Method and apparatus for csi reporting in nodes used for wireless communication

Abstract

A method and apparatus for CSI reporting in a node used for wireless communication are disclosed. A first node receives at least one CSI reporting configuration; receives a first DCI on a first PDCCH, the first DCI triggering reporting of at least one CSI on a first PUSCH; 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; wherein a target CSI reporting configuration is used to configure reporting of the target CSI; and the target CSI reporting configuration indicates a first set of resources.

Description

Method and apparatus for CSI reporting in a node for wireless communication

[0001] The present application claims priority from the Chinese patent application No. 202410741829.0 filed on June 7, 2024, and entitled "Method and apparatus for CSI reporting in a node for wireless communication", the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] The present application relates to a transmission method and apparatus in a wireless communication system, and in particular to a method and apparatus for CSI (Channel State Information) reporting in a wireless communication system. BACKGROUND

[0003] In a conventional wireless communication, a UE (User Equipment) reports various assistance information, such as channel information, beam management related assistance information, positioning related assistance information, etc., by measuring downlink signals and / or channels. The channel information includes, but is not limited to, one or more of CRI (CSI-RS Resource Indicator), RI (Rank Indicator), PMI (Precoding Matrix Indicator), CQI (Channel quality indicator), or beam indication. The UE can use the information to select appropriate transmission parameters by itself or report the information. The network device selects appropriate transmission parameters for the UE according to the UE's report, such as cell camping, MCS (Modulation and Coding Scheme), TPMI (Transmitted Precoding Matrix Indicator), TCI (Transmission Configuration Indication), etc. In addition, the UE report can be used to optimize network parameters, such as better cell coverage, switching base stations according to UE location, etc.

[0004] 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 of AI (Artificial Intelligence) / ML (Machine Learning) technology is launched to explore its impact on system performance and system design. Compared with the traditional processing method, AI / ML has the characteristics of training and deployment. SUMMARY

[0005] The applicant 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 view of 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 taken as an example, and the present application is also applicable to scenarios such as future 6G systems, achieving similar technical effects as 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 traditional CSI (Channel State Information) reporting solutions; further, a unified design solution for different scenarios (such as other non-AI / ML scenarios, including but not limited to V2X (Vehicle to Everything), capacity enhancement systems, near-distance communication systems, NTN (Non Terrestrial Network), IoT (Internet of Things), URLLC (Ultra Reliable Low Latency Communication) networks, etc.) can also help to reduce hardware complexity and cost. In the case of no conflict, the embodiments in any node of the present application and the features in the embodiments can be applied to any other node. In the case of no conflict, the embodiments of the present application and the features in the embodiments can be arbitrarily combined with each other.

[0006] In particular, the explanation of the terminology, the noun, the function, the variable in the present application (if not specially stated) can refer to the definition in the specification agreement TS28 series, TS36 series, TS38 series, TS37 series of 3GPP. If necessary, the 3GPP standards TS38.211, TS38.212, TS38.213, TS38.214, TS38.215, TS38.321, TS38.331, TS38.305, TS38.304, TS37.355 can be referred to for the understanding of the present application.

[0007] The present application discloses a method in a first node used for wireless communication, characterized in that, comprising:

[0008] 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;

[0009] determining whether to send a target CSI on the first PUSCH; only when a first condition is met, sending the target CSI on the first PUSCH;

[0010] wherein a target CSI reporting configuration is used to configure the reporting of the target CSI, the target CSI reporting configuration being one of the at least one CSI reporting configuration, the target CSI being one of the at least one CSI; the target CSI reporting configuration indicates a first resource set, the first resource set being used for at least one of channel measurement or interference resource measurement of the target CSI, the first resource set including one or more RS resources; the first condition includes that a first symbol is not earlier than a first reference symbol and a second symbol is not earlier than a second reference symbol; the first symbol is a first uplink symbol in the first PUSCH for carrying the at least one CSI, the first reference symbol being a next uplink symbol whose CP starts at a first time interval after the end of the last symbol of the first PDCCH; the second symbol is a first uplink symbol in the first PUSCH for carrying the target CSI, the second reference symbol being a next uplink symbol whose CP starts at a second time interval after the end of the last symbol of a first RS in the first resource set; the second time interval depends on whether the generation mode of the target CSI is based on AI.

[0011] In the present application, the AI (Artificial Intelligence) includes ML (Machine Learning).

[0012] As an embodiment, the problem to be solved by the present application includes: how to determine whether to send CSI on PUSCH.

[0013] As an embodiment, the essence of the above method includes: sending CSI on PUSCH only when the first condition is met.

[0014] As an embodiment, the essence of the above method includes: the first condition includes at least two conditions.

[0015] As an embodiment, the essence of the above method includes: whether to send CSI on PUSCH depends on whether the generation mode of CSI is based on AI.

[0016] As an embodiment, the benefits of the above method include: supporting AI-based CSI reporting.

[0017] As an embodiment, the benefits of the above method include: ensuring consistency in the understanding of CSI reporting by the transmitting and receiving ends.

[0018] As an embodiment, the benefits of the above method include: improving the accuracy and effectiveness of CSI reporting.

[0019] 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, simplifying system design and reducing scheme complexity.

[0020] As an embodiment, the benefits of the above method include: enhancing the flexibility and robustness of the system, better adapting to various different transmission conditions and application scenarios, and improving the overall performance of the system.

[0021] According to one aspect of the present application, the calculation formula of the second time interval depends on a first parameter, and the first parameter depends on whether the generation mode of the target CSI is based on AI.

[0022] As an embodiment, the essence of the above method includes: the value of the second time interval depends on whether the generation mode of the target CSI is based on AI.

[0023] As an embodiment, the benefits of the above method include: compatible with existing system design and standards, and improving the forward and backward compatibility of the system.

[0024] As an embodiment, the benefits of the above method include: simplifying the design of the system and reducing the implementation complexity of the scheme.

[0025] According to an aspect of the present disclosure, when the generation manner of the target CSI is based on AI, the target CSI includes N information blocks, the N information blocks respectively include channel information of N time units, and N is a positive integer greater than 1; the second time interval depends on at least one of the N time units.

[0026] As an embodiment, the benefits of the above method include: compatibility with current system design and standards.

[0027] As an embodiment, the benefits of the above method include: enhancing the flexibility and robustness of the system, adapting to different transmission scenarios and applications.

[0028] As an embodiment, the benefits of the above method include: improving the overall performance of the system.

[0029] According to an aspect of the present disclosure, the first resource set is composed of one or more aperiodic RS resources, and the first RS is the latest RS in time in the first resource set triggered by the first DCI.

[0030] As an embodiment, the benefits of the above method include: compatibility with current system design and standards.

[0031] According to an aspect of the present disclosure, when the generation manner of the target CSI is based on AI, the target CSI includes N information blocks, the N information blocks respectively include channel information of N time units, and N is a positive integer greater than 1; the second time interval depends on at least one of the N time units.

[0032] As an embodiment, the first operation is based on training or AI.

[0033] As an embodiment, the benefits of the above method include: supporting AI / ML-based CSI reporting.

[0034] As an embodiment, the benefits of the above method include: improving the accuracy and effectiveness of CSI reporting.

[0035] As an embodiment, the benefits of the above method include: improving the overall performance of the system.

[0036] According to an aspect of the present disclosure, when the generation manner of the target CSI is based on AI, the target CSI includes N information blocks, the N information blocks respectively include channel information of N time units, and N is a positive integer greater than 1; the second time interval depends on at least one of the N time units.

[0037] As an embodiment, the benefits of the above method include: adapting to various different scenarios and terminals, and improving the adaptability and flexibility of the system.

[0038] As an embodiment, benefits of the above method include: simplifying system design, having good flexibility.

[0039] According to an aspect of the present application, when the generation manner of the target CSI is based on AI, the second time interval is associated with the first type identifier to which the generation manner of the target CSI is dependent.

[0040] As an embodiment, benefits of the above method include: simplifying system design, reducing implementation complexity of the scheme.

[0041] As an embodiment, benefits of the above method include: enhancing flexibility of the system, improving overall performance of the system.

[0042] According to an aspect of the present application, the generation manner of the target CSI based on AI includes: the target CSI indicates at least one resource in a second resource set, and the second resource set includes resources not belonging to the first resource set.

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

[0044] As an embodiment, benefits of the above method include: reducing measurement resources required to obtain the target CSI.

[0045] As an embodiment, benefits of the above method include: enhancing flexibility of the system, improving overall performance of the system.

[0046] According to an aspect of the present application, it comprises:

[0047] When the first condition is not met, the first DCI is ignored.

[0048] Wherein, no HARQ-ACK or transport block is multiplexed on the first PUSCH.

[0049] 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.

[0050] As an embodiment, benefits of the above method include: being compatible with current system design and standards, improving forward and backward compatibility of the system.

[0051] As an embodiment, benefits of the above method include: improving stability and robustness of the system.

[0052] The present application discloses a method in a second node used for wireless communication, characterized by comprising:

[0053] transmitting at least one CSI reporting configuration; transmitting a first DCI on a first PDCCH, the first DCI triggering reporting of at least one CSI on a first PUSCH, the at least one CSI reporting configuration being used to configure reporting of the at least one CSI;

[0054] wherein a target receiver of the first DCI determines whether to transmit a target CSI on the first PUSCH; the target receiver of the first DCI transmits the target CSI on the first PUSCH only when a first condition is satisfied; a target CSI reporting configuration is used to configure reporting of the target CSI, the target CSI reporting configuration being one of the at least one CSI reporting configuration, the target CSI being one of the at least one CSI; the target CSI reporting configuration indicates a first resource set, the first resource set being used for at least one of channel measurement or interference resource measurement of the target CSI, the first resource set including one or more RS resources; the first condition includes a first symbol not earlier than a first reference symbol and a second symbol not earlier than a second reference symbol; the first symbol is a first uplink symbol in the first PUSCH for carrying the at least one CSI, the first reference symbol being a next uplink symbol with CP starting at a first time interval after an end of a last symbol of the first PDCCH; the second symbol is a first uplink symbol in the first PUSCH for carrying the target CSI, the second reference symbol being a next uplink symbol with CP starting at a second time interval after an end of a last symbol of a first RS in the first resource set; the second time interval depending on whether a generation manner of the target CSI is based on AI.

[0055] According to an aspect of the present application, it features in comprising:

[0056] determining whether to receive a target CSI on the first PUSCH; receiving the target CSI on the first PUSCH only when the first condition is satisfied;

[0057] According to an aspect of the present application, a calculation formula of the second time interval depends on a first parameter, the first parameter depending on whether a generation manner of the target CSI is based on AI.

[0058] According to an aspect of the present application, when the generation manner of the target CSI is based on AI, the target CSI includes N information blocks, the N information blocks respectively including channel information of N time units, N being a positive integer greater than 1; the second time interval depending on at least one of the N time units.

[0059] According to an aspect of the present application, the first resource set consists of one or more aperiodic RS resources, and the first RS is the latest RS in time in the first resource set triggered by the first DCI.

[0060] According to an aspect of the present application, when the generation manner of the target CSI is based on AI, the generation manner of the target CSI comprises that the target receiver of the first DCI performs a first operation, and an input of the first operation depends on measurement based on the first resource set, and the target CSI depends on an output of the first operation.

[0061] According to an aspect of the present application, when the generation manner of the target CSI is based on AI, the generation manner of the target CSI is associated to a first type identifier.

[0062] According to an aspect of the present application, when the generation manner of the target CSI is based on AI, the second time interval depends on the first type identifier to which the generation manner of the target CSI is associated.

[0063] According to an aspect of the present application, when the generation manner of the target CSI is based on AI, the generation manner of the target CSI comprises that the target CSI indicates at least one resource in a second resource set, and the second resource set comprises resources not belonging to the first resource set.

[0064] According to an aspect of the present application, the method comprises:

[0065] When the first condition is not met, the method comprises:

[0066] According to an aspect of the present application, when the first condition is not met, the target receiver of the first DCI ignores the first DCI, and no HARQ-ACK or transport block is multiplexed on the first PUSCH.

[0067] According to an aspect of the present application, when the first condition is not met, the target receiver of the first DCI ignores the first DCI; and no HARQ-ACK or transport block is multiplexed on the first PUSCH.

[0068] The present application discloses a terminal, which comprises one or more processors and a memory.

[0069] The memory is coupled with the one or more processors, and is configured to store computer program codes, the computer program codes comprising computer instructions, the one or more processors invoking the computer instructions to cause the terminal to perform the method in the first node.

[0070] The present application discloses a base station, characterized in that the base station comprises one or more processors and a memory.

[0071] The memory is coupled with the one or more processors, and is configured to store computer program codes, the computer program codes comprising computer instructions, the one or more processors invoking the computer instructions to cause the base station to perform the method in the second node.

[0072] The present application discloses a first node used for wireless communication, characterized in that comprising:

[0073] The first receiver receives at least one CSI reporting configuration, and receives a first DCI on a first PDCCH, the first DCI triggering reporting of at least one CSI on a first PUSCH, the at least one CSI reporting configuration being used for configuring reporting of the at least one CSI.

[0074] The 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.

