Method for transmitting and receiving signal, and device therefor

Abstract

A method according to an embodiment of the present specification comprises the steps of: determining one or more reference signal (RS) resource indices; and determining quasi co-location (QCL)-related information for reception and / or transmission of a signal on the basis of at least one of the one or more RS resource indices. The one or more RS resource indices are based on an inference result related to a period.

Description

Method and device for transmitting and receiving signals

[0001] This specification relates to a method and device for transmitting and receiving signals.

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

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

[0004] The Rel-19 AI / ML standardization process completed the standardization process for beam prediction using NW / UE-side AI / ML. When a terminal reports predicted top-K beam(s), a method for performing P2 sweeping (Tx beam change) for the top-K beam(s) with low latency was discussed. However, specific details related to this were not introduced.

[0005] For Rel-19 UE-side AI / ML standard operations, overhead issues may arise. Specifically, beam configuration / instruction by the base station for the reported predicted Top-K beam(s) is required, and performance monitoring must be performed.

[0006] The purpose of this specification is to propose a method to solve the above-mentioned problems.

[0007] The technical problems to be achieved in this specification are not limited to the technical problems mentioned above, and other technical problems not mentioned can be clearly understood by a person having ordinary skill in the technical field to which this specification pertains from the description below.

[0008] A method according to an embodiment of the present disclosure for solving the above-described technical problem includes the steps of determining one or more Reference Signal (RS) resource indices and the step of determining information related to Quasi Co-Location (QCL) for reception and / or transmission of a signal based on at least one of the one or more RS resource indices. The one or more RS resource indices are characterized in that they are based on an inference result related to a period.

[0009] Information related to QCL is determined without separate signaling (e.g., beam indication), so that signaling overhead can be reduced in operations for beam determination / application.

[0010] According to an embodiment of the present specification, when beam prediction is performed using a NW / UE-side AI / ML model, signaling overhead related to beam indication is reduced. In other words, when inference for beam prediction of the model is performed, beam-related information (e.g., QCL information, TCI state, and / or spatial filter) is determined / applied based on the inference result, and separate signaling related to beam indication is not required. Therefore, signaling overhead is reduced compared to the existing method in which beam indication-related signaling is performed after reporting of the inference result.

[0011] According to embodiments of the present disclosure, models related to the same functionality are utilized, thereby reducing signaling overhead associated with performance monitoring. Specifically, since terminal-side models and network-side models related to the same functionality operate based on defined cycles, identical inference results can be expected for both terminals and base stations. Since the performance of each model can be monitored based on the same inference results for both terminals and base stations, separate performance monitoring reporting is unnecessary. Therefore, signaling overhead associated with performance monitoring can be reduced compared to existing methods that require performance monitoring reporting.

[0012] The effects that can be obtained from this specification are not limited to the effects mentioned above, and other effects that are not mentioned can be clearly understood by a person having ordinary skill in the technical field to which this specification belongs from the description below.

[0013] Figure 1 is a diagram to explain overall functions from an AI / ML model perspective.

[0014] Figure 2 illustrates a general form of AI / ML related procedures performed between a network and a terminal.

[0015] Figure 3 illustrates an example of AI / ML-based beam management operations.

[0016] Figure 4 illustrates an example of AI / ML-based CSI measurement / reporting operations.

[0017] Figure 5 illustrates an example of AI / ML-based positioning operation.

[0018] Figure 6 is a flowchart showing an example of a CSI-related procedure.

[0019] Figure 7 is a flowchart showing an example of a DL BM procedure.

[0020] Figure 8 is a flowchart for explaining operations by a terminal according to an embodiment of the present specification.

[0021] FIG. 9 is a flowchart for explaining operations by a base station according to an embodiment of the present specification.

[0022] FIG. 10 is a flowchart illustrating a signaling procedure according to an embodiment of the present specification.

[0023] FIG. 11 is a flowchart illustrating a method according to one embodiment of the present specification.

[0024] FIG. 12 is a flowchart illustrating a method according to another embodiment of the present specification.

[0025] FIG. 13 is a drawing showing the configuration of a first device and a second device according to an embodiment of the present specification.

[0026] As used herein, "A or B" can mean "only A," "only B," or "both A and B." In other words, as used herein, "A or B" can be interpreted as "A and / or B." For example, as used herein, "A, B or C" can mean "only A," "only B," "only C," or "any combination of A, B and C."

[0027] As used herein, a slash ( / ) or a comma can mean "and / or." For example, "A / B" can mean "A and / or B." Accordingly, "A / B" can mean "only A," "only B," or "both A and B." For example, "A, B, C" can mean "A, B, or C."

[0028] In this specification, "at least one of A and B" may mean "only A", "only B" or "both A and B". Additionally, in this specification, the expressions "at least one of A or B" or "at least one of A and / or B" may be interpreted identically to "at least one of A and B".

[0029] Additionally, in this specification, “at least one of A, B and C” can mean “only A,” “only B,” “only C,” or “any combination of A, B and C.” Additionally, “at least one of A, B or C” or “at least one of A, B and / or C” can mean “at least one of A, B and C.”

[0030] Additionally, parentheses used herein may mean "for example." Specifically, when indicated as "control information (PDCCH)", "PDCCH" may be proposed as an example of "control information." In other words, "control information" in this specification is not limited to "PDCCH," and "PDCCH" may be proposed as an example of "control information." Furthermore, even when indicated as "control information (i.e., PDCCH)", "PDCCH" may be proposed as an example of "control information."

[0031] In the following explanation, ‘when, if, in case of’ can be replaced with ‘based on’.

[0032] Technical features individually described in a single drawing in this specification may be implemented individually or simultaneously.

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

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

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

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

[0037] In this specification, a terminal is a user equipment (UE) or a consumer-side device, and may also be referred to as a base station / second node / IAB node / first node that receives / transmits signals from / to a Transmission-Reception Point (TRP). A terminal may correspond to a physical node or a logical node. A terminal may correspond to an endpoint on the user side, or may correspond to an intermediate point between other endpoints. In a communication between two points that is not limited to endpoints (including one-to-one / many-to-one / one-to-many / many-to-many communication), a terminal may correspond to a served node. A terminal may be a node with a fixed location, or a node with an unfixed location (or mobile).

[0038] In this specification, a base station (BS) is a device on the network side, and may also be called a second node / IAB node / x-NodeB (x-NodeB, x may be an abbreviation related to radio access technology (RAT)) / Transmission-Reception Point (TRP). A BS may correspond to a physical node or a logical node. A BS may correspond to an endpoint on the network side, or may correspond to an intermediate point between other endpoints. In communication between two points that are not limited to endpoints (including one-to-one / many-to-one / one-to-many / many-to-many communication), a BS may correspond to a serving node. A BS may be a node with a fixed location, or a node with an unfixed location.

[0039] In this specification, higher layer parameters may be set for the terminal, preset, or predefined. For example, the base station may transmit higher layer parameters to the terminal. For example, the terminal may transmit parameters such as capabilities to the base station as higher layer parameters. For example, the higher layer parameters may be transmitted via radio resource control (RRC) signaling or medium access control (MAC) signaling.

[0040] In this specification, the information / state / parameter being “configured or pre-configured” can be interpreted as the information / state / parameter being provided / pre-provided to the terminal through pre-defined signaling (e.g., SIB, MAC, RRC) from the base station. In this specification, the information / state / parameter being “defined or pre-defined” can be interpreted as the information / state / parameter being known in advance or pre-stored at the base station and the terminal without signaling between the base station and the terminal.

[0041] < AI / ML for Wireless Communication >

[0042] With the advancement of computing technology, artificial intelligence (AI) and machine learning (ML) are being adopted in various industries and technical fields. In the field of wireless communications, various discussions are underway to apply AI models trained based on ML, and the 3GPP standardization process refers to this as AI / ML. This specification describes "AI / ML" according to the terminology used in the 3GPP standardization process. However, "AI / ML" may be referred to by various other terms depending on the progress and implementation of future standards. For example, it may be referred to as "transmission / reception mode" or "signal / channel / operation / transmission / reception configuration" configured for AI / ML, but is not limited thereto. The meanings of the terms currently used in the 3GPP standardization process are briefly summarized as follows.

[0043] - AI / ML model: A data-driven algorithm that applies AI / ML technology to generate a set of outputs containing prediction information and / or decision parameters based on a set of inputs.

[0044] - Data collection: The process of collecting data required for AI / ML model training, data analysis, and inference from network nodes, management entities, or terminals.

[0045] - AI / ML Training: An online or offline process of training an AI model by learning features and patterns that best represent the data and obtain a trained AI / ML model for inference.

[0046] - Offline training: The process of training a model based on a previously collected data set, and the trained model is used or provided for future inference.

[0047] - Online training: This is a method in which the model is trained in real time when new training sample data is acquired and used for inference.

[0048] - AI / ML Inference: This is the process of making predictions or inducing decisions based on collected data and the AI ​​model using a trained AI model. Meanwhile, depending on whether the AI / ML model is set up on both the transmitting and receiving devices or only on one of them, it can be divided into (i) a two-sided model and (ii) a one-sided model. (i) In the case of the two-sided model, collaborative inference is performed through paired AI / ML models. Collaborative inference refers to cooperation between the network and the UE, in which one party performs part of the inference and the other party performs the rest of the inference. (ii) The one-sided model is divided into a UE-side model and a network-side model. In the one-sided model, inference is performed entirely by the UE / network-side model.

[0049] 1. Life Cycle Management (LCM) for AI / ML models

[0050] LCM for AI / ML models is a concept that encompasses all the overall procedures for AI / ML models, including data collection, model training, model deployment, model inference, model monitoring, and model updates.

[0051] LCM for AI / ML models can be broadly categorized into functionality-based LCM and model ID-based LCM. In functionality-based LCM, the network can instruct the activation / deactivation / fallback / switching of specific functions, even if the target AI / ML model may not be identified by the network. In model ID-based LCM, the network can instruct the activation / deactivation / selection / switching of AI / ML models identified by their AI / ML model ID.

[0052] Figure 1 is a diagram to explain overall functions from an AI / ML model perspective.

[0053] Referring to Fig. 1, a general AI / ML functional framework can be configured to include a data collection function (10), a model training function (20), a management function (30), an inference function (40), and a model storage function (50).

[0054] The Data Collection function (10) is a function that provides input data to the Model Training function (20), Management function (30), and Inference function (40). The Data Collection function (10) can perform data preparation and provide input data processed through data preparation.

[0055] 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).

[0056] The Model Training function (20) is a function that performs AI / ML model training, validation, and testing, and can generate model performance metrics that can be used as part of the AI / ML model testing procedure. If necessary, the Model Training function (20) can perform data preparation (e.g., data pre-processing and cleaning, forming, and transformation) based on the Training Data (11) transmitted from the Data Collection function (10).

