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 at least one CSI reporting configuration; receiving a first DCI on the first PDCCH, wherein the first DCI triggers the reporting of at least one CSI on the first PUSCH; determining whether to send the target CSI on the first PUSCH; transmitting the target CSI on the first PUSCH only when the first condition is satisfied; wherein the target CSI reporting configuration is used for configuring reporting of the target CSI; the target CSI reporting configuration indicates a first resource set; the first condition comprises that the first symbol is not earlier than the first reference symbol and the second symbol is not earlier than the second reference symbol; the second symbol is the first uplink symbol used for bearing the target CSI in the first PUSCH, and the second reference symbol is the next uplink symbol at a second time interval after the end of the last symbol of the first RS in the first resource set from the CP; the second time interval depends on whether the generation mode of the target CSI is based on the AI or not.

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 problems, 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, the use of a unified design scheme for different scenarios (such as other non-AI / ML scenarios, including but not limited to Vehicle to Everything (V2X), capacity enhancement systems, short-range communication systems, NTN (NonTerrestrial Network), IoT (Internet of Things), URLLC (UltraReliable Low Latency Communication) networks, etc.) 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 at least one CSI reporting configuration; receiving a first DCI on a first PDCCH, wherein the first DCI triggers reporting of at least one CSI on a first PUSCH, and the at least one CSI reporting configuration is used to configure reporting of the at least one CSI;

[0008] determining whether to send target CSI on the first PUSCH; and sending the target CSI on the first PUSCH only when a first condition is met;

[0009] Among them, the target CSI reporting configuration is used to configure the reporting of the target CSI, the target CSI reporting configuration is one of the at least one CSI reporting configuration, and the target CSI is one of the at least one CSI; 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 first condition includes that the first symbol is not earlier than the first reference symbol and the second symbol is not earlier than the second reference symbol; the first symbol is the first uplink symbol in the first PUSCH for carrying the at least one CSI, and the first reference symbol is the next uplink symbol with a CP starting at the first time interval after the end of the last symbol of the first PDCCH; the second symbol is the first uplink symbol in the first PUSCH for carrying the target CSI, and the second reference symbol is the next uplink symbol with a CP starting at the second time interval after the end of the last symbol of the first RS in the first resource set; the second time interval depends on whether the generation method of the target CSI is based on AI.

[0010] In this application, 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 CSI on PUSCH.

[0012] As an embodiment, the essence of the above method includes: sending CSI on PUSCH only when the first condition is met.

[0013] As an embodiment, the essence of the above method includes: the first condition includes at least two conditions.

[0014] As an embodiment, the essence of the above method includes: whether to send CSI on PUSCH depends on whether the generation method of CSI is based on AI.

[0015] As an embodiment, the benefits of the above method include: supporting AI-based CSI reporting.

[0016] As an embodiment, the benefits of the above method include: ensuring consistency in the understanding of CSI reporting by the transmitting and receiving ends.

[0017] As an embodiment, the benefits of the above method include: improving the accuracy and effectiveness of CSI reporting.

[0018] As an embodiment, the benefits of the above method include: whether to send CSI on PUSCH depends on the first time interval and the second time interval, which simplifies system design and reduces solution complexity.

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

[0020] According to one aspect of the present application, it is characterized in that the calculation formula of the second time interval depends on a first parameter, and the first parameter depends on whether the generation method of the target CSI is based on AI.

[0021] As an embodiment, the essence of the above method includes: the value of the second time interval depends on whether the generation method of the target CSI is based on AI.

[0022] 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.

[0023] As an embodiment, the benefits of the above method include: simplifying the design of the system and reducing the implementation complexity of the solution.

[0024] According to one aspect of the present application, it is characterized in that when the target CSI is generated based on AI, the target CSI includes N information blocks, and the N information blocks respectively include channel information of N time units, where N is a positive integer greater than 1; and the second time interval depends on at least one of the N time units.

[0025] As an embodiment, the benefits of the above method include: compatibility with current system designs and standards.

[0026] 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.

[0027] As an embodiment, the benefits of the above method include: improving the overall performance of the system.

[0028] According to one aspect of the present application, it is characterized in that the first resource set consists of one or more non-periodic RS resources, and the first RS is the latest RS in time in the first resource set triggered by the first DCI.

[0029] As an embodiment, the benefits of the above method include: compatibility with current system designs and standards.

[0030] According to one aspect of the present application, it is characterized in that the method for generating the target CSI is based on AI and includes: 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.

[0031] As an embodiment, the first operation is training-based or AI-based.

[0032] As an embodiment, the benefits of the above method include: supporting AI / ML-based CSI reporting.

[0033] As an embodiment, the benefits of the above method include: improving the accuracy and effectiveness of CSI reporting.

[0034] As an embodiment, the benefits of the above method include: improving the overall performance of the system.

[0035] According to one aspect of the present application, it is characterized in that the generation method of the target CSI is based on AI and includes: the generation method of the target CSI is associated with a first type of identifier.

[0036] As an embodiment, the benefits of the above method include: adapting to various scenarios and terminals, and improving the adaptability and flexibility of the system.

[0037] As an embodiment, the benefits of the above method include: simplifying system design and having good flexibility.

[0038] According to one aspect of the present application, it is characterized in that when the target CSI is generated based on AI, the second time interval depends on the first type of identifier associated with the target CSI generation method.

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

[0040] As an embodiment, the benefits of the above method include: enhancing the flexibility of the system and improving the overall performance of the system.

[0041] According to one aspect of the present application, it is characterized in that the target CSI is generated based on AI and includes: 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.

[0042] As an embodiment, the benefits of the above method include: reducing the overhead required to obtain the target CSI.

[0043] As an embodiment, the benefits of the above method include: reducing the measurement resources required to obtain the target CSI.

[0044] As an embodiment, the benefits of the above method include: enhancing the flexibility of the system and improving the overall performance of the system.

[0045] According to one aspect of the present application, it is characterized by comprising:

[0046] When the first condition is not met, ignoring the first DCI;

[0047] Therein, no HARQ-ACK or transport block is multiplexed on the first PUSCH.

[0048] As an embodiment, the essence of the above method includes: when the first condition is not met, CSI is not sent on PUSCH and the corresponding DCI is ignored.

[0049] As an embodiment, the benefits of the above method include: being compatible with current system designs and standards, and improving the forward and backward compatibility of the system.

[0050] As an embodiment, the benefits of the above method include: improving the stability and robustness of the system.

[0051] The present application discloses a method used in a second node of wireless communication, characterized by comprising:

[0052] Sending at least one CSI reporting configuration; sending a first DCI on a first PDCCH, wherein the first DCI triggers reporting of at least one CSI on a first PUSCH, and the at least one CSI reporting configuration is used to configure reporting of the at least one CSI;

[0053] The target recipient of the first DCI determines whether to send the target CSI on the first PUSCH; the target recipient of the first DCI sends the target CSI on the first PUSCH only when the first condition is met; the target CSI reporting configuration is used to configure the reporting of the target CSI, the target CSI reporting configuration is one of the at least one CSI reporting configuration, and the target CSI is one of the at least one CSI; 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 first The conditions include that the first symbol is not earlier than the first reference symbol and the second symbol is not earlier than the second reference symbol; the first symbol is the first uplink symbol in the first PUSCH for carrying the at least one CSI, and the first reference symbol is the next uplink symbol whose CP starts at the first time interval after the end of the last symbol of the first PDCCH; the second symbol is the first uplink symbol in the first PUSCH for carrying the target CSI, and the second reference symbol is the next uplink symbol whose CP starts at the second time interval after the end of the last symbol of the first RS in the first resource set; the second time interval depends on whether the generation method of the target CSI is based on AI.

[0054] According to one aspect of the present application, it is characterized by comprising:

[0055] determining whether to receive target CSI on the first PUSCH; and receiving the target CSI on the first PUSCH only when the first condition is met;

[0056] According to one aspect of the present application, it is characterized in that the calculation formula of the second time interval depends on a first parameter, and the first parameter depends on whether the generation method of the target CSI is based on AI.

[0057] According to one aspect of the present application, it is characterized in that when the target CSI is generated based on AI, the target CSI includes N information blocks, and the N information blocks respectively include channel information of N time units, where N is a positive integer greater than 1; and the second time interval depends on at least one of the N time units.

[0058] According to one aspect of the present application, it is characterized in that the first resource set consists of one or more non-periodic RS resources, and the first RS is the latest RS in time in the first resource set triggered by the first DCI.

[0059] According to one aspect of the present application, it is characterized in that the method for generating the target CSI is based on AI and includes: the method for generating the target CSI includes the target receiver of the first DCI 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.

[0060] According to one aspect of the present application, it is characterized in that the generation method of the target CSI is based on AI and includes: the generation method of the target CSI is associated with a first type of identifier.

[0061] According to one aspect of the present application, it is characterized in that when the target CSI is generated based on AI, the second time interval depends on the first type of identifier associated with the target CSI generation method.

[0062] According to one aspect of the present application, it is characterized in that the target CSI is generated based on AI and includes: 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.

[0063] According to one aspect of the present application, it is characterized by comprising:

[0064] When the first condition is not met, giving up receiving a signal on the first PUSCH, or giving up receiving the target CSI on the first PUSCH;

[0065] Therein, no HARQ-ACK or transport block is multiplexed on the first PUSCH.

[0066] According to one aspect of the present application, it is characterized in that when the first condition is not met, the target recipient of the first DCI ignores the first DCI; wherein no HARQ-ACK or transport block is multiplexed on the first PUSCH.

[0067] The present application discloses a terminal, characterized in that the terminal comprises: one or more processors and a memory;

[0068] 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.

[0069] The present application discloses a base station, characterized in that the base station comprises: one or more processors and a memory;

[0070] 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.

[0071] The present application discloses a first node used for wireless communication, characterized in that it includes:

[0072] A first receiver receives at least one CSI reporting configuration; receives a first DCI on a first PDCCH, wherein the first DCI triggers reporting of at least one CSI on a first PUSCH, and the at least one CSI reporting configuration is used to configure reporting of the at least one CSI;

[0073] A first processor determines whether to send a target CSI on the first PUSCH; and sends the target CSI on the first PUSCH only when a first condition is met;

[0074] Among them, the target CSI reporting configuration is used to configure the reporting of the target CSI, the target CSI reporting configuration is one of the at least one CSI reporting configuration, and the target CSI is one of the at least one CSI; 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 first condition includes that the first symbol is not earlier than the first reference symbol and the second symbol is not earlier than the second reference symbol; the first symbol is the first uplink symbol in the first PUSCH for carrying the at least one CSI, and the first reference symbol is the next uplink symbol with a CP starting at the first time interval after the end of the last symbol of the first PDCCH; the second symbol is the first uplink symbol in the first PUSCH for carrying the target CSI, and the second reference symbol is the next uplink symbol with a CP starting at the second time interval after the end of the last symbol of the first RS in the first resource set; the second time interval depends on whether the generation method of the target CSI is based on AI.

[0075] The present application discloses a second node used for wireless communication, characterized in that it includes:

[0076] The second processor sends at least one CSI reporting configuration; sends a first DCI on a first PDCCH, where the first DCI triggers reporting of at least one CSI on a first PUSCH, and the at least one CSI reporting configuration is used to configure reporting of the at least one CSI;

[0077] The target recipient of the first DCI determines whether to send the target CSI on the first PUSCH; the target recipient of the first DCI sends the target CSI on the first PUSCH only when the first condition is met; the target CSI reporting configuration is used to configure the reporting of the target CSI, the target CSI reporting configuration is one of the at least one CSI reporting configuration, and the target CSI is one of the at least one CSI; 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 first The conditions include that the first symbol is not earlier than the first reference symbol and the second symbol is not earlier than the second reference symbol; the first symbol is the first uplink symbol in the first PUSCH for carrying the at least one CSI, and the first reference symbol is the next uplink symbol whose CP starts at the first time interval after the end of the last symbol of the first PDCCH; the second symbol is the first uplink symbol in the first PUSCH for carrying the target CSI, and the second reference symbol is the next uplink symbol whose CP starts at the second time interval after the end of the last symbol of the first RS in the first resource set; the second time interval depends on whether the generation method of the target CSI is based on AI.

[0078] As an embodiment, compared with the traditional solution, this application has the following advantages:

[0079] - Ensure the consistency of the CSI reporting between the sender and receiver;

[0080] -Support AI-based CSI reporting;

[0081] -Reduce system measurement resources and overhead;

[0082] -Improve the system's forward and backward compatibility;

[0083] - Improve the flexibility and adaptability of the system;

[0084] - Improve system reliability and robustness;

[0085] -Simplify system design and reduce the complexity of solution implementation;

[0086] - Improve the accuracy and effectiveness of CSI reporting;

[0087] - Enhance the overall performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] 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:

[0089] Figure 1 A flowchart of a first DCI, at least one CSI reporting configuration and a target CSI according to an embodiment of the present application is shown;

[0090] Figure 2 A schematic diagram of a network architecture according to an embodiment of the present application is shown;

[0091] 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;

[0092] 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;

[0093] Figure 5 A flowchart of wireless transmission according to an embodiment of the present application is shown;

[0094] Figure 6 A schematic diagram showing a calculation formula for a second time interval according to an embodiment of the present application;

[0095] Figure 7 A schematic diagram showing N information blocks and N time units according to an embodiment of the present application is shown;

[0096] Figure 8 A schematic diagram of a first RS according to an embodiment of the present application is shown;

[0097] Fig. 9 A schematic diagram showing a first operation according to an embodiment of the present application;

[0098] Fig.10 A schematic diagram of a first type of identification according to an embodiment of the present application is shown;

[0099] Fig.11 A schematic diagram showing a relationship between a second time interval and a first type of identification according to an embodiment of the present application is shown;

[0100] Fig.12 A schematic diagram showing a second resource set according to an embodiment of the present application is shown;

[0101] Fig.13 A schematic diagram showing that the first condition according to an embodiment of the present application is not satisfied;

[0102] Fig.14 A schematic diagram showing a second operation according to an embodiment of the present application;

[0103] Fig.15A schematic diagram showing a first operation according to another embodiment of the present application;

[0104] Fig.16 A schematic diagram of deploying a first operation according to an embodiment of the present application is shown;

[0105] Fig.17 A schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of the present application is shown;

[0106] Fig.18 A schematic diagram based on artificial intelligence or machine learning according to an embodiment of the present application is shown;

[0107] Fig.19 A structural block diagram of a processing device used in a first node according to an embodiment of the present application is shown;

[0108] Fig. 20 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

[0109] 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. 20 The embodiments in Figure 5 The embodiments and appendix Figure 6 -Attached Fig. 20 The embodiments in , etc.

[0110] Example 1

[0111] Embodiment 1 illustrates a flowchart of a first DCI, at least one CSI reporting configuration 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.

[0112] In Embodiment 1, the first node in the present application receives at least one CSI reporting configuration in step 101; receives a first DCI on a first PDCCH in step 102; determines whether to send a target CSI on the first PUSCH in step 103; and sends the target CSI on the first PUSCH in step 104 only when the first condition is met;

[0113] Among them, the first DCI triggers the reporting of at least one CSI on the first PUSCH, and the at least one CSI reporting configuration is used to configure the reporting of the at least one CSI; the target CSI reporting configuration is used to configure the reporting of the target CSI, the target CSI reporting configuration is one of the at least one CSI reporting configuration, and the target CSI is one of the at least one CSI; 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 first condition includes that the first symbol is not earlier than the first reference symbol and the second symbol is not earlier than the second reference symbol; the first symbol is the first uplink symbol in the first PUSCH for carrying the at least one CSI, and the first reference symbol is the next uplink symbol whose CP starts at the first time interval after the end of the last symbol of the first PDCCH; the second symbol is the first uplink symbol in the first PUSCH for carrying the target CSI, and the second reference symbol is the next uplink symbol whose CP starts at the second time interval after the end of the last symbol of the first RS in the first resource set; the second time interval depends on whether the generation method of the target CSI is based on AI.

[0114] As an embodiment, the at least one CSI reporting configuration is carried by higher layer signaling.

[0115] As an embodiment, the at least one CSI reporting configuration is carried by RRC (Radio Resource Control) signaling.

[0116] As an embodiment, the at least one CSI reporting configuration is carried by an RRC IE (Information Element).

[0117] As an embodiment, the at least one CSI reporting configuration is carried by at least one RRC IE.

[0118] As an embodiment, the at least one CSI reporting configuration includes information in one or more fields in at least one RRC IE.

