Method and device for CSI (Channel State Information) reporting in node used for wireless communication

Abstract

The invention discloses a method and a device for CSI (Channel State Information) reporting in a node used for wireless communication. The first node receives the target CSI reporting configuration; the target CSI reporting configuration is used for configuring reporting of the target CSI; the generation mode of the target CSI is based on training or AI; sending the target information block; determining whether sending of the target CSI is abandoned; when the first condition is met, giving up to send the target CSI; wherein the determination whether to give up the sending of the target CSI depends on whether the first condition is satisfied; the first condition comprises that the generation mode of the target CSI is associated with a target identifier; the target identifier depends on the target information block; the method and the device support CSI reporting based on AI, determine whether to send or give up sending of CSI reporting, reduce resource overhead of a system, and improve overall performance of the system.

Description

Technical Field

[0001] The present application relates to a transmission method and apparatus in a wireless communication system, and more particularly to a scheme and apparatus for reporting CSI (Channel State Information) in a wireless communication system. Background Art

[0002] In traditional wireless communication, UE (User Equipment) reports various auxiliary information obtained by measuring downlink signals and / or channels, such as channel information, auxiliary information related to beam management, auxiliary information related to positioning, etc. The channel information includes but is not limited to one or more of CRI (CSI-RS Resource Indicator, channel state information reference signal resource indication), RI (Rank Indicator, rank indication), PMI (Precoding Matrix Indicator, precoding indication), CQI (Channel quality indicator) or beam indication. The UE can use this information to select appropriate transmission parameters by itself, or report this information. The network device selects appropriate transmission parameters for the UE according to the UE's report, such as the resident cell, MCS (Modulation and Coding Scheme, modulation and coding scheme), TPMI (Transmitted Precoding Matrix Indicator, transmission precoding matrix indication), TCI (Transmission Configuration Indication) and other parameters. In addition, UE reporting can be used to optimize network parameters, such as better cell coverage, switching base stations according to UE location, etc.

[0003] With the adoption of new technologies, the increase in the number of antennas, the diversification of application scenarios, and the improvement of system performance requirements, traditional measurement and reporting methods will bring a lot of redundant overhead. Therefore, in NR (New Radio) Rel-18 (Release-18), the research on AI (Artificial Intelligence) / ML (Machine Learning) technology was established to explore its impact on system performance and system design. Compared with traditional processing methods, AI / ML has the characteristics of being based on training and requiring deployment. Summary of the invention

[0004] The applicant has found through research that when AI / ML functions are introduced, the existing measurement mechanism, reporting mechanism and related configuration signaling may not be able to adapt to the needs of AI / ML. In response to the above problems, the present application discloses a solution. It should be noted that in the description of the above problem, the NR system is used as an example. The present application is also applicable to scenarios such as the future 6G system, and achieves technical effects similar to the NR system; further, although the original intention of the present application is for AI / ML scenarios, the present application can also be applied to other non-AI / ML scenarios, such as the traditional CSI (Channel State Information) reporting scheme; further, for different scenarios (such as other non-AI / ML scenarios, including but not limited to vehicle to everything (V2X), capacity enhancement system, short-range communication system, NTN (NonTerrestrialNetwork, non-terrestrial network), IoT (Internet of Things, Internet of Things), URLLC (UltraReliable Low Latency Communication, ultra-robust low-latency communication) network, etc.), the use of a unified design scheme also helps to reduce hardware complexity and cost. In the absence of conflict, the embodiments and features in any node of the present application can be applied to any other node. In the absence of conflict, the embodiments and features in the embodiments of the present application can be arbitrarily combined with each other.

[0005] In particular, the interpretation of the terminology, nouns, functions, and variables in this application (if not otherwise specified) can refer to the definitions in the 3GPP specification protocols TS28 series, TS36 series, TS38 series, and TS37 series. If necessary, reference can be made to 3GPP standards TS38.211, TS38.212, TS38.213, TS38.214, TS38.215, TS38.321, TS38.331, TS38.305, TS38.304, and TS37.355 to assist in understanding this application.

[0006] The present application discloses a method in a first node used for wireless communication, characterized by comprising:

[0007] receiving a target CSI reporting configuration; the target CSI reporting configuration is used to configure reporting of the target CSI; the target CSI is generated in a training-based or AI-based manner;

[0008] Sending a target information block; determining whether to abandon sending the target CSI; when the first condition is met, abandoning sending the target CSI;

[0009] The determination of whether to abandon sending the target CSI depends on whether the first condition is satisfied; the first condition includes that the generation method of the target CSI is associated with the target identifier; the target identifier depends on the target information block.

[0010] As an embodiment, the AI ​​(Artificial Intelligence) includes ML (Machine Learning).

[0011] As an embodiment, the problem to be solved by the present application includes: how to determine whether to send or give up sending CSI reporting.

[0012] As an embodiment, the essence of the above method includes: whether to abandon sending CSI report depends on whether the first condition is met; when the first condition is met, abandon sending CSI report.

[0013] As an embodiment, the essence of the above method includes: when the AI-based CSI generation method is associated with the target identifier, giving up sending the CSI report.

[0014] As an embodiment, the benefits of the above method include: supporting AI-based CSI reporting.

[0015] As an embodiment, the benefits of the above method include: ensuring consistency in the understanding of CSI reporting by the transmitting and receiving ends.

[0016] As an embodiment, the benefits of the above method include: improving the accuracy and effectiveness of CSI reporting.

[0017] As an embodiment, the benefits of the above method include: reducing the overhead of CSI reporting and improving system efficiency.

[0018] As an embodiment, the benefits of the above method include: simplifying system design and reducing solution complexity.

[0019] As an embodiment, the benefits of the above method include: enhancing the flexibility and robustness of the system and better adapting to various transmission conditions and application scenarios.

[0020] As an embodiment, the benefits of the above method include: improving the overall performance of the system.

[0021] According to one aspect of the present application, it is characterized in that the target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of channel measurement or interference resource measurement of the target CSI, and the first resource set includes one or more RS resources; the target CSI indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

[0022] As an embodiment, the benefits of the above method include: reducing the overhead required to obtain the target CSI.

[0023] As an embodiment, the benefits of the above method include: reducing the measurement resources required to obtain the target CSI.

[0024] As an embodiment, the benefits of the above method include: enhancing the flexibility of the system and improving the overall performance of the system.

[0025] According to one aspect of the present application, it is characterized in that the target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of channel measurement or interference resource measurement of the target CSI, and the first resource set includes one or more RS resources; the method for generating the target CSI includes performing a first operation, the input of the first operation depends on measurement based on the first resource set, and the target CSI depends on the output of the first operation.

[0026] As an embodiment, the first operation is training-based or AI-based.

[0027] As an embodiment, the benefits of the above method include: supporting AI / ML-based CSI reporting.

[0028] As an embodiment, the benefits of the above method include: improving the accuracy and effectiveness of CSI reporting.

[0029] As an embodiment, the benefits of the above method include: improving the overall performance of the system.

[0030] According to one aspect of the present application, it is characterized in that the generation method of the target CSI is associated with the target identifier including: the first operation is associated with the target identifier.

[0031] As an embodiment, the benefits of the above method include: simplifying system design and reducing the implementation complexity of the solution.

[0032] As an embodiment, the benefits of the above method include: enhancing the flexibility of the system and improving the overall performance of the system.

[0033] According to one aspect of the present application, it is characterized in that the generation method of the target CSI is associated with the target identifier, including: the target CSI reporting configuration indicates a first type identifier, the first type identifier indicated by the target CSI reporting configuration is the same as the target identifier, and the target identifier is a first type identifier.

[0034] As an embodiment, the benefits of the above method include: small changes to existing standards and system designs, and improved forward and backward compatibility of the system.

[0035] As an embodiment, the benefits of the above method include: enhancing the flexibility and robustness of the system.

[0036] According to one aspect of the present application, it is characterized in that the target information block indicates the target identifier.

[0037] As an embodiment, the essence of the above method includes: the determination of the target identifier depends on the target information block.

[0038] As an embodiment, the benefits of the above method include: the target information block is adapted to a variety of transmission scenarios and applications, thereby improving the flexibility of the system.

[0039] As an embodiment, the benefits of the above method include: simplifying system design and reducing the implementation complexity of the solution.

[0040] According to one aspect of the present application, it is characterized by comprising:

[0041] receiving at least one information block, the at least one information block configuring or indicating at least one signal;

[0042] The target information block is generated based on the reception or measurement of part or all of the at least one signal.

[0043] As an embodiment, the essence of the above method includes: the target information block carries information on reception or measurement of the first node signal.

[0044] As an embodiment, the essence of the above method includes: the target identification depends on the reception or measurement of part or all of the at least one signal.

[0045] As an embodiment, the benefits of the above method include: compatibility with existing system designs and standards, and improved forward and backward compatibility of the system.

[0046] As an embodiment, the benefits of the above method include: simplifying the design of the system and reducing the implementation complexity of the solution.

[0047] As an embodiment, the benefits of the above method include: improving the flexibility and overall performance of the system.

[0048] According to one aspect of the present application, it is characterized in that the target identification depends on the at least one information block.

[0049] As an embodiment, the benefits of the above method include: the at least one information block is adaptable to a variety of transmission scenarios and applications, thereby improving the flexibility of the system.

[0050] As an embodiment, the benefits of the above method include: establishing the internal connection of the system, simplifying the design of the system, and reducing the implementation complexity of the solution.

[0051] According to one aspect of the present application, it is characterized in that the first value is greater than a first threshold, and the first value depends on the target information block.

[0052] As an embodiment, the essence of the above method includes: introducing a first value and a first threshold, and establishing an association between them and the target information block.

[0053] As an embodiment, the benefits of the above method include: simplifying the design of the system and reducing the implementation complexity of the solution.

[0054] As an embodiment, the benefits of the above method include: improving the flexibility and overall performance of the system.

[0055] According to one aspect of the present application, it is characterized in that the trigger condition of the target information block includes that the first value is greater than a first threshold;

[0056] in,

[0057] The first value indicates the number of times the AI ​​model identified by the target identifier fails or fails, the first value is a non-negative integer, and the first threshold is a positive integer;

[0058] or,

[0059] The first value dependency is based on reception or measurement of part or all of the at least one signal.

[0060] As an embodiment, the essence of the above method includes: when the first value is greater than the first threshold value and is satisfied, the target information block may be triggered.

[0061] As an embodiment, the essence of the above method includes: the first value and the first threshold may have different meanings and implementations in different scenarios and applications.

[0062] As an embodiment, the benefits of the above method include: simplifying the design of the system and reducing the implementation complexity of the solution.

[0063] As an embodiment, the benefits of the above method include: enhancing the flexibility and robustness of the system and adapting to different transmission scenarios and applications.

[0064] As an embodiment, the benefits of the above method include: improving the overall performance of the system.

[0065] As an embodiment, the first node is a terminal.

[0066] The present application discloses a method used in a second node of wireless communication, characterized by comprising:

[0067] Sending a target CSI reporting configuration; the target CSI reporting configuration is used to configure reporting of the target CSI; the target CSI is generated based on training or based on AI;

[0068] receiving a target information block; determining whether to abandon receiving the target CSI; when a first condition is met, abandoning receiving the target CSI;

[0069] Among them, the target recipient of the target CSI reporting configuration determines whether to give up sending the target CSI; when the first condition is met, the target recipient of the target CSI reporting configuration gives up sending the target CSI; the target recipient of the target CSI reporting configuration determines whether to give up sending the target CSI depends on whether the first condition is met; the first condition includes the generation method of the target CSI associated with the target identifier; the target identifier depends on the target information block.

[0070] According to one aspect of the present application, it is characterized in that the target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of channel measurement or interference resource measurement of the target CSI, and the first resource set includes one or more RS resources; the target CSI indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

[0071] According to one aspect of the present application, it is characterized in that the target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of channel measurement or interference resource measurement of the target CSI, and the first resource set includes one or more RS resources; the method for generating the target CSI includes performing a first operation, the input of the first operation depends on measurement based on the first resource set, and the target CSI depends on the output of the first operation.

[0072] According to one aspect of the present application, it is characterized in that the generation method of the target CSI is associated with the target identifier including: the first operation is associated with the target identifier.

[0073] According to one aspect of the present application, it is characterized in that the generation method of the target CSI is associated with the target identifier, including: the target CSI reporting configuration indicates a first type identifier, the first type identifier indicated by the target CSI reporting configuration is the same as the target identifier, and the target identifier is a first type identifier.

[0074] According to one aspect of the present application, it is characterized in that the target information block indicates the target identifier.

[0075] According to one aspect of the present application, it is characterized by comprising:

[0076] sending at least one information block, the at least one information block configuring or indicating at least one signal;

[0077] The target information block is generated based on the reception or measurement of part or all of the at least one signal.

[0078] According to one aspect of the present application, it is characterized in that the target identification depends on the at least one information block.

[0079] According to one aspect of the present application, it is characterized in that the first value is greater than a first threshold, and the first value depends on the target information block.

[0080] According to one aspect of the present application, it is characterized in that the trigger condition of the target information block includes that the first value is greater than a first threshold;

[0081] in,

[0082] The first value indicates the number of times the AI ​​model identified by the target identifier fails or fails, the first value is a non-negative integer, and the first threshold is a positive integer;

[0083] or,

[0084] The first value dependency is based on reception or measurement of part or all of the at least one signal.

[0085] The present application discloses a terminal, characterized in that the terminal comprises: one or more processors and a memory;

[0086] The memory is coupled to the one or more processors, and is used to store computer program codes, where the computer program codes include computer instructions, and the one or more processors call the computer instructions to enable the terminal to execute the method in the first node.

[0087] As an embodiment, the terminal is a user equipment.

[0088] The present application discloses a base station, characterized in that the base station comprises: one or more processors and a memory;

[0089] The memory is coupled to the one or more processors, and is used to store computer program codes, where the computer program codes include computer instructions, and the one or more processors call the computer instructions to cause the base station to perform the method in the second node.

[0090] The present application discloses a first node used for wireless communication, characterized in that it includes:

[0091] A first receiver receives a target CSI reporting configuration; the target CSI reporting configuration is used to configure reporting of the target CSI; the target CSI is generated based on training or based on AI;

[0092] A first processor sends a target information block; determines whether to abandon sending the target CSI; when a first condition is met, abandons sending the target CSI;

[0093] The determination of whether to abandon sending the target CSI depends on whether the first condition is satisfied; the first condition includes that the generation method of the target CSI is associated with the target identifier; the target identifier depends on the target information block.

[0094] The present application discloses a second node used for wireless communication, characterized in that it includes:

[0095] The second processor sends a target CSI reporting configuration; the target CSI reporting configuration is used to configure the reporting of the target CSI; the generation method of the target CSI is based on training or AI; receives a target information block; determines whether to abandon receiving the target CSI; when the first condition is met, abandons receiving the target CSI;

[0096] Among them, the target recipient of the target CSI reporting configuration determines whether to give up sending the target CSI; when the first condition is met, the target recipient of the target CSI reporting configuration gives up sending the target CSI; the target recipient of the target CSI reporting configuration determines whether to give up sending the target CSI depends on whether the first condition is met; the first condition includes the generation method of the target CSI associated with the target identifier; the target identifier depends on the target information block.

[0097] As an embodiment, compared with the traditional solution, this application has the following advantages:

[0098] -Support AI-based CSI reporting;

[0099] -Reduce system measurement resources and overhead;

[0100] -Reduce system CSI reporting resources and overhead;

[0101] -Improve the system's forward and backward compatibility;

[0102] - Improve the flexibility and adaptability of the system;

[0103] - Improve system reliability and robustness;

[0104] -Simplify system design and reduce the complexity of solution implementation;

[0105] - Improve the accuracy and effectiveness of CSI reporting;

[0106] - Enhance the overall performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0108] Figure 1 A flowchart of a target CSI reporting configuration, a target information block and a target CSI according to an embodiment of the present application is shown;

[0109] Figure 2 A schematic diagram of a network architecture according to an embodiment of the present application is shown;

[0110] Figure 3 A schematic diagram showing an embodiment of a wireless protocol architecture of a user plane and a control plane according to an embodiment of the present application;

[0111] Figure 4 A schematic diagram showing a first communication device and a second communication device according to an embodiment of the present application is shown;

[0112] Figure 5 A flowchart of wireless transmission according to an embodiment of the present application is shown;

[0113] Figure 6 A schematic diagram showing a first resource set and a second resource set according to an embodiment of the present application is shown;

[0114] Figure 7 A schematic diagram showing a first operation according to an embodiment of the present application;

[0115] Figure 8 A schematic diagram showing a method of generating a target CSI according to an embodiment of the present application and associating it with a target identifier;

[0116] Fig. 9A schematic diagram showing a method for generating a target CSI according to another embodiment of the present application and associating it with a target identifier is shown;

[0117] Fig.10 A schematic diagram showing a target information block indicating a target identifier according to an embodiment of the present application is shown;

[0118] Fig.11 A schematic diagram showing at least one information block according to an embodiment of the present application;

[0119] Fig.12 A schematic diagram showing a target identification depending on at least one information block according to an embodiment of the present application;

[0120] Fig.13 A schematic diagram showing a first value and a first threshold according to an embodiment of the present application is shown;

[0121] Fig.14 A schematic diagram showing a triggering condition of a target information block according to an embodiment of the present application;

[0122] Fig.15 A schematic diagram showing a second operation according to an embodiment of the present application;

[0123] Fig.16 A schematic diagram showing a first operation according to another embodiment of the present application;

[0124] Fig.17 A schematic diagram of deploying a first operation according to an embodiment of the present application is shown;

[0125] Fig.18 A schematic diagram of RAN (Radio Access Network) domain (Domain) AI / ML function deployment according to an embodiment of the present application is shown;

[0126] Fig.19 A schematic diagram showing the deployment of AI / ML functions of a UE according to an embodiment of the present application is shown;

[0127] Fig. 20 A schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of the present application is shown;

[0128] Fig.21 A schematic diagram based on artificial intelligence or machine learning according to an embodiment of the present application is shown;

[0129] Fig. 22 A structural block diagram of a processing device used in a first node according to an embodiment of the present application is shown;

[0130] Fig.23 A structural block diagram of a processing device used in a second node according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0131] The technical solution of the present application will be further described in detail below in conjunction with the accompanying drawings. It should be noted that the embodiments and features in the embodiments of the present application can be arbitrarily combined with each other without conflict. Based on considerations such as performance, flexibility, complexity, overhead, and compatibility, those skilled in the art are motivated to flexibly combine the embodiments in different drawings without conflict, such as but not limited to the attached drawings. Figure 1 The embodiments and appendix Figure 5 -Attached Fig.23 The embodiments in Figure 5 The embodiments and appendix Figure 6 -Attached Fig.23 The embodiments in , etc.

[0132] Example 1

[0133] Embodiment 1 illustrates a flow chart of a target CSI reporting configuration, a target information block, and a target CSI according to an embodiment of the present application, as shown in the attached figure. Figure 1 Attached Figure 1 In the illustrated 100, each box represents a step. In particular, the order of the steps in the boxes does not represent a specific time sequence between the steps.

[0134] In Embodiment 1, the first node in the present application receives a target CSI reporting configuration in step 101; sends a target information block in step 102; determines whether to abandon sending the target CSI in step 103; and abandons sending the target CSI in step 104 when the first condition is met;

[0135] Among them, the target CSI reporting configuration is used to configure the reporting of the target CSI; the generation method of the target CSI is training-based or AI-based; the determination of whether to abandon sending the target CSI depends on whether the first condition is met; the first condition includes the generation method of the target CSI being associated with the target identifier; the target identifier depends on the target information block.

[0136] As an embodiment, the target CSI reporting configuration is carried by higher layer signaling.

[0137] As an embodiment, the target CSI reporting configuration is carried by RRC (Radio Resource Control) signaling.

[0138] As an embodiment, the target CSI reporting configuration is carried by an RRC IE (Information Element).

[0139] As an embodiment, the target CSI reporting configuration is carried by at least one RRC IE.

[0140] As an embodiment, the target CSI reporting configuration includes information in one or more fields in at least one RRC IE.

[0141] As an embodiment, the target CSI reporting configuration includes information in one or more fields of each RRC IE in multiple RRC IEs.

[0142] As an embodiment, the target CSI reporting configuration includes part or all of the fields in the CSI-ReportConfig IE.

[0143] As an embodiment, the target CSI reporting configuration includes part or all of the fields in the ServingCellConfig IE.

[0144] As an embodiment, the target CSI reporting configuration includes part or all of the fields in the CSI-MeasConfig IE.

[0145] As an embodiment, the target CSI reporting configuration includes part or all of the fields in the ServingCellConfigCommon IE.

[0146] As an embodiment, the target CSI reporting configuration includes part or all of the fields in the ServingCellConfigCommonSIB IE.

[0147] As an embodiment, the target CSI reporting configuration is transmitted on the PDSCH.

[0148] As an embodiment, the CSI reporting configured by the target CSI reporting configuration is periodic.

[0149] As an embodiment, the CSI reporting configured by the target CSI reporting configuration is semi-persistent.

[0150] As an embodiment, the CSI reporting configured by the target CSI reporting configuration is aperiodic.

[0151] As an embodiment, the target CSI reporting configuration is an RRC IE.

[0152] As an embodiment, the target CSI reporting configuration belongs to the CSI-ReportConfig IE.

[0153] As an embodiment, the target CSI reporting configuration belongs to ServingCellConfig IE.

[0154] As an embodiment, the target CSI reporting configuration belongs to the CSI-MeasConfig IE.

[0155] As an embodiment, the target CSI reporting configuration belongs to ServingCellConfigCommon IE.

[0156] As an embodiment, the target CSI reporting configuration belongs to ServingCellConfigCommonSIB IE.

[0157] As an embodiment, the target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of channel measurement or interference resource measurement of the target CSI, and the first resource set includes one or more RS resources.

[0158] As an embodiment, the method for generating the target CSI is training-based or AI-based, including: the method for generating the target CSI is based on training; the method for generating the target CSI is not training-based or AI-based, including: the method for generating the target CSI is not training-based.