[0057] Trained / Updated Model (21): If there is a Model Storage function (50), it is used to pass a trained, validated and tested AI / ML model to the Model Storage function (50) or to pass an updated version of the model to the Model Storage function (50).

[0058] The Management function (30) is a function that monitors the operation of the AI / ML model or AI / ML function. In addition, the Management function (30) may perform a decision to ensure appropriate inference operation based on data received from the Data Collection function (10) (i.e., Monitoring Data (12)) and / or data received from the Inference function (40) (i.e., Inference Output (41)).

[0059] Management Instruction (32) is information required as input to manage the Inference function (40). The relevant information may include selection / (de)activation / switching of an AI / ML model or AI / ML-based function, and may also include fallback to non-AI / ML operations (i.e., not relying on the inference process).

[0060] A Model Transfer / Delivery Request (33) can be used to request model(s) from Model Storage (50).

[0061] A Performance Feedback / Retraining Request (31) refers to information required as input to the Model Training function (20) (e.g., for the purpose of (re)training or updating the model).

[0062] The Inference function (40) is a function that provides output from the process of applying an AI / ML model or AI / ML function using data (i.e., Inference Data (13)) provided by Data Collection (10) as input. Data preparation (e.g., data preprocessing and cleaning, formatting, and transformation) may also be performed based on the Inference Data (13) delivered by Data Collection (10). If necessary, the Inference function (40) may also perform data preparation (e.g., data preprocessing and cleaning, forming, and transformation) based on the Inference Data (13) provided by Data Collection function (10).

[0063] Inference Output (41) is data used in the Management function (30) to monitor the performance of an AI / ML model or AI / ML function. Inference Output (41) may include the inference output of the AI / ML model generated by the Inference function (30), and the details of the inference output may vary depending on the use case.

[0064] The Model Storage function (50) stores a learned / updated model that can be used to perform the Inference function (40). The Model Storage function (50) illustrated in Fig. 1 can be used as a reference point (if any) when applicable to protocol termination, model transmission / delivery, and related processes. Furthermore, the Model Storage function (50) is merely an example and is not intended to limit the storage location of actual AI / ML models, and may be omitted.

[0065] Model Transfer / Delivery (51) is used to transfer AI / ML models to inference functions.

[0066] 2. General AI / ML-related procedures between networks and terminals

[0067] Figure 2 illustrates the general form of AI / ML-related procedures performed between a network and a terminal. While Figure 1 examined LCM from an AI / ML model perspective, Figure 2 describes the general form of procedures performed from a signaling / protocol perspective between a terminal and the network.

[0068] (1) Setting procedures related to AI / ML

[0069] Referring to FIG. 2, an AI / ML-related configuration procedure may be performed between the network and the terminal (B05). The AI / ML-related configuration procedure may include information exchange between the terminal and the network via at least one upper-layer signaling, and / or preparatory / follow-up operations at the terminal / network, respectively, before / after the upper-layer signaling.

[0070] Specifically, the AI / ML-related configuration procedure may include, but is not limited to, at least one of (i) AI / ML-related terminal capability reporting, (ii) data collection, (iii) model training, (iv) model transfer / transfer, (v) AI / ML function / model selection, and (vi) configuration for various operations performed based on AI / ML models (e.g., AI / ML-based CSI / Positioning / Beam Management).

[0071] (i) A terminal can report to the network its capabilities, such as models / functionalities supported by the terminal in relation to AI / ML, through UE Capability Reporting. The network can provide AI / ML-related settings to the terminal based on the AI / ML-related capabilities reported by the terminal.

[0072] (ii) AI / ML-related configuration procedures may include data collection and / or provision of configuration information related to AI / ML model training / inference, etc. Configuration information related to data collection may relate to how to configure the method / action of data collection, etc.

[0073] (iii) AI / ML-related configuration procedures may include online or offline AI / ML model training and / or providing configuration information for AI / ML model training. Configuration information for AI / ML model training may relate to how to configure the method / operation of training the AI / ML model, etc.

[0074] (iv) The AI / ML-related configuration procedure may include transmitting / delivering configuration information for the model. The configuration information for the model may include parameters configuring the AI / ML model and / or an identifier (ID) for the AI / ML model.

[0075] The AI / ML model provided can be either a network-trained model or a model that requires self-training on the terminal. Even if a network-trained model is provided, the terminal can perform fine-tuning / retraining processes as needed. Meanwhile, if a network-trained model is provided, the terminal can provide training data to the network.

[0076] Meanwhile, AI / ML models can be categorized into Type A models, which can be identified without over-the-air (OTA) signaling, and Type B models, which are identified through OTA signaling. A model ID can be assigned during the model identification process, which can be further subdivided into terminal-initiated and network-initiated methods.

[0077] (v) The AI / ML-related configuration procedure may include a configuration of how to select an AI / ML Functionality / model and / or a selection process for the AI / ML Functionality / model. Selection of the UE part in a UE-side AI / ML model or a two-sided AI / ML model may be performed through instructions / signaling from the network or may be performed by the UE itself. Selection of the AI / ML Functionality / model may be performed when multiple AI / ML Functionality / models are configured / provided.

[0078] (vi) The AI / ML-related setup procedure may include setup information for various inference operations performed based on AI / ML models, for example, AI / ML-based CSI measurement / reporting, AI / ML-based Positioning, and / or AI / ML-based Beam Management.

[0079] (2) Actions based on inference from AI / ML models

[0080] Referring back to FIG. 2, the network and / or the terminal may perform inference of the AI / ML model through the trained AI / ML model, and perform various operations based on the inference of the AI / ML model (B10). If the AI / ML model is a one-sided model, the inference of the AI / ML model may be performed on either the network or the terminal where the AI / ML model is configured. If the AI / ML model is a two-sided model, each part of the inference of the AI / ML model may be performed on the network and the terminal, and such inference may be performed cooperatively between the network and the terminal depending on the implementation.

[0081] (i) Actions performed based on the inference of the AI / ML model may include AI / ML-based CSI measurement / reporting. The AI / ML-based CSI measurement / reporting may be for improving CSI feedback, and may be related to overhead reduction / CSI compression, accuracy improvement, and / or CSI prediction.

[0082] (ii) The actions performed based on the inference of the AI / ML model may include AI / ML-based beam management. The AI / ML-based beam management may be related to beam prediction in the time domain, reducing overhead / delay in the spatial domain, and / or improving beam selection accuracy.

[0083] (iii) Actions performed based on the inference of the AI / ML model may include AI / ML-based positioning. AI / ML-based positioning may be relevant to improving positioning accuracy in various scenarios, such as non-line-of-sight environments.

[0084] (3) Procedures for AI / ML management

[0085] The network and / or terminal can perform procedures for managing AI / ML Functionality / model or its settings (B15).

[0086] The network and / or terminal may perform monitoring of AI / ML Functionality / model during the process of AI / ML model inference or operation based thereon (B10) for management procedures (B15).

[0087] Management procedures may include, for example, at least one of activation / deactivation, switching, model update, and / or fallback operations for AI / ML Functionality / models. Signaling for management procedures may use various 3GPP signaling methods, such as RRC, MAC-CE, and DCI.

[0088] As an example of model switching, multiple model groups are formed, and switching between them, groups can be performed based on models having a common model structure or partially common sub-structures, and models within the same group can be related to different input / output formats or processing.

[0089] Model updating is the process of changing the parameters used by the model to adapt them to changing channel conditions over time, and fine-tuning is an example of model updating.

[0090] fallback: In a wireless communication system using an AI / ML model, when the reliability of the AI / ML model is reduced due to internal / external environmental factors, it can mean not using the AI / ML model or operating in a default operation mode that is set / defined in advance.

[0091] For example, the decision to perform a management procedure may be made by the network. For example, the network may decide to perform a management procedure upon network initiation, or upon terminal initiation and request.

[0092] As another example, the decision to perform a management procedure can be made by the terminal. For example, the terminal's decision to perform a management procedure can be triggered by the satisfaction of an event condition set by the network, by reporting the terminal's decision to the network, or by the terminal performing the decision autonomously.

[0093] 3. Specific examples of actions based on AI / ML model inference

[0094] (1) Beam management

[0095] Figure 3 illustrates an example of AI / ML-based beam management operations.

[0096] Referring to FIG. 3, the network / terminal can perform a configuration procedure related to AI / ML-based beam management (C05). The network / terminal can exchange configuration information for upper layer signaling for AI / ML-based beam management and perform a configuration procedure for an AI / ML model to be used for AI / ML-based beam management. For example, at least one of information related to model inference, configuration for the first set / second set of beams, monitoring performance, data collection, and assistance information for beam measurement can be signaled.

[0097] The network / terminal can perform measurements on the first set of beams (C10). The beam measurements can be related to RSRP measurements.

[0098] The network / terminal can obtain information about the second set of beams based on the measurement results for the first set of beams (C15). For example, the network / terminal can perform AI / ML inference using the measurement results for the first set of beams as AI / ML input data. Beam ID information may be additionally provided as AI / ML input data. The information about the second set of beams may correspond to AI / ML output data. The AI / ML output data may be related to prediction of future beam quality, such as, but not limited to, the probability that each beam will become a top-N beam, and the predicted RSRP.

[0099] In some embodiments, the network / terminal may transmit and receive information about the acquired second set of beams.

[0100] Specifically, the AI / ML-based beam management operation may include at least one of the following BM-Case 1 and BM-Case 2.

[0101] - BM-Case 1: Prediction of the second set of DL beams in the spatial domain using the first set of beam measurements.

[0102] - BM-Case 2: Prediction of the second set of DL beams in the time domain using the first set of beam measurements.

[0103] In BM-Case 1 and / or 2, both AI / ML model training and inference can be performed in the network or at the terminal. The first set of beams and the second set of beams can be different beams. Alternatively, the first set of beams can be a subset of the second set of beams. Alternatively, particularly in BM-Case 2, the first set of beams and the second set of beams can be the same beams.

[0104] The report corresponding to the inference of the UE-side model for BM-Case 1 may relate to the predicted RSRP for the upper N beams. The report may include, for example, the predicted RSRP value, and for example, the predicted RSRP value may be reported together with the actually measured RSRP.

[0105] UE-side AI / ML model inference for BM-Case 2 can report inference results for N future time points in a single report. Each time point report can correspond to the report for BM-Case 1.

