Method for SRS transmission and reception and device therefor

Abstract

A method according to an embodiment of the present specification comprises the steps of: receiving configuration information including information about a sounding reference signal (SRS) resource; and transmitting an SRS on the basis of the SRS resource. The SRS is transmitted on the basis of a subset of a plurality of ports configured in the SRS resource. The configuration information includes information indicating a group related to the subset among a plurality of groups configured on the basis of the coherency between the plurality of ports.

Description

Method and apparatus for SRS transmission and reception

[0001] This specification relates to a method and apparatus for SRS transmission and reception.

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

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

[0004] Previously, base stations had no way to estimate the channel for ports where the terminal did not transmit, and in a Massive MIMO environment, as the number of antenna ports increases and bandwidth expands, RS overhead can become excessive.

[0005] To address the problem of excessive RS overhead mentioned above, a method of estimating channels for other ports from RS based on some ports (e.g., partial SRS transmission) using an AI / ML model may be considered. Meanwhile, according to existing methods, except for the method of transmitting three ports excluding the last port (e.g., port 1003 among ports 1000~1003) out of the four ports configured in the SRS resource (3-port SRS), there is no support for the operation of configuring / instructing some port(s) among the ports configured in the SRS resource (specified / defined to facilitate channel estimation for the remaining ports where transmission is not performed) to be used. Furthermore, if the index(s) of some port(s) among all ports of the SRS (or SRS resource) are explicitly configured / instructed, signaling overhead may become excessive as the number of supported ports gradually increases.

[0006] The purpose of this specification is to propose a method for solving the aforementioned problems.

[0007] The technical problems to be solved in this specification are not limited to those mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art to which this specification belongs from the description below.

[0008] A method according to an embodiment of the present specification for solving the aforementioned problem comprises the steps of receiving configuration information including information regarding a Sounding Reference Signal (SRS) resource and transmitting an SRS based on said SRS resource. The SRS is transmitted based on a subset of a plurality of ports configured in said SRS resource. The configuration information is characterized by including information indicating a group associated with said subset among a plurality of groups configured based on the coherency between said ports. Since one of the port groups configured in advance based on coherency is indicated, channel estimation accuracy and stability can be secured while reducing the signaling overhead required for partial port indication.

[0009] According to an embodiment of the present specification, the RS overhead required for channel estimation for all ports based on SRS transmission can be reduced.

[0010] In addition, since some port(s) based on coherency are utilized, it is ensured that ports within a group share similar channel characteristics, which can improve the accuracy and stability of channel estimation. In other words, SRS transmission can guarantee the accuracy of channel estimation for unused ports.

[0011] The effects obtainable in this specification are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art to which this specification belongs from the description below.

[0012] Figure 1 is a diagram illustrating the overall functions from the perspective of an AI / ML model.

[0013] Figure 2 illustrates a general form of an AI / ML-related procedure performed between a network and a terminal.

[0014] Figure 3 illustrates an example of AI / ML-based beam management operation.

[0015] Figure 4 illustrates an example of an AI / ML-based CSI measurement / reporting operation.

[0016] Figure 5 illustrates an example of an AI / ML-based positioning operation.

[0017] FIG. 6 is a diagram showing a partial port SRS-based channel estimation according to an embodiment of the present specification.

[0018] FIG. 7 illustrates a port group according to an embodiment of the present specification.

[0019] FIG. 8 is a flowchart illustrating a method according to one embodiment of the present specification.

[0020] FIG. 9 is a flowchart illustrating a method according to another embodiment of the present specification.

[0021] FIG. 10 is a drawing showing the configuration of a first device and a second device according to an embodiment of the present specification.

[0022] In this specification, "A or B" may mean "only A," "only B," or "both A and B." Alternatively, in this specification, "A or B" may be interpreted as "A and / or B." For example, in this specification, "A, B or C" may mean "only A," "only B," "only C," or "any combination of A, B and C."

[0023] A slash ( / ) or a comma used in this specification may mean "and / or." For example, "A / B" may mean "A and / or B." Accordingly, "A / B" may mean "only A," "only B," or "both A and B." For example, "A, B, C" may mean "A, B or C."

[0024] In this specification, "at least one of A and B" may mean "only A," "only B," or "both A and B." Additionally, in this specification, the expressions "at least one of A or B" or "at least one of A and / or B" may be interpreted as synonymous with "at least one of A and B."

[0025] Additionally, in this specification, "at least one of A, B and C" may mean "only A," "only B," "only C," or "any combination of A, B and C." Also, "at least one of A, B or C" or "at least one of A, B and / or C" may mean "at least one of A, B and C."

[0026] Additionally, parentheses used in this specification may mean "for example." Specifically, when indicated as "control information (PDCCH)," "PDCCH" may be proposed as an example of "control information." In other words, "control information" in this specification is not limited to "PDCCH," and "PDCCH" may be proposed as an example of "control information." Furthermore, even when indicated as "control information (i.e., PDCCH)," "PDCCH" may be proposed as an example of "control information."

[0027] In the following explanation, 'when, if, in case of' can be replaced with 'based on'.

[0028] Technical features described individually within a single drawing in this specification may be implemented individually or simultaneously.

[0029] Hereinafter, preferred embodiments according to the present specification will be described in detail with reference to the accompanying drawings. The detailed description disclosed below, together with the accompanying drawings, is intended to describe exemplary embodiments of the present specification and is not intended to represent the only embodiment in which the present specification may be practiced. The following detailed description includes specific details to provide a complete understanding of the present specification.

[0030] In this specification, a terminal is a user-side device (user equipment, UE) or a consumer-side device, and may also be referred to as a first node that receives / transmits signals from / to a base station / second node / IAB node / Transmission-Reception Point (TRP). A terminal may correspond to a physical node or a logical node. A terminal may correspond to a user-side endpoint or an intermediate point between other endpoints. In communication between two points not limited to endpoints (including one-to-one / many-to-one / one-to-many / many-to-many communication), a terminal may correspond to a served node. A terminal may be a fixed-location node or a non-fixed-location (or mobile) node.

[0031] In this specification, a Base Station (BS) is a device on the network side and may also be referred to as a second node / IAB node / x-NodeB (x-NodeB, where x may be an abbreviation related to Radio Access Technology (RAT)) / Transmission-Reception Point (TRP). A Base Station may correspond to a physical node or a logical node. A Base Station may correspond to an endpoint on the network side or an intermediate point between other endpoints. In communication between two points not limited to endpoints (including one-to-one / many-to-one / one-to-many / many-to-many communication), a Base Station may correspond to a serving node. A Base Station may be a node with a fixed location or a node with an indefinite location.

[0032] In this specification, higher layer parameters may be set for the terminal, pre-set, or pre-defined. For example, a base station may transmit higher layer parameters to the terminal. For example, the terminal may transmit parameters such as capability to the base station as higher layer parameters. For example, higher layer parameters may be transmitted via RRC (radio resource control) signaling or MAC (medium access control) signaling.

[0033] In this specification, information / state / parameters being "configured" or "pre-configured" may be interpreted as the information / state / parameters being provided / pre-provided to the terminal through pre-defined signaling (e.g., SIB, MAC, RRC) from the base station. In this specification, information / state / parameters being "defined" or "pre-defined" may be interpreted as being known or stored in advance by the base station and the terminal without signaling between the base station and the terminal.

[0034] < AI / ML for Wireless Communication >

[0035] With the advancement of computing technology, artificial intelligence (AI) and machine learning (ML) are being adopted across various industries and technological fields. In the field of wireless communication, various discussions are underway regarding the application of AI models trained on ML; notably, the 3GPP standardization process refers to this as AI / ML. In this specification, we use the term "AI / ML" following the terminology currently in use during the 3GPP standardization discussions; however, "AI / ML" may be referred to by various other terms depending on the progress of standardization and implementation. For example, it may be referred to as a "transmission / reception mode" or "signal / channel / operation / transmission / reception configuration" set for AI / ML, but is not limited thereto. The meanings of the terms currently used in the 3GPP standardization process are briefly summarized as follows.

[0036] - AI / ML Model: Refers to a data-driven algorithm that applies AI / ML technology to generate a set of outputs containing predictive information and / or decision parameters based on a set of inputs.

[0037] - Data collection: This is the process of collecting data necessary for AI / ML model training, data analysis, and inference from network nodes, management entities, or terminals.

[0038] - AI / ML Training: An online or offline process of training an AI model by learning features and patterns that best represent data and acquire an AI / ML model trained for inference.

[0039] - Offline training: A process of training a model based on previously collected data sets, where the trained model is used or provided for future inference.

[0040] - Online Training: A method in which the model is trained in real-time upon the acquisition of new training sample data and used for inference.

[0041] - AI / ML Inference: This is the process of making predictions or deriving decisions based on collected data and AI models using trained AI models. Meanwhile, depending on whether the AI / ML model is configured on both the transmitting and receiving devices or on only one, it can be classified into (i) two-sided models and (ii) one-sided models. In the case of (i) two-sided models, cooperative inference is performed through paired AI / ML models. Cooperative inference refers to cooperation between the network and the UE, where one side performs part of the inference and the other performs the remainder. (ii) One-sided models are divided into UE-side models and network-side models. In the case of one-sided models, inference is performed entirely by the UE / network-side models.

[0042] 1. Life Cycle Management (LCM) for AI / ML models

[0043] For AI / ML models, LCM is a concept that encompasses all overall procedures for the model, such as data collection, model training, model deployment, model inference, model monitoring, and model updates.

[0044] LCMs for AI / ML models can be broadly classified into functionality-based LCMs and model-ID-based LCMs. In functionality-based LCMs, the network can direct activation, deactivation, fallback, or switching for specific functions; in this case, the target AI / ML model may not be identified by the network. In model-ID (identifier)-based LCMs, the network can direct activation, deactivation, selection, or switching for AI / ML models identified based on their AI / ML model IDs.

[0045] Figure 1 is a diagram illustrating the overall functions from the perspective of an AI / ML model.

[0046] 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).

[0047] The Data Collection function (10) is a function that provides input data to the Model Training function (20), Management function (30), and Inference function (40). The Data Collection function (10) performs data preparation and can provide input data processed through data preparation.

[0048] 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).

