Terminal, wireless communication method and base station

Abstract

A terminal according to one embodiment of the present disclosure comprises: a reception unit for receiving a reference signal; and a control unit for exercising control to perform a prediction based on a second setting of the reference signal, using a machine learning model having been trained on the basis of a first setting of the reference signal. One embodiment of the present disclosure makes it possible to realize highly accurate channel estimation, highly efficient resource utilization, etc.

Description

[Technical field]

[0001] The present disclosure relates to a terminal, a wireless communication method, and a base station in a next-generation mobile communication system. [Background technology]

[0002] In the Universal Mobile Telecommunications System (UMTS) network, Long Term Evolution (LTE) has been specified for the purpose of achieving higher data rates and lower latency (Non-Patent Document 1). In addition, LTE-Advanced (3GPP Rel. 10-14) has been specified for the purpose of achieving higher capacity and greater sophistication of LTE (Third Generation Partnership Project (3GPP) Release (Rel.) 8, 9).

[0003] Successor systems to LTE (e.g., 5th generation mobile communication system (5G), 5G+ (plus), 6th generation mobile communication system (6G), New Radio (NR), 3GPP Rel. 15 or later, etc.) are also being considered. [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] 3GPP TS 36.300 V8.12.0 “Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Overall description; Stage 2 (Release 8)”, April 2010 Summary of the Invention [Problem to be solved by the invention]

[0005] Regarding future wireless communication technologies, the use of artificial intelligence (AI) techniques such as machine learning (ML) for network / device control and management is being considered.

[0006] However, when using an ML model in wireless communication, there is still no progress in considering which entity will implement the ML model, which entity will train the ML model, etc. If these are not properly specified, it may be difficult to achieve highly accurate channel estimation / highly efficient resource utilization, which may hinder improvement of communication throughput or communication quality.

[0007] Therefore, an object of the present disclosure is to provide a terminal, a wireless communication method, and a base station that can realize highly accurate channel estimation and highly efficient resource utilization. [Means for solving the problem]

[0008] A terminal according to one embodiment of the present disclosure has a receiving unit that receives a reference signal, and a control unit that controls making a prediction based on a second setting of the reference signal using a machine learning model trained based on a first setting of the reference signal. Effect of the Invention

[0009] According to one aspect of the present disclosure, highly accurate channel estimation, highly efficient resource utilization, and the like can be achieved. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram illustrating an example of resources for estimation. [Diagram 2] FIG. 2 is a diagram illustrating an example of a sequence for training an ML model according to embodiment A1. [Diagram 3]FIG. 3 is a diagram illustrating an example of a sequence for testing the ML model according to embodiment A1. [Figure 4] FIG. 4 is a diagram illustrating an example of a sequence for training an ML model according to embodiment A2. [Diagram 5] FIG. 5 is a diagram illustrating an example of a sequence for training the ML model according to embodiment B1. [Figure 6] FIG. 6 is a diagram illustrating an example of a sequence for testing the ML model according to embodiment B1. [Figure 7] FIG. 7 is a diagram illustrating an example of a sequence for training an ML model according to embodiment B2. [Figure 8] FIG. 8 is a diagram illustrating an example of a sequence for testing an ML model according to embodiment C1. [Figure 9] 9A and 9B are diagrams illustrating an example of mode switching control. [Figure 10] FIG. 10 is a diagram illustrating an example of transmission and reception of information according to the third embodiment. [Figure 11] FIG. 11 is a diagram showing an example of what technology is used in each embodiment of the present disclosure. [Figure 12] FIG. 12 is a diagram illustrating an example of a schematic configuration of a wireless communication system according to an embodiment. [Figure 13] FIG. 13 is a diagram illustrating an example of the configuration of a base station according to an embodiment. [Figure 14] FIG. 14 is a diagram illustrating an example of the configuration of a user terminal according to an embodiment. [Figure 15] FIG. 15 is a diagram illustrating an example of the hardware configuration of a base station and a user terminal according to an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] (Application of Artificial Intelligence (AI)) Technology to Wireless Communications) Regarding future wireless communication technologies, the use of AI technology for network / device control and management is being considered.

[0012] For example, in future wireless communication technologies, particularly in communications using beams, there is a demand for high accuracy in channel estimation (which may also be called channel measurement) for beam management, decoding of received signals, and the like.

[0013] Channel estimation may be performed using at least one of, for example, a Channel State Information Reference Signal (CSI-RS), a Synchronization Signal (SS), a Synchronization Signal / Physical Broadcast Channel (SS / PBCH) block, a DeModulation Reference Signal (DMRS), a Sounding Reference Signal (SRS), etc.

[0014] In conventional wireless communication technologies, highly accurate channel estimation requires a large amount of estimation resources (e.g., resources for transmitting reference signals) and requires channel estimation for all antenna ports used.

[0015] FIG. 1 is a diagram showing an example of estimation resources. In this example, a resource configuration for three slots of NR communication is illustrated. For example, in the first slot, a terminal (user terminal, also called User Equipment (UE)) receives Downlink Control Information (DCI) for scheduling a Downlink Shared Channel (Physical Downlink Shared Channel (PDSCH)), an Uplink Shared Channel (Physical Uplink Shared Channel (PUSCH)), etc., a PDSCH, a DMRS for the PDSCH, and a CSI-RS. In the second and third slots, the UE transmits a PUSCH and a DMRS for the PUSCH.

[0016] Increasing resources such as DMRS and CSI-RS to achieve highly accurate channel estimation reduces resources for data transmission and reception (eg, PDSCH resources and PUSCH resources).

[0017] In the future, it will be considered to realize highly accurate channel estimation with fewer resources by using AI techniques such as machine learning (ML). Such channel estimation may be called AI-aided estimating.

[0018] However, when using an ML model in wireless communication, there is still no progress in considering which entity will implement the ML model, which entity will train the ML model, etc. If these are not properly specified, it may be difficult to achieve highly accurate channel estimation / highly efficient resource utilization, which may hinder improvement of communication throughput or communication quality.

[0019] Therefore, the present inventors came up with the idea of ​​using an ML model, particularly for the physical layer (Physical (PHY) layer).

[0020] In one embodiment of the present disclosure, a UE / base station (BS) trains an ML model in a training mode and executes the ML model in a test mode (also called a test mode, etc.). In the test mode, validation of the accuracy of the ML model trained in the training mode may be performed.

[0021] In this disclosure, the UE / BS may input channel state information, reference signal measurements, etc. to the ML model and output highly accurate channel state information / measurements / beam selection / position, etc.

[0022] In this disclosure, AI may be interpreted as an object (also called a subject, object, data, function, program, etc.) having (performing) at least one of the following characteristics: - inferences based on observed or collected information; Choices based on observed or collected information; · Predictions based on observed or collected information.

[0023] In the present disclosure, the object may be, for example, an apparatus such as a terminal or a base station, a device, etc. Furthermore, the object may correspond to a program included in the apparatus.

[0024] In addition, in the present disclosure, an ML model may be interpreted as an object having (implementing) at least one of the following characteristics: - Producing estimates by feeding information, -Predicting estimates by giving information, - Discover features by providing information, · Selecting behavior by providing information.

[0025] In addition, in the present disclosure, the ML model may be interpreted as at least one of an AI model, predictive analytics, a predictive analysis model, etc. In addition, the ML model may be derived using at least one of regression analysis (e.g., linear regression analysis, multiple regression analysis, logistic regression analysis), a support vector machine, a random forest, a neural network, deep learning, etc. In the present disclosure, the model may be interpreted as at least one of an encoder, a decoder, a tool, etc.

[0026] Based on input information, the ML model outputs at least one piece of information, such as an estimate, a prediction, a selected action, a classification, etc.

[0027] ML models may include supervised learning, unsupervised learning, reinforcement learning, etc. Supervised learning may be used to learn general rules that map inputs to outputs. Unsupervised learning may be used to learn features of data. Reinforcement learning may be used to learn actions to maximize a goal.

[0028] Each embodiment described below will be mainly described assuming that supervised learning is used for the ML model, but is not limited to this.

[0029] In the present disclosure, "A / B" and "at least one of A and B" may be read as interchangeable.

[0030] In the present disclosure, terms such as activate, deactivate, indicate, select, configure, update, and determine may be interchangeable. In the present disclosure, terms such as support, control, controllable, operate, and operate may be interchangeable.

[0031] In the present disclosure, Radio Resource Control (RRC), RRC parameters, RRC messages, higher layer parameters, information elements (IEs), and configurations may be interchangeable. In the present disclosure, Medium Access Control (MAC) control elements (CEs), update commands, and activation / deactivation commands may be interchangeable.

[0032] In this disclosure, the terms index, ID, indicator, and resource ID may be interchangeable. In this disclosure, the terms sequence, list, set, group, cluster, and subset may be interchangeable.

[0033] In this disclosure, terms such as implementation, operation, operation, execution, etc. may be interchangeable. Also, in this disclosure, terms such as testing, after-training, live use, actual use, etc. may be interchangeable. Signal may be interchangeable with signal / channel.

[0034] In the present disclosure, the training mode may correspond to a mode in which the UE / BS transmits / receives signals for an ML model (in other words, a mode of operation during a training period). In the present disclosure, the test mode may correspond to a mode in which the UE / BS implements an ML model (e.g., implements a trained ML model to predict outputs) (in other words, a mode of operation during a test period).