[0106] For performance monitoring of the UE-side model for BM-Case 1 / 2, (i) network side performance monitoring and / or (ii) UE-assisted performance monitoring may be supported. (i) For network side performance monitoring, the UE may report information necessary for the network to calculate performance metrics, for example, measurement results (e.g., RSRP) and / or RS index for a resource set for monitoring. (ii) For UE-assisted performance monitoring, the UE may also calculate performance metrics.

[0107] Regarding the NW-side model for BM-Case 1 / 2, quantization of reported RSRPs may be supported, e.g., differential RSRP reporting may be supported along with existing quantization steps and ranges. The reported content may include information about the RSRP and the corresponding upper N beams, where N may be configured by the network.

[0108] Regarding the configuration of the first set of beams and the second set of beams in the UE-side model of BM Case-1, two resource sets may be separately configured for each of the first set and the second set, and the corresponding resource sets may be provided through the CSI reporting configuration. The terminal may perform inference / measurement for the resource set of the first set of beams. The terminal may not be expected to perform measurement / inference for the resource set of the second set of beams. The beam information of the inference report may include resource set information for the first set.

[0109] In relation to the UE-side model, relevant IDs can be provided via the CSI framework. UEs can assume identical / similar characteristics for DL ​​transmission beams / sets (lists) with the same relevant ID.

[0110] Regarding UE-assisted performance monitoring for the UE-side models of BM-Case 1 and 2, the following methods can be considered.

[0111] i) Compare prediction results based on resources for monitoring and use the top 1 or top K beam prediction accuracy.

[0112] ii) Use RSRP difference information based on RSRP measurements of resources for monitoring and actual RSRP measurements for at least one of the top N predicted beams.

[0113] iii) Use the difference information between the measured RSRP and the predicted RSRP for the corresponding beam of the resources for monitoring.

[0114] iv) Probability information about the predicted beam being one of the top 1 or N beams.

[0115] Quantization of RSRP can be supported for reporting inference results for UE-side models, and differential RSRP with existing quantization steps can be supported. The scope of RSRP reporting is that differential RSRP can be supported among multiple beams in the case of BM-case 1, and differential RSRP can be supported among multiple beams at multiple viewpoints in the case of BM-case 2.

[0116] For BM-Case 2 of the UE-side model, the network can be configured to report inferences for N future time points to the terminal.

[0117] (2) CSI prediction and / or compression (CSI prediction / compression)

[0118] Figure 4 illustrates an example of AI / ML-based CSI measurement / reporting operations.

[0119] Referring to FIG. 4, the network / terminal may perform a configuration procedure related to AI / ML-based CSI (D05). The network / terminal may exchange configuration information for upper-layer signaling for AI / ML-based CSI measurement / reporting, and perform a configuration procedure for an AI / ML model to be used for AI / ML-based CSI. For example, at least one of information related to model inference, configuration of RS / resources to be used for CSI measurement, monitoring performance, data collection, and conditions / resources for CSI reporting may be signaled.

[0120] The terminal can perform CSI measurements based on AI / ML model inference (D10). The AI / ML model used by the terminal for CSI measurements may be a UE-side AI / ML model corresponding to a one-side AI / ML model, or an AI / ML model corresponding to the terminal portion of a two-side AI / ML model.

[0121] A terminal may report CSI to the network based on the CSI measurement results (D15). CSI reporting may be performed periodically or aperiodically depending on the configuration, and in the case of aperiodic CSI reporting, a network instruction (not shown) such as DCI that triggers it may be additionally signaled. CSI reporting may include AI / ML-based CSI content, and additionally (depending on the configuration / scheduling) may further include legacy CSI content (e.g., non-AI / ML-based RI, PMI, CQI, etc.). AI / ML-based CSI content may be related to at least one of 1) CSI compression to reduce the overhead of CSI reporting, and 2) CSI prediction for future time points in the time domain.

[0122] The network can obtain CSI based on the CSI report of the terminal.

[0123] If a two-sided AI / ML model is configured, the network can reconstruct CSI using the terminal's CSI report as input data to the network-configured AI / ML model (D20). The inference (output) of the network-configured AI / ML model can be the reconstructed CSI. In such a two-sided AI / ML model, the terminal-side AI / ML model can be understood as a CSI encoder, while the network-side AI / ML model can be understood as a concept similar to a CSI decoder.

[0124] CSI compression is a spatial-frequency domain CSI compression, which can be primarily based on two-sided AI / ML models. CSI prediction can primarily be based on one-sided, specifically, UE-side AI / ML models.

[0125] In CSI compression based on two-side AI / ML models, AI / ML model training may include at least one of (i) Type 1, in which either the terminal or the network jointly trains two-side AI / ML models, (ii) Type 2, in which the terminal and the network each jointly train their respective two-side AI / ML model parts, and (iii) Type 3, in which the terminal and the network each separately train their respective two-side AI / ML model parts, with the terminal training being primarily related to CSI generation and the network training being primarily related to CSI reconstruction. Joint training means that the CSI generation / reconstruction model is trained in the same loop for forward / backward delays, and separate training may mean a sequential method in which either the terminal or the network starts training first and then the other performs training.

[0126] (3) Positioning

[0127] Figure 5 illustrates an example of AI / ML-based positioning operation.

[0128] Referring to FIG. 5, the network / terminal may perform a configuration procedure related to AI / ML-based positioning (E05). The network / terminal may exchange configuration information for upper-layer signaling for AI / ML-based positioning and perform a configuration procedure for an AI / ML model to be used for AI / ML-based positioning. For example, at least one of information related to model inference, positioning configuration for RS, monitoring performance, data collection, and assistance information for positioning measurement may be signaled.

[0129] The network / terminal can perform measurements for positioning (E10). The measurements for positioning may be related to PRS and / or SRS measurements.

[0130] Based on the measurement results, the network / terminal can obtain information about terminal positioning (E15). For example, the network / terminal can use the PRS / SRS measurement results as AI / ML input data to perform AI / ML inference. Information about terminal positioning may correspond to AI / ML output data. The AI / ML output data may be, for example, terminal location or assistance information that serves as the basis for determining terminal location, but is not limited thereto.

[0131] Depending on the embodiment, the network / terminal may transmit and receive information about the acquired terminal positioning.

[0132] Specifically, the AI / ML-based positioning may include at least one of (i) direct AI / ML positioning and / or (ii) AI / ML-assisted positioning. (i) In direct AI / ML positioning, the output of the AI / ML model includes the UE location. (ii) In AI / ML-assisted positioning, the output of the AI / ML model may be a new measurement result and / or an improvement to an existing measurement (e.g., LoS / NLoS identification, timing and / or angle of the measurement, likelihood of the measurement, etc.).

[0133] The data sample collected for training data collection may include at least one of the following Parts:

[0134] - Part A: Channel measurements, quality indicators of channel measurements, timestamps of channel measurements

[0135] - Part B: ground truth label (or its approximation), label quality indicator, label timestamp

[0136] The training data samples for Part A and Part B may be for the same terminal (e.g., PRU or Non-PRU UE) and for the same location associated with Part B.

[0137] Measurements for positioning can be divided into sample-based measurements and path-based measurements.

[0138] - In sample-based measurements, the measurement consists of Nt' samples of the estimated channel response in the time domain. Timing information for the Nt' samples is reported with a timing granularity T, where T = 2k x Tc. k denotes a timing reporting granularity factor, and Tc denotes the fundamental time unit of the wireless communication system. Nt' and k can be signaled parameters. Timing information can be defined as a value relative to a reference time.

[0139] - Path-based measurement refers to the measurement defined in existing wireless communication systems.

[0140] AI / ML-based positioning may relate to at least one of the following specific cases:

[0141] - (i) Case 1: UE-based positioning using a UE-side model, in the case of direct AI / ML or AI / ML assisted positioning.

[0142] - (ii) Case 2a: UE-assisted / LMF-based positioning using the UE-side model, in the case of AI / ML assisted positioning

[0143] - (iii) Case 2b: UE-assisted / LMF-based positioning using the LMF-side model, in the case of direct AI / ML positioning

[0144] - (iv) Case 3a: NG-RAN node assisted positioning using the gNB-side model, in the case of AI / ML assisted positioning

[0145] - (v) Case 3b: NG-RAN node assisted positioning using LMF-side model, in case of direct AI / ML positioning

[0146] (i) In relation to model performance monitoring in Case 1, the following options may be considered for calculating model performance metrics in label-based model monitoring.

[0147] 1) Option A. Calculate monitoring metrics on the target terminal side.

[0148] - Option A-1: ​​At least some of the information about the target terminal's ground truth label is generated by the LMF and provided to the target terminal. For example, the target terminal and / or base station may send measurement results to the LMF, which may then derive information about the ground truth label.

[0149] - Option A-2: At least part of the information for position calculation assistance data is provided from the LMF to the target UE.

[0150] - Option A-3: This is a method of reusing assistance data previously provided from the LMF to the target terminal, in which PRU measurement results and corresponding PRU location information are provided from the LMF to the target terminal.

[0151] - Option A-4: PRU measurements and PRU locations are provided from the PRU to the target UE.

[0152] 2) Option B. LMF calculates monitoring metrics

[0153] - Option B-1: At least the inference results of the target terminal (i.e., the model output corresponding to the channel measurements of the target terminal) can be transmitted to the LMF by the target terminal.

[0154] - Option B-2: The channel measurement of the PRU is provided to the target terminal through the LMF, and the inference result (i.e., the model output corresponding to the channel measurement of the PRU) can be transmitted from the target terminal to the LMF.

[0155] (iv) With regard to generating training data in Case 3a, at least LMF can generate labels and data related to them (e.g., timestamps).

[0156] (iv) For calculating model performance monitoring metrics in label-based model monitoring in Case 3a, the following options A and B may be considered.

[0157] - Option A: NG-RAN nodes perform monitoring metric calculations for their own models.

[0158] - Option B: LMF performs monitoring metric calculations for models located on NG-RAN nodes.

[0159] (iv) For generating training data for Case 3a and (v) Case 3b, measurements and related data (e.g., timestamp) can be generated at the TRP / base station.

[0160] (v) For base station channel measurements reported to the LMF (location management server) in Case 3b, timing information can be expressed as a relative value to the UL RTOA reference time T0+tSRS.

[0161] (iii) Time domain channel measurements supported for reporting in Case 2b and (v) Case 3b may include timing information and / or power information corresponding to timing information.

[0162] For the definition of a sample-based measurement, Nt' samples can be selected from a list of consecutive Nt samples, and the Nt samples can have a time granularity of T.

[0163] In relation to sample-based measurements, the Nt' samples selected for measurement of the network may be those having the highest power.

[0164] For positioning based on Case 3b, for sample-based measurements, LMF can signal parameter values ​​such as Nt, Nt', k, etc. to the base station.