[0075] The target CSI reporting configuration is used for configuring reporting of the target CSI, the target CSI reporting configuration being one of the at least one CSI reporting configuration, and the target CSI being one of the at least one CSI; the target CSI reporting configuration indicates a first resource set, the first resource set being used for at least one of channel measurement or interference resource measurement of the target CSI, the first resource set comprising one or more RS resources; the first condition comprises that a first symbol is not earlier than a first reference symbol and a second symbol is not earlier than a second reference symbol; the first symbol is a first uplink symbol in the first PUSCH for carrying the at least one CSI, and the first reference symbol is a next uplink symbol with a CP starting at a first time interval after an end of a last symbol of the first PDCCH; the second symbol is a first uplink symbol in the first PUSCH for carrying the target CSI, and the second reference symbol is a next uplink symbol with a CP starting at a second time interval after an end of a last symbol of a first RS in the first resource set; and the second time interval depends on whether a generation manner of the target CSI is based on AI.

[0076] The application discloses a second node used for wireless communication, which is characterized by comprising:

[0077] a second processor configured to send at least one CSI reporting configuration, and send 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 for configuring reporting of the at least one CSI.

[0078] wherein a target receiver of the first DCI determines whether to send a target CSI on the first PUSCH; the target receiver of the first DCI sends the target CSI on the first PUSCH only when a first condition is met; a target CSI reporting configuration is used for configuring 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 comprises one or more RS resources; the first condition comprises that a first symbol is not earlier than a first reference symbol and a second symbol is not earlier than a second reference symbol; the first symbol is a first uplink symbol in the first PUSCH for carrying the at least one CSI, and the first reference symbol is a next uplink symbol with a CP starting at a first time interval after the end of a last symbol of the first PDCCH; the second symbol is a first uplink symbol in the first PUSCH for carrying the target CSI, and the second reference symbol is a next uplink symbol with a CP starting at a second time interval after the end of a last symbol of a first RS in the first resource set; and the second time interval depends on whether a generation mode of the target CSI is based on AI.

[0079] As an embodiment, compared with a conventional scheme, the application has the following advantages:

[0080] -ensuring consistency of CSI reporting understanding of a transceiver;

[0081] -supporting AI-based CSI reporting;

[0082] -reducing system measurement resources and overheads;

[0083] -improving forward and backward compatibility of the system;

[0084] -improving flexibility and adaptability of the system;

[0085] -improving reliability and robustness of the system;

[0086] - simplify system design, reduce complexity of scheme implementation;

[0087] - improve accuracy and effectiveness of CSI reporting;

[0088] - enhance overall performance of system. BRIEF DESCRIPTION OF DRAWINGS

[0089] Other features, objects, and advantages of the application will become more apparent from the following detailed description when read in connection with the following accompanying drawings:

[0090] Fig. 1 shows a flowchart of a first DCI, at least one CSI reporting configuration and a target CSI according to one embodiment of the present application;

[0091] Fig. 2 shows a schematic diagram of a network architecture according to one embodiment of the present application;

[0092] Fig. 3 shows a schematic diagram of an embodiment of a radio protocol architecture for the user and control planes according to one embodiment of the present application;

[0093] Fig. 4 shows a schematic diagram of a first communication device and a second communication device according to one embodiment of the present application;

[0094] Fig. 5 shows a flowchart of a wireless transmission according to one embodiment of the present application;

[0095] Fig. 6 shows a schematic diagram of a calculation formula of a second time interval according to one embodiment of the present application;

[0096] Fig. 7 shows a schematic diagram of N information blocks and N time units according to one embodiment of the present application;

[0097] Fig. 8 shows a schematic diagram of a first RS according to one embodiment of the present application;

[0098] Fig. 9 shows a schematic diagram of a first operation according to one embodiment of the present application;

[0099] Fig. 10 shows a schematic diagram of a first type of identification according to one embodiment of the present application;

[0100] Fig. 11 shows a schematic diagram of a second time interval and a first type of identification relationship according to one embodiment of the present application;

[0101] Fig. 12 shows a schematic diagram of a second resource set according to one embodiment of the present application;

[0102] Fig. 13 shows a schematic diagram of a first condition not being met according to one embodiment of the present application;

[0103] FIG. 14 shows a schematic diagram of a second operation according to an embodiment of the present application;

[0104] FIG. 15 shows a schematic diagram of a first operation according to another embodiment of the present application;

[0105] FIG. 16 shows a schematic diagram of deploying a first operation according to an embodiment of the present application;

[0106] FIG. 17 shows a schematic diagram of an artificial intelligence or machine learning based processing system according to an embodiment of the present application;

[0107] FIG. 18 shows a schematic diagram of an artificial intelligence or machine learning based processing system according to an embodiment of the present application;

[0108] FIG. 19 shows a structural block diagram of a processing device for use in a first node according to an embodiment of the present application;

[0109] FIG. 20 shows a structural block diagram of a processing device for use in a second node according to an embodiment of the present application. DETAILED DESCRIPTION

[0110] The technical solutions of the present application will be further described in detail below with reference to the accompanying drawings. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other arbitrarily without conflict. Based on performance, flexibility, complexity, overhead and compatibility, etc., the person skilled in the art has the motivation to flexibly combine the embodiments in different drawings without conflict, for example, but not limited to, the embodiments in FIG. 1 and the embodiments in FIGS. 5-20, the embodiments in FIG. 5 and the embodiments in FIGS. 6-20, etc.

[0111] Embodiment 1

[0112] Embodiment 1 shows 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 FIG. 1. In 100 shown in FIG. 1, each block represents a step. In particular, the order of the steps in the blocks does not represent a specific time sequence between the steps.

[0113] 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 only when a first condition is met, sends the target CSI on the first PUSCH in step 104;

[0114] The first DCI triggers reporting of at least one CSI on a first PUSCH, at least one CSI reporting configuration is used to configure reporting of the at least one CSI, a target CSI reporting configuration is used to configure reporting of a target CSI, the target CSI reporting configuration is one of the at least one CSI reporting configuration, 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, the first resource set includes one or more RS resources, the first condition includes that a first symbol is not earlier than a first reference symbol and a second symbol is not earlier than a second reference symbol, the first symbol is a first uplink symbol in the first PUSCH for carrying the at least one CSI, the first reference symbol is a next uplink symbol whose CP starts at a first time interval after the end of a last symbol of the first PDCCH, the second symbol is a first uplink symbol in the first PUSCH for carrying the target CSI, and the second reference symbol is a next uplink symbol whose CP starts at a second time interval after the end of a last symbol of a first RS in the first resource set, and the second time interval depends on whether generation of the target CSI is based on AI.

[0115] As an embodiment, the at least one CSI reporting configuration is carried by higher layer signaling.

[0116] As an embodiment, the at least one CSI reporting configuration is carried by RRC (Radio Resource Control) signaling.

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

[0118] As an embodiment, the at least one CSI reporting configuration is carried by at least one RRC IE.

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

[0120] As an embodiment, the at least one CSI reporting configuration includes information in one or more fields in each of a plurality of RRC IEs.

[0121] As an embodiment, the at least one CSI reporting configuration includes some or all fields in a CSI-ReportConfig IE.

[0122] As one embodiment, the at least one CSI reporting configuration includes some or all fields in a ServingCellConfig IE.

[0123] As one embodiment, the at least one CSI reporting configuration includes some or all fields in a CSI-MeasConfig IE.

[0124] As one embodiment, the at least one CSI reporting configuration includes some or all fields in a ServingCellConfigCommon IE.

[0125] As one embodiment, the at least one CSI reporting configuration includes some or all fields in a ServingCellConfigCommonSIB IE.

[0126] As one embodiment, any of the at least one CSI reporting configuration is transmitted on PDSCH.

[0127] As one embodiment, any of the at least one CSI reporting configuration configures periodic CSI reporting.

[0128] As one embodiment, any of the at least one CSI reporting configuration configures semi-persistent CSI reporting.

[0129] As one embodiment, any of the at least one CSI reporting configuration configures aperiodic CSI reporting.

[0130] As one embodiment, any of the at least one CSI reporting configuration is an RRC IE.

[0131] As one embodiment, any of the at least one CSI reporting configuration belongs to a CSI-ReportConfig IE.

[0132] As one embodiment, any of the at least one CSI reporting configuration belongs to a ServingCellConfig IE.

[0133] As one embodiment, any of the at least one CSI reporting configuration belongs to a CSI-MeasConfig IE.

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

[0135] As one embodiment, any of the at least one CSI reporting configuration belongs to a ServingCellConfigCommonSIB IE.

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

[0137] As one embodiment, the first resource set includes one or more downlink RS resources.

[0138] As one embodiment, the first resource set includes periodic RS resources.

[0139] As one embodiment, the first resource set includes semi-persistent RS resources.

[0140] As one embodiment, the first resource set includes aperiodic RS resources.

[0141] As one embodiment, the first resource set consists of one or more aperiodic RS resources.

[0142] As one embodiment, a resource in the first resource set includes at least one of an antenna port, a TCI (Transmission Configuration Indication) state, QCL (Quasi Co-Location) information, a time-frequency resource, a time-frequency code resource, a beam, an RS resource, a vector, or a matrix.

[0143] As one embodiment, the first resource set includes one or more RS (Reference Signal) resource sets, and one RS resource set includes one or more RS resources.

[0144] As one 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.

[0145] As one embodiment, the first resource set comprises at least one RS resource set for channel measurement.

[0146] As one embodiment, the first resource set comprises at least one RS resource set for channel measurement, and at least one RS resource set for interference measurement.

[0147] As one embodiment, the first resource set comprises at least one RS resource set for interference measurement.

[0148] As one sub-embodiment of the above embodiment, one RS resource set for channel measurement comprises one or more RS resources.

[0149] As one sub-embodiment of the above embodiment, one RS resource set for interference measurement comprises one or more RS resources.

[0150] As one embodiment, one RS resource set for channel measurement comprises one or more RS resources, and any RS resource in the one RS resource set for channel measurement is a CSI-RS resource or a synchronization signal resource.

[0151] As one embodiment, one RS resource set for interference measurement comprises one or more RS resources, and any RS resource in the one RS resource set for interference measurement is a CSI-IM resource or a NZP (non-zero power) CSI-RS resource for interference measurement.

[0152] As one embodiment, the first resource set comprises 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.

[0153] As one embodiment, the synchronization signal resource comprises at least a resource occupied by a synchronization signal.

[0154] As one embodiment, the synchronization signal resource is an SSB (Synchronization Signal Block).

[0155] As one embodiment, the synchronization signal resource is an SS / PBCH (synchronization signal / physical broadcast channel) block resource.

[0156] As one embodiment, the first resource set consists of at least one aperiodic CSI-RS resource for channel measurement.

[0157] As one embodiment, the first resource set consists of at least one aperiodic CSI-RS resource for channel measurement, at least one aperiodic CSI-IM resource for interference measurement, or at least one aperiodic NZP CSI-RS resource for interference measurement.

[0158] As one embodiment, the first resource set consists of one or more aperiodic RS resources; the first resource set consists of at least one aperiodic CSI-RS resource for channel measurement, at least one aperiodic CSI-IM resource for interference measurement, or at least one aperiodic NZP CSI-RS resource for interference measurement.

[0159] As one embodiment, the first resource set consists of at least one CSI-RS resource.

[0160] As one embodiment, the first resource set includes at least one of CSI-RS resource or SS / PBCH block resource.

[0161] As one embodiment, the reference resource is a CSI reference resource.

[0162] As one embodiment, the reference resource is a CSI reference resource of the target CSI.

[0163] As one embodiment, the benefits of the above method include: maintaining existing standards and system design, reducing complexity.

[0164] As one embodiment, the target CSI reporting configuration indicates at least one resource configuration, and the at least one resource configuration indicates the first resource set.

[0165] As one embodiment, the target CSI reporting configuration includes at least one resource configuration, and the at least one resource configuration indicates the first resource set.

[0166] As one embodiment, one resource configuration is used to configure CSI resources.

[0167] As one embodiment, one resource configuration is an IE CSI-ResourceConfig.

[0168] As one embodiment, one resource configuration is carried by RRC IE.

[0169] As an embodiment, a resource configuration is carried by a CSI-ResourceConfig IE.

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

[0171] As an embodiment, the target CSI reporting configuration indicates an identity of the first resource set.

[0172] As an embodiment, the first DCI (Downlink Control Information) triggers the target CSI on a first PUSCH (Physical uplink shared channel).

[0173] As an embodiment, the first DCI triggers reporting of at least one CSI on a first PUSCH, the reporting of the at least one CSI including the target CSI.

[0174] As an embodiment, the first DCI triggers reporting of at least one CSI on a first PUSCH, the reporting of the at least one CSI including the target CSI; and the target CSI reporting configuration is used to configure the reporting of the target CSI.

[0175] As an embodiment, the receiving the first DCI on the first PDCCH comprises receiving a second signal on the first PDCCH, the second signal carrying the first DCI.

[0176] As an embodiment, the first PDCCH comprises a plurality of REs (Resource Elements).

[0177] Typically, one RE occupies one symbol in time domain and one subcarrier in frequency domain.

[0178] As an embodiment, the first PDCCH occupies at least one symbol in time domain and at least one subcarrier in frequency domain.

[0179] As an embodiment, the first PDCCH occupies at least one symbol in time domain and at least one RB (resource block) in frequency domain.

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

[0181] As an embodiment, the symbol is a multi-carrier symbol.

[0182] As an embodiment, the multi-carrier symbol is an OFDM (Orthogonal Frequency Division Multiplexing) symbol.

[0183] As an embodiment, the symbol is an output of transform precoding after OFDM symbol generation.

[0184] As an embodiment, the multi-carrier symbol is an SC-FDMA (Single Carrier-Frequency Division Multiple Access) symbol.

