] 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).

[0111] 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).

[0112] 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).

[0113] 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)).

[0114] 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).

[0115] A Model Transfer / Delivery Request (33) can be used to request model(s) from Model Storage (50).

[0116] 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).

[0117] 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).

[0118] 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.

[0119] 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.

[0120] Model Transfer / Delivery (51) is used to transfer an AI / ML model to an inference function.

[0121] 2. General AI / ML related procedures between the network and the terminal

[0122] 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.

[0123] (1) AI / ML related setup procedure

[0124] 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.

[0125] 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).

[0126] (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.

[0127] (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.

[0128] (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.

[0129] (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.

[0130] 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.

[0131] 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.

[0132] (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.

[0133] (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.

[0134] (2) Operation based on inference by AI / ML models

[0135] 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.

[0136] (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.

[0137] (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.

[0138] (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.

[0139] (3) Procedures for AI / ML management

[0140] The network and / or terminal can perform a procedure for managing AI / ML Functionality / model or settings therefor (S530).

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 3. Specific operation examples based on AI / ML model inference

[0149] Beam management

[0150] Figure 6 illustrates an example of AI / ML-based beam management operation.

[0151] 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.

[0152] The network / terminal can perform measurements on the first set of beams (S620). The beam measurements may be related to RSRP measurements.

[0153] 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.

[0154] According to an embodiment, the network / terminal can transmit and receive information about the acquired second set of beams.

[0155] Specifically, AI / ML-based beam management operations may include at least one of the following BM-Case 1 and BM-Case 2.

[0156] - BM-Case 1: Prediction of the second set of DL beams in the spatial domain through the first set of beam measurements

[0157] - BM-Case 2: Prediction of the second set of DL beams in the time domain through the first set of beam measurements

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Regarding UE-assisted performance monitoring for the UE-side models of BM-Case 1 and 2, the following methods may be considered.

[0166] i) Compare prediction results based on resources for monitoring and use the top 1 or top K beam prediction accuracy.

[0167] 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.

[0168] iii) Use the difference information between the measured RSRP and the predicted RSRP for the corresponding beam of the resources for monitoring.

[0169] iv) Probability information that the predicted beam will become one of the top 1 or N beams

[0170] 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.

[0171] For BM-Case 2 of the UE-side model, the network can be configured to report inferences about N future times to the terminal.

[0172] In the Rel-18 AI / ML study item, performance analysis and potential specification impact were studied through evaluation when NW-side AI / ML models and / or UE-side AI / ML models operate in three use cases: CSI compression / CSI prediction, beam management, and positioning (see TR38.843). In the subsequent Rel-19, standardization is underway for the use cases of beam management, CSI prediction, and positioning.

[0173] Meanwhile, regarding specific UE-side AI / ML related reporting scenarios, the terminal may not always be able to perform actuation / inference for all models / functionalities (due to issues such as the terminal's computing power and memory), and may need to receive specific models / functionalities from the NW side or / and UE side servers. This means that even if the terminal possesses the capability for specific AI / ML related CSI reporting and positioning related reporting, it may be determined whether it can perform such reporting depending on whether the UE-side AI / ML model / functionality related to the reporting is currently available. As shown in Tables 2 to 9 below, Rel-19 standardization discussions are underway to allow the availability of such models / functionalities to be reported to the base station through an applicability report procedure.

[0174] The definitions of terms related to the applicability report during the RAN2 discussion are as shown in Table 2 below.

[0175]

[0176] Figure 7 shows an example of the procedure for an applicability report. The agreements regarding applicability reporting during the RAN2 discussions are shown in Tables 3 through 7 and Figure 7.

[0177]

[0178]

[0179]

[0180]

[0181]

[0182] The agreement regarding applicability reporting during the RAN1 discussion is as shown in Tables 8 and 9.

[0183]

[0184]

[0185] To summarize the discussion in Tables 2 through 9 above, similar to the existing UE capability report, the terminal can perform reporting on one or more AI / ML supported functionalities in Step 2, and in Step 4, the terminal can report to the NW via an applicable functionality report (or applicability report) regarding one or more currently available (applicable) functionalities among the one or more AI / ML supported functionalities. In this case, the applicable functionality report can be reported via UAI reporting via OtherConfig or / and the signaling procedure of RRCReconfigurationComplete. This report can be used to report one or more available functionalities or / and one or more inapplicable functionalities. In addition, since the terminal's status regarding available / unavailable functionality may change even after the initial report, a subsequent report may be performed regarding changes in applicable / inapplicable functionality.