[0035] In this disclosure, training mode may refer to a mode in which a particular signal transmitted in test mode is transmitted with a large overhead (e.g., a large amount of resources).

[0036] In the present disclosure, the training mode may refer to a mode that refers to a first configuration (e.g., a first DMRS configuration, a first CSI-RS configuration). In the present disclosure, the test mode may refer to a mode that refers to a second configuration (e.g., a second DMRS configuration, a second CSI-RS configuration) different from the first configuration. The first configuration may be configured with at least one of more time resources, frequency resources, code resources, and ports (antenna ports) related to measurements than the second configuration.

[0037] Hereinafter, embodiments according to the present disclosure will be described in detail with reference to the drawings. Wireless communication methods according to the embodiments may be applied independently or in combination.

[0038] In the following embodiments, to explain the ML model for communication between UE and BS, the relevant subjects are UE and BS, but the application of each embodiment of the present disclosure is not limited to this. For example, for communication between other subjects (e.g., communication between UE and UE), the UE and BS in the following embodiments may be read as a first UE and a second UE. In other words, the UE, BS, etc. in the present disclosure may be read as any UE / BS.

[0039] (Wireless communication method) <First embodiment> In the first embodiment, which entity implements the ML model and which entity trains the ML model will be described. For the former, there are three embodiments AC, and for the latter, there are three embodiments 1-3, and by combining these, there are nine embodiments A1-C3.

[0040] A specific example of the use of an ML model will be described at the end of the first embodiment.

[0041] [Embodiment A1] In embodiment A1, the ML model trained by the BS is implemented by the BS.

[0042] 2 is a diagram showing an example of a sequence for training the ML model according to embodiment A1. The UE calculates information for training the ML model (step S101). The UE transmits at least one of the information calculated in step S101 and a signal for training the ML model to the BS (step S102). The BS trains the ML model based on the information / signal received in step S102 (step S103). Note that, when a signal for training the ML model is transmitted in step S102, step S101 may be omitted.

[0043] In addition, in the present disclosure, information for training an ML model may be an ML model report described later in the third embodiment, or may be information used as input to the ML model.

[0044] In step S101, the UE may calculate information for training an ML model based on information / signals received from the BS.

[0045] 3 is a diagram showing an example of a sequence for testing an ML model according to embodiment A1. The UE calculates information for input of the ML model (step S201). The UE transmits at least one of the information calculated in step S201 and a signal for testing the ML model to the BS (step S202). The BS executes the trained ML model based on the information / signal received in step S202 (for example, using the information as input) (step S203). Note that, when transmitting a signal for testing the ML model in step S202, step S201 may be omitted.

[0046] In addition, in this disclosure, testing of an ML model may mean testing a trained ML model.

[0047] [Embodiment A2] In embodiment A2, the BS implements the ML model trained by the UE.

[0048] 4 is a diagram showing an example of a sequence for training an ML model according to embodiment A2. The UE trains the ML model (step S111). The UE transmits information on the ML model trained in step S111 to the BS (step S112).

[0049] In step S111, the UE may perform training of the ML model based on the information / signal received from the BS. For example, the UE may perform training of the ML model based on at least one of the information for training the ML model and the signal for training the ML model.

[0050] The information on the trained ML model in step S112 will be described later in the third embodiment.

[0051] The sequence for testing the ML model according to embodiment A2 may be similar to embodiment A1, except that the trained ML model is based on the information of the trained ML model in step S112.

[0052] [Embodiment A3] In embodiment A3, the BS implements the ML model that is trained by both the UE and the BS.

[0053] The training of the ML model according to embodiment A3 corresponds to a case where the training of the ML models according to embodiments A1 and A2 is performed (for example, in parallel, simultaneously, at different times). That is, the BS trains the ML model as shown in Fig. 2, and obtains the trained ML model. Meanwhile, the UE trains the ML model as shown in Fig. 4, and obtains the trained ML model, and transmits information of the trained ML model to the BS.

[0054] The BS may update (e.g., fine-tune) the ML model that it has trained based on information of the ML model that the UE has trained and received from the UE. The BS may also perform transfer learning of the ML model based on information of the ML model that the UE has trained and received from the UE. The BS may also perform the ML model by combining the ML model that it has trained with the ML model that it has received from the UE (e.g., the output of the ML model that it has received from the UE may be used as the input of the ML model that it has trained). The BS may also perform the above-mentioned update, transfer learning, and the like based on information of multiple trained ML models that it has received from multiple UEs with which it communicates.

[0055] The sequence for testing the ML model according to embodiment A3 may be the same as that of embodiment A1, except that the trained ML model corresponds to the ML model to which the above-mentioned update / transfer learning is applied.

[0056] [Embodiment B1] In embodiment B1, the UE implements the ML model trained by the BS.

[0057] Fig. 5 is a diagram showing an example of a sequence for training an ML model according to embodiment B1. Steps S301, S302, and S303 may be similar to steps S101, S102, and S103 in Fig. 2, respectively, and therefore will not be described repeatedly.

[0058] The UE receives from the BS information on the ML model trained by the BS in step S303 (step S304). The information on the ML model trained in step S304 will be described later in the third embodiment.

[0059] 6 is a diagram showing an example of a sequence for testing an ML model according to embodiment B1. The UE receives at least one of information for inputting the ML model and a signal for testing the ML model from the BS (step S401). The UE executes the trained ML model based on the information / signal received in step S401 (for example, using the above information as input) (step S402).

[0060] [Embodiment B2] In embodiment B2, the UE implements the ML model trained by the UE.

[0061] 7 is a diagram showing an example of a sequence for training an ML model according to embodiment B2. The UE receives at least one of information for training the ML model and a signal for training the ML model from the BS (step S311). The BS performs training of the ML model based on the information / signal received in step S311 (step S312).

[0062] The sequence for testing the ML model according to embodiment B2 may be similar to that of embodiment B1.

[0063] [Embodiment B3] In embodiment B3, the UE implements an ML model that is trained by both the UE and the BS.

[0064] The training of the ML model according to embodiment B3 corresponds to a case where the training of the ML models according to embodiments B1 and B2 is performed (for example, in parallel, simultaneously, at different times). That is, the BS trains the ML model as shown in Fig. 5, obtains the trained ML model, and transmits information of the trained ML model to the UE. Meanwhile, the UE trains the ML model as shown in Fig. 7, and obtains the trained ML model.

[0065] The UE may update (e.g., fine-tune) the ML model that it has trained based on information of the ML model trained by the BS received from the BS. The UE may also perform transfer learning of the ML model based on information of the ML model trained by the BS received from the BS. The UE may also perform a combination of the ML model that it has trained and the ML model received from the BS (e.g., the output of the ML model received from the BS may be used as an input of the ML model that it has trained). The UE may also perform the above-mentioned update, transfer learning, etc. based on information of multiple trained ML models received from one or more BSs / one or more other UEs with which it communicates.

[0066] The sequence for testing the ML model according to embodiment B3 may be the same as that of embodiment B1, except that the trained ML model corresponds to the ML model to which the above-mentioned update / transfer learning is applied.

[0067] [Embodiment C1] In embodiment C1, the ML model trained by the BS is implemented by both the UE and the BS. Note that in embodiments C1-C3, the trained ML model may be composed of multiple ML models, and may include an ML model implemented by the UE and an ML model implemented by the BS.

[0068] The sequence for training the ML model according to embodiment C1 may be the same as that according to embodiment B1. However, the information of the trained ML model notified to the UE may be the information of the ML model implemented by the UE among the ML models trained by the BS.

[0069] 8 is a diagram showing an example of a sequence for testing an ML model according to embodiment C1. The UE receives at least one of information for inputting the ML model and a signal for testing the ML model from the BS (step S601). The UE executes the trained ML model based on the information / signal received in step S401 (for example, using the above information as input) (step S602).

[0070] The UE transmits the output of the ML model in step S602 to the BS (step S603). The BS uses the output of the ML model of the UE transmitted in step S603 as input to implement the trained ML model (step S604).

[0071] [Embodiment C2] In embodiment C2, the ML model trained by the UE is implemented by both the UE and the BS.

[0072] The sequence for training the ML model according to embodiment C1 may be the same as that according to embodiment A2. However, the information of the trained ML model notified to the BS may be the information of the ML model implemented by the BS among the ML models trained by the UE.

[0073] The sequence for testing the ML model according to embodiment C2 may be similar to that of embodiment C1.

[0074] [Embodiment C3] In embodiment C3, the UE and the BS implement an ML model that has been trained by both the UE and the BS.

[0075] The training of the ML model according to embodiment C3 corresponds to a case where the training of the ML models according to embodiments C1 and C2 is performed (for example, in parallel, simultaneously, at different times). That is, the BS trains the ML model as in embodiment C1, obtains the trained ML model, and transmits information of the trained ML model to the UE. On the other hand, the UE trains the ML model as in embodiment C2, obtains the trained ML model, and transmits information of the trained ML model to the BS.

[0076] Note that information about the trained ML models does not necessarily need to be transmitted to each other.