[0165] < CSI-related actions >

[0166] Figure 6 is a flowchart showing an example of a CSI-related procedure.

[0167] Referring to FIG. 6, in order to perform one of the purposes of CSI-RS, a terminal (e.g., user equipment, UE) receives configuration information related to CSI from a base station (e.g., general Node B, gNB) through RRC (radio resource control) signaling (S610).

[0168] The configuration information related to the above CSI may include at least one of CSI-IM (interference management) resource related information, CSI measurement configuration related information, CSI resource configuration related information, CSI-RS resource related information, or CSI report configuration related information.

[0169] CSI resource configuration related information can be expressed as CSI-ResourceConfig IE. The CSI resource configuration related information defines a group including at least one of a non-zero power (NZP) CSI-RS resource set, a CSI-IM resource set, or a CSI-SSB resource set. That is, the CSI resource configuration related information includes a CSI-RS resource set list, and the CSI-RS resource set list can include at least one of an NZP CSI-RS resource set list, a CSI-IM resource set list, or a CSI-SSB resource set list. A CSI-RS resource set is identified by a CSI-RS resource set ID, and one resource set includes at least one CSI-RS resource. Each CSI-RS resource is identified by a CSI-RS resource ID.

[0170] Information related to the CSI report configuration includes a reportConfigType parameter indicating a time domain behavior and a reportQuantity parameter indicating a CSI-related quantity to be reported. The time domain behavior may be periodic, aperiodic, or semi-persistent.

[0171] The above reportQuantity parameter may be related to at least one of a channel quality indicator (CQI), a precoding matrix indicator (PMI), a CSI-RS resource indicator (CRI), an SSB resource block indicator (SSBRI), a layer indicator (LI), a rank indicator (RI), and a layer 1-reference signal received strength (L1-Reference Signal Received Strength (RSRP).

[0172] Measurement resources may include configurations for downlink signals and / or downlink resources on which a terminal will perform measurements to determine feedback information. Measurement resources may be configured as ZP and / or NZP CSI-RS resource sets associated with CSI reporting configurations. The NZP CSI-RS resource set may include a CSI-RS set or an SSB set. For example, L1-RSRP may be measured for a CSI-RS set or an SSB set.

[0173] The terminal measures CSI based on configuration information related to the CSI (S620). The CSI measurement may include (1) a process of receiving a CSI-RS by the terminal (S621) and (2) a process of computing CSI using the received CSI-RS (S622). The terminal reports the CSI to the base station (S630).

[0174] Resource setting

[0175] Each CSI resource setting 'CSI-ResourceConfig' contains a configuration for S≥1 CSI resource sets (given by the higher layer parameter csi-RS-ResourceSetList). A CSI resource setting corresponds to a CSI-RS-resourcesetlist, where S represents the number of configured CSI-RS resource sets. Wherein, the list of S≥1 CSI resource sets contains either or both of NZP CSI-RS resource set(s) and SS / PBCH block (SSB) set(s) used for L1-RSRP computation, or contains CSI-IM resource set(s).

[0176] One or more CSI resource settings for channel measurement (CM) and interference measurement (IM) are configured via higher layer signaling.

[0177] - CSI-IM resource for interference measurement.

[0178] - NZP CSI-RS resources for interference measurement.

[0179] - NZP CSI-RS resources for channel measurement.

[0180] That is, the CMR (channel measurement resource) can be NZP CSI-RS for CSI acquisition, and the IMR (Interference measurement resource) can be NZP CSI-RS for CSI-IM and IM.

[0181] Here, CSI-IM (or ZP CSI-RS for IM) is mainly used for inter-cell interference measurement.

[0182] And, NZP CSI-RS for IM is mainly used for intra-cell interference measurement from multi-user.

[0183] A UE may assume that the CSI-RS resource(s) configured for channel measurement for one CSI reporting and the CSI-IM / NZP CSI-RS resource(s) for interference measurement (when NZP CSI-RS resource(s) are used for interference measurement) are in a QCL relationship with respect to 'QCL-TypeD' per resource.

[0184] As we have seen, resource setting can mean a resource set list.

[0185] For aperiodic CSI, each trigger state set using the higher layer parameter CSI-AperiodicTriggerState is associated with one or more CSI-ReportConfigs, and each CSI-ReportConfig is linked to a periodic or semi-persistent or aperiodic resource setting.

[0186] One reporting setting can be linked to up to three resource settings.

[0187] < Beam Management (BM) >

[0188] BM procedures are L1 (layer 1) / L2 (layer 2) procedures for acquiring and maintaining a set of base station (e.g., gNB, TRP, etc.) and / or terminal (e.g., UE) beams that can be used for downlink (DL) and uplink (UL) transmission / reception, and may include the following procedures and terminology.

[0189] - Beam measurement: An operation in which a base station or UE measures the characteristics of a received beam-forming signal.

[0190] - Beam determination: An operation in which a base station or UE selects its own transmit beam (Tx beam) / receive beam (Rx beam).

[0191] - Beam sweeping: The operation of covering a spatial area using a transmit and / or receive beam over a predetermined time interval in a predetermined manner.

[0192] - Beam report: An operation in which a UE reports information about a beam-formed signal based on beam measurement.

[0193] The BM procedure can be divided into (1) a DL BM procedure using SS (synchronization signal) / PBCH (physical broadcast channel) Block or CSI-RS, and (2) a UL BM procedure using SRS (sounding reference signal).

[0194] Additionally, each BM procedure may include Tx beam sweeping to determine the Tx beam and Rx beam sweeping to determine the Rx beam.

[0195] DL BM

[0196] The DL BM procedure may include (1) transmission of beamformed DL RSs (reference signals) (e.g., CSI-RS or SS Block (SSB)) of the base station and (2) beam reporting of the terminal.

[0197] Here, beam reporting may include preferred DL RS ID(identifier)(s) and corresponding L1-RSRP (Reference Signal Received Power).

[0198] The above DL RS ID may be an SSBRI (SSB Resource Indicator) or a CRI (CSI-RS Resource Indicator).

[0199] An example of beamforming using SSB and CSI-RS is described in detail below.

[0200] Both SSB and CSI-RS beams can be used for beam measurement. The measurement metric is L1-RSRP per resource / block. SSB is used for coarse beam measurement, and CSI-RS can be used for fine beam measurement. SSB can be used for both Tx beam sweeping and Rx beam sweeping.

[0201] Rx beam sweeping using SSB can be performed by the UE changing the Rx beam for the same SSBRI across multiple SSB bursts, where one SS burst contains one or more SSBs, and one SS burst set contains one or more SSB bursts.

[0202] Below we will look at the DL BM procedure.

[0203] Figure 7 is a flowchart showing an example of a DL BM procedure.

[0204] The configuration for beam report using SSB is performed during CSI / beam configuration in RRC connected state (or RRC connected mode).

[0205] - The terminal receives configuration information from the base station. As a specific example, the terminal receives a CSI-ResourceConfig IE including a CSI-SSB-ResourceSetList including SSB resources used for BM from the base station (S710).

[0206] Table 1 shows an example of the CSI-ResourceConfig IE. As shown in Table 1, BM configuration using SSB is not defined separately, and SSB is configured as a CSI-RS resource.

[0207]

[0208] In Table 1, the csi-SSB-ResourceSetList parameter indicates a list of SSB resources used for beam management and reporting in a single CSI-RS resource set. Here, the SSB resource set can be set to {SSBx1, SSBx2, SSBx3, SSBx4, …}. For example, the SSB index can be defined from 0 to 63.

[0209] - 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 (S720).

[0210] - The terminal transmits a beam report to the base station. For example, if CSI-ReportConfig related to reporting on SSBRI (SSB Resource Indicator) and L1-RSRP is set, the terminal reports the best SSBRI and its corresponding L1-RSRP to the base station (S730).

[0211] That is, when the reportQuantity of the above CSI-ReportConfig IE is set to 'ssb-Index-RSRP', the terminal reports the best SSBRI and the corresponding L1-RSRP to the base station.

[0212] And, if the terminal sets the CSI-RS resource in the same OFDM symbol(s) as the SSB (SS / PBCH Block) and 'QCL-TypeD' is applicable, the terminal can assume that the CSI-RS and SSB are quasi co-located from the 'QCL-TypeD' perspective.

[0213] Here, the QCL TypeD may mean that the antenna ports are QCL-connected from a spatial Rx parameter perspective. When a terminal receives multiple DL antenna ports in a QCL Type D relationship, the same reception beam may be applied. In addition, the terminal does not expect the CSI-RS to be configured in an RE that overlaps with the SSB RE.

[0214] < Description of Rel-17 / 18 beam management >

[0215] In Rel-17, both the DL TCI state and the UL TCI state can be indicated through DL DCI (e.g., DCI format 1-1 or 1-2), or only the UL TCI state can be indicated without indicating the DL TCI state. Therefore, the methods used for UL beam and power control (PC) configuration in the existing R15 / R16 are replaced in Rel-17 with the above UL TCI state indication method. More specifically, in R17, one UL TCI state can be indicated through the TCI field of DL DCI, and the UL TCI state is applied to all PUSCHs and all PUCCHs after a certain time called the beam application time, and can be applied to some or all of the indicated SRS resource sets. In addition, the base station can perform a terminal common beam update by using DCI and / or MAC-CE to perform indication / update with one beam in common (using a joint or separate TCI state) for specific DL / UL channel / RS combinations of multiple terminals. The target channels / RS of common beam update include UE-dedicated CORESET, UE-dedicated reception on PDSCH for DL, DG / CG-PUSCH, all or a subset of dedicated PUCCH for UL, and additionally, AP CSI-RS for tracking / BM, SRS can be set as target channels / RS.In Rel-18, considering the M-TRP environment, the method of indicating multiple UL TCI states (and / or DL ​​TCI states) through the TCI field of DL DCI has been standardized, and the uplink / downlink resources to which multiple indicated TCIs are applied can be defined / configured depending on the S-DCI based M-TRP environment and the M-DCI based M-TRP environment.

[0216] In this document, ' / ' means 'and', 'or', or 'and / or' depending on the context.

[0217] In this specification, 'beam' may mean a source RS for a 'spatial filter' or a 'spatial relation', and may be interpreted as a QCL (type-D) RS or a TCI state or (in the case of uplink) a spatial relation RS.

[0218] For example, in this specification, 'beam' may mean a spatial filter determined based on the reference RS or the source RS. The spatial filter may include a spatial domain filter, a spatial domain transmission filter, and a spatial domain receive filter. For example, in this specification, 'beam' may be interpreted / replaced with a reference signal index (RS index), a reference signal resource index (RS resource index), and / or a resource indicator (e.g., RS index, SSB index, CSI-RS resource index, SRS resource index, SSB Resource Indicator (SSBRI), CSI-RS Resource Indicator (CRI), etc.).