[0119] As an embodiment, the at least one CSI reporting configuration includes information in one or more fields of each RRC IE in multiple RRC IEs.

[0120] As an embodiment, the at least one CSI reporting configuration includes part or all of the fields in the CSI-ReportConfig IE.

[0121] As an embodiment, the at least one CSI reporting configuration includes part or all of the fields in the ServingCellConfig IE.

[0122] As an embodiment, the at least one CSI reporting configuration includes part or all of the fields in the CSI-MeasConfig IE.

[0123] As an embodiment, the at least one CSI reporting configuration includes part or all of the fields in the ServingCellConfigCommon IE.

[0124] As an embodiment, the at least one CSI reporting configuration includes part or all of the fields in ServingCellConfigCommonSIBIE.

[0125] As an embodiment, any CSI reporting configuration of the at least one CSI reporting configuration is transmitted on the PDSCH.

[0126] As an embodiment, the CSI reporting configured in any CSI reporting configuration in the at least one CSI reporting configuration is periodic.

[0127] As an embodiment, the CSI reporting configured in any one of the at least one CSI reporting configuration is semi-persistent.

[0128] As an embodiment, the CSI reporting configured in any CSI reporting configuration of the at least one CSI reporting configuration is aperiodic.

[0129] As an embodiment, any CSI reporting configuration in the at least one CSI reporting configuration is an RRCIE.

[0130] As an embodiment, any CSI reporting configuration of the at least one CSI reporting configuration belongs to the CSI-ReportConfig IE.

[0131] As an embodiment, any CSI reporting configuration of the at least one CSI reporting configuration belongs to the ServingCellConfig IE.

[0132] As an embodiment, any CSI reporting configuration of the at least one CSI reporting configuration belongs to the CSI-MeasConfig IE.

[0133] As an embodiment, any CSI reporting configuration of the at least one CSI reporting configuration belongs to the ServingCellConfigCommon IE.

[0134] As an embodiment, any CSI reporting configuration of the at least one CSI reporting configuration belongs to ServingCellConfigCommonSIB IE.

[0135] As an embodiment, the first resource set includes one or more RS resources.

[0136] As an embodiment, the first resource set includes one or more downlink RS resources.

[0137] As an embodiment, the first resource set includes periodic RS resources.

[0138] As an embodiment, the first resource set includes semi-persistent RS resources.

[0139] As an embodiment, the first resource set includes aperiodic RS resources.

[0140] As an embodiment, the first resource set consists of one or more non-periodic RS resources.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] As an embodiment, the first resource set includes at least one RS resource set for channel measurement.

[0145] 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.

[0146] As an embodiment, the first resource set includes at least one RS resource set for interference measurement.

[0147] As a sub-embodiment of the above embodiment, an RS resource set for channel measurement includes one or more RS resources.

[0148] As a sub-embodiment of the above embodiment, an RS resource set for interference measurement includes one or more RS resources.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] As an embodiment, the synchronization signal resources include at least resources occupied by the synchronization signal.

[0153] As an embodiment, the synchronization signal resource is SSB (Synchronization Signal Block).

[0154] As an embodiment, the synchronization signal resource is a SS / PBCH (synchronization signal / physical broadcast channel) block resource.

[0155] As an embodiment, the first resource set consists of at least one non-periodic CSI-RS resource used for channel measurement.

[0156] 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.

[0157] 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.

[0158] As an embodiment, the first resource set consists of at least one CSI-RS resource.

[0159] As an embodiment, the first resource set includes at least one of CSI-RS resources or SS / PBCH block resources.

[0160] As an embodiment, the reference resource is a CSI reference resource.

[0161] As an embodiment, the reference resource is a CSI reference resource of the target CSI.

[0162] As an embodiment, the benefits of the above method include: using existing standards and system designs to reduce complexity.

[0163] 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.

[0164] 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.

[0165] As an embodiment, a resource configuration is used to configure CSI resources.

[0166] As an embodiment, a resource configuration is an IE CSI-ResourceConfig.

[0167] As an embodiment, a resource configuration is carried by RRC IE.

[0168] As an embodiment, a resource configuration is carried by a CSI-ResourceConfig IE.

[0169] As an embodiment, the target CSI reporting configuration indicates configuration information of the first resource set.

[0170] As an embodiment, the target CSI reporting configuration indicates an identifier of the first resource set.

[0171] As an embodiment, the first DCI (Downlink Control Information) triggers the target CSI on the first PUSCH (Physical uplink shared channel).

[0172] As an embodiment, the first DCI triggers the reporting of at least one CSI on the first PUSCH, and the reporting of the at least one CSI includes the target CSI.

[0173] As an embodiment, the first DCI triggers the reporting of at least one CSI on the first PUSCH, and the at least one CSI reporting includes the target CSI; the target CSI reporting configuration is used to configure the reporting of the target CSI.

[0174] As an embodiment, receiving the first DCI on the first PDCCH includes: receiving a second signal on the first PDCCH, wherein the second signal carries the first DCI.

[0175] As an embodiment, the first PDCCH includes multiple REs (Resource Element).

[0176] Typically, one RE occupies one symbol in the time domain and one subcarrier in the frequency domain.

[0177] As an embodiment, the first PDCCH occupies at least one symbol in the time domain, and the first PDCCH occupies at least one subcarrier in the frequency domain.

[0178] As an embodiment, the first PDCCH occupies at least one symbol in the time domain, and the first PDCCH occupies at least one RB (resource block) in the frequency domain.

[0179] As an embodiment, the symbol is a single carrier symbol.

[0180] As an embodiment, the symbol is a multi-carrier symbol.

[0181] As an embodiment, the multi-carrier symbol is an OFDM (Orthogonal Frequency Division Multiplexing) symbol.

[0182] As an embodiment, the symbol is obtained by performing OFDM symbol generation on the output of a transform precoding.

[0183] As an embodiment, the multi-carrier symbol is an SC-FDMA (Single Carrier-Frequency Division Multiple Access) symbol.

[0184] As an embodiment, the multi-carrier symbol is a DFT-S-OFDM (Discrete Fourier Transform Spread OFDM) symbol.

[0185] As an embodiment, the multi-carrier symbol is a FBMC (Filter Bank Multi Carrier) symbol.

[0186] As an embodiment, the multi-carrier symbol includes a CP (Cyclic Prefix).

[0187] As an embodiment, the first DCI includes a CSI request field, and the CSI request field in the first DCI triggers at least one CSI on the first PUSCH.

[0188] As an embodiment, the first DCI includes a CSI request field, and the CSI request field in the first DCI indicates at least one CSI reporting configuration, and the at least one CSI reporting configuration is used to configure the reporting of the at least one CSI.

[0189] As an embodiment, the first DCI includes a CSI request field, the CSI request field in the first DCI indicates at least one CSI reporting configuration, the target CSI reporting configuration is one of the at least one CSI reporting configuration, the at least one CSI reporting configuration is used to configure the reporting of the at least one CSI, and the target CSI reporting configuration is used to configure the reporting of the target CSI.

[0190] As an embodiment, generation of the target CSI relies on measurements obtained based on the first resource set.

[0191] As an embodiment, generation of the target CSI relies on channel measurement and / or interference measurement obtained based on the first resource set.

[0192] As an embodiment, measurements based on the first resource set are used to generate the target CSI.

[0193] As an embodiment, channel measurement and / or interference measurement based on the first resource set is used to generate the target CSI.

[0194] As an embodiment, measurement based on one or more RS resources in the first resource set is used to generate the target CSI.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] As an embodiment, the channel measurement obtained based on the first resource set refers to: the channel measurement obtained in the first resource set.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] As an embodiment, the interference measurement obtained based on the first resource set refers to: interference measurement obtained in the first resource set.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] As an embodiment, the channel matrix is ​​in spatial-frequency domain.

[0208] As an embodiment, the channel matrix is ​​in angular-delay domain projection.

[0209] 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.

[0210] As an embodiment, the at least one CSI is the target CSI, or the at least one CSI includes multiple CSIs, and the target CSI is one of the multiple CSIs.

[0211] As an embodiment, the at least one CSI includes only one CSI, the at least one CSI is the target CSI, and the at least one CSI reporting configuration is the target CSI reporting configuration.

[0212] As an embodiment, the at least one CSI includes multiple CSIs, the at least one CSI reporting configuration includes multiple CSI reporting configurations, and the multiple CSI reporting configurations are used to configure the multiple CSIs respectively.

[0213] As an embodiment, the at least one CSI includes multiple CSIs, the at least one CSI reporting configuration includes multiple CSI reporting configurations, the multiple CSI reporting configurations are used to configure the multiple CSIs respectively, and the target CSI is any CSI among the multiple CSIs.

[0214] As an embodiment, the target CSI includes at least one CSI reporting amount.

[0215] 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 (CapabilityIndex), or TDCP (Time Domain Channel Properties, time domain channel properties) One or more.

[0216] As an embodiment, the target CSI includes one or more of a channel matrix, an eigenvector, an eigenvalue, or a precoding matrix.

[0217] 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).

[0218] 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.

[0219] As an embodiment, the probability information indicates the probability that the corresponding beam is the optimal one or more beams.

[0220] As an embodiment, the probability information indicates the probability that the corresponding RS resource is the optimal one or more RS resources.

[0221] As an embodiment, the confidence information indicates the accuracy of RSRP.

[0222] As an embodiment, the confidence information indicates the accuracy of the differential RSRP.

[0223] 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.

[0224] 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.

[0225] As an embodiment, the target CSI includes RSRP.

[0226] As an embodiment, the target CSI includes at least one resource indication and RSRP.

[0227] As an embodiment, the target CSI includes at least one resource indication.

[0228] 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.

[0229] 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.

[0230] 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).

[0231] As an embodiment, the target CSI includes predicted CSI.

[0232] As an embodiment, the target CSI includes CSI for a period of time in the future.

[0233] As an embodiment, the target CSI includes predicted beam information.

[0234] As an embodiment, the target CSI includes beam information for a period of time in the future.

[0235] 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.

[0236] As a sub-embodiment of the above embodiment, the future period of time includes at least one time unit after the current time unit.

[0237] As a sub-embodiment of the above embodiment, the future period of time includes at least one time slot after the current time slot.

[0238] As a sub-embodiment of the above embodiment, the future period of time includes at least one symbol after the current symbol.

[0239] As an embodiment, the benefits of the above method include: reducing channel measurement overhead.

[0240] 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.

[0241] As an embodiment, the target CSI includes compressed CSI.

[0242] As an embodiment, the compressed CSI is non-codebook based.

[0243] 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.

[0244] 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.

[0245] As an embodiment, the benefits of the above method include: saving CSI feedback overhead and improving the overall performance of the system.

[0246] As an embodiment, the target CSI is based on AI.

[0247] As an embodiment, the target CSI is not AI-based.

[0248] As an embodiment, the reporting amount included in the target CSI depends on whether the generation of the target CSI is based on AI.

[0249] 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.

[0250] 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.

[0251] 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 AI; when the generation method of the target CSI is based on AI, the target CSI is based on a non-codebook; when the generation of the target CSI is not based on AI, the target CSI is based on a codebook.

[0252] As an embodiment, when the target CSI is not generated based on AI, the target CSI belongs to the CSI defined in 3GPP Rel-18.

[0253] As an embodiment, when the target CSI is generated based on AI, the target CSI does not belong to the CSI defined in 3GPP Rel-18 and earlier versions.

[0254] As an embodiment, when the target CSI is generated based on AI, the target CSI includes predicted CSI, or predicted beam information, or compressed CSI.

[0255] As an embodiment, the benefits of the above method include: supporting an AI-based CSI reporting solution.

[0256] As an embodiment, the advantages of the above method include: little modification to existing systems and standards.

[0257] As an embodiment, the benefits of the above method include: enhancing the flexibility of the system and improving the overall performance of the system.

[0258] As an embodiment, the target CSI indicates at least one RS resource in the first resource set.

[0259] 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.

[0260] As an embodiment, when the target CSI is generated based on 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.

[0261] As an embodiment, when the target CSI is generated based on 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 AI, the target CSI indicates at least one RS resource in the first resource set.

[0262] As an embodiment, the benefits of the above method include: reducing the overhead required to obtain the target CSI.

[0263] As an embodiment, the benefits of the above method include: reducing the measurement resources required to obtain the target CSI.

[0264] As an embodiment, the advantages of the above method include: little modification to existing systems and standards.

[0265] As an example, in the case where the generation of the target CSI is not based on 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:

[0266] 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×tAfter 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 1 is the rank or number of layers, in one case 1 is a positive integer not greater than t, in another case the precoding matrix is ​​the identity matrix, in which case t=1; 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.

[0267] 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.

[0268] As an embodiment, the determining whether to send the target CSI on the first PUSCH includes: determining whether the target CSI is sent on the first PUSCH and the target CSI is valid.

[0269] As an embodiment, the determining whether to send the target CSI on the first PUSCH includes: determining whether to send the target CSI on the first PUSCH or to ignore the first DCI.

[0270] As an embodiment, the determining whether to send the target CSI on the first PUSCH includes: determining whether to send the target CSI on the first PUSCH or giving up sending the target CSI on the first PUSCH.

[0271] Typically, the target CSI is sent on the first PUSCH only when a first condition is satisfied; wherein the target CSI sent on the first PUSCH is valid.

[0272] As an embodiment, the target CSI being valid includes: the target CSI is an updated CSI.

[0273] 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.

[0274] 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.

[0275] 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.

[0276] 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.

[0277] 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.

[0278] 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.

[0279] As an embodiment, the first resource set consists of one or more non-periodic RS resources; the target CSI is valid including: the target CSI is generated based on the measurement of the non-periodic RS resources in the first resource set triggered by the first DCI.

[0280] As an embodiment, the first resource set consists of one or more non-periodic RS resources; the target CSI is valid including: the target CSI is updated based on the measurement of the non-periodic RS resources in the first resource set triggered by the first DCI.

[0281] As an embodiment, the first PUSCH includes multiple REs (Resource Element).

[0282] As an embodiment, the first PUSCH occupies at least one symbol in the time domain, and the first PUSCH occupies at least one subcarrier in the frequency domain.

[0283] As an embodiment, the first PUSCH occupies at least one symbol in the time domain, and the first PUSCH occupies at least one RB (resource block) in the frequency domain.

[0284] As an embodiment, sending the target CSI on the first PUSCH includes: sending a first signal on the first PUSCH; wherein the first signal carries the target CSI.

[0285] As an embodiment, the first signal includes a baseband signal.

[0286] As an embodiment, the first signal includes a wireless signal.

[0287] As an embodiment, the first signal includes a radio frequency signal.

[0288] As an embodiment, the sending of the target CSI on the first PUSCH includes: the target CSI is used to generate a signal sent on the first PUSCH after channel coding.

[0289] As an embodiment, the sending of the target CSI on the first PUSCH includes: the target CSI is used to generate a signal sent on the first PUSCH after channel coding and modulation.

[0290] As an embodiment, the sending of the target CSI on the first PUSCH includes: the target CSI is used to generate a signal sent on the first PUSCH after bit sequence generation and channel coding.

[0291] As an embodiment, the sending of the target CSI on the first PUSCH includes: the target CSI is used to generate a signal sent on the first PUSCH after bit sequence generation, channel coding, and modulation.

[0292] As an embodiment, the sending of the target CSI on the first PUSCH includes: the target CSI is used to generate a signal sent on the first PUSCH after bit sequence generation, code block segmentation and CRC attachment, channel coding, rate matching, and code block concatenation.

[0293] As an embodiment, sending the target CSI on the first PUSCH includes: the target CSI is multiplexed into the first PUSCH after bit sequence generation, code block segmentation and CRC addition, channel coding, rate matching, and code block connection.

[0294] Typically, the first symbol takes into account a timing advance.

[0295] Typically, both the first symbol and the first reference symbol take into account timing advance.

[0296] As an embodiment, the first symbol is the first uplink symbol in the first PUSCH used to carry the at least one CSI.

[0297] As an embodiment, the first symbol is the first uplink symbol in the first PUSCH used to carry the target CSI.

[0298] As an embodiment, the first reference symbol is Z ref , the Z ref For the specific meaning, refer to Section 5.4 of 3GPP TS 38.214.

[0299] Typically, the next uplink symbol refers to: an earliest uplink symbol in time.

[0300] Typically, the last symbol of the first PDCCH refers to: the latest symbol occupied by the first PDCCH.