[0049] The Model Training function (20) is a function that performs AI / ML model training, validation, and testing, and can generate model performance metrics that can be used as part of the AI / ML model testing procedure. If necessary, the Model Training function (20) can perform data preparation (e.g., data pre-processing and cleaning, forming and transformation) based on the Training Data (11) delivered from the Data Collection function (10).

[0050] Trained / Updated Model (21): If there is a Model Storage function (50), it is used to transfer trained, validated, and tested AI / ML models to the Model Storage function (50) or to transfer updated versions of the models to the Model Storage function (50).

[0051] The Management function (30) is a function that monitors the operation of an AI / ML model or an AI / ML function. Additionally, the Management function (30) may make decisions to ensure appropriate inference operations based on data received from the Data Collection function (10) (i.e., Monitoring Data (12)) and / or data received from the Inference function (40) (i.e., Inference Output (41)).

[0052] Management Instruction (32) is information required as input to manage the Inference function (40). The relevant information may include the selection / (de)activation / switching of an AI / ML model or an AI / ML-based function, and may also include a fallback to a non-AI / ML operation (i.e., not relying on the inference process).

[0053] A Model Transfer / Delivery Request (33) can be used to request model(s) from Model Storage (50).

[0054] Performance Feedback / Retraining Request (31) refers to information required as input to Model Training function (20) (e.g., for the purpose of retraining or updating the model).

[0055] The inference function (40) is a function that provides output from the process of applying an AI / ML model or AI / ML function using data (i.e., inference data (13)) provided by the data collection (10) as input. Data preparation (e.g., data preprocessing and cleaning, formatting and transformation) may also be performed based on the inference data (13) delivered by the data collection (10). If necessary, the inference function (40) may also perform data preparation (e.g., data pre-processing and cleaning, forming and transformation) based on the inference data (13) provided by the data collection function (10).

[0056] Inference Output (41) is data used in the Management function (30) to monitor the performance of an AI / ML model or AI / ML function. Inference Output (41) may include the inference output of an AI / ML model generated by the Inference function (30), and the details of the inference output may vary depending on the use case.

[0057] The Model Storage function (50) is a function that stores a trained / updated model that can be used to perform the Inference function (40). The Model Storage function (50) exemplified in FIG. 1 can be used as a reference point (if any) applicable to protocol termination, model transmission / delivery, and related processes. Additionally, the Model Storage function (50) is an example and is not intended to restrict the storage location of the actual AI / ML model, and may be omitted.

[0058] Model Transfer / Delivery (51) is used to transfer an AI / ML model to an inference function.

[0059] 2. General AI / ML related procedures between the network and the terminal

[0060] Figure 2 illustrates the general form of AI / ML-related procedures performed between a network and a terminal. While Figure 1 examined the LCM from the perspective of an AI / ML model, Figure 2 describes the general form of procedures performed between a terminal and a network from the perspective of signaling / protocols.

[0061] (1) AI / ML related setup procedure

[0062] Referring to FIG. 2, an AI / ML-related configuration procedure may be performed between the network and the terminal (B05). The AI / ML-related configuration procedure may include information exchange through at least one upper-layer signaling between the terminal and the network, and / or prior preparation / subsequent operations at the terminal / network respectively before / after the upper-layer signaling.

[0063] Specifically, the configuration procedure related to AI / ML may include, but is not limited to, at least one of the following: (i) reporting the capability of the AI / ML-related terminal, (ii) data collection, (iii) model training, (iv) model delivery / transmission, (v) selection of AI / ML functions / models, and (vi) configuration of various operations performed based on the AI / ML model (e.g., AI / ML-based CSI / Positioning / Beam Management).

[0064] (i) The terminal can inform the network of its capabilities, such as models and functions related to AI / ML, that it supports through UE Capability reporting. The network can provide AI / ML-related settings to the terminal based on the terminal's capabilities related to AI / ML reported by the terminal.

[0065] (ii) AI / ML-related configuration procedures may include data collection related to the training / inference of AI / ML models and / or the provision of configuration information regarding data collection. The configuration information regarding data collection may relate to how to configure the method / operation of data collection.

[0066] (iii) AI / ML-related configuration procedures may include training AI / ML models online or offline and / or providing configuration information for AI / ML model training. The configuration information for AI / ML model training may relate to how to configure the method / behavior, etc., of training the AI / ML model.

[0067] (iv) AI / ML-related configuration procedures may include transmitting / transmitting configuration information for a model. The configuration information for a model may include parameters that constitute the AI / ML model and / or an identifier (ID) for the AI / ML model.

[0068] The provided AI / ML model may be a model trained by the network or a model that requires self-training at the terminal. Even when a model trained by the network is provided, the terminal may perform fine-tuning or retraining as necessary. Meanwhile, if a model trained by the network is provided, the terminal may provide data for training to the network.

[0069] Meanwhile, AI / ML models can be classified into Type A models, which can be identified without OTA (over-the-air) signaling, and Type B models, which are identified through OTA signaling. A model ID may be assigned during the model identification process, and this process can be subdivided into methods initiated by the terminal and methods identified by the network.

[0070] (v) The configuration procedure related to AI / ML may include the configuration of how to select AI / ML Functionality / models and / or the selection process for AI / ML Functionality / models. In UE-side AI / ML models or two-sided AI / ML models, the selection of the UE part may be performed through instructions / signaling from the network or the terminal may select it itself. The selection of AI / ML Functionality / models may be performed when multiple AI / ML Functionality / models are configured / provided.

[0071] (vi) The configuration procedure related to AI / ML may include configuration information for various inference operations performed based on AI / ML models, e.g., AI / ML-based CSI measurement / reporting, AI / ML-based positioning, and / or AI / ML-based beam management.

[0072] (2) Operation based on inference by AI / ML models

[0073] Referring again to FIG. 2, the network and / or terminal can perform inference of the AI / ML model through the trained AI / ML model and perform various operations based on the inference of the AI / ML model (B10). If the AI / ML model is a one-sided model, the inference of the AI / ML model can be performed at either the network or the terminal where the AI / ML model is configured. If the AI / ML model is a two-sided model, each part of the inference of the AI / ML model can be performed at the network and the terminal, and depending on the implementation, such inference can be performed cooperatively between the network and the terminal.

[0074] (i) Actions performed based on the inference of an AI / ML model may include AI / ML-based CSI measurement / reporting. AI / ML-based CSI measurement / reporting is intended to improve CSI feedback and may be related to overhead reduction / CSI compression, accuracy improvement, and / or CSI prediction.

[0075] (ii) Actions performed based on the inference of an AI / ML model may include AI / ML-based beam management. AI / ML-based beam management may be related to beam prediction in the time domain, reduction of overhead / latency in the spatial domain, and / or improvement of beam selection accuracy.

[0076] (iii) Actions performed based on the inference of an AI / ML model may include AI / ML-based positioning. AI / ML-based positioning may be relevant to improving positioning accuracy in various scenarios, for example, in non-line-of-sight environments.

[0077] (3) Procedures for AI / ML management

[0078] The network and / or terminal can perform procedures for the management of AI / ML Functionality / model or the settings therefor (B15).

[0079] The network and / or terminal may perform monitoring of AI / ML Functionality / model during the AI / ML model inference or operation based thereon (B10) for the management procedure (B15).

[0080] The management procedure may include, for example, at least one of activation / deactivation, switching, model update, and / or fallback operation for AI / ML Functionality / model. For the signaling of the management procedure, various 3GPP signaling schemes, such as RRC, MAC-CE, DCI, etc., may be used.

[0081] As an example of model switching, multiple model groups are configured, and switching between them can be performed based on models having a common model structure or partially common substructures, and models within the same group may be associated with different input / output formats or processing.

[0082] Model updating involves modifying the parameters used by the model to suit channel conditions that change over time, and fine-tuning is an example of model updating.

[0083] Fallback: In a wireless communication system using an AI / ML model, this may refer to the operation of not using the AI / ML model or operating in a pre-configured / defined default mode when the reliability of the AI / ML model decreases due to internal or external environmental factors.

[0084] For example, the decision on whether to perform a management procedure can be made by the network. For instance, the network may decide to perform the management procedure upon network initiation, or the network may decide to perform the management procedure upon terminal initiation and request.

[0085] As another example, the decision on whether to perform a management procedure can be made by the terminal. For instance, the terminal's decision on the management procedure may be triggered when an event condition set by the network is satisfied, performed by reporting the terminal's decision to the network, or performed autonomously by the terminal.

[0086] 3. Specific operation examples based on AI / ML model inference

[0087] (1) Beam management

[0088] Figure 3 illustrates an example of AI / ML-based beam management operation.

[0089] Referring to FIG. 3, the network / terminal can perform a configuration procedure related to AI / ML-based beam management (C05). The network / terminal can perform a configuration procedure for an AI / ML model to be used for AI / ML-based beam management, and an exchange of configuration information for upper-layer signaling for AI / ML-based beam management. For example, at least one of information related to model inference, configuration for a first set / second set beam, monitoring performance, and assistance information for data collection and beam measurement may be signaled.

[0090] The network / terminal can perform measurements on the first set of beams (C10). The beam measurements may be related to RSRP measurements.

[0091] A network / terminal can obtain information about a second set of beams based on measurement results for a first set of beams (C15). For example, the network / terminal can perform AI / ML inference by using the measurement results for the first set of beams as AI / ML input data. Beam ID information may also be additionally provided as AI / ML input data. Information about the second set of beams may correspond to AI / ML output data. The AI / ML output data may be related to, for example, the probability that each beam will become a top-N beam, the predicted RSRP, etc., for predicting future beam quality, but is not limited thereto.

[0092] According to an embodiment, the network / terminal can transmit and receive information about the acquired second set of beams.

[0093] Specifically, AI / ML-based beam management operations may include at least one of the following BM-Case 1 and BM-Case 2.

[0094] - BM-Case 1: Prediction of the second set of DL beams in the spatial domain through the first set of beam measurements

[0095] - BM-Case 2: Prediction of the second set of DL beams in the time domain through the first set of beam measurements

[0096] In BM-Case 1 and / or 2, both AI / ML model training and inference may be performed on the network or on the terminal. The first set of beams and the second set of beams may be different beams. Or the first set of beams may be a subset of the second set of beams. Or, particularly in BM-Case 2, the first set of beams and the second set of beams may be the same beam.