[0186] In addition, for this applicability report, an associated ID introduced in RAN1 to maintain consistency between the training phase and the inference phase may be optionally set in the CSI-ReportConfig related to inference (see Option A of the background regarding applicability in Table 8 above) and / or the inference-related parameter set (see Option B of the background regarding applicability in Table 8 above). As this associated ID is optionally set by NW, the associated ID may or may not be set in the CSI-ReportConfig for training data collection and / or the CSI-ReportConfig for inference. If an associated ID is set for both the inference phase and the training phase, resulting in the same associated ID, the terminal can perform inference without ambiguity in the inference phase and report the inference result to the base station by comparing the associated ID related to / assigned to the AI / ML model / functionality (trained by the terminal or provided by the NW / UE side server) with the associated ID set in the CSI-ReportConfig for inference purposes. However, ambiguity may occur in the terminal's inference operation when considering the cases below.

[0187] Case 1) When neither the CSI report configuration set for training nor the CSI report configuration set for inference has an associated ID set

[0188] Case 2) When the associated ID is not set in the CSI report-related configuration configured for training, but is set in the CSI report-related configuration configured for inference

[0189] Case 3) When the associated ID is not set in the CSI report configuration configured for inference, but is set in the CSI report configuration configured for training

[0190] Case 4) When the associated ID set in the CSI report configuration for training and the associated ID set in the CSI report configuration for inference are different

[0191] For example, in the case of Case 1 above, since the base station does not assign an associated ID to maintain consistency between training and inference, the terminal may need to perform inference and inference-related reporting by utilizing a generalized AI / ML model. Additionally, when utilizing such a generalized AI / ML model, the terminal's judgment regarding the applicability of a specific functionality may also become ambiguous.

[0192] For example, in the case of Case 2 and / or Case 4 above, the terminal may also perform inference and inference-related reporting using a generalized AI / ML model, and / or in the applicability report procedure mentioned above, the terminal may report that the inference-related CSI reporting settings mentioned in Case 2 / 4 are not applicable.

[0193] For example, in the case of Case 3 above, while the terminal is performing inference and inference-related reporting using a generalized AI / ML model, the base station may assign an associated ID to the terminal's training-related CSI report configuration (due to reasons such as training completion). The terminal may additionally need to perform the operation of requesting / reporting to the base station a CSI report configuration for inference purposes that is associated with / corresponds to the training-related CSI report configuration to which such an associated ID has been assigned. This specification aims to resolve the aforementioned problems.

[0194] Based on this background, the present specification proposes a solution for cases where an associated ID is not set in a reporting setting for training or / and inference purposes, or where a specific inference-related report is non-applicable or inapplicable, when a terminal performs beam prediction, CSI prediction, and positioning-related reporting using UE-sided AI / ML, and proposes a subsequent terminal operation.

[0195] In the present invention, ' / ' can be interpreted as 'and', 'or', or 'and / or' depending on the context.

[0196] Proposal 1

[0197] Due to the absence of associated ID settings, the terminal operation in Cases 1 through 4 above may result in i) the terminal performing inference and inference-related reporting using a generalized model, or ii) the terminal performing inference and inference-related reporting regarding non-applicable functionality, potentially leading to degradation of UE-side AI / ML performance. Considering this possibility, the base station may perform more sophisticated / strict / fine performance monitoring of the terminal's inference and inference-related reporting.