[0159] As an embodiment, the method for generating the target CSI is training-based or AI-based, including: the method for generating the target CSI is based on AI; the method for generating the target CSI is not training-based or AI-based, including: the method for generating the target CSI is not based on AI.

[0160] As an embodiment, the method for generating the target CSI is training-based or AI-based, including: the method for generating the target CSI uses an AI model; the method for generating the target CSI is not training-based or AI-based, including: the method for generating the target CSI does not use an AI model.

[0161] As an embodiment, the generation method of the target CSI is based on training or AI, including: the target CSI includes information based on artificial intelligence or machine learning; the generation method of the target CSI is not based on training or AI, including: the target CSI does not include information based on artificial intelligence or machine learning.

[0162] As an embodiment, the generation method of the target CSI is based on training or AI, including: the target CSI includes information generated based on a neural network (Neural Network); the generation method of the target CSI is not based on training or AI, including: the target CSI does not include information generated based on a neural network (Neural Network).

[0163] As an embodiment, the generation method of the target CSI is based on training or AI, including: the target CSI includes information generated based on CNN (Conventional Neural Networks, convolutional neural network); the generation method of the target CSI is not based on training or AI, including: the target CSI does not include information generated based on CNN.

[0164] As an embodiment, the generation method of the target CSI is training-based or AI-based, including: the generation of the target CSI includes the sender of the target CSI performing a first operation, the input of the first operation depends on the measurement based on the first resource set, and the target CSI depends on the output of the first operation; the generation method of the target CSI is not training-based or AI-based, including: the generation of the target CSI does not include the sender of the target CSI performing the first operation.

[0165] As an embodiment, the generation method of the target CSI is based on training or AI, including: the generation of the target CSI includes the sender of the target CSI performing a first operation, the input of the first operation depends on the measurement based on the first resource set, the target CSI depends on the output of the first operation, and the first operation is associated with a first type of identifier; the generation method of the target CSI is not based on training or AI, including: the generation of the target CSI does not include the sender of the target CSI performing the first operation.

[0166] As an embodiment, the target CSI generation method is training-based or AI-based, including: the target CSI reporting configuration indicates a first type of identifier; the target CSI generation method is not training-based or AI-based, including: the target CSI reporting configuration does not indicate a first type of identifier.

[0167] As an embodiment, the target CSI generation method is training-based or AI-based, including: the target CSI reporting configuration indicates a first type of identifier, and the first operation is associated with the first type of identifier indicated by the target CSI reporting configuration; the target CSI generation method is not training-based or AI-based, including: the target CSI reporting configuration does not indicate the first type of identifier.

[0168] As an embodiment, the generation method of the target CSI is based on training or AI, including: the generation of the target CSI is associated with a first type of identifier; the generation method of the target CSI is not based on training or AI, including: the generation of the target CSI is not associated with the first type of identifier.

[0169] As an embodiment, the generation of the target CSI is associated with the first type of identifier, including: the generation of the target CSI uses an AI model identified by the first type of identifier.

[0170] As an embodiment, the generation of the target CSI is associated with the first type of identifier, including: an AI entity identified by the first type of identifier generates the target CSI.

[0171] As an embodiment, the generation of the target CSI is associated with the first type of identifier, including: the target CSI is generated by an AI entity, and the first type of identifier is used to identify the AI ​​entity or function.

[0172] As an embodiment, the generation of the target CSI is associated with the first type of identifier, including: the generation of the target CSI belongs to an AI function, and the first type of identifier is used to identify the AI ​​function.

[0173] As an embodiment, the benefits of the above method include: simplifying system design and reducing implementation complexity.

[0174] As an embodiment, the benefits of the above method include: improving the flexibility and overall performance of the system.

[0175] As an embodiment, the target CSI includes at least one CSI reporting amount.

[0176] As an embodiment, the target CSI includes PMI (Precoding Matrix Indicator), CRI (CSI-RS Resource Indicator, channel state information reference signal resource indicator), SS / PBCH block resource indicator (SS / PBCH Block Resource indicator, SSBRI), beam indication, resource indication, CQI (Channel quality indicator, channel quality indication), RI (Rank Indicator, rank indication), layer indication (Layer Indicator, LI), RSRP (reference signal received power, reference signal received power), SINR (signal-to-noise and interference ratio, signal-to-interference and noise ratio), capability index (Capability Index), or TDCP (Time Domain Channel Properties, time domain channel properties) One or more.

[0177] As an embodiment, the target CSI includes a channel matrix.

[0178] As an embodiment, the target CSI includes a eigenvector and / or an eigenvalue.

[0179] As an embodiment, the target CSI includes a precoding matrix.

[0180] As an embodiment, the target CSI includes one or more of a channel matrix, an eigenvector, an eigenvalue, or a precoding matrix.

[0181] As an embodiment, the target CSI includes one or more of a beam indication, a CRI (CSI-RS Resource Indicator, channel state information reference signal resource indicator), a SS / PBCH block resource indicator (SS / PBCH Block Resource Indicator, SSBRI), or a RSRP (reference signal received power, reference signal received power).

[0182] As an embodiment, the target CSI includes one or more of beam indication, number of beams, CRI, SS / PBCH block resource indicator, number of CRI or SSBRI, RSRP, differential RSRP, probability information, or confidence information.

[0183] As an embodiment, the probability information indicates the probability that the corresponding beam is the optimal one or more beams.

[0184] As an embodiment, the probability information indicates the probability that the corresponding RS resource is the optimal one or more RS resources.

[0185] As an embodiment, the confidence information indicates the accuracy of RSRP.

[0186] As an embodiment, the confidence information indicates the accuracy of the differential RSRP.

[0187] As an embodiment, the essence of the above method includes: monitoring the AI ​​model based on performance parameters related to the CSI reporting of the AI.

[0188] As an embodiment, the benefits of the above method include: improving the performance of the AI-based CSI reporting solution and improving the overall performance of the system.

[0189] As an embodiment, the target CSI includes RSRP.

[0190] As an embodiment, the target CSI includes at least one resource indication and RSRP.

[0191] As an embodiment, the target CSI includes at least one resource indication.

[0192] As an embodiment, the target CSI includes at least one resource indication, and one resource indication in the target CSI is used to indicate a beam or RS resource.

[0193] As an embodiment, the target CSI includes at least one resource indication, and one resource indication in the target CSI is used to indicate a beam, a CSI-RS resource or a SS / PBCH block resource.

[0194] As an embodiment, the target CSI includes at least one resource indication; a resource indication in the target CSI is used to indicate a beam, or a resource indication in the target CSI is a CRI (CSI-RS Resource Indicator, channel state information reference signal resource indicator) or a SS / PBCH block resource indicator (SS / PBCH Block Resource indicator, SSBRI).

[0195] As an embodiment, the target CSI includes a first indication and RSRP, and the first indication is used to indicate a resource.

[0196] As an embodiment, the target CSI includes a first indication and RSRP, and the first indication is used to indicate an RS resource.

[0197] As an embodiment, the target CSI includes beam indication and RSRP.

[0198] As an embodiment, the target CSI includes RS resource indication and RSRP.

[0199] As an embodiment, the target CSI includes a beam indication or an RS resource indication.

[0200] As an embodiment, the target CSI includes predicted CSI.

[0201] As an embodiment, the target CSI includes CSI for a period of time in the future.

[0202] As an embodiment, the target CSI includes predicted beam information.

[0203] As an embodiment, the target CSI includes beam information for a period of time in the future.

[0204] As an embodiment, the target CSI includes a prediction of temporal CSI.

[0205] As a sub-embodiment of the above embodiment, the future period of time includes at least one time domain resource after the current time domain resource.

[0206] As a sub-embodiment of the above embodiment, the future period of time includes at least one time unit after the current time unit.

[0207] As a sub-embodiment of the above embodiment, the future period of time includes at least one time slot after the current time slot.

[0208] As a sub-embodiment of the above embodiment, the future period of time includes at least one symbol after the current symbol.

[0209] As an embodiment, the benefits of the above method include: reducing channel measurement overhead.

[0210] As an embodiment, the benefits of the above method include: improving the accuracy and real-time performance of CSI reporting and enhancing the overall performance of the system.

[0211] As an embodiment, the target CSI includes compressed CSI.

[0212] As an embodiment, the compressed CSI is non-codebook based.

[0213] As an embodiment, the compressed CSI does not belong to the CSI defined in 3GPP Rel-18, nor does it belong to the CSI defined in versions before 3GPP Rel-18.

[0214] As an embodiment, the channel parameters recovered by the target receiver of the compressed CSI based on the compressed CSI are unknown to the sender of the compressed CSI.

[0215] As an embodiment, the benefits of the above method include: saving CSI feedback overhead and improving the overall performance of the system.

[0216] As an embodiment, the target CSI is based on AI.

[0217] As an embodiment, the target CSI is not AI-based.

[0218] As an embodiment, the reporting amount included in the target CSI depends on whether the generation of the target CSI is based on AI.

[0219] As an embodiment, the reporting amount included in the target CSI depends on whether the generation method of the target CSI is based on AI.

[0220] As an embodiment, whether the target CSI includes confidence information depends on whether the generation method of the target CSI is based on AI; only when the generation method of the target CSI is based on AI, the target CSI includes confidence information.

[0221] As an embodiment, whether the target CSI is based on a non-codebook depends on whether the generation method of the target CSI is based on training or AI; when the generation method of the target CSI is based on training or AI, the target CSI is based on a non-codebook; when the generation of the target CSI is not based on training or AI, the target CSI is based on a codebook.

[0222] As an embodiment, when the target CSI is not generated based on training or AI, the target CSI belongs to the CSI defined in 3GPP Rel-18.

[0223] As an embodiment, when the target CSI is generated based on training or AI, the target CSI does not belong to the CSI defined in 3GPP Rel-18 and earlier versions.

[0224] As an embodiment, when the target CSI is generated based on training or AI, the target CSI includes predicted CSI, or predicted beam information, or compressed CSI.

[0225] As an embodiment, when the target CSI is generated based on training or AI, the target CSI includes CSI based on artificial intelligence or machine learning.

[0226] As an embodiment, when the target CSI is generated based on training or AI, the target CSI includes CSI generated based on a neural network (NeuralNetwork).

[0227] As an embodiment, when the target CSI is generated based on training or AI, the target CSI includes CSI generated based on CNN (Conventional Neural Networks, convolutional neural network).

[0228] As an embodiment, the benefits of the above method include: supporting an AI-based CSI reporting solution.

[0229] As an embodiment, the advantages of the above method include: little modification to existing systems and standards.

[0230] As an embodiment, the benefits of the above method include: enhancing the flexibility of the system and improving the overall performance of the system.

[0231] As an embodiment, for the case where the generation of the target CSI is not based on training or AI, how to generate the target CSI is determined by the manufacturer of the first node, or is implementation-dependent. A typical but non-limiting implementation is described below:

[0232] The first node first performs measurement on the first resource set to obtain a channel parameter matrix H r×t , where r and t are the number of receiving antennas and the number of antenna ports of the target CSI-RS resource respectively; for the channel parameter matrix H r×t After power adjustment, the adjusted channel parameter matrix is Where P is the ratio of the assumed PDSCH EPRE to the target CSI-RSEPRE (i.e., the first power control offset); when the precoding matrix W is used t×l Under the condition of , the channel parameter matrix after precoding is Where l is the rank or the number of layers, in one case l is a positive integer not greater than t, in another case the precoding matrix is ​​the identity matrix, in which case t=l; H is calculated using criteria such as SINR (Signal Interference Noise Ratio), EESM (Exponential Effective SINR Mapping), or RBIR (Received Block mean mutual Information Ratio). r×t ·W t×l The equivalent channel capacity is then used to determine the CQI included in the target CSI report by looking up a table or the like. Generally speaking, the calculation of the equivalent channel capacity requires the first node to estimate the interference (including noise), and the first node can obtain more accurate interference measurement by measuring the second opportunity set in the present application. Generally speaking, the direct mapping of the equivalent channel capacity to the CQI value depends on the receiver performance, or hardware-related factors such as the modulation method.

[0233] As an embodiment, in the case where the generation of the target CSI is based on AI, how to generate the target CSI is determined by the manufacturer of the first node, or is implementation-related. Without loss of generality, the AI ​​model or parameters used to generate the target CSI are determined by the manufacturer of the first node.

[0234] As an embodiment, generation of the target CSI relies on measurements obtained based on the first resource set.

[0235] As an embodiment, generation of the target CSI relies on channel measurement and / or interference measurement obtained based on the first resource set.

[0236] As an embodiment, measurements based on the first resource set are used to generate the target CSI.

[0237] As an embodiment, channel measurement and / or interference measurement based on the first resource set is used to generate the target CSI.

[0238] As an embodiment, measurement based on one or more RS resources in the first resource set is used to generate the target CSI.

[0239] As an embodiment, measurement based on a transmission timing no later than a reference resource in one or more RS resources in the first resource set is used to generate the target CSI.

[0240] As an embodiment, measurement based on the latest one or more transmission opportunities no later than the reference resource in one or more RS resources in the first resource set is used to generate the target CSI.

[0241] As an embodiment, the channel measurement obtained based on the first resource set refers to: the channel measurement obtained based on at least one reference signal transmitted in the first resource set.

[0242] As an embodiment, the channel measurement obtained based on the first resource set refers to: the channel measurement obtained in the first resource set.

[0243] As an embodiment, the channel measurement obtained based on the first resource set includes at least one of a channel matrix, a raw channel matrix, an eigenvector and an eigenvalue.

[0244] As an embodiment, the channel measurement obtained based on the first resource set includes one or more of BLER, delay spread, Doppler spread, Doppler shift, average delay, average gain, path loss and RSRP.

[0245] As an embodiment, the interference measurement obtained based on the first resource set refers to: interference measurement obtained based on at least one reference signal transmitted in the first resource set.

[0246] As an embodiment, the interference measurement obtained based on the first resource set refers to: interference measurement obtained in the first resource set.

[0247] As an embodiment, the interference measurement obtained based on the first resource set includes at least one of interference power, interference variance or interference power spectral density.

[0248] As an embodiment, the interference measurement obtained based on the first resource set includes at least one of an interference channel matrix, an interference covariance matrix, an interference eigenvector, an interference eigenvalue, and an interference beam.

[0249] As an embodiment, the measurement based on the first resource set includes: a channel matrix obtained based on the measurement of the first resource set.

[0250] As an embodiment, the measurement based on the first resource set includes: preprocessing a channel matrix obtained based on the measurement of the first resource set to obtain a matrix or vector.

[0251] As an embodiment, the channel matrix is ​​in spatial-frequency domain.

[0252] As an embodiment, the channel matrix is ​​in angular-delay domain projection.

[0253] As an embodiment, the preprocessing includes one or more of quantization, DFT (Discrete Fourier Transform), matrix decomposition, matrix transformation or projection, transformation from spatial domain to angular domain, transformation from angular domain to spatial domain, transformation from frequency domain to time domain and transformation from time domain to frequency domain, truncation, padding, mapping, and labeling.

[0254] As an embodiment, the sending of the target CSI includes: sending the target CSI on a first physical channel.

[0255] As an embodiment, sending the target CSI includes: sending a first signal on a first physical channel; wherein the first signal carries the target CSI.

[0256] As an embodiment, the first signal includes a baseband signal.

[0257] As an embodiment, the first signal includes a wireless signal.

[0258] As an embodiment, the first signal includes a radio frequency signal.

[0259] As an embodiment, the sending of the target CSI includes: the target CSI is used to generate a signal sent on a first physical channel after being channel encoded.

[0260] As an embodiment, the sending of the target CSI includes: the target CSI is used to generate a signal sent on a first physical channel after channel coding and modulation.

[0261] As an embodiment, the sending of the target CSI includes: the target CSI is used to generate a signal sent on a first physical channel after bit sequence generation and channel coding.

[0262] As an embodiment, the sending of the target CSI includes: the target CSI is used to generate a signal sent on a first physical channel after bit sequence generation, channel coding, and modulation.

[0263] As an embodiment, the sending target CSI includes: the target CSI is used to generate a signal sent on a first physical channel after bit sequence generation (bitsequence generation), code block segmentation (code block segmentation) and CRC addition (attachment), channel coding (channel coding), rate matching (rate matching), and code block concatenation (codeblock concatenation).

[0264] As an embodiment, the sending of the target CSI includes: the target CSI is multiplexed into the first physical channel after bit sequence generation, code block segmentation and CRC addition, channel coding, rate matching, and code block connection.

[0265] As an embodiment, the benefits of the above method include: using existing system designs and standards.

[0266] As an embodiment, the first physical channel is an uplink channel.

[0267] As an embodiment, the first physical channel is PUSCH (Physical Uplink Shared Channel).

[0268] As an embodiment, the first physical channel is PUCCH (Physical Uplink Control Channel).

[0269] As an embodiment, the first physical channel includes multiple REs (Resource Element).

[0270] As an embodiment, the first physical channel occupies at least one symbol in the time domain, and the first physical channel occupies at least one subcarrier in the frequency domain.

[0271] As an embodiment, the first physical channel occupies at least one symbol in the time domain, and the first physical channel occupies at least one RB (resource block) in the frequency domain.

[0272] Typically, one RE occupies one symbol in the time domain and one subcarrier in the frequency domain.

[0273] As an embodiment, the symbol is a single carrier symbol.

[0274] As an embodiment, the symbol is a multi-carrier symbol.

[0275] As an embodiment, the symbol is a 6G single carrier symbol.

[0276] As an embodiment, the symbol is a 6G multi-carrier symbol.

[0277] As an embodiment, the multi-carrier symbol is an OFDM (Orthogonal Frequency Division Multiplexing) symbol.

[0278] As an embodiment, the symbol is obtained by performing OFDM symbol generation on the output of a transform precoding.

[0279] As an embodiment, the multi-carrier symbol is an SC-FDMA (Single Carrier-Frequency Division Multiple Access) symbol.

[0280] As an embodiment, the multi-carrier symbol is a DFT-S-OFDM (Discrete Fourier Transform Spread OFDM) symbol.

[0281] As an embodiment, the multi-carrier symbol is a FBMC (Filter Bank Multi Carrier) symbol.

[0282] As an embodiment, the multi-carrier symbol includes a CP (Cyclic Prefix).

[0283] As an embodiment, the benefits of the above method include: using existing system designs and standards.

[0284] Typically, the determining whether to abandon sending the target CSI depends on whether the first condition is satisfied.

[0285] As an embodiment, the first condition being satisfied includes: the generation method of the target CSI is associated with the target identifier.

[0286] As an embodiment, the first condition being satisfied includes: the generation method of the target CSI is associated with at least one identifier, and the at least one identifier includes a target identifier.

[0287] As an embodiment, the first condition being satisfied includes: the generation method of the target CSI is associated with a first type of identifier, and the first type of identifier is a target identifier.

[0288] As an embodiment, the first condition being satisfied includes: the target CSI generation method is associated with a first type of identifier, and the first type of identifier is associated with a target identifier.

[0289] As an embodiment, the first condition being satisfied includes: the target CSI is generated using an AI model, and the AI ​​model is associated with a target identifier.

[0290] As an embodiment, the first condition being satisfied includes: the method of generating the target CSI includes executing a first operation, and the first operation is associated with a target identifier.

[0291] As an embodiment, the first condition not being met includes: the generation method of the target CSI is not associated with the target identifier.

[0292] As an embodiment, the first condition not being met includes: the generation method of the target CSI is associated with at least one identifier, and the at least one identifier does not include the target identifier.

[0293] As an embodiment, the first condition not being met includes: the generation method of the target CSI is associated with a first type of identifier, and the first type of identifier is not a target identifier.

[0294] As an embodiment, the first condition not being met includes: the target CSI is not generated based on training or AI.

[0295] As an embodiment, the first condition not being met includes: the target CSI is generated without using an AI model.

[0296] As an embodiment, the first condition not being met includes: the method of generating the target CSI does not include performing the first operation.

[0297] As an embodiment, the first condition not being met includes: the generation method of the target CSI is not associated with the first type of identifier.

[0298] As an embodiment, the benefits of the above method include: supporting AI-based CSI reporting.

[0299] As an embodiment, the benefits of the above method include: improving system flexibility and simplifying system design.

[0300] As an embodiment, only when the first condition is met, the first node gives up sending the target CSI.

[0301] As an embodiment, when the first condition is met, the first node ignores the target CSI.

[0302] As an embodiment, when the first condition is met, the first node abandons sending the target CSI and ignores the target CSI.

[0303] As an embodiment, when the first condition is met, the first node sends the target CSI, and the target CSI is not updated.

[0304] As an embodiment, when the first condition is not met, the first node sends the target CSI, and the target CSI is valid.

[0305] As an embodiment, when the first condition is met, the first node abandons sending the target CSI; when the first condition is not met, the first node sends the target CSI.

[0306] As an embodiment, when the first condition is met, the first node abandons sending the target CSI; when the first condition is not met, the first node sends the target CSI, and the target CSI is valid.

[0307] As an embodiment, when the generation method of the target CSI is associated with the target identifier, the first node abandons sending the target CSI; when the generation method of the target CSI is not associated with the target identifier, the first node sends the target CSI.

[0308] As an embodiment, when the generation method of the target CSI is associated with the target identifier, the first node abandons sending the target CSI; when the generation method of the target CSI is not associated with the target identifier, the first node sends the target CSI, and the target CSI is valid.

[0309] As an embodiment, the benefits of the above method include: improving the flexibility and overall performance of the system.

[0310] As an embodiment, the benefits of the above method include: improving the stability and robustness of the system.

[0311] As an embodiment, the benefits of the above method include: little modification to existing systems and standards.

[0312] As an embodiment, the target CSI being valid includes: the target CSI is an updated CSI.

[0313] As an embodiment, the target CSI being valid includes: the target CSI is different from the CSI that is configured for reporting the target CSI most recently on the first PUSCH.

[0314] As an embodiment, the target CSI being valid includes: the target CSI may be different from the most recent CSI configured for reporting the target CSI on the first PUSCH.

[0315] As an embodiment, the target CSI being valid includes: the target CSI is not necessarily the same as the most recent CSI configured for reporting the target CSI on the first PUSCH.