[0097] The report corresponding to the inference of the UE-side model for BM-Case 1 may relate to the RSRP for the predicted top N beams. The report may include, for example, the predicted RSRP values, and as an example, the predicted RSRP values ​​may be reported together with the actual measured RSRP.

[0098] UE-side AI / ML model inference for BM-Case 2 can report inference results for N future time points through a single report. The report for each time point can correspond to the report in BM-Case 1.

[0099] For performance monitoring of the UE-side model for BM-Case 1 / 2, (i) network-side performance monitoring and / or (ii) UE-assisted performance monitoring may be supported. (i) For network-side performance monitoring, the terminal may report information necessary for the network to calculate performance metrics, for example, by reporting measurement results (e.g., RSRP) and / or RS index for a set of resources for monitoring. (ii) For UE-assisted performance monitoring, the terminal may calculate performance metrics.

[0100] With respect to the NW-side model for BM-Case 1 / 2, quantization of the reported RSRP may be supported, for example, differential RSRP reporting may be supported along existing quantization steps and ranges. The reported content may include information on the RSRP and the corresponding upper N beam, where N can be set by the network.

[0101] With respect to the configuration of the first set of beams and the second set of beams of the UE-side model of BM Case-1, two resource sets may be configured separately for each of the first set and the second set, and the resource sets may be provided through CSI reporting settings. The terminal may perform inference / measurement on the resource set of the first set of beams. The terminal may not be expected to perform measurement / inference on the resource set of the second set of beams. The beam information in the inference report may include resource set information for the first set.

[0102] In relation to the UE-side model, the associated ID may be provided through the CSI framework. The terminal may assume identical / similar characteristics for DL ​​transmit beams / sets (lists) for the same associated ID.

[0103] Regarding UE-assisted performance monitoring for the UE-side models of BM-Case 1 and 2, the following methods may be considered.

[0104] i) Compare prediction results based on resources for monitoring and use the top 1 or top K beam prediction accuracy.

[0105] ii) Use RSRP difference information based on RSRP measurements of resources for monitoring and actual RSRP measurements for at least one of the top N prediction beams.

[0106] iii) Use the difference information between the measured RSRP and the predicted RSRP for the corresponding beam of the resources for monitoring.

[0107] iv) Probability information that the predicted beam will become one of the top 1 or N beams

[0108] For reporting inference results for the UE-side model, quantization of RSRP may be supported, and differential RSRP with existing quantization steps may be supported. The scope of RSRP reporting is such that differential RSRP among multiple beams is supported in the case of BM-case 1, and differential RSRP among multiple beams at multiple time points is supported in the case of BM-case 2.

[0109] For BM-Case 2 of the UE-side model, the network can be configured to report inferences about N future times to the terminal.

[0110] (2) CSI prediction and / or compression

[0111] Figure 4 illustrates an example of an AI / ML-based CSI measurement / reporting operation.

[0112] Referring to FIG. 4, the network / terminal can perform a configuration procedure related to AI / ML-based CSI (D05). The network / terminal can perform a configuration procedure for an AI / ML model to be used for AI / ML-based CSI, and for the exchange of configuration information for upper-layer signaling for AI / ML-based CSI measurement / reporting. For example, at least one of information related to model inference, settings for RS / resources to be used for CSI measurement, monitoring performance, data collection, and conditions / resources for CSI reporting may be signaled.

[0113] The terminal can perform CSI measurements based on AI / ML model inference (D10). The AI / ML model used by the terminal for CSI measurements may be a UE-side AI / ML model corresponding to a one-side AI / ML model, or an AI / ML model corresponding to the terminal part of a two-side AI / ML model.

[0114] The terminal may report CSI to the network based on the results of CSI measurements (D15). CSI reporting may be performed periodically or non-periodically depending on the configuration, and in the case of non-period CSI reporting, network instructions (not shown), such as DCI, that trigger it may be additionally signaled. CSI reporting may include AI / ML-based CSI content and may additionally include legacy CSI content (e.g., non-AI / ML-based RI, PMI, CQI, etc.) (depending on the configuration / scheduling). AI / ML-based CSI content may be related to at least one of 1) CSI compression to reduce the overhead of CSI reporting and 2) CSI prediction for future time points in the time domain.

[0115] The network can acquire CSI based on the terminal's CSI report.

[0116] If a two-sided AI / ML model is configured, the network can reconstruct the CSI by using the terminal's CSI report as input data to the AI / ML model configured in the network (D20). The inference (output) of the AI / ML model configured in the network may be the reconstructed CSI. In such a two-sided AI / ML model, the terminal-side AI / ML model part can be understood as a CSI encoder, and the network-side AI / ML model part can be understood as a concept similar to a CSI decoder.

[0117] CSI compression is CSI compression in the spatial-frequency domain and can primarily be based on two-sided AI / ML models. CSI prediction can primarily be based on one-sided, specifically UE-side AI / ML models.

[0118] In CSI compression based on a two-sided AI / ML model, AI / ML model training may include at least one of the following: (i) Type 1, in which the two-sided AI / ML model is jointly trained at either the terminal or the network; (ii) Type 2, in which the terminal and the network each jointly train the corresponding parts of the two-sided AI / ML model; and (iii) Type 3, in which the terminal and the network each separately train the corresponding parts of the two-sided AI / ML model, wherein the training of the terminal is mainly related to CSI generation and the training of the network is mainly related to CSI reconstruction. Joint training means that the CSI generation / reconstruction models are trained in the same loop for forward / backward delays, and separate training may mean a sequential method in which one of the terminals or the network starts training first, and then the other performs training.

[0119] (3) Positioning

[0120] Figure 5 illustrates an example of an AI / ML-based positioning operation.

[0121] Referring to FIG. 5, the network / terminal can perform a configuration procedure related to AI / ML-based positioning (E05). The network / terminal can perform a configuration procedure for an AI / ML model to be used for AI / ML-based positioning, and an exchange of configuration information for upper-layer signaling for AI / ML-based positioning. For example, at least one of information related to model inference, configuration for positioning RS, monitoring performance, and assistance information for data collection and positioning measurement may be signaled.

[0122] The network / terminal can perform a measurement for positioning (E10). The measurement for positioning may be related to PRS and / or SRS measurements.

[0123] The network / terminal can obtain information about terminal positioning based on measurement results (E15). For example, the network / terminal can perform AI / ML inference by using measurement results for PRS / SRS as AI / ML input data. 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.

[0124] According to an embodiment, the network / terminal can transmit and receive information regarding the acquired terminal positioning.

[0125] Specifically, AI / ML-based positioning may include at least one of (i) direct AI / ML positioning and / or (ii) AI / ML-assisted positioning. In (i) direct AI / ML positioning, the output of the AI / ML model includes the UE location. In (ii) AI / ML-assisted positioning, the output of the AI / ML model may be a new measurement result and / or an improvement on an existing measurement (e.g., LoS / NLoS identification, timing and / or angle of the measurement, likelihood of the measurement, etc.).

[0126] The data sample collected for training data collection may include at least one of the following parts.

[0127] - Part A: Channel Measurement, Quality Indicators of Channel Measurement, Time Stamps of Channel Measurement

[0128] - Part B: ground truth label (or its approximation), label quality indicators, label timestamp

[0129] The training data samples in Part A and Part B are for the same terminal (e.g., PRU or Non-PRU UE) and may be for the same location associated with Part B.

[0130] Measurements for positioning can be classified into sample-based measurements and path-based measurements.

[0131] - In sample-based measurements, the measurement consists of Nt' samples of the channel response estimated in the time domain. Timing information for the Nt' samples is reported as the timing granularity T, where T = 2k x Tc. k represents the timing reporting granularity factor, and Tc represents the base time unit of the corresponding wireless communication system. Nt' and k may be signaling parameters. Timing information can be defined as a value relative to a reference time.

[0132] - Path-based measurement refers to a measurement defined in existing wireless communication systems.

[0133] AI / ML-based positioning may be related to at least one of the following detailed cases.

[0134] - (i) Case 1: UE-based positioning using a UE-side model, in the case of direct AI / ML or AI / ML assisted positioning

[0135] - (ii) Case 2a: UE-assisted / LMF-based positioning using a UE-side model, in the case of AI / ML-assisted positioning

[0136] - (iii) Case 2b: UE-assisted / LMF-based positioning using an LMF-side model, in the case of direct AI / ML positioning

[0137] - (iv) Case 3a: NG-RAN node assisted positioning using a gNB-side model, in the case of AI / ML assisted positioning

[0138] - (v) Case 3b: NG-RAN node-assisted positioning using an LMF-side model, in the case of direct AI / ML positioning

[0139] (i) Regarding model performance monitoring in Case 1, the following options may be considered for calculating model performance metrics in label-based model monitoring.

[0140] 1) Option A. Calculate monitoring metrics on the target terminal side

[0141] - Option A-1: ​​At least part of the information regarding the ground truth label of the target terminal is generated in the LMF and provided to the target terminal. For example, the target terminal and / or base station sends measurement results to the LMF, and the LMF can derive information regarding the ground truth label from this.

[0142] - Option A-2: At least some of the information regarding location calculation assistance data is provided from the LMF to the target UE.

[0143] - Option A-3: As a method to reuse assistance data previously provided from the LMF to the target terminal, the PRU measurement results and the corresponding PRU location information are provided from the LMF to the target terminal.

[0144] - Option A-4: PRU measurement and PRU location are provided from the PRU to the target UE.

[0145] 2) Option B. LMF calculates monitoring metrics

[0146] - Option B-1: At least the inference result of the target terminal (i.e., the model output corresponding to the channel measurement of the target terminal) can be transmitted to the LMF by the target terminal.

[0147] - Option B-2: Channel measurements of the PRU are provided to the target terminal through the LMF, and inference results (i.e., model output corresponding to the channel measurements of the PRU) can be transmitted from the target terminal to the LMF.

[0148] (iv) Regarding the generation of training data in Case 3a, at least LMF can generate labels and related data (e.g., time stamp).

[0149] (iv) For calculating model performance monitoring metrics in label-based model monitoring in Case 3a, the following options A and B may be considered.

[0150] - Option A: The NG-RAN node performs monitoring metric calculations for its model

[0151] - Option B: LMF performs monitoring metric calculations for models located on the NG-RAN node.