[0301] Typically, the first reference symbol is the next uplink symbol of the CP starting at (starting) a first time interval after the end of the last symbol of the first PDCCH (after the end of the last symbol of the first PDCCH), including: the first reference symbol is the earliest uplink symbol that is later than the last symbol of the first PDCCH and satisfies a reference condition, and the reference condition includes that the time interval between the end of the last symbol of the first PDCCH is not less than the first time interval.

[0302] As an embodiment, the first time interval is a real number or an integer.

[0303] As an embodiment, the unit of the first time interval is millisecond (ms).

[0304] As an embodiment, the unit of the first time interval is symbol.

[0305] As an embodiment, the first time interval is T proc,CSI , the T proc,CSI For the specific meaning, refer to Section 5.4 of 3GPP TS 38.214.

[0306] The first time interval is (Z)(2048+144·κ2 -μ ·T C +T switch ; wherein, the Z, the κ, the μ, the T C and the T switch For the specific meaning, refer to Section 5.4 of 3GPP TS38.214.

[0307] As an embodiment, the benefits of the above method include: using existing system design and standards to enhance system consistency.

[0308] Typically, the second symbol takes into account a timing advance.

[0309] Typically, both the second symbol and the second reference symbol take timing advance into consideration.

[0310] As an embodiment, the second symbol is the first uplink symbol in the first PUSCH used to carry the at least one CSI.

[0311] As an embodiment, the second symbol is the first uplink symbol in the first PUSCH used to carry the target CSI.

[0312] As an embodiment, the second reference symbol is Z′ ref (n), the Z′ ref For the specific meaning of (n), please refer to Section 5.4 of 3GPP TS38.214.

[0313] Typically, the second reference symbol is the next uplink symbol in a second time interval after the end of the last symbol of the first RS in the first resource set starting at the CP, including: the second reference symbol is the earliest uplink symbol that is later than the last symbol of the first RS in the first resource set and satisfies a reference condition, wherein the reference condition includes that the time interval between the end of the last symbol of the first RS in the first resource set is not less than the second time interval.

[0314] As an embodiment, the second time interval is a real number or an integer.

[0315] As an embodiment, the unit of the second time interval is millisecond (ms).

[0316] As an embodiment, the unit of the second time interval is symbol.

[0317] As an embodiment, the second time interval is T′ proc,CSI , the T′ proc,CSI For the specific meaning, refer to Section 5.4 of 3GPP TS38.214.

[0318] As an embodiment, the at least one CSI includes only one CSI, the at least one CSI is the target CSI, the at least one CSI reporting configuration is the target CSI reporting configuration, and the first symbol is the second symbol.

[0319] As an embodiment, the at least one CSI includes multiple CSIs, the at least one CSI reporting configuration includes multiple CSI reporting configurations, the multiple CSI reporting configurations are used to configure the multiple CSIs respectively, and the first symbol and the second symbol are the same or the first symbol and the second symbol are different.

[0320] As an embodiment, the first RS is an RS in the first resource set.

[0321] As an embodiment, the first RS is the latest non-periodic RS in time in the first resource set.

[0322] As an embodiment, the first RS is the earliest non-periodic RS in time in the first resource set.

[0323] As an embodiment, the first RS is the latest or earliest non-periodic RS in time in the first resource set.

[0324] As an embodiment, the first resource set consists of one or more non-periodic RS resources, and the first RS is an RS in the first resource set triggered by the first DCI.

[0325] As an embodiment, the first resource set consists of one or more non-periodic RS resources, and the first RS is the latest RS in time among the first resource set triggered by the first DCI.

[0326] As an embodiment, the first resource set consists of one or more non-periodic RS resources, and the first RS is the latest or earliest RS in time among the first resource set triggered by the first DCI.

[0327] As an embodiment, the benefits of the above method include: using existing system design and standards to enhance system consistency.

[0328] Typically, the determining whether to send the target CSI on the first PUSCH depends on whether the first condition is satisfied.

[0329] As an embodiment, when the first condition is not met, it includes: when the first symbol is earlier than the first reference symbol, or the second symbol is earlier than the second reference symbol, the first condition is not met; when the first symbol is not earlier than the first reference symbol and the second symbol is not earlier than the second reference symbol, the first condition is met.

[0330] As an embodiment, the first resource set consists of one or more non-periodic RS resources; when the first condition is not met, it includes: when the first symbol is earlier than the first reference symbol, or the second symbol is earlier than the second reference symbol, the first condition is not met; when the first symbol is not earlier than the first reference symbol and the second symbol is not earlier than the second reference symbol, the first condition is met.

[0331] As an embodiment, the benefits of the above method include: little modification to existing systems and standards.

[0332] As an embodiment, the benefits of the above method include: enhancing the reliability and robustness of the system.

[0333] As an embodiment, sending the target CSI on the first PUSCH only when the first condition is met includes: when the first condition is not met, ignoring the first DCI.

[0334] As an embodiment, sending the target CSI on the first PUSCH only when the first condition is met includes: when the first condition is not met, giving up sending the target CSI on the first PUSCH.

[0335] As an embodiment, sending the target CSI on the first PUSCH only when the first condition is met includes: when the first condition is met, sending the target CSI on the first PUSCH and the target CSI is valid; when the first condition is not met, sending the target CSI on the first PUSCH and the target CSI is not updated.

[0336] As an embodiment, no HARQ-ACK or transport block is multiplexed on the first PUSCH; sending the target CSI on the first PUSCH only when the first condition is met includes: when the first condition is not met, ignoring the first DCI.

[0337] As an embodiment, no HARQ-ACK or transport block is multiplexed on the first PUSCH; sending the target CSI on the first PUSCH only when the first condition is met includes: when the first condition is not met, giving up sending the target CSI on the first PUSCH.

[0338] As an embodiment, a HARQ-ACK or a transport block is multiplexed on the first PUSCH; sending the target CSI on the first PUSCH only when the first condition is met includes: when the first condition is met, sending the target CSI on the first PUSCH and the target CSI is valid; when the first condition is not met, sending the target CSI on the first PUSCH and the target CSI is not updated.

[0339] Typically, no HARQ-ACK or transport block is multiplexed on the first PUSCH.

[0340] As an embodiment, the benefits of the above method include: improving the flexibility and overall performance of the system.

[0341] As an embodiment, the benefits of the above method include: improving the stability and robustness of the system.

[0342] As an embodiment, the benefits of the above method include: little modification to existing systems and standards.

[0343] As an embodiment, the method for generating the target CSI is based on AI, including: the method for generating the target CSI is based on training; the method for generating the target CSI is not based on AI, including: the method for generating the target CSI is not based on training.

[0344] As an embodiment, the method for generating the target CSI is based on AI, including: the method for generating the target CSI uses an AI model; the method for generating the target CSI is not based on AI, including: the method for generating the target CSI does not use an AI model.

[0345] As an embodiment, the generation method of the target CSI is based on 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 AI, including: the target CSI does not include information based on artificial intelligence or machine learning.

[0346] As an embodiment, the generation method of the target CSI is based on 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 AI, including: the target CSI does not include information generated based on a neural network (Neural Network).

[0347] As an embodiment, the generation method of the target CSI is based on AI, including: the target CSI includes information generated based on CNN (Conventional Neural Networks); the generation method of the target CSI is not based on AI, including: the target CSI does not include information generated based on CNN.

[0348] As an embodiment, the method for generating the target CSI based on AI includes: 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 method for generating the target CSI is not based on AI includes: the generation of the target CSI does not include the sender of the target CSI performing the first operation.

[0349] As an embodiment, the target CSI generation method is based on AI and includes: the target CSI reporting configuration indicates a first type of identifier; the target CSI generation method is not based on AI and includes: the target CSI reporting configuration does not indicate a first type of identifier.

[0350] As an embodiment, the method for generating the target CSI based on AI includes: the generation of the target CSI is associated with a first type of identifier; the method for generating the target CSI not based on AI includes: the generation of the target CSI is not associated with a first type of identifier.

[0351] As an embodiment, the benefits of the above method include: simplifying system design and reducing implementation complexity.

[0352] As an embodiment, the benefits of the above method include: improving the flexibility and overall performance of the system.

[0353] As an embodiment, when the target CSI is not generated based on AI, the second time interval is (Z′)(2048+144)·κ2 -μ ·T c ; wherein, the Z′, the κ, the μ, the T C For the specific meaning, refer to Section 5.4 of 3GPP TS 38.214.

[0354] As an embodiment, when the target CSI is not generated based on AI, the second time interval is T′ proc，CSI ; said T′ proc，CSI For the specific meaning, refer to Section 5.4 of 3GPP TS38.214.

[0355] As an embodiment, the advantages of the above method include: little modification to existing standards and systems.

[0356] As an embodiment, the second time interval depends on whether the target CSI is generated based on AI, including: a method for determining the second time interval depends on whether the target CSI is generated based on AI.

[0357] As an embodiment, the second time interval depends on whether the generation method of the target CSI is based on AI, including: the calculation formula of the second time interval depends on whether the generation method of the target CSI is based on AI.

[0358] As an embodiment, the second time interval depends on whether the target CSI is generated based on AI, including: the calculation formula of the second time interval is different when the target CSI is generated based on AI or not.

[0359] As an embodiment, the calculation formula of the second time interval depends on whether the generation method of the target CSI is based on AI, including: when the generation method of the target CSI is based on AI, the calculation formula of the second time interval is a first calculation formula; when the generation method of the target CSI is not based on AI, the calculation formula of the second time interval is a second calculation formula; wherein the first calculation formula and the second calculation formula are different.

[0360] As an embodiment, the calculation formula of the second time interval depends on whether the generation method of the target CSI is based on AI, including: when the generation method of the target CSI is based on AI, the second time interval is (Z′)(2048+144)·κ2 -μ ·T C +W, wherein w is an integer or a real number greater than zero; when the target CSI is not generated based on AI, the second time interval is (Z′)(2048+144·K2 -μ ·T C ; wherein, the Z′, the κ, the μ, the T C For the specific meaning, refer to Section 5.4 of 3GPP TS38.214.

[0361] As an embodiment, the calculation formula of the second time interval depends on whether the generation method of the target CSI is based on AI, including: when the generation method of the target CSI is based on AI, the second time interval is a·[(Z′)(2048+144)·κ2 -μ ·T C ], wherein a is an integer or real number greater than 1; when the target CSI is not generated based on AI, the second time interval is (Z′)(2048+144·K2 -μ ·T C ; wherein, the Z′, the κ, the μ, the T C For the specific meaning, refer to Section 5.4 of 3GPP TS38.214.

[0362] As an embodiment, the benefits of the above method include: reserving more CSI calculation time for AI-based CSI reporting, better supporting AI models and calculations, and improving the accuracy and effectiveness of CSI reporting.

[0363] As an embodiment, the benefits of the above method include: improving the overall performance of the system.

[0364] As an embodiment, the second time interval depends on whether the target CSI is generated based on AI, including: when the target CSI is generated based on AI, the second time interval is a first reference interval; when the target CSI is not generated based on AI, the second time interval is a second reference interval.

[0365] As an embodiment, the first reference interval is not equal to the second reference interval.

[0366] As an embodiment, the first reference interval is a real number, and the second reference interval is a real number.

[0367] As an embodiment, the first reference interval is an integer, and the second reference interval is an integer.

[0368] As an embodiment, the unit of the first reference interval is millisecond (ms), and the unit of the second reference interval is millisecond.

[0369] As an embodiment, the unit of the first reference interval is symbol, and the unit of the second reference interval is symbol.

[0370] As an embodiment, the first reference interval includes multiple candidate values.

[0371] As an embodiment, the first reference interval is calculated by a formula.

[0372] As an embodiment, the first reference interval is fixed.

[0373] As an embodiment, the first reference interval is configurable.

[0374] As an embodiment, the first reference interval is configured by higher layer signaling.

[0375] As an embodiment, the target CSI is generated using an AI model; and the first reference interval depends on the AI ​​model.

[0376] As an embodiment, the target CSI depends on the output of the first operation, and the first reference interval depends on the first operation.

[0377] As an embodiment, generation of the target CSI is associated with a first type of identifier; and the first reference interval depends on the first type of identifier.

[0378] As an embodiment, the first reference interval depends on UE capability information.

[0379] As an embodiment, the benefits of the above method include: improving the stability and robustness of the system.

[0380] As an embodiment, the benefits of the above method include: improving the flexibility and overall performance of the system.

[0381] As an embodiment, the second reference interval includes multiple candidate values.

[0382] As an embodiment, the second reference interval is calculated by a formula.

[0383] As an embodiment, the second reference interval is fixed.

[0384] As an embodiment, the second reference interval is configurable.

[0385] As an embodiment, the second reference interval is configured by higher layer signaling.

[0386] As an embodiment, the second reference interval is (Z′)(2048+144·κ2 -μ ·T C ; wherein, the Z′, the κ, the μ, the T C For the specific meaning, refer to Section 5.4 of 3GPP TS38.214.

[0387] As an embodiment, the second reference interval is T′ proc ,; said T′ proc For the specific meaning, please refer to Section 5.4 of 3GPPTS38.214.

[0388] As an embodiment, the essence of the above method includes: setting different CSI calculation times for AI-based CSI reporting and non-AI-based CSI reporting.

[0389] As an embodiment, the advantages of the above method include: little modification to existing standards and systems.

[0390] 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 and requirements.

[0391] As an embodiment, the second time interval depends on whether the generation method of the target CSI is based on AI, including: the parameter on which the second time interval depends depends on whether the generation method of the target CSI is based on AI.

[0392] As an embodiment, the second time interval depends on whether the generation method of the target CSI is based on AI, including: the parameter on which the calculation formula of the second time interval depends on whether the generation method of the target CSI is based on AI.

[0393] As an embodiment, the second time interval depends on whether the generation method of the target CSI is based on AI, including: a calculation formula of the second time interval depends on a first parameter, and the first parameter depends on whether the generation method of the target CSI is based on AI.

[0394] As an embodiment, the dependence of the second time interval on whether the generation method of the target CSI is based on AI includes: whether the calculation formula of the second time interval depends on a second parameter and 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 calculation formula of the second time interval depends on the second parameter.

[0395] As an embodiment, the calculation formula of the second time interval depends on the second parameter, including: the second time interval and the second parameter are in a linear relationship.

[0396] As an embodiment, the calculation formula of the second time interval depends on the second parameter, including: the second time interval and the second parameter are in a non-linear relationship.

[0397] As an embodiment, the calculation formula of the second time interval depends on the second parameter and includes: when the generation method of the target CSI is based on AI, the second time interval is (Z′)(2048+144)·κ2 -μ ·T C +W, wherein the Z′, the κ, the μ, the T C For the specific meaning of, refer to Section 5.4 of 3GPP TS38.214; W is the second parameter.

[0398] As an embodiment, the unit of the second parameter is millisecond (ms).

[0399] As an embodiment, the unit of the second parameter is symbol.

[0400] As an embodiment, the second parameter is an integer or a real number greater than 0.

[0401] As an embodiment, the calculation formula of the second time interval depends on the second parameter and includes: when the generation method of the target CSI is based on AI, the second time interval is a·[(Z′)(2048+144)·K2 -μ ·T C ], wherein the Z′, the K, the μ, the T C For the specific meaning of, refer to Section 5.4 of 3GPP TS38.214; a is the second parameter.

[0402] As an embodiment, the second parameter is an integer or a real number greater than 1.

[0403] As an embodiment, the essence of the above method includes: reserving a longer CSI calculation time for AI-based CSI reporting.

[0404] As an embodiment, the benefits of the above method include: better support for AI models and calculations, and improved accuracy and effectiveness of CSI reporting.

[0405] As an embodiment, the second time interval depends on whether the generation method of the target CSI is based on AI, including: whether the second time interval depends on the first capability parameter and 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 second time interval depends on the first capability parameter.

[0406] As an embodiment, the second time interval depends on whether the generation method of the target CSI is based on AI, including: when the generation method of the target CSI is based on AI, the second time interval depends on a first capability parameter; when the generation method of the target CSI is not based on AI, the second time interval depends on a second capability parameter; the first capability parameter and the second capability parameter are both capability parameters reported by the first node.

[0407] As an embodiment, the second time interval depends on whether the generation method of the target CSI is based on AI, including: when the generation method of the target CSI is based on AI, the second time interval depends on the first capability parameter and the second capability parameter; when the generation method of the target CSI is not based on AI, the second time interval only depends on the second capability parameter; the first capability parameter and the second capability parameter are both capability parameters reported by the first node.