[0185] As an embodiment, the multi-carrier symbol is a DFT-S-OFDM (Discrete Fourier Transform Spread OFDM) symbol.

[0186] As an embodiment, the multi-carrier symbol is an FBMC (Filter Bank Multi Carrier) symbol.

[0187] As an embodiment, the multi-carrier symbol comprises a CP (Cyclic Prefix).

[0188] As an embodiment, the first DCI comprises a CSI request field, the CSI request field in the first DCI triggering at least one CSI on a first PUSCH.

[0189] As an embodiment, the first DCI comprises a CSI request field, the CSI request field in the first DCI indicating at least one CSI reporting configuration, the at least one CSI reporting configuration being used for configuring reporting of the at least one CSI.

[0190] As an embodiment, the first DCI comprises a CSI request field, the CSI request field in the first DCI indicating at least one CSI reporting configuration, a target CSI reporting configuration being one of the at least one CSI reporting configuration, the at least one CSI reporting configuration being used for configuring reporting of the at least one CSI, the target CSI reporting configuration being used for configuring reporting of the target CSI.

[0191] As one embodiment, the generation of the target CSI relies on measurements obtained based on the first set of resources.

[0192] As one embodiment, the generation of the target CSI relies on channel measurements and / or interference measurements obtained based on the first set of resources.

[0193] As one embodiment, measurements based on the first set of resources are used for generating the target CSI.

[0194] As one embodiment, channel measurements and / or interference measurements based on the first set of resources are used for generating the target CSI.

[0195] As one embodiment, measurements based on one or more RS resources in the first set of resources are used for generating the target CSI.

[0196] As one embodiment, measurements based on no later than a transmission occasion of a reference resource among one or more RS resources in the first set of resources are used for generating the target CSI.

[0197] As one embodiment, measurements based on no later than one or more transmission occasions of a reference resource among one or more RS resources in the first set of resources are used for generating the target CSI.

[0198] As one embodiment, the channel measurements obtained based on the first set of resources refer to channel measurements obtained based on at least one reference signal transmitted in the first set of resources.

[0199] As one embodiment, the channel measurements obtained based on the first set of resources refer to channel measurements obtained in the first set of resources.

[0200] As one embodiment, the channel measurements obtained based on the first set of resources comprise at least one of a channel matrix, a raw channel matrix, an eigenvector, and an eigenvalue.

[0201] As one embodiment, the channel measurements obtained based on the first set of resources comprise one or more of a BLER, a delay spread, a Doppler spread, a Doppler shift, a mean delay, a mean gain, a path loss, and an RSRP.

[0202] As one embodiment, the interference measurements obtained based on the first set of resources refer to interference measurements obtained based on at least one reference signal transmitted in the first set of resources.

[0203] As an embodiment, the interference measurement obtained based on the first set of resources refers to an interference measurement obtained in the first set of resources.

[0204] As an embodiment, the interference measurement obtained based on the first set of resources comprises at least one of an interference power, an interference variance, or an interference power spectral density.

[0205] As an embodiment, the interference measurement obtained based on the first set of resources comprises at least one of an interference channel matrix, an interference covariance matrix, an interference eigenvector, an interference eigenvalue, or an interference beam.

[0206] As an embodiment, the measurement based on the first set of resources comprises a channel matrix obtained based on the measurement for the first set of resources.

[0207] As an embodiment, the measurement based on the first set of resources comprises a matrix or a vector obtained by pre-processing a channel matrix obtained based on the measurement for the first set of resources.

[0208] As an embodiment, the channel matrix is in a spatial-frequency domain.

[0209] As an embodiment, the channel matrix is in an angular-delay domain projection.

[0210] As an embodiment, the pre-processing comprises one or more of quantization, DFT (Discrete Fourier Transform), matrix decomposition, matrix transformation or projection, spatial-to-angular domain transformation, angular-to-spatial domain transformation, frequency-to-time domain transformation and time-to-frequency domain transformation, truncation, padding, mapping, labeling.

[0211] As an embodiment, the at least one CSI is the target CSI, or the at least one CSI comprises a plurality of CSIs, and the target CSI is one of the plurality of CSIs.

[0212] As an embodiment, the at least one CSI comprises 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.

[0213] As an embodiment, the at least one CSI comprises a plurality of CSIs, and the at least one CSI reporting configuration comprises a plurality of CSI reporting configurations, each of the plurality of CSI reporting configurations being used to configure one of the plurality of CSIs.

[0214] As an embodiment, the at least one CSI comprises a plurality of CSIs, the at least one CSI reporting configuration comprises a plurality of CSI reporting configurations, the plurality of CSI reporting configurations are respectively used for configuring the plurality of CSIs, and the target CSI is any one of the plurality of CSIs.

[0215] As an embodiment, the target CSI comprises at least one CSI reporting quantity.

[0216] As an embodiment, the target CSI comprises one or more of a PMI (Precoding Matrix Indicator), a CRI (CSI-RS Resource Indicator), an SS / PBCH Block Resource indicator (SSBRI), a beam indication, a resource indication, a CQI (Channel Quality Indicator), an RI (Rank Indicator), a LI (Layer Indicator), an RSRP (reference signal received power), an SINR (signal-to-noise and interference ratio), a Capability Index, or a TDCP (Time Domain Channel Properties).

[0217] As an embodiment, the target CSI comprises one or more of a channel matrix, an eigenvector, an eigenvalue, or a precoding matrix.

[0218] As an embodiment, the target CSI comprises one or more of a beam indication, a CRI (CSI-RS Resource Indicator), an SS / PBCH Block Resource indicator (SSBRI), or an RSRP (reference signal received power).

[0219] As an embodiment, the target CSI comprises one or more of a beam indication, a number of beams, a CRI, an SS / PBCH block resource indicator, a number of CRIs or SSBRs, an RSRP, a differential RSRP, probability information, or confidence information.

[0220] As an embodiment, the probability information indicates a probability that the corresponding beam is one of the one or more optimal beams.

[0221] As an embodiment, the probability information indicates a probability that the corresponding RS resource is one of the one or more optimal RS resources.

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

[0223] As an embodiment, the confidence information indicates an accuracy of the differential RSRP.

[0224] As an embodiment, the method further comprises monitoring the AI model based on a performance parameter of the AI-based CSI reporting.

[0225] As an embodiment, the method further comprises improving the performance of the AI-based CSI reporting scheme and improving the overall performance of the system.

[0226] As an embodiment, the target CSI comprises an RSRP.

[0227] As an embodiment, the target CSI comprises at least one resource indicator and an RSRP.

[0228] As an embodiment, the target CSI comprises at least one resource indicator.

[0229] As an embodiment, the target CSI comprises at least one resource indicator, and one of the resource indicators in the target CSI is used to indicate a beam or an RS resource.

[0230] As an embodiment, the target CSI comprises at least one resource indicator, and one of the resource indicators in the target CSI is used to indicate a beam, a CSI-RS resource, or an SS / PBCH block resource.

[0231] As one embodiment, the target CSI includes at least one resource indication; one resource indication in the target CSI is used to indicate a beam, or one resource indication in the target CSI is a CRI (CSI-RS Resource Indicator) or a SS / PBCH Block Resource indicator (SSBRI).

[0232] As one embodiment, the target CSI includes predicted CSI.

[0233] As one embodiment, the target CSI includes CSI for a future time period.

[0234] As one embodiment, the target CSI includes predicted beam information.

[0235] As one embodiment, the target CSI includes beam information for a future time period.

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

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

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

[0239] As one sub-embodiment of the above embodiment, the future time period includes at least one symbol after a current symbol.

[0240] As one embodiment, the benefits of the above method include reducing channel measurement overhead.

[0241] As one embodiment, the benefits of the above method include improving the accuracy and timeliness of CSI reporting, and enhancing the overall system performance.

[0242] As one embodiment, the target CSI includes compressed CSI.

[0243] As one embodiment, the compressed CSI is based on non-codebook.

[0244] As one embodiment, the compressed CSI is neither defined in 3GPP Rel-18 nor defined in versions before 3GPP Rel-18.

[0245] As an embodiment, the target receiver of the compressed CSI is unaware of the channel parameters recovered by the compressed CSI for the transmitter of the compressed CSI.

[0246] As an embodiment, the benefits of the above method include: saving CSI feedback overhead, improving the overall performance of the system.

[0247] As an embodiment, the target CSI is AI-based.

[0248] As an embodiment, the target CSI is not AI-based.

[0249] As an embodiment, the amount of reporting included in the target CSI depends on whether the generation of the target CSI is AI-based.

[0250] As an embodiment, the amount of reporting included in the target CSI depends on whether the generation method of the target CSI is AI-based.

[0251] As an embodiment, whether the target CSI includes confidence information depends on whether the generation method of the target CSI is AI-based; only when the generation method of the target CSI is AI-based, the target CSI includes confidence information.

[0252] As an embodiment, whether the target CSI is non-codebook-based depends on whether the generation method of the target CSI is AI-based; when the generation method of the target CSI is AI-based, the target CSI is non-codebook-based; when the generation of the target CSI is not AI-based, the target CSI is codebook-based.

[0253] As an embodiment, when the generation method of the target CSI is not AI-based, the target CSI belongs to the CSI defined in 3GPP Rel-18.

[0254] As an embodiment, when the generation method of the target CSI is AI-based, the target CSI does not belong to the CSI defined in 3GPP Rel-18 and earlier versions.

[0255] As an embodiment, when the generation method of the target CSI is AI-based, the target CSI includes predicted CSI, or predicted beam information, or compressed CSI.

[0256] As an embodiment, the benefits of the above method include: supporting AI-based CSI reporting schemes.

[0257] As an embodiment, the benefits of the above method include: little modification to existing systems and standards.

[0258] As an embodiment, the benefits of the above method include: enhancing the flexibility of the system, improving the overall performance of the system.

[0259] As an embodiment, the target CSI indicates at least one RS resource in the first resource set.

[0260] As an embodiment, the target CSI indicates at least one resource in a second resource set, which includes resources not belonging to the first resource set.

[0261] As an embodiment, when the generation of the target CSI is based on AI, the target CSI indicates at least one resource in a second resource set, which includes resources not belonging to the first resource set.

[0262] As an embodiment, when the generation of the target CSI is based on AI, the target CSI indicates at least one resource in a second resource set, which includes resources not belonging to the first resource set; when the generation of the target CSI is not based on AI, the target CSI indicates at least one RS resource in the first resource set.

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

[0264] As an embodiment, the benefits of the above method include: reducing the measurement resources required to obtain the target CSI.

[0265] As an embodiment, the benefits of the above method include: little modification to existing systems and standards.

[0266] As an embodiment, for 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:

[0267] The first node first performs measurement on the first set of resources to obtain a channel parameter matrix, where and are the number of receive antennas and the number of antenna ports of the target CSI-RS resource, respectively; performs power adjustment on the channel parameter matrix, and the adjusted channel parameter matrix is, where is a hypothetical PDSCH EPRE-to-target CSI-RS EPRE ratio (i.e., the first power control offset); under the condition of using a precoding matrix, the precoded channel parameter matrix is, where is the rank or the number of layers, which is a positive integer not greater than in one case, and in another case, the precoding matrix is a unit matrix, and at this time; an equivalent channel capacity calculated using, for example, a SINR (Signal Interference Noise Ratio) criterion, an EESM (Exponential Effective SINR Mapping) criterion, or an RBIR (Received Block mean mutual Information Ratio) criterion, and then the target CSI report including CQI is determined by the equivalent channel capacity through table lookup or the like. Generally, the calculation of the equivalent channel capacity needs the first node to estimate interference (including noise), and the first node can obtain more accurate measured interference by using the measurement of the second set of occasions in the present application. Generally, the direct mapping of the equivalent channel capacity to the value of CQI depends on the receiver performance or the modulation mode and other hardware-related factors.

[0268] As an embodiment, for the case that 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 in other words, 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.

[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 and the target CSI is valid.

[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 ignore the first DCI.

[0271] 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 abandon sending the target CSI on the first PUSCH.

[0272] Typically, the target CSI is valid on the first PUSCH only when the first condition is satisfied; wherein the target CSI transmitted on the first PUSCH is valid.

[0273] As one embodiment, the target CSI being valid comprises that the target CSI is an updated CSI.

[0274] As one embodiment, the target CSI being valid comprises that the target CSI is different from a latest one of CSI reporting configuration for the target CSI on the first PUSCH.

[0275] As one embodiment, the target CSI being valid comprises that the target CSI can be different from a latest one of CSI reporting configuration for the target CSI on the first PUSCH.

[0276] As one embodiment, the target CSI being valid comprises that the target CSI is not necessarily the same as a latest one of CSI reporting configuration for the target CSI on the first PUSCH.

[0277] As one embodiment, the target CSI being valid comprises that whether the target CSI is different from a latest one of CSI reporting configuration for the target CSI on the first PUSCH depends on a measurement of a latest RS occasion of the first set of resources no later than a CSI reference resource of the target CSI.

[0278] As one embodiment, the target CSI being valid comprises that the target CSI is generated based on at least a measurement of a latest RS occasion of the first set of resources no later than a CSI reference resource of the target CSI.

[0279] As one embodiment, the target CSI being valid comprises that the target CSI is an updated CSI based on at least a measurement of a latest RS occasion of the first set of resources no later than a CSI reference resource of the target CSI.