[0198] For example, the above 'more sophisticated / strict / fine performance monitoring' may refer to periodic (and / or semi-persistent) performance monitoring performed on the UE-side and / or NW-side, or reports on the results of performance monitoring, rather than aperiodic performance monitoring or / or reports on the results of performance monitoring. As a specific example, if the associated ID is not set in the configuration related to the CSI report for training and / or the configuration related to the CSI report for inference, the NW may perform periodic performance monitoring and / or semi-persistent performance monitoring instead of performing aperiodic performance monitoring. As a specific example, if the associated ID is not set in the configuration related to the CSI report for training and / or the configuration related to the CSI report for inference, the UE may perform periodic and / or semi-persistent performance monitoring reports instead of reporting on the results of aperiodic performance monitoring.For example, the period of the report (or performance monitoring report) regarding the performance monitoring and / or performance monitoring result may be set / instructed by the base station to be greater than or equal to a specific period value, and said specific period may be predefined in the specification or pre-set by the base station. Or / and the terminal may expect that such periodic and / or semi-persistent performance monitoring reports and / or performance monitoring reports of a specific period will be set. In this case, said specific period may be set shorter than the period of performance monitoring (and / or performance monitoring report) when an associated ID is set in both the CSI report-related configuration for training and the CSI report-related configuration for inference. In other words, the value of said specific period may be set smaller than the value of the period of performance monitoring (and / or performance monitoring report) when an associated ID is set in both the CSI report-related configuration for training and the CSI report-related configuration for inference.In other words, performance monitoring (and / or performance monitoring report) in the cases of Case 1 to Case 4 above may be performed more frequently than performance monitoring (and / or performance monitoring report) when an associated ID is set in both the CSI report configuration for training and the CSI report configuration for inference.

[0199] As another example of more sophisticated / strict / fine performance monitoring, multiple monitoring options (e.g., NW-side performance monitoring, UE-assisted performance monitoring, and / or UE-side performance monitoring, etc.) may be simultaneously configured by the base station for performance monitoring of a specific AI / ML inference result reporting operation of the terminal. In this case, the terminal may need to perform performance monitoring and monitoring reporting for the configured multiple monitoring options. In other words, the base station may need to configure the multiple monitoring options in the cases of Case 1 through Case 4. Or / and, the terminal may expect the multiple monitoring options to be configured by the base station in the cases of Case 1 through Case 4. As a specific example, when NW-side performance monitoring and UE-assisted performance monitoring are configured / instructed simultaneously, the terminal may need to report on performance monitoring metrics / outputs based on UE-assisted reporting settings (simultaneously) in addition to reporting ground-truth CSI / beam / positioning information related to specific inference result reporting.

[0200] As another example of more sophisticated / strict / fine performance monitoring, when a terminal calculates / calculates the correct / incorrect rate by comparing the inference result with ground-truth CSI / beam / positioning information during UE-assisted performance monitoring reporting, a stricter value may be set for the threshold value serving as the standard for the correct answer and / or the threshold value for the correct / incorrect rate. Alternatively, the terminal may expect that a threshold value relatively stricter than the threshold value serving as the standard for the correct answer and / or the threshold value for the correct / incorrect rate will be set in cases 1 through 4 above, where the associated ID is set in both the configuration related to the CSI report for training and the configuration related to the CSI report for inference. For example, in the discussion on beam prediction accuracy within the Rel-19 AI / ML beam management discussion below, whether a specific inference result report instance is correct or not can be determined by the M value set by the base station. The larger the M value, the higher the probability that the inference result sample is correct (low hurdle / threshold), and conversely, the smaller the M value, the lower the probability that it is correct (high hurdle / threshold). In this case, for Case 1 to Case 4, the terminal may be set to an M value lower than a specific value. Or / and, the terminal may expect to be set to an M value lower than a specific value for Case 1 to Case 4.

[0201] Alternatively, a separate table or indicator based on the presence or absence of an associated ID may be configured or defined within the specification (e.g., TS38.214 / TS38.331). Depending on the presence or absence of the indicator, the terminal may determine or apply different threshold values, monitoring granularity, and / or monitoring reporting cycles by citing the table differently. For example, Table A (when the associated ID is configured) and Table B (when the associated ID is not configured) may be configured, defined, or supported, and the aforementioned monitoring-related parameter set (e.g., threshold value, monitoring granularity, and / or monitoring reporting cycle) may be defined within the table. Therefore, regarding which values ​​to apply to indicators (e.g., codepoints) indicated by RRC / MAC-CE / DCI, etc., monitoring and monitoring-related reporting can be performed by interpreting the codepoints by applying the table differently as described above, depending on the presence or absence of the associated ID. Or / and when multiple tables are set, the base station can control monitoring performance by setting / instructing the multiple tables using RRC / MAC CE, etc.