[0408] As an embodiment, the first capability parameter and the second capability parameter represent different capability parameters reported by the first node.

[0409] As an embodiment, the first capability parameter represents the AI-related capability parameter reported by the first node.

[0410] As an embodiment, the second capability parameter represents a capability parameter reported by the first node that is not related to AI.

[0411] As an embodiment, the second capability parameter includes beamReportTiming IE.

[0412] As an embodiment, the second capability parameter includes beamSwitchTiming IE.

[0413] As an embodiment, the second capability parameter includes codebookType IE.

[0414] As an embodiment, the second capability parameter includes beamReportTiming IE and beamSwitchTiming IE.

[0415] As an embodiment, the second capability parameter includes at least one of codebookType IE, beamReportTimingIE and beamSwitchTiming IE.

[0416] As an embodiment, the essence of the above method includes: considering UE capability information when determining the second time interval; and considering different UE capability information for AI-based and non-AI-based CSI reporting.

[0417] As an embodiment, the benefits of the above method include: enhancing the reliability and robustness of the system.

[0418] As an embodiment, the second time interval being dependent on the first capability parameter includes: a calculation formula of the second time interval being dependent on the first capability parameter.

[0419] As an embodiment, the second time interval being dependent on the first capability parameter includes: the second time interval and the first capability parameter are in a linear relationship.

[0420] As an embodiment, the second time interval depends on the first capability parameter, including: the second time interval and the first capability parameter are in a nonlinear relationship.

[0421] As an embodiment, the second time interval being dependent on the first capability parameter includes: the second time interval being dependent on the first parameter, and the first parameter being dependent on the first capability parameter.

[0422] As an embodiment, the second time interval being dependent on the first capability parameter includes: a calculation formula of the second time interval being dependent on the first parameter, and a value of the first parameter being dependent on the first capability parameter.

[0423] As an embodiment, the second time interval being dependent on the second capability parameter includes: a calculation formula of the second time interval being dependent on the second capability parameter.

[0424] As an embodiment, the second time interval being dependent on the second capability parameter includes: the second time interval and the second capability parameter are in a linear relationship.

[0425] As an embodiment, the second time interval being dependent on the second capability parameter includes: the second time interval and the second capability parameter are in a nonlinear relationship.

[0426] As an embodiment, the second time interval being dependent on a second capability parameter includes: the second time interval being dependent on a first parameter, and the first parameter being dependent on the second capability parameter.

[0427] As an embodiment, the second time interval being dependent on a second capability parameter includes: a calculation formula of the second time interval being dependent on a first parameter, and a value of the first parameter being dependent on the second capability parameter.

[0428] As an embodiment, the benefits of the above method include: enhancing the reliability and robustness of the system.

[0429] As an embodiment, the benefits of the above method include: improving the flexibility and overall performance of the system.

[0430] Example 2

[0431] 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.

[0432] Attached Figure 2The network architecture 200 of LTE (Long-Term Evolution), LTE-A (Long-Term Evolution Advanced) and future 5G systems is illustrated. The network architecture 200 of LTE, LTE-A and future 5G systems is called EPS (Evolved Packet System) 200. The 5G NR or LTE network architecture 200 may be referred to as 5GS (5G System) / EPS (Evolved Packet System) 200 or some other suitable terminology. 5GS / EPS200 may include one or more UEs (User Equipment) 201, a UE 241 communicating with UE 201 via a sidelink, NG-RAN (Next Generation Radio Access Network) 202, 5GC (5G Core Network) / EPC (Evolved Packet Core) 210, HSS (Home Subscriber Server) / UDM (Unified Data Management) 220, and Internet service 230. 5GS / EPS200 may be interconnected with other access networks, but these entities / interfaces are not shown for simplicity. Figure 2As shown, 5GS / EPS200 provides packet switching services, however, those skilled in the art will readily appreciate that the various concepts presented throughout this application can be extended to networks that provide circuit switching services. NG-RAN202 includes NR (New Radio) Node B (gNB) 203 and other gNBs204. gNB203 provides user and control plane protocol terminations toward UE201. gNB203 can be connected to other gNBs204 via an Xn interface (e.g., backhaul). gNB203 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 (transmit receive point), or some other suitable term. gNB203 provides an access point to 5GC / EPC210 for UE201. Examples of UE 201 include cellular phones, smart phones, session initiation protocol (SIP) phones, laptop computers, personal digital assistants (PDAs), satellite radios, global positioning systems, multimedia devices, video devices, digital audio players (e.g., MP3 players), cameras, game consoles, drones, aircraft, narrowband physical network 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 UE 201 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. gNB203 is connected to 5GC / EPC210 via S1 / NG interface. 5GC / EPC210 includes MME (Mobility Management Entity) / AMF (Authentication Management Field) / SMF (Session Management Function) 211, other MME / AMF / SMF214, S-GW (Service Gateway) / UPF (User Plane Function) 212, and P-GW (Packet Data Network Gateway) / UPF213. MME / AMF / SMF211 is a control node that handles signaling between UE201 and 5GC / EPC210. ​​In general, MME / AMF / SMF211 provides bearer and connection management.All user IP (Internet Protocol) packets are transmitted through S-GW / UPF212, which is itself connected to P-GW / UPF213. P-GW provides UE IP address allocation and other functions. P-GW / UPF213 is connected to Internet service 230. Internet service 230 includes operator-corresponding Internet protocol services, which may specifically include Internet, Intranet, IMS (IP Multimedia Subsystem) and Packet switching services.

[0433] As an embodiment, the first node in the present application includes the UE201.

[0434] As an embodiment, the second node in the present application includes the gNB203.

[0435] As an embodiment, the UE 201 includes a mobile phone.

[0436] As an embodiment, the UE 201 includes a vehicle such as a car.

[0437] As an embodiment, the gNB203 is a macro cell base station.

[0438] As an embodiment, the gNB203 is a micro cell base station.

[0439] As an embodiment, the gNB203 is a pico cell base station.

[0440] As an embodiment, the gNB203 is a home base station (Femtocell).

[0441] As an embodiment, the gNB203 is a base station device that supports large delay difference.

[0442] As an embodiment, the gNB203 is a flying platform device.

[0443] As an embodiment, the gNB203 is a satellite device.

[0444] As an embodiment, the gNB203 is a testing device (e.g., a transceiver that simulates some functions of a base station, a signaling tester).

[0445] 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.

[0446] 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.

[0447] As an embodiment, the wireless link between the UE 201 and the gNB 203 includes a cellular network link.

[0448] As an embodiment, the UE 201 and the gNB 203 are connected via a Uu air interface.

[0449] As an embodiment, the sender of the at least one CSI reporting configuration includes the gNB203.

[0450] As an embodiment, the receiver of the at least one CSI reporting configuration includes the UE 201.

[0451] As an embodiment, the sender of the target CSI reporting configuration includes the gNB203.

[0452] As an embodiment, the receiver of the target CSI reporting configuration includes the UE 201.

[0453] As an embodiment, the sender of the first DCI includes the gNB203.

[0454] As an embodiment, the recipient of the first DCI includes the UE 201.

[0455] As an embodiment, the sender of the first resource set includes the gNB203.

[0456] As an embodiment, the recipient of the first resource set includes the UE201.

[0457] As an embodiment, the sender of the at least one CSI includes the UE 201.

[0458] As an embodiment, the at least one CSI receiver includes the gNB203.

[0459] As an embodiment, the sender of the target CSI includes the UE 201.

[0460] As an embodiment, the receiver of the target CSI includes the gNB203.

[0461] As an embodiment, the UE 201 supports the 6G system.

[0462] As an embodiment, the gNB203 supports the 6G system.

[0463] As an embodiment, the UE 201 at least supports the 5G system.

[0464] As an embodiment, the gNB203 at least supports the 5G system.

[0465] As an embodiment, the UE 201 supports AI.

[0466] As an embodiment, the gNB203 supports AI.

[0467] Example 3

[0468] 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.

[0469] 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. 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 between 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 with 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.).

[0470] As an example, Figure 3 The wireless protocol architecture in is applicable to the first node in this application.

[0471] As an example, Figure 3 The wireless protocol architecture in is applicable to the second node in this application.

[0472] As an embodiment, the higher layer in the present application refers to a layer above the physical layer.

[0473] As an embodiment, the at least one CSI reporting configuration is generated in the RRC306.

[0474] As an embodiment, the target CSI reporting configuration is generated in the RRC306.

[0475] As an embodiment, the reference signal in the first resource set is generated by the PHY301 or the PHY351.

[0476] As an embodiment, the at least one CSI is generated by the PHY301 or the PHY351.

[0477] As an embodiment, the at least one CSI is generated by the MAC302 or the MAC352.

[0478] As an embodiment, the target CSI is generated by the PHY301 or the PHY351.

[0479] As an embodiment, the target CSI is generated by the MAC302 or the MAC352.

[0480] Example 4

[0481] 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 4is a block diagram of a first communication device 410 and a second communication device 450 communicating with each other in an access network.

[0482] 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 .

[0483] 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.

[0484] In transmission from the first communication device 410 to the second communication device 450, at the first communication device 410, 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 the DL, the controller / processor 475 provides header compression, encryption, packet segmentation and reordering, multiplexing between logical and transport channels, and allocation of radio resources 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.

[0485] 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 (DownLink, downlink), 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.

[0486] 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.

[0487] 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.

[0488] 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 at least one CSI reporting configuration; receives a first DCI on a first PDCCH, the first DCI triggers the reporting of at least one CSI on a first PUSCH, and the at least one CSI reporting configuration is used to configure the reporting of the at least one CSI; determines whether to send a target CSI on the first PUSCH; sends the target CSI on the first PUSCH only when a first condition is met; the target CSI reporting configuration is used to configure the reporting of the target CSI, the target CSI reporting configuration is one of the at least one CSI reporting configuration, and the target CSI is one of the at least one CSI; the target CSI reporting configuration indicates a first resource set, and the first resource set is used for channel measurement of the target CSI Or at least one of the interference resource measurements, the first resource set includes one or more RS resources; the first condition includes that the first symbol is not earlier than the first reference symbol and the second symbol is not earlier than the second reference symbol; the first symbol is the first uplink symbol in the first PUSCH for carrying the at least one CSI, and the first reference symbol is the next uplink symbol with a CP starting at the first time interval after the end of the last symbol of the first PDCCH; the second symbol is the first uplink symbol in the first PUSCH for carrying the target CSI, and the second reference symbol is the next uplink symbol with a CP starting at the second time interval after the end of the last symbol of the first RS in the first resource set; the second time interval depends on whether the generation method of the target CSI is based on AI.

[0489] As an embodiment, the second communication device 450 includes: a memory storing a computer-readable instruction program, the computer-readable instruction program generates an action when executed by at least one processor, the action including: receiving at least one CSI reporting configuration; receiving a first DCI on a first PDCCH, the first DCI triggering the reporting of at least one CSI on a first PUSCH, the at least one CSI reporting configuration being used to configure the reporting of the at least one CSI; determining whether to send a target CSI on the first PUSCH; sending the target CSI on the first PUSCH only when a first condition is met; wherein the target CSI reporting configuration is used to configure the reporting of the target CSI, the target CSI reporting configuration is one of the at least one CSI reporting configuration, and the target CSI is one of the at least one CSI; the target CSI reporting configuration indicates A first resource set is indicated, and 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 first condition includes that the first symbol is not earlier than the first reference symbol and the second symbol is not earlier than the second reference symbol; the first symbol is the first uplink symbol in the first PUSCH for carrying the at least one CSI, and the first reference symbol is the next uplink symbol with a CP starting at the first time interval after the end of the last symbol of the first PDCCH; the second symbol is the first uplink symbol in the first PUSCH for carrying the target CSI, and the second reference symbol is the next uplink symbol with a CP starting at the second time interval after the end of the last symbol of the first RS in the first resource set; the second time interval depends on whether the generation method of the target CSI is based on AI.

[0490] 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 together with the at least one processor. The first communication device 410 device at least: sends at least one CSI reporting configuration; sends a first DCI on a first PDCCH, the first DCI triggers the reporting of at least one CSI on a first PUSCH, and the at least one CSI reporting configuration is used to configure the reporting of the at least one CSI; wherein the target recipient of the first DCI determines whether to send the target CSI on the first PUSCH; only when the first condition is met, the target recipient of the first DCI sends the target CSI on the first PUSCH; the target CSI reporting configuration is used to configure the reporting of the target CSI, the target CSI reporting configuration is one of the at least one CSI reporting configuration, and the target CSI is one of the at least one CSI; the target CSI reporting configuration indicates a first resource set, and the 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 first condition includes that the first symbol is not earlier than the first reference symbol and the second symbol is not earlier than the second reference symbol; the first symbol is the first uplink symbol in the first PUSCH for carrying the at least one CSI, and the first reference symbol is the next uplink symbol with a CP starting at the first time interval after the end of the last symbol of the first PDCCH; the second symbol is the first uplink symbol in the first PUSCH for carrying the target CSI, and the second reference symbol is the next uplink symbol with a CP starting at the second time interval after the end of the last symbol of the first RS in the first resource set; the second time interval depends on whether the generation method of the target CSI is based on AI.

[0491] As an embodiment, the first communication device 410 includes: a memory storing a computer-readable instruction program, the computer-readable instruction program generates an action when executed by at least one processor, the action including: sending at least one CSI reporting configuration; sending a first DCI on a first PDCCH, the first DCI triggering the reporting of at least one CSI on a first PUSCH, the at least one CSI reporting configuration being used to configure the reporting of the at least one CSI; wherein the target recipient of the first DCI determines whether to send the target CSI on the first PUSCH; only when the first condition is met, the target recipient of the first DCI sends the target CSI on the first PUSCH; the target CSI reporting configuration is used to configure the reporting of the target CSI, the target CSI reporting configuration is one of the at least one CSI reporting configurations, and the target CSI is one of the at least one CSI 1. The target CSI reporting configuration indicates a first resource set, and 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 first condition includes that the first symbol is not earlier than the first reference symbol and the second symbol is not earlier than the second reference symbol; the first symbol is the first uplink symbol in the first PUSCH used to carry the at least one CSI, and the first reference symbol is the next uplink symbol with a CP starting at the first time interval after the end of the last symbol of the first PDCCH; the second symbol is the first uplink symbol in the first PUSCH used to carry the target CSI, and the second reference symbol is the next uplink symbol with a CP starting at the second time interval after the end of the last symbol of the first RS in the first resource set; the second time interval depends on whether the generation method of the target CSI is based on AI.

[0492] As an embodiment, the first node in the present application includes the second communication device 450.

[0493] As an embodiment, the second node in the present application includes the first communication device 410.

[0494] 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 at least one 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 at least one CSI reporting configuration.

[0495] 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 first DCI; 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 first DCI.

[0496] 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.

[0497] 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 on the first PUSCH; 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 on the first PUSCH.

[0498] Example 5

[0499] 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.

[0500] For the second node U1, deploy the second operation in step S511; send at least one CSI reporting configuration in step S512; send the first DCI on the first PDCCH in step S513; send a signal in the first resource set in step S514; receive the target CSI on the first PUSCH only when the first condition is met in step S515; and perform the second operation in step S516.

[0501] For the first node U2, deploy a first operation in step S521; receive at least one CSI reporting configuration in step S522; receive a first DCI on a first PDCCH in step S523; receive a signal in a first resource set in step S524; perform a first operation in step S525; determine whether to send a target CSI on a first PUSCH in step S526; and send a target CSI on a first PUSCH only when the first condition is met in step S527.

[0502] In embodiment 5, the first DCI triggers reporting of at least one CSI on the first PUSCH, and the at least one CSI reporting configuration is used to configure reporting of the at least one CSI; the target CSI reporting configuration is used to configure reporting of the target CSI, the target CSI reporting configuration is one of the at least one CSI reporting configuration, and the target CSI is one of the at least one CSI; 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 first condition includes that the first symbol is not Earlier than the first reference symbol and the second symbol is not earlier than the second reference symbol; the first symbol is the first uplink symbol in the first PUSCH for carrying the at least one CSI, and the first reference symbol is the next uplink symbol with a CP starting at the first time interval after the end of the last symbol of the first PDCCH; the second symbol is the first uplink symbol in the first PUSCH for carrying the target CSI, and the second reference symbol is the next uplink symbol with a CP starting at the second time interval after the end of the last symbol of the first RS in the first resource set; the second time interval depends on whether the generation method of the target CSI is based on AI.

