Terminal, base station, and communication method

Abstract

The present invention improves the efficiency of radio communication. A terminal according to the present invention comprises a control circuit and a transmission circuit. The control circuit uses a past intermediate output of an artificial intelligence model on a terminal side to generate a report of channel state information obtained by compressing the amount of information via inference processing of the artificial intelligence model. The intermediate output includes the channel state information of immediately previous time and information related to a rank selected in the past. The transmission circuit transmits the report of the channel state information.

Description

Terminal, base station and communication method

[0001] The present disclosure relates to a terminal, a base station, and a communication method.

[0002] In recent years, the expansion and diversification of wireless services has led to the expectation of rapid development of the Internet of Things (IoT). Mobile communications are now being used in a wide range of applications, from smartphones and other information terminals to automobiles, homes, home appliances, and industrial equipment. To support this diversification, significant improvements in the performance and functionality of mobile communication systems are required, addressing various requirements, such as increased system capacity, an increased number of connected devices, and low latency. Fifth-generation mobile communication systems (5G) boast high-capacity and ultra-high-speed data transfer (eMBB: enhanced Mobile Broadband), massive machine-type communication (mMTC: massive Machine-Type Communication), and ultra-reliable and low-latency communication (URLLC), providing flexible wireless communications to meet diverse needs.

[0003] RP-221348, “Revised SID: Study on Artificial Intelligence (AI) / Machine Learning (ML) for NR Air Interface,”Qualcomm (Moderator), June 2022.C. K. Wen, W. T. Shih, and S. Jin, “Deep learning for massive MIMO CSI feedback,” IEEE Wireless Communications Letters, Vol.7, No.5, October 2018.W. Liu, W. Tian, H. Xiao, S. Jin, X. Liu, J. Shen, “EVCsiNet: Eigenvector-based CSI feedback under 3GPP link-level channels,” IEEE Wireless Communications Letters, Vol.10, No.12, December 2021.H. Xiao, Z. Wang, D. Li, W. Tian, X. Liu, S. Jin, J. Shen, Z. Zhang, N. Yang, “AI enlightens wireless communication: A transformer backbone for CSI feedback,” China Communications, June 2022.RP-234039, “New WID on Artificial Intelligence (AI) / Machine Learning (ML) for NR Air Interface,” Qualcomm (Moderator), December 2023.R1-2400546, “Discussion on two-sided AI / ML model based CSI compression,” Xiaomi, February 26th - March 1st, 2024.3GPP TS38.211, “NR Physical channels and modulation (Release 18),” March 2024.3GPP TS38.212, “NR Multiplexing and channel coding (Release 18),” March 2024.3GPP TS38.213, “NR Physical layer procedures for control (Release 18),” March 2024.3GPP TS38.214, “NR Physical layer procedures for data (Release 18),” March 2024.

[0004] However, there is room for improvement in the efficiency of wireless communications.

[0005] Non-limiting embodiments of the present disclosure contribute to providing a terminal, a base station, and a communication method that can improve the efficiency of wireless communication.

[0006] A terminal according to one embodiment of the present disclosure includes a control circuit that uses past intermediate outputs of an artificial intelligence model on the terminal side to generate a report of channel state information in which the amount of information is compressed by an inference process of the artificial intelligence model, and the intermediate output includes the channel state information at a previous time and information about a rank selected in the past, and a transmission circuit that transmits the report of channel state information.

[0007] These comprehensive or specific aspects may be realized as a system, an apparatus, a method, an integrated circuit, a computer program, or a recording medium, or may be realized as any combination of a system, an apparatus, a method, an integrated circuit, a computer program, and a recording medium.

[0008] According to an embodiment of the present disclosure, it is possible to improve the efficiency of wireless communication.

[0009] Further advantages and benefits of one embodiment of the present disclosure will become apparent from the specification and drawings. Such advantages and / or benefits may be provided by some embodiments and features described in the specification and drawings, respectively, but not necessarily all of them may be provided to obtain one or more identical features.

[0010] 1. Diagram showing an example of Channel State Information (CSI) compression in the spatial and frequency domains using Machine Learning (ML) / Artificial Intelligence (AI) technology. 2. Diagram showing an example of CSI compression in the time domain using AI / ML technology. 3. Diagram showing an example of an AI / MI model that performs CSI compression in the spatial, time, and frequency domains. 4. Diagram showing an example of an AI / MI model that performs CSI compression in the spatial, time, and frequency domains. 5. Diagram showing an example of setting an AI / MI model for each rank. 6. Diagram showing an example of CSI compression in the spatial, time, and frequency domains with an AI / MI model set for each rank. 7. Diagram showing an example of setting an AI / MI model for each layer. 8. Diagram showing an example of CSI compression in the spatial, time, and frequency domains with an AI / MI model set for each layer. 9. Diagram showing an example of updating AI / ML models for all ranks. 10. Diagram showing an example of AI / ML processing. 11. Block diagram showing an example of the configuration of a portion of a base station. 12. Block diagram showing an example of the configuration of a portion of a terminal. 13. Diagram showing an example of AI / ML processing. 14. Diagram showing an example of AI / ML processing. 15. Diagram showing an example of AI / ML processing. 16. Flowchart showing an example of the operation of a terminal. 17. Diagram showing an example of an architecture. 18. Block diagram showing an example of a base station. 19. Block diagram showing an example of a terminal. 19. Diagram of an exemplary architecture of a 3GPP NR system. Diagram of an exemplary functional division in O-RAN

[0011] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings.

[0012] The 3rd Generation Partnership Project (3GPP), an international standardization organization, is working on the specification of New Radio (NR) as one of the 5G radio interfaces. The basic functions of eMBB and URLLC were specified in Release 15, and from Release 16 onwards, URLLC will be extended to include Industrial IoT, Vehicle-to-Everything (V2X), and non-terrestrial networks (NTN) including satellites. The 3GPP extended specifications will also be called "5G-Advanced" from Release 18.

[0013] Furthermore, advances in artificial intelligence (AI) (hereinafter also referred to as "AI / ML") technologies such as machine learning (ML) have been remarkable, and the application of AI / ML to mobile communications is also being considered. 3GPP is also studying and standardizing the application of AI / ML to various applications, and has begun studying the application of AI / ML to wireless interfaces since Release 18 (see, for example, Non-Patent Document 1). Representative use cases for utilizing AI / ML technologies include, for example, channel state information (CSI) feedback, beam control, and position estimation.

[0014] In NR, for example, an access scheme based on Orthogonal Frequency Division Multiplexing (OFDM) is adopted for downlink transmission. Furthermore, Multiple-Input Multiple Output (MIMO) is adopted to improve communication quality or increase data rates. To effectively utilize MIMO performance, closed-loop control is introduced, in which a terminal (e.g., user equipment (UE)) estimates the channel (e.g., measures and generates CSI) and feeds back the CSI from the terminal to a base station (e.g., gNB). The base station performs downlink communication by applying, for example, transmit precoding based on the fed-back CSI. CSI feedback is expected to achieve high performance with a smaller amount of control information.

[0015] In 3GPP Release 18, the application of AI / ML technology to the radio interface has been discussed. For example, a method for compressing CSI information in the spatial and frequency domains (hereinafter also referred to as the space-frequency domain) using AI / ML technology for CSI feedback (hereinafter also referred to as "CSI compression") is being considered. For example, machine learning (ML) can automatically extract features by training an AI / ML model (e.g., an artificial intelligence model) such as a neural network using a huge amount of training data. CSI compression can utilize an autoencoder, an algorithm primarily used for dimensional compression and reconstruction of image data. For example, as shown in Figure 1, a CSI matrix represented by a two-dimensional space-frequency domain is considered as an image. An encoder used for compression in the autoencoder is used for CSI compression processing on the terminal side, and a decoder used for reconstruction is used for CSI reconstruction processing on the base station or network (e.g., also referred to as "base station / network") side (see, for example, Non-Patent Document 2 or Non-Patent Document 3).

[0016] In addition, for CSI compression using ML, an algorithm called Transformer, which compresses the information of an entire sentence while taking into account context in natural language processing, can also be used (see, for example, Non-Patent Document 4).

[0017] Furthermore, in 3GPP Release 19, in order to improve the performance of CSI compression in the space-frequency domain or reduce the amount of processing, CSI compression that uses information in the time domain (e.g., temporal domain) in addition to the space-frequency domain is also being considered (see, for example, Non-Patent Document 5). One example of using time-domain information for CSI compression is a method in which past CSI information is used in both an encoder that compresses CSI in a terminal and a decoder that reconstructs CSI on the base station / network side.

[0018] For example, in CSI compression using past CSI information in both the encoder of the terminal and the decoder of the base station / network, as shown in FIG. 2, the input to the AI / ML model (encoder or decoder) used for CSI compression at a certain time (e.g., time t) is the current CSI matrix (or precoding matrix) (Input(V t ) and the output from the AI / ML model in the past (e.g., time t-1) (past accumulated CSI). This allows for more efficient CSI compression, as the CSI feedback is differentially informed with respect to previous CSI reports, for example.

[0019] Rank adaptation is widely used in MIMO systems for mobile wireless communications, such as NR. Rank adaptation adaptively switches the number of multiplexed signals (rank) in MIMO transmission according to the state of the wireless channel (e.g., reception quality (e.g., SINR: Signal to Interference and Noise Ratio) or correlation of fading fluctuations between antennas), enabling robust and highly spectrally efficient transmission against time-varying wireless channels.

[0020] In the method using past CSI information, for example, an algorithm called Convolutional Long Short Term Memory (LTSM) (ConvLSTM: Convolutional LTSM), which is a type of recurrent neural network (RNN) that is also used for video frame prediction, may be used to construct an AI / ML model by combining LSTM with a machine learning algorithm for CSI compression (e.g., Transformer) as shown in FIG. 3 (see, for example, Non-Patent Document 6). The input of the AI / ML model for CSI compression in the spatial, time, and frequency domains (hereinafter also referred to as the space-time-frequency domains) includes both CSI information (or precoding information) at the current time and accumulated information from the past (past / accumulated CSI). The output of the encoder is compressed CSI (or precoding) information (CSI feedback C). t ), and the intermediate output of the LSTM part of the encoder is accumulated information (past / accumulated CSI) that is used as an input (additional input) to the AI / ML model used for CSI compression at the next time point. Similar to the encoder, the decoder restores the CSI (or precoding) information based on both the compressed CSI (or precoding) information and past accumulated information (past / accumulated CSI) calculated on the decoder side.