[0077] The BS may update (e.g., fine-tune) the ML model that it has trained based on information of the ML model that the UE has trained and received from the UE. The BS may also perform transfer learning of the ML model based on information of the ML model that the UE has trained and received from the UE. The BS may also perform a combination of the ML model that it has trained and the ML model that it has received from the UE (e.g., the output of the ML model that it has received from the UE may be used as an input of the ML model that it has trained). The BS may also perform the above-mentioned update, transfer learning, and the like based on information of multiple trained ML models that it has received from multiple UEs with which it communicates.

[0078] The UE may update (e.g., fine-tune) the ML model that it has trained based on information of the ML model trained by the BS received from the BS. The UE may also perform transfer learning of the ML model based on information of the ML model trained by the BS received from the BS. The UE may also perform a combination of the ML model that it has trained and the ML model received from the BS (e.g., the output of the ML model received from the BS may be used as an input of the ML model that it has trained). The UE may also perform the above-mentioned update, transfer learning, etc. based on information of multiple trained ML models received from one or more BSs / one or more other UEs with which it communicates.

[0079] The sequence for testing the ML model according to embodiment C3 may be the same as that of embodiment C1, except that the trained ML model corresponds to the ML model to which the above-mentioned update / transfer learning is applied.

[0080] [Specific examples of using ML models] [[AI-assisted DMRS estimation]] The ML model in the first embodiment may be used for AI-aided DMRS estimation. The input of the ML model may be a low-density DMRS (or a measurement result based thereon), and the output may be a high-density DMRS (or a measurement result based thereon). By using this ML model, it is possible to estimate high-density DMRS measurements while reducing the communication overhead of the DMRS.

[0081] In addition, in the present disclosure, "density" may refer to the density or number of at least one of time resources, frequency resources, code resources, and ports (antenna ports).

[0082] The period of the high-density DMRS as the training signal is preferably set in the UE. The UE / BS may use the measurement result of the high-density DMRS (training DMRS) to train an ML model for learning the correlation between resource elements of the DMRS. The UE / BS may use the trained ML model to perform channel estimation based on the low-density DMRS (test DMRS).

[0083] Any one or any combination of measurement results based on a high-density DMRS, estimation results based on a low-density DMRS extracted from the high-density DMRS, density / resource information of the high-density DMRS, and density / resource information of the low-density DMRS corresponds to training data for the ML model.

[0084] For modeling of the ML model for AI-assisted DMRS estimation, it is preferable to use at least one of the embodiments A1, A3, B2, B3, etc.

[0085] [[AI-assisted CSI-RS estimation]] The ML model in the first embodiment may be used for AI-assisted CSI-RS estimation. The input of the ML model may be a low-density CSI-RS (or a measurement result based thereon), and the output may be a high-density CSI-RS (or a measurement result based thereon). By using this ML model, it is possible to estimate high-density CSI-RS measurements while reducing the communication overhead of the CSI-RS.

[0086] Preferably, CSI resource indicators for the high-density DMRS (training DMRS) and the low-density DMRS (test DMRS) and CSI report quality are configured in the UE. The UE may train an ML model using the measurement results of the high-density CSI-RS / low-density DMRS. The UE may use the trained ML model to perform channel estimation equivalent to the high-density CSI-RS based on the low-density CSI-RS and perform CSI reporting.

[0087] The training data for the ML model includes any one or any combination of measurement results based on a high-density CSI-RS, estimation results based on a low-density CSI-RS extracted from the high-density CSI-RS, density / resource information of the high-density CSI-RS, and density / resource information of the low-density CSI-RS.

[0088] For modeling an ML model for AI-assisted CSI-RS estimation, it is preferable to use, for example, embodiment B2.

[0089] [[AI-Assisted CSI Report]] The ML model in the first embodiment may be used for AI-assisted CSI reporting. The input of the ML model may be a low-overhead CSI report (e.g., an existing CSI report including PMI, etc.), and the output may be an accurate high-overhead CSI report (e.g., a detailed channel matrix, a UE position, an optimal PMI, etc.). By using this ML model, it is possible to estimate a highly accurate CSI report while reducing the communication overhead of the CSI report.

[0090] The content of each CSI report is preferably configured in the UE. The BS may use the low-overhead / high-overhead CSI reports to train an ML model. The BS may use the trained ML model (which may be referred to as an AI decoder) to recover the high-overhead CSI report based on the low-overhead CSI report.

[0091] The low-overhead CSI report, the high-overhead CSI report, CSI-RS information (e.g., density) corresponding to the low-overhead CSI report, and CSI-RS information corresponding to the high-overhead CSI report, or any combination of these, corresponds to training data for the ML model.

[0092] For modeling the ML model for this AI-assisted CSI report, it is preferable to use, for example, embodiments A1 and A2.

[0093] An ML model for AI-assisted CSI reporting may be used at both the BS and the UE. The input of this ML model is the received CSI-RS (or its estimation), and the output may be accurate CSI (e.g., detailed channel matrix, UE location, optimal PMI, etc.). The use of this ML model allows for high-accuracy CSI estimation while reducing the CSI communication overhead.

[0094] Preferably, the CSI resource indicator, encoded information size, etc. are configured in the UE. The UE may train a model using the received CSI-RS and the encoded information size. The BS may train an ML model that takes the encoded information as input and outputs accurate CSI. The UE may encode or compress the CSI based on the received CSI-RS using the trained ML model (which may be referred to as an AI encoder) and transmit it. The BS may decode the received encoded or compressed CSI using the trained ML model (which may be referred to as an AI decoder) to obtain accurate CSI.

[0095] The CSI-RS estimation result, the encoded information size, the accurate CSI, and / or information on the CSI-RS (e.g., density) correspond to training data for the ML model.

[0096] For modeling the ML model for this AI-assisted CSI report, it is preferable to use, for example, embodiments C2 and C3.

[0097] According to the first embodiment described above, an ML model for wireless communication trained by an appropriate subject can be used.

[0098] <Second embodiment> The second embodiment concerns the distinction between training and test modes.

[0099] The UE / BS may determine which mode the UE / BS is in based on at least one of the following: (2-1) Reporting mode information to BS, (2-2) Receiving mode information (instructions) from the BS, (2-3) Specific rules / patterns.

[0100] The information in (2-1) above may be transmitted using physical layer signaling (e.g., UCI), higher layer signaling (e.g., RRC signaling, MAC CE), a specific signal / channel, or a combination thereof.

[0101] The information in (2-2) above may be transmitted using physical layer signaling (e.g., DCI), higher layer signaling (e.g., RRC signaling, MAC CE), a specific signal / channel, or a combination thereof.

[0102] The rules / patterns in (2-3) above may be determined in advance by a specification, or may be notified using physical layer signaling (e.g., DCI), higher layer signaling (e.g., RRC signaling, MAC CE), a specific signal / channel, or a combination of these.

[0103] The information regarding the modes (2-1) and (2-2) above may be referred to as trigger information, and may include information indicating a training mode or a test mode, may include information indicating a mode switch (or indicating that a mode has been switched / ended), may include information indicating that a specific mode (e.g., a test mode) may be performed, or may include information indicating that after receiving the trigger information, an operation will be performed based on a specific rule / pattern (2-3) above.

[0104] The mode-related information may indicate the number of samples until training / testing is completed (e.g., 50, 100, 200 samples). A UE operating in training (test) mode may transition to test (training) mode after transmitting / receiving the number of samples.

[0105] The information about the mode may also indicate the time (e.g., number of slots (e.g., 5, 10 slots), number of symbols) until training / test is completed. For example, a UE operating in training (test) mode may transition to test (training) mode after the time has elapsed.

[0106] 9A and 9B are diagrams showing an example of mode switching control. In this example, it is assumed that the mode operation can be switched on a slot-by-slot basis.

[0107] In Fig. 9A, in the first four slots shown, the UE operates in training mode. In the fifth slot, the UE receives trigger information (e.g., MAC CE). The trigger information specifies an operation of switching between test mode and training mode for each slot. Note that in Fig. 9A, the mode switching operation is performed from the next slot after the trigger information is received, but this operation may start a certain time after the trigger information is received, or after an ACKnowledge (ACK) for the trigger information is transmitted (or a certain time after this transmission).

[0108] In Fig. 9B, trigger information is notified based on DCI. A UE that has been operating in training mode (e.g., training an ML model of a PDSCH DMRS) in the first two slots shown in the figure may operate in test mode from the next fourth slot onwards when it receives DCI indicating an instruction to end training in the third slot.

[0109] According to the second embodiment described above, the timing of the training mode and the test mode can be appropriately controlled.

[0110] <Third embodiment> The third embodiment relates to the transmission and reception of information of an ML model.

[0111] Information of at least one of the trained ML model and the pre-trained ML model (hereinafter also referred to as ML model information, ML model report, etc.) may be transmitted / received from one entity (UE / BS) to another entity (BS / UE / another UE / another BS).

[0112] The UE / BS may train / tune / update (e.g., fine-tune) / transfer learn an ML model based on information from a received, trained, or pre-trained ML model, and may also combine a ML model that it has trained with a received ML model (e.g., use the output of a received ML model as input for a ML model that it has trained).

[0113] The pre-trained ML model may be selected from a plurality of models based on the location (location information) of the UE / BS, channel measurement results at the UE / BS, etc. Information on the pre-trained ML model may be stored in the UE / BS in advance, or may be set by higher layer signaling.