[0503] As an embodiment, the first node U2 is the first node in this application.

[0504] As an embodiment, the second node U1 is the second node in this application.

[0505] 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.

[0506] 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.

[0507] As an embodiment, the air interface between the second node U1 and the first node U2 includes a wireless interface between user equipments.

[0508] As an embodiment, the second node U1 is a base station maintaining a service cell of the first node U2.

[0509] 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.

[0510] 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.

[0511] As an embodiment, sending a signal in the first resource set refers to: sending a wireless signal in the first resource set.

[0512] As an embodiment, sending a signal in the first resource set refers to: sending a reference signal in the first resource set.

[0513] As an embodiment, receiving a signal in the first resource set refers to: receiving a wireless signal in the first resource set.

[0514] As an embodiment, receiving a signal in the first resource set refers to: receiving a reference signal in the first resource set.

[0515] As an example, Figure 5 In the embodiment, when the target CSI is generated based on AI, the step in box F54 exists.

[0516] As an example, Figure 5 The steps in box F52 exist.

[0517] As an example, Figure 5 In the embodiment, when the target CSI is generated based on 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.

[0518] As an example, Figure 5 In the embodiment, when the target CSI is generated based on AI, the step in box F54 exists, the step in box F55 does not exist, and the first node adopts a single side AI model.

[0519] 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.

[0520] As an embodiment, the deployment of the second operation is earlier than the sending of the at least one CSI reporting configuration.

[0521] As an embodiment, the deployment of the second operation is later than the sending of the at least one CSI reporting configuration.

[0522] 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.

[0523] As an example, Figure 5 In the embodiment, when the target CSI is generated based on 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.

[0524] As an example, Figure 5 In the embodiment, when the target CSI is generated based on 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.

[0525] As an embodiment, the deployment of the first operation is earlier than the reception of the at least one CSI reporting configuration.

[0526] As an embodiment, the deployment of the first operation is later than the reception of the at least one CSI reporting configuration.

[0527] As an embodiment, the output of the first operation includes the target CSI; and the input of the second operation includes the target CSI.

[0528] As an embodiment, the first node is a consumer.

[0529] As an embodiment, the first node is a consumer of an AI function.

[0530] As an embodiment, the first node is a user of AI inference.

[0531] As an embodiment, the first node is an AI trained user.

[0532] As an embodiment, the first node is a MnS (Management Service) user.

[0533] As an embodiment, the first node is a producer of AI inference.

[0534] As an embodiment, the first node is a producer of AI training.

[0535] Example 6

[0536] Embodiment 6 illustrates a schematic diagram of a calculation formula for the second time interval according to an embodiment of the present application; Figure 6 shown.

[0537] In Embodiment 6, the calculation formula of the second time interval depends on a first parameter, and the first parameter depends on whether the target CSI is generated based on AI.

[0538] As an embodiment, the first parameter is a real number or an integer.

[0539] As an embodiment, the first parameter is a real number or an integer greater than 0.

[0540] As an embodiment, the first parameter is a real number or an integer greater than 1.

[0541] As an embodiment, the unit of the first parameter is millisecond (ms).

[0542] As an embodiment, the unit of the first parameter is second (s).

[0543] As an embodiment, the unit of the first parameter is symbol.

[0544] As an embodiment, the unit of the first parameter is a time slot.

[0545] As an embodiment, the unit of the first parameter is a subframe.

[0546] As an embodiment, the value of the first parameter is configurable.

[0547] As an embodiment, the value of the first parameter is fixed.

[0548] As an embodiment, the first parameter includes one or more candidate values.

[0549] As an embodiment, the value of the first parameter depends on UE capability information.

[0550] As an embodiment, the value of the first parameter depends on UE capability information; the UE capability information includes at least one of codebookType IE, beamReportTiming IE and beamSwitchTiming IE.

[0551] As an embodiment, the essence of the above method includes: considering UE capability information when determining the second time interval.

[0552] As an embodiment, the benefits of the above method include: enhancing the reliability and robustness of the system.

[0553] As an embodiment, the first parameter is Z′.

[0554] As an embodiment, the first parameter is Z′(m).

[0555] As an embodiment, the first parameter is Z 1 ′、Z′ 2 , Z′ 3 one of them.

[0556] As an embodiment, the Z′, the Z′(m), the Z′ 1 , the Z′ 2 , the Z′ 3 For the specific meaning, refer to Section 5.4 of 3GPP TS38.214.

[0557] As an embodiment, the benefits of the above method include: using existing standards and system designs, and reducing implementation complexity.

[0558] As an embodiment, the second time interval and the first parameter are functionally related.

[0559] As an embodiment, the second time interval and the first parameter are in a mapping relationship.

[0560] As an embodiment, the second time interval and the first parameter are in a linear relationship.

[0561] As an embodiment, the second time interval and the first parameter are in a non-linear relationship.

[0562] As an embodiment, the second time interval is (Z′)(2048+144·κ2 -μ ·T C , where Z′, κ, μ, and T C For the specific meaning of, refer to Section 5.4 of 3GPP TS38.214; the first parameter is the Z′.

[0563] As an embodiment, the second time interval is (Z′)(2048+144)·κ2 -μ ·T C +W, where Z′, κ, μ, T C For the specific meaning of, refer to Section 5.4 of 3GPP TS38.214; W is an integer or a real number greater than 0; and the first parameter is Z′.

[0564] As an embodiment, the second time interval is a·[(Z′)(2048+144)·κ2 -μ ·T C ], wherein the Z′, the κ, the μ, the T C For the specific meaning of, refer to Section 5.4 of 3GPP TS38.214; a is an integer or a real number greater than 1; and the first parameter is Z′.

[0565] As an embodiment, the second time interval is Wherein, Z′(m), κ, μ, T C For the specific meaning of, refer to Section 5.4 of 3GPP TS38.214; the first parameter is the Z′(m).

[0566] As an embodiment, the second time interval is (Z 1 ′)(2048+144·κ2 -μ ·T C , wherein the Z 1 ′, the κ, the μ, the T C For the specific meaning of , refer to Section 5.4 of 3GPP TS38.214; the first parameter is the Z 1 ′.

[0567] As an embodiment, the second time interval is (Z′ 2 )(2048+144·κ2 -μ ·T C , where Z′ 2, the κ, the μ, the T C For the specific meaning of , refer to Section 5.4 of 3GPP TS38.214; the first parameter is the Z′ 2 .

[0568] As an embodiment, the second time interval is (Z′ 3 )(2048+144·κ2 -μ ·T C , where Z′ 3 , the κ, the μ, the T C For the specific meaning of , refer to Section 5.4 of 3GPP TS38.214; the first parameter is the Z′ 3 .

[0569] As an embodiment, the second time interval is (2Z′ 2 )(2048+144·K2 -μ ·T C , where Z′ 2 , the κ, the μ, the T C For the specific meaning of , refer to Section 5.4 of 3GPP TS38.214; the first parameter is the Z′ 2 .

[0570] As an embodiment, the benefits of the above method include: using existing standards and system designs, and reducing implementation complexity.

[0571] As an embodiment, the benefits of the above method include: improving the flexibility and overall performance of the system.

[0572] As an embodiment, the determination of the first parameter depends on whether the target CSI is generated based on AI.

[0573] As an embodiment, when the target CSI is not generated based on AI, the first parameter is Z′; when the target CSI is generated based on AI, the first parameter is not Z′.

[0574] As an embodiment, when the target CSI is not generated based on AI, the first parameter is Z 1 '; When the target CSI is generated based on AI, the first parameter is not Z 1 ′.

[0575] As an embodiment, when the target CSI is not generated based on AI, the first parameter is Z′ 2 ; When the target CSI is generated based on AI, the first parameter is not Z′ 2 .

[0576] As an embodiment, the reported amount of the target CSI includes RSRP; when the generation method of the target CSI is not based on AI, the first parameter is Z′ 3 ; When the target CSI is generated based on AI, the first parameter is not Z′ 3 .

[0577] As an embodiment, when the target CSI is generated based on AI, the first parameter is not Z′ 1 , Z′ 2 , Z′ 3 Any one of .

[0578] As an embodiment, when the target CSI is generated based on AI, the first parameter is not Z′(m) defined in version 18 and earlier versions of 3GPP TS 38.214.

[0579] As an embodiment, the essence of the above method includes: processing AI-based and non-AI-based solutions according to different situations.

[0580] As an embodiment, the benefits of the above method include: better adaptability to various application scenarios and good flexibility.

[0581] As an embodiment, the value of the first parameter depends on whether the target CSI is generated based on AI.

[0582] As an embodiment, when the target CSI is generated based on AI, the candidate value of the first parameter belongs to a first candidate value range, and the first candidate value range includes one or more candidate values; when the target CSI is not generated based on AI, the candidate value of the first parameter belongs to a second candidate value range, and the first candidate value range includes one or more candidate values.

[0583] As an embodiment, any candidate value in the first candidate value range and the second candidate value range is a real number or an integer.

[0584] As an embodiment, any candidate value in the first candidate value range and the second candidate value range is a real number or an integer greater than 0.

[0585] As an embodiment, any candidate value in the first candidate value range and the second candidate value range is a real number or an integer greater than 1.

[0586] As an embodiment, the unit of any candidate value in the first candidate value range and the second candidate value range is millisecond (ms).

[0587] As an embodiment, the unit of any candidate value in the first candidate value range and the second candidate value range is second (s).

[0588] As an embodiment, the unit of any candidate value in the first candidate value range and the second candidate value range is a symbol.

[0589] As an embodiment, the unit of any candidate value in the first candidate value range and the second candidate value range is a time slot.

[0590] As an embodiment, the unit of any candidate value in the first candidate value range and the second candidate value range is a subframe.

[0591] As an embodiment, the second candidate value range includes 8, 11, 21, and 36.

[0592] As an embodiment, the second candidate value range includes 16, 30, 42, 85, 340, and 680.

[0593] As an embodiment, the second candidate value range includes 37, 69, 140, 140, 560, and 1120.

[0594] As an embodiment, the second candidate value range includes X 0 , X 1 , X 2 , X 3 , X 5 , X 6 ; where X 0 , X 1 , X 2 , X 3 , X 5 , X 6 For the specific meaning, refer to Section 5.4 of 3GPP TS38.214.

[0595] As an embodiment, the essence of the above method includes: processing AI-based and non-AI-based solutions according to different situations.

[0596] As an embodiment, the benefits of the above method include: using existing standards and system designs, and reducing implementation complexity.

[0597] As an embodiment, the first candidate value range and the second candidate value range are different.

[0598] As an embodiment, the first candidate value range and the second candidate value range include the same number of candidate values.

[0599] As an embodiment, the first candidate value range is sorted from large to small according to the candidate values; the second candidate value range is sorted from large to small according to the candidate values; any candidate value in the sorted first candidate value range is greater than the candidate value at the same position in the sorted second candidate value range.

[0600] As an embodiment, the essence of the above method includes: reserving a longer CSI calculation time for AI-based CSI reporting.

[0601] As an embodiment, the benefits of the above method include: better support for AI models and calculations, and improved accuracy and effectiveness of CSI reporting.

[0602] As an embodiment, whether the first parameter depends on the first capability parameter 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 first parameter depends on the first capability parameter.

[0603] As an embodiment, when the target CSI is generated based on AI, the first parameter depends on a first capability parameter; when the target CSI is not generated based on AI, the first parameter depends on a second capability parameter; the first capability parameter and the second capability parameter are both capability parameters reported by the first node.

[0604] As an embodiment, when the target CSI is generated based on AI, the first parameter depends on a first capability parameter and a second capability parameter; when the target CSI is not generated based on AI, the first parameter only depends on the second capability parameter; the first capability parameter and the second capability parameter are both capability parameters reported by the first node.

[0605] As an embodiment, the first capability parameter and the second capability parameter represent different capability parameters reported by the first node.

[0606] As an embodiment, the first capability parameter represents the AI-related capability parameter reported by the first node.

[0607] As an embodiment, the second capability parameter represents a capability parameter reported by the first node that is not related to AI.

[0608] As an embodiment, the second capability parameter includes beamReportTiming IE.

[0609] As an embodiment, the second capability parameter includes beamSwitchTiming IE.

[0610] As an embodiment, the second capability parameter includes codebookType IE.

[0611] As an embodiment, the second capability parameter includes beamReportTiming IE and beamSwitchTiming IE.

[0612] As an embodiment, the second capability parameter includes at least one of codebookType IE, beamReportTimingIE and beamSwitchTiming IE.

[0613] As an embodiment, the essence of the above method includes: considering UE capability information when determining the second time interval; and considering different UE capability information for AI-based and non-AI-based CSI reporting.

[0614] As an embodiment, the benefits of the above method include: enhancing the reliability and robustness of the system.

[0615] Example 7

[0616] Embodiment 7 illustrates a schematic diagram of N information blocks and N time units according to an embodiment of the present application; Figure 7 As shown. Figure 7 In the figure, information block #1, ..., information block #N are N information blocks; time unit #1, ..., time unit #N are N time units.

[0617] In Example 7, when the target CSI is generated based on AI, the target CSI includes N information blocks, and the N information blocks respectively include channel information of N time units, where N is a positive integer greater than 1; and the second time interval depends on at least one of the N time units.

[0618] As an embodiment, the target CSI includes N information blocks, and the N information blocks respectively include CSI of N time units, where N is a positive integer greater than 1; the generation of any information block among the N information blocks depends on the measurement based on the first resource set.

[0619] As an embodiment, the N information blocks respectively include predicted CSI of N time units.

[0620] As an embodiment, the N information blocks respectively include predicted beam information of N time units.

[0621] As an embodiment, the N information blocks respectively include compressed CSI of N time units.

[0622] As an embodiment, any information block among the N information blocks indicates at least one resource in the first resource set.

[0623] As an embodiment, the essence of the above method includes: supporting joint reporting of CSI of multiple time units.

[0624] As an embodiment, the essence of the above method includes: supporting AI-based CSI prediction or compression scheme.

[0625] As an embodiment, the benefits of the above method include: reducing system overhead and information feedback delay, and enhancing the transmission efficiency of the system.

[0626] As an embodiment, the benefits of the above method include: improving the accuracy and real-time performance of CSI reporting and improving the overall performance of the system.

[0627] As an embodiment, the target CSI includes multiple information blocks, and the number of information blocks included in the target CSI is not less than the N.

[0628] As an embodiment, the target CSI includes multiple information blocks, and the multiple information blocks included in the target CSI include the N information blocks.

[0629] As an embodiment, the benefits of the above method include: enhancing the flexibility and robustness of the system.

[0630] As an embodiment, a time unit includes one or more time slots.

[0631] As an embodiment, one time unit includes one or more subframes.

[0632] As an embodiment, one time unit includes a plurality of consecutive symbols.

[0633] As an embodiment, the N time units are mutually orthogonal.

[0634] As an embodiment, the N time units are different from each other.

[0635] As an embodiment, there are two overlapping time units among the N time units.

[0636] As an embodiment, the N time units are continuous.

[0637] As an embodiment, the N time units are equally spaced.

[0638] As an embodiment, the interval between any two adjacent time units among the N time units is P time units, where P is a positive integer.

[0639] As an embodiment, the benefits of the above method include: using existing system designs and standards.

[0640] As an embodiment, the benefits of the above method include: enhancing the flexibility and robustness of the system.

[0641] Generally speaking, how the first node determines the N or at least one of the N time units is determined by the hardware equipment vendor. Some non-limiting implementation methods are described below:

[0642] As an embodiment, the first node determines the N time units based on changes in the channel.

[0643] As an embodiment, the first node determines the N time units based on the time correlation of the channel.

[0644] As an embodiment, the first node determines the N time units based on the moving speed.

[0645] As an embodiment, the first node determines the N time units based on at least one of channel variation, channel time correlation, or moving speed.

[0646] As an embodiment, the first node determines that the N time units are time units in which the channel changes faster (for example, the change is greater than a threshold).

[0647] As an embodiment, the first node determines the N time units based on its moving speed.

[0648] As an embodiment, the first node determines that the N time units are time units with low time correlation (for example, lower than a threshold).

[0649] As an embodiment, the second time interval being dependent on at least one of the N time units includes: the second time interval being dependent on a target time unit among the N time units.

[0650] As an embodiment, the target time unit is the earliest time unit among the N time units.

[0651] As an embodiment, the target time unit is the latest time unit among the N time units.