[0280] As one embodiment, the first set of resources consists of one or more aperiodic RS resources; the target CSI being valid comprises that the target CSI is generated based on a measurement of an aperiodic RS resource of the first set of resources triggered by the first DCI.

[0281] As an embodiment, the first set of resources consists of one or more aperiodic RS resources; the target CSI is valid including that the target CSI is updated based on measurement of the aperiodic RS resource in the first set of resources triggered by the first DCI.

[0282] As an embodiment, the first PUSCH includes a plurality of REs (Resource Elements).

[0283] As an embodiment, the first PUSCH occupies at least one symbol in time domain; the first PUSCH occupies at least one subcarrier in frequency domain.

[0284] As an embodiment, the first PUSCH occupies at least one symbol in time domain; the first PUSCH occupies at least one RB (resource block) in frequency domain.

[0285] As an embodiment, the transmitting the target CSI on the first PUSCH includes transmitting a first signal on the first PUSCH; wherein the first signal carries the target CSI.

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

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

[0288] As an embodiment, the first signal includes a radio frequency signal.

[0289] As an embodiment, the transmitting the target CSI on the first PUSCH includes that the target CSI is used to generate a signal transmitted on the first PUSCH after channel coding.

[0290] As an embodiment, the transmitting the target CSI on the first PUSCH includes that the target CSI is used to generate a signal transmitted on the first PUSCH after channel coding and modulation.

[0291] As an embodiment, the transmitting the target CSI on the first PUSCH includes that the target CSI is used to generate a signal transmitted on the first PUSCH after bit sequence generation, channel coding.

[0292] As an embodiment, the transmitting the target CSI on the first PUSCH includes that the target CSI is used to generate a signal transmitted on the first PUSCH after bit sequence generation, channel coding, and modulation.

[0293] As one embodiment, the sending the target CSI on the first PUSCH comprises that 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, code block concatenation.

[0294] As one embodiment, the sending the target CSI on the first PUSCH comprises that the target CSI is multiplexed to the first PUSCH after bit sequence generation, code block segmentation and CRC attachment, channel coding, rate matching, code block concatenation.

[0295] Typically, the first symbol considers a timing advance.

[0296] Typically, both the first symbol and the first reference symbol consider a timing advance.

[0297] As one embodiment, the first symbol is a first uplink symbol in the first PUSCH for carrying the at least one CSI.

[0298] As one embodiment, the first symbol is a first uplink symbol in the first PUSCH for carrying the target CSI.

[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 a latest symbol occupied by the first PDCCH.

[0301] Typically, the first reference symbol is the next uplink symbol starting a first time interval after the end of the last symbol of the first PDCCH comprises that the first reference symbol is an earliest uplink symbol later than the last symbol of the first PDCCH and satisfying a reference condition, the reference condition comprising that a time interval between the end of the last symbol of the first PDCCH and the first reference symbol 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 benefits of the above method include: maintaining existing system design and standards, and enhancing system consistency.

[0306] Typically, the second symbol takes into account timing advance.

[0307] Typically, both the second symbol and the second reference symbol take into account timing advance.

[0308] As an embodiment, the second symbol is the first uplink symbol in the first PUSCH for carrying the at least one CSI.

[0309] As an embodiment, the second symbol is the first uplink symbol in the first PUSCH for carrying the target CSI.

[0310] Typically, the second reference symbol is the next uplink symbol after the end of the last symbol of the first RS in the first resource set by a second time interval, including: the second reference symbol is the earliest uplink symbol later than the last symbol of the first RS in the first resource set and satisfying a reference condition, the reference condition including that the time interval between the second reference symbol and the end of the last symbol of the first RS in the first resource set is not less than the second time interval.

[0311] As an embodiment, the second time interval is a real number or an integer.

[0312] As an embodiment, the unit of the second time interval is millisecond (ms).

[0313] As an embodiment, the unit of the second time interval is symbol.

[0314] As an embodiment, the at least one CSI comprises 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.

[0315] As an embodiment, the at least one CSI comprises multiple CSIs, the at least one CSI reporting configuration comprises multiple CSI reporting configurations, the multiple CSI reporting configurations are respectively used for configuring the multiple CSIs, and the first symbol is the same as the second symbol or the first symbol is different from the second symbol.

[0316] As an embodiment, the first RS is one RS in the first resource set.

[0317] As an embodiment, the first RS is the latest aperiodic RS in the first resource set in time.

[0318] As an embodiment, the first RS is the earliest aperiodic RS in the first resource set in time.

[0319] As an embodiment, the first RS is the latest or the earliest aperiodic RS in the first resource set in time.

[0320] As an embodiment, the first resource set is composed of one or more aperiodic RS resources, and the first RS is one RS in the first resource set triggered by the first DCI.

[0321] As an embodiment, the first resource set is composed of one or more aperiodic RS resources, and the first RS is the latest RS in the first resource set triggered by the first DCI in time.

[0322] As an embodiment, the first resource set is composed of one or more aperiodic RS resources, and the first RS is the latest or the earliest RS in the first resource set triggered by the first DCI in time.

[0323] As an embodiment, the above method has the benefits of: maintaining the existing system design and standard, and enhancing the consistency of the system.

[0324] Typically, the determination of whether to send the target CSI on the first PUSCH depends on whether the first condition is met.

[0325] As one embodiment, the when the first condition is not satisfied includes: the first condition is not satisfied 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 satisfied when the first symbol is not earlier than the first reference symbol and the second symbol is not earlier than the second reference symbol.

[0326] As one embodiment, the first resource set consists of one or more aperiodic RS resources; the when the first condition is not satisfied includes: the first condition is not satisfied 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 satisfied when the first symbol is not earlier than the first reference symbol and the second symbol is not earlier than the second reference symbol.

[0327] As one embodiment, the above method has the benefit of: small changes to existing systems and standards.

[0328] As one embodiment, the above method has the benefit of: enhancing the reliability and robustness of the system.

[0329] As one embodiment, the transmitting the target CSI on the first PUSCH only when the first condition is satisfied includes: ignoring the first DCI when the first condition is not satisfied.

[0330] As one embodiment, the transmitting the target CSI on the first PUSCH only when the first condition is satisfied includes: dropping the transmitting the target CSI on the first PUSCH when the first condition is not satisfied.

[0331] As one embodiment, the transmitting the target CSI on the first PUSCH only when the first condition is satisfied includes: transmitting the target CSI on the first PUSCH and the target CSI is valid when the first condition is satisfied; transmitting the target CSI on the first PUSCH and the target CSI is not updated when the first condition is not satisfied.

[0332] As one embodiment, no HARQ-ACK or transport block is multiplexed on the first PUSCH; the transmitting the target CSI on the first PUSCH only when the first condition is satisfied includes: ignoring the first DCI when the first condition is not satisfied.

[0333] As an example, no HARQ-ACK or transport block is multiplexed on the first PUSCH; the transmitting the target CSI on the first PUSCH only when the first condition is met comprises: dropping the transmitting the target CSI on the first PUSCH when the first condition is not met.

[0334] As an example, a HARQ-ACK or a transport block is multiplexed on the first PUSCH; the transmitting the target CSI on the first PUSCH only when the first condition is met comprises: transmitting the target CSI on the first PUSCH and the target CSI is valid when the first condition is met; transmitting the target CSI on the first PUSCH and the target CSI is not updated when the first condition is not met.

[0335] Typically, no HARQ-ACK or transport block is multiplexed on the first PUSCH.

[0336] As an example, the above method has benefits including: improving flexibility and overall performance of the system.

[0337] As an example, the above method has benefits including: improving stability and robustness of the system.

[0338] As an example, the above method has benefits including: making small changes to existing systems and standards.

[0339] As an example, the target CSI is generated based on AI comprises: the target CSI is generated based on training; the target CSI is not generated based on AI comprises: the target CSI is not generated based on training.

[0340] As an example, the target CSI is generated based on AI comprises: the target CSI is generated using an AI model; the target CSI is not generated based on AI comprises: the target CSI is not generated using an AI model.

[0341] As an example, the target CSI is generated based on AI comprises: the target CSI includes information based on artificial intelligence or machine learning; the target CSI is not generated based on AI comprises: the target CSI does not include information based on artificial intelligence or machine learning.

[0342] As an example, the target CSI is generated in a manner based on AI includes that the target CSI includes information generated based on a neural network; the target CSI is generated in a manner not based on AI includes that the target CSI does not include information generated based on a neural network.

[0343] As an example, the target CSI is generated in a manner based on AI includes that the target CSI includes information generated based on a CNN; the target CSI is generated in a manner not based on AI includes that the target CSI does not include information generated based on a CNN.

[0344] As an example, the target CSI is generated in a manner based on AI includes that the generation of the target CSI includes a first operation performed by a transmitter of the target CSI, an input of the first operation depends on a measurement based on the first set of resources, and the target CSI depends on an output of the first operation; the target CSI is generated in a manner not based on AI includes that the generation of the target CSI does not include the first operation performed by the transmitter of the target CSI.

[0345] As an example, the target CSI is generated in a manner based on AI includes that a reporting configuration of the target CSI indicates a first type of identification; the target CSI is generated in a manner not based on AI includes that the reporting configuration of the target CSI does not indicate the first type of identification.

[0346] As an example, the target CSI is generated in a manner based on AI includes that the generation of the target CSI is associated to a first type of identification; the target CSI is generated in a manner not based on AI includes that the generation of the target CSI is not associated to the first type of identification.

[0347] As an example, the above method has advantages including: simplifying system design, reducing implementation complexity.

[0348] As an example, the above method has advantages including: improving flexibility and overall performance of the system.

[0349] As an example, the above method has advantages including: little change to existing standards and systems.

[0350] As an example, the second time interval depends on whether the target CSI is generated in a manner based on AI includes that a determination method of the second time interval depends on whether the target CSI is generated in a manner based on AI.

[0351] As an embodiment, the second time interval depends on whether the generation manner of the target CSI is based on AI includes: the calculation formula of the second time interval depends on whether the generation manner of the target CSI is based on AI.

[0352] As an embodiment, the second time interval depends on whether the generation manner of the target CSI is based on AI includes: the calculation formula of the second time interval is different in the case that the generation manner of the target CSI is based on AI and the case that the generation manner of the target CSI is not based on AI.

[0353] As an embodiment, the calculation formula of the second time interval depends on whether the generation manner of the target CSI is based on AI includes: when the generation manner of the target CSI is based on AI, the calculation formula of the second time interval is a first calculation formula; when the generation manner 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.

[0354] 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.

[0355] As an embodiment, the benefits of the above method include: improving the overall performance of the system.

[0356] As an embodiment, the second time interval depends on whether the generation manner of the target CSI is based on AI includes: when the generation manner of the target CSI is based on AI, the second time interval is a first reference interval; when the generation manner of the target CSI is not based on AI, the second time interval is a second reference interval.

[0357] As an embodiment, the first reference interval is not equal to the second reference interval.

[0358] As an embodiment, the first reference interval is a real number, and the second reference interval is a real number.

[0359] As an embodiment, the first reference interval is an integer, and the second reference interval is an integer.

[0360] As an embodiment, the unit of the first reference interval is millisecond (ms), and the unit of the second reference interval is millisecond.

[0361] As an embodiment, the unit of the first reference interval is a symbol, and the unit of the second reference interval is a symbol.

[0362] As an embodiment, the first reference interval comprises a plurality of candidate values.

[0363] As an embodiment, the first reference interval is calculated by a formula.

[0364] As an embodiment, the first reference interval is fixed.

[0365] As an embodiment, the first reference interval is configurable.

[0366] As an embodiment, the first reference interval is configured by higher layer signaling.

[0367] As an embodiment, the generation of the target CSI uses an AI model; the first reference interval depends on the AI model.

[0368] As an embodiment, the target CSI depends on the output of the first operation, and the first reference interval depends on the first operation.

[0369] As an embodiment, the generation of the target CSI is associated with a first type of identification; the first reference interval depends on the first type of identification.

[0370] As an embodiment, the first reference interval depends on UE capability information.

[0371] As an embodiment, the benefits of the above method include improving the stability and robustness of the system.

[0372] As an embodiment, the benefits of the above method include improving the flexibility and overall performance of the system.

[0373] As an embodiment, the second reference interval comprises a plurality of candidate values.

[0374] As an embodiment, the second reference interval is calculated by a formula.

[0375] As an embodiment, the second reference interval is fixed.

[0376] As an embodiment, the second reference interval is configurable.

[0377] As an embodiment, the second reference interval is configured by higher layer signaling.

[0378] 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.

[0379] As an embodiment, the benefits of the above method include small changes to existing standards and systems.

[0380] As an embodiment, the benefits of the above method include: improving the flexibility of the system, adapting to different scenarios and requirements of transmission and application.

[0381] As an embodiment, the second time interval depending on whether the generation mode of the target CSI is based on AI includes: the parameters on which the second time interval depends depend on whether the generation mode of the target CSI is based on AI.

[0382] As an embodiment, the second time interval depending on whether the generation mode of the target CSI is based on AI includes: the parameters on which the calculation formula of the second time interval depends depend on whether the generation mode of the target CSI is based on AI.

[0383] As an embodiment, the second time interval depending on whether the generation mode of the target CSI is based on AI includes: the calculation formula of the second time interval depends on a first parameter, and the first parameter depends on whether the generation mode of the target CSI is based on AI.

[0384] As an embodiment, the second time interval depending on whether the generation mode of the target CSI is based on AI includes: whether the calculation formula of the second time interval depends on a second parameter depends on whether the generation mode of the target CSI is based on AI; only when the generation mode of the target CSI is based on AI, the calculation formula of the second time interval depends on the second parameter.