[0316] As an embodiment, the target CSI is valid including: whether the target CSI is different from the CSI configured earlier than the most recent one for the target CSI reporting on the first PUSCH depends on the measurement of the most recent RS timing of the CSI reference resource in the first resource set that is no later than the target CSI.

[0317] As an embodiment, the target CSI being valid includes: the target CSI is generated based on the measurement of the most recent RS opportunity of a CSI reference resource that is no later than the target CSI in at least the first resource set.

[0318] As an embodiment, the target CSI being valid includes: the target CSI is CSI updated based on measurement of a latest RS timing of a CSI reference resource in at least the first resource set that is no later than the target CSI.

[0319] As an embodiment, the target CSI is valid including: the target CSI is generated based on the measurement of RS resources in the first resource set.

[0320] As an embodiment, the target CSI being valid includes: the target CSI is updated based on the measurement of RS resources in the first resource set.

[0321] As an embodiment, the benefits of the above method include: little modification to existing systems and standards.

[0322] As an embodiment, the method for generating the target CSI is associated with the target identifier, including: the method for generating the target CSI uses an AI model identified by the target identifier.

[0323] As an embodiment, the method for generating the target CSI is not associated with the target identifier, including: the method for generating the target CSI does not use the AI ​​model identified by the target identifier.

[0324] As an embodiment, the generation method of the target CSI is associated with the target identifier, including: the AI ​​entity identified by the target identifier generates the target CSI.

[0325] As an embodiment, the generation method of the target CSI is not associated with the target identifier, including: the generator of the target CSI is not the AI ​​entity identified by the target identifier.

[0326] As an embodiment, the generation method of the target CSI is associated with the target identifier, including: the target CSI is generated by an AI entity, and the target identifier is used to identify the AI ​​entity or function.

[0327] As an embodiment, the generation method of the target CSI is not associated with the target identifier, including: the target CSI is generated by an AI entity, and the target identifier is not used to identify the AI ​​entity or function.

[0328] As an embodiment, the generation method of the target CSI is associated with the target identifier, including: the target CSI is used for the AI ​​function identified by the target identifier.

[0329] As an embodiment, the generation method of the target CSI is not associated with the target identifier, including: the target CSI is not used for the AI ​​function identified by the target identifier.

[0330] As an embodiment, the target CSI generation method is associated with the target identifier, including: the target CSI generation method is associated with at least one identifier, and the at least one identifier includes a target identifier.

[0331] As an embodiment, the generation method of the target CSI is not associated with the target identifier, including: the generation method of the target CSI is associated with at least one identifier, and the at least one identifier does not include the target identifier.

[0332] As an embodiment, the target CSI generation method is associated with the target identifier, including: the target CSI generation method is associated with a first type of identifier, and the first type of identifier is associated with the target identifier.

[0333] As an embodiment, the generation method of the target CSI is not associated with the target identifier, including: the generation method of the target CSI is associated with a first type of identifier, and the first type of identifier is not associated with the target identifier.

[0334] As an embodiment, the target CSI generation method is associated with the target identifier, including: the target CSI generation method includes executing a first operation, and the first operation is associated with the target identifier.

[0335] As an embodiment, the method for generating the target CSI is not associated with the target identifier, including: the method for generating the target CSI includes performing a first operation, and the first operation is not associated with the target identifier.

[0336] As an embodiment, the target CSI generation method is associated with the target identifier, including: the target CSI generation method is associated with the target identifier through the target CSI reporting configuration.

[0337] As an embodiment, the target CSI generation method is not associated with the target identifier, including: the target CSI generation method is not associated with the target identifier through the target CSI reporting configuration.

[0338] As an embodiment, the generation method of the target CSI is associated with the target identifier, including: the target CSI reporting configuration indicates the target identifier; the target CSI reporting configuration indicates the generation method of the target CSI.

[0339] As an embodiment, the generation method of the target CSI is not associated with the target identifier, including: the target CSI reporting configuration indicates a first type of identifier; the target CSI reporting configuration indicates the generation method of the target CSI; and the target identifier is not the first type of identifier.

[0340] As an embodiment, the benefits of the above method include: supporting AI-based CSI reporting.

[0341] As an embodiment, the benefits of the above method include: improving the flexibility of the system and adapting to different scenarios and applications.

[0342] As an embodiment, the target identifier is a non-negative integer.

[0343] As an embodiment, the target identifier is a character string.

[0344] As an embodiment, the target identifier is a model identifier.

[0345] As an embodiment, the target identifier is used to identify an AI model, an AI entity, or an AI function.

[0346] As an embodiment, the target identifier is used by the first node to determine an AI model, AI entity or AI function.

[0347] As an embodiment, the target CSI reporting configuration indicates the use of the AI ​​model, AI entity or AI function by indicating the target identifier.

[0348] As an embodiment, the target CSI reporting configuration obtains the input of the AI ​​entity / function / inference associated with the target identifier by indicating the target identifier.

[0349] As an embodiment, the benefits of the above method include: identifying an AI model, AI entity or AI function through the target identifier, simplifying system design, and unifying the understanding of different AI models, AI entities or AI functions among multiple nodes.

[0350] As an embodiment, the target identifier is used to identify or indicate a resource set.

[0351] As an embodiment, the target identifier is used to identify or indicate a resource set, and the measurement of the resource set is used to obtain a training data set.

[0352] As an embodiment, the target identifier is used to identify or indicate a resource set.

[0353] As an embodiment, the target identifier is used to identify or indicate a training data set.

[0354] As an embodiment, the benefits of the above method include: identifying an AI training or an AI training data set to identify the inferences generated by the AI ​​training or the AI ​​training data set, establishing a consensus between different AI functions, and further simplifying the system design.

[0355] As an embodiment, the target identifier being dependent on the target information block includes: the target information block being used to determine the target identifier.

[0356] As an embodiment, the target identifier being dependent on the target information block includes: the target information block indicating the target identifier.

[0357] As an embodiment, the target identifier being dependent on the target information block includes: the target information block explicitly indicating the target identifier.

[0358] As an embodiment, the target identifier being dependent on the target information block includes: the target information block implicitly indicating the target identifier.

[0359] As an embodiment, the benefits of the above method include: simplifying system design and reducing the complexity of system implementation.

[0360] As an embodiment, the target identifier being dependent on the target information block includes: the target information block indicating at least one identifier, and the at least one identifier including the target identifier.

[0361] As an embodiment, the target identifier being dependent on the target information block includes: the target information block indicating a first type of identifier, and the first type of identifier being associated with the target identifier.

[0362] As an embodiment, the benefits of the above method include: improving the flexibility of the system and adapting to different scenarios and applications.

[0363] As an embodiment, the target identifier being dependent on the target information block includes: the target identifier being dependent on at least one information block, and the at least one information block includes the target information block.

[0364] As an embodiment, the target identifier being dependent on the target information block includes: the target identifier being dependent on at least one information block, and the target information block being dependent on at least one information block.

[0365] As an embodiment, the target identifier being dependent on the target information block includes: the target identifier being dependent on at least one information block, and the target information block being generated by the at least one information block.

[0366] As an embodiment, the target identifier depends on the target information block, including: the target identifier depends on at least one information block, the at least one information block configures or indicates at least one signal, and the target information block is generated based on the reception or measurement of part or all of the at least one signal.

[0367] As an embodiment, the benefits of the above method include: improving the flexibility and adaptability of the system.

[0368] As an embodiment, the target identifier depends on the target information block, including: the target information block indicates at least one AI model, AI entity or AI function, and the at least one AI model, AI entity or AI function is associated with the target identifier.

[0369] As an embodiment, the target identifier depends on the target information block, including: the target information block indicates the AI ​​model, AI entity or AI function identified by the target identifier.

[0370] As an embodiment, the target identifier depends on the target information block, including: the target information block indicates that the AI ​​model identified by the target identifier fails or is invalid.

[0371] As an embodiment, the benefits of the above method include: supporting AI-based CSI reporting.

[0372] As an embodiment, the benefits of the above method include: simplifying system design and reducing implementation complexity.

[0373] As an embodiment, the benefits of the above method include: improving the accuracy and effectiveness of CSI reporting and improving the overall performance of the system.

[0374] As an embodiment, the target identifier depends on the target information block, including: the target information block indicates a performance indicator of the AI ​​model identified by the target identifier.

[0375] As an embodiment, the performance indicator includes probability information.

[0376] As an embodiment, the AI ​​model is used for beam management, and the probability information indicates the probability that the corresponding beam is the optimal one or more beams.

[0377] As an embodiment, the AI ​​model is used for beam management, and the probability information represents the probability that the corresponding RS resource is the optimal one or more RS resources.

[0378] As an embodiment, the performance indicator includes a difference between a real measured RSRP and a predicted RSRP.

[0379] As an embodiment, the performance indicator includes at least one of L1-RSRP and L1-SINR.

[0380] As an embodiment, the performance indicator includes confidence information.

[0381] As an embodiment, the confidence information indicates the accuracy of RSRP.

[0382] As an embodiment, the confidence information indicates the accuracy of differential RSRP.

[0383] As an embodiment, the AI ​​model CSI prediction and / or CSI compression, the performance indicator includes the difference between the actual measured channel matrix and the predicted channel matrix.

[0384] As an embodiment, the AI ​​model CSI prediction and / or CSI compression, the performance indicator includes SGCS (cosinesimilarity) between the actual measured channel matrix and the predicted channel matrix.

[0385] As an embodiment, the AI ​​model CSI prediction and / or CSI compression, the performance indicator includes the difference between the actual measured channel eigenvector (eigenvector) and the predicted channel eigenvector (eigenvector).

[0386] As an embodiment, the AI ​​model CSI prediction and / or CSI compression, the performance indicator includes SGCS (cosinesimilarity) between the actual measured channel eigenvector (eigenvector) and the predicted channel eigenvector (eigenvector).

[0387] As an embodiment, the AI ​​model performs CSI prediction and / or CSI compression, and the performance indicator includes the difference between the actual measured PMI (Precoding Matrix Indicator) and the predicted PMI (Precoding Matrix Indicator).

[0388] As an embodiment, the AI ​​model CSI prediction and / or CSI compression, the performance indicator includes SGCS (cosine similarity) between the actual measured PMI (Precoding Matrix Indicator) and the predicted PMI (Precoding Matrix Indicator).

[0389] As an embodiment, the performance indicator is a system physical layer performance indicator.

[0390] As an embodiment, the performance indicator is a system link layer performance indicator.

[0391] As an embodiment, the performance indicator is a system higher-layer performance indicator.

[0392] As an embodiment, the performance indicator is an overall system performance indicator.

[0393] As an embodiment, the performance indicators include one or more of signal-to-noise ratio, link data rate, peak rate, throughput, traffic density, average user data rate, minimum user data rate, bit error rate, block error rate, bit error rate, spectrum efficiency, energy efficiency, data transmission delay, air interface delay, number of link connections, number of device connections, and maximum mobility support.

[0394] As an embodiment, the essence of the above method includes: monitoring the AI ​​model based on performance parameters related to the CSI reporting of the AI.

[0395] As an embodiment, the benefits of the above method include: improving the performance of the AI-based CSI reporting solution and improving the overall performance of the system.

[0396] As an embodiment, the target identifier being dependent on the target information block includes: the target information block includes first information, and the target identifier being dependent on the first information.

[0397] As an embodiment, the target identifier depends on the target information block, including: the target information block includes one or more HARQ-ACK bits, and the target identifier depends on the one or more HARQ-ACK bits.

[0398] As an embodiment, the target identifier depends on the target information block, including: the target information block includes the number of times one or more AI models fail or become invalid, and the target identifier depends on the number of times the one or more AI models fail or become invalid.

[0399] As an embodiment, the benefits of the above method include: improving the stability and robustness of the system.

[0400] As an embodiment, the benefits of the above method include: improving the performance of the AI-based CSI reporting solution and improving the overall performance of the system.

[0401] As an embodiment, the target identifier being dependent on the target information block includes: the target information block indicating a first value, and the target identifier being dependent on the first value.

[0402] As an embodiment, the target identifier being dependent on the target information block includes: the target information block indicating a first value, and the target identifier being dependent on the first value and a first threshold.

[0403] As an embodiment, the target identifier being dependent on the target information block includes: the target information block indicating a first value, and the target identifier being dependent on a magnitude relationship between the first value and a first threshold.

[0404] As an embodiment, the benefits of the above method include: simplifying system design and reducing implementation complexity.

[0405] Example 2

[0406] Embodiment 2 illustrates a schematic diagram of a network architecture according to an embodiment of the present application, as shown in the attached Figure 2 shown.

[0407] Attached Figure 2The network architecture 200 is described. The network architecture 200 is a 5G NR (New Radio) / LTE (Long-Term Evolution) / LTE-A (Long-Term Evolution Advanced) system, or the network architecture 200 is a 5G+ network architecture, or the network architecture 200 is a 6G network architecture, or the network architecture 200 is a network architecture adopted by 3GPP in the future evolution; the network architecture 200 may be called 5GS (5G System) / EPS (Evolved Packet System), or the network architecture 200 may be called 6GS (6G System); the network architecture 200 includes at least one of UE (User Equipment) 201, RAN (Radio Access Network) 202, core network 210, HSS (Home Subscriber Server) / UDM (Unified Data Management) 220 and Internet service 230. The network architecture 200 may be interconnected with other access networks, but these entities / interfaces are not shown for simplicity. As shown, the network architecture 200 provides packet switching services, but it will be readily understood by those skilled in the art that the various concepts presented throughout this application can be extended to networks or other cellular networks that provide circuit switching services. The RAN includes a node 203. The RAN may also include other nodes 204. The node 203 provides user and control plane protocol terminations toward the UE 201. The node 203 may be connected to the other nodes 204 via an Xn interface (e.g., backhaul) / X2 interface. The node 203 may also be referred to as a base station, a base transceiver station, a radio base station, a radio transceiver, a transceiver function, a basic service set (BSS), an extended service set (ESS), a TRP (transmitting receiving node) or some other suitable terminology. The core network 210 is 5GC (5G Core Network) / EPC (Evolved Packet Core), or the core network 210 is 6GC; the node 203 provides an access point to the core network 210 for the UE201.Examples of UE201 include cellular phones, smart phones, session initiation protocol (SIP) phones, laptop computers, personal digital assistants (PDAs), satellite radios, non-terrestrial base station communications, satellite mobile communications, global positioning systems, multimedia devices, video devices, digital audio players (e.g., MP3 players), cameras, game consoles, drones, aircraft, narrowband Internet of Things devices, machine type communication devices, land vehicles, cars, wearable devices, or any other similar functional devices. Those skilled in the art may also refer to UE201 as a mobile station, a subscriber station, a mobile unit, a subscriber unit, a wireless unit, a remote unit, a mobile device, a wireless device, a wireless communication device, a remote device, a mobile subscriber station, an access terminal, a mobile terminal, a wireless terminal, a remote terminal, a handset, a user agent, a mobile client, a client, or some other suitable term. Node 203 is connected to the core network 210 via an S1 / NG interface. The core network 210 includes MME (Mobility Management Entity) / AMF (Authentication Management Field) / SMF (Session Management Function) 211, other MME / AMF / SMF 214, S-GW (Service Gateway) / UPF (User Plane Function) 212 and P-GW (Packet Data Network Gateway) / UPF 213. MME / AMF / SMF 211 is a control node that handles signaling between UE 201 and the core network 210. In general, MME / AMF / SMF 211 provides bearer and connection management. All user IP (Internet Protocol) packets are transmitted through S-GW / UPF 212, which itself is connected to P-GW / UPF 213. P-GW provides UE IP address allocation and other functions. P-GW / UPF 213 is connected to Internet service 230. The Internet service 230 includes the operator's corresponding Internet protocol service, which may specifically include the Internet, intranet, IMS (IP Multimedia Subsystem) and packet switching services.

[0408] As an embodiment, the first node in the present application includes the UE201.

[0409] As an embodiment, the second node in the present application includes the gNB203.

[0410] As an embodiment, the UE 201 includes a mobile phone.

[0411] As an embodiment, the UE 201 includes a vehicle such as a car.

[0412] As an embodiment, the gNB203 is a macro cell base station.

[0413] As an embodiment, the gNB203 is a micro cell base station.

[0414] As an embodiment, the gNB203 is a pico cell base station.

[0415] As an embodiment, the gNB203 is a home base station (Femtocell).

[0416] As an embodiment, the gNB203 is a base station device that supports large delay difference.

[0417] As an embodiment, the gNB203 is a flying platform device.

[0418] As an embodiment, the gNB203 is a satellite device.

[0419] As an embodiment, the gNB203 is a testing device (e.g., a transceiver that simulates some functions of a base station, a signaling tester).

[0420] As an embodiment, the wireless link from the UE 201 to the gNB 203 is an uplink, and the uplink is used to perform uplink transmission.

[0421] As an embodiment, the wireless link from the gNB 203 to the UE 201 is a downlink, and the downlink is used to perform downlink transmission.

[0422] As an embodiment, the wireless link between the UE 201 and the gNB 203 includes a cellular network link.

[0423] As an embodiment, the UE 201 and the gNB 203 are connected via a Uu air interface.

[0424] As an embodiment, the sender of the target CSI reporting configuration includes the gNB203.

[0425] As an embodiment, the receiver of the target CSI reporting configuration includes the UE 201.

[0426] As an embodiment, the sender of the target information block includes the UE 201.

[0427] As an embodiment, the receiver of the target information block includes the gNB203.

[0428] As an embodiment, the sender of the first resource set includes the gNB203.

[0429] As an embodiment, the recipient of the first resource set includes the UE201.

[0430] As an embodiment, the sender of the target CSI includes the UE 201.

[0431] As an embodiment, the receiver of the target CSI includes the gNB203.

[0432] As an embodiment, the UE 201 supports the 6G system.

[0433] As an embodiment, the gNB203 supports the 6G system.

[0434] As an embodiment, the UE 201 at least supports the 5G system.

[0435] As an embodiment, the gNB203 at least supports the 5G system.

[0436] As an embodiment, the UE 201 supports AI.

[0437] As an embodiment, the gNB203 supports AI.

[0438] Example 3

[0439] Embodiment 3 illustrates a schematic diagram of an embodiment of a wireless protocol architecture of a user plane and a control plane according to an embodiment of the present application, as shown in the attached figure. Figure 3 shown.

[0440] Embodiment 3 shows a schematic diagram of an embodiment of a wireless protocol architecture of a user plane and a control plane according to the present application, as shown in the attached Figure 3 shown.

[0441] Figure 3 is a schematic diagram illustrating an embodiment of a radio protocol architecture for a user plane 350 and a control plane 300, Figure 3The radio protocol architecture of the control plane 300 for a first communication node device (UE, gNB or RSU in V2X) and a second communication node device (gNB, UE or RSU in V2X), or between two UEs is shown in three layers: Layer 1, Layer 2 and Layer 3. Layer 1 (L1 layer) is the lowest layer and implements various PHY (physical layer) signal processing functions. The L1 layer will be referred to as PHY301 in this article. Layer 2 (L2 layer) 305 is above PHY301 and is responsible for the link between the first communication node device and the second communication node device, or between two UEs. The L2 layer 305 includes a MAC (Medium Access Control) sublayer 302, an RLC (Radio Link Control) sublayer 303, and a PDCP (Packet Data Convergence Protocol) sublayer 304, which terminate at the second communication node device. The PDCP sublayer 304 provides multiplexing between different radio bearers and logical channels. The PDCP sublayer 304 also provides security by encrypting data packets, and provides support for inter-zone mobility of the first communication node device between the second communication node device. The RLC sublayer 303 provides segmentation and reassembly of upper layer data packets, retransmission of lost data packets, and reordering of data packets to compensate for out-of-order reception due to HARQ. The MAC sublayer 302 provides multiplexing between logical and transport channels. The MAC sublayer 302 is also responsible for allocating various radio resources (e.g., resource blocks) in a cell between the first communication node devices. The MAC sublayer 302 is also responsible for HARQ operations. The RRC (Radio Resource Control) sublayer 306 in layer 3 (L3 layer) in the control plane 300 is responsible for obtaining radio resources (i.e., radio bearers) and configuring the lower layer using RRC signaling between the second communication node device and the first communication node device. The radio protocol architecture of the user plane 350 includes layer 1 (L1 layer) and layer 2 (L2 layer). The radio protocol architecture used for the first communication node device and the second communication node device in the user plane 350 is substantially the same as the corresponding layers and sublayers in the control plane 300 for the physical layer 351, the PDCP sublayer 354 in the L2 layer 355, the RLC sublayer 353 in the L2 layer 355, and the MAC sublayer 352 in the L2 layer 355, but the PDCP sublayer 354 also provides header compression for upper layer data packets to reduce radio transmission overhead.The L2 layer 355 in the user plane 350 also includes a SDAP (Service Data Adaptation Protocol) sublayer 356, which is responsible for mapping between QoS flows and data radio bearers (DRBs) to support the diversity of services. Although not shown in the figure, the first communication node device may have several upper layers above the L2 layer 355, including a network layer (e.g., an IP layer) terminated at the P-GW on the network side and an application layer terminated at the other end of the connection (e.g., a remote UE, a server, etc.).

[0442] As an example, Figure 3 The wireless protocol architecture in is applicable to the first node in this application.

[0443] As an example, Figure 3 The wireless protocol architecture in is applicable to the second node in this application.

[0444] As an embodiment, the higher layer in the present application refers to a layer above the physical layer.

[0445] As an embodiment, the target CSI reporting configuration is generated in the RRC306.

[0446] As an embodiment, the signal in the first resource set is generated by the PHY301 or the PHY351.

[0447] As an embodiment, the target information block is generated in the PHY301 or the PHY351.

[0448] As an embodiment, the target information block is generated by the MAC302 or the MAC352.

[0449] As an embodiment, the target CSI is generated by the PHY301 or the PHY351.

[0450] As an embodiment, the target CSI is generated by the MAC302 or the MAC352.

[0451] Example 4

[0452] Embodiment 4 illustrates a schematic diagram of a first communication device and a second communication device according to an embodiment of the present application, as shown in the attached figure. Figure 4 Attached Figure 4 is a block diagram of a first communication device 410 and a second communication device 450 communicating with each other in an access network.