[0114] Performance information (performance report) of an ML model may be transmitted / received from one entity to another entity. The performance report may be transmitted together with the ML model information or may be transmitted separately. The performance report may include at least one of the estimation (prediction) accuracy (or error) of an ML model, the number of samples used to train the ML model, etc.

[0115] 10 is a diagram showing an example of information transmission and reception according to the third embodiment. In this example, trained ML model reports (which may be accompanied by performance reports) may be transmitted and received between BS-UE1, BS-UE2, UE1-UE2, etc. Each UE / BS may use the received information to update (tune) the ML model.

[0116] The ML model report / performance report (hereinafter also simply referred to as the ML model report) may include at least one of one or more weights used in each layer of the ML model, information on the pre-trained ML model on which the ML model is based, information on a specific ML model (which may be called a reference ML model), and differences from the reference ML model (e.g., differences in each weight, differences in the number of intermediate layers, etc.). Here, the information on the pre-trained ML model / reference ML model may be, for example, indexes of one or more ML models when multiple ML models are indexed (numbered).

[0117] The parameters of the ML model indicated by the ML model report may be indicated at a predetermined granularity (unit). The granularity of the parameters of the ML model may be at least one of the input parameters, output parameters, and intermediate layer parameters (e.g., weights, hyperparameters (e.g., the number of intermediate layers), etc.) of the ML model.

[0118] The granularity of the parameters of the ML model may be determined in advance by a specification (may be determined by a specific rule), or may be notified to the UE / BS using physical layer signaling (e.g., DCI, UCI), higher layer signaling (e.g., RRC signaling, MAC CE), a specific signal / channel, or a combination of these. The notified granularity may be called recommended granularity, specified granularity, etc.

[0119] The parameters of the ML model indicated by the ML model report may be expressed as at least one of absolute values ​​and relative values, and the parameters of the ML model indicated by the ML model report may be expressed as at least one of discrete values ​​(or digital values) and continuous values ​​(or analog values).

[0120] The ML model report may be transmitted using physical layer signaling (e.g., DCI, UCI), higher layer signaling (e.g., RRC signaling, MAC CE), a specific signal / channel, or a combination of these. The ML model report / performance report may be transmitted in the same cell as the cell for this ML model (the cell for which the ML model is used) or in a different cell (e.g., a cell for which the ML model is not used, a primary cell, a specific secondary cell, etc.).

[0121] The ML model report may be transmitted and received periodically / semi-persistently / non-periodically. The transmission period of the ML model report may be set in the UE by higher layer signaling. When the periodic transmission / reception of the ML model report is configured by the higher layer signaling, the UE may perform the periodic transmission / reception of the ML model report. When the semi-persistent transmission / reception of the ML model report is triggered by the DCI, the UE may perform the semi-persistent transmission / reception of the ML model report.

[0122] Regarding transmission and reception of aperiodic ML model reports, the UE may transmit / receive the ML model report when requested (or triggered) by another UE / BS. The UE may also transmit / receive the ML model report when a condition is satisfied. The condition may be predetermined by a specification, or information regarding the condition may be notified from the other UE / BS using higher layer signaling or the like. The condition may be at least one of, for example, the reception quality (e.g., Reference Signal Received Power (RSRP)) of a specific channel / signal falling below a threshold, the specific channel / signal being retransmitted (or decoding failed), and the like.

[0123] In addition, the UE may transmit information about the UE (which may be referred to as UE information) to the BS to prompt the BS to select a pre-trained ML model to be used by the UE. The UE information may be information for identifying the location of the UE, and for example, the location information of the present disclosure may include at least one of information obtained using a positioning system such as a Global Positioning System (GPS), information of a base station adjacent to (or serving) the UE (e.g., a base station / cell identifier (ID), a BS-UE distance, a direction of the UE as seen from the BS, etc.), and a specific address of the UE (e.g., an Internet Protocol (IP) address). The UE information may also include a reference signal for positioning (e.g., a Positioning Reference Signal (PRS), a CSI-RS, an SRS, etc.).

[0124] The UE information may include information about its implementation (e.g., antenna location / position / orientation, antenna panel location / orientation, number of antennas, number of antenna panels, etc.).

[0125] According to the third embodiment described above, ML model information can be suitably shared among a plurality of devices (UE, BS).

[0126] <Fourth embodiment> A fourth embodiment relates to reporting a desired form of information for an ML model.

[0127] The UE may report or receive from another entity (BS / another UE) a desired form of information (which may be referred to as a desired form of information) for training the ML model. The desired form may be reported using physical layer signaling (e.g., DCI, UCI), higher layer signaling (e.g., RRC signaling, MAC CE), a specific signal / channel, or a combination thereof.

[0128] The UE may report to or receive from another entity (BS / another UE) the desired format of information for input of the ML model (in other words, information for testing of the ML model).

[0129] Note that the desired format may refer to at least one of, for example, a desired density, a desired period (or offset), etc., for a reference signal (e.g., DMRS, CSI-RS) to measure for training / input of an ML model.

[0130] For example, in the case of AI-assisted CSI-RS estimation, the UE may calculate a density / resource pattern that is preferred for training an ML model (in other words, that is likely to contribute to improving the estimation accuracy of the ML model) based on measurement results of the CSI-RS transmitted from the BS, and report information about the preferred density / resource pattern to the BS, which may then transmit the CSI-RS using the preferred density / resource pattern.

[0131] The desired format reports of the fourth embodiment may be sent and received periodically / semi-persistently / aperiodically in a similar manner as described for the ML model reports of the third embodiment.

[0132] According to the fourth embodiment described above, it is possible to appropriately specify the configuration of the reference signal for improving the accuracy of the ML model.

[0133] <Fifth embodiment> The fifth embodiment relates to application conditions of an ML model.

[0134] The conditions for applying the ML model may be determined in advance by a specification, or may be notified to the UE / BS using physical layer signaling (e.g., DCI, UCI), higher layer signaling (e.g., RRC signaling, MAC CE), a specific signal / channel, or a combination of these.

[0135] It may be assumed that a UE under conditions in which an ML model is not applicable cannot train the ML model or perform estimation using the ML model (e.g., perform estimation operations specified up to Rel. 16) until the conditions in which the ML model is applicable are met.

[0136] In addition, sharing of ML models (in other words, notifying other UEs of information about an ML model trained by a UE and using it) may be performed based on at least one of the following: the UE's position (e.g., the distance between the BS and the UE, the direction of the UE from the BS, etc.), the UE's implementation (e.g., the antenna position, the number of antennas, etc.), the time elapsed since training, etc.

[0137] According to the fifth embodiment described above, the ML model can be appropriately applied only to favorable UE / BS.

[0138] <Other> FIG. 11 is a diagram showing an example of what technology is used for each embodiment of the present disclosure. Note that a circle (o) may mean that it can be applied, and may not mean that it must be applied. Also, a cross (x) may mean that it does not apply. Note that this is merely an example, and the combinations of o and x for each embodiment are not limited to those shown.

[0139] At least one of the above embodiments may be applied only to UEs that have reported or support a particular UE capability.

[0140] The specific UE capabilities may indicate at least one of the following: Whether to support AI-assisted PHY operation; Whether to support AI-assisted DMRS estimation; Whether to support AI-assisted CSI-RS estimation; Whether to support AI-assisted CSI reporting; Whether or not you can train an ML model, Whether or not you can report capabilities for pre-trained ML models; Whether to support extended DMRS configuration; Whether to support extended CSI-RS resource mapping; Whether or not a particular sampling pattern is supported; Whether to support notification of ML model information, · Whether to support distinguishing between training mode and test mode.

[0141] The UE capabilities may be reported per frequency, per frequency range (e.g., Frequency Range 1 (FR1), Frequency Range 2 (FR2)), or per subcarrier spacing (SubCarrier Spacing (SCS)). In other words, the UE may apply ML model-based control only to a specific frequency / FR / SCS.

[0142] The UE capabilities may be reported jointly for Time Division Duplex (TDD) and Frequency Division Duplex (FDD), or may be reported independently.

[0143] Also, at least one of the above-mentioned embodiments may be applied when the UE is configured with specific information related to the above-mentioned embodiments by higher layer signaling. For example, the specific information may be information indicating that the ML model-based control is enabled, any RRC parameters for a specific release (e.g., Rel. 18), etc.

[0144] (Wireless communication system) A configuration of a wireless communication system according to an embodiment of the present disclosure will be described below. In this wireless communication system, communication is performed using any one of the wireless communication methods according to the above embodiments of the present disclosure or a combination of these methods.

[0145] 12 is a diagram showing an example of a schematic configuration of a wireless communication system according to an embodiment. The wireless communication system 1 may be a system that realizes communication using Long Term Evolution (LTE), 5th generation mobile communication system New Radio (5G NR), or the like, which are specified by the Third Generation Partnership Project (3GPP).

[0146] Furthermore, the wireless communication system 1 may support dual connectivity between a plurality of Radio Access Technologies (RATs) (Multi-RAT Dual Connectivity (MR-DC)). MR-DC may include dual connectivity between LTE (Evolved Universal Terrestrial Radio Access (E-UTRA)) and NR (E-UTRA-NR Dual Connectivity (EN-DC)), dual connectivity between NR and LTE (NR-E-UTRA Dual Connectivity (NE-DC)), and the like.