[0385] As an embodiment, the calculation formula of the second time interval depending on the second parameter includes: the second time interval and the second parameter are in a linear relationship.

[0386] As an embodiment, the calculation formula of the second time interval depending on the second parameter includes: the second time interval and the second parameter are in a non-linear relationship.

[0387] As an embodiment, the unit of the second parameter is millisecond (ms).

[0388] As an embodiment, the unit of the second parameter is symbol.

[0389] As an embodiment, the second parameter is an integer or a real number greater than 0.

[0390] As an embodiment, the second parameter is an integer or a real number greater than 1.

[0391] As an embodiment, the essence of the above method includes: reserving longer CSI calculation time for AI-based CSI reporting.

[0392] As an embodiment, benefits of the above method include better support of AI model and computation, and improved accuracy and effectiveness of CSI reporting.

[0393] As an embodiment, whether the second time interval depends on whether the generation manner of the target CSI is based on AI includes: whether the second time interval depends on a first capability parameter depending on whether the generation manner of the target CSI is based on AI; the second time interval depends on the first capability parameter only when the generation manner of the target CSI is based on AI.

[0394] As an embodiment, whether the second time interval depends on whether the generation manner of the target CSI is based on AI includes: when the generation manner of the target CSI is based on AI, the second time interval depends on a first capability parameter; when the generation manner 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.

[0395] As an embodiment, whether the second time interval depends on whether the generation manner of the target CSI is based on AI includes: when the generation manner of the target CSI is based on AI, the second time interval depends on a first capability parameter and a second capability parameter; when the generation manner of the target CSI is not based on AI, the second time interval depends only on the second capability parameter; the first capability parameter and the second capability parameter are both capability parameters reported by the first node.

[0396] As an embodiment, the first capability parameter and the second capability parameter represent different capability parameters reported by the first node.

[0397] As an embodiment, the first capability parameter represents an AI-related capability parameter reported by the first node.

[0398] As an embodiment, the second capability parameter represents an AI-unrelated capability parameter reported by the first node.

[0399] As an embodiment, the second capability parameter includes a beamReportTiming IE.

[0400] As an embodiment, the second capability parameter includes a beamSwitchTiming IE.

[0401] As an embodiment, the second capability parameter includes a codebookType IE.

[0402] As an embodiment, the second capability parameter comprises at least one of codebookType IE, beamReportTiming IE, and beamSwitchTiming IE.

[0403] As an embodiment, the second capability parameter comprises at least one of codebookType IE, beamReportTiming IE, and beamSwitchTiming IE.

[0404] As an embodiment, the method further comprises: 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.

[0405] As an embodiment, the method has the advantage of enhancing the reliability and robustness of the system.

[0406] As an embodiment, the second time interval depending on the first capability parameter comprises: a calculation formula of the second time interval depending on the first capability parameter.

[0407] As an embodiment, the second time interval depending on the first capability parameter comprises: the second time interval and the first capability parameter being in a linear relationship.

[0408] As an embodiment, the second time interval depending on the first capability parameter comprises: the second time interval and the first capability parameter being in a nonlinear relationship.

[0409] As an embodiment, the second time interval depending on the first capability parameter comprises: the second time interval depending on a first parameter, and the first parameter depending on the first capability parameter.

[0410] As an embodiment, the second time interval depending on the first capability parameter comprises: a calculation formula of the second time interval depending on a first parameter, and a value of the first parameter depending on the first capability parameter.

[0411] As an embodiment, the second time interval depending on the second capability parameter comprises: a calculation formula of the second time interval depending on the second capability parameter.

[0412] As an embodiment, the second time interval depending on the second capability parameter comprises: the second time interval and the second capability parameter being in a linear relationship.

[0413] As an embodiment, the second time interval depending on the second capability parameter comprises: the second time interval and the second capability parameter being in a nonlinear relationship.

[0414] As one embodiment, the second time interval depending on the second capability parameter comprises: the second time interval depending on a first parameter, and the first parameter depending on the second capability parameter.

[0415] As one embodiment, the second time interval depending on the second capability parameter comprises: a calculation formula of the second time interval depending on a first parameter, and a value of the first parameter depending on the second capability parameter.

[0416] As one embodiment, the above method has the benefit of enhancing the reliability and robustness of the system.

[0417] As one embodiment, the above method has the benefit of improving the flexibility and overall performance of the system.

[0418] Embodiment 2

[0419] Embodiment 2 illustrates a schematic diagram of a network architecture according to one embodiment of the present application, as shown in FIG. 2.

[0420] FIG. 2 illustrates a network architecture 200 for LTE (Long-Term Evolution), LTE-A (Long-Term Evolution Advanced), and future 5G systems. The network architecture 200 for LTE, LTE-A, and future 5G systems is referred to as EPS (Evolved Packet System) 200. The 5G NR or LTE network architecture 200 can be referred to as 5GS (5G System) / EPS (Evolved Packet System) 200 or some other suitable terminology. The 5GS / EPS 200 can include one or more UEs (User Equipment) 201, a UE 241 in sidelink communication with the UE 201, a NG-RAN (Next Generation Radio Access Network) 202, a 5GC (5G Core Network) / EPC (Evolved Packet Core) 210, a HSS (Home Subscriber Server) / UDM (Unified Data Management) 220, and Internet services 230. The 5GS / EPS 200 can interconnect with other access networks, but these entities / interfaces are not shown for simplicity. As shown in FIG. 2, the 5GS / EPS 200 provides packet-switched services, however, those skilled in the art will readily appreciate that the various concepts presented throughout this application are amenable to use with networked systems providing circuit-switched services. The NG-RAN 202 includes NR (New Radio) nodes 203 (gNBs) and other nodes 204 (other gNBs). The nodes 203 provide user and control plane protocol terminations toward the UEs 201. The nodes 203 can be connected to the other nodes 204 via an Xn interface (e.g., backhaul). The nodes 203 can also be referred to as base stations, base transceiver stations, radio base stations, radio transceivers, transceiver functions, basic service sets (BSSs), extended service sets (ESSs), TRPs (Transmission Reception Points), or some other suitable terminology. The nodes 203 provide access to the 5GC / EPC 210 for the UEs 201. Examples of UEs 201 include a cellular phone, a smart phone, a session initiation protocol (SIP) phone, a laptop, a personal digital assistant (PDA), a satellite radio, a global positioning system, a multimedia device, a video device, a digital audio player (e.g., MP3 player), a camera, a game console, a drone, a UAV, a narrowband physical web device, a machine type communication device, a land vehicle, an automobile, a wearable device, or any other similar functional device.A person of ordinary skill in the art can also refer to the 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 terminology. The node 203 is connected to the 5GC / EPC 210 through an S1 / NG interface. The 5GC / EPC 210 includes a MME (Mobility Management Entity) / AMF (Authentication Management Field) / SMF (Session Management Function) 211, other MME / AMF / SMF 214, a S-GW (Service Gateway) / UPF (User Plane Function) 212, and a P-GW (Packet Date Network Gateway) / UPF 213. The MME / AMF / SMF 211 is a control node that handles signaling between the UE 201 and the 5GC / EPC 210. Generally, the MME / AMF / SMF 211 provides bearer and connection management. All user IP (Internet Protocal) packets are transferred through the S-GW / UPF 212, which is itself connected to the P-GW / UPF 213. The P-GW provides UE IP address allocation as well as other functions. The P-GW / UPF 213 is connected to Internet services 230. The Internet services 230 include operator corresponding Internet protocol services, which can specifically include the Internet, an intranet, an IMS (IP Multimedia Subsystem), and a packet switching service.

[0421] As one embodiment, the first node in the present application includes the UE 201.

[0422] As one embodiment, the second node in the present application includes the node 203.

[0423] As one embodiment, the UE 201 includes a mobile phone.

[0424] As one embodiment, the UE 201 includes a vehicle, such as a car.

[0425] As one embodiment, the node 203 is a macro cell base station.

[0426] As one embodiment, the node 203 is a micro cell (Micro Cell) base station.

[0427] As one embodiment, the node 203 is a pico cell (Pico Cell) base station.

[0428] As one embodiment, the node 203 is a femto cell (Femtocell).

[0429] As one embodiment, the node 203 is a base station device supporting large latency difference.

[0430] As one embodiment, the node 203 is a flying platform device.

[0431] As one embodiment, the node 203 is a satellite device.

[0432] As one embodiment, the node 203 is a test device (e.g. transceiver emulating part of base station functionality, signaling tester).

[0433] As one embodiment, the wireless link from the UE 201 to the node 203 is an uplink, which is used to perform uplink transmission.

[0434] As one embodiment, the wireless link from the node 203 to the UE 201 is a downlink, which is used to perform downlink transmission.

[0435] As one embodiment, the wireless link between the UE 201 and the node 203 comprises a cellular network link.

[0436] As one embodiment, the UE 201 and the node 203 are connected through a Uu air interface.

[0437] As one embodiment, the sender of the at least one CSI reporting configuration comprises the node 203.

[0438] As one embodiment, the receiver of the at least one CSI reporting configuration comprises the UE 201.

[0439] As one embodiment, the sender of the target CSI reporting configuration comprises the node 203.

[0440] As one embodiment, the receiver of the target CSI reporting configuration comprises the UE 201.

[0441] As one embodiment, the sender of the first DCI comprises the node 203.

[0442] As an embodiment, the receiver of the first DCI comprises the UE 201.

[0443] As an embodiment, the transmitter of the first resource set comprises the node 203.

[0444] As an embodiment, the receiver of the first resource set comprises the UE 201.

[0445] As an embodiment, the transmitter of the at least one CSI comprises the UE 201.

[0446] As an embodiment, the receiver of the at least one CSI comprises the node 203.

[0447] As an embodiment, the transmitter of the target CSI comprises the UE 201.

[0448] As an embodiment, the receiver of the target CSI comprises the node 203.

[0449] As an embodiment, the UE 201 supports a 6G system.

[0450] As an embodiment, the node 203 supports a 6G system.

[0451] As an embodiment, the UE 201 supports at least a 5G system.

[0452] As an embodiment, the node 203 supports at least a 5G system.

[0453] As an embodiment, the UE 201 supports AI.

[0454] As an embodiment, the node 203 supports AI.

[0455] Embodiment 3

[0456] Embodiment 3 illustrates a schematic diagram of an embodiment of a wireless protocol architecture of user plane and control plane according to an embodiment of the present application, as shown in FIG. 3.

[0457] 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 3 showing three layers of the radio protocol architecture for 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: Layer 1, Layer 2, and Layer 3. Layer 1 (LI layer) is the lowest layer and implements various PHY (Physical layer) signal processing functions. The LI layer will be referred to as the PHY 301 herein. Layer 2 (L2 layer) 305 is above the PHY 301 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, a RLC (Radio Link Control) sublayer 303, and a PDCP (Packet Data Convergence Protocol) sublayer 304, which terminate the functions of 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 functions, such as ciphering of the data packets, and header compression. 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 the various radio resources (e.g., resource blocks) in one cell among the UEs. 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 the use of RRC signaling between the second communication node device and the first communication node device for configuring the lower layers. The radio protocol architecture for the user plane 350 includes Layer 1 (LI layer) and Layer 2 (L2 layer), which are 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 for the first communication node device and the second communication node device, but the PDCP sublayer 354 also provides header compression for upper layer data packets to reduce radio transmission overhead.A SDAP (Service Data Adaptation Protocol) sublayer 356 is also comprised in the L2 layer 355 in the user plane 350, the SDAP sublayer 356 is in charge of mapping between QoS flows and data radio bearers (DRBs) to support the diversity of services. Although not shown, the first communication node device can have several upper layers above the L2 layer 355, including a network layer (e.g., IP layer) that terminates at a P-GW on the network side and an application layer that terminates at the other end of the connection (e.g., a remote UE, a server, etc.).

[0458] As one embodiment, the wireless protocol architecture in FIG. 3 is applicable to the first node in the present application.

[0459] As one embodiment, the wireless protocol architecture in FIG. 3 is applicable to the second node in the present application.

[0460] As one embodiment, the higher layer in the present application refers to the layer above the physical layer.

[0461] As one embodiment, the at least one CSI is generated at the RRC 306.

[0462] As one embodiment, the target CSI is generated at the RRC 306.

[0463] As one embodiment, the reference signal in the first resource set is generated at the PHY 301 or the PHY 351.

[0464] As one embodiment, the at least one CSI is generated at the PHY 301 or the PHY 351.

[0465] As one embodiment, the at least one CSI is generated at the MAC 302 or the MAC 352.

[0466] As one embodiment, the target CSI is generated at the PHY 301 or the PHY 351.

[0467] As one embodiment, the target CSI is generated at the MAC 302 or the MAC 352.

[0468] Embodiment 4

[0469] Embodiment 4 illustrates a schematic diagram of a first communication device and a second communication device according to one embodiment of the present application, as shown in FIG. 4. FIG. 4 is a block diagram of a first communication device 410 and a second communication device 450 that communicate with each other in an access network.

[0470] 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 antennas 420.

[0471] 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 antennas 452.