[0021] When rank adaptation is applied, the rank (or the number of layers) may change over time because it depends on the state of the wireless propagation path (channel). For example, when an AI / ML model is configured to apply inference processing for each rank or layer, if the rank or the number of layers changes over time, the accumulated information from the past (past / accumulated CSI) may not be continuously updated, as shown in Figure 4. In this case, performance degradation may occur in space-time-frequency domain CSI compression using past CSI information.

[0022] FIG. 5 shows an example of setting an AI / ML model for each rank in CSI compression in the space-frequency domain. FIG. 6 shows an example of extending the AI / ML model setting shown in FIG. 5 to CSI compression in the space-time-frequency domain, where the rank value changes over time.

[0023] As shown in Figures 5 and 6, when an AI / ML model is set for each rank, for example, an AI / ML model is trained for each rank, and in the inference process, rank-specific inference processing (e.g., encoder processing or decoder processing) is performed using the AI / ML model corresponding to the rank determined in the rank selection.

[0024] Fig. 6 shows an example in which the rank value calculated from the state of CSI information at time t-1 and the state of CSI information at time t is 2, and the rank value calculated from the state of CSI information at time t+1 is 1. For example, as shown in Fig. 6, at time t, an AI / ML model corresponding to rank 2 is used, and the input to the AI / ML model is the CSI information H t , and the accumulated information (past / accumulated CSI) S from time t-1 for the AI / ML model corresponding to rank 2 t-1 (2) Also, at time t, the output of the encoder is the compressed CSI (or precoding) information c t The intermediate output of the encoder is the accumulated information S t (2) is.

[0025] Here, as shown in Fig. 6, the rank value calculated from the state of CSI information at time t+1 is 1. In this case, the input to the AI / ML model corresponding to rank 1 is the CSI information H t+1 , and the accumulated information S from time t for the AI / ML model corresponding to rank 1 t (1) However, as shown in Figure 6, at the previous time t, the accumulated information S for the AI / ML model corresponding to rank 2 t (2)is output, but the accumulated information S for the AI / ML model corresponding to rank 1 t (1) is not output, and the AI / ML model corresponding to rank 1 used at time t+1 cannot use the accumulated information from time t.

[0026] Figure 7 shows an example of setting an AI / ML model for each layer in CSI compression in the space-frequency domain, and Figure 8 shows an example of extending the AI / ML model shown in Figure 7 to CSI compression in the space-time-frequency domain, showing a case where the rank value (or the number of layers) changes over time.

[0027] 7 and 8, when an AI / ML model is set for each layer, for example, an AI / ML model is trained for each layer, and in the inference process, an individual inference process (e.g., encoder process or decoder process) is performed for the layer corresponding to the rank determined in the rank selection. Alternatively, for example, a unified AI / ML model common to multiple layers may be trained, and the layer-specific inference process may be performed using the unified AI / ML model.

[0028] 8 shows an example in which the rank value calculated from the state of the CSI information at time t−1 and the state of the CSI information at time t+1 is 2, and the rank value calculated from the state of the CSI information at time t is 1. For example, as shown in FIG. 8, at time t, an AI / ML model corresponding to layer 1 is used, and the input to the AI / ML model is the CSI information H t , and accumulated information S from time t-1 for the AI / ML model corresponding to layer 1 t-1 (1) Also, at time t, the output of the encoder is the compressed CSI (or precoding) information c t The intermediate output of the encoder is the accumulated information S for the AI / ML model corresponding to Layer 1. t (1) is.

[0029] Here, as shown in Fig. 8, the rank value calculated from the state of the CSI information at time t+1 is 2. In this case, the input to the AI / ML model corresponding to layer 1 is the CSI information H t+1 , and accumulated information S from time t for the AI / ML model corresponding to Layer 1 t (1) On the other hand, the input to the AI / ML model corresponding to Layer 2 includes the CSI information H t+1 , and accumulated information S from time t for the AI / ML model corresponding to layer 2 t (2) However, as shown in Figure 8, at the previous time t, the accumulated information S for the AI / ML model corresponding to layer 2 is t (2) Since the output is not available, the AI / ML model corresponding to Layer 2 cannot use the accumulated information from time t.

[0030] In this way, the performance of CSI compression may be degraded due to the influence of loss of accumulated information (past / accumulated CSI) caused by a change in rank or the number of layers over time in rank adaptation.

[0031] One method for eliminating the impact of missing stored information due to temporal changes in rank or number of layers caused by the rank adaptation described above is to separate chains of stored information in the time domain between ranks or layers, as shown in FIG. 9 , so that the terminal and base station / network update the AI / ML models for all ranks or layers regardless of the rank selection result at each time. FIG. 9 illustrates an example of setting an AI / ML model for each rank. In this method, for example, only the chain of stored information corresponding to the rank determined by rank selection or the AI / ML model of the layer corresponding to the determined rank is actually used in the inference process for CSI compression or reconstruction, but the chain of stored information corresponding to other AI / ML models (AI / ML models not corresponding to the selected rank or layer) is also used to perform the inference process for updating the stored information. This allows past stored information for different ranks or layers to be used and updated even when the rank changes over time.

[0032] However, with this method, inference processing is performed using all AI / ML models, including not only the AI / ML models actually used in the inference processing for CSI compression or reconstruction, but also other AI / ML models that are not actually used. Since the AI / ML models are kept in continuous operation, there is a possibility that the power consumption or memory usage of the terminal and base station / network will increase.

[0033] In one non-limiting embodiment of the present disclosure, a method for eliminating or mitigating the effect of changing the rank or number of layers in rank adaptation in space-time-frequency domain CSI compression using past CSI information is described.

[0034] For example, in training an AI / ML model, a chain of accumulated information in the time domain is configured to learn temporal fluctuations in rank in addition to temporal channel fluctuations. Figure 10 illustrates an example of updating an AI / ML model (e.g., a chain of accumulated information in the time domain) in a non-limiting example embodiment of the present disclosure. As shown in Figure 10, accumulated information from a previous time instance (past / accumulated CSI, intermediate output of the AI / ML model) input to the AI / ML model at a certain time instance may include information on rank adaptation (e.g., information on the rank selected at the previous time instance) in addition to the accumulated CSI information.

[0035] This makes it possible to prevent inconsistencies from occurring in the chain of accumulated information (past / accumulated CSI) even when the rank or the number of layers changes over time.

[0036] Non-limiting embodiments of the present disclosure will be described below.

[0037] [Overview of Communication System] A communication system according to an aspect of the present disclosure includes, for example, at least one base station and at least one terminal.

[0038] FIG. 11 is a block diagram showing a configuration example of a portion of a base station 100 according to an embodiment of the present disclosure, and FIG. 12 is a block diagram showing a configuration example of a portion of a terminal 200 according to an embodiment of the present disclosure.

[0039] In the base station 100 shown in Fig. 11, a communication unit (e.g., corresponding to a receiving circuit) receives a channel state information report (e.g., a CSI report) in which the amount of information is compressed by inference processing of an AI / ML model on the terminal side. A control unit (e.g., corresponding to a control circuit) reconstructs the channel state information report using past intermediate outputs of the AI / ML model on the network side. Here, the past intermediate outputs include channel state information at a previous time and information about a previously selected rank.

[0040] In the terminal 200 shown in FIG. 12 , a control unit (e.g., corresponding to a control circuit) uses past intermediate outputs (e.g., accumulated information) of the terminal-side AI / ML model to generate a channel state information report (CSI report) in which the amount of information is compressed by inference processing of the terminal-side AI / ML model. Here, the past intermediate outputs include channel state information at the immediately previous time and information about a rank selected in the past. A communication unit (e.g., corresponding to a transmission circuit) transmits the channel state information report.

[0041] (Embodiment 1) Terminal 200 inputs CSI information measured and generated by terminal 200 to a CSI compression unit (encoder), generates a CSI report (e.g., compressed CSI), and reports the CSI report to base station 100 (e.g., base station / network). The base station / network uses a CSI reconstruction unit (decoder) to reconstruct (restore) the CSI report (compressed CSI) reported from terminal 200. These processes can also be considered as inference processes using AI / ML models (two-sided models) on both the terminal side and the base station / network side.

[0042] Here, a CSI report configuration framework based on at least a subband configuration and a rank notification (e.g., a layer configuration) may be applied to a CSI report (e.g., a compressed CSI report) based on an inference process using an AI / ML model. Furthermore, the CSI report (e.g., a compressed CSI) may be transmitted via uplink control information (UCI) on a physical uplink control channel (PUCCH) or a physical uplink shared channel (PUSCH). For example, a periodic CSI report, an aperiodic CSI report, or a semi-persistent CSI report may be used as the CSI report.

[0043] Terminal 200 may be notified of, for example, information regarding resources for CSI reports of at least one of Periodic CSI, Semi-persistent CSI, and Aperiodic CSI (e.g., information regarding resources such as PUCCH resources and PUSCH resources; also referred to as "CSI report configuration") using Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling, Downlink Control Information (DCI), or a combination thereof.

[0044] The CSI report configuration may include, for example, information regarding a reporting period, an offset, etc. Furthermore, the CSI report configuration may include a configuration ID (e.g., CSI-ReportConfigId). The configuration ID may identify parameters such as the type of CSI reporting method and the reporting period. Furthermore, the CSI report configuration may include information indicating which reference signal (e.g., CSI-RS) is used to report the measured CSI.

[0045] The input of the AI / ML model of the CSI compression unit (encoder) includes both CSI information (or precoding information) at the current time and accumulated information (past / accumulated CSI) from past time points, as shown in Figure 2 or 3. The output of the encoder is compressed CSI (or precoding) information, and the intermediate output of the encoder is accumulated information (past / accumulated CSI) to be used as input to the encoder (AI / ML model) at the next time point.

[0046] The CSI reconstruction unit (decoder), like the encoder, may restore the CSI (or precoding) information based on both the compressed CSI (or precoding) information and the accumulated information from the past (past / accumulated CSI) calculated on the decoder side, for example, as shown in FIG. 2 or FIG. 3 .

[0047] In this embodiment, a chain of AI / ML models and time-domain accumulated information (e.g., updating of accumulated information) is configured so that temporal fluctuations in rank can be learned in addition to temporal channel fluctuations.

[0048] FIG. 13 shows an example of the structure of an AI / ML model and a chain of accumulated information in the time domain.

[0049] As shown in Figure 13, rank selection and CSI compression (encoder processing) are processed simultaneously. For example, as shown in Figure 13, an AI model configuration is applied that outputs compressed CSI (e.g., CSI feedback "C") and a selected rank (e.g., Rank value "RI") to learn temporal channel and rank variations. For example, the AI / ML model outputs both a CSI report and a rank value through inference processing.

[0050] Furthermore, in FIG. 13, the accumulated information (past / accumulated CSI) may include CSI information at a past time instance and rank information selected at a past time instance.