[0453] The first communication device 410 includes a controller / processor 475 , a memory 476 , a receive processor 470 , a transmit processor 416 , a multi-antenna receive processor 472 , a multi-antenna transmit processor 471 , a transmitter / receiver 418 and an antenna 420 .

[0454] The second communication device 450 includes a controller / processor 459, a memory 460, a data source 467, a transmit processor 468, a receive processor 456, a multi-antenna transmit processor 457, a multi-antenna receive processor 458, a transmitter / receiver 454 and an antenna 452.

[0455] In the transmission from the first communication device 410 to the second communication device 450, at the first communication device 410, the upper layer data packets from the core network are provided to the controller / processor 475. The controller / processor 475 implements the functionality of the L2 layer. In DL (DownLink), the controller / processor 475 provides header compression, encryption, packet segmentation and reordering, multiplexing between logical and transport channels, and radio resource allocation to the second communication device 450 based on various priority metrics. The controller / processor 475 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the second communication device 450. The transmit processor 416 and the multi-antenna transmit processor 471 implement various signal processing functions for the L1 layer (i.e., the physical layer). The transmit processor 416 implements coding and interleaving to facilitate forward error correction (FEC) at the second communication device 450, as well as constellation mapping based on various modulation schemes (e.g., binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), M-phase shift keying (M-PSK), M-quadrature amplitude modulation (M-QAM)). The multi-antenna transmit processor 471 performs digital spatial precoding, including codebook-based precoding and non-codebook-based precoding, and beamforming processing on the coded and modulated symbols to generate one or more parallel streams. The transmit processor 416 then maps each parallel stream to a subcarrier, multiplexes the modulated symbols with a reference signal (e.g., a pilot) in the time domain and / or frequency domain, and then uses an inverse fast Fourier transform (IFFT) to generate a physical channel carrying a time-domain multi-carrier symbol stream. The multi-antenna transmit processor 471 then performs a transmit analog precoding / beamforming operation on the time-domain multi-carrier symbol stream. Each transmitter 418 converts the baseband multi-carrier symbol stream provided by the multi-antenna transmit processor 471 into a radio frequency stream, and then provides it to a different antenna 420.

[0456] In the transmission from the first communication device 410 to the second communication device 450, at the second communication device 450, each receiver 454 receives a signal through its corresponding antenna 452. Each receiver 454 recovers the information modulated onto the RF carrier and converts the RF stream into a baseband multi-carrier symbol stream and provides it to the receiving processor 456. The receiving processor 456 and the multi-antenna receiving processor 458 implement various signal processing functions of the L1 layer. The multi-antenna receiving processor 458 performs a receiving analog precoding / beamforming operation on the baseband multi-carrier symbol stream from the receiver 454. The receiving processor 456 uses a fast Fourier transform (FFT) to convert the baseband multi-carrier symbol stream after the receiving analog precoding / beamforming operation from the time domain to the frequency domain. In the frequency domain, the physical layer data signal and the reference signal are demultiplexed by the receiving processor 456, wherein the reference signal will be used for channel estimation, and the data signal is recovered after multi-antenna detection in the multi-antenna receiving processor 458 to any parallel stream destined for the second communication device 450. The symbols on each parallel stream are demodulated and recovered in the receiving processor 456, and soft decisions are generated. The receiving processor 456 then decodes and deinterleaves the soft decisions to recover the upper layer data and control signals transmitted by the first communication device 410 on the physical channel. The upper layer data and control signals are then provided to the controller / processor 459. The controller / processor 459 implements the functions of the L2 layer. The controller / processor 459 may be associated with a memory 460 storing program codes and data. The memory 460 may be referred to as a computer-readable medium. In DL, the controller / processor 459 provides multiplexing, packet reassembly, decryption, header decompression, and control signal processing between the transmission and logical channels to recover the upper layer data packets from the core network. The upper layer data packets are then provided to all protocol layers above the L2 layer. Various control signals may also be provided to L3 for L3 processing. The controller / processor 459 is also responsible for error detection using confirmation (ACK) and / or negative confirmation (NACK) protocols to support HARQ operations.

[0457] In the transmission from the second communication device 450 to the first communication device 410, at the second communication device 450, a data source 467 is used to provide upper layer data packets to the controller / processor 459. The data source 467 represents all protocol layers above the L2 layer. Similar to the transmission function at the first communication device 410 described in DL, the controller / processor 459 implements header compression, encryption, packet segmentation and reordering, and multiplexing between logical and transport channels based on the radio resource allocation of the first communication device 410, and implements L2 layer functions for the user plane and the control plane. The controller / processor 459 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the first communication device 410. The transmit processor 468 performs modulation mapping and channel coding processing, and the multi-antenna transmit processor 457 performs digital multi-antenna spatial precoding, including codebook-based precoding and non-codebook-based precoding, and beamforming processing. Then, the transmit processor 468 modulates the generated parallel stream into a multi-carrier / single-carrier symbol stream, which is then provided to different antennas 452 via the transmitter 454 after analog precoding / beamforming operations in the multi-antenna transmit processor 457. Each transmitter 454 first converts the baseband symbol stream provided by the multi-antenna transmit processor 457 into a radio frequency symbol stream, and then provides it to the antenna 452.

[0458] In the transmission from the second communication device 450 to the first communication device 410, the function at the first communication device 410 is similar to the reception function at the second communication device 450 described in the transmission from the first communication device 410 to the second communication device 450. Each receiver 418 receives a radio frequency signal through its corresponding antenna 420, converts the received radio frequency signal into a baseband signal, and provides the baseband signal to the multi-antenna reception processor 472 and the reception processor 470. The reception processor 470 and the multi-antenna reception processor 472 jointly implement the functions of the L1 layer. The controller / processor 475 implements the L2 layer functions. The controller / processor 475 can be associated with a memory 476 that stores program codes and data. The memory 476 can be referred to as a computer-readable medium. The controller / processor 475 provides demultiplexing between transmission and logical channels, packet reassembly, decryption, header decompression, control signal processing to recover the upper layer data packets from the second communication device 450. The upper layer data packets from the controller / processor 475 can be provided to the core network. The controller / processor 475 is also responsible for error detection using an ACK and / or NACK protocol to support HARQ operations.

[0459] As an embodiment, the second communication device 450 includes: at least one processor and at least one memory, the at least one memory including computer program code; the at least one memory and the computer program code are configured to be used together with the at least one processor. The second communication device 450 device at least: receives a target CSI reporting configuration; the target CSI reporting configuration is used to configure the reporting of the target CSI; the generation method of the target CSI is training-based or AI-based; sends a target information block; determines whether to abandon sending the target CSI; when a first condition is met, abandons sending the target CSI; wherein, the determination whether to abandon sending the target CSI depends on whether the first condition is met; the first condition includes that the generation method of the target CSI is associated with a target identifier; the target identifier depends on the target information block.

[0460] As an embodiment, the second communication device 450 includes: a memory storing a computer-readable instruction program, wherein the computer-readable instruction program generates actions when executed by at least one processor, and the actions include: receiving a target CSI reporting configuration; the target CSI reporting configuration is used to configure the reporting of the target CSI; the target CSI is generated in a training-based or AI-based manner; sending a target information block; determining whether to abandon sending the target CSI; when a first condition is met, abandoning sending the target CSI; wherein the determination whether to abandon sending the target CSI depends on whether the first condition is met; the first condition includes that the target CSI generation method is associated with a target identifier; the target identifier depends on the target information block.

[0461] As an embodiment, the first communication device 410 includes: at least one processor and at least one memory, the at least one memory including computer program code; the at least one memory and the computer program code are configured to be used with the at least one processor. The first communication device 410 device at least: sends a target CSI reporting configuration; the target CSI reporting configuration is used to configure the reporting of the target CSI; the generation method of the target CSI is based on training or AI; receives a target information block; determines whether to give up receiving the target CSI; when a first condition is met, gives up receiving the target CSI; wherein the target recipient of the target CSI reporting configuration determines whether to give up sending the target CSI; when the first condition is met, the target recipient of the target CSI reporting configuration gives up sending the target CSI; the target recipient of the target CSI reporting configuration determines whether to give up sending the target CSI depending on whether the first condition is met; the first condition includes that the generation method of the target CSI is associated with the target identifier; the target identifier depends on the target information block.

[0462] As an embodiment, the first communication device 410 includes: a memory storing a computer-readable instruction program, and the computer-readable instruction program generates actions when executed by at least one processor, and the actions include: sending a target CSI reporting configuration; the target CSI reporting configuration is used to configure the reporting of the target CSI; the generation method of the target CSI is training-based or AI-based; receiving a target information block; determining whether to give up receiving the target CSI; when a first condition is met, giving up receiving the target CSI; wherein, the target recipient of the target CSI reporting configuration determines whether to give up sending the target CSI; when the first condition is met, the target recipient of the target CSI reporting configuration gives up sending the target CSI; the target recipient of the target CSI reporting configuration determines whether to give up sending the target CSI depending on whether the first condition is met; the first condition includes that the generation method of the target CSI is associated with a target identifier; the target identifier depends on the target information block.

[0463] As an embodiment, the first node in the present application includes the second communication device 450.

[0464] As an embodiment, the second node in the present application includes the first communication device 410.

[0465] As an embodiment, at least one of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, and the data source 467} is used to receive the target CSI reporting configuration; and at least one of {the antenna 420, the transmitter 418, the transmitting processor 416, the multi-antenna transmitting processor 471, the controller / processor 475, and the memory 476} is used to send the target CSI reporting configuration.

[0466] As an embodiment, at least one of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, and the data source 467} is used to receive the RS resources in the first resource set; and at least one of {the antenna 420, the transmitter 418, the transmitting processor 416, the multi-antenna transmitting processor 471, the controller / processor 475, and the memory 476} is used to send the RS resources in the first resource set.

[0467] As an embodiment, at least one of {the antenna 420, the receiver 418, the receiving processor 470, the multi-antenna receiving processor 472, the controller / processor 475, and the memory 476} is used to receive the target information block; and at least one of {the antenna 452, the transmitter 454, the transmitting processor 468, the multi-antenna transmitting processor 457, the controller / processor 459, the memory 460, and the data source 467} is used to send the target information block.

[0468] As an embodiment, at least one of {the antenna 420, the receiver 418, the receive processor 470, the multi-antenna receive processor 472, the controller / processor 475, and the memory 476} is used to receive the target CSI; and at least one of {the antenna 452, the transmitter 454, the transmit processor 468, the multi-antenna transmit processor 457, the controller / processor 459, the memory 460, and the data source 467} is used to send the target CSI.

[0469] Example 5

[0470] Embodiment 5 illustrates a flowchart of wireless transmission according to an embodiment of the present application, as shown in the attached Figure 5 As shown in the attached Figure 5 In the embodiment, the second node U1 and the first node U2 are communication nodes transmitted via the air interface. Figure 5 In the process, the steps in box F51 to box F55 are respectively optional.

[0471] For the second node U1, deploy the second operation in step S511; send the target CSI reporting configuration in step S512; send a signal in the first resource set in step S513; receive the target information block in step S514; determine whether to give up receiving the target CSI in step S515; when the first condition is met in step S516, give up receiving the target CSI; and perform the second operation in step S517.

[0472] For the first node U2, deploy the first operation in step S521; receive the target CSI reporting configuration in step S522; receive the signal in the first resource set in step S523; perform the first operation in step S524; send the target information block in step S525; determine whether to give up sending the target CSI in step S526; and give up sending the target CSI when the first condition is met in step S527.

[0473] In Example 5, the target CSI reporting configuration is used to configure the reporting of the target CSI; the generation method of the target CSI is training-based or AI-based; the determination of whether to abandon sending the target CSI depends on whether the first condition is met; the first condition includes the generation method of the target CSI being associated with the target identifier; the target identifier depends on the target information block.

[0474] As an embodiment, the first node U2 is the first node in this application.

[0475] As an embodiment, the second node U1 is the second node in this application.

[0476] As an embodiment, the air interface between the second node U1 and the first node U2 includes a wireless interface between a base station device and a user equipment.

[0477] As an embodiment, the air interface between the second node U1 and the first node U2 includes a wireless interface between a relay node device and a user equipment.

[0478] As an embodiment, the air interface between the second node U1 and the first node U2 includes a wireless interface between user equipments.

[0479] As an embodiment, the second node U1 is a base station maintaining a service cell of the first node U2.

[0480] As an example, Figure 5 The step in box F53 exists; the method in the first node used for wireless communication includes: receiving a signal in the first resource set.

[0481] As an example, Figure 5 The step in box F53 exists; the method in the second node used for wireless communication includes: sending a signal in the first resource set.

[0482] As an embodiment, sending a signal in the first resource set refers to: sending a wireless signal in the first resource set.

[0483] As an embodiment, sending a signal in the first resource set refers to: sending a reference signal in the first resource set.

[0484] As an embodiment, receiving a signal in the first resource set refers to: receiving a wireless signal in the first resource set.

[0485] As an embodiment, receiving a signal in the first resource set refers to: receiving a reference signal in the first resource set.

[0486] As an example, Figure 5 In the example, when the target CSI is generated based on training or AI, the step in box F54 exists.

[0487] As an example, Figure 5 The steps in box F52 exist.

[0488] As an example, Figure 5 In the embodiment, when the target CSI is generated based on training or AI, the step in box F54 exists, the step in box F55 exists, and the first node and the second node adopt a two-sided AI model.

[0489] As an example, Figure 5 In the embodiment, when the target CSI is generated based on training or AI, the steps in box F54 exist, the steps in box F55 do not exist, and the first node adopts a single side AI model.

[0490] As an example, Figure 5 The step in box F51 in the above-mentioned method in the second node used for wireless communication includes: deploying the second operation.

[0491] As an embodiment, the deployment of the second operation is earlier than the sending of the target CSI reporting configuration.

[0492] As an embodiment, the deployment of the second operation is later than the sending of the target CSI reporting configuration.

[0493] As an example, Figure 5 The step in box F55 in is present, and the method in the second node used for wireless communication includes: performing the second operation.

[0494] As an example, Figure 5 In the embodiment, when the target CSI is generated based on training or AI, the steps in box F54 exist, the steps in box F55 exist, the first operation is used for CSI compression, the second operation is used for CSI recovery, and the first node and the second node adopt a two-sided AI model.

[0495] As an example, Figure 5In the embodiment, when the target CSI is generated based on training or AI, the step in box F54 exists, the step in box F55 does not exist, the first operation is used for beam prediction, and the first node adopts a single side AI model.

[0496] As an embodiment, the deployment of the first operation is earlier than the reception of the target CSI reporting configuration.

[0497] As an embodiment, the deployment of the first operation is later than the reception of the target CSI reporting configuration.

[0498] As an embodiment, the output of the first operation includes the target CSI; and the input of the second operation includes the target CSI.

[0499] As an embodiment, the first node is a consumer.

[0500] As an embodiment, the first node is a consumer of an AI function.

[0501] As an embodiment, the first node is a user of AI inference.

[0502] As an embodiment, the first node is an AI trained user.

[0503] As an embodiment, the first node is a MnS (Management Service) user.

[0504] As an embodiment, the first node is a producer of AI inference.

[0505] As an embodiment, the first node is a producer of AI training.

[0506] Example 6

[0507] Embodiment 6 illustrates a schematic diagram of a first resource set and a second resource set according to an embodiment of the present application; Figure 6 As shown. Figure 6 In the above, resource #1, ..., resource #n, ... represent the resources in the first resource set; resource #1, ..., resource #m, ... represent the resources in the second resource set.

[0508] In Example 6, the target CSI reporting configuration indicates a first resource set, which is used for at least one of channel measurement or interference resource measurement of the target CSI, and the first resource set includes one or more RS resources; the target CSI indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

[0509] As an embodiment, the first resource set includes one or more RS resources.

[0510] As an embodiment, the first resource set includes one or more downlink RS resources.

[0511] As an embodiment, the first resource set includes periodic RS resources.

[0512] As an embodiment, the first resource set includes semi-persistent RS resources.

[0513] As an embodiment, the first resource set includes aperiodic RS resources.

[0514] As an embodiment, the first resource set consists of one or more non-periodic RS resources.

[0515] As an embodiment, the resources in the first resource set include at least one of antenna ports, TCI (Transmission Configuration Indication) status, QCL (Quasi Co-Location) information, time-frequency resources, time-frequency code resources, beams, RS resources, vectors, or matrices.

[0516] As an embodiment, the first resource set includes one or more RS (Reference Signal) resource sets, and an RS resource set includes one or more RS resources.

[0517] As an embodiment, the first resource set includes at least one of at least one CSI-RS resource set, at least one CSI-SSB (Channel State Information-Synchronization Signal Block) resource set, or at least one CSI-IM (Channel State Information-Interference Measurement) resource set.

[0518] As an embodiment, the first resource set includes at least one RS resource set for channel measurement.

[0519] As an embodiment, the first resource set includes at least one RS resource set for channel measurement and at least one RS resource set for interference measurement.

[0520] As an embodiment, the first resource set includes at least one RS resource set for interference measurement.

[0521] As a sub-embodiment of the above embodiment, an RS resource set for channel measurement includes one or more RS resources.

[0522] As a sub-embodiment of the above embodiment, an RS resource set for interference measurement includes one or more RS resources.

[0523] As an embodiment, an RS resource set for channel measurement includes one or more RS resources, and any RS resource in the RS resource set for channel measurement is a CSI-RS resource or a synchronization signal resource.

[0524] As an embodiment, an RS resource set for interference measurement includes one or more RS resources, and any RS resource in the RS resource set for interference measurement is a CSI-IM resource or an NZP (non-zero power) CSI-RS resource for interference measurement.

[0525] As an embodiment, the first resource set includes one or more RS resources, and any RS resource in the first resource set is a CSI-RS (Channel State Information Reference Signal) resource or a synchronization signal resource.

[0526] As an embodiment, the synchronization signal resources include at least resources occupied by the synchronization signal.

[0527] As an embodiment, the synchronization signal resource is SSB (Synchronization Signal Block).

[0528] As an embodiment, the synchronization signal resource is a SS / PBCH (synchronization signal / physical broadcast channel) block resource.

[0529] As an embodiment, the first resource set consists of at least one non-periodic CSI-RS resource used for channel measurement.

[0530] As an embodiment, the first resource set consists of at least one of at least one non-periodic CSI-RS resource for channel measurement, at least one non-periodic CSI-IM resource for interference measurement, or at least one non-periodic NZP CSI-RS resource for interference measurement.

[0531] As an embodiment, the first resource set consists of one or more non-periodic RS resources; the first resource set consists of at least one non-periodic CSI-RS resource for channel measurement, at least one non-periodic CSI-IM resource for interference measurement, or at least one non-periodic NZP CSI-RS resource for interference measurement.

[0532] As an embodiment, the first resource set consists of at least one CSI-RS resource.

[0533] As an embodiment, the first resource set includes at least one of CSI-RS resources or SS / PBCH block resources.

[0534] As an embodiment, the first resource set includes at least one transmission opportunity of each RS resource in the first resource set that is no later than a reference resource.

[0535] As an embodiment, the first resource set includes at least one transmission timing of each RS resource in at least one RS resource in the first resource set that is no later than the reference resource.

[0536] As an embodiment, the first resource set includes at least one transmission opportunity of at least one RS resource in the first resource set that is no later than a reference resource.

[0537] As an embodiment, the first resource set includes a most recent transmission timing of at least one RS resource in the first resource set that is no later than a reference resource.

[0538] As an embodiment, the first resource set includes one or more recent transmission opportunities of at least one RS resource in the first resource set that are no later than a reference resource.

[0539] As an embodiment, the first resource set includes all transmission opportunities of at least one RS resource in the first resource set that are no later than a reference resource.

[0540] As an embodiment, the reference resource is a CSI reference resource.

[0541] As an embodiment, the reference resource is a CSI reference resource of the target CSI.

[0542] As an embodiment, the benefits of the above method include: using existing standards and system designs to reduce complexity.

[0543] As an embodiment, when the generation of the target CSI is based on AI, the first resource set includes at least one transmission opportunity for each RS resource in some RS resources in the first resource set; when the generation of the target CSI is not based on AI, the first resource set includes at least one transmission opportunity for each RS resource in the first resource set.

[0544] As an embodiment, when the generation of the target CSI is based on AI, the first resource set includes at least one transmission opportunity of only the first RS in the first resource set; when the generation of the target CSI is not based on AI, the first resource set includes at least one transmission opportunity for each RS resource in the first resource set.

[0545] As an embodiment, the essence of the above method includes: configuring different transmission opportunity sets for AI-based and non-AI-based solutions respectively.

[0546] As an embodiment, the benefits of the above method include: fully considering the processing capabilities based on AI and not based on AI, and having greater flexibility and adaptability.

[0547] As an embodiment, when the generation of the target CSI is based on AI, the first resource set includes one or more transmission opportunities of at least one RS resource in the first resource set that are no later than the most recent transmission opportunity of a reference resource; when the generation of the target CSI is not based on AI, the first resource set includes one or more transmission opportunities of at least one RS resource in the first resource set that are no later than the most recent transmission opportunity of the reference resource.

[0548] As an embodiment, the essence of the above method includes: for solutions that are not based on AI, the current system design and standards are used.

[0549] As an embodiment, the essence of the above method includes: configuring more transmission opportunities for the AI-based solution.

[0550] As an embodiment, the benefits of the above method include: enhancing the forward and backward compatibility of the system and making small changes to existing systems and standards.

[0551] As an embodiment, the target CSI reporting configuration indicates at least one resource configuration, and the at least one resource configuration indicates the first resource set.

[0552] As an embodiment, the target CSI reporting configuration includes at least one resource configuration, and the at least one resource configuration indicates the first resource set.

[0553] As an embodiment, a resource configuration is used to configure CSI resources.

[0554] As an embodiment, a resource configuration is an IE CSI-ResourceConfig.

[0555] As an embodiment, a resource configuration is carried by RRC IE.

[0556] As an embodiment, a resource configuration is carried by a CSI-ResourceConfig IE.