[0202]

[0203] For example, the operation of the embodiments of Proposal 1 above may be performed only when an associated ID is not set in the CSI-ReportConfig or / and positioning-related reporting settings in which the terminal performs inference result reporting. Or / and, in order to determine whether the base station applies the embodiments of Proposal 1 above to a specific terminal, the base station may be configured / instructed to report whether the terminal's (applicable / non-applicable) UE-side AI / ML model / functionality is generalized or / and the degree of generalization (e.g., distinguishing generalization levels through quantized values ​​and then determining one of them). For example, the generalization level may be expressed as a natural number, and as a specific example, the generalization level may have a value from 1 to 4. The terminal may report the generalization level for a specific applicable / non-applicable functionality using 2 bits. Such reporting may be transmitted via the UCI of PUCCH / PUSCH or via the applicability report described above. At this time, the applicability report can be transmitted via UAI reporting and / or RRCReconfigurationComplete messages through OtherConfig.

[0204] Effects of Proposal 1

[0205] Since there is a high probability that performance issues will occur when a terminal performs inference result reporting through an inference report configuration in the environments of Case 1 to Case 4, the base station can perform maintenance of the terminal's UE-side AI / ML model after receiving the monitoring results from the terminal through the stricter performance monitoring of Proposal 1. If the performance monitoring results are poor, the base station can deactivate the terminal's AI / ML-related report or perform switching / activation to another applicable AI / ML model / functionality other than the AI / ML model / functionality currently used by the terminal for inference, thereby enabling the terminal to stably perform AI / ML-related reporting operations.

[0206] Proposal 2

[0207] In Cases 1 through 4 above, the terminal may perform reporting on requests for the assignment of an associated ID or / and requests for changes to an associated ID for specific CSI / beam reporting settings or / and positioning-related reporting settings (for training purposes or / and inference purposes). And / or, the terminal may perform reporting on requests for the assignment of an associated ID or / and requests for changes to an associated ID for specific applicable / non-applicable functionality. In other words, the reporting may include requests for the assignment of an associated ID for applicable / non-applicable functionality and / or requests for changes to an associated ID for applicable / non-applicable functionality. The terminal's reporting may be i) reported by a performance monitoring result reporting setting related to the inference result reporting setting (in other words, together with a monitoring result report), or ii) reported by a specific uplink channel / RS (e.g., PUCCH, PUSCH, SRS, or PRACH). or / and, it may be reported through the applicability report mentioned in the background of the invention (e.g., UAI reporting via OtherConfig or / and RRCReconfigurationComplete).

[0208] Following the above report, the base station may, based on the above report of the terminal, assign / change an associated ID (through RRC reconfiguration, etc.) for (the above-reported) i) a specific training data collection measurement / reporting setting, ii) a specific inference result reporting related setting (e.g., CSI-ReportConfig for inference regarding beam / CSI prediction, positioning related report configuration), or / and iii) a specific applicable / non-applicable functionality of the terminal. Additionally, for more dynamic assignment / change of associated ID operations, the base station may assign / change / update an associated ID for i) the above-reported specific training data collection measurement / reporting setting, ii) the above-reported specific inference result reporting related setting, or / and iii) the terminal's specific applicable / non-applicable functionality through MAC-CE / DCI signaling.

[0209] As another example, the terminal may perform a report requesting the base station for settings related to inference result reporting, specific applicable / non-applicable functionality of the terminal, or / and a training data collection procedure related to a specific associated ID. In other words, the report may include a request for settings related to inference result reporting, specific applicable / non-applicable functionality of the terminal, or / and a training data collection procedure related to a specific associated ID. The report of such a terminal may be reported i) by a performance monitoring result reporting setting related to the inference result reporting setting (in other words, together with a monitoring result report), or ii) by a specific uplink channel / RS (e.g., PUCCH, PUSCH, SRS, or PRACH) alone. Or / and, it may be reported through the applicability report mentioned in the background of the invention (e.g., UAI reporting via OtherConfig or / and RRCReconfigurationComplete). Based on the above report, the base station may perform settings related to a specific inference result report (as reported above), specific applicable / non-applicable functionality of the terminal, or / and CSI-ReportConfig for data collection related to a specific associated ID, or / and positioning-related reporting settings. In this case, the above reporting settings may be transmitted / instructed / configured to the terminal through RRC reconfiguration, MAC CE activation, DCI triggering, etc.