[0051] In this way, the AI / ML model of the CSI compressor (encoder) uses past rank information to learn not only temporal channel fluctuations but also temporal rank fluctuations, and outputs CSI reports and ranks through inference processing that takes rank fluctuations into account. This makes it possible to learn the AI / ML model and the chain of time-domain accumulated information (updating accumulated information) that takes rank fluctuations into account even when the rank or number of layers changes over time.

[0052] FIG. 14 shows an example of the relationship between the input and output of the AI / ML model of each of the encoder and decoder corresponding to a certain time (e.g., time instance #t) in a setting of an AI / ML model that simultaneously outputs CSI compression and rank selection.

[0053] In FIG. 14, the input of the AI / ML model (AI / ML model on the terminal side) of the CSI compressor (encoder) is CSI information (or precoding information) H t , and accumulated information from past time (time instance#t-1) (past / accumulated CSI) S t-1 Here, the accumulated information from the past includes, as described above, CSI information at the past time and rank information selected at the past time (for example, time-series information of past ranks).

[0054] In FIG. 14, the output of the AI / ML model (AI / ML model on the terminal side) of the CSI compressor (encoder) includes the selected rank value RI t , compressed CSI (or precoding information appropriate for the selected rank) C t The intermediate output of the CSI compressor (encoder) contains the accumulated CSI (past / accumulated CSI) S, which is used as the input for the next time step. t Includes:

[0055] The terminal 200 reports the selected rank value and the compressed CSI information to the base station / network via CSI feedback.

[0056] In FIG. 14, the input of the AI / ML model (AI / ML model on the base station / network side) of the CSI reconstruction unit (decoder) is the rank value RI selected in terminal 200. t , compressed CSI (or precoding information appropriate for the selected rank) C t , and past (e.g., time instance #t-1) accumulated CSI S' calculated on the decoder side. t-1 Includes:

[0057] In FIG. 14, the output of the AI / ML model of the CSI reconstruction unit (decoder) (AI / ML model on the base station / network side) is the reconstructed (restored) CSI (or precoding information) V' t The intermediate output of the CSI reconstruction unit (decoder) contains the accumulated CSI (past / accumulated CSI) S', which is used as the input for the next time step. t Includes:

[0058] According to this embodiment, during the learning stage, it is possible to learn the AI / ML model and the chain of accumulated information (past / accumulated CSI) taking rank fluctuations into account. Therefore, even if the rank or number of layers changes over time, the terminal 200 and the base station / network can use past accumulated information regardless of rank fluctuations. In addition, since the accumulated information can be updated appropriately, it is possible to prevent inconsistencies from occurring in the chain of accumulated information.

[0059] Furthermore, in the inference process at a certain time instance, only one AI / ML model that performs both CSI compression and rank selection is executed, which prevents an increase in power consumption or memory usage of the terminal 200 and base station / network, and also has the advantage of simplifying AI / ML model management.

[0060] (Embodiment 2) Terminal 200 inputs CSI information measured and generated by terminal 200 to a CSI compression unit (encoder), generates a CSI report (e.g., compressed CSI), and reports the CSI report to base station 100 (e.g., base station / network). The base station / network uses a CSI reconstruction unit (decoder) to reconstruct (restore) the CSI report (compressed CSI) reported from terminal 200. These processes can also be considered as inference processes using AI / ML models (two-sided models) on both the terminal side and the base station / network side.

[0061] Here, a CSI report configuration framework based on at least a subband configuration and a rank indication (e.g., a layer configuration) may be applied to a CSI report (e.g., a compressed CSI report) based on inference processing using an AI / ML model. Furthermore, the CSI report (e.g., a compressed CSI) may be transmitted in UCI on a PUCCH or a PUSCH. For example, a periodic CSI report, an aperiodic CSI report, or a semi-persistent CSI report may be used as the CSI report.

[0062] Terminal 200 may be notified of, for example, information regarding resources for CSI reports of at least one of Periodic CSI, Semi-persistent CSI, and Aperiodic CSI (e.g., information regarding resources such as PUCCH resources and PUSCH resources (e.g., CSI report configuration)) using RRC signaling, MAC signaling, downlink control information (DCI), or a combination thereof.

[0063] The CSI report configuration may include, for example, information regarding a reporting period, an offset, etc. Furthermore, the CSI report configuration may include a configuration ID (e.g., CSI-ReportConfigId). The configuration ID may identify parameters such as the type of CSI reporting method and the reporting period. Furthermore, the CSI report configuration may include information indicating which reference signal (e.g., CSI-RS) is used to report the measured CSI.

[0064] The input of the AI / ML model of the CSI compression unit (encoder) includes both CSI information (or precoding information) at the current time and accumulated information (past / accumulated CSI) from past time points, as shown in Figure 2 or 3. The output of the encoder is compressed CSI (or precoding) information, and the intermediate output of the encoder is accumulated information (past / accumulated CSI) to be used as input to the encoder (AI / ML model) at the next time point.

[0065] The CSI reconstruction unit (decoder), like the encoder, may restore the CSI (or precoding) information based on both the compressed CSI (or precoding) information and the accumulated information from the past (past / accumulated CSI) calculated on the decoder side, for example, as shown in FIG. 2 or FIG. 3 .

[0066] In this embodiment, a chain of AI / ML models and time-domain accumulated information (e.g., updating of time-domain accumulated information) is configured so that temporal fluctuations in rank can be learned in addition to temporal channel fluctuations.

[0067] FIG. 15 shows an example of the structure of an AI / ML model and a chain of accumulated information in the time domain.

[0068] As shown in Fig. 15, an AI / ML model is set for each rank. Also, as shown in Fig. 15, rank selection and CSI compression are processed separately. Terminal 200 and base station / network perform rank-specific inference processing (e.g., encoder processing or decoder processing) using an AI / ML model corresponding to the rank determined by rank selection (rank 1 or rank 2 in the example of Fig. 15).

[0069] Also, in FIG. 15 , in order to learn temporal channel fluctuations and rank fluctuations, past / accumulated CSI (e.g., represented by “S”) may include CSI information at past time instances and rank information selected at past times.

[0070] Also, as shown in FIG. 15, to learn the temporal channel variation and rank variation, a chain of time-domain accumulated information (e.g., updates) is connected to the AI / ML model across different ranks.

[0071] For example, as shown in FIG. 15, at time t-1 (time instance#t-1), rank 2 is selected and an AI / ML model corresponding to rank 2 is used, and at time t (time instance#t), rank 1 is selected and an AI / ML model corresponding to rank 1 is used. That is, different ranks are selected at time t-1 and time t (for example, the rank is changed). In this case, as shown in FIG. 15, accumulated information S t-1 is input to the AI / ML model corresponding to rank 1 used at time t.

[0072] Similarly, for example, as shown in FIG. 15, at time t (time instance #t+1), rank 2 is selected and the AI / ML model corresponding to rank 2 is used. That is, a different rank is selected between time t and time t+1 (for example, the rank is changed). In this case, as shown in FIG. 15, accumulated information S t is input to the AI / ML model corresponding to rank 2 used at time t+1.

[0073] In this way, the accumulated information (intermediate output) from the AI / ML model corresponding to the rank determined at the previous time is input to the AI / ML model corresponding to the rank determined at the current time, regardless of the rank. As a result, the AI / ML model in the CSI compression unit (encoder) uses past rank information to learn temporal channel fluctuations as well as temporal rank fluctuations, and outputs a CSI report (compressed CSI) through inference processing that takes rank fluctuations into account. This makes it possible to learn the AI / ML model and the chain of accumulated information in the time domain (updating the accumulated information) that takes rank fluctuations into account, even when the rank or number of layers changes over time.

[0074] Below, we will explain an example of the relationship between the input and output of the AI / ML model for each of the encoder and decoder corresponding to a certain time in the setting of the AI / ML model when CSI compression and rank selection are processed separately as shown in Figure 15.

[0075] The CSI compressor (encoder) may be configured with, for example, an AI / ML model for rank selection and an AI / ML model for CSI compression. Note that the rank selection function may be configured with, for example, an AI / ML model, or an existing rank selection method that does not use AI / ML technology may be applied.

[0076] The input to the rank selection function (rank selection model) may include, for example, CSI information (or precoding information) at the current time, and the output of the rank selection function may include, for example, a selected rank value.

[0077] The inputs of the AI / ML model for CSI compression (the terminal-side AI / ML model) include the rank value selected at the current time, CSI information at the current time, and accumulated information at past times (past / accumulated CSI). Here, the accumulated information at past times is accumulated information output from the AI / ML model corresponding to the rank selected at the immediately previous time, as described above.

[0078] The output of the AI / ML model for CSI compression (the terminal-side AI / ML model) includes the selected rank value and compressed CSI (or precoding information appropriate for the selected rank), and the intermediate output of the AI / ML model for CSI compression includes accumulated information (past / accumulated CSI) to be used as input for the next time.

[0079] The terminal 200 reports the selected rank value and the compressed CSI information to the base station / network via CSI feedback.

[0080] The inputs of the AI / ML model of the CSI reconstruction unit (decoder) (AI / ML model on the base station / network side) include the rank value selected in terminal 200, compressed CSI (or precoding information appropriate for the selected rank), and past / accumulated CSI calculated on the decoder side.

[0081] The output of the AI / ML model of the CSI reconstruction unit (decoder) (the AI / ML model on the base station / network side) contains the reconstructed (restored) CSI (or precoding information), and the intermediate output of the CSI reconstruction unit (decoder) contains the accumulated information (past / accumulated CSI) that will be used as the input for the next time.

[0082] According to this embodiment, the accumulated information used in the AI / ML model at a certain time (time instance) can be obtained from the output (intermediate output) of the AI / ML model of each of the encoder and decoder corresponding to the rank selected at the immediately previous time, regardless of the rank. This makes it possible to learn, in the learning stage, the AI / ML model and the chain of accumulated information (past / accumulated CSI) that takes rank fluctuations into account. Therefore, even if the rank or the number of layers changes over time, the terminal 200 and the base station / network can use past accumulated information regardless of rank fluctuations and can appropriately update the accumulated information, thereby preventing inconsistencies from occurring in the chain of accumulated information.

[0083] Furthermore, in the inference process at a certain time instance, only one AI / ML model corresponding to a specific rank (e.g., a rank determined by rank selection) is executed, thereby preventing an increase in power consumption or memory usage of the terminal 200 and base station / network.