[0557] As an embodiment, the target CSI reporting configuration indicates configuration information of the first resource set.

[0558] As an embodiment, the target CSI reporting configuration indicates an identifier of the first resource set.

[0559] As an embodiment, the target CSI reporting configuration indicates at least one resource configuration, and the at least one resource configuration indicates the second resource set.

[0560] As an embodiment, the target CSI reporting configuration includes at least one resource configuration, and the at least one resource configuration indicates the second resource set.

[0561] As an embodiment, the second resource set includes the first resource set and resources outside the first resource set.

[0562] As an embodiment, the first resource set includes one or more RS resources, the second resource set includes one or more RS resources, and the second resource set includes the first resource set and RS resources outside the first resource set.

[0563] As an embodiment, the number of resources included in the first resource set is less than the number of resources included in the second resource set.

[0564] As an embodiment, the number of RS resources included in the first resource set is less than the number of RS resources included in the second resource set.

[0565] As an embodiment, the second resource set includes resources that do not belong to the first resource set.

[0566] As an embodiment, the second resource set includes antenna ports that do not belong to the first resource set.

[0567] As an embodiment, the second resource set includes resources that do not belong to the first resource set, and the resources in the second resource set include at least one of antenna ports, TCI status, QCL information, frequency resources, time-frequency code resources, beams, RS resources, vectors, or matrices.

[0568] As an embodiment, the second resource set includes at least one training data set.

[0569] As an embodiment, the second resource set is used to train an AI model.

[0570] As an embodiment, the second resource set includes one or more RS (Reference Signal) resource sets, and an RS resource set includes one or more RS resources.

[0571] As an embodiment, the second resource set includes at least one of at least one CSI-RS resource set, at least one CSI-SSB (Channel State Information-Synchronization Signal Block) resource set, or at least one CSI-IM (Channel State Information-Interference Measurement) resource set.

[0572] As an embodiment, the second resource set includes at least one RS resource set for channel measurement, and an RS resource set for channel measurement includes one or more RS resources.

[0573] As an embodiment, the second resource set includes at least one RS resource set for channel measurement, and at least one RS resource set for interference measurement; an RS resource set for channel measurement includes one or more RS resources, and an RS resource set for interference measurement includes one or more RS resources.

[0574] As an embodiment, the second resource set includes at least one RS resource set for interference measurement; an RS resource set for interference measurement includes one or more RS resources.

[0575] As an embodiment, the second resource set includes one or more RS resources.

[0576] As an embodiment, the second resource set includes one or more downlink RS resources.

[0577] As an embodiment, the second resource set includes one or more RS resources, and any RS resource in the second resource set is a CSI-RS (Channel State Information Reference Signal) resource or a synchronization signal resource.

[0578] As an embodiment, the target CSI reporting configuration indicates at least one resource configuration, and the at least one resource configuration indicates the first resource set and the second resource set.

[0579] As an embodiment, the target CSI reporting configuration includes at least one resource configuration, and the at least one resource configuration indicates the first resource set and the second resource set.

[0580] As an embodiment, the target CSI reporting configuration indicates a resource configuration, and the one resource configuration indicates the first resource set and the second resource set.

[0581] As an embodiment, the target CSI reporting configuration indicates two resource configurations, and the two resource configurations respectively indicate the first resource set and the second resource set.

[0582] As an embodiment, the target CSI reporting configuration indicates configuration information of the second resource set.

[0583] As an embodiment, the target CSI reporting configuration indicates an identifier of the second resource set.

[0584] As an embodiment, the target CSI reporting configuration is used to indicate the second resource set from a reference resource set.

[0585] As an embodiment, the target CSI reporting configuration indicates a first type of identifier, and the second resource set depends on the first type of identifier.

[0586] As an embodiment, the second resource set being dependent on the first type of identifier includes: the first type of identifier is used to identify the second resource set.

[0587] As an embodiment, the second resource set depends on the first type of identifier, including: the first type of identifier is used to identify a reference resource set, and the reference resource set includes the second resource set.

[0588] As an embodiment, the second resource set depends on the first type of identifier, including: the first type of identifier is used to identify a reference resource set, the reference resource set includes the second resource set, and the target CSI reporting configuration is used to indicate the second resource set from the reference resource set.

[0589] As an embodiment, the information other than the target CSI reporting configuration indicates the second resource set.

[0590] As an embodiment, the information other than the target CSI reporting configuration indicating the second resource set includes higher layer parameters.

[0591] As an embodiment, the information other than the target CSI reporting configuration indicating the second resource set includes RRC parameters.

[0592] As an embodiment, the information other than the target CSI reporting configuration indicating the second resource set includes a partial or entire field of an RRC IE.

[0593] As an embodiment, the information other than the target CSI reporting configuration indicating the second resource set includes MAC CE.

[0594] As an embodiment, the information other than the target CSI reporting configuration indicating the second resource set includes DCI (downlink control information).

[0595] As an embodiment, the target CSI indicates at least one resource in the first resource set.

[0596] As an embodiment, the target CSI indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

[0597] As an embodiment, when the target CSI is generated based on training or AI, the target CSI indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

[0598] As an embodiment, when the target CSI is generated based on training or AI, the target CSI indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set; when the target CSI is not generated based on training or AI, the target CSI indicates at least one resource in the first resource set.

[0599] As an embodiment, the benefits of the above method include: reducing the overhead required to obtain the target CSI.

[0600] As an embodiment, the benefits of the above method include: reducing the measurement resources required to obtain the target CSI.

[0601] As an embodiment, the advantages of the above method include: little modification to existing systems and standards.

[0602] As an embodiment, whether the resources indicated by the target CSI belong to the first resource set depends on whether the generation of the target CSI is based on training or AI; when the generation of the target CSI is based on training or AI, the resources indicated by the target CSI belong to the second resource set, and the second resource set includes resources that do not belong to the first resource set; when the generation of the target CSI is not based on training or AI, the resources indicated by the target CSI belong to the first resource set.

[0603] As an embodiment, whether the RS resources indicated by the target CSI belong to the first resource set depends on whether the generation of the target CSI is based on training or based on AI; when the generation of the target CSI is based on training or based on AI, the resources indicated by the target CSI belong to the second resource set, and the second resource set includes resources that do not belong to the first resource set; only when the generation of the target CSI is not based on training or based on AI, the resources indicated by the target CSI belong to the first resource set.

[0604] As an embodiment, the benefits of the above method include: supporting AI-based solutions and improving system flexibility while being compatible with existing systems and standards.

[0605] As an embodiment, the advantages of the above method include: little modification to existing systems and standards.

[0606] As an embodiment, the target CSI is generated based on AI, and the first node is not required to measure the second resource set.

[0607] As an embodiment, the target CSI is generated based on AI, the first resource set is used for measurement, and the second resource set is used for prediction.

[0608] As an embodiment, the target CSI is generated based on AI, the first resource set is used for measurement, and the second resource set is used for prediction.

[0609] As an embodiment, the target CSI is generated based on AI, and only the first resource set of the first resource set and the second resource set is used for measurement.

[0610] As an embodiment, that only the first resource set among the first resource set and the second resource set is used for measurement includes: that only the first resource set among the first resource set and the second resource set is used for measurement by the first node.

[0611] As an embodiment, the fact that only the first resource set among the first resource set and the second resource set is used for measurement includes: the first resource set is used by the first node for measurement, and the first node is not required to measure part or all of the resources in the second resource set.

[0612] As an embodiment, the first node is not required to measure the second resource set, including: the first node does not measure part or all of the resources in the second resource set.

[0613] As an embodiment, the first node is not required to measure the second resource set including: whether the first node measures part or all of the resources in the second resource set is implementation-related or independently determined by the first node.

[0614] Example 7

[0615] Embodiment 7 illustrates a schematic diagram of a first operation according to an embodiment of the present application; Figure 7 shown.

[0616] In Example 7, the target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of channel measurement or interference resource measurement of the target CSI, and the first resource set includes one or more RS resources; the method for generating the target CSI includes performing a first operation, the input of the first operation depends on the measurement based on the first resource set, and the target CSI depends on the output of the first operation.

[0617] As an embodiment, the first operation is training-based or AI-based.

[0618] As an embodiment, the first operation is obtained through training.

[0619] As an embodiment, the first operation is based on a neural network.

[0620] As an embodiment, the first operation includes an AI entity.

[0621] As an embodiment, the first operation includes a portion of an AI entity.

[0622] As an embodiment, the first operation includes a part of an AI entity for inference.

[0623] As an embodiment, the first operation is performed by an AI entity.

[0624] As an embodiment, the first operation is performed by an AI function.

[0625] As an embodiment, the AI ​​function includes at least one of an AI inference function, an AI training function, and an AI management function.

[0626] As an embodiment, the training for obtaining the first operation is performed by the first node.

[0627] As an embodiment, the training for obtaining the first operation is performed by an MDA function (Management Data Analytics Function).

[0628] As an embodiment, the training for obtaining the first operation is performed by a MDAS (Management Data Analytics Service) producer.

[0629] As an embodiment, the training for obtaining the first operation is performed by NWDAF (Network Data Analytics Function).

[0630] As an embodiment, the training for obtaining the first operation is performed by a core network.

[0631] As an embodiment, the training for obtaining the first operation is performed by an AI training producer.

[0632] As an embodiment, the first operation includes inference.

[0633] As an embodiment, the first operation is AI inference.

[0634] As an embodiment, the first operation includes AI inference for CSI.

[0635] As an embodiment, the first operation is AI inference for CSI.

[0636] As an embodiment, the first operation includes AI inference for at least one of beam prediction, CSI prediction, CSI estimation, or CSI compression.

[0637] As an embodiment, the benefits of the above method include: improving the performance of CSI (including beam) measurement and reporting, including more accurate CSI, lower reference signal overhead and reporting overhead, thereby improving the overall system performance.

[0638] As an embodiment, the benefits of the above method include: more accurate and complete CSI, lower reference signal overhead, and improved real-time performance of CSI.

[0639] As an embodiment, the first operation requires deployment.

[0640] As an embodiment, the first operation is obtained by loading.

[0641] As an embodiment, the first operation is obtained by loading from a serving cell of the first node.

[0642] As an embodiment, the first operation is obtained from maintaining base station loading of a serving cell of the first node.

[0643] As an embodiment, the first operation is obtained by loading from the core network.

[0644] As an embodiment, the first operation includes one or more of convolution, pooling, cascading and activation.

[0645] As an embodiment, the first operation includes at least one of a fully connected layer, a pooling layer, at least one convolutional layer, and at least one encoding layer.

[0646] As an embodiment, a coding layer includes at least one convolutional layer and one pooling layer.

[0647] As an embodiment, in the convolution layer, at least one convolution kernel is used to convolve the input to generate a corresponding feature map, and at least one feature map output by the convolution layer is reshaped into a vector input to the fully connected layer; the fully connected layer converts the one vector into an output.

[0648] As an embodiment, part or all of the convolution kernel size, number of convolution layers, convolution step size, pooling kernel size, pooling kernel step size, pooling function, activation function and number of feature maps of the first operation are obtained through training.

[0649] As an embodiment, part or all of the convolution kernel, pooling kernel, pooling function, activation function, parameters of the pooling function and parameters of the activation function of the first operation are obtained through training.

[0650] As an embodiment, the first operation includes preprocessing.

[0651] As an embodiment, the preprocessing includes one or more of matrix decomposition, matrix transformation and projection.

[0652] As an embodiment, the preprocessing includes one or more of quantization, transformation from spatial domain to angular domain, transformation from angular domain to spatial domain, transformation from frequency domain to time domain, and transformation from time domain to frequency domain.

[0653] As an embodiment, the preprocessing includes at least one of truncation and / or padding, DFT (DiscreteFourier Transform), mapping, and labeling.

[0654] As an embodiment, the first operation includes post-processing.

[0655] As an embodiment, the post-processing includes at least one of DFT (Discrete Fourier Transform), quantization, truncation and / or padding.

[0656] As an embodiment, the post-processing includes one or more of a transformation from the angle domain to the spatial domain, a transformation from the spatial domain to the angle domain, a transformation from the time domain to the frequency domain, and a transformation from the frequency domain to the time domain.

[0657] As an embodiment, the measurement based on the first resource set includes channel information before compression, and the output of the first operation includes channel information after compression.

[0658] As an embodiment, the above method has the following benefits: it is suitable for channel compression and saves feedback overhead.

[0659] As an embodiment, the measurement based on the first resource set includes channel information obtained by measurement, and the output of the first operation includes predicted channel information.

[0660] As an embodiment, the measurement based on the first resource set includes channel information obtained by measurement, and the output of the first operation includes spatial beam prediction.

[0661] As an embodiment, the benefits of the above method include: reducing RS resource overhead and reducing feedback delay.

[0662] As an embodiment, the measurement based on the first resource set includes historical channel information, and the output of the first operation includes predicted channel information.

[0663] As an embodiment, the measurement based on the first resource set includes historical channel information, and the output of the first operation includes temporal beam prediction.

[0664] As an embodiment, the benefits of the above method include: reducing the channel information feedback delay and improving the real-time performance of channel information acquisition.

[0665] As an embodiment, the measurement based on the first resource set includes current channel information, and the output of the first operation includes channel information after a period of time.

[0666] As an embodiment, the benefits of the above method include: improving CSI accuracy and real-time performance, and reducing RS overhead.

[0667] As an embodiment, the measurement based on the first resource set includes incomplete channel information, and the output of the first operation includes complete channel information.

[0668] As an embodiment, the benefits of the above method include: reducing RS overhead and improving the accuracy and completeness of CSI.

[0669] As an embodiment, the measurement based on the first resource set includes channel information of P1 antenna ports, and the output of the first operation includes channel information of P2 antenna ports, and the P1 and the P2 are positive integers greater than 1 respectively, and the P1 is less than the P2.

[0670] As a sub-embodiment of the above embodiment, the P1 antenna ports are a proper subset of the P2 antenna ports.

[0671] As a sub-embodiment of the above embodiment, the P2 antenna ports belong to the second resource set.

[0672] As an embodiment, the input of the first operation also includes the second resource set.

[0673] As an embodiment, the output of the first operation includes one or more of a beam indication, a CRI (CSI-RS Resource Indicator, channel state information reference signal resource indicator), a SS / PBCH block resource indicator (SS / PBCH Block Resource indicator, SSBRI), or a RSRP (reference signal received power, reference signal received power).

[0674] As an embodiment, the output of the first operation includes one or more of PMI, CRI, CQI, RI, LI, SSBRI, RSRP, SINR, capability index and TDCP.

[0675] As an embodiment, the output of the first operation includes one or more of a channel impulse response, a small-scale characteristic, and a channel matrix.

[0676] As an embodiment, the output of the first operation includes one or more of delay spread, Doppler spread, Doppler shift, average delay and average gain.

[0677] As an embodiment, the output of the first operation includes the target CSI.

[0678] As an embodiment, the input of the first operation depends on measurements based on the first set of resources.

[0679] As an embodiment, the generation of the target CSI is based on AI, and the input of the first operation depends on the measurement based on the first resource set.

[0680] As an embodiment, the input of the first operation depends on the measurement based on the first resource set, including: the measurement (channel measurement and / or interference measurement) based on the first resource set is used to generate the input of the first operation.

[0681] As an embodiment, the target CSI depends on the output of the first operation.

[0682] As an embodiment, the target CSI depends on the output of the first operation, including: the target CSI includes the output of the first operation.

[0683] As an embodiment, the target CSI depends on the output of the first operation, including: the target CSI includes the post-processed output of the first operation.

[0684] As an embodiment, the target CSI being dependent on the output of the first operation includes: the output of the first operation being used to generate the target CSI.

[0685] As an embodiment, the target CSI depends on the output of the first operation, including: the output of the first operation is used to generate the target CSI after post-processing.

[0686] As an embodiment, the input of the first operation depends on the measurement based on the first resource set, and the target CSI depends on the output of the first operation.

[0687] As an embodiment, the generation of the target CSI includes performing a first operation, an input of the first operation depends on a measurement based on the first resource set, and the target CSI depends on an output of the first operation.

[0688] As an embodiment, the benefits of the above method include: supporting AI-based solutions and improving the accuracy and real-time nature of information reporting.

[0689] As an embodiment, the benefits of the above method include: improving the overall performance of the system.

[0690] As an embodiment, the first type identifier is a non-negative integer.

[0691] As an embodiment, the first type of identifier is a character string.

[0692] As an embodiment, the first type of identifier is a model identifier.

[0693] As an embodiment, the first type of identifier is used to identify an AI model, AI entity or AI function.

[0694] As an embodiment, the first type of identifier is used by the first node to determine an AI model, AI entity or AI function.

[0695] As an embodiment, the target CSI reporting configuration indicates the use of the AI ​​model, AI entity or AI function by indicating the first type of identifier.

[0696] As an embodiment, the target CSI reporting configuration obtains the input of the AI ​​entity / function / inference associated with the first type of identifier by indicating the first type of identifier.

[0697] As an embodiment, the first type of identifier is used to identify or indicate a resource set.

[0698] As an embodiment, the first type of identifier is used to identify or indicate a resource set, and the measurement of the resource set is used to obtain a training data set.

[0699] As an embodiment, the first type of identifier is used to identify or indicate a resource set.

[0700] As an embodiment, the first type of identifier is used to identify or indicate a training data set.

[0701] As an embodiment, the first operation is associated with a first type of identification.

[0702] As an embodiment, the first operation is associated with a first type of identifier, and the target identifier is one of the first type of identifiers.

[0703] As an embodiment, the target CSI reporting configuration indicates a first type of identifier, and the first operation is associated with the first type of identifier.

[0704] As an embodiment, the target CSI reporting configuration indicates the first operation by indicating the first type of identifier.

[0705] As an embodiment, the AI ​​model adopted by the first operation is identified by the first category identifier.

[0706] As an embodiment, the AI ​​entity or AI function to which the first operation belongs is identified by the first type identifier.

[0707] As an embodiment, the AI ​​entity or AI function that performs the first operation is identified by the first type identifier.

[0708] As an embodiment, the benefits of the above method include: identifying an AI model / entity / function through the first type of identifier, simplifying the design and unifying the understanding of different AI entities / functions among multiple nodes.

[0709] As an embodiment, the first type of identifier is used to identify or indicate a reference resource set, and measurements on the reference resource set are used to obtain a training data set for the first operation.

[0710] As an embodiment, the first type of identifier is used to identify configuration information of a reference resource set, and measurements of the reference resource set are used to obtain a training data set for the first operation.

[0711] As an embodiment, the training for obtaining the first operation is identified by the first category identifier.

[0712] As an embodiment, the data set used for training the first operation is identified by the first category identifier.

[0713] As an embodiment, the target CSI reporting configuration indicates the first operation by indicating the first type of identifier.

[0714] As an embodiment, the benefits of the above method include: by identifying an AI training or an AI training data set to identify the inferences generated by this AI training or AI training data set, a consensus is established between different AI functions, and the design is further simplified.

[0715] As an embodiment, the first operation performs spatial beam prediction for a second resource set based on the measurement of the first resource set, and the second resource set depends on the first type of identifier.

[0716] As an embodiment, the benefits of the above method include: reducing RS resource overhead and reducing feedback delay.

[0717] As an embodiment, the first operation predicts channel information for a second resource set based on measurement of the first resource set, and the second resource set depends on the first type of identifier.

[0718] As an embodiment, the channel information in the present application includes beam information.

[0719] As an embodiment, the first operation performs temporal beam prediction for a second resource set based on historical measurements of the first resource set, and the second resource set depends on the first type of identifier.

[0720] As an embodiment, the benefits of the above method include: reducing beam feedback delay and improving the real-time performance of beam acquisition.

[0721] As an embodiment, the first operation performs temporal channel information prediction for a second resource set based on historical measurements of the first resource set, and the second resource set depends on the first type of identifier.

[0722] As an embodiment, the benefits of the above method include: reducing the channel information feedback delay and improving the real-time performance of channel information acquisition.

[0723] Example 8

[0724] Embodiment 8 illustrates a schematic diagram of associating a target CSI generation method with a target identifier according to an embodiment of the present application; Figure 8 shown.

[0725] In Embodiment 8, the target CSI generation method is associated with the target identifier, including: the first operation is associated with the target identifier.

[0726] As an embodiment, the method for generating the target CSI is not associated with the target identifier, including: the method for generating the target CSI does not include the first operation.

[0727] As an embodiment, the generation method of the target CSI is not associated with the target identifier, including: the first operation is not associated with the target identifier.

[0728] As an embodiment, the method for generating the target CSI is not associated with the target identifier, including: the method for generating the target CSI includes performing a first operation, and the first operation is not associated with the target identifier.

[0729] As an embodiment, the method for generating the target CSI is not associated with the target identifier, including: the method for generating the target CSI includes performing a first operation, the first operation is associated with a first category identifier, the first category identifier associated with the first operation is different from the target identifier, and the target identifier is a first category identifier.

[0730] As an embodiment, the benefits of the above method include: improving the flexibility of the system and adapting to transmission and applications in different scenarios.

[0731] As an embodiment, the benefits of the above method include: improving the reliability and robustness of the system.

[0732] As an embodiment, the first operation being associated with the target identifier includes: the first operation being associated with at least one identifier, and the at least one identifier includes the target identifier.

[0733] As an embodiment, the first operation is not associated with the target identifier, which includes: the first operation is associated with at least one identifier, and the at least one identifier does not include the target identifier.

[0734] As an embodiment, the benefits of the above method include: improving the flexibility of the system.

[0735] As an embodiment, the first operation being associated with the target identifier includes: the first operation being identified by the target identifier.

[0736] As an embodiment, the first operation is not associated with the target identifier, including: the first operation is not identified by the target identifier.

[0737] As an embodiment, the first operation is associated with the target identifier, including: the first operation is associated with a first-category identifier, the first-category identifier associated with the first operation is the same as the target identifier, and the target identifier is a first-category identifier.

[0738] As an embodiment, the first operation is not associated with the target identifier, which includes: the first operation is associated with a first-category identifier, the first-category identifier associated with the first operation is different from the target identifier, and the target identifier is one of the first-category identifiers.