[0652] As an embodiment, the target time unit is a designated time unit among the N time units.

[0653] As an embodiment, the second time interval being dependent on a target time unit among the N time units includes: the second time interval being dependent on a time interval between the target time unit and the last symbol of the first PDCCH.

[0654] As an embodiment, the second time interval depends on the time interval between the target time unit and the last symbol of the first PDCCH, including: the second time interval depends on the time interval between the starting symbol of the target time unit and the last symbol of the first PDCCH.

[0655] As an embodiment, the second time interval being dependent on at least one of the N time units includes: the second time interval being dependent on a target symbol in the N time units.

[0656] As an embodiment, the target symbol is the earliest symbol among the N time units.

[0657] As an embodiment, the target symbol is the latest symbol in the N time units.

[0658] As an embodiment, the target symbol is a designated symbol in the N time units.

[0659] As an embodiment, the second time interval being dependent on the target symbol in the N time units includes: the second time interval being dependent on the time interval between the target symbol and the last symbol of the first PDCCH.

[0660] As an embodiment, the benefits of the above method include: enhancing the flexibility of the system.

[0661] As an embodiment, the advantages of the above method include: little change to existing standards and system design.

[0662] As an embodiment, the second time interval being dependent on at least one of the N time units includes: the second time interval being dependent on the N.

[0663] As an embodiment, the second time interval depends on N and includes: N belongs to one of V1 candidate value ranges, any candidate value range in the V1 candidate value ranges includes one or more positive integers, and V1 is a positive integer greater than 1; V1 time intervals correspond one-to-one to the V1 candidate value ranges, respectively, and the second time interval is a time interval in the V1 time interval corresponding to the candidate value range to which N belongs.

[0664] As an embodiment, the second time interval being dependent on the N includes: the second time interval being dependent on a first parameter, and the first parameter being dependent on the N.

[0665] As an embodiment, the second time interval being dependent on the N includes: the second time interval and the first parameter are in a linear relationship, and the first parameter is dependent on the N.

[0666] As an embodiment, the second time interval being dependent on N includes: the second time interval and the first parameter are in a linear relationship, and the first parameter and N are in a linear relationship.

[0667] As an embodiment, the first parameter depends on N, including: N belongs to one of V1 candidate value ranges, any candidate value range in the V1 candidate value ranges includes one or more positive integers, and V1 is a positive integer greater than 1; V1 candidate values ​​of the first parameter correspond one-to-one to the V1 candidate value ranges, respectively, and the first parameter is a candidate value among the V1 candidate values ​​of the first parameter corresponding to the candidate value range to which N belongs.

[0668] As an embodiment, the essence of the above method includes: the CSI calculation time depends on the amount of information carried by the CSI report or the number of time units targeted by the CSI report.

[0669] As an embodiment, the benefits of the above method include: more accurately setting the CSI calculation time, effectively utilizing system resources, and improving the accuracy and real-time performance of CSI reporting.

[0670] As an embodiment, the benefits of the above method include: enhancing the flexibility of the system and the overall performance of the system.

[0671] As an embodiment, the second time interval being dependent on at least one of the N time units includes: the second time interval being dependent on the length of the N time units.

[0672] As an embodiment, the second time interval being dependent on the length of the N time units includes: the second time interval and the length of the N time units are in a linear relationship.

[0673] As an embodiment, the second time interval depends on the length of the N time units, including: the length of the N time units belongs to one of V1 candidate value ranges, any candidate value range in the V1 candidate value ranges includes one or more real numbers or integers, and V1 is a positive integer greater than 1; the V1 time intervals correspond one-to-one to the V1 candidate value ranges, respectively, and the second time interval is a time interval in the V1 time interval corresponding to the candidate value range to which the length of the N time units belongs.

[0674] As an embodiment, the second time interval being dependent on the length of the N time units includes: the second time interval being dependent on a first parameter, and the first parameter being dependent on the length of the N time units.

[0675] As an embodiment, the second time interval being dependent on the length of the N time units includes: the second time interval and the first parameter are in a linear relationship, and the first parameter and the length of the N time units are in a linear relationship.

[0676] As an embodiment, the essence of the above method includes: the CSI calculation time depends on the length of the time unit for CSI reporting.

[0677] As an embodiment, the benefits of the above method include: more accurately setting the CSI calculation time, effectively utilizing system resources, and improving the accuracy and real-time performance of CSI reporting.

[0678] As an embodiment, the benefits of the above method include: enhancing the flexibility of the system and the overall performance of the system.

[0679] Example 8

[0680] Embodiment 8 A schematic diagram of a first RS according to an embodiment of the present application; as shown in the attached Figure 8 As shown. Figure 8 In which, RS#1, ..., RS#n, ..., RS#M are one or more non-periodic RS resources in the first resource set.

[0681] In Embodiment 8, the first resource set consists of one or more non-periodic RS resources, and the first RS is the latest RS in time in the first resource set triggered by the first DCI.

[0682] As an embodiment, the first RS is an RS in the first resource set.

[0683] As an embodiment, the first RS is the latest non-periodic RS in time in the first resource set.

[0684] As an embodiment, the first RS is the last RS in the first resource set triggered by the first DCI.

[0685] As an embodiment, the first RS is the last non-periodic RS in the first resource set triggered by the first DCI.

[0686] As an embodiment, the benefits of the above method include: using existing system design and standards to enhance system consistency.

[0687] Example 9

[0688] Embodiment 9 illustrates a schematic diagram of a first operation according to an embodiment of the present application; Fig. 9 shown.

[0689] In Embodiment 9, the method for generating the target CSI is based on AI and includes: the method for generating 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.

[0690] As an embodiment, the first operation is training-based or AI-based.

[0691] As an embodiment, the first operation is obtained through training.

[0692] As an embodiment, the first operation is based on a neural network.

[0693] As an embodiment, the first operation includes an AI entity.

[0694] As an embodiment, the first operation includes a portion of an AI entity.

[0695] As an embodiment, the first operation includes a part of an AI entity for inference.

[0696] As an embodiment, the first operation is performed by an AI entity.

[0697] As an embodiment, the first operation is performed by an AI function.

[0698] As an embodiment, the AI ​​function includes at least one of an AI inference function, an AI training function, and an AI management function.

[0699] As an embodiment, the training for obtaining the first operation is performed by the first node.

[0700] As an embodiment, the training for obtaining the first operation is performed by an MDA function (Management Data Analytics Function).

[0701] As an embodiment, the training for obtaining the first operation is performed by a MDAS (Management Data Analytics Service) producer.

[0702] As an embodiment, the training for obtaining the first operation is performed by NWDAF (Network Data Analytics Function).

[0703] As an embodiment, the training for obtaining the first operation is performed by a core network.

[0704] As an embodiment, the training for obtaining the first operation is performed by an AI training producer.

[0705] As an embodiment, the first operation includes inference.

[0706] As an embodiment, the first operation is AI inference.

[0707] As an embodiment, the first operation includes AI inference for CSI.

[0708] As an embodiment, the first operation is AI inference for CSI.

[0709] As an embodiment, the first operation includes AI inference for at least one of beam prediction, CSI prediction, CSI estimation, or CSI compression.

[0710] 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.

[0711] 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.

[0712] As an embodiment, the first operation requires deployment.

[0713] As an embodiment, the first operation is obtained by loading.

[0714] As an embodiment, the first operation is obtained by loading from a serving cell of the first node.

[0715] As an embodiment, the first operation is obtained from maintaining base station loading of a serving cell of the first node.

[0716] As an embodiment, the first operation is obtained by loading from the core network.

[0717] As an embodiment, the first operation includes one or more of convolution, pooling, cascading and activation.

[0718] 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.

[0719] As an embodiment, a coding layer includes at least one convolutional layer and one pooling layer.

[0720] 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.

[0721] 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.

[0722] 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.

[0723] As an embodiment, the first operation includes preprocessing.

[0724] As an embodiment, the preprocessing includes one or more of matrix decomposition, matrix transformation and projection.

[0725] 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.

[0726] As an embodiment, the preprocessing includes at least one of truncation and / or padding, DFT (DiscreteFourier Transform), mapping, and labeling.

[0727] As an embodiment, the first operation includes post-processing.

[0728] As an embodiment, the post-processing includes at least one of DFT (Discrete Fourier Transform), quantization, truncation and / or padding.

[0729] 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.

[0730] 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.

[0731] As an embodiment, the above method has the following benefits: it is suitable for channel compression and saves feedback overhead.

[0732] 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.

[0733] 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.

[0734] As an embodiment, the benefits of the above method include: reducing RS resource overhead and reducing feedback delay.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] As an embodiment, the benefits of the above method include: improving CSI accuracy and real-time performance, and reducing RS overhead.

[0740] 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.

[0741] As an embodiment, the benefits of the above method include: reducing RS overhead and improving the accuracy and completeness of CSI.

[0742] 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.

[0743] As a sub-embodiment of the above embodiment, the P1 antenna ports are a proper subset of the P2 antenna ports.

[0744] As a sub-embodiment of the above embodiment, the P2 antenna ports belong to the second resource set.

[0745] As an embodiment, the input of the first operation also includes the second resource set.

[0746] 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).

[0747] 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.

[0748] 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.

[0749] 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.

[0750] As an embodiment, the output of the first operation includes the target CSI.

[0751] As an embodiment, the input of the first operation depends on measurements based on the first set of resources.

[0752] 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.

[0753] 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.

[0754] As an embodiment, the target CSI depends on the output of the first operation.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] As an embodiment, the benefits of the above method include: improving the overall performance of the system.

[0763] As an embodiment, the first operation is associated with the first type of identification.

[0764] 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.

[0765] As an embodiment, the AI ​​model adopted by the first operation is identified by the first category identifier.

[0766] As an embodiment, the AI ​​entity or AI function to which the first operation belongs is identified by the first type identifier.

[0767] As an embodiment, the AI ​​entity or AI function that performs the first operation is identified by the first type identifier.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] As an embodiment, the training for obtaining the first operation is identified by the first category identifier.

[0772] As an embodiment, the data set used for training the first operation is identified by the first category identifier.

[0773] As an embodiment, the target CSI reporting configuration indicates the first operation by indicating the first type of identifier.

[0774] 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.

[0775] Example 10

[0776] Embodiment 10 illustrates a schematic diagram of a first type of identification according to an embodiment of the present application; Fig.10 shown.

[0777] In Embodiment 10, the target CSI generation method is based on AI and includes: the target CSI generation method is associated with a first type of identifier.

[0778] As an embodiment, the first type identifier is a non-negative integer.

[0779] As an embodiment, the first type of identifier is a character string.

[0780] As an embodiment, the first type of identifier is a model identifier.

[0781] As an embodiment, the first type of identifier is used to identify an AI model, AI entity or AI function.

[0782] As an embodiment, the first type of identifier is used by the first node to determine an AI model, AI entity or AI function.

[0783] 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.

[0784] As an embodiment, the benefits of the above method include: identifying an AI model, AI entity or AI function through the first type of identifier, simplifying system design, and unifying the understanding of different AI models, AI entities or AI functions among multiple nodes.

[0785] As an embodiment, the first type of identifier is used to identify or indicate a resource set.

[0786] 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.

[0787] As an embodiment, the first type of identifier is used to identify or indicate a training data set.

[0788] 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.

[0789] As an embodiment, the generation method of the target CSI is associated with the first type of identifier, including: the generation of the target CSI is associated with the first type of identifier through the target CSI reporting configuration.

[0790] As an embodiment, the target CSI generation method is associated with the first type of identifier, including: the target CSI reporting configuration indicates the first type of identifier; the target CSI reporting configuration indicates the generation of the target CSI.

[0791] As an embodiment, the method for generating the target CSI is associated with the first type of identifier, including: 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, the target CSI depends on the output of the first operation, and the first operation is associated with the first type of identifier.

[0792] As an embodiment, the benefits of the above method include: supporting AI-based CSI reporting.

[0793] As an embodiment, the method for generating the target CSI is associated with the first type of identifier, including: the method for generating the target CSI uses an AI model identified by the first type of identifier.

[0794] As an embodiment, the generation method of the target CSI is associated with the first type of identifier, including: the AI ​​entity identified by the first type of identifier generates the target CSI.

[0795] As an embodiment, the generation method 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.

[0796] As an embodiment, the generation method of the target CSI is associated with the first type of identifier, including: the generation method of the target CSI belongs to an AI function, and the first type of identifier is used to identify the AI ​​function.

[0797] As an embodiment, the method for generating the target CSI is not associated with the first type of identifier, including: the method for generating the target CSI includes the target receiver of the target CSI reporting configuration 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 not associated with the first type of identifier.

[0798] As an embodiment, the method for generating the target CSI is not associated with the first type of identifier, including: the method for generating the target CSI does not use the AI ​​model identified by the first type of identifier.

[0799] As an embodiment, the generation method of the target CSI is not associated with the first type of identifier, including: the AI ​​entity identified by the first type of identifier is not used to generate the target CSI.

[0800] As an embodiment, the generation method of the target CSI is not associated with the first type of identifier, including: the target CSI is generated by an AI entity, and the first type of identifier is not used to identify the AI ​​entity or function.

[0801] As an embodiment, the generation method of the target CSI is not associated with the first type of identifier, including: the generation method of the target CSI belongs to an AI function, and the first type of identifier is not used to identify the AI ​​function.

[0802] As an embodiment, the benefits of the above method include: simplifying system design and reducing the implementation complexity of the solution.

[0803] 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.

[0804] Embodiment 11

[0805] Embodiment 11 illustrates a schematic diagram of a second time interval and a first type of identification relationship according to an embodiment of the present application; Fig.11 shown.

[0806] In Embodiment 11, when the target CSI is generated in an AI-based manner, the second time interval depends on the first type of identifier associated with the target CSI generation method.

[0807] As an embodiment, the first category identifier is the first category identifier associated with the generation method of the target CSI, the first category identifier belongs to one of V identifier sets, any identifier set in the V identifier sets includes one or more first category identifiers, and V is a positive integer greater than 1; the first reference symbol depends on the identifier set to which the first category identifier belongs.

[0808] As an embodiment, the first category identifier is the first category identifier associated with the generation method of the target CSI, the first category identifier belongs to one of V identifier sets, any identifier set in the V identifier sets includes one or more first category identifiers, and V is a positive integer greater than 1; the second time interval depends on the identifier set to which the first category identifier belongs.

[0809] As an embodiment, the calculation formula of the second time interval depends on the first type of identifier associated with the generation method of the target CSI.

[0810] As an embodiment, the first category identifier is the first category identifier associated with the generation method of the target CSI, the first category identifier belongs to one of V identifier sets, any identifier set in the V identifier sets includes one or more first category identifiers, and V is a positive integer greater than 1; the calculation formula of the second time interval depends on the identifier set to which the first category identifier belongs.

[0811] As an embodiment, the first category identifier is the first category identifier associated with the generation method of the target CSI, the first category identifier belongs to one of V identifier sets, any identifier set in the V identifier sets includes one or more first category identifiers, and V is a positive integer greater than 1; the calculation formula for the second time interval includes V formulas, the V formulas correspond one-to-one to the V identifier sets, and the calculation formula for the second time interval is a calculation formula corresponding to the identifier set to which the first category identifier belongs.

[0812] As an embodiment, the second time interval depends on the first parameter, and the first parameter depends on the first type of identifier associated with the generation method of the target CSI.

[0813] As an embodiment, the second time interval and the first parameter are in a linear relationship, and the first parameter depends on the first type of identifier associated with the target CSI generation method.

[0814] As an embodiment, the second time interval and the first parameter are in a linear relationship; the first parameter belongs to one of V candidate value ranges, any candidate value range in the V candidate value ranges includes one or more integers or real numbers V is a positive integer greater than 1; the first category identifier belongs to one of V identifier sets, any identifier set in the V identifier sets includes one or more first category identifiers, and V is a positive integer greater than 1; the V candidate value ranges and the V identifier sets correspond one-to-one, and the first parameter is a candidate value in the candidate value range of the V candidate value ranges corresponding to the identifier set to which the first category identifier belongs.

[0815] As an embodiment, the second time interval is (Z′)(2048+144)·κ2 -μ ·T C , where Z′, κ, μ, and T C For the specific meaning of, refer to Section 5.4 of 3GPP TS38.214; the Z′ depends on the first type of identifier associated with the generation method of the target CSI.