[0472] In the transmission from the first communication device 410 to the second communication device 450, at the first communication device 410, upper layer packets from a core network are provided to the controller / processor 475. The controller / processor 475 implements functionality of the L2 layer. In the DL, the controller / processor 475 provides header compression, ciphering, packet segmentation and reordering, multiplexing between logical and transport channels, and radio resource allocations for 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 LI layer (i.e., physical layer). The transmit processor 416 implements coding and interleaving to facilitate forward error correction (FEC) at the second communication device 450, and 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 pre-coding on the coded and modulated symbols, including codebook-based and non-codebook-based pre-coding, and beamforming processing, generating one or more parallel streams. The transmit processor 416 then maps to each of the parallel streams to the subcarriers, multiplexes the modulated symbols with reference signals (e.g., pilot) in time domain and / or frequency domain, and then performs an inverse fast Fourier transform (IFFT) to generate time domain multi-carrier symbol streams. The multi-antenna transmit processor 471 then performs transmit analog pre-coding / beamforming operations on the time domain multi-carrier symbol streams. Each transmitter 418 converts the baseband multi-carrier symbol streams provided by the multi-antenna transmit processor 471 into radio frequency signals, and then provides the radio frequency signals to the different antennas 420, where the transmitter / receiver 418 is understood to be either a transmitter 418 or a receiver 418.

[0473] In 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 respective antenna 452. Each receiver 454 recovers information modulated onto an RF carrier and converts the RF stream into a baseband multi-carrier symbol stream that provides to the receive processor 456, where transmitter / receiver 454 is understood to be either a transmitter 454 or a receiver 454. The receive processor 456 and the multi-antenna receive processor 458 implement various signal processing functions of the Ll layer. The multi-antenna receive processor 458 performs receive analog precoding / beamforming operation on the baseband multi-carrier symbol stream from the receivers 454. The receive processor 456 converts the baseband multi-carrier symbol stream from the receive analog precoding / beamforming operation from the time domain to the frequency domain using a Fast Fourier Transform (FFT). In the frequency domain, the physical layer data signals and the reference signals are demultiplexed by the receive processor 456, where the reference signals will be used for channel estimation, and the data signals are recovered after multi-antenna detection in the multi-antenna receive processor 458 for any parallel streams destined for the second communication device 450. The symbols on each parallel stream are demodulated and recovered in the receive processor 456 and generate soft decisions. The receive processor 456 then decodes and de-interleaves the soft decisions to recover the upper layer data and control signals transmitted by the first communication device 410 on the physical channels. 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 can be associated with a memory 460 that stores program codes and data. The memory 460 can be referred to as a computer readable medium. In the DL (DownLink), the controller / processor 459 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, control signal processing to recover 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 can also be provided to the L3 for L3 processing. The controller / processor 459 is also responsible for error detection using an acknowledgement (ACK) and / or negative acknowledgement (NACK) protocol to support HARQ operations.

[0474] 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 packets to a controller / processor 459. The data source 467 represents all protocol layers above the L2 layer. Similar to the transmit function described at the first communication device 410 in the DL, the controller / processor 459 implements header compression, ciphering, packet segmentation and reordering, and multiplexing between logical and transport channels based on radio resource allocations for the first communication device 410, implements L2 layer functionality for the user plane and control plane. The controller / processor 459 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the first communication device 410. A transmit processor 468, in conjunction with a multi-antenna transmit processor 457, performs modulation mapping, channel coding processing, digital multi-antenna spatial processing, including codebook-based and non-codebook-based precoding, and beamforming processing, and then the transmit processor 468 generates parallel streams of symbols that are modulated onto different carriers, and the modulated symbol streams are then provided to different antennas 452 via transmitters 454 after analog precoding / beamforming at the multi-antenna transmit processor 457. Each transmitter 454 modulates a respective symbol stream, converts the modulated symbol stream from digital form to analog form, and transmits the analog signal via the corresponding antenna 452.

[0475] In the transmission from the second communication device 450 to the first communication device 410, the functionality at the first communication device 410 is similar to the functionality described in connection with the reception at the second communication device 450 in the transmission from the first communication device 410 to the second communication device 450. Each receiver 418 receives a signal from its respective antenna 420, converts the received signal to a baseband signal, and provides the baseband signal to a multi-antenna receive processor 472 and a receive processor 470. The receive processor 470 and the multi-antenna receive processor 472, in conjunction with the controller / processor 475, implement the L1 layer functions. The controller / processor 475 implements L2 layer functionality. 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 transport and logical channels, packet reassembly, deciphering, header decompression, control signal processing to recover upper layer packets from the second communication device 450. Upper layer packets from the controller / processor 475 can be provided to a core network. The controller / processor 475 is also responsible for error detection using an ACK and / or NACK protocol to support HARQ operations.

[0476] As an embodiment, the second communication device 450 comprises at least one processor and at least one memory including computer program code; the at least one memory and the computer program code configured to, with the at least one processor, cause the second communication device 450 to perform at least the following: receive at least one CSI reporting configuration; receive a first DCI on a first PDCCH, the first DCI triggering reporting of at least one CSI on a first PUSCH, the at least one CSI reporting configuration being used for configuring reporting of the at least one CSI; determine whether to send a target CSI on the first PUSCH; send the target CSI on the first PUSCH only when a first condition is met; the target CSI reporting configuration being used for configuring reporting of the target CSI, the target CSI reporting configuration being one of the at least one CSI reporting configuration, the target CSI being one of the at least one CSI; the target CSI reporting configuration indicating a first set of resources, the first set of resources being used for at least one of channel measurement or interference resource measurement of the target CSI, the first set of resources comprising one or more RS resources; the first condition comprising a first symbol not being earlier than a first reference symbol and a second symbol not being earlier than a second reference symbol; the first symbol being a first uplink symbol in the first PUSCH for carrying the at least one CSI, the first reference symbol being a next uplink symbol with a CP starting at a first time interval after an end of a last symbol of the first PDCCH; the second symbol being a first uplink symbol in the first PUSCH for carrying the target CSI, the second reference symbol being a next uplink symbol with a CP starting at a second time interval after an end of a last symbol of a first RS in the first set of resources; the second time interval depending on whether a generation manner of the target CSI is based on AI.

[0477] As an embodiment, the second communication device 450 comprises: a memory storing a computer readable program, the computer readable program, when executed by at least one processor, generates actions comprising: receiving at least one CSI reporting configuration; receiving a first DCI on a first PDCCH, the first DCI triggering reporting of at least one CSI on a first PUSCH, the at least one CSI reporting configuration being used for configuring 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 a target CSI reporting configuration is used for configuring reporting of the target CSI, the target CSI reporting configuration being one of the at least one CSI reporting configuration, the target CSI being one of the at least one CSI; the target CSI reporting configuration indicating a first resource set, the first resource set being used for at least one of channel measurement or interference resource measurement of the target CSI, the first resource set comprising one or more RS resources; the first condition comprising a first symbol not earlier than a first reference symbol and a second symbol not earlier than a second reference symbol; the first symbol being a first uplink symbol in the first PUSCH for carrying the at least one CSI, the first reference symbol being a next uplink symbol with CP starting at a first time interval after an end of a last symbol of the first PDCCH; the second symbol being a first uplink symbol in the first PUSCH for carrying the target CSI, the second reference symbol being a next uplink symbol with CP starting at a second time interval after an end of a last symbol of a first RS in the first resource set; the second time interval depending on whether a generation manner of the target CSI is based on AI.

[0478] As an embodiment, the first communication device 410 comprises at least one processor and at least one memory including computer program code; the at least one memory and the computer program code configured to, with the at least one processor, cause the first communication device 410 to perform the following: transmitting at least one CSI reporting configuration; transmitting a first DCI on a first PDCCH, the first DCI triggering reporting of at least one CSI on a first PUSCH, the at least one CSI reporting configuration being used for configuring reporting of the at least one CSI; wherein a target receiver of the first DCI determines whether to transmit a target CSI on the first PUSCH; the target receiver of the first DCI transmits the target CSI on the first PUSCH only when a first condition is met; a target CSI reporting configuration is used for configuring reporting of the target CSI, the target CSI reporting configuration being one of the at least one CSI reporting configuration, the target CSI being one of the at least one CSI; the target CSI reporting configuration indicates a first resource set, the first resource set being used for at least one of channel measurement or interference resource measurement of the target CSI, the first resource set comprising one or more RS resources; the first condition comprises a first symbol not earlier than a first reference symbol and a second symbol not earlier than a second reference symbol; the first symbol is a first uplink symbol in the first PUSCH for carrying the at least one CSI, the first reference symbol being a next uplink symbol with CP starting at a first time interval after an end of a last symbol of the first PDCCH; the second symbol is a first uplink symbol in the first PUSCH for carrying the target CSI, the second reference symbol being a next uplink symbol with CP starting at a second time interval after an end of a last symbol of a first RS in the first resource set; the second time interval depends on whether a generation manner of the target CSI is based on AI.

[0479] As an embodiment, the first communication device 410 comprises: a memory storing a computer readable program of instructions which, when executed by at least one processor, causes actions comprising: transmitting at least one CSI reporting configuration; transmitting a first DCI on a first PDCCH, the first DCI triggering reporting of at least one CSI on a first PUSCH, the at least one CSI reporting configuration being used for configuring reporting of the at least one CSI; wherein a target receiver of the first DCI determines whether to transmit a target CSI on the first PUSCH; the target receiver of the first DCI transmits the target CSI on the first PUSCH only when a first condition is met; a target CSI reporting configuration is used for configuring reporting of the target CSI, the target CSI reporting configuration being one of the at least one CSI reporting configuration, the target CSI being one of the at least one CSI; the target CSI reporting configuration indicates a first resource set, the first resource set being used for at least one of channel measurement or interference resource measurement of the target CSI, the first resource set comprising one or more RS resources; the first condition comprises a first symbol not earlier than a first reference symbol and a second symbol not earlier than a second reference symbol; the first symbol is a first uplink symbol in the first PUSCH for carrying the at least one CSI, the first reference symbol being a next uplink symbol with CP starting at a first time interval after an end of a last symbol of the first PDCCH; the second symbol is a first uplink symbol in the first PUSCH for carrying the target CSI, the second reference symbol being a next uplink symbol with CP starting at a second time interval after an end of a last symbol of a first RS in the first resource set; the second time interval depends on whether a generation manner of the target CSI is based on AI.

[0480] As an embodiment, the first node in the present application comprises the second communication device 450.

[0481] As an embodiment, the second node in the present application comprises the first communication device 410.

[0482] 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, the data source 467} is used for receiving the at least one CSI reporting configuration; at least one of {the antenna 420, the transmitter 418, the transmitting processor 416, the multi-antenna transmitting processor 471, the controller / processor 475, the memory 476} is used for transmitting the at least one CSI reporting configuration.

[0483] As one embodiment, at least one of {the antenna 452, the receiver 454, the receive processor 456, the multi-antenna receive processor 458, the controller / processor 459, the memory 460, the data source 467} is configured to receive the first DCI; at least one of {the antenna 420, the transmitter 418, the transmit processor 416, the multi-antenna transmit processor 471, the controller / processor 475, the memory 476} is configured to transmit the first DCI.

[0484] As one embodiment, at least one of {the antenna 452, the receiver 454, the receive processor 456, the multi-antenna receive processor 458, the controller / processor 459, the memory 460, the data source 467} is configured to receive the RS resource in the first resource set; at least one of {the antenna 420, the transmitter 418, the transmit processor 416, the multi-antenna transmit processor 471, the controller / processor 475, the memory 476} is configured to transmit the RS resource in the first resource set.

[0485] As one 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, the memory 476} is configured 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, the data source 467} is configured to transmit the target CSI on the first PUSCH.

[0486] Embodiment 5

[0487] Embodiment 5 illustrates a flowchart of wireless transmission according to one embodiment of the present application, as shown in FIG. 5. In FIG. 5, the second node U1 and the first node U2 are communication nodes for transmission over an air interface. In FIG. 5, the steps in the block F51 to the block F55 are optional, respectively.

[0488] For the second node U1, the second operation is deployed in the step S511; at least one CSI reporting configuration is transmitted in the step S512; the first DCI is transmitted on the first PDCCH in the step S513; a signal is transmitted in the first resource set in the step S514; the target CSI is received on the first PUSCH only when the first condition is satisfied in the step S515; the second operation is performed in the step S516.

[0489] For the first node U2, deploying the first operation in step S521; receiving the at least one CSI reporting configuration in step S522; receiving the first DCI on the first PDCCH in step S523; receiving the signal in the first resource set in step S524; performing the first operation in step S525; determining whether to transmit the target CSI on the first PUSCH in step S526; transmitting the target CSI on the first PUSCH only when the first condition is satisfied in step S527.

[0490] In embodiment 5, the first DCI triggers reporting of at least one CSI on the first PUSCH, the at least one CSI reporting configuration is used for configuring reporting of the at least one CSI; a target CSI reporting configuration is used for configuring reporting of the target CSI, the target CSI reporting configuration is one of the at least one CSI reporting configuration, 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, the first resource set comprises one or more RS resources; the first condition comprises that a first symbol is not earlier than a first reference symbol and a second symbol is not earlier than a second reference symbol; the first symbol is a first uplink symbol in the first PUSCH for carrying the at least one CSI, the first reference symbol is a next uplink symbol whose CP starts at a first time interval after the end of a last symbol of the first PDCCH; the second symbol is a first uplink symbol in the first PUSCH for carrying the target CSI, the second reference symbol is a next uplink symbol whose CP starts at a second time interval after the end of a last symbol of a first RS in the first resource set; the second time interval depends on whether the generation manner of the target CSI is based on AI.