[0219] For example, a beam associated with UL may be referred to as i) a spatial filter (for uplink transmission or uplink reception), ii) a spatial domain filter (for uplink transmission or uplink reception), iii) an uplink spatial domain transmission filter, iv) an uplink spatial domain receive filter, v) an uplink transmit spatial filter (UL Tx spatial filter), or vi) an uplink receive spatial filter (UL Rx spatial filter).

[0220] For example, a beam associated with DL may be referred to as i) a spatial filter (for downlink transmission or downlink reception), ii) a spatial domain filter (for downlink transmission or downlink reception), iii) a downlink spatial domain transmission filter, iv) a downlink spatial domain receive filter, v) a downlink transmit spatial filter (DL Tx spatial filter) or vi) a downlink receive spatial filter (DL Rx spatial filter).

[0221] In the NR standard, QCL setting and spatialRelation setting by TCI state setting are utilized to set the UL / DL transmission / reception beam of the terminal. In the Rel-15 NR standard, RRC and MAC CE signaling are mainly used for UL / DL number / transmission beam. Dynamic signaling was allowed only for the reception beam of the PDSCH using the TCI state field of the DL grant DCI. The Rel-17 / 18 NR standard introduced a unified TCI framework. Specifically, a method was introduced to dynamically manage the common beam by indicating the reception / transmission beam using the indicated TCI using DCI. Meanwhile, in the Rel-18 AI / ML study item, a study was conducted on performance evaluation and specification impact in the spatial beam prediction and temporal beam prediction sub-use cases in the beam management field. The study discussed the NW / UE-side AI / ML operation that predicts the best beam of Set A based on Set B measurements. For UE-side AI / ML, the terminal is required to measure Set B and report the predicted Set A beam. In the latter case, standardization discussions are underway regarding what beam-related information (e.g., beam ID, RSRP, beam pattern ID, beam group ID, and bitmap information) should be included when the terminal performs Set B measurement / report.

[0222] < Background related to AI / ML beam management >

[0223] In the Rel-18 AI / ML study item, we conducted a study on performance analysis and potential specification impact through evaluation when NW and / or UE-side AI / ML models operate in three use cases: CSI compression / prediction, beam management, and positioning. In particular, in the beam management use case, we divided the sub-use cases into BM-case1 and BM-case2, and studied performance analysis and potential specification impact for spatial domain beam prediction and temporal beam prediction. The WID goals of AI / ML BM, BM-case1, and BM-case2 are summarized in Tables 2 to 4 below.

[0224] - AI / ML BM's WID goals

[0225]

[0226] - BM-case1: Spatial domain downlink beam prediction for beam set A based on measurement results for beam set B.

[0227]

[0228] - BM-case2: Temporal downlink beam prediction for beam set A based on past measurement results for beam set B.

[0229]

[0230] Additionally, an example of the operation for data collection of AI / ML models in the Beam management use case is shown in Table 5 below.

[0231]

[0232] Additionally, an example of the operation for inference of AI / ML model in Beam management use case is as shown in Table 6 below.

[0233]

[0234] As cited above, the Rel-18 AI / ML study discussed NW / UE sided AI / ML operations that predict the best beam of Set A based on Set B measurements. In the case of UE sided AI / ML, the terminal is required to measure Set B and report the predicted Set A beam. In the case of NW sided AI / ML, the terminal is required to report the Set B measurement. In the former, standardization discussions are underway regarding what information (e.g., predicted top-K beam, RSRP of predicted top-K beam) to report when reporting the predicted Set A beam. In the latter, standardization discussions are underway regarding what information (e.g., beam ID, RSRP, beam pattern ID, beam group ID, and bitmap information) to consist of when the terminal performs Set B measurement / report.

[0235] The standardization agreements reached to date are as shown in Table 7 below.

[0236]

[0237] The symbols / abbreviations / terms used in this specification are as follows.

[0238] BM: beam management

[0239] CQI: channel quality indicator

[0240] CRI: CSI-RS (channel state information - reference signal) resource indicator

[0241] CSI: channel state information

[0242] CSI-IM: channel state information - interference measurement

[0243] CSI-RS: channel state information - reference signal

[0244] DMRS: demodulation reference signal

[0245] FDM: frequency division multiplexing

[0246] FFT: fast Fourier transform

[0247] IFDMA: interleaved frequency division multiple access

[0248] IFFT: inverse fast Fourier transform

[0249] L1-RSRP: Layer 1 reference signal received power

[0250] L1-RSRQ: Layer 1 reference signal received quality

[0251] MAC: medium access control

[0252] NZP: non-zero power

[0253] OFDM: orthogonal frequency division multiplexing

[0254] PDCCH: physical downlink control channel

[0255] PDSCH: physical downlink shared channel

[0256] PMI: precoding matrix indicator

[0257] RE: resource element

[0258] RI: Rank indicator

[0259] RRC: radio resource control

[0260] RSSI: received signal strength indicator

[0261] Rx: Reception

[0262] QCL: quasi co-location

[0263] SINR: signal to interference and noise ratio

[0264] SSB (or SS / PBCH block): synchronization signal block (including primary synchronization signal, secondary synchronization signal and physical broadcast channel)

[0265] TDM: time division multiplexing

[0266] TRP: transmission and reception point

[0267] TRS: tracking reference signal

[0268] Tx: transmission

[0269] UE: user equipment

[0270] ZP: zero power

[0271] Below, the technical problems to be solved by this specification are specifically described.

[0272] Currently, the Rel-19 AI / ML work item of 5G advanced only considers one-sided AI / ML (e.g., NW-sided AI / ML model or UE-sided AI / ML model) for beam management. However, in future releases or 6G 3GPP standards, AI / ML beam prediction operations may be considered in an environment where AI / ML model-based operations are performed on the NW side and AI / ML model-based operations are performed simultaneously on the UE side. If the same AI / ML model is used for inference purposes on the NW and UE sides, the base station and the terminal can obtain the same inference results, which can reduce the overhead in the configuration signaling between the base station and the terminal for a specific operation. Here, the same AI / ML model may refer to a model related to the same functionality. For example, a model related to the same functionality may refer to an AI / ML model related to a specific identical use-case (e.g., beam prediction, CSI prediction, etc.) or identical sub-use-case (e.g., within beam prediction, hierarchies such as spatial domain DL Tx beam,prediction, temporal DL Tx beam prediction, etc.). For example, a model associated with the same functionality may mean an AI / ML model associated with a CSI-ReportConfig having a specific identical reportQuantity (e.g., 'p-cri-r19', 'p-cri-RSRP-r19', 'p-ssb-index-r19' or 'p-ssb-index-RSRP-r19').For example, if a base station and a terminal perform spatial domain Tx beam prediction and / or temporal Tx beam prediction using the same AI / ML model (e.g., through model transfer), the base station and the terminal can obtain the same prediction result for the optimal Tx beam in the near future. To this end, an operation can be set / defined in which the base station and the terminal promise to apply the Tx beam to be applied in the near future based on the predicted result through some kind of setting / definition.

[0273] Based on this background, this specification proposes a beam management method based on AI / ML of a base station and a terminal, and proposes a downlink / uplink number / transmission operation of the subsequent terminal.

[0274] In this specification, ' / ' can be interpreted as 'and', 'or', or 'and / or' depending on the context.

[0275] Below, beam can mean DL, UL, or / and joint TCI state.

[0276] Proposal 1

[0277] When the base station and the terminal perform inference for DL ​​Tx beam prediction using an AI / ML model, the base station and the terminal can perform downlink transmission / reception (without configuring / instructing the downlink transmission / reception beam) by assuming / configuring / defining at least one of the predicted top-K beams as the serving DL Tx beam for a specific period(s) / time window of the present / future time.

[0278] For example, the AI / ML model may be based on a model trained identically, such as through model transfer. For example, the AI / ML model may be based on a model trained on a dataset. For example, the AI / ML model may be based on a model related to the same functionality. For example, the AI / ML model may be based on a model with (similar) accuracy.

[0279] For example, the specific cycle may be a cycle of inference performed by the base station and the terminal. For example, the specific cycle may be a cycle in which the inference result is assumed / configured / defined and applied as a serving DL Tx beam. As a specific example, the terminal / base station may determine / change / update the serving DL Tx beam assumption / configuration / definition for each cycle.

[0280] For example, when the above operation having a period of T is performed by the base station and the terminal, the base station and the terminal can perform AI / ML model inference for beam prediction before time T for the serving DL Tx beam to be assumed / set during the period T to 2T. The top-1 beam predicted simultaneously (identically) by the base station and the terminal can be assumed / set / defined as the serving DL Tx beam during the period T to 2T, and transmission / reception of DL channel / RS can be performed. The transmission / reception operation in the above example may be an operation of assuming / setting / defining the serving DL Tx beam for the downlink signal as the predicted beam when there is downlink signal transmission during the period T to 2T. Downlink transmission may not exist during the period T to 2T.

[0281] Before the 2T to 3T section, the base station and the terminal can perform inference before the 2T point and repeat the same operation as described above.

[0282] For the operation of the above proposal 1, the configuration of the Set B beam set can be performed at the terminal for the prediction of the Set A beam set as discussed in Rel-19 AI / ML WI. The terminal can perform AI / ML model inference based on the measurement result for the Set B beam. The terminal can report the measurement result for the Set B beam to the base station so that the base station can also perform AI / ML model inference by reporting the inference input data. In the operation of the proposal 1, the configuration / instruction of the base station for the DL Tx beam may not exist. The terminal may have to report the measurement result for the Set B beam set to the base station at every cycle of the proposal 1. Therefore, the time cycle for applying the predicted beam and the cycle for reporting the Set B beam measurement result of the terminal may be the same.

[0283] In one embodiment, terminal operations related to assumption / setting / definition of the serving DL Tx beam of the above proposal 1 may be as follows.

[0284] Based on periodic beam reporting (e.g., L1-RSRP / SINR of CSI-RS resources, L1-RSRP / SINR of SSB) of the terminal, the terminal can periodically change assumptions (e.g., QCL assumption, TCI state) for the base station DL Tx beam (without Tx beam-related instructions from the base station). The base station DL Tx beam may be a predicted beam (e.g., predicted RS resource index, predicted SSBRI, predicted CRI). The base station DL Tx beam may be determined based on an inference result of AI / ML models of the base station and the terminal. For example, the change cycle of the base station DL Tx beam may be a cycle of beam reporting. For example, the change cycle of the base station DL Tx beam may be based on a separate base station setting / instruction.