[0147] In EN-DC, the LTE (E-UTRA) base station (eNB) is the master node (MN) and the NR base station (gNB) is the secondary node (SN). In NE-DC, the NR base station (gNB) is the MN and the LTE (E-UTRA) base station (eNB) is the SN.

[0148] The wireless communication system 1 may support dual connectivity between multiple base stations in the same RAT (for example, dual connectivity in which both the MN and the SN are NR base stations (gNBs) (NR-NR Dual Connectivity (NN-DC))).

[0149] The wireless communication system 1 may include a base station 11 that forms a macrocell C1 with a relatively wide coverage, and base stations 12 (12a-12c) that are arranged in the macrocell C1 and form a small cell C2 that is narrower than the macrocell C1. A user terminal 20 may be located in at least one of the cells. The arrangement and number of each cell and user terminal 20 are not limited to the embodiment shown in the figure. Hereinafter, when there is no need to distinguish between the base stations 11 and 12, they will be collectively referred to as a base station 10.

[0150] The user terminal 20 may be connected to at least one of the multiple base stations 10. The user terminal 20 may utilize at least one of carrier aggregation (CA) using multiple component carriers (CC) and dual connectivity (DC).

[0151] Each CC may be included in at least one of a first frequency band (Frequency Range 1 (FR1)) and a second frequency band (Frequency Range 2 (FR2)). The macro cell C1 may be included in FR1, and the small cell C2 may be included in FR2. For example, FR1 may be a frequency band of 6 GHz or less (sub-6 GHz), and FR2 may be a frequency band above 24 GHz (above-24 GHz). Note that the frequency bands and definitions of FR1 and FR2 are not limited to these, and for example, FR1 may correspond to a frequency band higher than FR2.

[0152] Furthermore, the user terminal 20 may perform communication in each CC using at least one of Time Division Duplex (TDD) and Frequency Division Duplex (FDD).

[0153] The multiple base stations 10 may be connected by wire (e.g., optical fiber conforming to Common Public Radio Interface (CPRI), X2 interface, etc.) or wirelessly (e.g., NR communication). For example, when NR communication is used as a backhaul between the base stations 11 and 12, the base station 11 corresponding to the upper station may be called an Integrated Access Backhaul (IAB) donor, and the base station 12 corresponding to the relay station (relay) may be called an IAB node.

[0154] The base station 10 may be connected to the core network 30 directly or via another base station 10. The core network 30 may include at least one of, for example, an Evolved Packet Core (EPC), a 5G Core Network (5GCN), a Next Generation Core (NGC), and the like.

[0155] The user terminal 20 may be a terminal compatible with at least one of communication methods such as LTE, LTE-A, and 5G.

[0156] In the wireless communication system 1, a wireless access scheme based on Orthogonal Frequency Division Multiplexing (OFDM) may be used. For example, in at least one of the downlink (DL) and the uplink (UL), Cyclic Prefix OFDM (CP-OFDM), Discrete Fourier Transform Spread OFDM (DFT-s-OFDM), Orthogonal Frequency Division Multiple Access (OFDMA), Single Carrier Frequency Division Multiple Access (SC-FDMA), etc. may be used.

[0157] The radio access scheme may be called a waveform. In the wireless communication system 1, other radio access schemes (for example, other single-carrier transmission schemes, other multi-carrier transmission schemes) may be used as the UL and DL radio access schemes.

[0158] In the wireless communication system 1, a downlink shared channel (Physical Downlink Shared Channel (PDSCH)) shared by each user terminal 20, a broadcast channel (Physical Broadcast Channel (PBCH)), a downlink control channel (Physical Downlink Control Channel (PDCCH)), etc. may be used as a downlink channel.

[0159] In addition, in the wireless communication system 1, an uplink shared channel (Physical Uplink Shared Channel (PUSCH)) shared by each user terminal 20, an uplink control channel (Physical Uplink Control Channel (PUCCH)), a random access channel (Physical Random Access Channel (PRACH)), etc. may be used as an uplink channel.

[0160] The PDSCH transmits user data, higher layer control information, a System Information Block (SIB), etc. The PUSCH may transmit user data, higher layer control information, etc. Furthermore, the PBCH may transmit a Master Information Block (MIB).

[0161] Lower layer control information may be transmitted by the PDCCH. The lower layer control information may include, for example, Downlink Control Information (DCI) including scheduling information of at least one of the PDSCH and the PUSCH.

[0162] In addition, DCI for scheduling PDSCH may be called DL assignment, DL DCI, etc., and DCI for scheduling PUSCH may be called UL grant, UL DCI, etc. In addition, PDSCH may be replaced with DL data, and PUSCH may be replaced with UL data.

[0163] A control resource set (CORESET) and a search space may be used to detect the PDCCH. The CORESET corresponds to resources to search for DCI. The search space corresponds to a search region and a search method for PDCCH candidates. One CORESET may be associated with one or multiple search spaces. The UE may monitor a CORESET associated with a certain search space based on the search space configuration.

[0164] One search space may correspond to PDCCH candidates corresponding to one or more aggregation levels. One or more search spaces may be called a search space set. Note that the terms "search space," "search space set," "search space setting," "search space set setting," "CORESET," and "CORESET setting" in the present disclosure may be read as interchangeable terms.

[0165] The PUCCH may transmit uplink control information (UCI) including at least one of channel state information (CSI), delivery acknowledgement information (which may be called, for example, Hybrid Automatic Repeat reQuest ACKnowledgement (HARQ-ACK), ACK / NACK, etc.), and a scheduling request (SR). The PRACH may transmit a random access preamble for establishing a connection with a cell.

[0166] In the present disclosure, a downlink, an uplink, etc. may be expressed without adding "link." Also, various channels may be expressed without adding "Physical" to the beginning of the channels.

[0167] In the wireless communication system 1, a synchronization signal (SS), a downlink reference signal (DL-RS), etc. may be transmitted. In the wireless communication system 1, as the DL-RS, a cell-specific reference signal (CRS), a channel state information reference signal (CSI-RS), a demodulation reference signal (DMRS), a positioning reference signal (PRS), a phase tracking reference signal (PTRS), etc. may be transmitted.

[0168] The synchronization signal may be, for example, at least one of a Primary Synchronization Signal (PSS) and a Secondary Synchronization Signal (SSS). A signal block including the SS (PSS, SSS) and the PBCH (and the DMRS for the PBCH) may be called an SS / PBCH block, an SS Block (SSB), or the like. In addition, the SS, SSB, and the like may also be called a reference signal.

[0169] Furthermore, in the wireless communication system 1, a sounding reference signal (SRS), a demodulation reference signal (DMRS), etc. may be transmitted as an uplink reference signal (UL-RS). Note that the DMRS may be called a user equipment specific reference signal (UE-specific reference signal).

[0170] (base station) 13 is a diagram showing an example of the configuration of a base station according to an embodiment. The base station 10 includes a control unit 110, a transceiver unit 120, a transceiver antenna 130, and a transmission line interface 140. Note that one or more of each of the control unit 110, the transceiver unit 120, the transceiver antenna 130, and the transmission line interface 140 may be provided.

[0171] In this example, the functional blocks of the characteristic parts of the present embodiment are mainly shown, and the base station 10 may be assumed to have other functional blocks necessary for wireless communication. Some of the processes of each unit described below may be omitted.

[0172] The control unit 110 performs overall control of the base station 10. The control unit 110 can be configured with a controller, a control circuit, and the like that are explained based on common understanding in the technical field to which the present disclosure relates.

[0173] The control unit 110 may control signal generation, scheduling (e.g., resource allocation, mapping), etc. The control unit 110 may control transmission and reception using the transmission and reception unit 120, the transmission and reception antenna 130, and the transmission path interface 140, measurement, etc. The control unit 110 may generate data, control information, sequences, etc. to be transmitted as signals, and transfer them to the transmission and reception unit 120. The control unit 110 may perform call processing (setting, release, etc.) of communication channels, status management of the base station 10, management of radio resources, etc.

[0174] The transceiver unit 120 may include a baseband unit 121, a radio frequency (RF) unit 122, and a measurement unit 123. The baseband unit 121 may include a transmission processing unit 1211 and a reception processing unit 1212. The transceiver unit 120 may be configured with a transmitter / receiver, an RF circuit, a baseband circuit, a filter, a phase shifter, a measurement circuit, a transceiver circuit, etc., which are described based on common understanding in the technical field related to the present disclosure.

[0175] The transmitting / receiving unit 120 may be configured as an integrated transmitting / receiving unit, or may be configured from a transmitting unit and a receiving unit. The transmitting unit may be configured from a transmission processing unit 1211 and an RF unit 122. The receiving unit may be configured from a reception processing unit 1212, an RF unit 122, and a measurement unit 123.

[0176] The transmitting / receiving antenna 130 can be composed of an antenna described based on common understanding in the technical field to which the present disclosure pertains, such as an array antenna.

[0177] The transceiver 120 may transmit the above-mentioned downlink channel, synchronization signal, downlink reference signal, etc. The transceiver 120 may receive the above-mentioned uplink channel, uplink reference signal, etc.

[0178] The transceiver 120 may form at least one of a transmission beam and a reception beam using digital beamforming (eg, precoding), analog beamforming (eg, phase rotation), or the like.