[0491] As an embodiment, the first node U2 is the first node in the present application.

[0492] As an embodiment, the second node U1 is the second node in the present application.

[0493] As an embodiment, the air interface between the second node U1 and the first node U2 comprises a wireless interface between a base station device and a user equipment.

[0494] As an embodiment, the air interface between the second node U1 and the first node U2 comprises a wireless interface between a relay node device and a user equipment.

[0495] As one embodiment, the air interface between the second node U1 and the first node U2 comprises a wireless interface between a user equipment and a user equipment.

[0496] As one embodiment, the second node U1 is a serving cell maintaining base station of the first node U2.

[0497] As one embodiment, the step in block F53 in FIG. 5 exists; the method in the first node used for wireless communication comprises: receiving a signal in the first resource set.

[0498] As one embodiment, the step in block F53 in FIG. 5 exists; the method in the second node used for wireless communication comprises: transmitting a signal in the first resource set.

[0499] As one embodiment, transmitting a signal in the first resource set means transmitting a wireless signal in the first resource set.

[0500] As one embodiment, transmitting a signal in the first resource set means transmitting a reference signal in the first resource set.

[0501] As one embodiment, receiving a signal in the first resource set means receiving a wireless signal in the first resource set.

[0502] As one embodiment, receiving a signal in the first resource set means receiving a reference signal in the first resource set.

[0503] As one embodiment, in FIG. 5, when the generation manner of the target CSI is based on AI, the step in block F54 exists.

[0504] As one embodiment, the step in block F52 in FIG. 5 exists.

[0505] As one embodiment, in FIG. 5, when the generation manner of the target CSI is based on AI, the step in block F54 exists, the step in block F55 exists, and the first node and the second node adopt a two-sided AI model.

[0506] As one embodiment, in FIG. 5, when the generation manner of the target CSI is based on AI, the step in block F54 exists, the step in block F55 does not exist, and the first node adopts a single side AI model.

[0507] As one embodiment, the step in block F51 in FIG. 5 exists, and the method in the second node used for wireless communication comprises: deploying the second operation.

[0508] As one embodiment, the deployment of the second operation is earlier than the sending of the at least one CSI reporting configuration.

[0509] As one embodiment, the deployment of the second operation is later than the sending of the at least one CSI reporting configuration.

[0510] As one embodiment, the step in block F55 in FIG. 5 exists, and the method in the second node for wireless communication comprises: performing the second operation.

[0511] As one embodiment, in FIG. 5, when the generation manner of the target CSI is based on AI, the step in block F54 exists, the step in block F55 exists, the first operation is for CSI compression, the second operation is for CSI recovery, and the first node and the second node adopt a two-sided AI model.

[0512] As one embodiment, in FIG. 5, when the generation manner of the target CSI is based on AI, the step in block F54 exists, the step in block F55 does not exist, the first operation is for beam prediction, and the first node adopts a single side AI model.

[0513] As one embodiment, the deployment of the first operation is earlier than the receiving of the at least one CSI reporting configuration.

[0514] As one embodiment, the deployment of the first operation is later than the receiving of the at least one CSI reporting configuration.

[0515] As one embodiment, the output of the first operation comprises the target CSI; and the input of the second operation comprises the target CSI.

[0516] As one embodiment, the first node is a user (consumer).

[0517] As one embodiment, the first node is a user (consumer) of an AI function.

[0518] As one embodiment, the first node is a user of AI inference.

[0519] As one embodiment, the first node is a user of AI training.

[0520] As one embodiment, the first node is a MnS (Management Service) user.

[0521] As an embodiment, the first node is a producer of AI inference.

[0522] As an embodiment, the first node is a producer of AI training.

[0523] Embodiment 6

[0524] Embodiment 6 illustrates a diagram of a formula of the second time interval according to an embodiment of the present application; as shown in FIG. 6.

[0525] In embodiment 6, the formula of the second time interval depends on a first parameter, which depends on whether the generation of the target CSI is based on AI.

[0526] As an embodiment, the first parameter is a real number or an integer.

[0527] As an embodiment, the first parameter is a real number or an integer greater than 0.

[0528] As an embodiment, the first parameter is a real number or an integer greater than 1.

[0529] As an embodiment, the unit of the first parameter is millisecond (ms).

[0530] As an embodiment, the unit of the first parameter is second (s).

[0531] As an embodiment, the unit of the first parameter is symbol.

[0532] As an embodiment, the unit of the first parameter is slot.

[0533] As an embodiment, the unit of the first parameter is subframe.

[0534] As an embodiment, the value of the first parameter is configurable.

[0535] As an embodiment, the value of the first parameter is fixed.

[0536] As an embodiment, the first parameter includes one or more candidate values.

[0537] As an embodiment, the value of the first parameter depends on UE capability information.

[0538] As an embodiment, the first parameter is dependent on UE capability information; the UE capability information includes at least one of codebookType IE, beamReportTiming IE and beamSwitchTiming IE.

[0539] As an embodiment, the essence of the above method includes: considering UE capability information when determining the second time interval.

[0540] As an embodiment, the benefit of the above method includes: enhancing the reliability and robustness of the system.

[0541] As an embodiment, the second time interval and the first parameter are a functional relationship.

[0542] As an embodiment, the second time interval and the first parameter are a mapping relationship.

[0543] As an embodiment, the second time interval and the first parameter are a linear relationship.

[0544] As an embodiment, the second time interval and the first parameter are a nonlinear relationship.

[0545] As an embodiment, the determination of the first parameter depends on whether the generation mode of the target CSI is based on AI.

[0546] As an embodiment, when the generation mode of the target CSI is not based on AI, the first parameter is; when the generation mode of the target CSI is based on AI, the first parameter is not.

[0547] As an embodiment, when the generation mode of the target CSI is not based on AI, the first parameter is; when the generation mode of the target CSI is based on AI, the first parameter is not.

[0548] As an embodiment, when the generation mode of the target CSI is not based on AI, the first parameter is; when the generation mode of the target CSI is based on AI, the first parameter is not.

[0549] As an embodiment, the reporting amount of the target CSI includes RSRP; when the generation mode of the target CSI is not based on AI, the first parameter is; when the generation mode of the target CSI is based on AI, the first parameter is not.

[0550] As an embodiment, when the generation mode of the target CSI is based on AI, the first parameter is not defined in the version 18 and previous versions of 3GPP TS 38.214.

[0551] As an embodiment, the essence of the above method includes: processing AI-based and non-AI-based schemes on a case-by-case basis.

[0552] As an embodiment, the benefits of the above method include: better adaptation to various application scenarios, good flexibility.

[0553] As an embodiment, the value of the first parameter depends on whether the generation method of the target CSI is AI-based.

[0554] As an embodiment, when the generation method of the target CSI is AI-based, 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 generation method of the target CSI is not AI-based, 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.

[0555] 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.

[0556] 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.

[0557] 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.

[0558] As an embodiment, any candidate value in the first candidate value range and the second candidate value range has a unit of milliseconds (ms).

[0559] As an embodiment, any candidate value in the first candidate value range and the second candidate value range has a unit of seconds (s).

[0560] As an embodiment, any candidate value in the first candidate value range and the second candidate value range has a unit of symbols.

[0561] As an embodiment, any candidate value in the first candidate value range and the second candidate value range has a unit of slots.

[0562] As an embodiment, any candidate value in the first candidate value range and the second candidate value range has a unit of subframes.

[0563] As an embodiment, the second candidate value range includes 8, 11, 21, 36.

[0564] As an embodiment, the second candidate value range includes 16, 30, 42, 85, 340, 680.

[0565] As an embodiment, the second candidate value range includes 37, 69, 140, 140, 560, 1120.

[0566] As an embodiment, the essence of the above method includes: processing AI-based and non-AI-based schemes on a case-by-case basis.

[0567] As an embodiment, the benefits of the above method include: maintaining existing standards and system designs, and reducing implementation complexity.

[0568] As an embodiment, the first candidate value range and the second candidate value range are different.

[0569] As an embodiment, the first candidate value range and the second candidate value range include the same number of candidate values.

[0570] As an embodiment, the first candidate value range is sorted in descending order of candidate values; the second candidate value range is sorted in descending order of 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.

[0571] As an embodiment, the essence of the above method includes: reserving longer CSI calculation time for AI-based CSI reporting.

[0572] 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.

[0573] As an embodiment, whether the first parameter depends on the first capability parameter depends on whether the generation method of the target CSI is AI-based; only when the generation method of the target CSI is AI-based, the first parameter depends on the first capability parameter.

[0574] As an embodiment, when the generation method of the target CSI is AI-based, the first parameter depends on the first capability parameter; when the generation method of the target CSI is not AI-based, the first parameter depends on the second capability parameter; the first capability parameter and the second capability parameter are both capability parameters reported by the first node.

[0575] As an embodiment, when the generation manner of the target CSI is based on AI, the first parameter depends on a first capability parameter and a second capability parameter; when the generation manner of the target CSI is not based on AI, the first parameter depends on only the second capability parameter; the first capability parameter and the second capability parameter are both the capability parameters reported by the first node.

[0576] As an embodiment, the first capability parameter and the second capability parameter represent different capability parameters reported by the first node.

[0577] As an embodiment, the first capability parameter represents an AI-related capability parameter reported by the first node.

[0578] As an embodiment, the second capability parameter represents an AI-unrelated capability parameter reported by the first node.

[0579] As an embodiment, the second capability parameter includes a beamReportTiming IE.

[0580] As an embodiment, the second capability parameter includes a beamSwitchTiming IE.

[0581] As an embodiment, the second capability parameter includes a codebookType IE.

[0582] As an embodiment, the second capability parameter includes a beamReportTiming IE and a beamSwitchTiming IE.

[0583] As an embodiment, the second capability parameter includes at least one of a codebookType IE, a beamReportTiming IE, and a beamSwitchTiming IE.

[0584] As an embodiment, the essence of the above method includes: when determining the second time interval, considering UE capability information; and considering different UE capability information for AI-based and non-AI-based CSI reporting.

[0585] As an embodiment, the benefits of the above method include: enhancing the reliability and robustness of the system.

[0586] Embodiment 7

[0587] Embodiment 7 illustrates a schematic diagram of N information blocks and N time units according to an embodiment of the present application; as shown in FIG. 7. In FIG. 7, information block #1, …, information block #N are N information blocks; time unit #1, …, time unit #N are N time units.

[0588] In Example 7, when the target CSI is generated based on AI, the target CSI includes N information blocks, each of which includes channel information of N time units, and N is a positive integer greater than 1; and the second time interval depends on at least one of the N time units.

[0589] As an example, the target CSI includes N information blocks, each of which includes CSI of N time units, and N is a positive integer greater than 1; and generation of any information block of the N information blocks depends on measurement based on the first resource set.

[0590] As an example, the N information blocks each include predicted CSI of N time units.

[0591] As an example, the N information blocks each include predicted beam information of N time units.

[0592] As an example, the N information blocks each include compressed CSI of N time units.

[0593] As an example, any information block of the N information blocks indicates at least one resource in the first resource set.

[0594] As an example, the essence of the above method includes supporting joint reporting of CSI of multiple time units.

[0595] As an example, the essence of the above method includes supporting an AI-based CSI prediction or compression scheme.

[0596] As an example, the benefits of the above method include reducing system overhead and information feedback delay, and enhancing transmission efficiency of the system.

[0597] As an example, the benefits of the above method include improving accuracy and real-time performance of CSI reporting, and improving overall performance of the system.

[0598] As an example, the target CSI includes multiple information blocks, and the number of information blocks included in the target CSI is not less than N.

[0599] As an example, the target CSI includes multiple information blocks, and the multiple information blocks included in the target CSI include the N information blocks.

[0600] As an example, the benefits of the above method include enhancing flexibility and robustness of the system.

[0601] As an example, one time unit includes one or more slots.

[0602] As an example, a time unit comprises one or more subframes.

[0603] As an example, a time unit comprises a plurality of consecutive symbols.

[0604] As an example, the N time units are orthogonal to each other.

[0605] As an example, the N time units are different from each other.

[0606] As an example, there are two time units in the N time units that overlap.

[0607] As an example, the N time units are consecutive.

[0608] As an example, the N time units are equally spaced.

[0609] As an example, the interval between any two adjacent time units in the N time units is P time units, where P is a positive integer.

[0610] As an example, the benefits of the above method include: using existing system design and standards.

[0611] As an example, the benefits of the above method include: enhancing the flexibility and robustness of the system.

[0612] In general, how the first node determines the N or at least one of the N time units is determined by the hardware device manufacturer, and some non-limiting examples are described below:

[0613] As an example, the first node determines the N time units based on the variation of the channel.

[0614] As an example, the first node determines the N time units based on the time correlation of the channel.

[0615] As an example, the first node determines the N time units based on the moving speed.

[0616] As an example, the first node determines the N time units based on at least one of the variation of the channel, the time correlation of the channel, or the moving speed.

[0617] As an example, the first node determines the N time units to be time units with fast channel variation (e.g., variation greater than a threshold).

[0618] As one embodiment, the first node determines the N time units based on its moving speed.

[0619] As one embodiment, the first node determines the N time units to be time units with low time correlation, such as below a threshold.

[0620] As one embodiment, the second time interval depending on at least one of the N time units comprises the second time interval depending on a target time unit of the N time units.

[0621] As one embodiment, the target time unit is the earliest one of the N time units.

[0622] As one embodiment, the target time unit is the latest one of the N time units.