[0084] (Embodiment 3) Terminal 200 inputs CSI information measured and generated by terminal 200 to a CSI compression unit (encoder), generates a CSI report (e.g., compressed CSI), and reports the CSI report to base station 100 (e.g., base station / network). The base station / network uses a CSI reconstruction unit (decoder) to reconstruct (restore) the CSI report (compressed CSI) reported from terminal 200. These processes can also be considered as inference processes using AI / ML models (two-sided models) on both the terminal side and the base station / network side.

[0085] Here, a CSI report configuration framework based on at least a subband configuration and a rank indication (e.g., a layer configuration) may be applied to a CSI report (e.g., a compressed CSI report) based on inference processing using an AI / ML model. Furthermore, the CSI report (e.g., a compressed CSI) may be transmitted in UCI on a PUCCH or a PUSCH. For example, a periodic CSI report, an aperiodic CSI report, or a semi-persistent CSI report may be used as the CSI report.

[0086] Terminal 200 may be notified of, for example, information regarding resources for CSI reports of at least one of Periodic CSI, Semi-persistent CSI, and Aperiodic CSI (e.g., information regarding resources such as PUCCH resources and PUSCH resources (e.g., CSI report configuration)) using RRC signaling, MAC signaling, downlink control information (DCI), or a combination thereof.

[0087] The CSI report configuration may include, for example, information regarding a reporting period, an offset, etc. Furthermore, the CSI report configuration may include a configuration ID (e.g., CSI-ReportConfigId). The configuration ID may identify parameters such as the type of CSI reporting method and the reporting period. Furthermore, the CSI report configuration may include information indicating which reference signal (e.g., CSI-RS) is used to report the measured CSI.

[0088] The input of the AI / ML model of the CSI compression unit (encoder) includes both CSI information (or precoding information) at the current time and accumulated information (past / accumulated CSI) from past time points, as shown in Figure 2 or 3. The output of the encoder is compressed CSI (or precoding) information, and the intermediate output of the encoder is accumulated information (past / accumulated CSI) to be used as input to the encoder (AI / ML model) at the next time point.

[0089] The CSI reconstruction unit (decoder), like the encoder, may restore the CSI (or precoding) information based on both the compressed CSI (or precoding) information and the accumulated information from the past (past / accumulated CSI) calculated on the decoder side, for example, as shown in FIG. 2 or FIG. 3 .

[0090] In this embodiment, a chain of AI / ML models and time-domain accumulated information (e.g., updating of time-domain accumulated information) is configured so that temporal fluctuations in rank can be learned in addition to temporal channel fluctuations.

[0091] FIG. 16 shows an example of the structure of an AI / ML model and a chain of accumulated information in the time domain.

[0092] As shown in Fig. 16, an AI / ML model is set for each layer. Also, as shown in Fig. 16, rank selection and CSI compression are processed separately. Terminal 200 and base station / network perform layer-specific inference processing (e.g., encoder processing or decoder processing) using an AI / ML model corresponding to the layer according to the rank determined by rank selection (rank 1 or rank 2 in the example of Fig. 16).

[0093] Also, in FIG. 16 , in order to learn temporal channel fluctuations and rank fluctuations, past / accumulated CSI (e.g., represented by “S”) may include CSI information at past time instances and rank information selected at past times.

[0094] 16, in order to learn temporal channel fluctuations and rank fluctuations, a chain of accumulated information in the time domain (e.g., updates) is connected to AI / ML models between different layers. Furthermore, the input to the AI / ML model of the CSI compressor (encoder) may be information obtained by pre-processing the measured channel information and converting it into information for each layer (e.g., precoding information such as eigenvectors).

[0095] For example, as shown in FIG. 16 , at time t-1 (time instance#t-1), rank 2 is selected and an AI / ML model corresponding to layers 1 and 2 is used, and at time t (time instance#t), rank 1 is selected and an AI / ML model corresponding to layer 1 is used. That is, different ranks are selected between time t-1 and time t (for example, the rank is changed). In this case, as shown in FIG. 16 , accumulated information S , which is the intermediate output of the AI / ML model corresponding to layer 1 used at time t-1, is used. t-1 (1) , and accumulated information S which is the intermediate output of the AI / ML model corresponding to layer 2 used at time t-1 t-1 (2) is input to the AI / ML model corresponding to layer 1 used at time t.

[0096] Similarly, for example, as shown in FIG. 16, at time t (time instance #t+1), rank 2 is selected and the AI / ML model corresponding to layers 1 and 2 is used. That is, different ranks are selected at time t and time t+1 (for example, the rank is changed). In this case, as shown in FIG. 16, accumulated information S t (1) are input to the AI / ML models corresponding to layers 1 and 2 used at time t+1.

[0097] In this way, the accumulated information (intermediate output) from the AI / ML model corresponding to the layer corresponding to the rank determined at the previous time is input to the AI / ML model corresponding to the layer corresponding to the rank determined at the current time, regardless of the rank (or layer). As a result, the AI / ML model in the CSI compression unit (encoder) learns temporal rank fluctuations in addition to temporal channel fluctuations using past rank information, and outputs a CSI report (compressed CSI) through inference processing that takes rank fluctuations into account. This makes it possible to learn the AI / ML model and the chain of accumulated information in the time domain (updating the accumulated information) that takes rank fluctuations into account, even when the rank or the number of layers changes over time.

[0098] Below, we will explain an example of the relationship between the input and output of the AI / ML model for each of the encoder and decoder corresponding to a certain time in the setting of the AI / ML model when CSI compression and rank selection are processed separately as shown in Figure 16.

[0099] The CSI compressor (encoder) may be configured with, for example, an AI / ML model for rank selection and an AI / ML model for CSI compression. Note that the rank selection function may be configured with, for example, an AI / ML model, or an existing rank selection method that does not use AI / ML technology may be applied.

[0100] The input to the rank selection function (rank selection model) may include, for example, CSI information (or precoding information) at the current time, and the output of the rank selection function may include, for example, a selected rank value.

[0101] The inputs of the AI / ML model for CSI compression (the terminal-side AI / ML model) include the rank value selected at the current time, CSI information at the current time, and accumulated information from past times (past / accumulated CSI). Note that the CSI information at the current time may be information obtained by pre-processing measured channel information and converting it into information for each layer (e.g., precoding information such as eigenvectors). Here, the accumulated information from past times is accumulated information output from the AI / ML model corresponding to each layer according to the rank selected at the previous time, as described above.

[0102] For example, in FIG. 16 , at time instance #t where rank 1 is selected, the accumulated information (past / accumulated CSI) from the AI / ML model corresponding to layer 1 at time instance #t−1 and the accumulated information from the AI / ML model corresponding to layer 2 are input to the AI / ML model corresponding to layer 1.

[0103] Also, for example, in FIG. 16 , at time instance #t+1 where rank 2 is selected, the accumulated information (past / accumulated CSI) from the AI / ML model corresponding to layer 1 at time instance #t is input to the AI / ML model corresponding to layer 1, and the accumulated information from the AI / ML model corresponding to layer 1 at time instance #t is input to the AI / ML model corresponding to layer 2.

[0104] In this way, the input to the AI / ML model corresponding to each layer at the current time instance may include accumulated information from the AI / ML models corresponding to all layers according to the rank number selected at the previous time instance.

[0105] The output of the AI / ML model for CSI compression (the terminal-side AI / ML model) includes the selected rank value and compressed CSI (or precoding information appropriate for the selected rank), and the intermediate output of the AI / ML model for CSI compression includes accumulated information (past / accumulated CSI) to be used as input for the next time.

[0106] The terminal 200 reports the selected rank value and the compressed CSI information to the base station / network via CSI feedback.

[0107] The inputs of the AI / ML model of the CSI reconstruction unit (decoder) (AI / ML model on the base station / network side) include the rank value selected in terminal 200, compressed CSI (or precoding information appropriate for the selected rank), and past / accumulated CSI calculated on the decoder side.

[0108] The output of the AI / ML model of the CSI reconstruction unit (decoder) (the AI / ML model on the base station / network side) contains the reconstructed (restored) CSI (or precoding information), and the intermediate output of the CSI reconstruction unit (decoder) contains the accumulated information (past / accumulated CSI) that will be used as the input for the next time.

[0109] According to this embodiment, the accumulated information used in the AI / ML model at a certain time (time instance) can be obtained from the output (intermediate output) of the AI / ML model of each encoder and decoder corresponding to each layer according to the rank selected at the previous time, regardless of the rank. This makes it possible to learn the AI / ML model and the chain of accumulated information (past / accumulated CSI) that takes rank fluctuations into account in the learning stage. Therefore, even if the rank or the number of layers changes over time, the terminal 200 and the base station / network can use past accumulated information and appropriately update the accumulated information, thereby preventing inconsistencies from occurring in the chain of accumulated information.

[0110] Furthermore, in the inference process at a certain time instance, only the AI / ML model corresponding to each layer according to a specific rank (e.g., a rank determined by rank selection) is executed, thereby preventing an increase in power consumption or memory usage of the terminal 200 and base station / network.

[0111] The first to third embodiments have been described above.

[0112] As described above, in a non-limiting example of the present disclosure, terminal 200 generates a CSI report in which the amount of information is compressed by inference processing of the terminal-side AI / ML model using past intermediate outputs (accumulated information) of the terminal-side AI / ML model, and transmits the CSI report. Furthermore, base station 100 receives the CSI report in which the amount of information is compressed by inference processing of the terminal-side AI / ML model, and reconstructs the CSI report using past intermediate outputs of the base station / network-side AI / ML model. Here, the past intermediate outputs include CSI information from the immediately previous time and information about a previously selected rank.

[0113] As a result, even if the rank or number of layers changes over time in rank adaptation, for example, the terminal 200 and the base station / network can suppress inconsistencies in the chain of past accumulated information (past / accumulated CSI) input to the AI / ML model, thereby improving the performance of CSI compression.

[0114] Furthermore, in a non-limiting example of the present disclosure, inference processing for CSI compression or reconstruction is performed using only the AI / ML model that is actually used at each time instance. This prevents an increase in power consumption or memory usage of the terminal 200 and the base station / network without having to continuously operate all AI / ML models.

[0115] Therefore, according to non-limiting examples of the present disclosure, the efficiency of wireless communication can be improved.

[0116] (Another embodiment 1) When an AI / ML model is used on the terminal 200 side and the base station / network side, learning may be coordinated between vendors. When learning is coordinated between vendors, for example, as shown in Fig. 17, a method is possible in which a base station / network (gNB / NW) learns an encoder and decoder model, and transfers a data set used and generated in the learning to the terminal 200 (UE), and the terminal 200 uses the acquired data set to learn an encoder model (or a reference decoder model) on the terminal side.

[0117] In this method, the data set transferred from the base station / network to the terminal 200 may include, for example, the following data:

[0118] The dataset for encoder model training may include, for example, time-series data for the target CSI (CSI input to the encoder), the rank value, and the CSI feedback (encoder output corresponding to the compressed CSI).