[0210] Alternatively, instead of requesting the assignment or modification of an associated ID, the terminal may request the base station to set a time window, time period, or timing gap for model switching / update / transfer. This may operate as a terminal implementation when the terminal is equipped with a model suitable for multiple associated IDs or a relatively more generalized model and switches between them; however, since the terminal cannot properly perform operations such as inference / monitoring during such model switching / update / transfer time intervals, the model switching / update / transfer time interval refers to a time interval during which the terminal does not perform the inference / monitoring. In other words, i) in the case of periodic / semi-persistent reporting associated with inference / monitoring, the terminal may not perform the corresponding report during the model switching / update / transfer time interval, and ii) in the case of aperiodic reporting, the terminal does not expect scheduling for the corresponding inference / monitoring report during the model switching / update / transfer time interval. Alternatively, the terminal may ignore the scheduling and drop reports related to the scheduling even if it receives the scheduling during the model switching / update / transfer time interval.Additionally, when the terminal performs switching / update on the terminal-side model, a request for a training data collection procedure associated with the aforementioned associated ID may be reported to the base station, along with a request for setting a time window / time period / timing gap, etc., for model switching / update / transfer.

[0211] As another example of Proposal 2, particularly in Case 4 above, the base station may provide / instruct / transmit information to the terminal in advance regarding one or more associated IDs having characteristics similar to a specific associated ID. Even if the associated ID related to training (of measurement / reporting settings) is different from the associated ID related to inference (of inference result reporting settings), the terminal may perform inference and inference result reporting by utilizing the configured inference-related reporting settings based on the information regarding the one or more associated IDs. At this time, the terminal may determine the applicability of a specific functionality based on the information regarding the one or more associated IDs. This operation may be utilized particularly in Case 4 above, for example, in the case of offline training, in situations where the base station did not set an associated ID during the training phase but set an associated ID during the inference phase (e.g., a new cell environment due to mobility).

[0212] As another example of Proposal 2, if the base station does not have an associated ID set in the inference-related reporting settings as in Case 1 and / or Case 3, or / and if the associated ID set in the training-related reporting settings and the inference-related reporting settings are different as in Case 4, the base station may set / instruct the terminal to perform performance monitoring on inactive inference-related reporting settings among the terminal's inference reporting settings (e.g., inference-related reporting settings related to functionality that the terminal has not reported as applicable, inference-related reporting settings related to functionality that the terminal has reported as non-applicable, inference-related reporting settings that have not been activated / triggered by MAC CE / DCI, etc.) and report a monitoring result. Upon receiving a performance monitoring result report for such inactive inference-related reporting settings from the terminal, the base station may assign a new or change an associated ID for the terminal's (inactive) inference-related reporting settings based on the monitoring result (using the RRC / MAC-CE / DCI, etc. described above).

[0213] Effects of Proposal 2

[0214] In order to prevent performance degradation due to the absence of an associated ID setting in the environments of Case 1 to Case 4 (such as error cases), the terminal can perform an operation requesting a base station to assign / change a clear associated ID and / or request necessary training data collection, thereby resolving the problems and ambiguities of Case 1 to Case 4. In particular, in a case like Case 3, where an associated ID is set for the training setting but not for the inference setting, the terminal can perform a request to assign / change an appropriate associated ID for the inference setting, so Proposal 2 can be more effective in Case 3.

[0215] Proposal 3

[0216] Cases 1 through 4 above may be considered as cases where the base station activates an inference result reporting setting related to a functionality that the terminal has not reported as applicable (or a functionality reported as inapplicable). In this case, the terminal may report that the functionality activated by the base station is not applicable or / and request functionality switching / deactivation. The terminal report may be reported i) by a performance monitoring result reporting setting related to the inference result reporting setting (in other words, together with a monitoring result report), or ii) by a specific uplink channel / RS (e.g., PUCCH, PUSCH, SRS, or PRACH). Or / and may be reported through the applicability report mentioned in the background of the invention (e.g., UAI reporting via OtherConfig or / and RRCReconfigurationComplete). Following the terminal report, the base station may i) perform deactivation on the functionality that the terminal reported as non-applicable (or inapplicable) and / or the functionality for which a functionality switching / deactivation request was received, and / or ii) perform switching / activation to another applicable AI / ML model / functionality other than the AI / ML model / functionality that the terminal is currently using for inference.

[0217] Effects of Proposal 3

[0218] Proposal 3, like Proposal 2, can resolve the problems and ambiguities of Cases 1 through 4 by performing a report that the functionality activated by the base station is not applicable and / or a report requesting functionality switching / deactivation in order to prevent performance degradation due to the absence of associated ID setting in the environments of Cases 1 through 4.