[0179] The transceiver 120 (transmission processing unit 1211) may perform Packet Data Convergence Protocol (PDCP) layer processing, Radio Link Control (RLC) layer processing (e.g., RLC retransmission control), Medium Access Control (MAC) layer processing (e.g., HARQ retransmission control), etc. on data, control information, etc. obtained from the control unit 110, and generate a bit string to be transmitted.

[0180] The transceiver 120 (transmission processing unit 1211) may perform transmission processing such as channel coding (which may include error correction coding), modulation, mapping, filtering, Discrete Fourier Transform (DFT) processing (if necessary), Inverse Fast Fourier Transform (IFFT) processing, precoding, and digital-to-analog conversion on the bit string to be transmitted, and output a baseband signal.

[0181] The transceiver unit 120 (RF unit 122) may perform modulation to a radio frequency band, filtering, amplification, and the like on the baseband signal, and transmit the radio frequency band signal via the transceiver antenna .

[0182] On the other hand, the transceiver unit 120 (RF unit 122) may perform amplification, filtering, demodulation to a baseband signal, etc. on the radio frequency signal received by the transceiver antenna .

[0183] The transceiver 120 (reception processing unit 1212) may apply reception processing such as analog-to-digital conversion, Fast Fourier Transform (FFT) processing, Inverse Discrete Fourier Transform (IDFT) processing (if necessary), filtering, demapping, demodulation, decoding (which may include error correction decoding), MAC layer processing, RLC layer processing, and PDCP layer processing to the acquired baseband signal, thereby acquiring user data, etc.

[0184] The transceiver 120 (measurement unit 123) may perform measurements on the received signal. For example, the measurement unit 123 may perform Radio Resource Management (RRM) measurements, Channel State Information (CSI) measurements, etc., based on the received signal. The measurement unit 123 may measure received power (e.g., Reference Signal Received Power (RSRP)), received quality (e.g., Reference Signal Received Quality (RSRQ), Signal to Interference plus Noise Ratio (SINR), Signal to Noise Ratio (SNR)), signal strength (e.g., Received Signal Strength Indicator (RSSI)), propagation path information (e.g., CSI), etc. The measurement results may be output to the control unit 110.

[0185] The transmission path interface 140 may transmit and receive signals (backhaul signaling) between devices included in the core network 30, other base stations 10, etc., and may acquire and transmit user data (user plane data) for the user terminal 20, control plane data, etc.

[0186] The transmitting section and receiving section of the base station 10 in the present disclosure may be configured by at least one of the transmitting / receiving section 120, the transmitting / receiving antenna 130, and the transmission path interface 140.

[0187] In addition, the transmission / reception unit 120 may receive a reference signal (for example, DMRS, SRS).

[0188] The control unit 110 may perform control to perform prediction based on the second setting of the reference signal, using a machine learning model trained based on the first setting of the reference signal.

[0189] (User terminal) 14 is a diagram showing an example of the configuration of a user terminal according to an embodiment. The user terminal 20 includes a control unit 210, a transceiver unit 220, and a transceiver antenna 230. Note that the user terminal 20 may include one or more of each of the control unit 210, the transceiver unit 220, and the transceiver antenna 230.

[0190] In this example, the functional blocks of the characteristic parts of the present embodiment are mainly shown, and the user terminal 20 may be assumed to have other functional blocks necessary for wireless communication. Some of the processes of each unit described below may be omitted.

[0191] The control unit 210 performs overall control of the user terminal 20. The control unit 210 can be configured with a controller, a control circuit, and the like that are explained based on common understanding in the technical field to which the present disclosure relates.

[0192] The control unit 210 may control signal generation, mapping, etc. The control unit 210 may control transmission / reception, measurement, etc. using the transmission / reception unit 220 and the transmission / reception antenna 230. The control unit 210 may generate data, control information, sequences, etc. to be transmitted as signals, and transfer them to the transmission / reception unit 220.

[0193] The transmitting / receiving unit 220 may include a baseband unit 221, an RF unit 222, and a measurement unit 223. The baseband unit 221 may include a transmission processing unit 2211 and a reception processing unit 2212. The transmitting / receiving unit 220 may be configured with a transmitter / receiver, an RF circuit, a baseband circuit, a filter, a phase shifter, a measurement circuit, a transmitting / receiving circuit, and the like, which are described based on common understanding in the technical field related to the present disclosure.

[0194] The transmitting / receiving unit 220 may be configured as an integrated transmitting / receiving unit, or may be composed of a transmitting unit and a receiving unit. The transmitting unit may be composed of a transmission processing unit 2211 and an RF unit 222. The receiving unit may be composed of a reception processing unit 2212, an RF unit 222, and a measurement unit 223.

[0195] The transmitting / receiving antenna 230 can be composed of an antenna described based on common understanding in the technical field to which this disclosure pertains, such as an array antenna.

[0196] The transceiver 220 may receive the above-mentioned downlink channel, synchronization signal, downlink reference signal, etc. The transceiver 220 may transmit the above-mentioned uplink channel, uplink reference signal, etc.

[0197] The transceiver 220 may form at least one of a transmission beam and a reception beam using digital beamforming (eg, precoding), analog beamforming (eg, phase rotation), or the like.

[0198] The transceiver unit 220 (transmission processing unit 2211) may perform PDCP layer processing, RLC layer processing (e.g., RLC retransmission control), MAC layer processing (e.g., HARQ retransmission control), etc. on data, control information, etc. obtained from the control unit 210, and generate a bit string to be transmitted.

[0199] The transceiver 220 (transmission processing unit 2211) may perform transmission processing such as channel coding (which may include error correction coding), modulation, mapping, filtering, DFT processing (if necessary), IFFT processing, precoding, and digital-to-analog conversion on the bit sequence to be transmitted, and output a baseband signal.

[0200] Whether or not to apply the DFT process may be based on the setting of transform precoding. When transform precoding is enabled for a certain channel (e.g., PUSCH), the transceiver 220 (transmission processor 2211) may perform the DFT process as the transmission process to transmit the channel using a DFT-s-OFDM waveform, and may not perform the DFT process as the transmission process if transform precoding is enabled for the channel.

[0201] The transceiver unit 220 (RF unit 222) may perform modulation to a radio frequency band, filtering, amplification, and the like on the baseband signal, and transmit the radio frequency band signal via the transceiver antenna 230.

[0202] On the other hand, the transceiver unit 220 (RF unit 222) may perform amplification, filtering, demodulation to a baseband signal, etc. on the radio frequency signal received by the transceiver antenna 230.

[0203] The transceiver 220 (reception processing unit 2212) may apply reception processing such as analog-to-digital conversion, FFT processing, IDFT processing (if necessary), filtering, demapping, demodulation, decoding (which may include error correction decoding), MAC layer processing, RLC layer processing, and PDCP layer processing to the acquired baseband signal, and acquire user data, etc.

[0204] The transceiver 220 (measurement unit 223) may perform measurements on the received signal. For example, the measurement unit 223 may perform RRM measurements, CSI measurements, etc. based on the received signal. The measurement unit 223 may measure received power (e.g., RSRP), received quality (e.g., RSRQ, SINR, SNR), signal strength (e.g., RSSI), propagation path information (e.g., CSI), etc. The measurement results may be output to the control unit 210.

[0205] In addition, the transmitting section and the receiving section of the user terminal 20 in the present disclosure may be configured by at least one of the transmitting / receiving section 220, the transmitting / receiving antenna 230, and the transmission path interface 240.

[0206] In addition, the transceiver 220 may receive a reference signal (eg, DMRS, CSI-RS).

[0207] The control unit 210 may control a prediction based on a second setting (e.g., a setting for a test mode) of the reference signal using a machine learning model trained based on a first setting (e.g., a setting for a training mode) of the reference signal.

[0208] The transceiver 220 may receive information instructing training of the machine learning model based on the first setting or prediction based on the second setting.

[0209] The transceiver unit 220 may receive information about the trained machine learning model from a base station 10 or another user terminal 20.

[0210] The control unit 210 may control the transmission of information of a desired format for the machine learning model to the base station 10 or other user terminals 20.

[0211] (Hardware configuration) The block diagrams used to explain the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of at least one of hardware and software. The method of realizing each functional block is not particularly limited. That is, each functional block may be realized by using one device that is physically or logically combined, or may be realized by using two or more devices that are physically or logically separated and directly or indirectly connected (for example, by wire, wirelessly, etc.). The functional blocks may be realized by combining the one device or the multiple devices with software.

[0212] Here, the functions include, but are not limited to, judgment, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, deeming, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs the function of transmission may be called a transmitting unit, a transmitter, and the like. In either case, as described above, the method of realization is not particularly limited.

[0213] For example, a base station, a user terminal, etc. in an embodiment of the present disclosure may function as a computer that performs processing of the wireless communication method of the present disclosure. Fig. 15 is a diagram showing an example of a hardware configuration of a base station and a user terminal according to an embodiment. The above-mentioned base station 10 and user terminal 20 may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, etc.

[0214] In this disclosure, the terms "apparatus," "circuit," "device," "section," "unit," and the like can be read interchangeably. The hardware configurations of the base station 10 and the user terminal 20 may be configured to include one or more of the devices shown in the drawings, or may be configured to exclude some of the devices.