[0816] As an embodiment, the second time interval is (Z′)(2048+144)·κ2 -μ ·T C +W, where Z′, κ, μ, T C For the specific meaning of, refer to Section 5.4 of 3GPP TS38.214; the W depends on the first type of identifier associated with the generation method of the target CSI.

[0817] As an embodiment, the essence of the above method includes: setting different CSI calculation times for CSI reports associated with different first-category identifiers.

[0818] As an embodiment, the essence of the above method includes: when setting the CSI calculation time, considering the impact of unused AI models, AI entities or AI functions.

[0819] As an embodiment, the benefits of the above method include: better support for AI models and calculations, and improved accuracy and effectiveness of CSI reporting.

[0820] As an embodiment, the benefits of the above method include: improving the overall performance of the system.

[0821] Example 12

[0822] Embodiment 12 illustrates a schematic diagram of a second resource set according to an embodiment of the present application; Fig.12 As shown. Fig.12 In the above, resource #1, ..., resource #m, ... are at least one resource in the second resource set.

[0823] In Embodiment 12, the target CSI is generated based on AI and includes: 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.

[0824] As an embodiment, the second resource set includes the first resource set and resources outside the first resource set.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] As an embodiment, the second resource set includes at least one training data set.

[0829] As an embodiment, the second resource set includes one or more RS (Reference Signal) resource sets, and one RS resource set includes one or more RS resources.

[0830] As an embodiment, the second resource set includes at least one of at least one CSI-RS resource set, at least one CSI-SSB resource set, or at least one CSI-IM resource set.

[0831] 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 resource or a synchronization signal resource.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] As an embodiment, the target CSI reporting configuration indicates configuration information of the second resource set.

[0836] As an embodiment, the target CSI reporting configuration indicates an identifier of the second resource set.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] As an embodiment, the information other than the target CSI reporting configuration indicates the second resource set.

[0842] As an embodiment, the information other than the target CSI reporting configuration indicating the second resource set includes higher layer parameters.

[0843] As an embodiment, the information other than the target CSI reporting configuration indicating the second resource set includes RRC parameters.

[0844] As an embodiment, the information other than the target CSI reporting configuration indicating the second resource set includes MAC CE.

[0845] As an embodiment, the information other than the target CSI reporting configuration indicating the second resource set includes DCI (downlink control information).

[0846] As an embodiment, the target CSI is generated based on AI, and the first node is not required to measure the second resource set.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] Embodiment 13

[0855] Embodiment 13 illustrates a schematic diagram in which the first condition according to an embodiment of the present application is not satisfied; Fig.13 shown.

[0856] In embodiment 13, when the first condition is not met, the first DCI is ignored; wherein no HARQ-ACK or transport block is multiplexed on the first PUSCH.

[0857] As an embodiment, when the first condition is not satisfied includes: when the first symbol is earlier than the first reference symbol.

[0858] As an embodiment, when the first condition is not satisfied includes: when the second symbol is earlier than the second reference symbol.

[0859] As an embodiment, when the first condition is not satisfied includes: when the first symbol is earlier than the first reference symbol or the second symbol is earlier than the second reference symbol.

[0860] As an embodiment, no HARQ-ACK or transport block is multiplexed on the first PUSCH; when the first condition is not met, the first DCI is ignored.

[0861] As an embodiment, the first resource set consists of one or more non-periodic RS resources; no HARQ-ACK or transport block is multiplexed on the first PUSCH; when the first condition is not met, the first node ignores the first DCI.

[0862] As an embodiment, the first resource set consists of one or more periodic or semi-persistent RS resources; when the first condition is not met, the first node ignores the first DCI; wherein no HARQ-ACK or transport block is multiplexed on the first PUSCH.

[0863] As an embodiment, the first resource set consists of one or more periodic or semi-persistent RS resources; when the first condition is not met, the first processor ignores the first DCI; wherein no HARQ-ACK or transport block is multiplexed on the first PUSCH.

[0864] As an embodiment, HARQ-ACK or transport block is multiplexed on the first PUSCH; when the first condition is not met, the target CSI is sent on the first PUSCH and the target CSI is not updated.

[0865] As an embodiment, the first resource set consists of one or more non-periodic RS resources; HARQ-ACK or transport block is multiplexed on the first PUSCH; when the first condition is not met, the target CSI is sent on the first PUSCH and the target CSI is not updated.

[0866] As an embodiment, when the first condition is not met, the first node ignores the first DCI, or sends the target CSI on the first PUSCH and the target CSI is not updated.

[0867] As an embodiment, when the first condition is not met, the first processor ignores the first DCI, or sends the target CSI on the first PUSCH and the target CSI is not updated.

[0868] As an embodiment, ignoring the first DCI includes: giving up sending a signal on the first PUSCH.

[0869] As an embodiment, ignoring the first DCI includes: giving up sending the target CSI on the first PUSCH.

[0870] As an embodiment, ignoring the first DCI includes: giving up sending the at least one CSI on the first PUSCH.

[0871] As an embodiment, the sending of the target CSI on the first PUSCH and the target CSI not being updated include: the first node is not expected to send the target CSI on the first PUSCH and the target CSI is valid.

[0872] As an embodiment, the sending of the target CSI on the first PUSCH and the target CSI being not updated includes: the first node is not expected to send the target CSI on the first PUSCH and the target CSI is updated.

[0873] As an embodiment, the target CSI is not updated, including: the first node is not expected to update the target CSI.

[0874] As an embodiment, the target CSI is not updated including: whether the target CSI is actually updated is determined by the first node itself or is implementation-related.

[0875] As an embodiment, the target CSI is not updated including: the target CSI is not valid.

[0876] As an embodiment, the target CSI is not updated including: the target CSI is the same as the CSI that is most recently configured for reporting the target CSI on the first PUSCH.

[0877] As an embodiment, the target CSI is not updated including: the target CSI is independent of 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.

[0878] As an embodiment, the target CSI is not updated including: the target CSI is irrelevant to the measurement of the most recent RS opportunity based on the CSI reference resource in the first resource set that is no later than the target CSI.

[0879] As an embodiment, the target CSI being not updated includes: the target CSI is not CSI updated based on measurement of a most recent RS opportunity of a CSI reference resource in at least the first resource set that is no later than the target CSI.

[0880] As an embodiment, the first resource set consists of one or more non-periodic RS resources; the target CSI is not updated including: the target CSI is independent of the measurement of the non-periodic RS resources triggered by the first DCI in the first resource set.

[0881] As an embodiment, the first resource set consists of one or more non-periodic RS resources; the target CSI is not updated including: the target CSI is not generated based on the measurement of the non-periodic RS resources in the first resource set triggered by the first DCI.

[0882] As an embodiment, the first resource set consists of one or more non-periodic RS resources; the target CSI is not updated including: the target CSI is not updated based on the measurement of the non-periodic RS resources in the first resource set triggered by the first DCI.

[0883] Embodiment 14

[0884] Embodiment 14 illustrates a schematic diagram of a second operation according to an embodiment of the present application; Fig.14 shown.

[0885] In Embodiment 14, 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.

[0886] 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.

[0887] As an embodiment, the target CSI includes the first CSI.

[0888] As an embodiment, the first CSI is used to generate the target CSI after post-processing.

[0889] As an embodiment, the first CSI includes N sub-CSIs, and the N information blocks respectively carry the N sub-CSIs.

[0890] As an embodiment, the first CSI includes the output of the first operation.

[0891] As an embodiment, the second CSI includes recovery of at least part of the input of the first operation.

[0892] As an embodiment, the second 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 (CapabilityIndex), or TDCP (Time Domain Channel Properties, time domain channel properties) One or more.

[0893] As an embodiment, the second CSI includes one or more of a channel matrix, an eigenvector, an eigenvalue, or a precoding matrix.

[0894] As an embodiment, the second operation is a reverse operation of the first operation.

[0895] As an embodiment, the second operation is based on training.

[0896] As an embodiment, the training for obtaining the second operation is performed by the target receiver of the target CSI.

[0897] As an embodiment, the training for obtaining the second operation is performed by an MDA function.

[0898] As an embodiment, the training for obtaining the second operation is performed by an MDAS producer.

[0899] As an embodiment, the training for obtaining the second operation is performed by NWDAF.

[0900] As an embodiment, the training for obtaining the second operation is performed by a core network.

[0901] As an embodiment, the training for obtaining the second operation is performed by an AI (Artificial Intelligence) training producer.

[0902] As an embodiment, the first operation and the second operation are obtained through different trainings.

[0903] As an embodiment, the first operation and the second operation are obtained through independent training.

[0904] 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.

[0905] As an embodiment, the first operation and the second operation are obtained through joint training.

[0906] As an embodiment, the benefits of the above method include: optimizing system performance.

[0907] As an embodiment, the training of the second operation depends on the first operation.

[0908] As an embodiment, the producer of the second operation trains the second operation according to the output of the first operation.

[0909] Embodiment 15

[0910] Embodiment 15 illustrates a schematic diagram of a first operation according to another embodiment of the present application; Fig.15 In Example 15, the first operation includes K1 sub-operations, and K1 is a positive integer not greater than 1.

[0911] In Example 15, the K1 sub-operations are respectively represented as sub-operation #0, ..., sub-operation #(K1-1).

[0912] As an embodiment, each of the K1 sub-operations is based on training.

[0913] As an embodiment, at least one of the K1 sub-operations is based on training.

[0914] As an embodiment, each training-based sub-operation in the K1 sub-operations is based on the same executor of the training.

[0915] As an embodiment, two of the K1 sub-operations are based on different executors of the training.

[0916] As an embodiment, at least one sub-operation among the K1 sub-operations needs to be deployed.

[0917] As an embodiment, at least one of the K1 sub-operations needs to be loaded.

[0918] As an embodiment, all the sub-operations that need to be loaded in the K1 sub-operations are loaded from the same producer.

[0919] As an embodiment, two of the K1 sub-operations that need to be loaded are loaded from different producers.

[0920] As an embodiment, at least one of the K1 sub-operations is not based on training.

[0921] 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.

[0922] As an embodiment, one or more of the K1 sub-operations are AI-based.

[0923] As an embodiment, one or more of the K1 sub-operations include inference.

[0924] As an embodiment, one or more of the K1 sub-operations include AI inference.

[0925] As an embodiment, one or more of the K1 sub-operations include AI inference for CSI.

[0926] As an embodiment, one or more of the K1 sub-operations include preprocessing.

[0927] As an embodiment, one or more of the K1 sub-operations include post-processing.

[0928] As an embodiment, there are two sub-operations in the K1 sub-operations that are serial, as shown in the attached Fig.15 All sub-operations in (a), sub-operation #2 to sub-operation #(K1-1) in 15(b), and sub-operation #0 to sub-operation #(K1-4) in 15(c).

[0929] 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.

[0930] As an embodiment, there are two sub-operations in the K1 sub-operations that are parallel, as shown in the attached Fig.15 (b) Sub-operation #0 and sub-operation #1, attached Fig.15 Sub-operation #(K1-3) and sub-operation #(K1-2) in (c).

[0931] 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.

[0932] As an embodiment, the K1 sub-operations include one or more of convolution, pooling, cascading or activation.

[0933] As an embodiment, there is a sub-operation among the K1 sub-operations including a fully connected layer.

[0934] As an embodiment, there is a sub-operation among the K1 sub-operations including a pooling layer.

[0935] As an embodiment, there is a sub-operation among the K1 sub-operations including at least one convolutional layer.

[0936] As an embodiment, there is a sub-operation among the K1 sub-operations that includes at least one encoding layer.

[0937] 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.

[0938] As an embodiment, a coding layer includes at least one convolutional layer and one pooling layer.

[0939] Example 16

[0940] Embodiment 16 illustrates a schematic diagram of a first operation of deploying according to an embodiment of the present application; Fig.16 shown.

[0941] In embodiment 16, the first processor deploys the first operation.

[0942] As an embodiment, the deployment includes obtaining the first operation.

[0943] As an embodiment, the deploying includes obtaining an AI entity.

[0944] As an embodiment, the deploying includes obtaining an AI entity that performs the first operation.

[0945] As an embodiment, the deploying includes obtaining an AI entity including an AI function for performing the first operation.

[0946] As an embodiment, the deploying includes loading the first operation.

[0947] As an embodiment, the deploying includes making a request to load the first operation.

[0948] As an example, Fig.16 The request in is a request made by the first node to load the first operation.

[0949] As an example, Fig.16 The response in is a response to the request for loading the first operation made by the first node.

[0950] As an embodiment, the first node is connected Fig.16 The first operation is obtained in response to the above.

[0951] As an embodiment, the first operation is obtained by loading from a serving cell of the first node.

[0952] As an embodiment, the first operation is obtained by loading from a base station maintaining a service cell of the first node.

[0953] As an embodiment, the first operation is obtained by loading from the core network.

[0954] As an embodiment, the first operation is obtained by loading from a first producer.

[0955] As an embodiment, the first producer attaches Fig.16 The response in provides the first operation to the first node.

[0956] As an embodiment, the deployment is performed by an AI function.

[0957] As an embodiment, the deployment is completed by an AI function deployed on the first node.

[0958] As an embodiment, the deployment is performed by an AI deployment function.

[0959] As an embodiment, the deployment is completed by an AI deployment function deployed on the first node.

[0960] As an embodiment, the deployment is performed by AI inference function.

[0961] As an embodiment, the deployment is accomplished by an AI inference function deployed on the first node.

[0962] As an embodiment, the deployment is performed by an AI entity.

[0963] As an embodiment, the deployment is completed by an AI entity deployed on the first node.

[0964] As an embodiment, the deployment is completed by an AI entity having a deployment function.

[0965] As an embodiment, the deployment is completed by an AI entity with deployment function deployed on the first node.

[0966] As an embodiment, the deployment is performed by an AI entity having an inference function.

[0967] As an embodiment, the deployment is completed by an AI entity with inference function deployed on the first node.

[0968] As an embodiment, the deploying includes obtaining the first operation from a first producer.

[0969] As an embodiment, the deploying includes making a request to a first producer to load the first operation.

[0970] As an embodiment, the deploying includes loading the first operation from a first producer.

[0971] As an embodiment, the first producer generates and provides at least one of an AL entity and an AL function.

[0972] As an embodiment, the first producer is the producer of the first operation.

[0973] As an embodiment, the first producer includes an AL entity producer.

[0974] As an embodiment, the first producer includes an AL function producer.

[0975] As an embodiment, the first producer includes an AL deployment producer.

[0976] As an embodiment, the first producer includes an AL loading producer.

[0977] As an embodiment, the first producer includes an AL training producer.

[0978] As an embodiment, the first producer includes an AL inference producer.

[0979] As an embodiment, the first producer includes a producer of deployment of an AL entity.

[0980] As an embodiment, the first producer includes a loaded producer of an AL entity.

[0981] As an embodiment, the first producer includes a MnS (Management Service) producer.

[0982] As an embodiment, the sender of the target CSI reporting configuration is the first producer.

[0983] As an embodiment, the sender of the target CSI reporting configuration is different from the first producer.

[0984] As an embodiment, the training for obtaining the first operation is performed by the first producer.

[0985] As an embodiment, a performer of the training for obtaining the first operation is different from the first producer.

[0986] Embodiment 17

[0987] Embodiment 17 illustrates a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of the present application; Fig.17 Attached Fig.17 (a) comprising a third processor, a fourth processor and a fifth processor, Fig.17(b) includes a third processor, a fourth processor, a fifth processor and a sixth processor.

[0988] In Example 17(a), the third processor sends a first data set to the fourth processor and 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. Fig.17 (a),The first type of feedback is optional.

[0989] In Example 17(b), the third processor sends a first data set to the fourth processor and 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 the fifth processor sends the first-category output to the sixth processor. Fig.17 (b), the first type of feedback and the second type of feedback are optional.

[0990] As an example, Fig.17 In (a), the fifth processor sends the first type of output to the second node in the present application.

[0991] As an example, Fig.17 (a) A single-side AI model is used for beam prediction or channel information prediction, and the fifth processor performs the first operation, where the first operation is used for beam prediction or channel information prediction.

[0992] As an example, Fig.17 (b) A two-sided AI model is used for CSI compression, the first operation is used to compress CSI, the second operation is used to restore CSI, the fifth processor performs the first operation, and the sixth processor includes the second operation.

[0993] As an embodiment, the AI ​​includes ML (Machine Learning) inference.

[0994] As an embodiment, the fifth processor performs the first operation.

[0995] As an embodiment, the sixth processor includes the second operation.

[0996] 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 update of the target first type parameter group.