[0219] The embodiments of the above proposals 1 to 3 may be operated by one or more combinations.

[0220] i) when a two-sided model is utilized in CSI compression, there may be an inference result reporting setting related to the said two-sided model (the terminal-side model) or / and ii) when positioning information is reported using terminal UE-side AI / ML in positioning, there may be a (reporting) setting related to the said reporting. Proposals 1 to 3 above may be applied in an extended manner even when an associated ID is set or not set in the inference result reporting setting in each of the above scenarios.

[0221] 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 Proposal 3) is as follows.

[0222] 1) The terminal (base station) receives (transmits) settings related to UE capability reporting.

[0223] 2) The terminal (base station) transmits (receives) the UE capability report

[0224] The above UE capability report may include information on supported functionality.

[0225] 3) The terminal (base station) receives (transmits) settings related to reporting applicable / non-applicable functionality

[0226] 4) The terminal (base station) transmits (receives) an applicable / non-applicable functionality report.

[0227] The above applicable / non-applicable functionality report may include information regarding applicable / non-applicable functionality (inference-related reporting settings). In addition, it may include reporting information of the embodiments of Proposals 1 to 3.

[0228] 5) The terminal (base station) receives (transmits) report settings related to specific functionality and transmits (receives) AI / ML-related reports.

[0229] In the case where the situation of Case 1 to Case 4 occurs in 5) above, the terminal / base station can perform the operation of the embodiments of 1 to 3 above.

[0230] 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.

[0231] 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 Proposal 1 to Proposal 3) can be processed by the device of FIG. 10 to be described later (e.g., the processor (102, 202) of FIG. 10).

[0232] 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 Proposal 3) may be stored in memory (e.g., 104, 204 in FIG. 10) in the form of instructions / programs (e.g., instruction, executable code) for driving at least one processor (e.g., 102, 202 in FIG. 10).

[0233] The embodiments described above will be explained in detail below with reference to FIGS. 8 and FIG. 9 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.

[0234] FIG. 8 is a flowchart illustrating a method according to one embodiment of the present specification.

[0235] Referring to FIG. 8, the method according to an embodiment of the present specification includes a step of receiving configuration information related to CSI (S810) and a step of transmitting a request related to an associated identifier (S830).

[0236] In S810, the terminal receives configuration information related to channel state information from the base station.

[0237] In S830, the terminal transmits a request associated with an associated ID to the base station.

[0238] For example, the above association identifier may be associated with i) specific CSI / beam reporting settings and / or positioning-related reporting settings for training and / or inference purposes. And / or the above association identifier may be associated with specific applicable functionality and / or inapplicable functionality.

[0239] The above request related to the above association identifier relates to the setting and / or change of the above association identifier.

[0240] For example, the terminal may report to the base station a request to set (assign) or change an associated ID associated with i) specific CSI / beam reporting settings and / or positioning-related reporting settings for training and / or inference purposes. And / or the terminal may report to the base station through a request to set or change an associated ID associated with specific applicable functionality and / or inapplicable functionality.

[0241] According to one embodiment, the request related to the association identifier is transmitted based on the fact that the association identifier is not included in the configuration information. The case where the association identifier is not included in the configuration information may be a case such as Case 1 to Case 4 described above.

[0242] For example, the above request related to the above association identifier may be included in the CSI.

[0243] As a specific example, the above CSI may further include a prediction accuracy indicator (PAI). In other words, the request associated with the above association identifier may be reported to the base station through the CSI together with the PAI.

[0244] For example, the request associated with the above-mentioned association identifier may be transmitted through a report related to functionality applicability.

[0245] For example, the above configuration information may include a CSI report configuration (e.g., CSI-ReportConfig). In this case, the CSI report configuration may include information related to an associated identifier that is configured and / or changed based on the request related to the associated identifier.

[0246] For example, the above-mentioned associated identifier may be set or changed based on i) RRC Reconfiguration, ii) MAC control element (MAC-CE) and / or iii) downlink control information (DCI).

[0247] For example, the request associated with the above-mentioned association identifier may further include a request for data collection configuration.

[0248] For example, the data collection setting may be associated with an associated identifier that is set and / or changed based on the request related to the associated identifier.

[0249] For example, the request associated with the above-mentioned association identifier may further include a request associated with a time interval for changing the terminal-side model (UE-side model).

[0250] This embodiment may be based on the aforementioned Proposal 2.