[0152] (iv) For generating training data for Case 3a and (v) Case 3b, measurements and related data (e.g., timestamps) may be generated at TRP / base stations.

[0153] (v) For base station channel measurements reported to the LMF (location management server) in Case 3b, timing information can be expressed as a value relative to the UL RTOA reference time T0+tSRS.

[0154] (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 the timing information.

[0155] For the definition of a sample-based measurement, Nt' samples may be selected from a list of consecutive Nt samples, and the Nt samples may have a particle size at time T.

[0156] In relation to sample-based measurements, the Nt' samples selected for network measurement may be those with the highest power.

[0157] Regarding positioning based on Case 3b, for sample-based measurements, LMF can signal parameter values ​​such as Nt, Nt', and k to the base station.

[0158] Beam Management (BM)

[0159] BM procedures are L1 (layer 1) / L2 (layer 2) procedures for acquiring and maintaining a set of base station (e.g., gNB, TRP, etc.) and / or terminal (e.g., UE) beams that can be used for downlink (DL) and uplink (UL) transmission / reception, and may include the following procedures and terms.

[0160] - Beam measurement: An operation in which a base station or UE measures the characteristics of a received beamforming signal.

[0161] - Beam determination: The operation in which a base station or UE selects its transmit beam (Tx beam) / receive beam (Rx beam).

[0162] - Beam sweeping: An operation that covers a spatial area using transmitting and / or receiving beams for a set time interval in a predetermined manner.

[0163] - Beam report: An operation in which the UE reports information about the beam-formed signal based on beam measurements.

[0164] The BM procedure can be divided into (1) a DL BM procedure using an SS (synchronization signal) / PBCH (physical broadcast channel) Block or CSI-RS, and (2) a UL BM procedure using an SRS (sounding reference signal).

[0165] In addition, each BM procedure may include Tx beam sweeping to determine the Tx beam and Rx beam sweeping to determine the Rx beam.

[0166] DL BM

[0167] The DL BM procedure may include (1) transmission to beamformed DL RS (reference signals) of the base station (e.g., CSI-RS or SS Block (SSB)) and (2) beam reporting of the terminal.

[0168] Here, beam reporting may include preferred DL RS ID(identifier)(s) and the corresponding L1-RSRP(Reference Signal Received Power).

[0169] The above DL RS ID may be SSBRI (SSB Resource Indicator) or CRI (CSI-RS Resource Indicator).

[0170] An example of beam forming using SSB and CSI-RS will be examined in detail below.

[0171] SSB beams and CSI-RS beams can be used for beam measurement. The measurement metric is L1-RSRP per resource / block. SSB is used for coarse beam measurement, while CSI-RS can be used for fine beam measurement. SSB can be used for both Tx beam sweeping and Rx beam sweeping.

[0172] Rx beam sweeping using SSBs can be performed as the UE changes the Rx beam across multiple SSB bursts for the same SSBRI. Here, one SS burst includes one or more SSBs, and one set of SS bursts includes one or more SSB bursts.

[0173] The DL BM procedure is examined below.

[0174] Configuration for beam reporting using SSB is performed during CSI / beam configuration in the RRC connected state (or RRC connected mode).

[0175] - The terminal receives configuration information from the base station. As a specific example, the terminal receives from the base station a CSI-ResourceConfig IE containing a CSI-SSB-ResourceSetList containing SSB resources used for BM.

[0176] Table 1 shows an example of CSI-ResourceConfig IE. As shown in Table 1, BM configuration using SSB is not defined separately, and SSB is configured like a CSI-RS resource.

[0177]

[0178] In Table 1, the csi-SSB-ResourceSetList parameter represents a list of SSB resources used for beam management and reporting in a single CSI-RS resource set. Here, the SSB resource set can be set to {SSBx1, SSBx2, SSBx3, SSBx4, …}. For example, the SSB index can be defined from 0 to 63.

[0179] - 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.

[0180] - The terminal transmits a beam report to the base station. As a specific example, if a CSI-ReportConfig related to reporting on SSBRI (SSB Resource Indicator) and L1-RSRP is configured, the terminal reports the best SSBRI and the corresponding L1-RSRP to the base station.

[0181] That is, if the reportQuantity of the above CSI-ReportConfig IE is set to 'ssb-Index-RSRP', the terminal reports the best SSBRI and the corresponding L1-RSRP to the base station.

[0182] And, if the terminal has a CSI-RS resource configured in the same OFDM symbol(s) as the SSB (SS / PBCH Block) and 'QCL-TypeD' is applicable, the terminal can assume that the CSI-RS and SSB are quasi-co-located in terms of 'QCL-TypeD'.

[0183] Here, the above QCL Type D may mean that the antenna ports are QCL-connected in terms of spatial Rx parameters. When a terminal receives multiple DL antenna ports that are in a QCL Type D relationship, it is acceptable to apply the same receiving beam. Additionally, the terminal does not expect CSI-RS to be established in an RE that overlaps with the RE of the SSB.

[0184] The configuration for beam reporting using CSI-RS is performed in the same manner as the configuration for beam reporting using SSB described above, so a redundant explanation is omitted. The operation of the beam reporting procedure using CSI is described below.

[0185] - The terminal receives configuration information from the base station. As a specific example, the terminal receives from the base station a CSI-ResourceConfig IE containing a CSI-SSB-ResourceSetList containing CSI resources used for BM (e.g., NZP CSI-RS resource set IE).

[0186] - The terminal receives CSI-RS resources within the NZP CSI-RS resource set through different Tx beams (DL spatial domain transmission filters) of the base station.

[0187] - The terminal selects (or determines) the best beam.

[0188] - The terminal reports the ID and associated quality information (e.g., L1-RSRP) for the selected beam to the base station. In this case, the reportQuantity of the CSI report config can be set to 'cri-RSRP'.

[0189] < SRS Related Operations >

[0190] The terminal may receive one or more Sounding Reference Symbol (SRS) resource sets configured by the (higher layer parameter) SRS-ResourceSet (via higher layer signaling, RRC signaling, etc.). For each SRS resource set, the UE may be configured with K≥1 SRS resources (higher layer parameter SRS-resource). Here, K is a natural number, and the maximum value of K is indicated by SRS_capability.

[0191] Below, we will examine the UL BM procedure.

[0192] The terminal receives RRC signaling (e.g., SRS-Config IE) containing usage parameters from the base station. For example, the usage parameter may be set to 'beam management', 'codebook', 'nonCodebook', or 'antennaSwitching'.

[0193] Table 2 shows an example of an SRS-Config IE (Information Element), which is used for SRS transmission configuration. An SRS-Config IE includes a list of SRS-Resources and a list of SRS-ResourceSets. Each SRS resource set represents a set of SRS-resources.

[0194] The network can trigger the transmission of an SRS resource set using the configured aperiodicSRS-ResourceTrigger (L1 DCI).

[0195]

[0196] In Table 2, 'usage' represents a higher layer parameter indicating whether the SRS resource set is used for beam management, or for codebook-based or non-codebook-based transmission. 'spatialRelationInfo' is a parameter representing the configuration of the spatial relation between the reference RS and the target SRS. Here, the reference RS can be an SSB, CSI-RS, or SRS corresponding to the L1 parameter 'SRS-SpatialRelationInfo'. The above usage is configured per SRS resource set.

[0197] - The terminal determines the Tx beam for the SRS resource to be transmitted based on the SRS-SpatialRelation Info included in the above SRS-Config IE. Here, the SRS-SpatialRelation Info is configured per SRS resource and indicates whether to apply the same beam used in the SSB, CSI-RS, or SRS for each SRS resource. Additionally, SRS-SpatialRelationInfo may or may not be configured for each SRS resource.

[0198] - If SRS-SpatialRelationInfo is configured in the SRS resource, transmission is performed by applying the same beam used in the SSB, CSI-RS, or SRS. However, if SRS-SpatialRelationInfo is not configured in the SRS resource, the terminal arbitrarily determines a Tx beam and transmits the SRS through the determined Tx beam.

[0199] - Additionally, the terminal may or may not receive feedback regarding the SRS from the base station.

[0200] <Sounding reference singal (SRS)>

[0201] In Rel-15 NR, spatialRelationInfo can be used to indicate which transmission beam to use when a base station transmits a UL channel to a terminal. By configuring the RRC, the base station can indicate which UL transmission beam to use when transmitting PUCCH and SRS by setting a DL reference signal (e.g., SSB-RI, CRI(P / SP / AP)) or SRS (i.e., SRS resource) as a reference RS for the target UL channel and / or target RS. Additionally, when the base station schedules a PUSCH to a terminal, the transmission beam designated by the base station for SRS transmission is indicated as the transmission beam for PUSCH via the SRI field and is used as the terminal's PUSCH transmission beam.

[0202] < Explanation regarding Rel-17 / 18 beam management >

[0203] In Rel-17, DL DCI (e.g., DCI format 1-1 or 1-2) can indicate both the DL TCI state and the UL TCI state, or it can indicate only the UL TCI state without specifying the DL TCI state. Consequently, the methods used in the existing R15 / R16 for configuring UL beam and power control (PC) are replaced in Rel-17 by the aforementioned method of indicating the UL TCI state. More specifically, in R17, a single UL TCI state can be indicated through the TCI field of the DL DCI; this UL TCI state is applied to all PUSCHs and all PUCCHs after a certain period known as the beam application time, and can be applied to some or all of the indicated SRS resource sets. Additionally, the base station can utilize DCI and / or MAC-CE to perform a terminal common beam update, which performs indication / updates for multiple specific DL / UL channel / RS combinations using a single beam (utilizing joint or separate TCI states). For the target channel / RS of the common beam update, UE-dedicated CORESET and UE-dedicated reception on PDSCH are available for DL, and DG / CG-PUSCH and all or subset of dedicated PUCCH are available for UL, and additionally, AP CSI-RS for tracking / BM and SRS can be set as target channel / RS.In Rel-18, considering the M-TRP environment, the method of indicating multiple UL TCI states (and / or DL ​​TCI states) through the TCI field of DL DCI was standardized, and depending on the S-DCI based M-TRP environment and the M-DCI based M-TRP environment, uplink and downlink resources to which each indicated TCI is applied can be defined / configured.