[0119] The data set for training the reference decoder model may include, for example, time-series data of the rank value, the CSI feedback (encoder output corresponding to compressed CSI), and the reconstructed target CSI (decoder output). After training the reference decoder model, the terminal 200 may train the encoder model using the reference decoder model.

[0120] The dataset for training the encoder model and the reference decoder model may include, for example, time series data for the target CSI (CSI input to the encoder), rank value, CSI feedback (encoder output corresponding to compressed CSI), and reconstructed target CSI (decoder output).

[0121] In either case, at least time series data of the selected rank value (rank fluctuation) may be included in the data set exchanged from the base station / network to terminal 200. By including the rank value in the data set, it is possible to avoid inconsistencies in rank selection between the encoder and decoder.

[0122] (Another embodiment 2) A chain of AI / ML models that performs CSI compression in the space-frequency domain using past / accumulated CSI in the time domain may be reset or reconstructed every N CSI reports.

[0123] Resetting or rebuilding the chain of an AI / ML model may be applied not only to the inference stage of an AI / ML model, but also to the learning stage.

[0124] The value of N may be, for example, a fixed value or may be set by a base station / network. For example, the value of N may be set to 10 assuming an instantaneous PUSCH Block Error Rate (BLER) of 10% in general network operation. Note that the value of N is not limited to 10 and may be another value. For example, the value of N may be set according to an expected BLER value.

[0125] Also, instead of counting the number of CSI reports, the value of N may be a value determined by a time unit such as M ms or X frames / subframes / slots / symbols.

[0126] According to this embodiment, even if UCI loss occurs, for example, the chain of accumulated information in the time domain is reset or reconstructed every certain interval (for example, every interval corresponding to N CSI reports), thereby mitigating the influence of inconsistency in accumulated information between the encoder of terminal 200 and the decoder on the base station / network side.

[0127] As a method for resetting or reconstructing the chain of accumulated information every certain interval, for example, terminal 200 may not input accumulated information to the AI / ML model every time a certain interval elapses (the input of accumulated information to the AI / ML model may be stopped). For example, at a certain time when a certain interval elapses, terminal 200 may not input accumulated information, but may input CSI information (or precoding information) in the space-frequency domain, perform inference (encoding processing using an AI / ML model), generate a CSI report (e.g., compressed CSI), and report the CSI report to the base station / network. Similarly, at a certain time when a certain interval elapses, the base station / network may not input accumulated information, but may input a CSI report from terminal 200, perform inference (decoding processing using an AI / ML model), and restore the CSI information.

[0128] Alternatively, as a method of resetting or reconstructing the chain of accumulated information every certain period, for example, the base station / network and terminal 200 may input an averaged or statistical value using past information into the AI / ML model as accumulated information every time a certain period has passed.

[0129] For example, the value averaged or statistically generated using past information may be the average value of accumulated information obtained by inference processing (encoding and decoding using an AI / ML model) over the past N_average time opportunities.

[0130] Furthermore, in the inference process (encoding by an AI / ML model) of terminal 200, terminal 200 may average or statistically generate CSI information (or precoding information) before CSI compression, which is one of the inputs of the inference process for the past N_average time instances. Then, an intermediate output when the inference process is performed using the averaged or statistically generated CSI information as input may be input to the AI / ML model as accumulated information when resetting or reconstructing the AI / ML model chain.

[0131] Furthermore, in the inference process (decoding by the AI / ML model) on the base station / network side, the base station / network may average or statisticize the CSI-compressed CSI report, which is one of the inputs of the inference process for the past N_average time opportunities. Then, the intermediate output of the inference process using the averaged or statisticized CSI report as input may be input to the AI / ML model as accumulated information when resetting or reconstructing the chain of the AI / ML model.

[0132] The value of N_average may be the same as or different from the value of the interval length N for resetting or reconstructing the chain of accumulated information in the time domain.

[0133] (Another embodiment 3) The conditions for resetting or reconstructing the chain of AI / ML models that perform CSI compression in the space-frequency domain using accumulated information (past / accumulated CSI) in the time domain are not limited to the conditions (e.g., a certain interval or number of times) described in the above-mentioned other embodiment 2, and the chain of AI / ML models may be reset or reconstructed according to other methods (conditions).

[0134] For example, the reception quality (e.g., SINR) at the previous time (time instance) may be compared with the reception quality (SINR) at the current time t, and whether to reset or reconstruct the chain of AI / ML models may be determined based on the difference. For example, if the difference in SINR is equal to or less than a threshold, terminal 200 and base station / network may determine that the previously accumulated information is valid and may not need to reset or reconstruct the chain of AI / ML models (e.g., may continue the chain of AI / ML models). Furthermore, for example, if the difference in SINR is greater than a threshold, terminal 200 and base station / network may determine that the previously accumulated information is invalid and may reset or reconstruct the chain of AI / ML models.

[0135] Furthermore, for example, whether to reset or reconstruct the chain of AI / ML models may be determined based on the fluctuation range of the rank between the previous time instance and the current time instance. For example, if the fluctuation range of the rank is equal to or less than a threshold, the terminal 200 and the base station / network may determine that the previously accumulated information is valid and may not reset or reconstruct the chain of AI / ML models (for example, the chain of AI / ML models may be continued). For example, if the fluctuation range of the rank is greater than a threshold, the terminal 200 and the base station / network may determine that the previously accumulated information is invalid and may reset or reconstruct the chain of AI / ML models.

[0136] [Example of Operation of Base Station / Network and Terminal 200] FIG. 18 is a diagram showing an example of operation of the base station / network (base station 100) and the terminal 200. As shown in FIG.

[0137] The base station / network notifies the terminal 200 of the configuration related to CSI measurement and reporting (S101).

[0138] The terminal 200 performs CSI measurement based on, for example, settings related to CSI measurement and reporting (S102), generates a CSI report based on rank information and CSI compression (S103), and transmits the rank information and the generated CSI report to the base station / network (S104).

[0139] [Regarding Architecture] Fig. 19 shows an example of an architecture including a function for processing AI / ML. Note that part of the network architecture may have the configuration shown in Fig. 19. Furthermore, the function for processing AI / ML may be included in an Access and Mobility Management Function (AMF) or a RAN (gNB).

[0140] [Configuration of Base Station] Fig. 20 is a block diagram showing an example configuration of base station 100. In Fig. 20, base station 100 has a control unit 101, a signal generation unit 102, a transmission unit 103, a reception unit 104, an extraction unit 105, a demodulation unit 106, and a decoding unit 107.

[0141] At least one of the control unit 101, the signal generation unit 102, the extraction unit 105, the demodulation unit 106, and the decoding unit 107 shown in Fig. 20 may be included in the control unit shown in Fig. 11. Also, at least one of the transmission unit 103 and the reception unit 104 shown in Fig. 20 may be included in the communication unit shown in Fig. 11.

[0142] The control unit 101, for example, determines control information related to CSI measurement and reporting of the terminal 200 and outputs the determined information to the signal generation unit 102. The control information related to CSI measurement and reporting may include, for example, the configuration of a reference signal for CSI measurement, such as CSI-RS, and information related to a CSI report configuration for the terminal 200 to report CSI. Furthermore, the control unit 101 may output the CSI report input from the decoding unit 107 to a function for processing AI / ML (for example, a data collection function or a learning function of an AI / ML model). The function for processing AI / ML may be included in the base station 100 or in a node different from the base station 100.

[0143] Furthermore, the control unit 101 determines, for example, information for the terminal 200 to receive a downlink signal, and outputs the determined information to the signal generation unit 102. The information for the terminal 200 to receive a downlink signal may include, for example, information on resource allocation of a downlink data channel (e.g., PDSCH: Physical Downlink Shared Channel) or a downlink control channel (e.g., PDCCH: Physical Downlink Control Channel), and information on a coding and modulation scheme (e.g., MCS: Modulation and Coding Scheme).

[0144] Furthermore, the control unit 101 determines, for example, information used by the terminal 200 to transmit an uplink signal (e.g., a CSI report), and outputs the determined information to the signal generation unit 102, the extraction unit 105, the demodulation unit 106, and the decoding unit 107. The information used by the terminal 200 to transmit an uplink signal may include, for example, information on resource allocation of an uplink data channel (e.g., a Physical Uplink Shared Channel (PUSCH)) or an uplink control channel (e.g., a Physical Uplink Control Channel (PUCCH)), information on a coding / modulation scheme (e.g., MCS), and information on a CSI report.

[0145] The signal generation unit 102 generates a data signal or a control signal bit sequence using, for example, information input from the control unit 101, and applies encoding as necessary. The signal generation unit 102 also modulates the encoded bit sequence to generate a modulated signal (for example, a symbol sequence), and maps the modulated signal to the radio resource specified by the control unit 101. The signal generation unit 102 outputs the mapped signal to the transmission unit 103.

[0146] The transmitting unit 103 performs, for example, OFDM or other transmission waveform generation processing on the signal input from the signal generating unit 102. Furthermore, in the case of OFDM transmission using a cyclic prefix (CP), for example, the transmitting unit 103 performs an inverse fast Fourier transform (IFFT) processing on the signal and adds a CP to the signal after the IFFT. Furthermore, the transmitting unit 103 performs, for example, RF processing such as D / A conversion or up-conversion on the signal and transmits the radio signal to the terminal 200 via an antenna.

[0147] The receiving unit 104 performs RF processing such as downconvert or A / D conversion on an uplink signal received from the terminal 200 via an antenna. In addition, in the case of OFDM transmission, the receiving unit 104 performs Fast Fourier Transform (FFT) processing on the received signal, and outputs the resulting frequency domain signal to the extracting unit 105.

[0148] The extraction unit 105 extracts, for example, based on information input from the control unit 101, a radio resource portion from which an uplink signal (for example, a PUSCH or a PUCCH) is transmitted, from the received signal input from the receiving unit 104, and outputs the extracted radio resource portion to the demodulation unit 106.

[0149] The demodulation unit 106 demodulates the uplink signal (for example, PUSCH or PUCCH) input from the extraction unit 105, for example, based on information input from the control unit 101. The demodulation unit 106 outputs the demodulation result to the decoding unit 107, for example.

[0150] The decoding unit 107 performs error correction decoding on the uplink signal (for example, PUSCH or PUCCH) based on, for example, the information input from the control unit 101 and the demodulation result input from the demodulation unit 106, and obtains a decoded received bit sequence. For example, if the decoded received bit sequence includes a CSI report from the terminal 200, the decoding unit 107 outputs the information to the control unit 101.