[0739] As an embodiment, the first operation is associated with the target identifier, including: the AI ​​model adopted by the first operation is identified by the target identifier.

[0740] As an embodiment, the first operation is not associated with the target identifier, including: the first operation does not adopt the AI ​​model identified by the target identifier.

[0741] As an embodiment, the first operation is not associated with the target identifier, including: the first operation does not adopt an AI model.

[0742] As an embodiment, the first operation being associated with the target identifier includes: the AI ​​entity to which the first operation belongs is identified by the target identifier.

[0743] As an embodiment, the first operation is not associated with the target identifier includes: the AI ​​entity to which the first operation belongs is not identified by the target identifier.

[0744] As an embodiment, the first operation is associated with the target identifier, including: the execution of the first operation is for the AI ​​function identified by the target identifier.

[0745] As an embodiment, the first operation is not associated with the target identifier, including: the execution of the first operation is not for the AI ​​function identified by the target identifier.

[0746] As an embodiment, the benefits of the above method include: supporting AI-based CSI reporting.

[0747] As an embodiment, the benefits of the above method include: simplifying system design and reducing the implementation complexity of the solution.

[0748] As an embodiment, the first operation is associated with the target identifier including: the first operation is associated with the target identifier through the target CSI reporting configuration.

[0749] As an embodiment, the first operation is not associated with the target identifier, including: the first operation is not associated with the target identifier through the target CSI reporting configuration.

[0750] As an embodiment, the first operation is associated with the target identifier, including: the target CSI reporting configuration indicates a first type of identifier; the target CSI reporting configuration indicates the first operation; and the target identifier is the first type of identifier.

[0751] As an embodiment, the first operation is not associated with the target identifier, including: the target CSI reporting configuration indicates a first type of identifier; the target CSI reporting configuration indicates the first operation; and the target identifier is not the first type of identifier.

[0752] As an embodiment, the advantages of the above method include: little modification to existing systems and standards.

[0753] As an embodiment, the benefits of the above method include: improving the forward and backward compatibility of the system.

[0754] Example 9

[0755] Embodiment 9 illustrates a schematic diagram of associating a target CSI generation method with a target identifier according to another embodiment of the present application; Fig. 9 shown.

[0756] In Embodiment 9, the target CSI generation method is associated with the target identifier, including: the target CSI reporting configuration indicates a first type identifier, the first type identifier indicated by the target CSI reporting configuration is the same as the target identifier, and the target identifier is a first type identifier.

[0757] As an embodiment, the target CSI generation method is associated with the target identifier, including: the target CSI generation method is associated with the target identifier through the target CSI reporting configuration.

[0758] As an embodiment, the target CSI generation method is not associated with the target identifier, including: the target CSI generation method is not associated with the target identifier through the target CSI reporting configuration.

[0759] As an embodiment, the target CSI generation method is associated with the target identifier, including: the target CSI generation method is associated with a first type of identifier through the target CSI reporting configuration, and the first type of identifier is the same as the target identifier.

[0760] As an embodiment, the target CSI generation method is not associated with the target identifier, including: the target CSI generation method is associated with a first type of identifier through the target CSI reporting configuration, and the first type of identifier is different from the target identifier.

[0761] As an embodiment, the target CSI generation method is associated with the target identifier, including: the target CSI generation method is associated with at least one identifier through the target CSI reporting configuration, and the at least one identifier includes the target identifier.

[0762] As an embodiment, the target CSI generation method is not associated with the target identifier, including: the target CSI generation method is associated with at least one identifier through the target CSI reporting configuration, and the at least one identifier does not include the target identifier.

[0763] As an embodiment, the advantages of the above method include: little modification to existing systems and standards.

[0764] As an embodiment, the benefits of the above method include: improving the forward and backward compatibility of the system.

[0765] As an embodiment, the generation method of the target CSI is not associated with the target identifier, including: the target CSI reporting configuration indicates a first type identifier, and the first type identifier indicated by the target CSI reporting configuration is different from the target identifier.

[0766] As an embodiment, the target CSI generation method is associated with the target identifier, including: the target CSI reporting configuration indicates at least one identifier, and the at least one identifier includes the target identifier.

[0767] As an embodiment, the generation method of the target CSI is not associated with the target identifier, including: the target CSI reporting configuration indicates at least one identifier, and the at least one identifier does not include the target identifier.

[0768] As an embodiment, the benefits of the above method include: improving the flexibility of the system.

[0769] As an embodiment, the generation method of the target CSI is associated with the target identifier, including: the target CSI reporting configuration indicates a first type of identifier; the target CSI reporting configuration indicates the generation method of the target CSI; the target identifier is the same as the first type of identifier.

[0770] As an embodiment, the generation method of the target CSI is not associated with the target identifier, including: the target CSI reporting configuration indicates a first type of identifier; the target CSI reporting configuration indicates the generation method of the target CSI; the target identifier and the first type of identifier are different.

[0771] As an embodiment, the benefits of the above method include: simplifying system design and reducing implementation complexity.

[0772] Example 10

[0773] Embodiment 10 illustrates a schematic diagram of a target information block indicating a target identifier according to an embodiment of the present application; Fig.10 shown.

[0774] In embodiment 10, the target information block indicates the target identifier.

[0775] As an embodiment, the target information block explicitly indicates the target identifier.

[0776] As an embodiment, the target information block implicitly indicates the target identifier.

[0777] As an embodiment, the benefits of the above method include: simplifying system design and reducing implementation complexity.

[0778] As an embodiment, the target information block indicating the target identifier includes: the target information block indicates at least one identifier, and the at least one identifier includes the target identifier.

[0779] As an embodiment, the target information block indicating the target identifier includes: the target information block indicating a first type of identifier, and the first type of identifier is the same as the target identifier.

[0780] As an embodiment, the target information block indicating the target identifier includes: the target information block indicating a first type of identifier, and the first type of identifier is associated with the target identifier.

[0781] As an embodiment, the benefits of the above method include: improving the flexibility of the system and adapting to different scenarios and applications.

[0782] As an embodiment, the target information block indicates the target identifier, including: the target information block indicates at least one AI model, AI entity or AI function, and the at least one AI model, AI entity or AI function is associated with the target identifier.

[0783] As an embodiment, the target information block indicating the target identifier includes: the target information block indicating the AI ​​model, AI entity or AI function identified by the target identifier.

[0784] As an embodiment, the target information block indicating the target identifier includes: the target information block indicating that the AI ​​model identified by the target identifier fails or is invalid.

[0785] As an embodiment, the target information block indicating the target identifier includes: the target information block indicating a performance indicator of the AI ​​model identified by the target identifier.

[0786] As an embodiment, the benefits of the above method include: supporting AI-based CSI reporting.

[0787] As an embodiment, the benefits of the above method include: simplifying system design and reducing implementation complexity.

[0788] As an embodiment, the benefits of the above method include: improving the accuracy and effectiveness of CSI reporting and improving the overall performance of the system.

[0789] As an embodiment, the target information block indicating the target identifier includes: the target information block includes first information, and the first information indicates the target identifier.

[0790] As an embodiment, the benefits of the above method include: improving the stability and robustness of the system.

[0791] Embodiment 11

[0792] Embodiment 11 illustrates a schematic diagram of at least one information block according to an embodiment of the present application; Fig.11 shown.

[0793] In embodiment 11, at least one information block is received, wherein the at least one information block configures or indicates at least one signal; wherein the target information block is generated based on reception or measurement of part or all of the at least one signal.

[0794] As an embodiment, the at least one information block is indicated by a physical layer signaling.

[0795] As an embodiment, the at least one information block is indicated by at least one physical layer signaling.

[0796] As an embodiment, the at least one information block is indicated by higher layer signaling.

[0797] As an embodiment, the at least one information block belongs to a DCI (Downlink Control Information, downlink control information).

[0798] As an embodiment, the at least one information block belongs to at least one DCI (Downlink Control Information, downlink control information).

[0799] As an embodiment, the target identifier is used to identify or indicate a resource set; the at least one signal belongs to the resource set identified or indicated by the target identifier.

[0800] As an embodiment, the target identifier is used to identify or indicate a resource set, and the measurement of the resource set is used to obtain a training data set; the at least one signal belongs to the resource set identified or indicated by the target identifier.

[0801] As an embodiment, the target identifier is used to identify or indicate a training data set; and the measurement of the at least one signal is used to obtain part or all of the data in the training data set.

[0802] As an embodiment, the target identifier is used to identify or indicate a testing data set; and the measurement of the at least one signal is used to obtain part or all of the data in the testing data set.

[0803] As an embodiment, the target identifier is used to identify or indicate a validation data set; and the measurement of the at least one signal is used to obtain part or all of the data in the validation data set.

[0804] As an embodiment, the benefits of the above method include: simplifying system design and reducing implementation complexity.

[0805] As an embodiment, the at least one signal includes at least one PDSCH.

[0806] As an embodiment, the at least one signal includes at least one PDSCH; the target information block is generated based on the reception or measurement of part or all of the at least one signal and includes: the target information block includes all HARQ-ACK bits of the at least one PDSCH.

[0807] As an embodiment, the at least one signal includes at least one reference signal.

[0808] As an embodiment, the at least one signal includes at least one reference signal; the measurement of the at least one reference signal is used for training the AI ​​model identified by the target identifier.

[0809] As an embodiment, the at least one signal includes at least one reference signal; the measurement of the at least one reference signal is used for testing of the AI ​​model identified by the target identifier.

[0810] As an embodiment, the at least one signal includes at least one reference signal; the measurement of the at least one reference signal is used for validation of the AI ​​model identified by the target identifier.

[0811] As an embodiment, the at least one signal includes at least one reference signal; the target information block is generated based on the reception or measurement of part or all of the at least one signal and includes: the target information block includes the reception quality of the at least one reference signal.

[0812] As a sub-embodiment of the above embodiment, the reception quality is RSRP.

[0813] As a sub-embodiment of the above embodiment, the reception quality is SINR.

[0814] As a sub-embodiment of the above embodiment, the reception quality is BLER.

[0815] As an embodiment, the measurement of the at least one signal is used to generate the input of the AI ​​model identified by the target identifier; the target information block is generated based on the reception or measurement of part or all of the at least one signal, including: the target information block includes performance indicators of the AI ​​model identified by the target identifier.

[0816] As an embodiment, the measurement of the at least one signal is used to obtain part or all of the data in the training data set; the target information block is generated based on the reception or measurement of part or all of the at least one signal, including: the target information block includes performance indicators of the AI ​​model identified by the target identifier.

[0817] As an embodiment, the measurement of the at least one signal is used to obtain part or all of the data in a testing data set; the target information block is generated based on the reception or measurement of part or all of the at least one signal, including: the target information block includes performance indicators of the AI ​​model identified by the target identifier.

[0818] As an embodiment, the measurement of the at least one signal is used to obtain part or all of the data in a validation data set; the target information block is generated based on the reception or measurement of part or all of the at least one signal, including: the target information block includes performance indicators of the AI ​​model identified by the target identifier.

[0819] As an embodiment, the essence of the above method includes: monitoring the AI ​​model based on performance parameters related to the CSI reporting of the AI.

[0820] As an embodiment, the benefits of the above method include: improving the performance of the AI-based CSI reporting solution and improving the overall performance of the system.

[0821] As an embodiment, the target information block indicates a performance indicator of the AI ​​model identified by the target identifier, and the performance indicator of the AI ​​model identified by the target identifier is determined based on the reception or measurement of part or all of the at least one signal.

[0822] As an embodiment, the target information block indicates that the AI ​​model identified by the target identifier fails or is invalid, and the failure or invalidity of the AI ​​model identified by the target identifier is determined based on the reception or measurement of part or all of the at least one signal.

[0823] As an embodiment, the target information block indicates the number of times the AI ​​model identified by the target identifier fails or fails, and the number of times the AI ​​model identified by the target identifier fails or fails is determined based on the reception or measurement of part or all of the at least one signal.

[0824] As an embodiment, the benefits of the above method include: simplifying system design and reducing implementation complexity.

[0825] As an embodiment, the benefits of the above method include: improving the stability and robustness of the system.

[0826] Example 12

[0827] Embodiment 12 illustrates a schematic diagram of a target identification according to an embodiment of the present application depending on at least one information block; Fig.12 shown.

[0828] In embodiment 12, the target identifier depends on the at least one information block.

[0829] As an embodiment, the at least one information block indicates the target identifier.

[0830] As an embodiment, the benefits of the above method include: simplifying system design and reducing implementation complexity.

[0831] As an embodiment, the at least one information block indicates at least one identifier, and the at least one identifier includes the target identifier.

[0832] As an embodiment, the at least one information block indicates a first type of identifier, and the first type of identifier is the same as the target identifier.

[0833] As an embodiment, the at least one information block indicates a first type of identifier, and the first type of identifier is associated with the target identifier.

[0834] As an embodiment, the at least one information block indicates at least one AI model, AI entity or AI function, and the at least one AI model, AI entity or AI function is associated with the target identifier.

[0835] As an embodiment, the at least one information block indicates the AI ​​model, AI entity or AI function identified by the target identifier.

[0836] As an embodiment, the benefits of the above method include: improving the flexibility of the system and adapting to different scenarios and applications.

[0837] As an embodiment, the at least one information block indicates at least one reference signal, and the measurement of the at least one reference signal indicated by the at least one information block is used for training the AI ​​model identified by the target identifier.

[0838] As an embodiment, the at least one information block indicates at least one reference signal, and the measurement of the at least one reference signal indicated by the at least one information block is used for testing of the AI ​​model identified by the target identifier.

[0839] As an embodiment, the at least one information block indicates at least one reference signal, and the measurement of the at least one reference signal indicated by the at least one information block is used for validation of the AI ​​model identified by the target identifier.

[0840] As an embodiment, the target identifier is used to identify or indicate a resource set; the at least one information block indicates at least one reference signal, and the at least one reference signal indicated by the at least one information block belongs to the resource set identified or indicated by the target identifier.

[0841] As an embodiment, the target identifier is used to identify or indicate a resource set, and the measurement of the resource set is used to obtain a training data set; the at least one information block indicates at least one reference signal, and the at least one reference signal indicated by the at least one information block belongs to the resource set identified or indicated by the target identifier.

[0842] As an embodiment, the target identifier is used to identify or indicate a training data set; the at least one information block indicates at least one reference signal, and the measurement of the at least one reference signal indicated by the at least one information block is used to obtain part or all of the data in the training data set.

[0843] As an embodiment, the target identifier is used to identify or indicate a resource set, and the measurement of the resource set is used to obtain a testing data set; the at least one information block indicates at least one reference signal, and the at least one reference signal indicated by the at least one information block belongs to the resource set identified or indicated by the target identifier.

[0844] As an embodiment, the target identifier is used to identify or indicate a testing data set; the at least one information block indicates at least one reference signal, and the measurement of the at least one reference signal indicated by the at least one information block is used to obtain part or all of the data in the testing data set.

[0845] As an embodiment, the target identifier is used to identify or indicate a resource set, and the measurement of the resource set is used to obtain a validation data set; the at least one information block indicates at least one reference signal, and the at least one reference signal indicated by the at least one information block belongs to the resource set identified or indicated by the target identifier.

[0846] As an embodiment, the target identifier is used to identify or indicate a validation data set; the at least one information block indicates at least one reference signal, and the measurement of the at least one reference signal indicated by the at least one information block is used to obtain part or all of the data in the validation data set.

[0847] As an embodiment, the benefits of the above method include: supporting AI-based CSI reporting.

[0848] As an embodiment, the benefits of the above method include: enhancing the flexibility of the system and adapting to different scenarios and applications.

[0849] As an embodiment, the at least one information block is used to determine the target identification.

[0850] As an embodiment, the at least one information block includes one or more HARQ-ACK bits, and the target identifier depends on the one or more HARQ-ACK bits.

[0851] As an embodiment, the measurement of the at least one information block is used to determine the target identity.

[0852] As an embodiment, the at least one information block indicates at least one reference signal, and measurement of the at least one reference signal indicated by the at least one information block is used to determine the target identity.

[0853] As an embodiment, the at least one information block indicates at least one reference signal, and the measurement of the at least one reference signal indicated by the at least one information block is used to generate a performance indicator of the AI ​​model identified by the target identifier.

[0854] As an embodiment, the at least one information block indicates at least one reference signal, and the measurement of the at least one reference signal indicated by the at least one information block is used to generate a failure or invalidation of the AI ​​model identified by the target identifier.

[0855] As an embodiment, the at least one information block indicates at least one reference signal, and the measurement of the at least one reference signal indicated by the at least one information block is used to generate the number of failures or failures of the AI ​​model identified by the target identifier.

[0856] As an embodiment, the benefits of the above method include: simplifying system design and reducing implementation complexity.

[0857] As an embodiment, the benefits of the above method include: improving the reliability and robustness of the system.

[0858] Embodiment 13

[0859] Embodiment 13 illustrates a schematic diagram of a first value and a first threshold according to an embodiment of the present application; Fig.13 shown.

[0860] In embodiment 13, the first value is greater than a first threshold, and the first value depends on the target information block.

[0861] As an embodiment, the first value is an integer, and the first threshold is an integer.

[0862] As an embodiment, the first value is a positive integer, and the first threshold is a positive integer.

[0863] As an embodiment, the first value is a real number, and the first threshold is a real number.

[0864] As an embodiment, the first value is a positive real number, and the first threshold is a positive real number.

[0865] As an embodiment, the first value is a non-negative real number, and the first threshold is a non-negative real number.

[0866] As an embodiment, the first value is a positive real number not greater than 1, and the first threshold is a positive real number less than 1.

[0867] As an embodiment, the first value is a logical Boolean value, and the first threshold is a logical Boolean value.

[0868] As an embodiment, the target information block includes the first value.

[0869] As an embodiment, the target information block includes at least one value, and the at least one value includes the first value.

[0870] As an embodiment, the target information block indicates the first value.

[0871] As an embodiment, the target information block indicates at least one value, and the at least one value includes the first value.

[0872] As an embodiment, the benefits of the above method include: simplifying system design and reducing implementation complexity.

[0873] As an embodiment, the at least one signal includes at least one reference signal; the target information block indicates a first value, and the first value is the reception quality of the at least one reference signal.

[0874] As an embodiment, the at least one signal includes at least one reference signal; the target information block indicates a first value, which is the difference between the reception quality measured by the at least one reference signal and the predicted reception quality of the at least one reference signal.

[0875] As an embodiment, the essence of the above method includes: monitoring CSI reporting through system performance parameters.

[0876] As an embodiment, the benefits of the above method include: improving the performance of the CSI reporting solution and improving the overall performance of the system.

[0877] As an embodiment, the target information block indicates a first value, which is a performance indicator of the AI ​​model identified by the target identifier.

[0878] As an embodiment, the target information block indicates a first value, and the first value indicates a failure or invalidation of the AI ​​model identified by the target identifier.

[0879] As an embodiment, the target information block indicates a first value, which is the number of failures or invalidations of the AI ​​model identified by the target identifier.

[0880] As an embodiment, the essence of the above method includes: monitoring the AI ​​model based on performance parameters related to the CSI reporting of the AI.

[0881] As an embodiment, the benefits of the above method include: improving the performance of the AI-based CSI reporting solution and improving the overall performance of the system.

[0882] As an embodiment, the target information block includes one or more bits, and the first value depends on the number of bits with a value of 0 in the target information block.

[0883] As an embodiment, the target information block includes one or more bits, and the first value is the number of bits whose value is 0 in the target information block.

[0884] As an embodiment, the target information block includes one or more bits, and the first value is equal to the number of bits with a value of 0 in the target information block divided by the total number of bits included in the target information block.

[0885] As an embodiment, the target information block includes one or more HARQ-ACK bits, and the first value depends on the number of HARQ-ACK bits whose value is NACK in the target information block.

[0886] As an embodiment, the target information block includes one or more HARQ-ACK bits, and the first value is the number of HARQ-ACK bits whose value is NACK in the target information block.

[0887] As an embodiment, the target information block includes one or more HARQ-ACK bits, and the first value is equal to the number of HARQ-ACK bits with a value of NACK in the target information block divided by the total number of HARQ-ACK bits included in the target information block.

[0888] As an embodiment, the benefits of the above method include: small changes to existing standards and system designs, and enhanced forward and backward compatibility of the system.

[0889] As an embodiment, the benefits of the above method include: improving the reliability and robustness of the system.

[0890] As an embodiment, the target information block includes N information sub-blocks, any of the N information sub-blocks includes at least one bit, and N is a positive integer greater than 1; the N information sub-blocks are used to determine a first value, and the first value is greater than a first threshold.

[0891] As an embodiment, the N information sub-blocks are transmitted on the same physical layer channel.

[0892] As an embodiment, at least two information sub-blocks among the N information sub-blocks are transmitted on different physical layer channels.

[0893] As an embodiment, the N information sub-blocks are transmitted on the same PUSCH.

[0894] As an embodiment, the N information sub-blocks are transmitted on the same PUCCH.

[0895] As an embodiment, at least two information sub-blocks among the N information sub-blocks are transmitted on different physical layer channels, and the physical layer channel is PUCCH or PUSCH.

[0896] As an embodiment, the benefits of the above method include: small changes to existing standards and system designs, and enhanced forward and backward compatibility of the system.

[0897] As an embodiment, any information sub-block among the N information sub-blocks includes a HARQ-ACK bit.

[0898] As an embodiment, the first value depends on the number of HARQ-ACK bits whose value is NACK in the N information sub-blocks.

[0899] As an embodiment, the first value is the number of HARQ-ACK bits whose value is NACK in the N information sub-blocks.

[0900] As an embodiment, the first value is equal to the number of HARQ-ACK bits whose values ​​are NACK in the N information sub-blocks divided by the total number of HARQ-ACK bits included in the N information sub-blocks.

[0901] As an embodiment, the benefits of the above method include: small changes to existing standards and system designs, and enhanced forward and backward compatibility of the system.

[0902] As an embodiment, the benefits of the above method include: improving the reliability and robustness of the system.

[0903] As an embodiment, any one of the N information sub-blocks indicates that the AI ​​model identified by the target identifier fails or is invalid, and the first value depends on the failure or invalidity of the AI ​​model identified by the target identifier indicated by the N information sub-blocks.