[0215] For example, although only one processor 1001 is shown, there may be multiple processors. Also, the processes may be performed by one processor, or the processes may be performed by two or more processors simultaneously, sequentially, or in other manners. Also, the processor 1001 may be implemented by one or more chips.

[0216] Each function in the base station 10 and the user terminal 20 is realized, for example, by loading a specific software (program) onto hardware such as a processor 1001 and a memory 1002, so that the processor 1001 performs calculations, controls communications via a communication device 1004, and controls at least one of reading and writing of data in the memory 1002 and the storage 1003.

[0217] The processor 1001, for example, operates an operating system to control the entire computer. The processor 1001 may be configured with a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc. For example, at least a part of the above-mentioned control unit 110 (210), transmission / reception unit 120 (220), etc. may be realized by the processor 1001.

[0218] Moreover, the processor 1001 reads out programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002, and executes various processes according to the programs. As the programs, programs that cause a computer to execute at least a part of the operations described in the above-mentioned embodiments are used. For example, the control unit 110 (210) may be realized by a control program stored in the memory 1002 and operated by the processor 1001, and the other functional blocks may be realized in a similar manner.

[0219] The memory 1002 is a computer-readable recording medium, and may be configured by at least one of, for example, a Read Only Memory (ROM), an Erasable Programmable ROM (EPROM), an Electrically EPROM (EEPROM), a Random Access Memory (RAM), and other suitable storage media. The memory 1002 may be called a register, a cache, a main memory (primary storage device), and the like. The memory 1002 can store a program (program code), a software module, and the like that is executable to implement a wireless communication method according to an embodiment of the present disclosure.

[0220] Storage 1003 is a computer-readable recording medium and may be constituted by at least one of, for example, a flexible disk, a floppy disk, a magneto-optical disk (e.g., a compact disk (e.g., a Compact Disc ROM (CD-ROM)), a digital versatile disk, a Blu-ray disk), a removable disk, a hard disk drive, a smart card, a flash memory device (e.g., a card, stick, key drive), a magnetic stripe, a database, a server, or other suitable storage medium. Storage 1003 may also be referred to as an auxiliary storage device.

[0221] The communication device 1004 is hardware (transmission / reception device) for performing communication between computers via at least one of a wired network and a wireless network, and is also called, for example, a network device, a network controller, a network card, a communication module, etc. The communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc., in order to realize at least one of Frequency Division Duplex (FDD) and Time Division Duplex (TDD). For example, the above-mentioned transmission / reception unit 120 (220), transmission / reception antenna 130 (230), etc. may be realized by the communication device 1004. The transmission / reception unit 120 (220) may be implemented as a transmission unit 120a (220a) and a reception unit 120b (220b) that are physically or logically separated.

[0222] The input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that accepts input from the outside. The output device 1006 is an output device (e.g., a display, a speaker, a Light Emitting Diode (LED) lamp, etc.) that performs output to the outside. The input device 1005 and the output device 1006 may be integrated into one device (e.g., a touch panel).

[0223] In addition, each device such as the processor 1001 and the memory 1002 is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between each device.

[0224] Furthermore, the base station 10 and the user terminal 20 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), etc., and some or all of the functional blocks may be realized using the hardware. For example, the processor 1001 may be implemented using at least one of these pieces of hardware.

[0225] (Modification) In addition, the terms explained in this disclosure and the terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings. For example, a channel, a symbol, and a signal (signal or signaling) may be read as mutually interchangeable. A signal may also be a message. A reference signal may also be abbreviated as RS, and may also be called a pilot, pilot signal, etc. depending on the applied standard. A component carrier (CC) may also be called a cell, a frequency carrier, a carrier frequency, etc.

[0226] A radio frame may be composed of one or more periods (frames) in the time domain. Each of the one or more periods (frames) constituting a radio frame may be called a subframe. Furthermore, a subframe may be composed of one or more slots in the time domain. A subframe may have a fixed time length (e.g., 1 ms) that is independent of numerology.

[0227] Here, the numerology may be a communication parameter applied to at least one of the transmission and reception of a signal or channel, and may indicate at least one of, for example, a subcarrier spacing (SCS), a bandwidth, a symbol length, a cyclic prefix length, a transmission time interval (TTI), a number of symbols per TTI, a radio frame structure, a specific filtering process performed by a transceiver in the frequency domain, and a specific windowing process performed by a transceiver in the time domain.

[0228] A slot may be composed of one or more symbols (such as an Orthogonal Frequency Division Multiplexing (OFDM) symbol, a Single Carrier Frequency Division Multiple Access (SC-FDMA) symbol, etc.) in the time domain. A slot may also be a time unit based on numerology.

[0229] A slot may include multiple minislots. Each minislot may be composed of one or multiple symbols in the time domain. A minislot may also be called a subslot. A minislot may be composed of fewer symbols than a slot. A PDSCH (or PUSCH) transmitted in a time unit larger than a minislot may be called PDSCH (PUSCH) mapping type A. A PDSCH (or PUSCH) transmitted using a minislot may be called PDSCH (PUSCH) mapping type B.

[0230] A radio frame, a subframe, a slot, a minislot, and a symbol each represent a time unit for transmitting a signal. A different name may be used for the radio frame, the subframe, the slot, the minislot, and the symbol. Note that the time units such as a frame, a subframe, a slot, a minislot, and a symbol in the present disclosure may be read as interchangeable with each other.

[0231] For example, one subframe may be called a TTI, multiple consecutive subframes may be called a TTI, or one slot or one minislot may be called a TTI. That is, at least one of the subframe and the TTI may be a subframe (1 ms) in the existing LTE, a period shorter than 1 ms (for example, 1-13 symbols), or a period longer than 1 ms. Note that the unit representing the TTI may be called a slot, minislot, etc., instead of a subframe.

[0232] Here, TTI refers to, for example, the minimum time unit for scheduling in wireless communication. For example, in an LTE system, a base station performs scheduling to allocate radio resources (such as frequency bandwidth and transmission power that can be used by each user terminal) to each user terminal in TTI units. Note that the definition of TTI is not limited to this.

[0233] The TTI may be a transmission time unit for a channel-encoded data packet (transport block), a code block, a code word, etc., or may be a processing unit for scheduling, link adaptation, etc. When a TTI is given, the time interval (e.g., the number of symbols) in which a transport block, a code block, a code word, etc. are actually mapped may be shorter than the TTI.

[0234] In addition, when one slot or one minislot is called TTI, one or more TTIs (i.e., one or more slots or one or more minislots) may be the minimum time unit of scheduling. Also, the number of slots (minislots) constituting the minimum time unit of scheduling may be controlled.

[0235] A TTI having a time length of 1 ms may be called a normal TTI (TTI in 3GPP Rel. 8-12), a normal TTI, a long TTI, a normal subframe, a normal subframe, a long subframe, a slot, etc. A TTI shorter than a normal TTI may be called a shortened TTI, a short TTI, a partial or fractional TTI, a shortened subframe, a short subframe, a minislot, a subslot, a slot, etc.

[0236] In addition, a long TTI (e.g., a normal TTI, a subframe, etc.) may be interpreted as a TTI having a time length exceeding 1 ms, and a short TTI (e.g., a shortened TTI, etc.) may be interpreted as a TTI having a TTI length shorter than the TTI length of a long TTI and equal to or greater than 1 ms.

[0237] A resource block (RB) is a resource allocation unit in the time domain and the frequency domain, and may include one or more consecutive subcarriers in the frequency domain. The number of subcarriers included in an RB may be the same regardless of the numerology, and may be, for example, 12. The number of subcarriers included in an RB may be determined based on the numerology.

[0238] In addition, an RB may include one or more symbols in the time domain, and may have a length of one slot, one minislot, one subframe, or one TTI. Each of one TTI, one subframe, etc. may be composed of one or more resource blocks.

[0239] In addition, one or more RBs may be called a physical resource block (PRB), a sub-carrier group (SCG), a resource element group (REG), a PRB pair, an RB pair, etc.

[0240] Furthermore, a resource block may be composed of one or more resource elements (REs). For example, one RE may be a radio resource region of one subcarrier and one symbol.

[0241] A Bandwidth Part (BWP), which may also be referred to as a fractional bandwidth, may represent a subset of contiguous common resource blocks (RBs) for a numerology on a carrier, where the common RBs may be identified by the index of the RBs relative to a common reference point of the carrier. PRBs may be defined in a BWP and numbered within the BWP.

[0242] The BWP may include a UL BWP (a BWP for UL) and a DL BWP (a BWP for DL). One or more BWPs may be configured for a UE within one carrier.

[0243] At least one of the configured BWPs may be active, and the UE may not expect to transmit or receive a given signal / channel outside the active BWP. Note that the terms "cell", "carrier", etc. in this disclosure may be replaced with "BWP".

[0244] The above-mentioned structures of radio frames, subframes, slots, minislots, and symbols are merely examples. For example, the number of subframes included in a radio frame, the number of slots per subframe or radio frame, the number of minislots included in a slot, the number of symbols and RBs included in a slot or minislot, the number of subcarriers included in an RB, the number of symbols in a TTI, the symbol length, the cyclic prefix (CP) length, and other configurations can be changed in various ways.