[0151] [Configuration of Terminal] Fig. 21 is a block diagram showing an example configuration of a terminal 200 according to an embodiment of the present disclosure. For example, in Fig. 21, the terminal 200 includes a receiving unit 201, an extracting unit 202, a demodulating unit 203, a decoding unit 204, a control unit 205, a signal generating unit 206, and a transmitting unit 207.

[0152] At least one of the extraction unit 202, demodulation unit 203, decoding unit 204, control unit 205, and signal generation unit 206 shown in Fig. 21 may be included in the control unit shown in Fig. 12. Also, at least one of the reception unit 201 and transmission unit 207 shown in Fig. 21 may be included in the communication unit shown in Fig. 12.

[0153] The receiving unit 201 receives, for example, a downlink signal (e.g., a downlink data signal or a downlink control signal) from the base station 100 via an antenna, and performs RF processing such as downconvert or A / D conversion on the radio received signal to obtain a received signal (baseband signal). Furthermore, when receiving an OFDM signal, the receiving unit 201 performs FFT processing on the received signal to convert it into the frequency domain. The receiving unit 201 outputs the received signal to the extracting unit 202.

[0154] The extraction unit 202 extracts a radio resource portion that may include a downlink control signal from the received signal input from the receiving unit 201, based on, for example, information related to the radio resource of the downlink control signal input from the control unit 205, and outputs the extracted radio resource portion to the demodulation unit 203. Furthermore, based on information related to the radio resource of the data signal input from the control unit 205, the extraction unit 202 extracts a radio resource portion that includes a downlink data signal from the received signal, and outputs the extracted radio resource portion to the demodulation unit 203. Furthermore, the extraction unit 202 extracts a radio resource portion that includes a reference signal for CSI measurement (e.g., CSI-RS) from the received signal, and outputs the extracted radio resource portion to the control unit 205.

[0155] The demodulation unit 203 demodulates the signal (for example, PDCCH or PDSCH) input from the extraction unit 202 based on information input from the control unit 205 , for example, and outputs the demodulation result to the decoding unit 204 .

[0156] The decoding unit 204 performs error correction decoding of the PDCCH or PDSCH using, for example, information input from the control unit 205 and the demodulation result input from the demodulation unit 203, and obtains, for example, a control signal or a downlink data signal. The decoding unit 204 outputs the control signal to the control unit 205.

[0157] The control unit 205 identifies information related to downlink transmission based on, for example, information obtained from a control signal input from the decoding unit 204, and outputs the information to the extraction unit 202, the demodulation unit 203, and the decoding unit 204. The control unit 205 also identifies information related to uplink transmission based on, for example, information obtained from a control signal input from the decoding unit 204, and outputs the information to the signal generation unit 206. The control unit 205 also performs CSI measurement using the CSI-RS input from the extraction unit 202, generates control information related to CSI reporting based on the CSI measurement results using the above-described method, and outputs the control information to the signal generation unit 206. The control unit 205 may also output information related to CSI reporting to a function that processes AI / ML based on the CSI measurement results.

[0158] The signal generation unit 206 generates an uplink data signal or an uplink control signal based on control information related to the CSI report input from the control unit 205, information related to uplink transmission, or information related to the coded CSI report input from the AI / ML processing function, encodes and modulates the bit string of the generated signal, and maps it to radio resources. The signal generation unit 206 outputs the uplink signal onto which the signal has been mapped to, for example, the transmission unit 207.

[0159] The transmitter 207 generates a transmission signal waveform, such as OFDM, for the signal input from the signal generator 206. Furthermore, in the case of OFDM transmission or DFT-s-OFDM transmission using a CP, for example, the transmitter 207 performs IFFT processing on the signal and adds a CP to the signal after IFFT. Alternatively, when the transmitter 207 generates a single-carrier waveform such as a DFT-s-OFDM waveform, a DFT unit (not shown) may be added before the signal generator 206. Furthermore, the transmitter 207 performs RF processing, such as D / A conversion and up-conversion, on the transmission signal, and transmits the radio signal to the base station 100 via an antenna.

[0160] The above describes the embodiments according to non-limiting examples of the present disclosure.

[0161] [Use Cases] The use cases to which the above-described embodiments or modifications are applied are not limited to CSI compression (CSI feedback enhancement), and can be applied to any use case in which an AI / ML model is deployed on the terminal side and the base station / network side. For example, examples of use cases in which an AI / ML model is deployed on the terminal side and the base station / network side include CSI prediction using an AI / ML model, beam prediction in the spatial or time domain using an AI / ML model, positioning accuracy enhancement using an AI / ML model, and optimization of parameters and configurations of a wireless interface using AI / ML.

[0162] Furthermore, if the processor or AI / ML accelerator is general-purpose for AI / ML processing, AI / ML processing for different use cases may be executed simultaneously, or multiple AI / ML processing for different use cases may be selected.

[0163] [AI / ML Model] In non-limiting examples of the present disclosure, an "AI / ML model (or model)" may include a physical model, a binary model, an executable model, a converted model, a source code model, a non-executable model, a raw model, a logical model, or other model formats.

[0164] In addition, different AI / ML models may be used depending on parameters such as the cell, site, Transmission and Reception Point (TRP), beam, location, terminal movement speed, wireless channel such as multipath, cell congestion status, and transmitted traffic.

[0165] Furthermore, in the present disclosure, the signal / message / signaling used for notification may be a control plane message (e.g., UCI or MAC-CE), an RRC signal, or a notification in DCI, which is physical layer signaling.

[0166] Furthermore, the parameter values ​​used in the above embodiment are merely examples, and other values ​​may be used.

[0167] (Supplementary Note) Information indicating whether the terminal 200 supports the functions, operations, or processes described in each of the above-described embodiments and each supplementary note may be transmitted (or notified) from the terminal 200 to the base station 100, for example, as capability information or capability parameters of the terminal 200.

[0168] The capability information may include an information element (IE) that individually indicates whether or not the terminal 200 supports at least one of the functions, operations, or processes described in the above-described embodiments, modifications, and supplements. Alternatively, the capability information may include an information element that indicates whether or not the terminal 200 supports a combination of any two or more of the functions, operations, or processes described in the above-described embodiments, modifications, and supplements.

[0169] For example, the base station 100 may determine (or decide or assume) functions, operations, or processes that the terminal 200 that transmitted the capability information supports (or does not support) based on the capability information received from the terminal 200. The base station 100 may perform operations, processes, or controls according to the determination result based on the capability information. For example, the base station 100 may control processing related to CSI reporting based on the capability information received from the terminal 200.

[0170] Note that the fact that terminal 200 does not support some of the functions, operations, or processes described in the above-described embodiments, modifications, and supplementary notes may be interpreted as meaning that such some of the functions, operations, or processes are restricted in terminal 200. For example, information or a request regarding such restrictions may be notified to base station 100.

[0171] Information regarding the capabilities or limitations of terminal 200 may, for example, be defined in a standard, or may be implicitly notified to base station 100 in association with information known at base station 100 or information transmitted to base station 100.

[0172] The above has described the embodiments, modifications, and supplementary notes according to a non-limiting example of the present disclosure.

[0173] (Control Signal) In the present disclosure, a downlink control signal (or downlink control information) related to an embodiment of the present disclosure may be, for example, a signal (or information) transmitted in a Physical Downlink Control Channel (PDCCH) of a physical layer, or a signal (or information) transmitted in a Medium Access Control Control Element (MAC CE) or Radio Resource Control (RRC) of a higher layer. Furthermore, the signal (or information) is not limited to being notified by a downlink control signal, but may be predefined in a specification (or standard) or preconfigured in a base station and a terminal.

[0174] In the present disclosure, an uplink control signal (or uplink control information) related to an embodiment of the present disclosure may be, for example, a signal (or information) transmitted in a PUCCH of a physical layer, or a signal (or information) transmitted in a MAC CE or RRC of a higher layer. Furthermore, the signal (or information) is not limited to being notified by an uplink control signal, but may be predefined in a specification (or standard) or preconfigured in a base station and a terminal. Furthermore, the uplink control signal may be replaced with, for example, uplink control information (UCI), 1st stage sidelink control information (SCI), or 2nd stage SCI.

[0175] (Base Station) In an embodiment of the present disclosure, the base station may be a Transmission Reception Point (TRP), a cluster head, an access point, a Remote Radio Head (RRH), an eNodeB (eNB), a gNodeB (gNB), a Base Station (BS), a Base Transceiver Station (BTS), a parent device, a gateway, or the like. In sidelink communication, a terminal may play the role of a base station. Instead of a base station, a relay device that relays communication between an upper node and a terminal may be used. Alternatively, a roadside unit may be used.

[0176] (Uplink / Downlink / Sidelink) An embodiment of the present disclosure may be applied to, for example, any of the uplink, downlink, and sidelink. For example, an embodiment of the present disclosure may be applied to a Physical Uplink Shared Channel (PUSCH), a Physical Uplink Control Channel (PUCCH), or a Physical Random Access Channel (PRACH) in the uplink, a Physical Downlink Shared Channel (PDSCH), a PDCCH, or a Physical Broadcast Channel (PBCH) in the downlink, or a Physical Sidelink Shared Channel (PSSCH), a Physical Sidelink Control Channel (PSCCH), or a Physical Sidelink Broadcast Channel (PSBCH) in the sidelink.

[0177] The PDCCH, PDSCH, PUSCH, and PUCCH are examples of a downlink control channel, a downlink data channel, an uplink data channel, and an uplink control channel, respectively. The PSCCH and PSSCH are examples of a sidelink control channel and a sidelink data channel. The PBCH and PSBCH are examples of a broadcast channel, and the PRACH is an example of a random access channel.

[0178] (Data Channel / Control Channel) An embodiment of the present disclosure may be applied to, for example, either a data channel or a control channel. For example, the channel in an embodiment of the present disclosure may be replaced with any of the data channels PDSCH, PUSCH, and PSSCH, or the control channels PDCCH, PUCCH, PBCH, PSCCH, and PSBCH.

[0179] (Reference Signal) In one embodiment of the present disclosure, a reference signal is, for example, a signal known by both a base station and a mobile station, and may also be called a Reference Signal (RS) or a pilot signal. The reference signal may be any of a Demodulation Reference Signal (DMRS), a Channel State Information - Reference Signal (CSI-RS), a Tracking Reference Signal (TRS), a Phase Tracking Reference Signal (PTRS), a Cell-specific Reference Signal (CRS), or a Sounding Reference Signal (SRS).