[0285] In one embodiment, the base station operation related to the assumption / setting / definition of the serving DL Tx beam of the above proposal 1 may be as follows.

[0286] The base station can periodically change the spatial Tx filter for the DL Tx beam (without providing a Tx beam-related instruction to the terminal) based on the terminal's periodic beam report (e.g., L1-RSRP / SINR of CSI-RS resources, L1-RSRP / SINR of SSB). The base station DL Tx beam may be a predicted beam (e.g., predicted RS resource index, predicted SSBRI, predicted CRI). The base station DL Tx beam may be determined based on the inference result of the AI / ML model of the base station and the terminal. For example, the change period of the base station DL Tx beam may be a period of beam report. For example, the change period of the base station DL Tx beam may be based on a separate base station setting / instruction.

[0287] Operations based on the embodiments described above may include not only assumption / configuration / definition of a serving DL Tx beam, but also i) assumption / configuration / definition of a serving DL Tx-Rx beam pair using an inference result (for a predicted beam pair) for a beam pair of a DL Tx beam and an Rx beam and / or ii) assumption / configuration / definition of an Rx beam using an inference result (for a predicted Rx beam) for a DL Rx beam.

[0288] Additionally, the operation based on the above-described embodiments may also include assumption / setting / definition of the terminal UL Tx beam using the inference result (for the predicted Tx beam) for the terminal UL Tx beam.

[0289] In addition, when performing the operation of the above proposal 1 for the serving DL Tx beam, the serving DL Tx-Rx beam pair and / or the Rx beam (if beam correspondence is established / assumed), the operation for beam direction / change can also be applied to the UL Tx beam of the terminal (using the terminal DL Rx beam).

[0290] For convenience of explanation, the operation of the above proposal 1 is referred to as 'indication-less beam management' below.

[0291] Problem 1: Even if the base station receives the best 4 beams in the NR legacy beam reporting from the terminal, it does not always utilize the best beam as the DL beam. Instead, it configures / instructs the terminal to use an appropriate DL serving beam by considering the base station scheduler and DL multi-UE interference. As in the above-described situation, even if indication-less beam management is performed, there may be cases where a beam other than the predicted top-K beam must be configured / assumed as the DL serving beam. Embodiments for solving this problem are described in detail below.

[0292] Example 1 of Proposal 1)

[0293] The base station may determine that assuming / configuring / defining at least one of the predicted top-K beams as a DL Tx beam for a specific period / time window between base station terminals during an operation such as the above proposal 1 is not beneficial to system / link-level performance. The base station may use DCI, etc. (similar to the indicated TCI update in the NR unified TCI framework) to instruct / update the terminals on the DL serving beam to be applied for the specific period / time window.

[0294] In this case, the terminal can suspend the indication-less beam management operation of the above proposal 1 during the period / time window of the specific point in time and perform downlink reception using the DL Tx beam information instructed / updated from the base station. For example, the terminal can suspend or continue the indication-less beam management operation (by base station configuration / instruction) after the period / time window of the specific point in time.

[0295] The advantages of the above embodiment 1 are as follows.

[0296] Since the entity indicating the DL TCI state in the current NR is the base station, no separate fallback behavior is required even when the indicated-less beam management is in operation. Specifically, depending on whether the DL TCI state to be utilized for a specific period / time window is indicated by the DCI as the base station beam indication, it can be determined whether the indicated DL TCI state will be indicated as the DL serving beam or whether the beam prediction result will be set / assumed / defined as the DL serving beam. The operation according to Embodiment 1 can be performed by the base station implementation / NW implementation. In other words, the operation related to beam indication / decision / application suitable for the network situation can be performed while minimizing the implementation complexity.

[0297] Example 2 of Proposal 1)

[0298] An operation may be performed to confirm that the inference results of the base station and the terminal are performed identically at every cycle corresponding to N times the cycle of the above proposal 1 (where N is a natural number). For example, the terminal may report the terminal-side AI / ML model inference result to the base station at every N*T cycle. The terminal report on the inference result may be included in the measurement result report for the Set B beam set described in the above proposal 1. For example, if the terminal was reporting the measurement result for the Set B beam set at every T cycle, the terminal may report the predicted top-K Set A beam (in addition to the Set B beam measurement result) at every N*T sparse cycle.

[0299] For example, when reporting measurement results, a terminal can use a 1-bit field to report information indicating whether the predicted beams of the base station and the terminal match or not.

[0300] During the indication-less beam management operation, the terminal may assume that the base station's actual DL serving beam differs from the terminal's inference result in a specific time window. For example, the assumption (e.g., QCL assumption, TCI state) regarding the base station DL Tx beam that the terminal assumes will change may differ from the actually transmitted base station DL Tx beam. For example, the quality of the actually transmitted base station DL Tx beam may be below a certain threshold.

[0301] As described above, if the actual DL serving beam of the base station is different from the inference result of the terminal, the terminal may report the 1-bit field together when reporting the measurement result for the Set B beam set after that point in time.

[0302] As an example, the 1-bit field may always be included in the Set B beam measurement report.

[0303] For example, reporting of the 1-bit field may be performed in an event-triggered manner. Specifically, if an event occurs, the 1-bit field may be included in the Set B beam measurement report. Specifically, if an event occurs, the terminal may report the 1-bit field to the base station based on a separate uplink resource.

[0304] The advantages of the above embodiment 2 are as follows.

[0305] The base station can confirm that the inference performance of the AI / ML model on the base station side is the same as the AI / ML model inference performance on the terminal side, and can continue the assumption / configuration / definition operation for the serving DL Tx beam of Proposal 1. If the AI / ML model inference result on the base station side is different from the AI / ML model inference result on the terminal side, the base station can turn off the operation of Proposal 1 (using RRC / MAC CE signaling) or perform DL Tx beam indication for a future specific period / time window using dynamic signaling such as DCI. In other words, since an indication-less beam management operation is performed as long as the inference results on both the terminal and the base station remain the same, signaling overhead can be reduced while maintaining the accuracy of beam management.

[0306] Example 3 of Proposal 1)

[0307] Considering the introduction of online learning in 6G, the following actions can be performed. Information based on the above-described embodiments can be used as input data for training / inference of AI / ML models at the base station or / and terminal. This is described in detail below.

[0308] For example, a base station-side DL Tx beam indication may be performed to modify operations related to assumptions / configurations / definitions for the serving DL Tx beam of Proposal 1 as mentioned in Embodiment 1. The input data may include i) whether the DL Tx beam indication of Embodiment 1 is present or / and ii) the result of the indicated DL Tx beam (e.g., which DL RS is indicated for the DL Tx beam or which DL TCI state is indicated, etc.).

[0309] In relation to the above indication-less beam management operation, this embodiment 3 can be utilized as follows.

[0310] It can be assumed that the explicit DL Tx beam indication (for DL ​​serving beam modification) of the base station of Example 1 is not being performed. This may mean that the operations related to the indication-less beam management of Proposal 1 of the base station and the terminal are being performed correctly. For example, the fact that there was no explicit DL Tx beam indication for a certain period of time can be utilized as an input of the AI / ML model on the base station / terminal side. As a specific example, the AI / ML model on the base station / terminal side can be trained based on the input. The AI / ML model on the base station / terminal side can be trained so that the current state is maintained.

[0311] On the other hand, it can be assumed that the base station continues to perform explicit DL Tx beam indication to modify the DL serving beam as in Example 1. Whether explicit DL Tx beam indication is performed and the indicated DL Tx beam can be used as inputs to the AI / ML model on the base station / terminal side. For example, the AI / ML model on the base station / terminal side can be trained / adjusted / updated based on the input so that the operation related to the indication-less beam management of Proposal 1 is modified in the correct direction. For example, the inference result for the predicted beam of the AI / ML model on the base station / terminal side can be fine-tuned based on the input.

[0312] The above embodiments may be operated by a combination of one or more embodiments.

[0313] Below, the operation of the Receiver (Entity A) is specifically described with reference to Fig. 8.

[0314] Figure 8 is a flowchart for explaining operations by a terminal according to an embodiment of the present specification.

[0315] Referring to FIG. 8, an example of terminal operation based on at least one of the embodiments described above (e.g., at least one of the embodiments of Proposal 1) is as follows.

[0316] In S810, the terminal receives settings related to beam measurement / reporting from the base station. The settings may include measurement / reporting settings related to Set B beams and / or Set A beams to be used as input data for AI / ML model inference of the base station / terminal.

[0317] In step S820, the terminal transmits a beam report to the base station. The transmission of the beam report may have a periodic / semi-persistent time domain behavior. The beam report may include measurement results and / or inference results.

[0318] In S830, the terminal may change the assumptions (e.g., QCL assumption, TCI state) for the base station DL Tx beam based on the beam report. The change in the assumption for the beam may be performed using AI / ML beam prediction related to the beam report. The beam report and the change in the assumption for the beam may be periodic.

[0319] Below, the operation of the Transmitter (Entity B) is specifically described with reference to Fig. 9.

[0320] FIG. 9 is a flowchart for explaining operations by a base station according to an embodiment of the present specification.

[0321] Referring to FIG. 9, an example of base station operation based on at least one of the embodiments described above (e.g., at least one of the embodiments of Proposal 1) is as follows.

[0322] In S910, the base station transmits settings related to beam measurement / reporting to the terminal. The settings may include measurement / reporting settings related to Set B beam or / and Set A beam, which are to be used as input data for AI / ML model inference of the base station / terminal.

[0323] In step S920, the base station receives a beam report from the terminal. The transmission of the beam report by the terminal may have a periodic / semi-persistent time domain behavior. The beam report may include measurement results and / or inference results.

[0324] In S930, the base station can change the Tx spatial filter for the base station DL Tx beam based on the beam report. The change in the beam can be performed using AI / ML beam prediction related to the beam report. The beam report and the change in the beam can be periodic.

[0325] The terminal / base station operations described in FIGS. 8 and 9 are merely examples, and each operation (or step) is not necessarily essential, and the beam management operations of the terminal / base station according to the above-described embodiments may be omitted or added depending on the terminal / base station implementation method.

[0326] Below, the signaling procedure of Receiver & Transmitter is described with reference to Fig. 10.

[0327] FIG. 10 is a flowchart illustrating a signaling procedure according to an embodiment of the present specification.

[0328] Specifically, the operation of the above-described base station / terminal is flow charted as in Fig. 10.

[0329] S1010 corresponds to S810 of FIG. 8 and S910 of FIG. 9, and S1020 corresponds to S820 of FIG. 8 and S920 of FIG. 9. After S1010 and S1020, the base station / terminal can perform beam direction / change.

[0330] In S1030, the terminal can receive a beam indication from the base station (NW). As described above, the explicit beam indication in S1030 may be omitted.