[0251] According to one embodiment, the CSI may be reported according to a preset or defined period based on the fact that the associated identifier is not included in the setting information. This embodiment may be based on Proposal 1 described above.

[0252] Operations based on S810 to S830 described above can be implemented by the device of FIG. 10. For example, referring to FIG. 10, a terminal (200) can control one or more transceivers (230) and / or one or more memories (240) to perform operations based on S810 to S830.

[0253] The embodiments described above will be explained in detail below in terms of base station operation.

[0254] S910 to S930 described below correspond to S810 to S830 described in FIG. 8. 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. 8 corresponding to the operation.

[0255] FIG. 9 is a flowchart illustrating a method according to another embodiment of the present specification.

[0256] Referring to FIG. 9, a method according to another embodiment of the present specification includes i) a step of transmitting configuration information related to CSI (S910) and ii) a step of receiving a request related to an associated identifier (S930).

[0257] In step S910, the base station transmits configuration information related to channel state information to the terminal (user equipment, UE).

[0258] In step S930, the base station receives a request from the terminal associated with an associated ID.

[0259] The above request related to the above association identifier relates to the setting and / or change of the above association identifier.

[0260] Operations based on the above-described S910 to S930 can be implemented by the device of FIG. 10. For example, referring to FIG. 10, a base station (100) can control one or more transceivers (130) and / or one or more memories (140) to perform operations based on S910 to S930.

[0261] 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).

[0262] 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. 10.

[0263] FIG. 10 is a drawing showing the configuration of a first device and a second device according to an embodiment of the present specification.

[0264] The first device (100) may include a processor (110), an antenna unit (120), a transceiver (130), and a memory (140).

[0265] 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).

[0266] 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.

[0267] 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.

[0268] The second device (200) may include a processor (210), an antenna unit (220), a transceiver (230), and a memory (240).

[0269] 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).

[0270] 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.

[0271] 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.

[0272] 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.

[0273] 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.

[0274] 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.

[0275] 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. A method performed by a terminal (user equipment, UE), A step of receiving configuration information related to channel state information from a base station; and The method includes the step of transmitting a request associated with an associated ID to the base station; A method characterized in that the request related to the above-mentioned association identifier is related to the setting and / or change of the above-mentioned association identifier.

2. In Paragraph 1, A method characterized in that the request related to the above association identifier is transmitted based on the fact that the association identifier is not included in the above setting information.

3. In Paragraph 1, A method characterized by the above request related to the above association identifier being included in the CSI.

4. In Paragraph 3, A method characterized in that the above CSI further includes a prediction accuracy indicator (PAI).

5. In Paragraph 4, A method characterized in that the above CSI is reported according to a preset or defined period based on the fact that the associated identifier is not included in the above setting information.

6. In Paragraph 1, A method characterized in that the request associated with the above-mentioned association identifier is transmitted through a report related to functionality applicability.

7. In Paragraph 1, The above setting information includes CSI reporting settings, and A method characterized in that the above CSI reporting setting includes information related to the association identifier that is set and / or changed based on the above request related to the association identifier.

8. In Paragraph 1, A method characterized in that the above-mentioned associated identifier is set and / or changed based on i) RRC Reconfiguration, ii) MAC control element (MAC-CE) and / or iii) downlink control information (DCI).

9. In Paragraph 1, The above request related to the above association identifier further includes a request for data collection configuration, and A method characterized in that the above data collection setting is related to the associated identifier that is set and / or changed based on the above request related to the above associated identifier.

10. In Paragraph 1, A method characterized in that the request associated with the above-mentioned association identifier further includes a request associated with a time interval for changing the terminal-side model (UE-side model).

11. In a terminal (user equipment, UE), 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, based on execution by the one or more processors, causing the terminal to perform all steps of the method according to any one of claims 1 to 10.

12. A device comprising one or more memories and one or more processors connected to the 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 10, based on execution by the one or more processors.

13. 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 cause the terminal to perform all steps of the method according to any one of claims 1 to 10.

14. In a method performed by a base station, A step of transmitting configuration information related to channel state information to a terminal (user equipment, UE); and The method includes the step of receiving a request related to an associated ID from the terminal; A method characterized in that the request related to the above-mentioned association identifier relates to the setting and / or change of the above-mentioned association identifier.

15. Regarding base stations, 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, having the base station perform all steps of the method according to claim 14.

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