[0180] (Time Interval) In one embodiment of the present disclosure, the unit of time resource is not limited to one or a combination of slots and symbols, but may be, for example, a time resource unit such as a frame, a superframe, a subframe, a slot, a time slot, a subslot, a minislot, a symbol, an Orthogonal Frequency Division Multiplexing (OFDM) symbol, a Single Carrier-Frequency Division Multiplexing Access (SC-FDMA) symbol, or another time resource unit. Furthermore, the number of symbols included in one slot is not limited to the number of symbols exemplified in the above-mentioned embodiment, and may be another number of symbols.

[0181] (Frequency Band) An embodiment of the present disclosure may be applied to either a licensed band or an unlicensed band.

[0182] (Communication) An embodiment of the present disclosure may be applied to communication between a base station and a terminal (Uu link communication), communication between terminals (Sidelink communication), or Vehicle to Everything (V2X) communication. For example, the channel in an embodiment of the present disclosure may be replaced with any of PSCCH, PSSCH, Physical Sidelink Feedback Channel (PSFCH), PSBCH, PDCCH, PUCCH, PDSCH, PUSCH, or PBCH.

[0183] An embodiment of the present disclosure may be applied to a terrestrial network, a non-terrestrial network (NTN) using a satellite or a high altitude pseudo satellite (HAPS), or a terrestrial network in which transmission delay is large compared to the symbol length or slot length, such as a network with a large cell size or an ultra-wideband transmission network.

[0184] (SBFD) In ​​one embodiment of the present disclosure, operations on uplink, downlink, and sidelink symbols may be applied to symbols (e.g., SBFD symbols) on which SBFD (Subband Non-Overlapping Full Duplex, Subband Full Duplex) operations or controls are performed. In SBFD symbols, a frequency domain (or frequency resource, frequency band) is divided into multiple frequency domains (e.g., subbands, RB sets, subbands, or sub-BWPs (Bandwidth Parts)). A terminal transmits and receives in different directions (e.g., downlink or uplink) in units of subbands, which are the divided domains. In SBFD symbols, a terminal may transmit and receive in one direction, either uplink or downlink, but not in the other direction. On the other hand, a base station may be capable of transmitting and receiving on both the uplink and downlink simultaneously. SBFD symbols may have a smaller frequency domain available for downlink use than symbols that transmit and receive only downlink use. Also, SBFD symbols may have a smaller frequency domain available for uplink use than symbols that transmit and receive only uplink use.

[0185] In addition, in the SBFD symbol, a terminal may transmit and receive uplink and downlink simultaneously. In this case, the frequency domain in which the terminal transmits and the frequency domain in which the terminal receives may not be adjacent, but may be separated by a frequency interval (also called a frequency gap).

[0186] In addition, different transmission and reception directions in subband units, which are divided areas, may include transmission and reception of side links.

[0187] (XDD: Cross Division Duplex) In one embodiment of the present disclosure, the operation for uplink, downlink, and sidelink symbols may be applied to symbols (e.g., full duplex symbols) where full duplex operation or control is performed. In a full duplex symbol, both the terminal and the base station can simultaneously transmit and receive on the uplink and downlink. In a full duplex symbol, the terminal and the base station may simultaneously transmit and receive in an available frequency region (or frequency resource, frequency band), or may simultaneously transmit and receive in a partial frequency region (i.e., transmission or reception may be performed in other frequency regions). In this case, the frequency region in which the base station or terminal transmits and receives may not be adjacent, but may have a frequency interval (also called a frequency gap). Furthermore, for the purpose of, for example, reducing interference, either the terminal or the base station may simultaneously transmit and receive (i.e., the other may transmit or receive).

[0188] In addition, full duplex operation may be applied to an operation in which a terminal can simultaneously transmit and receive sidelinks, or to an operation in which a terminal can simultaneously transmit and receive sidelinks and uplinks or downlinks.

[0189] (Antenna Port) In one embodiment of the present disclosure, an antenna port refers to a logical antenna (antenna group) consisting of one or more physical antennas. For example, an antenna port does not necessarily refer to a single physical antenna, but may refer to an array antenna consisting of multiple antennas. For example, the number of physical antennas that an antenna port is composed of is not specified, and the antenna port may be specified as the smallest unit by which a terminal station can transmit a reference signal. Furthermore, an antenna port may also be specified as the smallest unit by which a weighting of a precoding vector is multiplied.

[0190] <5G NR System Architecture and Protocol Stack> The 5G NR system architecture generally assumes an NG-RAN (Next Generation - Radio Access Network) including gNBs. The gNBs provide UE-side termination of the NG radio access user plane (SDAP / PDCP / RLC / MAC / PHY) and control plane (RRC) protocols. The gNBs are connected to each other via an Xn interface. The gNBs are also connected to a Next Generation Core (NGC) via a Next Generation (NG) interface, more specifically to an Access and Mobility Management Function (AMF) (e.g., a specific core entity that performs AMF) via an NG-C interface, and to a User Plane Function (UPF) (e.g., a specific core entity that performs UPF) via an NG-U interface. The NG-RAN architecture is shown in Figure 22 (see, for example, 3GPP TS 38.300 v15.6.0, section 4).

[0191] <RRC connection setup and reconfiguration procedure> This shows the NAS part of the interaction between the UE, gNB, and AMF (5GC entity) when the UE transitions from RRC_IDLE to RRC_CONNECTED (see TS 38.300 v15.6.0).

[0192] RRC is a higher layer signaling protocol used to configure the UE and gNB. The AMF prepares UE context data (including, for example, PDU session context, security keys, UE radio capabilities, UE security capabilities, etc.) and sends it to the gNB along with an INITIAL CONTEXT SETUP REQUEST. The gNB then activates AS security together with the UE. This is done by the gNB sending a SecurityModeCommand message to the UE, and the UE responding with a SecurityModeComplete message to the gNB. The gNB then sends an RRCReconfiguration message to the UE, and upon receiving an RRCReconfigurationComplete from the UE, the gNB performs reconfiguration to set up Signaling Radio Bearer 2 (SRB2) and Data Radio Bearer (DRB). For signaling-only connections, the steps related to RRCReconfiguration are omitted because SRB2 and DRB are not set up. Finally, the gNB notifies the AMF that the setup procedure is complete with an INITIAL CONTEXT SETUP RESPONSE.

[0193] Therefore, the present disclosure provides a 5th Generation Core (5GC) entity (e.g., AMF, SMF, etc.) that includes: a control circuit that, upon operation, establishes a Next Generation (NG) connection with a gNodeB; and a transmitter that, upon operation, transmits an initial context setup message to the gNodeB via the NG connection so that a signaling radio bearer between the gNodeB and a user equipment (UE) is set up. Specifically, the gNodeB transmits Radio Resource Control (RRC) signaling, including a resource allocation configuration information element (IE), to the UE via the signaling radio bearer. The UE then transmits in uplink or receives in downlink based on the resource allocation configuration.

[0194] <QoS Control> The 5G Quality of Service (QoS) model is based on QoS flows and supports both QoS flows that require a guaranteed flow bit rate (Guaranteed Bit Rate QoS flows (GBR)) and QoS flows that do not require a guaranteed flow bit rate (non-GBR QoS flows). Thus, at the NAS level, a QoS flow is the finest granularity of QoS classification in a PDU session. A QoS flow is identified within a PDU session by a QoS Flow ID (QFI) carried in an encapsulation header over the NG-U interface.

[0195] For each UE, 5GC establishes one or more PDU sessions. For each UE, the NG-RAN establishes, for example, at least one Data Radio Bearer (DRB) for each PDU session. Additional DRBs for the QoS flows of that PDU session can be configured later (when this is up to the NG-RAN). The NG-RAN maps packets belonging to different PDU sessions to different DRBs. NAS-level packet filters in the UE and 5GC associate UL and DL packets with QoS flows, while AS-level mapping rules in the UE and NG-RAN associate UL and DL QoS flows with DRBs.

[0196] (Open-RAN) The base station described in each embodiment (for example, a 5G NR base station called a gNB) may be configured with three functional modules: a Centralized Unit (CU), a Distributed Unit (DU), and a Radio Unit (RU).

[0197] A CU may be referred to as a centralized node, aggregation node, central station, aggregation station, or centralized unit. A DU may be referred to as an O-RAN Distributed Unit (O-DU), distributed node, distributed station, or distributed unit. An RU may be referred to as an O-RAN Radio Unit (O-RU), radio equipment, radio node, radio station, antenna unit, or radio unit.

[0198] There are several split options for the functional split configuration (or functional split point) between CU, DU, and RU. The term "functional split point" is sometimes referred to as "split," "option," or "split option."

[0199] Examples of "division options" include the following division options 1 to 8. The functions of the base station described in each embodiment may be divided into a CU, a DU, and an RU by any of the following division options 1 to 8. For example, the CU, DU, and RU may be functionally divided, or the functions may be divided only between the CU and DU or only between the DU and RU. (1) Segmentation option 1: Between RRC (radio resource control) and PDCP (2) Segmentation option 2: Between PDCP and RLC (High-RLC) (3) Segmentation option 3: Between High-RLC and Low-RLC (4) Segmentation option 4: Between RLC (Low-RLC) and MAC (High-MAC) (5) Segmentation option 5: Between High-MAC and Low-MAC (6) Segmentation option 6: Between MAC (Low-MAC) and PHY (High-PHY) (7) Segmentation option 7: Between High-PHY and Low-PHY (8) Segmentation option 8: Between PHY (Low-PHY) and RF

[0200] The functional split point between the CU and O-DU may be split option 2. The section between the CU and O-DU is called midhaul, and the F1 interface is specified by 3GPP. The section between the O-DU and O-RU is called fronthaul, and the functional split point may be split option 7-2x, which is adopted as the O-RAN fronthaul specification.

[0201] Figure 23 shows an example of functionally dividing the gNB base station functions into CU, O-DU, and O-RU using Split Option 2 and Split Option 7-2x.

[0202] The CU may have, for example, a radio resource control (RRC) function, a service data adaptation protocol (SDAP) function, and a packet data convergence protocol (PDCP) function.

[0203] The O-DU may include, for example, a radio link control (RLC) function, a MAC function, and a higher physical layer (HIGH-PHY) function. The HIGH-PHY function may include an encoding function, a scrambling function, a modulation function, a layer mapping function, a precoding function, and a resource element (RE) mapping function for downlink (DL) transmission. The HIGH-PHY function may also include a decoding function, a descrambling function, a demodulation function, a layer demapping function, and a resource element (RE) demapping function for uplink (UL) reception.

[0204] The O-RU may have, for example, a LOW-PHY function and an RF function. The LOW-PHY function may also have, for downlink transmission, a beamforming function, an IFFT (Inverse First Fourier Transform) + CP (Cyclic Prefix) assignment function, and a D / A (Digital to Analog) conversion function. The LOW-PHY function may also have, for uplink reception, an A / D (Analog to Digital) conversion function, a CP removal + FFT (First Fourier Transform) function, and a beamforming function.