[0904] As an embodiment, any one of the N information sub-blocks indicates the number of times the AI ​​model identified by the target identifier fails or fails, and the first value is equal to the sum of the number of times the AI ​​model identified by the target identifier fails or fails as indicated by the N information sub-blocks.

[0905] As an embodiment, any one of the N information sub-blocks indicates the number of times the AI ​​model identified by the target identifier fails or fails, and the first value is equal to the average value of the number of times the AI ​​model identified by the target identifier fails or fails as indicated by the N information sub-blocks.

[0906] As an embodiment, any one of the N information sub-blocks indicates the number of times the AI ​​model identified by the target identifier fails or fails, and the first value is equal to the maximum value of the number of times the AI ​​model identified by the target identifier fails or fails as indicated by the N information sub-blocks.

[0907] As an embodiment, any one of the N information sub-blocks indicates a performance indicator of the AI ​​model identified by the target identifier, and the first value depends on the performance indicator of the AI ​​model identified by the target identifier indicated by the N information sub-blocks.

[0908] As an embodiment, any one of the N information sub-blocks indicates a performance indicator of the AI ​​model identified by the target identifier, and the first value is equal to the average value of the performance indicators of the AI ​​model identified by the target identifier indicated by the N information sub-blocks.

[0909] As an embodiment, any one of the N information sub-blocks indicates a performance indicator of the AI ​​model identified by the target identifier, and the first value is equal to the maximum value of the performance indicators of the AI ​​model identified by the target identifier indicated by the N information sub-blocks.

[0910] As an embodiment, any one of the N information sub-blocks indicates a performance indicator of the AI ​​model identified by the target identifier, and the first value is equal to the minimum value of the performance indicator of the AI ​​model identified by the target identifier indicated by the N information sub-blocks.

[0911] As an embodiment, the benefits of the above method include: more precise control of the system, and improved system stability and overall performance.

[0912] Embodiment 14

[0913] Embodiment 14 illustrates a schematic diagram of a trigger condition of a target information block according to an embodiment of the present application; Fig.14 shown.

[0914] In embodiment 14, the trigger condition of the target information block includes a first value being greater than a first threshold; wherein,

[0915] The first value indicates the number of times the AI ​​model identified by the target identifier fails or fails, the first value is a non-negative integer, and the first threshold is a positive integer;

[0916] or,

[0917] The first value dependency is based on reception or measurement of part or all of the at least one signal.

[0918] As an embodiment, the failure or ineffectiveness of the AI ​​model identified by the target identifier is determined based on the reception or measurement of part or all of the at least one signal.

[0919] As an embodiment, when the reception quality of the at least one reference signal is worse than a second threshold, the value of the first counter is increased by 1, and the first value is the value of the first counter.

[0920] As an embodiment, when the difference between the reception quality of the at least one reference signal and the predicted reception quality of the at least one reference signal is greater than a second threshold, the value of the first counter is increased by 1, and the first value is the value of the first counter.

[0921] As an embodiment, the value of the first counter is the number of times the AI ​​model identified by the target identifier fails or fails.

[0922] As an embodiment, the essence of the above method includes: monitoring the performance of the AI ​​model, and the monitoring results affect the transmission of the system.

[0923] As an embodiment, the benefits of the above method include: improving the accuracy and effectiveness of AI-based CSI reporting and improving the overall performance of the system.

[0924] As an embodiment, the target information block includes one or more HARQ-ACK bits, and the one or more HARQ-ACK bits are determined based on the reception or measurement of part or all of the at least one signal.

[0925] As an embodiment, when there is a HARQ-ACK bit with a value of NACK in the one or more HARQ-ACK bits, the value of the first counter is added to the number of HARQ-ACK bits with a value of NACK in the one or more HARQ-ACK bits, and the first value is the value of the first counter.

[0926] As an embodiment, when the number of HARQ-ACK bits whose value is NACK among the one or more HARQ-ACK bits is greater than a second threshold, the value of the first counter is increased by 1, and the first value is the value of the first counter.

[0927] As an embodiment, when the number of HARQ-ACK bits whose value is NACK in the one or more HARQ-ACK bits divided by the number of bits of the one or more HARQ-ACK bits is greater than a second threshold, the value of the first counter is increased by 1, and the first value is the value of the first counter.

[0928] As an embodiment, the value of the first counter is the number of HARQ-ACK bits whose value is NACK in the target information block.

[0929] As an embodiment, the benefits of the above method include: small changes to existing standards and system designs, and enhanced forward and backward compatibility of the system.

[0930] As an embodiment, the benefits of the above method include: improving the reliability and robustness of the system.

[0931] As an embodiment, the AI ​​model performance indicator identified by the target identifier is determined based on the reception or measurement of part or all of the at least one signal.

[0932] As an embodiment, when the AI ​​model performance indicator identified by the target identifier is less than a second threshold, the value of the first counter is increased by 1, and the first value is the value of the first counter.

[0933] As an embodiment, the essence of the above method includes: when the AI ​​model performance indicator (such as prediction accuracy) is less than a certain threshold, the AI ​​model fails or becomes invalid.

[0934] As an embodiment, when the AI ​​model performance indicator identified by the target identifier is greater than a second threshold, the value of the first counter is increased by 1, and the first value is the value of the first counter.

[0935] As an embodiment, the essence of the above method includes: when the AI ​​model performance indicator (such as the difference between the predicted value and the true value) is greater than a certain threshold, the AI ​​model fails or becomes invalid.

[0936] As an embodiment, the value of the first counter indicates the number of times the AI ​​model identified by the target identifier fails or fails.

[0937] As an embodiment, the essence of the above method includes: monitoring the AI ​​model based on performance parameters related to the CSI reporting of the AI.

[0938] As an embodiment, the benefits of the above method include: improving the performance of the AI-based CSI reporting solution and improving the overall performance of the system.

[0939] As an embodiment, the first counter is maintained at a higher layer of the first node.

[0940] As an embodiment, when the reception quality of at least one reference signal is worse than a second threshold, the physical layer of the first node sends a first type of indication to a higher layer, and after receiving the first type of indication, the higher layer of the first node adds 1 to the value of the first counter, and the first value is the value of the first counter.

[0941] As an embodiment, when the number of HARQ-ACK bits whose value is NACK in the one or more HARQ-ACK bits is greater than a second threshold, the physical layer of the first node sends a first type of indication to a higher layer, and after receiving the first type of indication, the higher layer of the first node adds 1 to the value of a first counter, and the first value is the value of the first counter.

[0942] As an embodiment, when the number of HARQ-ACK bits whose value is NACK in the one or more HARQ-ACK bits divided by the number of bits of the one or more HARQ-ACK bits is greater than a second threshold, the physical layer of the first node sends a first type of indication to a higher layer, and after receiving the first type of indication, the higher layer of the first node adds 1 to the value of a first counter, and the first value is the value of the first counter.

[0943] As an embodiment, when there is a HARQ-ACK bit with a value of NACK in the one or more HARQ-ACK bits, the physical layer of the first node sends a first type of indication to a higher layer, and after receiving the first type of indication, the higher layer of the first node adds the value of the first counter to the number of HARQ-ACK bits with a value of NACK in the one or more HARQ-ACK bits, and the first value is the value of the first counter.

[0944] As an embodiment, when the AI ​​model performance indicator identified by the target identifier is less than a second threshold, the physical layer of the first node sends a first type of indication to a higher layer. After receiving the first type of indication, the higher layer of the first node adds 1 to the value of the first counter, and the first value is the value of the first counter.

[0945] As an embodiment, when the AI ​​model performance indicator identified by the target identifier is greater than a second threshold, the physical layer of the first node sends a first type of indication to a higher layer. After receiving the first type of indication, the higher layer of the first node adds 1 to the value of the first counter, and the first value is the value of the first counter.

[0946] As an embodiment, the first type of indication indicates that the AI ​​model identified by the target identifier has failed or is invalid.

[0947] As an embodiment, the first type of indication indicates that the reception quality of the at least one reference signal is worse than a second threshold.

[0948] As an embodiment, the first type of indication indicates that a difference between the reception quality of the at least one reference signal and the predicted reception quality of the at least one reference signal is greater than a second threshold.

[0949] As an embodiment, the first type of indication indicates that the AI ​​model performance indicator identified by the target identifier is greater than a second threshold.

[0950] As an embodiment, the first type of indication indicates that the AI ​​model performance indicator identified by the target identifier is less than a second threshold.

[0951] As an embodiment, the first type of indication indicates that there is a HARQ-ACK bit with a value of NACK among the one or more HARQ-ACK bits.

[0952] As an embodiment, the first type of indication indicates the number of HARQ-ACK bits whose value is NACK among the one or more HARQ-ACK bits.

[0953] As an embodiment, the first type of indication indicates that the number of HARQ-ACK bits whose value is NACK among the one or more HARQ-ACK bits is greater than a second threshold.

[0954] As an embodiment, the first type of indication indicates that the number of HARQ-ACK bits whose value is NACK in the one or more HARQ-ACK bits divided by the number of bits of the one or more HARQ-ACK bits is greater than a second threshold.

[0955] As an embodiment, the benefits of the above method include: adopting a hierarchical structure, simplifying system design, and reducing implementation complexity.

[0956] Embodiment 15

[0957] Embodiment 15 illustrates a schematic diagram of a second operation according to an embodiment of the present application; Fig.15 shown.

[0958] In Embodiment 15, the output of the first operation includes a first CSI, the target CSI carries the first CSI, and the first CSI is used as an input of the second operation by a target receiver of the target CSI to generate a second CSI.

[0959] As an embodiment, the first operation is used for CSI compression, the second operation is used for CSI recovery, and the first node and the second node adopt a two-sided AI model.

[0960] As an embodiment, the target CSI includes the first CSI.

[0961] As an embodiment, the first CSI is used to generate the target CSI after post-processing.

[0962] As an embodiment, the first CSI includes N sub-CSIs, and the N information blocks respectively carry the N sub-CSIs.

[0963] As an embodiment, the first CSI includes the output of the first operation.

[0964] As an embodiment, the second CSI includes recovery of at least part of the input of the first operation.

[0965] As an embodiment, the second CSI includes one or more of PMI (Precoding Matrix Indicator), CRI (CSI-RS Resource Indicator), SS / PBCH block resource indicator (SS / PBCH Block Resource indicator, SSBRI), beam indication, resource indication, CQI (Channel quality indicator, channel quality indication), RI (Rank Indicator, rank indication), layer indication (Layer Indicator, LI), RSRP (reference signal received power, reference signal received power), SINR (signal-to-noise and interference ratio, signal-to-interference and noise ratio), capability index (Capability Index), or TDCP (Time Domain Channel Properties, time domain channel properties).

[0966] As an embodiment, the second CSI includes one or more of a channel matrix, an eigenvector, an eigenvalue, or a precoding matrix.

[0967] As an embodiment, the second operation is a reverse operation of the first operation.

[0968] As an embodiment, the second operation is based on training.

[0969] As an embodiment, the training for obtaining the second operation is performed by the target receiver of the target CSI.

[0970] As an embodiment, the training for obtaining the second operation is performed by an MDA function.

[0971] As an embodiment, the training for obtaining the second operation is performed by an MDAS producer.

[0972] As an embodiment, the training for obtaining the second operation is performed by NWDAF.

[0973] As an embodiment, the training for obtaining the second operation is performed by a core network.

[0974] As an embodiment, the training for obtaining the second operation is performed by an AI (Artificial Intelligence) training producer.

[0975] As an embodiment, the first operation and the second operation are obtained through different trainings.

[0976] As an embodiment, the first operation and the second operation are obtained through independent training.

[0977] As an embodiment, the benefits of the above method include: saving air interface overhead, having better flexibility, being adaptable to different terminals, and having better forward compatibility.

[0978] As an embodiment, the first operation and the second operation are obtained through joint training.

[0979] As an embodiment, the benefits of the above method include: optimizing system performance.

[0980] As an embodiment, the training of the second operation depends on the first operation.

[0981] As an embodiment, the producer of the second operation trains the second operation according to the output of the first operation.

[0982] Example 16

[0983] Embodiment 16 illustrates a schematic diagram of a first operation according to another embodiment of the present application; Fig.16 In Example 16, the first operation includes K1 sub-operations, and K1 is a positive integer not greater than 1.

[0984] In Example 16, the K1 sub-operations are respectively represented as sub-operation #0, ..., sub-operation #(K1-1).

[0985] As an embodiment, each of the K1 sub-operations is based on training.

[0986] As an embodiment, at least one of the K1 sub-operations is based on training.

[0987] As an embodiment, each training-based sub-operation in the K1 sub-operations is based on the same executor of the training.

[0988] As an embodiment, two of the K1 sub-operations are based on different executors of the training.

[0989] As an embodiment, at least one sub-operation among the K1 sub-operations needs to be deployed.

[0990] As an embodiment, at least one of the K1 sub-operations needs to be loaded.

[0991] As an embodiment, all the sub-operations that need to be loaded in the K1 sub-operations are loaded from the same producer.

[0992] As an embodiment, two of the K1 sub-operations that need to be loaded are loaded from different producers.

[0993] As an embodiment, at least one of the K1 sub-operations is not based on training.

[0994] As an embodiment, at least one of the K1 sub-operations is based on a codebook for precoding defined in 3GPP R18 or a version prior to 3GPP R18.

[0995] As an embodiment, one or more of the K1 sub-operations are AI-based.

[0996] As an embodiment, one or more of the K1 sub-operations include inference.

[0997] As an embodiment, one or more of the K1 sub-operations include AI inference.

[0998] As an embodiment, one or more of the K1 sub-operations include AI inference for CSI.

[0999] As an embodiment, one or more of the K1 sub-operations include preprocessing.

[1000] As an embodiment, one or more of the K1 sub-operations include post-processing.

[1001] As an embodiment, there are two sub-operations in the K1 sub-operations that are serial, as shown in the attached Fig.16 All sub-operations in (a), sub-operation #2 to sub-operation #(K1-1) in 16(b), and sub-operation #0 to sub-operation #(K1-4) in 16(c).

[1002] As an embodiment, two sub-operations are serial, which means that the output of one of the two sub-operations is used as the input of the other sub-operation of the two sub-operations.

[1003] As an embodiment, there are two sub-operations in the K1 sub-operations that are parallel, as shown in the attached Fig.16 (b) Sub-operation #0 and sub-operation #1, attached Fig.16 Sub-operation #(K1-3) and sub-operation #(K1-2) in (c).

[1004] As an embodiment, two sub-operations are parallel means that the outputs of the two sub-operations are used together as the input of another sub-operation.

[1005] As an embodiment, the K1 sub-operations include one or more of convolution, pooling, cascading or activation.

[1006] As an embodiment, there is a sub-operation among the K1 sub-operations including a fully connected layer.

[1007] As an embodiment, there is a sub-operation among the K1 sub-operations including a pooling layer.

[1008] As an embodiment, there is a sub-operation among the K1 sub-operations including at least one convolutional layer.

[1009] As an embodiment, there is a sub-operation among the K1 sub-operations that includes at least one encoding layer.

[1010] As an embodiment, there are two sub-operations among the K1 sub-operations, each of which includes a fully connected layer and at least one encoding layer.

[1011] As an embodiment, a coding layer includes at least one convolutional layer and one pooling layer.

[1012] Embodiment 17

[1013] Embodiment 17 illustrates a schematic diagram of a first operation of deploying according to an embodiment of the present application; Fig.17 shown.

[1014] In Embodiment 17, the first processor deploys the first operation.

[1015] As an embodiment, the deployment includes obtaining the first operation.

[1016] As an embodiment, the deploying includes obtaining an AI entity.

[1017] As an embodiment, the deploying includes obtaining an AI entity that performs the first operation.

[1018] As an embodiment, the deploying includes obtaining an AI entity including an AI function for performing the first operation.

[1019] As an embodiment, the deploying includes loading the first operation.

[1020] As an embodiment, the deploying includes making a request to load the first operation.

[1021] As an example, Fig.16 The request in is a request made by the first node to load the first operation.

[1022] As an example, Fig.16 The response in is a response to the request of the first node to load the first operation. As an embodiment, the first node attaches Fig.16 The first operation is obtained in response to the above.

[1023] As an embodiment, the first operation is obtained by loading from a serving cell of the first node.

[1024] As an embodiment, the first operation is obtained by loading from a base station maintaining a service cell of the first node.

[1025] As an embodiment, the first operation is obtained by loading from the core network.

[1026] As an embodiment, the first operation is obtained by loading from a first producer.

[1027] As an embodiment, the first producer attaches Fig.16 The response in provides the first operation to the first node.

[1028] As an embodiment, the deployment is performed by an AI function.

[1029] As an embodiment, the deployment is completed by an AI function deployed on the first node.

[1030] As an embodiment, the deployment is performed by an AI deployment function.

[1031] As an embodiment, the deployment is completed by an AI deployment function deployed on the first node.

[1032] As an embodiment, the deployment is performed by AI inference function.

[1033] As an embodiment, the deployment is accomplished by an AI inference function deployed on the first node.

[1034] As an embodiment, the deployment is performed by an AI entity.

[1035] As an embodiment, the deployment is completed by an AI entity deployed on the first node.

[1036] As an embodiment, the deployment is completed by an AI entity having a deployment function.

[1037] As an embodiment, the deployment is completed by an AI entity with deployment function deployed on the first node.

[1038] As an embodiment, the deployment is performed by an AI entity having an inference function.

[1039] As an embodiment, the deployment is completed by an AI entity with inference function deployed on the first node.

[1040] As an embodiment, the deploying includes obtaining the first operation from a first producer.

[1041] As an embodiment, the deploying includes making a request to a first producer to load the first operation.

[1042] As an embodiment, the deploying includes loading the first operation from a first producer.

[1043] As an embodiment, the first producer generates and provides at least one of an AL entity and an AL function.

[1044] As an embodiment, the first producer is the producer of the first operation.

[1045] As an embodiment, the first producer includes an AL entity producer.

[1046] As an embodiment, the first producer includes an AL function producer.

[1047] As an embodiment, the first producer includes an AL deployment producer.

[1048] As an embodiment, the first producer includes an AL loading producer.

[1049] As an embodiment, the first producer includes an AL training producer.

[1050] As an embodiment, the first producer includes an AL inference producer.

[1051] As an embodiment, the first producer includes a producer of deployment of an AL entity.

[1052] As an embodiment, the first producer includes a loaded producer of an AL entity.

[1053] As an embodiment, the first producer includes a MnS (Management Service) producer.

[1054] As an embodiment, the sender of the target CSI reporting configuration is the first producer.

[1055] As an embodiment, the sender of the target CSI reporting configuration is different from the first producer.

[1056] As an embodiment, the training for obtaining the first operation is performed by the first producer.

[1057] As an embodiment, a performer of the training for obtaining the first operation is different from the first producer.

[1058] Embodiment 18

[1059] Embodiment 18 illustrates a schematic diagram of RAN (Radio Access Network) domain (Domain) AI / ML function deployment according to an embodiment of the present application; as shown in the attached Fig.18 As shown. The gNB in ​​Example 18 can be replaced by network equipment such as eNB or 6G base station.

[1060] AI / ML related functions include ML training function (also called AI training, or AI / ML training), ML testing function, ML reasoning (also called AI reasoning, or AI / ML reasoning) function, etc. ML training function, ML testing function, and ML reasoning function can be deployed independently or co-located. The deployment of AI / ML related functions can be achieved through software, such as downloading and / or running executable files; it can also be achieved through a combination of software and hardware, such as accelerating specific computing units through hardware to increase computing speed or save power consumption.

[1061] For ML training functions, they can be deployed in a cross-domain management system or a domain-specific management system; the domain-specific management system is used to manage the RAN domain or the CN (Core Network) domain. For example, the ML training function for MDA (Management Data Analytics) can be deployed in MDAF (MDA function); ML training for network data analysis can be deployed in NWDAF (Network Data Analytics Function), that is, the ML training function is MTLF (Model Training logical function).

[1062] The ML reasoning function can also be deployed in a cross-domain management system or a domain-specific management system; for example, the ML reasoning function is MDAF, or the ML reasoning function is AnLF (Analytics logical function) located in NWDAF.

[1063] Similarly, ML testing capabilities can also be deployed in cross-domain management systems, or domain-specific management systems.

[1064] In Example 18, the RAN domain ML training function 1802 is located in the RAN domain management function 1803; and the ML reasoning function is located in the base station, that is, the AI / ML reasoning function 1804 is located in gNB1805, and the AI / ML reasoning function 1806 is located in gNB1807.

[1065] Attached Fig.18In the example, the management of the ML inference functions of multiple base stations is completed by the RAN domain management function 1803, that is, data interaction with the RAN domain MnS (Management Service) consumer / cross-domain management 1801 (see attached Fig.18 ).

[1066] Optionally, the management of the ML reasoning function can also be completed by the base station itself, that is, each base station can independently interact with the RAN domain MnS consumer / cross-domain management 1801 for data.

[1067] It should be noted that Example 18 is merely a non-restrictive implementation method; optionally, the ML training function of the RAN domain may also be deployed in the base station; or optionally, some base stations deploy the ML reasoning function and the ML training function of the RAN domain, while some base stations only deploy the ML reasoning function.

[1068] As an embodiment, a gNB (or base station) in Embodiment 18 is the second node of the present application.

[1069] As an embodiment, the second processor includes an attached Fig.18 An AL / ML inference function in, i.e., 1804 or 1806.

[1070] As an example, Fig.18 An AL / ML inference function performs ML training based on the target precoding vector; the target reference signal is different from the first reference signal; any reference signal among the multiple reference signals except the first reference signal is associated with a precoding vector, wherein the target precoding vector is associated with the target reference signal.

[1071] Embodiment 19

[1072] Embodiment 19 illustrates a schematic diagram of the AI / ML function deployment of a UE according to an embodiment of the present application; Fig.19 Attached Fig.19 The RAN domain ML training functionality 1905 is optional.