[0331] The effects of the embodiments described below (embodiments of Proposal 1) are explained.

[0332] The serving DL Tx beam for a specific period / time window can be indicated / changed only with the periodic beam report (used as input data for AI / ML inference) of the terminal, without a separate Tx beam-related setting / instruction by the base station. Therefore, signaling for beam indication can be saved. In addition, the base station can switch between AI / ML-based beam indication / change operation and non-AI / ML-based beam indication / change operation by performing beam indication for the subsequent period / time window without the need to define a separate fallback operation for the AI / ML-based beam management operation.

[0333] In a first aspect of the present specification, a method is provided, which is performed by a terminal (or first node) in a wireless communication system, comprising a step of performing an operation described in the present specification.

[0334] In a second aspect of the present disclosure, a terminal (or first node) of a wireless communication system is provided, the terminal (or first node) comprising: at least one transceiver; at least one processor configured to perform the operations described herein; and at least one computer memory operably connected to the at least one processor.

[0335] In a third aspect of the present disclosure, a device is provided comprising at least one processor; and at least one computer memory operably connected to the at least one processor and storing instructions that, when executed by the at least one processor, cause a terminal (or first node) to perform operations described herein.

[0336] In a fourth aspect of the present specification, a non-transitory computer-readable storage medium is provided that stores instructions that, when executed by at least one processor, cause a terminal (or first node) to perform an operation described herein.

[0337] In a fifth aspect of the present specification, a method is provided, which is performed by a base station (or a second node) in a wireless communication system, comprising the steps of performing the operations described herein.

[0338] In a sixth aspect of the present disclosure, a base station (or second node) of a wireless communication system is provided, the base station (or second node) comprising: at least one transceiver; at least one processor configured to perform the operations described herein; and at least one computer memory operably connected to the at least one processor.

[0339] In a seventh aspect of the present disclosure, a device is provided comprising at least one processor; and at least one computer memory operably connected to the at least one processor and storing instructions that, when executed by the at least one processor, cause a base station (or a second node) to perform operations described herein.

[0340] In an eighth aspect of the present disclosure, a non-transitory computer-readable storage medium is provided that stores instructions that, when executed by at least one processor, cause a base station (or a second node) to perform operations described herein.

[0341] Here, the operations described in this specification may be described separately for convenience, but unless specifically stated otherwise, the operations may be combined with each other.

[0342] In terms of implementation, the operations of the base station / terminal according to the embodiments described above (e.g., operations based on Proposal 1) can be processed by the device of FIG. 13 (e.g., the processor (110, 210) of FIG. 13).

[0343] In addition, the operations of the base station / terminal according to the above-described embodiment (e.g., operations based on Proposal 1) may be stored in a memory (e.g., 140, 240 of FIG. 13) in the form of commands / programs (e.g., instructions, executable codes) for driving at least one processor (e.g., 110, 210 of FIG. 13).

[0344] The embodiments described below are specifically described with reference to FIGS. 11 and 12 in terms of the operation of the terminal and base station. The methods described below are distinguished for convenience of explanation, and it is understood that some components of one method may be substituted for or combined with some components of another method.

[0345] FIG. 11 is a flowchart illustrating a method according to one embodiment of the present specification.

[0346] Referring to FIG. 11, a method according to one embodiment of the present specification includes a step of determining one or more RS resource indices (S1110) and a step of determining information related to QCL for reception and / or transmission of a signal based on at least one of the one or more RS resource indices (S1120).

[0347] In S1110, the terminal may determine one or more RS (Reference Signal) resource indices. For example, the one or more RS resource indices may be based on predicted RS resource index(es) (e.g., predicted top-K beam of Example 1).

[0348] For example, the predicted RS resource index(es) may include i) at least one predicted CRI (e.g., Predicted CSI-RS Resource Indicator, P-CRI) and / or ii) at least one predicted SSBRI (e.g., Predicted SS / PBCH Block Resource Indicator, P-SSBRI).

[0349] For example, the predicted RS resource index(es) may be determined from among RS resource indices within a second resource configuration (e.g., a configuration for a Set A beam set, Set A, and / or a CSI-ResourceConfig associated with prediction) based on measurements based on a first resource configuration (e.g., a configuration for a Set B beam set, Set B, and / or a CSI-ResourceConfig associated with measurement). The following describes in detail the operation for determining the predicted RS resource index(es).

[0350] The terminal performs L1-RSRP measurements on CSI-RS resources or SS / PBCH block resources associated with the first resource setting.

[0351] The terminal performs prediction on CSI-RS resources or SS / PBCH block resources associated with the second resource configuration based on the L1-RSRP measurements. For example, predicted L1 RSRPs (e.g., Predicted L1-RSRPs, P-L1-RSRPs) for CSI-RS resources or SS / PBCH block resources associated with the second resource configuration may be determined.

[0352] The terminal can determine reported P-CRI, P-SSBRI, and / or P-L1-RSRP based on the L1-RSRP measurements and the prediction.

[0353] For example, the reported P-CRI / P-SSBRI(s) may be determined based on the ranking of predicted L1 RSRPs (e.g., Predicted L1-RSRPs, P-L1-RSRPs) for CSI-RS resources or SS / PBCH block resources associated with the second resource configuration. In other words, the reported P-CRI / P-SSBRI(s) may be determined based on the order of the predicted L1 RSRPs.

[0354] The predicted RS resource index(es) (e.g., predicted top-K beam) may include P-CRI(s) or P-SSBRI(s) determined based on the order or ranking of the predicted L1 RSRPs.

[0355] In S1120, the terminal determines information related to QCL (Quasi Co-Location) for reception and / or transmission of a signal based on at least one of the one or more RS resource indices.

[0356] For example, the signal may include i) a downlink signal and / or ii) an uplink signal.

[0357] For example, the information related to the QCL may include i) a QCL assumption related to downlink, ii) a TCI state related to downlink and / or uplink (e.g., separate TCI state (DL / UL TCI state), joint TCI state) and / or iii) a spatial filter. The spatial filter may include at least one of i) a spatial domain filter related to DL and / or UL, ii) a spatial domain transmit filter related to DL and / or UL, iii) a spatial domain receive filter related to DL and / or UL, iv) a UL Tx spatial filter, v) a UL Rx spatial filter, vi) a DL Tx spatial filter and / or vii) a DL Rx spatial filter.

[0358] The above determination of the above information related to the above QCL may be based on at least one of the embodiments of Proposal 1. The embodiments of Proposal 1 are described in detail below.

[0359] In one embodiment, the one or more RS resource indices may be based on an inference result related to a period.

[0360] In one embodiment, the cycle may be associated with a model based on the same functionality. The model may include i) a UE-side model and ii) a network-side model.

[0361] In one embodiment, the period may be related to i) the time point at which the model's inference is performed and / or ii) the application period of the inference result.

[0362] For example, the inference operation of the terminal-side model and the inference operation of the network-side model may be performed at each point in time related to the cycle. As a specific example, assuming the cycle is T, in order to determine information related to QCL (Quasi Co-Location) for the period T to 2T, the terminal / base station may perform inference for prediction before point T.

[0363] For example, the first inference result of the terminal-side model and the second inference result of the network-side model may be applied based on each time section related to the cycle. As a specific example, assuming the cycle is T, the information related to the QCL determined before time T may be applied to the section T to 2T. As a specific example, the information related to the QCL determined before time 2T may be applied to the section 2T to 3T. More specifically, the information related to the QCL may be applied to downlink / uplink transmission / reception within the section T to 2T / section 2T to 3T.

[0364] In one embodiment, based on receiving a beam indication related to a specific time interval (e.g., 2T to 3T) among a plurality of time intervals (e.g., T to 2T, 2T to 3T, 3T to 4T, etc.) related to the period, information related to the QCL (Quasi Co-Location) for the specific time interval can be determined based on the beam indication. This embodiment can be based on Embodiment 1) of Proposal 1. Assuming that the period is T and a specific time interval is 2T to 3T among a plurality of time intervals (e.g., T to 2T, 2T to 3T, 3T to 4T, etc.), information applied to each interval will be described.

[0365] Information related to the QCL to be applied for T~2T (3T~4T) can be determined based on the inference results before time T (time 3T).

[0366] Information related to the QCL to be applied for 2T~3T can be determined based on the above beam instructions (e.g. instructions based on the TCI field of DCI).

[0367] The above example assumes that the indication-less beam management operation of Proposal 1 is suspended only for the specific time interval. Unlike the above example, the indication-less beam management operation of Proposal 1 may be suspended from the specific time interval. Specifically, the existing beam indication operation may be performed from the specific time interval (fallback to the existing beam indication operation is performed).

[0368] In one embodiment, the method may further include a step of transmitting a report including an inference result. Specifically, the terminal may transmit a report including a first inference result of the terminal-side model to the base station. The report may be transmitted every second period. The second period may be N times the period, and N may be a natural number. If the period is T, the second period may be N*T. The present embodiment may be based on Embodiment 2) of Proposal 1. For example, the report may be Channel State Information (CSI) or a CSI report. For example, the report may be based on a report (e.g., an inference result report) for the above-described BM-Case 1 and / or BM-Case 2 (see Tables 2 to 7).

[0369] For example, the report may include information based on report quantities related to the prediction (e.g., P-CRI(s), P-SSBRI(s) and / or P-L1-RSRP(s)).

[0370] In one embodiment, the input data of the terminal-side model and / or the network-side model may include i) whether a beam indication related to a specific time interval among a plurality of time intervals related to the period has been received and / or ii) at least one RS resource index (or a TCI state based on the beam indication) determined based on the beam indication. The present embodiment may be based on embodiment 3) of proposal 1.

[0371] In one embodiment, the method may further include a step of transmitting a report including information related to RS resource indices configured for measurement. Specifically, the terminal may transmit a report including information related to RS resource indices configured for measurement to the base station. At this time, the report may be transmitted based on the period. For example, the report may be channel state information (CSI) or a CSI report.

[0372] For example, the report may be based on a report (e.g., a measurement result report) for the BM-Case 1 and / or BM-Case 2 described above (see Tables 2 to 7). For example, the report may include measurement results for the Set B beam set of Proposal 1. For example, the report may include measurement-related information (e.g., CRI(s), SSBRI(s) and / or L1-RSRP(s)) based on the first resource configuration described above (e.g., CSI-ResourceConfig related to Set B and / or measurement).

[0373] For example, the report may include a field. Based on the field, it may be indicated whether at least one of the one or more RS resource indices (e.g., predicted beam) matches an RS resource index determined based on the reception of the signal (e.g., actual DL serving beam). The field may be based on the 1-bit field of Proposal 1.