[0204] In this specification, 'beam' may refer to a source RS for a 'spatial filter' or 'spatial relation', and may be interpreted as a QCL (type-D) RS, a TCI state, or (in the case of an uplink) a spatial relation RS.

[0205] For example, in this specification, 'beam' may refer to a spatial filter determined based on the reference RS or the source RS. The spatial filter may include a spatial domain filter, a spatial domain transmission filter, and a spatial domain receive filter. For example, in this specification, 'beam' may be interpreted or substituted with a reference signal index (RS index), a reference signal resource index (RS resource index), and / or a resource indicator (e.g., RS index, SSB index, CSI-RS resource index, SRS resource index, SSB Resource Indicator (SSBRI), CSI-RS Resource Indicator (CRI), etc.).

[0206] For example, a beam associated with a UL may be referred to as i) a spatial filter (for uplink transmission or uplink reception), ii) a spatial domain filter (for uplink transmission or uplink reception), iii) an uplink spatial domain transmission filter, iv) an uplink spatial domain receive filter, v) an uplink transmission spatial filter (UL Tx spatial filter) or vi) an uplink receive spatial filter (UL Rx spatial filter).

[0207] For example, a beam associated with DL may be referred to as i) a spatial filter (for downlink transmission or downlink reception), ii) a spatial domain filter (for downlink transmission or downlink reception), iii) a downlink spatial domain transmission filter, iv) a downlink spatial domain receive filter, v) a downlink transmission spatial filter (DL Tx spatial filter), or vi) a downlink receive spatial filter (DL Rx spatial filter).

[0208] The symbols / abbreviations / terms used in this specification are as follows.

[0209] BM: beam management

[0210] CQI: channel quality indicator

[0211] CRI: CSI-RS (channel state information - reference signal) resource indicator

[0212] CSI: channel state information

[0213] CSI-IM: channel state information - interference measurement

[0214] CSI-RS: channel state information - reference signal

[0215] DMRS: demodulation reference signal

[0216] FDM: frequency division multiplexing

[0217] FFT: fast Fourier transform

[0218] IFDMA: interleaved frequency division multiple access

[0219] IFFT: inverse fast Fourier transform

[0220] L1-RSRP: Layer 1 reference signal received power

[0221] L1-RSRQ: Layer 1 reference signal received quality

[0222] MAC: medium access control

[0223] NZP: non-zero power

[0224] OFDM: orthogonal frequency division multiplexing

[0225] PDCCH: physical downlink control channel

[0226] PDSCH: physical downlink shared channel

[0227] PMI: precoding matrix indicator

[0228] RE: resource element

[0229] RI: Rank indicator

[0230] RRC: radio resource control

[0231] RSSI: received signal strength indicator

[0232] Rx: Reception

[0233] QCL: quasi co-location

[0234] SINR: signal to interference and noise ratio

[0235] SSB (or SS / PBCH block): synchronization signal block (including primary synchronization signal, secondary synchronization signal and physical broadcast channel)

[0236] TDM: time division multiplexing

[0237] TRP: transmission and reception point

[0238] TRS: tracking reference signal

[0239] Tx: transmission

[0240] UE: user equipment

[0241] ZP: zero power

[0242] Due to advancements in computational processing technology and AI / ML technologies, the nodes and terminals constituting wireless communication networks are becoming more intelligent and sophisticated. In particular, as a result of this network intelligence, it is expected that various network decision parameter values ​​(e.g., transmit / receive power of each base station, transmit power of each terminal, precoder / beam of base stations and terminals, time / frequency resource allocation for each terminal, duplex method of each base station, etc.) can be rapidly optimized, derived, and applied according to diverse network environment parameters (e.g., distribution / location of base stations, distribution / location / material of buildings / furniture, location / movement direction / speed of terminals, weather information, etc.).

[0243] In a first aspect, a method is provided that includes the step of performing an operation described in the present specification, in a method performed by a terminal (or a first node).

[0244] In a second aspect, a terminal (or first node) of a wireless communication system is provided, comprising: at least one transceiver; at least one processor configured to perform the operation described herein; and at least one computer memory operably connected to the at least one processor.

[0245] In a third aspect, an apparatus 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 cause a terminal (or first node) to perform the operation described herein based on execution by the at least one processor.

[0246] In a fourth aspect, a non-transitory computer-readable storage medium is provided that stores instructions for a terminal (or first node) to perform the operation described herein, based on execution by at least one processor.

[0247] In a fifth aspect, a method is provided that includes the step of performing the operation described in the present specification, in a method performed by a base station (or a second node).

[0248] In a sixth aspect, a base station (or second node) of a wireless communication system is provided, comprising: at least one transceiver; at least one processor configured to perform the operation described herein; and at least one computer memory operably connected to the at least one processor.

[0249] In a seventh aspect, an apparatus 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 cause a base station (or a second node) to perform the operation described herein based on execution by the at least one processor.

[0250] In an eighth aspect, a non-transient computer-readable storage medium is provided that stores instructions for a base station (or a second node) to perform the operation described herein, based on execution by at least one processor.

[0251] Herein, the operations described in this specification may be described separately for convenience, but unless specifically stated otherwise, each operation may be combined with others.

[0252] In this specification, ' / ' means 'and', 'or', or 'and / or' depending on the context.

[0253] Previously, channel estimation accuracy was achieved by transmitting the RS (Reference Signal) across all antenna ports or all frequency resources, but in a Massive MIMO environment, as the number of antenna ports increased and the bandwidth expanded, a problem arose where the RS overhead became excessive.

[0254] Accordingly, the need to design RS transmission more efficiently by incorporating AI / ML techniques into recent wireless communication systems is being discussed.

[0255] For example, instead of transmitting RS to all ports as in the existing method, a method of transmitting RS only to some ports or resources can be considered.

[0256] For non-transmitting ports or resources, the base station can train an AI / ML model on channel measurements based on partial RS, and consequently infer a value similar to the channel information estimated with the entire RS.

[0257] While this partial transmission method has the advantage of significantly reducing transmission overhead, in order to minimize inference error, it is necessary to carefully decide which ports (or resources) to select for transmitting RS. This will be explained below with reference to Fig. 6.

[0258] FIG. 6 is a diagram illustrating a partial port SRS-based channel estimation according to an embodiment of the present specification. The SRS channel estimation based on the partial transmission method described above can be represented as in FIG. 6.

[0259] Referring to Fig. 6, channel information corresponding to some ports (2 ports / 4 ports) among the total ports (8 ports) becomes the input of an AI / ML model, and based on this, channel information corresponding to all ports can be inferred.

[0260] One of the important concepts here is coherency (degree of phase synchronization) between ports.

[0261] Generally, antenna ports on the terminal (UE) or base station (gNB) side can be classified as follows based on their ability to maintain the same phase reference.

[0262] For example, in the case of a terminal, the above coherency may mean the ability to maintain the phase from SRS transmission to PUSCH transmission with the corresponding UL precoding applied.

[0263] Full coherent

[0264] This means a state in which all antenna ports used by the terminal (UE) for UL transmission share the same phase reference or the same local oscillator to control the relative phase difference between the antenna ports, allowing them to be 'coherently' coupled.

[0265] Maximum beamforming gain can be obtained by precisely controlling the phase during transmission.

[0266] Partial coherent

[0267] This is a method in which the entire antenna port is divided into two or more groups, and while ports within each group share a phase reference ('coherent' transmission), the groups transmit with separated phases ('non-coherent').

[0268] Non-coherent

[0269] This is a state where each antenna port does not share a phase reference, meaning all ports (or all port groups) are completely isolated. While the burden of phase synchronization is minimized, it is difficult to obtain significant beamforming gain.

[0270] For example, in a Full Coherence environment, the channels of all ports can be restored relatively accurately even if only some representative ports are transmitted. On the other hand, in Partial Coherence or Non-Coherence states, the gap in inference performance can widen depending on how port combinations are selected, so appropriate port grouping is necessary.

[0271] This specification considers AI / ML-based SRS channel estimation. By utilizing AI / ML, channels corresponding to the remaining ports can be inferred by using only some of the ports of the SRS, as shown in FIG. 6. This allows existing operations to be performed without transmitting all ports of the SRS, thereby reducing signaling overhead.

[0272] In order to estimate channel information corresponding to all ports of an SRS based on AI / ML by transmitting only some ports of an SRS, it must be specified how to configure those some ports. In other words, the accuracy of inference regarding channel information corresponding to all ports may vary depending on how the some ports are configured. Below, we will examine specific examples for configuring some ports.

[0273] Proposal 1

[0274] Below, we examine how a terminal determines the port index when it receives partial port SRS settings / instructions from a base station.

[0275] To instruct the transmission of partial port SRS, the base station must configure / instruct the terminal on the ports to which the SRS will be transmitted. The terminal can receive this configuration / instruction from the base station through parameters for partial port SRS transmission. Information regarding the ratio of partial ports to total ports can be configured / instructed based on RRC signaling.

[0276] In this specification, 1000 to 1007 may be interpreted / replaced as SRS port / antenna port / port 1000 to SRS port / antenna port / port 1007. For example, 1004 may be interpreted / replaced as SRS port 1004, antenna port 1004, or port 1004.

[0277] In this case, the method of explicitly setting / instructing the indices of ports used for SRS transmission results in increased signaling overhead depending on the number of ports. Therefore, this specification proposes a method in which, when a terminal is set / instructed to a single port index, the port index corresponding to the port group to be transmitted is configured considering port-to-port coherency. In other words, the base station may set / instruct a single port index to the terminal. The terminal may transmit SRS based on the port(s) within the port group based on the set / instructed port index. This will be explained below with reference to FIG. 7.

[0278] FIG. 7 illustrates a port group according to an embodiment of the present specification.

[0279] In FIG. 7, the total number of ports of the SRS / SRS resources is assumed to be 8 ports (e.g., 1000–1007). Specifically, FIG. 7 illustrates port groups configured based on the antenna coherence relationship of the 8 ports.

[0280] (1) in FIG. 7 represents a 2-group partial coherent, and (2) in FIG. 7 represents a 4-group partial coherent. In other words, the ports belonging to each group (e.g., 1000, 1001, 1004, 1005) have coherency. Alternatively, the 8 ports (e.g., 1000–1007) can be classified / defined / configured into multiple port groups consisting of ports that have coherency. Alternatively, each port group can be configured / defined to include ports based on coherency.