[1073] The UE function 1904 is deployed in the first node of the present application, and the UE function 1904 includes an AI / ML reasoning function 1906; the AI / ML reasoning function 1906 uses an ML model (also called an AI model) for reasoning; an ML model is usually trained before being used for AI / ML reasoning.

[1074] As an embodiment, the UE function 1904 includes a RAN domain ML training function 1905, and the RAN domain ML training function 1905 runs the training data through the ML model to derive relevant losses, and adjusts the parameters of the ML model based on the calculated losses; the ML training includes at least one of ML initial training, ML re-training, and reinforcement learning.

[1075] The above embodiments can reduce the complexity of the base station, or save air interface resources caused by reporting training data; however, the above embodiments place high demands on the processing capability of the UE side.

[1076] Optionally, the UE function 1904 also includes a CN domain ML training function ( Fig.19 not included).

[1077] Optionally, the UE function 1904 also includes an AI / ML deployment function (deployment function) ( Fig.19 Not included in ), used to load ML models and data.

[1078] As an embodiment, the first node indicates whether the ML training function is supported (RAN domain or CN domain) through capability reporting, and the capability reporting is RRC signaling, or NAS (Non-Access Stratum) signaling.

[1079] As an embodiment, the ML model and related metadata are loaded by the first node from a network device or a remote server.

[1080] Optionally, the UE function 1904 is a MnS (Management Service) producer (Producer), providing data to the CN domain MnF (Management Function) 1901, and / or the RAN domain MnF 1902, and / or the cross-domain management system 1903 for management or analysis (as shown by the double arrow 1907).

[1081] Optionally, the UE function 1904 is an MnS consumer (Consumer), which loads data from the CN domain MnF (Management Function) 1901, and / or the RAN domain MnF 1902, and / or the cross-domain management system 1903 for AI / ML related management, such as management data requests, ML model activation, and / or ML training, etc. (as shown by the double arrow 1907).

[1082] As an embodiment, the first channel information in the present application is obtained through reasoning by the AI / ML reasoning function 1906.

[1083] As an embodiment, the RAN domain ML training function 1905 performs ML training according to a target precoding vector; wherein the target reference signal is different from the first reference signal; any reference signal among the multiple reference signals except the first reference signal is associated with a precoding vector, wherein the target precoding vector is associated with the target reference signal.

[1084] As an embodiment, the first processor includes an attached Fig.19 An AL / ML reasoning function in 1906.

[1085] As an embodiment, the ML model is based on a neural network.

[1086] As an embodiment, the ML model is based on CNN (Conventional Neural Networks, convolutional neural network).

[1087] As an embodiment, the ML model is based on the Transformer architecture.

[1088] Embodiment 20

[1089] Embodiment 20 illustrates a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of the present application; Fig. 20 Attached Fig. 20 Includes a third processor, a fourth processor, a fifth processor and a sixth processor.

[1090] In Example 20, the third processor sends a first data set to the fourth processor and sends a second data set to the fifth processor; the fourth processor generates a target first-category parameter group based on the first data set, and the fourth processor sends the generated target first-category parameter group to the fifth processor; the fifth processor processes the second data set using the target first-category parameter group to obtain a first-category output, and (optionally) the fifth processor sends the first-category output to the sixth processor. Fig. 20 In the embodiment, the first type of feedback and the second type of feedback are optional; the fourth processor includes ML training function; and the fifth processor includes ML reasoning function.

[1091] As an embodiment, the sixth processor includes ML testing functionality.

[1092] As an embodiment, the sixth processor includes performance monitoring / evaluation of the ML model.

[1093] As an embodiment, the fifth processor sends the first type of feedback to the fourth processor, and the first type of feedback is used to trigger recalculation or updating of the target first type parameter group, that is, triggering ML initial training or ML retraining.

[1094] As an embodiment, the sixth processor sends a second type of feedback to the third processor, and the second type of feedback is used to generate the first data set or the second data set, or the second type of feedback is used to trigger the sending of the first data set or the sending of the second data set.

[1095] As an embodiment, the third processor generates the first data set and the second data set based on measurement of a reference signal.

[1096] As an embodiment, the fifth processor belongs to the first node, and the sixth processor belongs to the second node.

[1097] As an embodiment, the first type of output includes the first channel information.

[1098] As an embodiment, the first type of output includes an index of the target reference signal.

[1099] As an embodiment, the second data set includes measurements for the first reference signal, or includes measurements for the second reference signal.

[1100] As an embodiment, the first data set includes training data (Training Data).

[1101] As an embodiment, the fourth processor is used to train the ML model, and the trained model is described by the target first-category parameter group.

[1102] As an embodiment, the fourth processor belongs to the first node.

[1103] The above embodiment avoids transferring the first data set to the second node.

[1104] As an embodiment, the fourth processor belongs to the second node.

[1105] The above embodiments support joint training and optimize system performance.

[1106] As an embodiment, the fourth processor belongs to the core network.

[1107] The above embodiments support joint training of the entire network, further optimizing system performance.

[1108] As an embodiment, the second data set includes inference data.

[1109] As an embodiment, the fifth processor belongs to the first node.

[1110] As an embodiment, the fifth processing machine constructs a model according to the target first-category parameter group, and then inputs the second data set into the constructed model to obtain the first-category output.

[1111] As an embodiment, the fifth processing machine generates a restored data set according to the first type of output, and an error between the restored data set and the second data set is used to generate the first type of feedback.

[1112] As an embodiment, the first type of feedback is used to reflect the performance of the trained model; when the performance of the trained model cannot meet the requirements, the fourth processor will recalculate the target first type parameter group.

[1113] As an embodiment, when the error is too large or has not been updated for too long, the performance of the trained model is considered to be unable to meet the requirements.

[1114] As an embodiment, the target first category parameter group includes: one or more of: convolution kernel size, number of convolution layers, convolution step size, pooling kernel size, pooling kernel step size, pooling function, activation function, or number of feature maps.

[1115] As an embodiment, the target first-category parameter group includes: one or more of a convolution kernel, a pooling kernel, a pooling function, an activation function, parameters of a pooling function, or parameters of an activation function.

[1116] Embodiment 21

[1117] Embodiment 21 illustrates a schematic diagram based on artificial intelligence or machine learning according to an embodiment of the present application; Fig.21 Attached Fig.21 It includes a third operation, a fourth operation, a fifth operation, a sixth operation and a seventh operation; the lines with arrows indicate the order of the process.

[1118] In Example 21, the third operation and the fourth operation belong to the first stage, the fifth operation belongs to the second stage, the sixth operation belongs to the third stage, and the seventh operation belongs to the fourth stage.

[1119] As an embodiment, the third operation includes AI / ML training, the fourth operation includes AI / ML testing, the fifth operation includes AI / ML emulation, the sixth operation includes AI / ML entity loading, and the seventh operation includes AI / ML inference.

[1120] As an embodiment, the first phase includes a training phase, the second phase includes an emulation phase, the third phase includes a deployment phase, and the fourth phase includes an emulation phase.

[1121] As an embodiment, the first stage includes AI / ML model training.

[1122] As an embodiment, the first stage includes AI / ML model training and AI / ML testing.

[1123] As an embodiment, the AI / ML model training includes initial training and re-training of one or a group of AI / ML entities.

[1124] As an embodiment, the AI / ML model training relies on training data.

[1125] As an embodiment, the AI / ML model training includes AI / ML entity validation.

[1126] As an embodiment, the AI / ML entity verification is used to evaluate the performance of the AI / ML entity.

[1127] As an embodiment, the AI / ML entity verification relies on verification data.

[1128] As an example, if the result of AI / ML entity verification does not meet expectations, the AI / ML model will be retrained.

[1129] As an embodiment, the AI / ML testing includes testing the verified AI / ML entity to estimate the performance of the trained AI / ML model.

[1130] As an example, if the result of the AI / ML test meets expectations, the AI / ML entity proceeds to the next stage; otherwise the AI / ML model will be retrained.

[1131] As an embodiment, the AI / ML testing relies on test data.

[1132] As an embodiment, the second stage includes AI / ML simulation, which performs inference of AI / ML entities in a simulation environment.

[1133] As an embodiment, the AI / ML simulation estimates the performance of AI / ML entity reasoning in a simulation environment before using the AI / ML entity.

[1134] As an embodiment, the second stage is optional.

[1135] As an embodiment, the third stage includes AI / ML entity loading, and the AI / ML entity loading is to obtain the trained AI / ML entity to obtain the desired AI / ML reasoning function.

[1136] As an embodiment, the third stage is optional.

[1137] As an example, when the training function and the inference function are co-located, the third stage is no longer required.

[1138] As an embodiment, the fourth stage includes AI / ML reasoning.

[1139] As an embodiment, the seventh operation includes the first operation.

[1140] As an embodiment, the seventh operation includes the second operation.

[1141] Embodiment 22

[1142] Embodiment 22 illustrates a structural block diagram of a processing device used in a first node according to an embodiment of the present application; Fig. 22 As shown in the attached Fig. 22 In the embodiment, the processing device 2200 in the first node includes a first receiver 2201 and a first processor 2202.

[1143] The first receiver 2201 receives a target CSI reporting configuration; the target CSI reporting configuration is used to configure reporting of the target CSI; the target CSI is generated based on training or AI;

[1144] The first processor 2202 sends a target information block; determines whether to abandon sending the target CSI; when a first condition is met, abandons sending the target CSI;

[1145] In Example 22, the determination of whether to abandon sending the target CSI depends on whether the first condition is satisfied; the first condition includes that the generation method of the target CSI is associated with the target identifier; the target identifier depends on the target information block.

[1146] As an embodiment, the target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of the channel measurement or interference resource measurement of the target CSI, and the first resource set includes one or more RS resources; the target CSI indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

[1147] As an embodiment, the target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of channel measurement or interference resource measurement of the target CSI, and the first resource set includes one or more RS resources; the method for generating the target CSI includes performing a first operation, the input of the first operation depends on measurement based on the first resource set, and the target CSI depends on the output of the first operation.

[1148] As an embodiment, the target CSI generation method is associated with the target identifier, including: the first operation is associated with the target identifier.

[1149] As an embodiment, the target CSI generation method is associated with the target identifier, including: the target CSI reporting configuration indicates a first type identifier, the first type identifier indicated by the target CSI reporting configuration is the same as the target identifier, and the target identifier is a first type identifier.

[1150] As an embodiment, the target information block indicates the target identifier.

[1151] As an embodiment, the first receiver 2201 receives at least one information block, and the at least one information block configures or indicates at least one signal; wherein the target information block is generated based on the reception or measurement of part or all of the at least one signal.

[1152] As an embodiment, the target identification depends on the at least one information block.

[1153] As an embodiment, the first value is greater than a first threshold, and the first value depends on the target information block.

[1154] As an embodiment, the trigger condition of the target information block includes that the first value is greater than a first threshold; wherein,

[1155] The first value indicates the number of times the AI ​​model identified by the target identifier fails or fails, the first value is a non-negative integer, and the first threshold is a positive integer;

[1156] or,

[1157] The first value dependency is based on reception or measurement of part or all of the at least one signal.

[1158] As an embodiment, the first processor 2202 deploys the first operation.

[1159] As an embodiment, the first operation is associated with the first type of identification.

[1160] As an embodiment, the first operation is training-based or AI-based.

[1161] As an embodiment, the second processor 2301 deploys the second operation.

[1162] As an embodiment, the second operation is training-based or AI-based.

[1163] As an embodiment, the second processor 2301 performs a second operation; wherein the output of the first operation includes the first CSI, the target CSI carries the first CSI, and the first CSI is used as an input of the second operation by the target recipient of the target CSI to generate a second CSI.

[1164] As an embodiment, the first node is user equipment.

[1165] As an embodiment, the user equipment is a terminal.

[1166] As an embodiment, the first node is a relay node device.

[1167] As an embodiment, the first receiver 2201 includes at least one of {antenna 452, receiver 454, receiving processor 456, multi-antenna receiving processor 458, controller / processor 459, memory 460, data source 467} in Embodiment 4.

[1168] As an embodiment, the first processor 2202 includes at least one of {antenna 452, receiver / transmitter 454, receiving processor 456, transmitting processor 468, multi-antenna receiving processor 458, multi-antenna transmitting processor 457, controller / processor 459, memory 460, data source 467} in Example 4.

[1169] Embodiment 23

[1170] Embodiment 23 illustrates a structural block diagram of a processing device used in a second node according to an embodiment of the present application; Fig.23 In the attached Fig.23 In the example, the processing device 2300 in the second node includes a second processor 2301.

[1171] The second processor 2301 sends a target CSI reporting configuration; receives a target information block; determines whether to abandon receiving the target CSI; when the first condition is met, abandons receiving the target CSI;

[1172] Among them, the target CSI reporting configuration is used to configure the reporting of the target CSI; the generation method of the target CSI is training-based or AI-based; the target recipient of the target CSI reporting configuration determines whether to abandon sending the target CSI; when the first condition is met, the target recipient of the target CSI reporting configuration abandons sending the target CSI; the target recipient of the target CSI reporting configuration determines whether to abandon sending the target CSI depending on whether the first condition is met; the first condition includes the generation method of the target CSI associated with the target identifier; the target identifier depends on the target information block.

[1173] As an embodiment, the target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of the channel measurement or interference resource measurement of the target CSI, and the first resource set includes one or more RS resources; the target CSI indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

[1174] As an embodiment, the target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of channel measurement or interference resource measurement of the target CSI, and the first resource set includes one or more RS resources; the method for generating the target CSI includes performing a first operation, the input of the first operation depends on measurement based on the first resource set, and the target CSI depends on the output of the first operation.

[1175] As an embodiment, the target CSI generation method is associated with the target identifier, including: the first operation is associated with the target identifier.

[1176] As an embodiment, the target CSI generation method is associated with the target identifier, including: the target CSI reporting configuration indicates a first type identifier, the first type identifier indicated by the target CSI reporting configuration is the same as the target identifier, and the target identifier is a first type identifier.

[1177] As an embodiment, the target information block indicates the target identifier.

[1178] As an embodiment, the second processor 2301 sends at least one information block, and the at least one information block configures or indicates at least one signal; wherein the target information block is generated based on the reception or measurement of part or all of the at least one signal.

[1179] As an embodiment, the target identification depends on the at least one information block.

[1180] As an embodiment, the first value is greater than a first threshold, and the first value depends on the target information block.

[1181] As an embodiment, the trigger condition of the target information block includes that the first value is greater than a first threshold; wherein,

[1182] The first value indicates the number of times the AI ​​model identified by the target identifier fails or fails, the first value is a non-negative integer, and the first threshold is a positive integer;

[1183] or,

[1184] The first value dependency is based on reception or measurement of part or all of the at least one signal.

[1185] As an embodiment, the second processor 2301 deploys the second operation.

[1186] As an embodiment, the second operation is training-based or AI-based.

[1187] As an embodiment, the second processor 2301 performs a second operation; wherein the output of the first operation includes the first CSI, the target CSI carries the first CSI, and the first CSI is used as an input of the second operation by the target recipient of the target CSI to generate a second CSI.

[1188] As an embodiment, the first processor 2202 deploys the first operation.

[1189] As an embodiment, the first operation is associated with the first type of identification.

[1190] As an embodiment, the first operation is training-based or AI-based.

[1191] As an embodiment, the second node is a base station device.

[1192] As an embodiment, the second node is user equipment.

[1193] As an embodiment, the user equipment is a terminal.

[1194] As an embodiment, the second node is a relay node device.

[1195] As an embodiment, the second processor 2301 includes at least one of {antenna 420, receiver / transmitter 418, receiving processor 470, transmitting processor 416, multi-antenna receiving processor 472, multi-antenna transmitting processor 471, controller / processor 475, memory 476} in Example 4.

[1196] A person of ordinary skill in the art can understand that all or part of the steps in the above method can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a hard disk or an optical disk. Optionally, all or part of the steps in the above embodiment can also be implemented using one or more integrated circuits. Accordingly, each module unit in the above embodiment can be implemented in the form of hardware or in the form of a software function module, and the present application is not limited to any specific form of software and hardware combination. The user equipment, terminal and UE in the present application include but are not limited to drones, communication modules on drones, remote-controlled aircraft, aircraft, small aircraft, mobile phones, tablet computers, notebooks, vehicle-mounted communication equipment, wireless sensors, Internet cards, Internet of Things terminals, RFID terminals, NB-IOT terminals, MTC (Machine Type Communication, Machine Type Communication) terminals, eMTC (enhancedMTC, enhanced MTC) terminals, data cards, Internet cards, vehicle-mounted communication equipment, low-cost mobile phones, low-cost tablet computers and other wireless communication devices. The base stations or system devices in this application include but are not limited to macrocell base stations, microcell base stations, home base stations, relay base stations, gNB (NR Node B) NR Node B, TRP (Transmitter Receiver Point) and other wireless communication devices.

[1197] The above is only a preferred embodiment of the present application and is not intended to limit the scope of protection of the present application. Any changes and modifications made based on the embodiments described in the specification, if similar partial or complete technical effects can be obtained, should be considered obvious and fall within the scope of protection of the present invention.

Claims

1. A method in a first node for wireless communication, characterized in that include: receiving a target CSI reporting configuration; the target CSI reporting configuration is used to configure reporting of the target CSI; the target CSI is generated in a training-based or AI-based manner; Sending a target information block; determining whether to abandon sending the target CSI; when the first condition is met, abandoning sending the target CSI; The determining whether to abandon sending the target CSI depends on whether the first condition is satisfied; the first condition includes that the generation method of the target CSI is associated with the target identifier; The target identification depends on the target information block.

2. The method according to claim 1, characterized in that The target CSI reporting configuration indicates a first resource set, which is used for at least one of channel measurement or interference resource measurement of the target CSI, and the first resource set includes one or more RS resources; the target CSI indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

3. The method according to claim 1 or 2, characterized in that: The target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of channel measurement or interference resource measurement of the target CSI, and the first resource set includes one or more RS resources; the method for generating the target CSI includes performing a first operation, the input of the first operation depends on measurement based on the first resource set, and the target CSI depends on the output of the first operation.

4. The method according to claim 3, characterized in that: The target CSI generation method is associated with the target identifier, including: the first operation is associated with the target identifier.

5. The method according to any one of claims 1 to 4, characterized in that: The target CSI generation method is associated with the target identifier, including: the target CSI reporting configuration indicates a first type identifier, the first type identifier indicated by the target CSI reporting configuration is the same as the target identifier, and the target identifier is a first type identifier.

6. The method according to any one of claims 1 to 5, characterized in that: The target information block indicates the target identifier.

7. The method according to any one of claims 1 to 5, characterized in that: include: receiving at least one information block, the at least one information block configuring or indicating at least one signal; The target information block is generated based on the reception or measurement of part or all of the at least one signal.

8. The method according to claim 7, characterized in that The target identification is dependent on the at least one information block.

9. The method according to claim 7 or 8, characterized in that: The first value is greater than a first threshold, and the first value depends on the target information block.

10. The method according to claim 7 or 8, characterized in that: The trigger condition of the target information block includes that the first value is greater than the first threshold; wherein, The first value indicates the number of times the AI ​​model identified by the target identifier fails or fails, the first value is a non-negative integer, and the first threshold is a positive integer; or, The first value dependency is based on reception or measurement of part or all of the at least one signal.

11. A terminal, characterized in that: The terminal includes: one or more processors and a memory; The memory is coupled to the one or more processors, and the memory is used to store computer program codes, where the computer program codes include computer instructions. The one or more processors call the computer instructions to enable the terminal to execute the method according to any one of claims 1 to 10.

12. A method in a second node for wireless communication, characterized in that: include: Sending a target CSI reporting configuration; the target CSI reporting configuration is used to configure reporting of the target CSI; the target CSI is generated based on training or based on AI; receiving a target information block; determining whether to abandon receiving the target CSI; When the first condition is met, giving up receiving the target CSI; Among them, the target recipient of the target CSI reporting configuration determines whether to give up sending the target CSI; when the first condition is met, the target recipient of the target CSI reporting configuration gives up sending the target CSI; the target recipient of the target CSI reporting configuration determines whether to give up sending the target CSI depends on whether the first condition is met; the first condition includes the generation method of the target CSI associated with the target identifier; the target identifier depends on the target information block.

13. The method according to claim 12, characterized in that The target CSI reporting configuration indicates a first resource set, which is used for at least one of channel measurement or interference resource measurement of the target CSI, and the first resource set includes one or more RS resources; the target CSI indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

14. The method according to claim 12 or 13, characterized in that The target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of channel measurement or interference resource measurement of the target CSI, and the first resource set includes one or more RS resources; the method for generating the target CSI includes performing a first operation, the input of the first operation depends on measurement based on the first resource set, and the target CSI depends on the output of the first operation.

15. The method according to claim 14, characterized in that The target CSI generation method is associated with the target identifier, including: the first operation is associated with the target identifier.

16. The method according to any one of claims 12 to 15, characterized in that The target CSI generation method is associated with the target identifier, including: the target CSI reporting configuration indicates a first type identifier, the first type identifier indicated by the target CSI reporting configuration is the same as the target identifier, and the target identifier is a first type identifier.

17. The method according to any one of claims 12 to 16, characterized in that The target information block indicates the target identifier.

18. The method according to any one of claims 12 to 16, characterized in that include: sending at least one information block, the at least one information block configuring or indicating at least one signal; The target information block is generated based on the reception or measurement of part or all of the at least one signal.

19. The method according to claim 18, characterized in that The target identification is dependent on the at least one information block.

20. The method according to claim 18 or 19, characterized in that The first value is greater than a first threshold, and the first value depends on the target information block.

21. The method according to claim 18 or 19, characterized in that The trigger condition of the target information block includes that the first value is greater than the first threshold; wherein, The first value indicates the number of times the AI ​​model identified by the target identifier fails or fails, the first value is a non-negative integer, and the first threshold is a positive integer; or, The first value dependency is based on reception or measurement of part or all of the at least one signal.

22. A base station, characterized in that: The base station includes: one or more processors and a memory; The memory is coupled to the one or more processors, and the memory is used to store computer program codes, where the computer program codes include computer instructions, and the one or more processors call the computer instructions to enable the base station to perform the method according to any one of claims 12 to 21.

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