[0205] In addition, if the O-DU does not have a precoding function, the O-RU may have a precoding function.

[0206] The O-RU may have functionality related to LBT (listen before talk).

[0207] The evolving Common Public Radio Interface (eCPRI) is specified as the communication method between the O-DU and O-RU in Split Option 7-2x. In Split Option 7-2x, eCPRI transmits and receives sampling sequences of the in-phase (I) and quadrature (Q) components of OFDM signals in the frequency domain, as well as information used for beamforming in antennas and time synchronization signals.

[0208] Information transmitted by the signals described in each embodiment (PDCCH, PUCCH, PDSCH, PUSCH, MAC CE, RRC, etc.) may be transmitted between the O-DU and the O-RU via the eCPRI User Plane (U-Plane) or Control Plane (C-Plane).

[0209] When the functions described in each embodiment are performed in the O-RU by functional division, the O-DU may control the O-RU by transmitting information for controlling the functions via a control signal (e.g., eCPRI) between the O-DU and the O-RU.

[0210] When the functions described in each embodiment are performed in the O-DU by functional division, the O-RU may receive the results of the functions performed in the O-DU via a control signal (e.g., eCPRI) and control the O-RU based on the received results.

[0211] The CU, O-DU, and O-RU may be deployed in physically different devices with their respective functions connected by optical fiber or the like, or some or all of their functions may be deployed in the same physical device.

[0212] The CU and O-DU may be logical entities implemented as software running on a server in the cloud or the like as a virtualized RAN (virtual Radio Access Network: vRAN). Also, some or all of the functions of the CU and O-DU may be provided as a virtualized network function (Network Functions Virtualization: NFV) service.

[0213] The transceiver does not have to be a radio transceiver, but may be, for example, a network transceiver, an optical transceiver, etc. The radio resources allocated by the O-DU may be resources for wireless communication between the O-RU and the UE.

[0214] The present disclosure can be realized in software, hardware, or software in conjunction with hardware.

[0215] Each functional block used in the description of the above embodiments may be partially or entirely realized as an LSI, which is an integrated circuit, and each process described in the above embodiments may be partially or entirely controlled by a single LSI or a combination of LSIs. The LSI may be composed of individual chips, or may be composed of a single chip that includes some or all of the functional blocks. The LSI may have data input and output. Depending on the degree of integration, the LSI may also be called an IC, system LSI, super LSI, or ultra LSI.

[0216] The integrated circuit method is not limited to LSI, and may be realized by a dedicated circuit, a general-purpose processor, or a dedicated processor. Also, a field programmable gate array (FPGA) that can be programmed after LSI manufacturing, or a reconfigurable processor that can reconfigure the connections and settings of circuit cells within the LSI, may be used. The present disclosure may be realized as digital processing or analog processing.

[0217] Furthermore, if an integrated circuit technology that can replace LSI emerges due to advances in semiconductor technology or other derivative technologies, it is natural that such technology may be used to integrate functional blocks. The application of biotechnology, etc. is also a possibility.

[0218] The present disclosure may be implemented in any type of apparatus, device, or system (collectively referred to as a communications apparatus) that has a communications function. The communications apparatus may include a radio transceiver and processing / control circuitry. The radio transceiver may include a receiver and a transmitter, or both functions. The radio transceiver (transmitter and receiver) may include a radio frequency (RF) module and one or more antennas. The RF module may include an amplifier, an RF modulator / demodulator, or the like. Non-limiting examples of communication devices include telephones (e.g., cell phones, smartphones), tablets, personal computers (PCs) (e.g., laptops, desktops, notebooks), cameras (e.g., digital still / video cameras), digital players (e.g., digital audio / video players), wearable devices (e.g., wearable cameras, smartwatches, tracking devices), game consoles, digital book readers, telehealth / telemedicine devices, communication-enabled vehicles or mobile transportation (e.g., cars, airplanes, ships), and combinations of the above devices.

[0219] The communication devices are not limited to portable or mobile devices, but also include any kind of non-portable or fixed equipment, devices, and systems, such as smart home devices (such as home appliances, lighting equipment, smart meters or measuring devices, control panels, etc.), vending machines, and any other "things" that may exist on an IoT (Internet of Things) network.

[0220] Communications include data communications via cellular systems, wireless LAN systems, communication satellite systems, and the like, as well as data communications via combinations of these.

[0221] A communications apparatus also includes devices such as controllers and sensors connected or coupled to a communications device that performs the communications functions described in this disclosure, such as controllers and sensors that generate control and data signals used by the communications device to perform the communications functions of the communications apparatus.

[0222] The communication apparatus also includes infrastructure facilities, such as base stations, access points, and any other apparatus, device, or system that communicates with or controls the various apparatuses listed above, but are not limited to these.

[0223] A terminal according to one embodiment of the present disclosure includes a control circuit that uses past intermediate outputs of an artificial intelligence model on the terminal side to generate a report of channel state information in which the amount of information is compressed by an inference process of the artificial intelligence model, and the intermediate output includes the channel state information at a previous time and information about a rank selected in the past, and a transmission circuit that transmits the report of channel state information.

[0224] In one embodiment of the present disclosure, the artificial intelligence model outputs both the channel state information report and the rank through the inference process.

[0225] In one embodiment of the present disclosure, the artificial intelligence model is configured for each rank, and the compression of the channel state information report and the selection of the rank are processed separately.

[0226] In one embodiment of the present disclosure, the control circuit performs the rank-specific inference process using the artificial intelligence model corresponding to the rank determined by the selection of the rank.

[0227] In one embodiment of the present disclosure, the intermediate output from the artificial intelligence model corresponding to the rank determined at the previous time is input to the artificial intelligence model corresponding to the rank determined at the current time, regardless of the rank.

[0228] In one embodiment of the present disclosure, the artificial intelligence model is configured for each layer, and the compression of the channel state information report and the selection of the rank are processed separately.

[0229] In one embodiment of the present disclosure, the control circuit performs the inference process individually for each layer using the artificial intelligence model corresponding to the layer according to the rank determined by the selection of the rank.

[0230] In one embodiment of the present disclosure, the intermediate output from the artificial intelligence model corresponding to the layer according to the rank determined at the previous time is input to the artificial intelligence model corresponding to the layer according to the rank determined at the current time, regardless of the rank.

[0231] In one embodiment of the present disclosure, the dataset for training the artificial intelligence model exchanged from the base station to the terminal includes at least time series data of the rank.

[0232] A base station according to one embodiment of the present disclosure includes a receiving circuit that receives a report of channel state information in which the amount of information is compressed by inference processing of an artificial intelligence model on a terminal side, and a control circuit that reconstructs the report of channel state information using past intermediate outputs of an artificial intelligence model on a network side, the intermediate outputs including the channel state information at a previous time and information regarding a rank selected in the past.

[0233] In a communication method according to one embodiment of the present disclosure, a terminal uses past intermediate outputs of an artificial intelligence model on the terminal side to generate a report of channel state information in which the amount of information is compressed by an inference process of the artificial intelligence model, and the intermediate output includes the channel state information at the previous time and information about a rank selected in the past, and transmits the report of channel state information.

[0234] In a communication method according to one embodiment of the present disclosure, a base station receives a report of channel state information in which the amount of information is compressed by an inference process of an artificial intelligence model on a terminal side, and reconstructs the report of channel state information using a past intermediate output of an artificial intelligence model on a network side, where the intermediate output includes the channel state information at a previous time and information about a rank selected in the past.

[0235] The disclosures of the specification, drawings and abstract contained in Japanese Patent Application No. 2024-114910, filed on July 18, 2024, are incorporated herein by reference in their entirety.

[0236] One embodiment of the present disclosure is useful in wireless communication systems.

[0237] 100 Base station 101, 205 Control unit 102, 206 Signal generation unit 103, 207 Transmission unit 104, 201 Reception unit 105, 202 Extraction unit 106, 203 Demodulation unit 107, 204 Decoding unit 200 Terminal

Claims

1. A terminal comprising: a control circuit that uses past intermediate outputs of an artificial intelligence model on the terminal side to generate a report of channel state information with a compressed amount of information through the inference process of the artificial intelligence model, the intermediate output including the channel state information at the previous time and information regarding a rank selected in the past; and a transmission circuit that transmits the report of channel state information.

2. The terminal according to claim 1, wherein the artificial intelligence model outputs both the channel state information report and the rank through the inference process.

3. The terminal according to claim 1, wherein the artificial intelligence model is set for each rank, and the compression of the channel state information report and the selection of the rank are processed separately.

4. The terminal according to claim 3, wherein the control circuit performs the inference process that is individual to the rank using the artificial intelligence model that corresponds to the rank determined by the selection of the rank.

5. The terminal according to claim 3, wherein the intermediate output from the artificial intelligence model corresponding to the rank determined at the immediately previous time is input to the artificial intelligence model corresponding to the rank determined at the current time, regardless of the rank.

6. The terminal according to claim 1, wherein the artificial intelligence model is configured for each layer, and the compression of the channel state information report and the selection of the rank are processed separately.

7. The terminal according to claim 6, wherein the control circuit performs the inference process for each layer using the artificial intelligence model corresponding to the layer according to the rank determined by the selection of the rank.

8. The terminal described in claim 6, wherein the intermediate output from the artificial intelligence model corresponding to the layer according to the rank determined at the previous time is input to the artificial intelligence model corresponding to the layer according to the rank determined at the current time, regardless of the rank.

9. The terminal according to claim 1, wherein the dataset for training the artificial intelligence model exchanged from the base station to the terminal includes at least time-series data of the rank.

10. A base station comprising: a receiving circuit that receives a report of channel state information in which the amount of information has been compressed by inference processing of an artificial intelligence model on the terminal side; and a control circuit that reconstructs the report of channel state information using past intermediate outputs of an artificial intelligence model on the network side, the intermediate outputs including the channel state information at the previous time and information regarding a rank selected in the past.

11. A communication method in which a terminal uses past intermediate outputs of an artificial intelligence model on the terminal side to generate a report of channel state information in which the amount of information is compressed through inference processing of the artificial intelligence model, the intermediate output including the channel state information at the immediately previous time and information regarding a rank selected in the past, and transmits the report of channel state information.

12. A communication method in which a base station receives a report of channel state information in which the amount of information has been compressed by inference processing of an artificial intelligence model on the terminal side, and reconstructs the report of channel state information using past intermediate outputs of an artificial intelligence model on the network side, the intermediate outputs including the channel state information at the previous time and information about a rank selected in the past.

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