[0245] In addition, the information, parameters, etc. described in the present disclosure may be represented using absolute values, may be represented using relative values ​​from a predetermined value, or may be represented using other corresponding information. For example, a radio resource may be indicated by a predetermined index.

[0246] The names used for parameters and the like in this disclosure are not limiting in any way. Furthermore, the formulas and the like using these parameters may differ from those explicitly disclosed in this disclosure. The various channels (PUCCH, PDCCH, etc.) and information elements may be identified by any suitable names, and therefore the various names assigned to these various channels and information elements are not limiting in any way.

[0247] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, the data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0248] Furthermore, information, signals, etc. may be output from a higher layer to a lower layer and / or from a lower layer to a higher layer. Information, signals, etc. may be input / output via a plurality of network nodes.

[0249] Input and output information, signals, etc. may be stored in a specific location (e.g., memory) or may be managed using a management table. Input and output information, signals, etc. may be overwritten, updated, or added to. Output information, signals, etc. may be deleted. Input information, signals, etc. may be transmitted to another device.

[0250] The notification of information is not limited to the aspects / embodiments described in the present disclosure, and may be performed using other methods. For example, the notification of information in the present disclosure may be performed by physical layer signaling (e.g., Downlink Control Information (DCI) and Uplink Control Information (UCI)), higher layer signaling (e.g., Radio Resource Control (RRC) signaling, broadcast information (Master Information Block (MIB) and System Information Block (SIB)), Medium Access Control (MAC) signaling), other signals, or a combination thereof.

[0251] The physical layer signaling may be called Layer 1 / Layer 2 (L1 / L2) control information (L1 / L2 control signal), L1 control information (L1 control signal), etc. The RRC signaling may be called an RRC message, for example, an RRC Connection Setup message, an RRC Connection Reconfiguration message, etc. The MAC signaling may be notified, for example, by using a MAC Control Element (CE).

[0252] Furthermore, notification of specified information (e.g., notification that "it is X") is not limited to explicit notification, but may be made implicitly (e.g., by not notifying the specified information or by notifying other information).

[0253] The determination may be made based on a value represented by a single bit (0 or 1), a Boolean value represented as true or false, or by comparing numerical values ​​(e.g., with a predetermined value).

[0254] Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

[0255] Additionally, software, instructions, information, etc. may be transmitted or received over a transmission medium. For example, if the software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave, etc.), then these wired and / or wireless technologies are included within the definition of transmission media.

[0256] As used in this disclosure, the terms "system" and "network" may be used interchangeably. A "network" may refer to devices included in the network (e.g., base stations).

[0257] In this disclosure, terms such as "precoding," "precoder," "weight (precoding weight)," "Quasi-Co-Location (QCL)," "Transmission Configuration Indication state (TCI state)," "spatial relation," "spatial domain filter," "transmit power," "phase rotation," "antenna port," "antenna port group," "layer," "number of layers," "rank," "resource," "resource set," "resource group," "beam," "beam width," "beam angle," "antenna," "antenna element," "panel," and the like may be used interchangeably.

[0258] In this disclosure, terms such as "Base Station (BS)", "radio base station", "fixed station", "NodeB", "eNB (eNodeB)", "gNB (gNodeB)", "access point", "Transmission Point (TP)", "Reception Point (RP)", "Transmission / Reception Point (TRP)", "panel", "cell", "sector", "cell group", "carrier", "component carrier", etc. may be used interchangeably. A base station may also be referred to by terms such as a macro cell, a small cell, a femto cell, a pico cell, etc.

[0259] A base station can accommodate one or more (e.g., three) cells. When a base station accommodates multiple cells, the entire coverage area of ​​the base station can be partitioned into multiple smaller areas, and each smaller area can also be provided with communication services by a base station subsystem (e.g., a small base station for indoor use (Remote Radio Head (RRH))). The term "cell" or "sector" refers to a part or the entire coverage area of ​​a base station and / or a base station subsystem that provides communication services in this coverage.

[0260] In this disclosure, the terms "Mobile Station (MS)", "user terminal", "User Equipment (UE)", "terminal", etc. may be used interchangeably.

[0261] A mobile station may also be referred to as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other suitable terminology.

[0262] At least one of the base station and the mobile station may be called a transmitting device, a receiving device, a wireless communication device, etc. At least one of the base station and the mobile station may be a device mounted on a moving body, the moving body itself, etc. The moving body may be a vehicle (e.g., a car, an airplane, etc.), an unmanned moving body (e.g., a drone, an autonomous vehicle, etc.), or a robot (manned or unmanned). At least one of the base station and the mobile station may include a device that does not necessarily move during communication operation. For example, at least one of the base station and the mobile station may be an Internet of Things (IoT) device such as a sensor.

[0263] Furthermore, the base station in the present disclosure may be read as a user terminal. For example, each aspect / embodiment of the present disclosure may be applied to a configuration in which communication between a base station and a user terminal is replaced with communication between a plurality of user terminals (which may be called, for example, Device-to-Device (D2D) or Vehicle-to-Everything (V2X)). In this case, the user terminal 20 may be configured to have the functions of the base station 10 described above. Furthermore, terms such as "uplink" and "downlink" may be read as terms corresponding to terminal-to-terminal communication (for example, "side"). For example, an uplink channel, a downlink channel, etc. may be read as a side channel.

[0264] Similarly, the user terminal in the present disclosure may be interpreted as a base station. In this case, the base station 10 may be configured to have the functions of the user terminal 20 described above.

[0265] In the present disclosure, an operation performed by a base station may be performed by its upper node in some cases. It is clear that in a network including one or more network nodes having base stations, various operations performed for communication with terminals may be performed by the base station, one or more network nodes other than the base station (such as, but not limited to, a Mobility Management Entity (MME) and a Serving-Gateway (S-GW)), or a combination thereof.

[0266] Each aspect / embodiment described in this disclosure may be used alone, in combination, or switched according to implementation. In addition, the processing procedures, sequences, flow charts, etc. of each aspect / embodiment described in this disclosure may be reordered unless inconsistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0267] Each aspect / embodiment described in the present disclosure may be implemented using any of a wide variety of standards, including Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 6th generation mobile communication system (6G), xth generation mobile communication system (xG) (xG (x is, for example, an integer or a decimal point)), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New radio access (NX), Future generation radio access (FX), Global System for Mobile communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE The present invention may be applied to systems using 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), other appropriate wireless communication methods, next-generation systems that are based on these, etc. Also, the present invention may be applied to a combination of multiple systems (for example, a combination of LTE or LTE-A and 5G).

[0268] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0269] Any reference to an element using a designation such as "first," "second," etc., used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must precede the second element in some way.

[0270] The term "determining" as used in this disclosure may encompass a wide variety of actions. For example, "determining" may be considered to be judging, calculating, computing, processing, deriving, investigating, looking up, search, inquiry (e.g., looking up in a table, database, or another data structure), ascertaining, and the like.

[0271] A "determining" may also be considered to be "determining" receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in a memory), etc.

[0272] Also, "determination" may be considered to be "deciding" resolving, selecting, choosing, establishing, comparing, etc. That is, "determination" may be considered to be "deciding" to perform some action.

[0273] Additionally, "judgment (decision)" may be interpreted as "assuming," "expecting," "considering," etc.

[0274] The "maximum transmit power" in this disclosure may mean the maximum value of transmit power, may mean the nominal UE maximum transmit power, or may mean the rated UE maximum transmit power.

[0275] As used in this disclosure, the terms "connected," "coupled," or any variation thereof, refer to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between the elements may be physical, logical, or a combination thereof. For example, "connected" may be read as "access."

[0276] In this disclosure, when two elements are connected, they may be considered to be "connected" or "coupled" to one another using one or more wires, cables, printed electrical connections, and the like, as well as using electromagnetic energy having wavelengths in the radio frequency range, microwave range, light (both visible and invisible) range, and the like, as some non-limiting and non-exhaustive examples.

[0277] In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "coupled" may also be interpreted in the same way as "different."

[0278] When the terms "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to the term "comprising." Further, when used in this disclosure, the term "or" is not intended to be an exclusive or.

[0279] In this disclosure, where articles have been added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0280] Although the invention according to the present disclosure has been described in detail above, it is clear to those skilled in the art that the invention according to the present disclosure is not limited to the embodiments described in the present disclosure. The invention according to the present disclosure can be implemented as modified and altered forms without departing from the spirit and scope of the invention defined based on the description of the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not have any limiting meaning on the invention according to the present disclosure.

Claims

1. A receiver for receiving a reference signal; A terminal having a control unit that controls making a prediction based on a second setting of the reference signal using a machine learning model trained based on a first setting of the reference signal.

2. The terminal according to claim 1 , wherein the receiving unit receives information instructing training of the machine learning model based on the first setting or prediction based on the second setting.

3. The terminal according to claim 1 or 2, wherein the receiving unit receives information about the trained machine learning model.

4. The terminal according to claim 1 , wherein the control unit performs control to transmit information in a desired format for the machine learning model.

5. receiving a reference signal; A wireless communication method for a terminal, comprising: a step of controlling a prediction based on a second setting of the reference signal using a machine learning model trained based on a first setting of the reference signal.

6. A receiver for receiving a reference signal; A base station having a control unit that controls making a prediction based on a second setting of the reference signal using a machine learning model trained based on a first setting of the reference signal.

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