[0281] When configuring a port index corresponding to a port group, as mentioned earlier, port coherency can be an important criterion for determining which ports will be transmitted. In other words, a port group based on the above-mentioned port index can be configured / defined / determined to include ports that have coherency with the configured / mentioned port index.

[0282] When considering a method of transmitting RS using only some ports, utilizing coherency information between ports allows AI / ML models to estimate channels more accurately. Since ports in a coherent relationship use the same oscillator or phase reference, the phase changes of the channels will be very similar. Therefore, the correlation between channels will be high, making it easier for AI / ML models to learn.

[0283] According to one embodiment, a terminal may receive a configuration / instruction for a partial port SRS from a base station. Specifically, port groups may be configured / defined / determined by grouping ports that are in a coherent relationship. One of the port groups may be configured / instructed to the terminal based on RRC signaling. The terminal may transmit an SRS based on the port group configured / instructed to the base station.

[0284] If the number of ports is N, N bits are required if all port indices are explicitly set / instructed (e.g., a bitmap indicating the usage / transmission status of each of the N ports). However, if they are set / instructed as in Proposal 1 above, bit, Since only bits are required, the feedback overhead required for partial port SRS configuration instructions can be reduced.

[0285] For example, a terminal may receive a setting related to the transmission of 1 / 2 of an 8-port SRS (e.g., 4-port SRS) based on RRC signaling from a base station. Based on the setting, 1 (e.g., a value representing the second port group among two port groups) may be indicated. Based on the setting, the terminal may transmit an SRS based on [1002, 1003, 1006, 1007] corresponding to the second port group. As a specific example, the setting may include i) information related to some port(s) (e.g., the number of some port(s) (4) or the ratio of some port(s) to the total number of ports set in the SRS resource (1 / 2)) and / or ii) information representing the port group related to said some port(s).

[0286] According to one embodiment, a method of transmitting partial port SRS based on configuring the same number of ports for each port group may be considered.

[0287] In this embodiment, a method of setting / instructing ports by port group based on RRC signaling and transmitting SRS based on those ports may be considered.

[0288] For example, a base station may set / instruct a terminal to transmit SRS based on 1 / 2 of 8 ports. When the coherency relationship for the antenna ports is as in (1) of FIG. 7, the base station may set / instruct the terminal to provide information indicating the ports within the port group. If the information is 0110, the terminal may determine the 2nd and 3rd ports of each port group as ports for SRS transmission. Accordingly, the terminal may transmit SRS based on [1001 1004] and [1003 1006]. In other words, the terminal may receive a setting from the base station. The setting may include i) information indicating SRS transmission based on 4 of the 8 ports and / or ii) information indicating at least one port per port group. In this case, the terminal expects to be set / instructed to transmit partial ports equal to the minimum number of port groups. Specifically, for two port groups based on eight ports, the terminal can expect partial port SRS transmission based on at least two ports to be established / instructed. As a specific example, the base station may transmit information (e.g., 0010) representing one port for each of the two port groups to the terminal. The terminal may transmit SRS based on i) 1004 of the first port group [1000 1001 1004 1005], and ii) 1006 of the second port group [1002 1003 1006 1007].

[0289] Proposal 2

[0290] Below, we examine how a terminal hops ports at regular intervals when it receives partial port SRS settings / instructions from a base station.

[0291] According to one embodiment, a base station may set / instruct a terminal based on RRC signaling a parameter indicating the hopping of a port (e.g., a partial port of an SRS). The terminal may transmit an SRS by hopping a port group index at regular intervals.

[0292] Training on various port groups can improve the generalization performance of AI / ML models for inference. If SRS is continuously transmitted only to a specific port group as in Proposal 1 above, there is a risk that the AI / ML model will overfit to the port group containing that port. Overfitting can be improved by hopping the port group index through which the terminal transmits SRS.

[0293] For example, when the coherency relationship for the antenna ports is as in (2) of FIG. 7, the terminal may receive a setting from the base station related to the transmission of SRS for some of the 8 ports. Specifically, the setting may include i) information related to some port(s) (e.g., the number of some port(s) (e.g., 2) or the ratio of some port(s) to the total number of ports set in the SRS resource (e.g., 1 / 4)) and / or ii) information indicating a port group related to said some port(s) (e.g., 10). Based on the fact that the ratio of said some ports is indicated as 1 / 4 and the information indicating the port group is indicated as 10 (e.g., 10 among 00, 01, 10, 11), the terminal transmits SRS based on [1002, 1006], which corresponds to the third port group among the four port groups. Additionally, port hopping (port group hopping) may be performed at each period. In other words, the terminal can transmit SRS based on port hopping (port group hopping). The port hopping (port group hopping) can be performed based on a set / defined period. In the above example, the port hopping (port group hopping) can be performed as follows.

[0294] [1002,1006]->[1003,1007]->[1000,1004]->[1001,1005]->[1002,1006]->...

[0295] Specifically, in the next cycle of [1002,1006], the terminal can transmit an SRS based on [1003,1007]. In the next cycle, the terminal can transmit an SRS based on [1000,1004]. In the next cycle, the terminal can transmit an SRS based on [1001,1005].

[0296] For example, when the coherency relationship for the antenna ports is as in (1) of FIG. 7, the terminal may receive a setting from the base station related to the transmission of SRS for some of the 8 ports. Specifically, the setting may include i) information related to some port(s) (e.g., the number of some port(s) (e.g., 2) or the ratio of some port(s) to the total number of ports set in the SRS resource (e.g., 1 / 4)) and / or ii) information indicating at least one port for each port group (e.g., 0010). Based on the ratio of the some ports being indicated as 1 / 4 and the information indicating the port groups being indicated as 0010 (e.g., a bitmap indicating the third port for each port group), the terminal transmits SRS based on [1004 1006] corresponding to the third port within each port group. Additionally, port hopping may be performed at each period. Specifically, port hopping may be performed as follows.

[0297] [1004 1006](3rd port per group)-> [1005 1007](4th port per group)-> [1000 1002](1st port per group)->[1001 1003](2nd port per group)->[1004 1006](3rd port per group)-> ..

[0298] Specifically, in the next cycle of [1004 1006], the terminal can transmit an SRS based on [1005 1007]. In the next cycle, the terminal can transmit an SRS based on [1000 1002]. In the next cycle, the terminal can transmit an SRS based on [1001 1003].

[0299] In the above proposal 1 / 2, the terminal can expect to receive a partial port SRS setting / instruction from the base station for a number of ports suitable for the partial coherent level. The terminal can signal the coherency relationship between ports as side information.

[0300] The above proposal 1 / 2 may be applied alone or in combination.

[0301] In the above-described scenario (AI / ML-based SRS channel estimation), the type of SRS information entered as model input may be based on i) a raw channel matrix, ii) eigenvectors calculated based on the raw channel matrix, iii) a precoder type calculated based on a codebook defined in conventional LTE / NR, etc. or a codebook extended from said codebook, or iv) a historical channel.

[0302] The type of the total channel information, which is the model output, can be based on the raw channel matrix, eigenvector, or precoder type described above.

[0303] When performing inference with AI / ML models by transmitting only a subset of ports, there is a risk that errors related to untransmitted ports will accumulate without being improved. In an environment where channels are rapidly changing, there is a need for a method to monitor and correct the performance of the current AI / ML model to determine how accurately it is inferring.

[0304] This specification proposes an operation in which a terminal, upon receiving a Partial Port SRS instruction from a base station, periodically transmits a Full-Port SRS. By ensuring that the entire channel information is obtained as ground truth, it is possible to continuously monitor and maintain the inference performance of AI / ML models while reducing overhead. This will be explained in detail below.

[0305] Proposal 3

[0306] Below, we examine how a terminal transmits a full port SRS at regular intervals when it receives a partial port SRS instruction from a base station.

[0307] A full port SRS can be utilized as a ground-truth SRS for performance monitoring. Therefore, it is necessary to transmit the full port SRS as ground-truth to monitor the performance of AI / ML models. However, frequent transmission of the full port SRS increases overhead. Considering this, the transmission of the full port SRS can be performed as follows.

[0308] According to one embodiment, the terminal can transmit SRS of some ports (e.g., 2 ports, 4 ports) based on a short period and transmit SRS of full ports (e.g., 8 ports) based on a long period.

[0309] For example, a terminal can receive settings related to partial port SRS from a base station. Based on RRC signaling, the terminal can receive information from the base station during the transmission period of a full port SRS (to be used as ground-truth).

[0310] Through this, the base station can monitor performance by comparing the inference values ​​of the AI / ML model with ground truth. Based on the monitoring results, the base station can modify model parameters or perform various subsequent actions, such as fallback operations.

[0311] Proposal 3-1

[0312] A method can be considered to configure the full port's SRS to be transmitted at regular intervals based on a single SRS resource.

[0313] The terminal receives period from the base station When configured / instructed, the terminal can determine the period of the SRS of the full port and the period of the SRS of the partial port as follows.

[0314] For example, the terminal uses the full port SRS transmission cycle of a single SRS resource as the partial port SRS transmission cycle. You can decide by the number of times.

[0315] For example, the terminal uses the partial port SRS transmission cycle of the full port SRS transmission cycle. You can decide by the number of times.

[0316] According to the present embodiment, the period / operation of partial port SRS and full port SRS can be directed / determined based on the settings for a single SRS resource, thereby reducing signaling overhead.

[0317] From the base station It can be assumed that a period of =5 is set / instructed. The terminal can determine the period of the full port SRS and the period of the partial port SRS as follows. For example, the terminal can determine that the full port SRS transmission period is 5 times the partial port SRS transmission period. For example, the terminal can determine that the partial port SRS transmission period is of the full port SRS transmission period It can be determined by the number of times. Based on the determined periods, the terminal can transmit partial port SRS and full port SRS.

[0318] As a specific example, when a Partial port SRS is transmitted every slot (where the transmission cycle of the Partial port SRS is 1 slot), the terminal can transmit a full port SRS every 5th slot. The base station can use the full port SRS as a ground-truth SRS for performance monitoring.

[0319] Proposal 3-2

[0320] A method can be considered in which full port SRS transmission is performed over a long period based on one SRS resource and partial port SRS transmission is performed over a short period based on the other SRS resource, based on two SRS resources.