[0997] 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.

[0998] As an embodiment, the third processor generates the first data set and the second data set based on measurements of a first type of wireless signal, where the first type of wireless signal includes a downlink RS.

[0999] As an embodiment, the fifth processor belongs to the first node, and the sixth processor belongs to the second node.

[1000] As an embodiment, the target CSI belongs to the first type of output.

[1001] As an embodiment, the second data set includes the input of the first operation.

[1002] As an embodiment, the second data set includes information obtained based on the target CSI reporting configuration and the M1 configurations.

[1003] As an embodiment, the first data set includes training data (Training Data).

[1004] As an embodiment, the fourth processor is a producer of the first operation.

[1005] As an embodiment, the fourth processor includes an AI training producer.

[1006] As an embodiment, the fourth processor includes an AI training function.

[1007] As an embodiment, the fourth processor is used for model training, and the trained model is described by the target first type parameter group.

[1008] As an embodiment, the fourth processor belongs to the first node.

[1009] The above embodiment avoids transferring the first data set to the second node.

[1010] As an embodiment, the fourth processor belongs to the second node.

[1011] The above embodiments support joint training and optimize system performance.

[1012] As an embodiment, the fourth processor belongs to the core network.

[1013] The above embodiments support joint training of the entire network to further optimize system performance.

[1014] As an embodiment, the second data set includes inference data.

[1015] As an embodiment, the fifth processor includes an AI inference producer.

[1016] As an embodiment, the fifth processor includes an AI inference function.

[1017] As an embodiment, the fifth processor belongs to the first node.

[1018] 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.

[1019] As an embodiment, the first operation is described by the target first type parameter group.

[1020] As an embodiment, the target first-category parameter group is used to construct the first operation.

[1021] As an embodiment, the fifth processor includes the second operation.

[1022] 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.

[1023] As a sub-embodiment of the above embodiment, the generation of the restored data set adopts a method similar to the second operation.

[1024] 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.

[1025] 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.

[1026] 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.

[1027] 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.

[1028] Embodiment 18

[1029] Embodiment 18 illustrates a schematic diagram based on artificial intelligence or machine learning according to an embodiment of the present application; Fig.18 Attached Fig.18 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.

[1030] In Example 18, 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.

[1031] As an embodiment, the third operation includes AI training, the fourth operation includes AI testing, the fifth operation includes AI emulation, the sixth operation includes AI entity loading, and the seventh operation includes AI inference.

[1032] 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.

[1033] As an embodiment, the first stage includes at least one of model training and testing.

[1034] As an embodiment, the AI ​​model training includes initial training and re-training of one or a group of AI entities.

[1035] As an embodiment, the AI ​​model training includes AI entity validation.

[1036] As an embodiment, the AI ​​entity verification is used to evaluate the performance of the AI ​​entity.

[1037] As an embodiment, if the result of the AI ​​entity verification does not meet expectations, the AI ​​model will be retrained.

[1038] As an embodiment, the AI ​​testing includes testing the verified AI entity to estimate the performance of the trained AI model.

[1039] As an embodiment, if the result of the AI ​​test meets expectations, the AI ​​entity proceeds to the next stage; otherwise the AI ​​model will be retrained.

[1040] As an embodiment, the second stage includes AI simulation, which performs inference of AI entities in a simulation environment.

[1041] As an embodiment, the AI ​​simulation is to estimate the performance of AI entity inference in a simulation environment before using the AI ​​entity.

[1042] As an embodiment, the second stage is optional.

[1043] As an embodiment, the third stage includes AI entity loading, and the AI ​​entity loading is to obtain the trained AI entity to obtain the desired AI inference function.

[1044] As an embodiment, the third stage is optional.

[1045] As an example, when the training function and the inference function are co-located, the third stage is no longer needed.

[1046] As an embodiment, the fourth stage includes AI inference.

[1047] As an embodiment, the seventh operation includes the first operation.

[1048] As an embodiment, the seventh operation includes the second operation.

[1049] Embodiment 19

[1050] Embodiment 19 illustrates a structural block diagram of a processing device used in a first node according to an embodiment of the present application; Fig.19 As shown in the attached Fig.19 In the embodiment, the processing device 1900 in the first node includes a first receiver 1901 and a first processor 1902.

[1051] A first receiver 1901 receives at least one CSI reporting configuration; receives a first DCI on a first PDCCH, where the first DCI triggers reporting of at least one CSI on a first PUSCH, and the at least one CSI reporting configuration is used to configure reporting of the at least one CSI;

[1052] The first processor 1902 determines whether to send target CSI on the first PUSCH; and sends the target CSI on the first PUSCH only when a first condition is met;

[1053] In embodiment 19, a target CSI reporting configuration is used to configure reporting of the target CSI, the target CSI reporting configuration is one of the at least one CSI reporting configurations, and the target CSI is one of the at least one CSI; 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 first condition includes that the first symbol is not earlier than the first reference symbol and the second symbol is not earlier than the second reference symbol; the first symbol is the first uplink symbol in the first PUSCH for carrying the at least one CSI, and the first reference symbol is the next uplink symbol with a CP starting at the first time interval after the end of the last symbol of the first PDCCH; the second symbol is the first uplink symbol in the first PUSCH for carrying the target CSI, and the second reference symbol is the next uplink symbol with a CP starting at the second time interval after the end of the last symbol of the first RS in the first resource set; the second time interval depends on whether the generation method of the target CSI is based on AI.

[1054] As an embodiment, the calculation formula of the second time interval depends on a first parameter, and the first parameter depends on whether the generation method of the target CSI is based on AI.

[1055] As an embodiment, when the target CSI is generated based on AI, the target CSI includes N information blocks, and the N information blocks respectively include channel information of N time units, where N is a positive integer greater than 1; and the second time interval depends on at least one of the N time units.

[1056] As an embodiment, the first resource set consists of one or more non-periodic RS resources, and the first RS is the latest RS in time among the first resource set triggered by the first DCI.

[1057] As an embodiment, the method for generating the target CSI is based on AI and includes: the method for generating 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.

[1058] As an embodiment, the method for generating the target CSI is based on AI and includes: the method for generating the target CSI is associated with a first type of identifier.

[1059] As an embodiment, when the target CSI is generated based on AI, the second time interval depends on the first type of identifier associated with the target CSI generation method.

[1060] As an embodiment, the target CSI is generated based on AI and includes: 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.

[1061] As an embodiment, any one of the N information blocks 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.

[1062] As an embodiment, the first processor 1902 ignores the first DCI when the first condition is not met; wherein no HARQ-ACK or transport block is multiplexed on the first PUSCH.

[1063] As an embodiment, the output of the first operation includes the first CSI, the target CSI carries the first CSI, and the first CSI is used as the input of the second operation by the target receiver of the target CSI to generate the second CSI.

[1064] As an embodiment, the first processor 1902 deploys the first operation.

[1065] As an embodiment, the first operation is associated with the first type of identification.

[1066] As an embodiment, the first operation is training-based or AI-based.

[1067] As an embodiment, the second operation is training-based or AI-based.

[1068] As an embodiment, the first node is user equipment.

[1069] As an embodiment, the first node is a relay node device.

[1070] As an embodiment, the first receiver 1901 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.

[1071] As an embodiment, the first processor 1902 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.

[1072] Embodiment 20

[1073] Embodiment 20 illustrates a structural block diagram of a processing device used in a second node according to an embodiment of the present application; Fig. 20 In the attached Fig. 20 In the example, the processing device 2000 in the second node includes a second processor 2001.

[1074] The second processor 2001 sends at least one CSI reporting configuration; sends a first DCI on a first PDCCH, where the first DCI triggers reporting of at least one CSI on a first PUSCH, and the at least one CSI reporting configuration is used to configure reporting of the at least one CSI;

[1075] In embodiment 20, the target recipient of the first DCI determines whether to send the target CSI on the first PUSCH; only when the first condition is met, the target recipient of the first DCI sends the target CSI on the first PUSCH; the target CSI reporting configuration is used to configure the reporting of the target CSI, the target CSI reporting configuration is one of the at least one CSI reporting configuration, and the target CSI is one of the at least one CSI; 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 first condition includes that the first symbol is not earlier than the first reference symbol and the second symbol is not earlier than the second reference symbol; the first symbol is the first uplink symbol in the first PUSCH for carrying the at least one CSI, and the first reference symbol is the next uplink symbol whose CP starts at the first time interval after the end of the last symbol of the first PDCCH; the second symbol is the first uplink symbol in the first PUSCH for carrying the target CSI, and the second reference symbol is the next uplink symbol whose CP starts at the second time interval after the end of the last symbol of the first RS in the first resource set; the second time interval depends on whether the generation method of the target CSI is based on AI.

[1076] As an embodiment, the second processor 2001 determines whether to receive target CSI on the first PUSCH; and receives the target CSI on the first PUSCH only when the first condition is met;

[1077] As an embodiment, the calculation formula of the second time interval depends on a first parameter, and the first parameter depends on whether the generation method of the target CSI is based on AI.

[1078] As an embodiment, when the target CSI is generated based on AI, the target CSI includes N information blocks, and the N information blocks respectively include channel information of N time units, where N is a positive integer greater than 1; and the second time interval depends on at least one of the N time units.

[1079] As an embodiment, the first resource set consists of one or more non-periodic RS resources, and the first RS is the latest RS in time among the first resource set triggered by the first DCI.

[1080] As an embodiment, the method for generating the target CSI is based on AI and includes: the method for generating the target CSI includes the target receiver of the first DCI 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.

[1081] As an embodiment, the method for generating the target CSI is based on AI and includes: the method for generating the target CSI is associated with a first type of identifier.

[1082] As an embodiment, when the target CSI is generated based on AI, the second time interval depends on the first type of identifier associated with the target CSI generation method.

[1083] As an embodiment, the target CSI is generated based on AI and includes: 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.

[1084] As an embodiment, any one of the N information blocks 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.

[1085] As an embodiment, the second processor 2001, when the first condition is not met, abandons receiving a signal on the first PUSCH, or abandons receiving the target CSI on the first PUSCH;

[1086] As an embodiment, when the first condition is not met, the target recipient of the first DCI ignores the first DCI; wherein no HARQ-ACK or transport block is multiplexed on the first PUSCH.

[1087] As an embodiment, the output of the first operation includes the first CSI, the target CSI carries the first CSI, and the first CSI is used as the input of the second operation by the target receiver of the target CSI to generate the second CSI.

[1088] As an embodiment, the second processor 2001 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.

[1089] As an embodiment, the second processor 2001 deploys the second operation.

[1090] As an embodiment, the first operation is associated with the first type of identification.

[1091] As an embodiment, the first operation is training-based or AI-based.

[1092] As an embodiment, the second operation is training-based or AI-based.

[1093] As an embodiment, the second node is a base station device.

[1094] As an embodiment, the second node is user equipment.

[1095] As an embodiment, the second node is a relay node device.

[1096] As an embodiment, the second processor 2001 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.

[1097] 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 (enhanced MTC, 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.

[1098] 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 at least one CSI reporting configuration; Receiving a first DCI on a first PDCCH, where the first DCI triggers reporting of at least one CSI on a first PUSCH, and the at least one CSI reporting configuration is used to configure reporting of the at least one CSI; determining whether to send target CSI on the first PUSCH; and sending the target CSI on the first PUSCH only when a first condition is met; Among them, the target CSI reporting configuration is used to configure the reporting of the target CSI, the target CSI reporting configuration is one of the at least one CSI reporting configuration, and the target CSI is one of the at least one CSI; 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 first condition includes that the first symbol is not earlier than the first reference symbol and the second symbol is not earlier than the second reference symbol; the first symbol is the first uplink symbol in the first PUSCH for carrying the at least one CSI, and the first reference symbol is the next uplink symbol with a CP starting at the first time interval after the end of the last symbol of the first PDCCH; the second symbol is the first uplink symbol in the first PUSCH for carrying the target CSI, and the second reference symbol is the next uplink symbol with a CP starting at the second time interval after the end of the last symbol of the first RS in the first resource set; the second time interval depends on whether the generation method of the target CSI is based on AI.

2. The method according to claim 1, characterized in that The calculation formula of the second time interval depends on a first parameter, and the first parameter depends on whether the generation method of the target CSI is based on AI.

3. The method according to claim 1 or 2, characterized in that: When the target CSI is generated based on AI, the target CSI includes N information blocks, and the N information blocks respectively include channel information of N time units, where N is a positive integer greater than 1; and the second time interval depends on at least one of the N time units.

4. The method according to any one of claims 1 to 3, characterized in that: The first resource set consists of one or more non-periodic RS resources, and the first RS is the latest RS in time in the first resource set triggered by the first DCI.

5. The method according to any one of claims 1 to 4, characterized in that: The method for generating the target CSI is based on AI and includes: the method for generating 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.

6. The method according to any one of claims 1 to 5, characterized in that: The method for generating the target CSI is based on AI and includes: the method for generating the target CSI is associated with a first type of identifier.

7. The method according to claim 6, characterized in that When the target CSI is generated in an AI-based manner, the second time interval depends on the first type of identifier associated with the target CSI generation manner.

8. The method according to any one of claims 1 to 7, characterized in that: The target CSI is generated based on AI and includes: 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.

9. The method according to any one of claims 1 to 8, characterized in that: include: When the first condition is not met, ignoring the first DCI; Therein, no HARQ-ACK or transport block is multiplexed on the first PUSCH.

10. 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 9.

11. A method in a second node for wireless communication, characterized in that: include: Send at least one CSI reporting configuration; Sending a first DCI on a first PDCCH, where the first DCI triggers reporting of at least one CSI on a first PUSCH, and the at least one CSI reporting configuration is used to configure reporting of the at least one CSI; The target recipient of the first DCI determines whether to send the target CSI on the first PUSCH; the target recipient of the first DCI sends the target CSI on the first PUSCH only when the first condition is met; the target CSI reporting configuration is used to configure the reporting of the target CSI, the target CSI reporting configuration is one of the at least one CSI reporting configuration, and the target CSI is one of the at least one CSI; 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 first The conditions include that the first symbol is not earlier than the first reference symbol and the second symbol is not earlier than the second reference symbol; the first symbol is the first uplink symbol in the first PUSCH for carrying the at least one CSI, and the first reference symbol is the next uplink symbol whose CP starts at the first time interval after the end of the last symbol of the first PDCCH; the second symbol is the first uplink symbol in the first PUSCH for carrying the target CSI, and the second reference symbol is the next uplink symbol whose CP starts at the second time interval after the end of the last symbol of the first RS in the first resource set; the second time interval depends on whether the generation method of the target CSI is based on AI.

12. The method according to claim 11, characterized in that include: Determine whether to receive target CSI on the first PUSCH; and receive the target CSI on the first PUSCH only when a first condition is met.

13. The method according to claim 11 or 12, characterized in that: The calculation formula of the second time interval depends on a first parameter, and the first parameter depends on whether the generation method of the target CSI is based on AI.

14. The method according to any one of claims 11 to 13, characterized in that When the target CSI is generated based on AI, the target CSI includes N information blocks, and the N information blocks respectively include channel information of N time units, where N is a positive integer greater than 1; and the second time interval depends on at least one of the N time units.

15. The method according to any one of claims 11 to 14, characterized in that The first resource set consists of one or more non-periodic RS resources, and the first RS is the latest RS in time in the first resource set triggered by the first DCI.

16. The method according to any one of claims 11 to 15, characterized in that The method for generating the target CSI is based on AI and includes: the method for generating the target CSI includes the target receiver of the first DCI 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.

17. The method according to any one of claims 11 to 16, characterized in that The method for generating the target CSI is based on AI and includes: the method for generating the target CSI is associated with a first type of identifier.

18. The method according to claim 17, characterized in that When the target CSI is generated in an AI-based manner, the second time interval depends on the first type of identifier associated with the target CSI generation manner.

19. The method according to any one of claims 11 to 18, characterized in that The target CSI is generated based on AI and includes: 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.

20. The method according to any one of claims 11 to 19, characterized in that include: When the first condition is not met, giving up receiving a signal on the first PUSCH, or giving up receiving the target CSI on the first PUSCH; Therein, no HARQ-ACK or transport block is multiplexed on the first PUSCH.

21. The method according to any one of claims 11 to 19, characterized in that When the first condition is not met, the target recipient of the first DCI ignores the first DCI; wherein no HARQ-ACK or transport block is multiplexed on the first PUSCH.

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 11 to 21.

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