[0374] In one embodiment, the reception and / or the transmission of the signal may be scheduled without a beam indication. As an example, the method may further include the step of receiving control information. Specifically, the terminal may receive control information (e.g., DCI) for scheduling the reception and / or the transmission of the signal from the base station. Information related to the beam indication (e.g., Transmission Configuration Indication, TCI, field) may be absent in the control information.

[0375] In one embodiment, the model based on the same functionality may be associated with a report configuration having a report quantity associated with a prediction.

[0376] The operations based on S1110 to S1120 described above can be implemented by the device of FIG. 13. For example, referring to FIG. 13, the terminal (200) can control one or more transceivers (230) and / or one or more memories (240) to perform the operations based on S1110 to S1120.

[0377] The embodiments described below are specifically described in terms of base station operation.

[0378] S1210 to S1220 described below correspond to S1110 to S1120 described in FIG. 11. Considering the above correspondence, redundant descriptions are omitted. That is, the specific description of the base station operation described below may be replaced with the description / example of FIG. 11 corresponding to the corresponding operation.

[0379] FIG. 12 is a flowchart illustrating a method according to another embodiment of the present specification.

[0380] Referring to FIG. 12, a method according to another embodiment of the present specification includes a step of determining one or more RS resource indices (S1210) and a step of determining information related to QCL for transmission and / or reception of a signal based on at least one of the one or more RS resource indices (S1220).

[0381] In S1210, the base station may determine one or more RS (Reference Signal) resource indices. For example, the one or more RS resource indices may be based on predicted RS resource index(es) (e.g., predicted top-K beam of Example 1).

[0382] In S1220, the base station determines information related to Quasi Co-Location (QCL) for transmission and / or reception of a signal based on at least one of the one or more RS resource indices. For example, the signal may include i) a downlink signal and / or ii) an uplink signal.

[0383] In one embodiment, the one or more RS resource indices may be based on an inference result related to a period.

[0384] In one embodiment, the method may further include a step of receiving a report including information related to RS resource indices set for measurement. Specifically, the base station may receive a report including information related to RS resource indices set for measurement from the terminal. At this time, the report may be received based on the period. The information related to the RS resource indices set for measurement may be used as an input of a network-side model for beam prediction. Specifically, the inference result may be determined based on the information related to the RS resource indices set for the measurement.

[0385] In one embodiment, the method may further include receiving a report including the inference result. Specifically, the base station may receive a report from the terminal including the first inference result of the terminal-side model.

[0386] In one embodiment, the method may further include a step of transmitting control information. Specifically, the base station may transmit control information (e.g., DCI) for scheduling the transmission and / or reception of the signal to the terminal.

[0387] The operations based on S1210 to S1220 described above can be implemented by the device of FIG. 13. For example, referring to FIG. 13, the base station (100) can control one or more transceivers (130) and / or one or more memories (140) to perform operations based on S1210 to S1220.

[0388] Hereinafter, a device to which an embodiment of the present specification can be applied (a device that implements a method / operation according to an embodiment of the present specification) is described with reference to FIG. 13.

[0389] FIG. 13 is a drawing showing the configuration of the first device and the second device according to an embodiment of the present specification.

[0390] The first device (100) may include a processor (110), an antenna unit (120), a transceiver (130), and a memory (140).

[0391] The processor (110) performs baseband-related signal processing and may include a higher layer processing unit (111) and a physical layer processing unit (115). The higher layer processing unit (111) may process operations of a MAC layer, an RRC layer, or higher layers. The physical layer processing unit (115) may process operations of a PHY layer. For example, when the first device (100) is a base station device in base station-terminal communication, the physical layer processing unit (115) may perform uplink reception signal processing, downlink transmission signal processing, etc. For example, when the first device (100) is a first terminal device in terminal-to-terminal communication, the physical layer processing unit (115) may perform downlink reception signal processing, uplink transmission signal processing, sidelink transmission signal processing, etc. In addition to performing baseband-related signal processing, the processor (110) may also control the overall operation of the first device (100).

[0392] The antenna unit (120) may include one or more physical antennas, and when it includes multiple antennas, it may support MIMO transmission and reception. The transceiver (130) may include an RF (Radio Frequency) transmitter and an RF receiver. The memory (140) may store information processed by the processor (110), and software, an operating system, applications, etc. related to the operation of the first device (100), and may also include components such as a buffer.

[0393] The processor (110) of the first device (100) may be configured to implement the operation of the base station in the base station-to-terminal communication (or the operation of the first terminal device in the terminal-to-terminal communication) in the embodiments described in the present disclosure.

[0394] The second device (200) may include a processor (210), an antenna unit (220), a transceiver (230), and a memory (240).

[0395] The processor (210) performs baseband-related signal processing and may include a higher layer processing unit (211) and a physical layer processing unit (215). The higher layer processing unit (211) may process operations of a MAC layer, an RRC layer, or higher layers. The physical layer processing unit (215) may process operations of a PHY layer. For example, when the second device (200) is a terminal device in base station-terminal communication, the physical layer processing unit (215) may perform downlink reception signal processing, uplink transmission signal processing, etc. For example, when the second device (200) is a second terminal device in terminal-to-terminal communication, the physical layer processing unit (215) may perform downlink reception signal processing, uplink transmission signal processing, sidelink reception signal processing, etc. In addition to performing baseband-related signal processing, the processor (210) may also control the overall operation of the second device (210).

[0396] The antenna unit (220) may include one or more physical antennas, and when it includes multiple antennas, it may support MIMO transmission and reception. The transceiver (230) may include an RF transmitter and an RF receiver. The memory (240) may store information processed by the processor (210), software, an operating system, applications, etc. related to the operation of the second device (200), and may also include components such as a buffer.

[0397] The processor (210) of the second device (200) may be configured to implement operations of the terminal in base station-to-terminal communication (or operations of the second terminal device in terminal-to-terminal communication) in the embodiments described in the present disclosure.

[0398] In the operation of the first device (100) and the second device (200), the same explanations given for the base station and the terminal (or the first terminal and the second terminal in the terminal-to-terminal communication) in the examples of the present disclosure may be applied, and redundant explanations are omitted.

[0399] Here, the wireless communication technology implemented in the device of the present disclosure may include LTE, NR, and 6G, as well as Narrowband Internet of Things (NB-IoT) for low-power communication. For example, NB-IoT technology may be an example of LPWAN (Low Power Wide Area Network) technology and may be implemented in standards such as LTE Cat NB1 and / or LTE Cat NB2, and is not limited to the above-described names.

[0400] Additionally or alternatively, the wireless communication technology implemented in the device of the present disclosure may perform communication based on LTE-M technology. For example, LTE-M technology may be an example of LPWAN technology and may be called by various names such as eMTC (enhanced Machine Type Communication). For example, LTE-M technology may be implemented by at least one of various standards such as 1) LTE CAT 0, 2) LTE Cat M1, 3) LTE Cat M2, 4) LTE non-BL (non-Bandwidth Limited), 5) LTE-MTC, 6) LTE Machine Type Communication, and / or 7) LTE M, and is not limited to the above-described names.

[0401] Additionally or alternatively, the wireless communication technology implemented in the device of the present disclosure may include at least one of ZigBee, Bluetooth, and Low Power Wide Area Network (LPWAN), which take low-power communication into account, and is not limited to the above-described names. For example, ZigBee technology can create personal area networks (PANs) related to small / low-power digital communication based on various standards such as IEEE 802.15.4, and may be called by various names.

Claims

In a method performed by a terminal, A step of determining one or more RS (Reference Signal) resource indices; and A step of determining information related to QCL (Quasi Co-Location) for receiving and / or transmitting a signal based on at least one of the above one or more RS resource indices; comprising: A method characterized in that the one or more RS resource indices are based on an inference result related to a cycle. In the first paragraph, The above cycle relates to models based on the same functionality, A method characterized in that the above model includes i) a terminal side model (UE side model) and ii) a network side model (NW side model). In the second paragraph, A method characterized in that the inference operation of the terminal-side model and the inference operation of the network-side model are performed at each point in time related to the cycle. In the second paragraph, A method characterized in that the first inference result of the terminal-side model and the second inference result of the network-side model are applied based on each time section related to the cycle. In the first paragraph, Based on receiving a beam indication related to a specific time interval among multiple time intervals related to the above cycle, A method characterized in that information related to the QCL (Quasi Co-Location) for the specific time interval is determined based on the beam indication. In the second paragraph, Further comprising a step of transmitting a report including the first inference result of the terminal side model; A method characterized in that the above report is transmitted every second period, the second period being N times the above period, and N being a natural number. In the second paragraph, A method characterized in that the input data of the terminal-side model and / or the network-side model includes i) whether a beam indication related to a specific time interval among a plurality of time intervals related to the period is received and / or ii) at least one RS resource index determined based on the beam indication. In the first paragraph, A method characterized in that a report is transmitted including information related to RS resource indices set for measurement based on the above cycle. In paragraph 8, The above report contains fields: A method characterized in that, based on the above field, it is indicated whether at least one of the one or more RS resource indices matches the RS resource index determined based on the reception of the signal. In the first paragraph, A method characterized in that said reception and / or said transmission of said signal is scheduled without beam indication. In Article 10, further comprising a step of receiving control information for scheduling the reception and / or transmission of the signal; A method characterized in that the control information contains no information related to the beam indication. In the second paragraph, A method characterized in that the model based on the same functionality is related to a report configuration having a report quantity related to a prediction. In the terminal, One or more transmitters and receivers; one or more processors; and One or more memories connected to said one or more processors and storing instructions, A terminal characterized in that the instructions, based on being executed by the one or more processors, cause the terminal to perform all steps of the method according to any one of claims 1 to 12. In a device comprising one or more memories and one or more processors connected to the one or more memories, A device characterized in that said one or more memories store instructions that cause said device to perform all steps of a method according to any one of claims 1 to 12, based on being executed by said one or more processors. In a non-transitory computer-readable storage medium storing instructions, A non-transitory computer-readable storage medium characterized in that the instructions executable by one or more processors cause a terminal to perform all steps of a method according to any one of claims 1 to 12. In a method performed by a base station, A step of determining one or more RS (Reference Signal) resource indices; and A step of determining information related to QCL (Quasi Co-Location) for transmission and / or reception of a signal based on at least one of the above one or more RS resource indices; comprising: A method characterized in that the one or more RS resource indices are based on an inference result related to a cycle. At the base station, One or more transmitters and receivers; one or more processors; and One or more memories connected to said one or more processors and storing instructions, A base station characterized in that the instructions, based on being executed by the one or more processors, cause the base station to perform all steps of the method according to claim 16.

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