[0321] In the above proposal 3-2, the terminal from the base station When configured / instructed, the terminal can determine the period of the SRS of the full port and the period of the SRS of the partial port as follows.

[0322] For example, the terminal has an SRS resource period for a long-period full-port SRS transmission that is the same as the SRS resource period for a short-period partial-port SRS transmission. You can decide by the number of times.

[0323] For example, the terminal has an SRS resource cycle for partial port SRS transmission that is the same as the SRS resource cycle for full port SRS transmission. You can decide by the number of times.

[0324] In this case, when a collision occurs between SRS resources where partial port SRS and full port SRS are transmitted at the same time, the full port SRS transmission may have priority over the partial port SRS transmission. In other words, based on the occurrence of the aforementioned collision, the terminal transmits the full port SRS.

[0325] From the base station It can be assumed that a period of =3 is set / instructed. The terminal can determine the period of the full port SRS and the period of the partial port SRS as follows. For example, the terminal can determine the full port SRS transmission period to be three times the partial port SRS transmission period. For example, the terminal can determine the partial port SRS transmission period to be of the full port SRS transmission period. You can decide by the number of times.

[0326] As a specific example, if the period of the SRS resource for partial port SRS transmission is 2 slots, the period of the SRS resource for full port SRS transmission can be set / determined / defined as 6 slots. In other words, the terminal can transmit partial port SRS every 2 slots and full port SRS every 6 slots. At this time, while partial port SRS is transmitted every 2 slots, a collision between the partial port SRS and the full port SRS may occur in the 6th slot. Based on the aforementioned priority, the full port SRS can be transmitted. The base station can use the full port SRS as a ground-truth SRS for performance monitoring.

[0327] Proposal 3-3

[0328] For a single SRS resource, a method to set / define different enable / disable cycles (e.g., muting cycle) for some ports may be considered.

[0329] In the above proposal 1-3, the terminal can receive a muting period for a specific port (e.g., port enabler) from the base station.

[0330] Specifically, different port enablers / disablers can be configured for each specific port / port group. The period for each port / port group may be an integer multiple of the enabler period for a specific port.

[0331] The following explains the 4-port SRS as an example.

[0332] According to one embodiment, for a 4-port SRS resource, a specific port (e.g., 2 ports) may be configured / defined / regulated to be disabled at specific intervals. In other words, for a 4-port SRS resource, the terminal may be configured / defined / regulated to transmit the SRS of a partial port based on specific intervals.

[0333] According to one embodiment, for a 4-port SRS resource, 2 of the 4 ports may be set to always be "on," and the remaining 2 of the 4 ports may be set to be "on" for a specific period. In other words, for a 4-port SRS resource, the terminal may be set / defined / regulated to transmit a full port (e.g., 4-port SRS) at each period.

[0334] According to one embodiment, a base station can dynamically turn on / off an enabler for some ports based on DCI / MAC-CE. In other words, a terminal can transmit an SRS based on the port(s) (partial port / full port) indicated based on DCI / MAC-CE (e.g., a port indicated as 'on').

[0335] In terms of implementation, the operations of the base station / terminal according to the embodiments described above (e.g., operations based on at least one of proposals 1 to 3) can be processed by the device of FIG. 10 (e.g., the processor (110, 210) of FIG. 10).

[0336] In addition, the operations of the base station / terminal according to the above-described embodiment (e.g., operations based on at least one of proposals 1 to 3) may be stored in memory (e.g., 140, 240 of FIG. 10) in the form of instructions / programs (e.g., instruction, executable code) for driving at least one processor (e.g., 110, 210 of FIG. 10).

[0337] The embodiments described above will be explained in detail below with reference to FIGS. 8 and FIG. 9 regarding the operation of the terminal and base station. The methods described below are distinguished only for convenience of explanation, and it is understood that a part of one method may be substituted with a part of another method or combined with one another and applied.

[0338] FIG. 8 is a flowchart illustrating a method according to one embodiment of the present specification.

[0339] Referring to FIG. 8, a method according to one embodiment of the present specification includes a setting information receiving step (S810) and an SRS transmission step (S820).

[0340] In S810, the terminal receives configuration information from the base station that includes information about a Sounding Reference Signal (SRS) resource. For example, the configuration information may be based on the SRS-Config of Table 2. The information about the SRS resource may be based on the SRS-Resource of Table 2. The configuration information may include information based on at least one of the above-described proposals 1 to 3.

[0341] In S820, the terminal transmits an SRS to the base station based on the above SRS resources.

[0342] According to one embodiment, the SRS may be transmitted based on a subset of a plurality of ports configured in the SRS resource. This embodiment may be based on at least one of Proposal 1 to Proposal 3. The subset may mean a part of the plurality of ports (e.g., 2 ports / 4 ports out of 8 ports).

[0343] For example, the above configuration information may include information indicating a group related to the subset among a plurality of groups configured based on the coherency among the plurality of ports. The coherency may be implicitly defined. In other words, the coherency may be configured / defined / determined based on the port index. This will be explained below with reference to FIG. 7. Referring to FIG. 7, the plurality of ports may be assumed to be eight ports (ports 1000 to 1007). The plurality of groups may be configured / defined / determined as follows based on the port index.

[0344] Referring to FIG. 7(a), the plurality of groups may include two groups. Each of the two groups may include four different ports. Specifically, the two groups may include i) a first group [ports 1000, 1001, 1004, and 1005] and ii) a second group [ports 1002, 1003, 1006, and 1007].

[0345] Referring to FIG. 7(b), the plurality of groups may include four groups. Each of the four groups may include two different ports. Specifically, the four groups may include i) a first group [ports 1000, 1004], ii) a second group [ports 1001, 1005], iii) a third group [ports 1002, 1006], and iv) a fourth group [ports 1003, 1007].

[0346] According to one embodiment, the setting information may include information related to the number of ports of the subset. This embodiment may be based on Proposal 1.

[0347] For example, based on the information regarding the number of ports, i) a ratio of the number of ports to the number of multiple ports (e.g., 1 / 2, 1 / 4 for 8 ports) or ii) the number of ports in the subset (e.g., 4, 2, etc.) may be indicated.

[0348] According to one embodiment, based on the information indicating the group, a group belonging to the subset among the plurality of groups may be indicated. This embodiment may be based on Proposal 1 / Proposal 2. Referring to the example according to (2) of FIG. 7 described above, the information indicating the group may indicate the third group ([1002, 1006]) among the four groups (e.g., '10' among 00, 01, 10, 11).

[0349] For example, the group belonging to the above subset may be changed to another group among the plurality of groups in each period. This embodiment may be based on Proposal 2. Referring to the example according to (2) of FIG. 7 described above, the information representing the group may represent the third group ([1002, 1006]) among the four groups (e.g., '10'). The third group may be changed to the fourth group ([1003, 1007]) in the next period. The fourth group may be changed to the first group ([1000, 1004]) in the next period. The change of the group per period may be represented as follows.

[0350] [1002,1006]->[1003,1007]->[1000,1004]->[1001,1005]->[1002,1006]->...

[0351] According to one embodiment, based on the information indicating the group, at least one port belonging to the subset for each group among the plurality of groups may be indicated. This embodiment may be based on Proposal 1 / Proposal 2. Referring to the example according to (1) of FIG. 7 described above, the information indicating the group may indicate the third port among four ports in each of the two groups (e.g., 0010).

[0352] For example, the at least one port for each of the above groups may be changed to at least one other port within the same group at each period. This embodiment may be based on Proposal 2. Referring to the example according to (1) of FIG. 7 described above, the information representing the group may represent the third port among the four ports within each of the two groups (e.g., 0010). The subset may consist of the third port (1004) of the first group and the third port (1006) of the second group.

[0353] The third port (1004 / 1006) within each group belonging to the above subset may be changed to the fourth port (1005 / 1007) within the same group in the next cycle. The fourth port within each group belonging to the above subset may be changed to the first port (1000 / 1002) within the same group in the next cycle. The change of ports within each group per cycle can be represented as follows.

[0354] [1004 1006](3rd port per group)-> [1005 1007](4th port per group)-> [1000 1002](1st port per group)->[1001 1003](2nd port per group)->[1004 1006](3rd port per group)-> ..

[0355] According to one embodiment, the SRS may be transmitted based on the subset in a slot based on a first period. The SRS may be transmitted based on the plurality of ports in a slot based on a second period greater than the first period. This embodiment may be based on Proposal 3.

[0356] According to one embodiment, the SRS resource may be based on a single SRS resource to which the first cycle and the second cycle are applied. A value representing the first cycle or the second cycle may be set in the SRS resource. The second cycle or the first cycle may be determined based on the value. This embodiment may be based on Proposal 3-1.

[0357] According to one embodiment, the SRS resource may include a first SRS resource and a second SRS resource associated with the plurality of ports. This embodiment may be based on Proposal 3-2. For example, the SRS resource may include a first SRS resource and a second SRS resource having the same number of ports. The plurality of ports (e.g., the same number of ports, ports 1000 to 1007) may be configured in each of the first SRS resource and the second SRS resource. For example, the SRS resource may include i) a first SRS resource (e.g., a 4-port SRS resource) in which ports belonging to the subset among the plurality of ports are configured, and ii) a second SRS resource (e.g., an 8-port SRS resource) in which the plurality of ports are configured.

[0358] For example, the first cycle can be applied to the first SRS resource. The second cycle can be applied to the second SRS resource.

[0359] For example, the transmission of the SRS based on the second SRS resource may have a higher priority than the transmission of the SRS based on the first SRS resource. As a specific example, the SRS based on the second SRS resource is transmitted based on the fact that a time domain resource (e.g., a second slot) for the transmission of the SRS based on the second SRS resource overlaps with a time domain resource (e.g., a first slot) for the transmission of the SRS based on the first SRS resource. In other words, the SRS based on the second period is transmitted based on the fact that the transmission of the SRS based on the first / second period collides.

[0360] According to one embodiment, the SRS resource may be based on a single SRS resource to which the first period and the second period are applied. The first period may be i) a periododicity related to the muting or disabling of the remaining ports excluding the subset among the plurality of ports set in the SRS resource, or ii) a periododicity related to the enabling of the ports belonging to the subset.

[0361] The second period above may be a periodicity related to the enable of the plurality of ports. This embodiment may be based on Proposal 3-3.

[0362] Operations based on S810 to S820 described above can be implemented by the device of FIG. 10. For example, referring to FIG. 10, the terminal (200) can control one or more transceivers (230) and / or one or more memories (240) to perform operations based on S810 to S820.

[0363] The embodiments described above will be explained in detail below in terms of base station operation.

[0364] S910 to S920 described below correspond to S810 to S820 described in FIG. 8. Considering the above correspondence, redundant descriptions are omitted. That is, the specific description of the base station operation described below can be replaced by the description / embodiment of FIG. 8 corresponding to the operation.

[0365] FIG. 9 is a flowchart illustrating a method according to another embodiment of the present specification.

[0366] Referring to FIG. 9, a method according to another embodiment of the present specification includes a setting information transmission step (S910) and an SRS reception step (S920).

[0367] In S910, the base station transmits configuration information to the terminal that includes information about the Sounding Reference Signal (SRS) resource.

[0368] In S920, the base station receives an SRS from the terminal based on the SRS resource.

[0369] According to one embodiment, the SRS may be received based on a subset of a plurality of ports configured in the SRS resource. More specifically, a base station may receive an SRS transmitted by a terminal based on a subset of the plurality of ports.

[0370] Operations based on the above-described S910 to S920 can be implemented by the device of FIG. 10. For example, referring to FIG. 10, a base station (100) can control one or more transceivers (130) and / or one or more memories (140) to perform operations based on S910 to S920.

[0371] The operations / terms based on the embodiments described above are described under the assumption of an existing system (e.g., a 5G system). However, this is for the convenience of explanation and is not intended to limit the scope of application of the technical problems and means for solving problems that are to be solved by this specification to a specific system. That is, the technical problems / technical issues / problems mentioned in this specification may exist in other systems (e.g., a 6G system). It is evident that the embodiments of this specification can be extended to solve problems that exist in other systems as well. Therefore, for the extended application of the embodiments of this specification to other systems, terms defined / described based on a 5G system may be replaced / changed with terms defined in other systems (or generalized terms not specific to one system). For example, PRACH, PUSCH, PUCCH, or SRS may be replaced / changed to uplink signals (or uplink channels). For example, SSB, CSI-RS, PDSCH, and PDCCH may be replaced / changed to downlink signals (or downlink channels).

[0372] Hereinafter, an apparatus to which the embodiments of the present specification can be applied (an apparatus implementing the method / operation according to the embodiments of the present specification) will be described with reference to FIG. 10.

[0373] FIG. 10 is a drawing showing the configuration of a first device and a second device according to an embodiment of the present specification.

[0374] The first device (100) may include a processor (110), an antenna unit (120), a transceiver (130), and a memory (140).

[0375] The processor (110) performs baseband-related signal processing and may include an upper layer processing unit (111) and a physical layer processing unit (115). The upper layer processing unit (111) may process operations of the MAC layer, RRC layer, or higher upper layers. The physical layer processing unit (115) may process operations of the PHY layer. For example, if the first device (100) is a base station device in base station-terminal communication, the physical layer processing unit (115) may perform uplink reception signal processing, downlink transmission signal processing, etc. For example, if the first device (100) is a first terminal device in terminal-terminal communication, the physical layer processing unit (115) may perform downlink reception signal processing, uplink transmission signal processing, sidelink transmission signal processing, etc. In addition to performing baseband-related signal processing, the processor (110) may also control the overall operation of the first device (100).

[0376] The antenna section (120) may include one or more physical antennas, and if it includes multiple antennas, it may support MIMO transmission and reception. The transceiver (130) may include an RF (Radio Frequency) transmitter and an RF receiver. The memory (140) may store information processed by the processor (110) and software, operating systems, applications, etc. related to the operation of the first device (100), and may include components such as a buffer.

[0377] The processor (110) of the first device (100) may be configured to implement the operation of the base station in base station-terminal communication (or the operation of the first terminal device in terminal-terminal communication) in the embodiments described in this disclosure.

[0378] The second device (200) may include a processor (210), an antenna unit (220), a transceiver (230), and a memory (240).

[0379] The processor (210) performs baseband-related signal processing and may include an upper layer processing unit (211) and a physical layer processing unit (215). The upper layer processing unit (211) may process operations of the MAC layer, RRC layer, or higher upper layers. The physical layer processing unit (215) may process operations of the PHY layer. For example, if the second device (200) is a terminal device in base station-terminal communication, the physical layer processing unit (215) may perform downlink reception signal processing, uplink transmission signal processing, etc. For example, if the second device (200) is a second terminal device in terminal-terminal communication, the physical layer processing unit (215) may perform downlink reception signal processing, uplink transmission signal processing, sidelink reception signal processing, etc. In addition to performing baseband-related signal processing, the processor (210) may also control the overall operation of the second device (210).

[0380] The antenna section (220) may include one or more physical antennas, and may support MIMO transmission and reception if it includes multiple antennas. The transceiver (230) may include an RF transmitter and an RF receiver. The memory (240) may store information processed by the processor (210) and software, operating systems, applications, etc. related to the operation of the second device (200), and may include components such as a buffer.

[0381] The processor (210) of the second device (200) may be configured to implement the operation of the terminal in base station-terminal communication (or the operation of the second terminal device in terminal-terminal communication) in the embodiments described in this disclosure.

[0382] In the operation of the first device (100) and the second device (200), the details described in the examples of the present disclosure regarding the base station and terminal (or the first terminal and the second terminal in terminal-to-terminal communication) in base station-to-terminal communication may be applied in the same way, and redundant descriptions are omitted.

[0383] Here, the wireless communication technology implemented in the device of the present disclosure may include LTE, NR, and 6G, as well as Narrowband Internet of Things (NB-IoT) for low-power communication. For example, NB-IoT technology may be an example of Low Power Wide Area Network (LPWAN) technology and may be implemented according to standards such as LTE Cat NB1 and / or LTE Cat NB2, but is not limited to the names mentioned above.

[0384] Additionally or alternatively, the wireless communication technology implemented in the device of the present disclosure may perform communication based on LTE-M technology. For example, LTE-M technology may be an example of LPWAN technology and may be referred to by various names such as eMTC (enhanced Machine Type Communication). For example, LTE-M technology may be implemented in at least one of various standards such as 1) LTE CAT 0, 2) LTE Cat M1, 3) LTE Cat M2, 4) LTE non-BL (non-Bandwidth Limited), 5) LTE-MTC, 6) LTE Machine Type Communication, and / or 7) LTE M, and is not limited to the names mentioned above.

[0385] Additionally or generally, the wireless communication technology implemented in the device of the present disclosure may include at least one of ZigBee, Bluetooth, and a Low Power Wide Area Network (LPWAN) for low-power communication, but is not limited to the names mentioned above. For example, ZigBee technology can create personal area networks (PANs) related to small / low-power digital communication based on various standards such as IEEE 802.15.4 and may be referred to by various names.

Claims

1. Regarding the method, A step of receiving configuration information including information about a Sounding Reference Signal (SRS) resource; and The method includes the step of transmitting an SRS based on the above SRS resources; The above SRS is transmitted based on a subset of multiple ports configured in the above SRS resource, and A method characterized in that the above configuration information includes information indicating a group related to the subset among a plurality of groups configured based on the coherency between the plurality of ports.

2. In Paragraph 1, A method characterized by the above setting information including information related to the number of ports of the above subset.

3. In Paragraph 2, A method characterized by indicating, based on the information related to the number of ports, i) the ratio of the number of ports to the number of the plurality of ports or ii) the number of ports of the subset.

4. In Paragraph 1, A method characterized by indicating a group belonging to a subset among the plurality of groups based on the information indicating the group.

5. In Paragraph 4, A method characterized in that the group belonging to the above subset is changed to another group among the plurality of groups at each period.

6. In Paragraph 1, A method characterized by indicating at least one port belonging to the subset for each group among the plurality of groups based on the information representing the group.

7. In Paragraph 6, A method characterized in that at least one port for each of the above groups is changed to at least one other port within the same group at each period.

8. In Paragraph 1, The above SRS is transmitted based on the above subset in a slot based on the first period, and A method characterized in that the above SRS is transmitted based on the plurality of ports in a slot based on a second period larger than the first period.

9. In Paragraph 8, The above SRS resource is based on a single SRS resource to which the above first cycle and the above second cycle are applied, and A value representing the first period or the second period is set in the above SRS resource, and A method characterized in that the second period or the first period is determined based on the value.

10. In Paragraph 8, The above SRS resource includes a first SRS resource and a second SRS resource associated with the plurality of ports, and The above first cycle is applied to the above first SRS resource, and A method characterized in that the above second cycle is applied to the above second SRS resource.

11. In Paragraph 10, A method characterized in that the transmission of the SRS based on the second SRS resource has a higher priority than the transmission of the SRS based on the first SRS resource.

12. In Paragraph 8, The above SRS resource is based on a single SRS resource to which the above first cycle and the above second cycle are applied, and The first period is a periododicity related to the muting or disabling of the remaining ports, excluding the subset, among the plurality of ports configured in the SRS resource, and A method characterized in that the second period is a periododicity associated with the enable of the plurality of ports.

13. In the terminal, One or more transmitters / receivers; One or more processors; and It includes one or more memories connected to the above one or more processors and storing instructions, A terminal characterized by the above instructions enabling the terminal to perform all steps of the method according to any one of claims 1 to 12, based on execution by the one or more processors.

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

15. In a non-transitory computer-readable storage medium for storing instructions, A non-transitory computer-readable storage medium characterized by instructions executable by one or more processors such that the terminal performs all steps of the method according to any one of claims 1 to 12.

16. Regarding the method, A step of transmitting configuration information including information about a Sounding Reference Signal (SRS) resource; and The method includes the step of receiving an SRS based on the above SRS resources; The above SRS is received based on a subset of multiple ports configured in the above SRS resource, and A method characterized in that the above configuration information includes information indicating a group related to the subset among a plurality of groups configured based on the coherency between the plurality of ports.

17. Regarding base stations, One or more transmitters / receivers; One or more processors; and It includes one or more memories connected to the above one or more processors and storing instructions, A base station characterized by the above instructions, based on execution by one or more processors, having the base station perform all steps of the method according to claim 16.

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