[0623] As one embodiment, the target time unit is a designated one of the N time units.

[0624] As one embodiment, the second time interval depending on a target time unit of the N time units comprises the second time interval depending on a time interval between the target time unit and a last symbol of the first PDCCH.

[0625] As one embodiment, the second time interval depending on a time interval between the target time unit and a last symbol of the first PDCCH comprises the second time interval depending on a time interval between a start symbol of the target time unit and a last symbol of the first PDCCH.

[0626] As one embodiment, the second time interval depending on at least one of the N time units comprises the second time interval depending on a target symbol of the N time units.

[0627] As one embodiment, the target symbol is the earliest one of the N time units.

[0628] As one embodiment, the target symbol is the latest one of the N time units.

[0629] As one embodiment, the target symbol is a designated one of the N time units.

[0630] As one embodiment, the second time interval depending on a target symbol of the N time units comprises the second time interval depending on a time interval between the target symbol and a last symbol of the first PDCCH.

[0631] As an embodiment, the benefits of the above method include: enhancing the flexibility of the system.

[0632] As an embodiment, the benefits of the above method include: small changes to existing standards and system design.

[0633] As an embodiment, the second time interval depending on at least one of the N time units includes: the second time interval depending on the N.

[0634] As an embodiment, the second time interval depending on the N includes: the N belonging to one of V1 candidate value ranges, any candidate value range of the V1 candidate value ranges including one or more positive integers, V1 being a positive integer greater than 1; V1 time intervals respectively and the V1 candidate value ranges one-to-one corresponding, the second time interval being one of the V1 time intervals corresponding to the candidate value range to which the N belongs.

[0635] As an embodiment, the second time interval depending on the N includes: the second time interval depending on a first parameter, the first parameter depending on the N.

[0636] As an embodiment, the second time interval depending on the N includes: the second time interval and the first parameter being a linear relationship, the first parameter depending on the N.

[0637] As an embodiment, the second time interval depending on the N includes: the second time interval and the first parameter being a linear relationship, the first parameter and the N being a linear relationship.

[0638] As an embodiment, the first parameter depending on the N includes: the N belonging to one of V1 candidate value ranges, any candidate value range of the V1 candidate value ranges including one or more positive integers, V1 being a positive integer greater than 1; V1 first parameter candidate values respectively and the V1 candidate value ranges one-to-one corresponding, the first parameter being one of the V1 first parameter candidate values corresponding to the candidate value range to which the N belongs.

[0639] As an embodiment, the essence of the above method includes: CSI calculation time depending on the amount of information carried by CSI reporting or the number of time units targeted by CSI reporting.

[0640] As an embodiment, the benefits of the above method include: more accurate setting of CSI calculation time, effective use of system resources, and improvement of the accuracy and real-time performance of CSI reporting.

[0641] As an embodiment, the benefits of the above method include: enhancing the flexibility of the system and the overall performance of the system.

[0642] As an embodiment, the second time interval depending on the length of the N time units comprises: the second time interval and the length of the N time units are linearly related.

[0643] As an embodiment, the second time interval depending on the length of the N time units comprises: the second time interval and the length of the N time units are linearly related.

[0644] As an embodiment, the second time interval depending on the length of the N time units comprises: the length of the N time units belongs to one of V1 candidate value ranges, any candidate value range of the V1 candidate value ranges comprises one or more real numbers or integers, V1 is a positive integer greater than 1; V1 time intervals respectively correspond to the V1 candidate value ranges one by one, and the second time interval is one of the V1 time intervals corresponding to the candidate value range to which the length of the N time units belongs.

[0645] As an embodiment, the second time interval depending on the length of the N time units comprises: the second time interval depends on a first parameter, and the first parameter depends on the length of the N time units.

[0646] As an embodiment, the second time interval depending on the length of the N time units comprises: the second time interval and the first parameter are linearly related, and the first parameter and the length of the N time units are linearly related.

[0647] As an embodiment, the essence of the above method comprises: the CSI calculation time depends on the length of the time unit to which the CSI reporting is directed.

[0648] As an embodiment, the advantage of the above method comprises: more accurate setting of the CSI calculation time, effective use of system resources, and improvement of the accuracy and real-time performance of the CSI reporting.

[0649] As an embodiment, the advantage of the above method comprises: enhancement of the flexibility of the system and the overall performance of the system.

[0650] Embodiment 8

[0651] Embodiment 8 is a schematic diagram of the first RS according to an embodiment of the present application; as shown in FIG. 8. In FIG. 8, RS#1, …, RS#n, …, RS#M are one or more aperiodic RS resources in the first resource set.

[0652] In Embodiment 8, the first set of resources consists of one or more aperiodic RS resources, and the first RS is the latest in time RS in the first set of resources triggered by the first DCI.

[0653] As an embodiment, the first RS is one RS in the first set of resources.

[0654] As an embodiment, the first RS is the latest in time aperiodic RS in the first set of resources.

[0655] As an embodiment, the first RS is the last RS in the first set of resources triggered by the first DCI.

[0656] As an embodiment, the first RS is the last aperiodic RS in the first set of resources triggered by the first DCI.

[0657] As an embodiment, the above method has the benefits including: maintaining existing system design and standards, and enhancing system consistency.

[0658] Embodiment 9

[0659] Embodiment 9 illustrates a schematic diagram of a first operation according to an embodiment of the present application; as shown in FIG. 9.

[0660] In Embodiment 9, the generation manner of the target CSI based on AI includes: the generation manner of the target CSI includes performing a first operation, an input of the first operation depends on measurement based on the first set of resources, and the target CSI depends on an output of the first operation.

[0661] As an embodiment, the first operation is based on training or AI.

[0662] As an embodiment, the first operation is obtained through training.

[0663] As an embodiment, the first operation is based on a neural network (NN).

[0664] As an embodiment, the first operation includes an AI entity.

[0665] As an embodiment, the first operation includes a part of an AI entity.

[0666] As an embodiment, the first operation includes a part of an AI entity for inference.

[0667] As an embodiment, the first operation is performed by an AI entity.

[0668] As one embodiment, the first operation is AI function execution.

[0669] As one embodiment, the AI function includes at least one of AI inference function, AI training function, AI management function.

[0670] As one embodiment, the training for obtaining the first operation is performed by the first node.

[0671] As one embodiment, the training for obtaining the first operation is performed by MDA function (Management Data Analytics Function).

[0672] As one embodiment, the training for obtaining the first operation is performed by MDAS (Management Data Analytics Service) producer.

[0673] As one embodiment, the training for obtaining the first operation is performed by NWDAF (Network Data Analytics Function).

[0674] As one embodiment, the training for obtaining the first operation is performed by core network.

[0675] As one embodiment, the training for obtaining the first operation is performed by AI training producer.

[0676] As one embodiment, the first operation includes inference.

[0677] As one embodiment, the first operation is AI inference.

[0678] As one embodiment, the first operation includes AI inference for CSI.

[0679] As one embodiment, the first operation is AI inference for CSI.

[0680] As one embodiment, the first operation includes AI inference for at least one of beam prediction, CSI prediction, CSI estimation, or CSI compression.

[0681] As one 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.

[0682] As one embodiment, benefits of the above method include more accurate and complete CSI, lower reference signal overhead, and improved real-time of CSI.

[0683] As one embodiment, the first operation is deployment requiring.

[0684] As one embodiment, the first operation is obtained by load.

[0685] As one embodiment, the first operation is obtained by load from a serving cell of the first node.

[0686] As one embodiment, the first operation is obtained by load from a maintaining base station of the serving cell of the first node.

[0687] As one embodiment, the first operation is obtained by load from a core network.

[0688] As one embodiment, the first operation includes one or more of convolution, pooling, concatenation, and activation.

[0689] As one embodiment, the first operation includes at least one of a fully connected layer, a pooling layer, at least one convolution layer, and at least one encoding layer.

[0690] As one embodiment, an encoding layer includes at least one convolution layer and a pooling layer.

[0691] As one embodiment, in a convolution layer, at least one convolution kernel is used to convolve an 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 a fully connected layer; the fully connected layer converts the one vector into an output.

[0692] As one embodiment, some or all of the convolution kernel size, the number of convolution layers, the convolution step, the pooling kernel size, the pooling kernel step, the pooling function, the activation function, and the number of feature maps of the first operation are obtained by training.

[0693] As one embodiment, some or all of the convolution kernel, the pooling kernel, the pooling function, the activation function, the parameters of the pooling function, and the parameters of the activation function of the first operation are obtained by training.

[0694] As one embodiment, the first operation includes pre-processing.

[0695] As one embodiment, the pre-processing includes one or more of matrix decomposition, matrix transformation, and projection.

[0696] As one embodiment, the pre-processing includes one or more of quantization, spatial-to-angle domain transformation, angle-to-spatial domain transformation, frequency-to-time domain transformation, and time-to-frequency domain transformation.

[0697] As one embodiment, the pre-processing includes at least one of truncation and / or padding, DFT (Discrete Fourier Transform), mapping, and labeling.

[0698] As one embodiment, the first operation includes post-processing.

[0699] As one embodiment, the post-processing includes at least one of DFT (Discrete Fourier Transform), quantization, truncation, and / or padding.

[0700] As one embodiment, the post-processing includes one or more of angle-to-spatial domain transformation, spatial-to-angle domain transformation, time-to-frequency domain transformation, and frequency-to-time domain transformation.

[0701] As one embodiment, the measurement based on the first set of resources includes pre-compression channel information, and the output of the first operation includes post-compression channel information.

[0702] As one embodiment, the benefits of the above method include applicability to channel compression, and saving feedback overhead.

[0703] As one embodiment, the measurement based on the first set of resources includes measured channel information, and the output of the first operation includes predicted channel information.

[0704] As one embodiment, the measurement based on the first set of resources includes measured channel information, and the output of the first operation includes spatial beam prediction.

[0705] As one embodiment, the benefits of the above method include reducing RS resource overhead, and reducing feedback delay.

[0706] As one embodiment, the measurement based on the first set of resources includes historic channel information, and the output of the first operation includes predicted channel information.

[0707] As one embodiment, the measurement based on the first set of resources includes historic channel information, and the output of the first operation includes Temporal beam prediction.

[0708] As an embodiment, benefits of the above method include: reducing channel information feedback delay, improving real-time of channel information acquisition.

[0709] As an embodiment, the measurement based on the first set of resources includes current channel information, and the output of the first operation includes channel information after a period of time.

[0710] As an embodiment, benefits of the above method include: improving CSI accuracy and real-time, reducing RS overhead.

[0711] As an embodiment, the measurement based on the first set of resources includes incomplete channel information, and the output of the first operation includes complete channel information.

[0712] As an embodiment, benefits of the above method include: reducing RS overhead, improving CSI accuracy and completeness.

[0713] As an embodiment, the measurement based on the first set of resources includes channel information of P1 antenna ports, and the output of the first operation includes channel information of P2 antenna ports, the P1 and the P2 are positive integers greater than 1 respectively, and the P1 is less than the P2.

[0714] As a sub-embodiment of the above embodiment, the P1 antenna ports are a proper subset of the P2 antenna ports.

[0715] As a sub-embodiment of the above embodiment, the P2 antenna ports belong to the second set of resources.

[0716] As an embodiment, the input of the first operation further includes the second set of resources.

[0717] As an embodiment, the output of the first operation includes one or more of beam indication, CRI (CSI-RS Resource Indicator), SS / PBCH Block Resource indicator (SSBRI), or RSRP (reference signal received power).

[0718] 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.

[0719] As an embodiment, the output of the first operation includes one or more of channel impulse response, small-scale characteristics, channel matrix.

[0720] As an embodiment, the output of the first operation comprises one or more of time delay spread, Doppler spread, Doppler shift, average time delay, and average gain.

[0721] As an embodiment, the output of the first operation comprises the target CSI.

[0722] As an embodiment, the input of the first operation is dependent on measurements based on the first set of resources.

[0723] As an embodiment, the generation of the target CSI is based on AI, and the input of the first operation is dependent on measurements based on the first set of resources.

[0724] As an embodiment, the input of the first operation being dependent on measurements based on the first set of resources comprises that measurements (channel measurements and / or interference measurements) based on the first set of resources are used to generate the input of the first operation.

[0725] As an embodiment, the target CSI is dependent on the output of the first operation.

[0726] As an embodiment, the target CSI being dependent on the output of the first operation comprises that the target CSI comprises the output of the first operation.

[0727] As an embodiment, the target CSI being dependent on the output of the first operation comprises that the target CSI comprises a post-processed output of the first operation.

[0728] As an embodiment, the target CSI being dependent on the output of the first operation comprises that the output of the first operation is used to generate the target CSI.

[0729] As an embodiment, the target CSI being dependent on the output of the first operation comprises that the output of the first operation, after being post-processed, is used to generate the target CSI.

[0730] As an embodiment, the input of the first operation is dependent on measurements based on the first set of resources, and the target CSI is dependent on the output of the first operation.

[0731] As an embodiment, the generation of the target CSI comprises performing a first operation, the input of the first operation is dependent on measurements based on the first set of resources, and the target CSI is dependent on the output of the first operation.

[0732] As an embodiment, the benefits of the above method comprise supporting AI-based schemes and improving the accuracy and real-time performance of information reporting.

[0733] As an embodiment, the benefits of the above method include: improving the overall performance of the system.

[0734] As an embodiment, the first operation is associated with the first type of identifier.

[0735] As an embodiment, the target CSI reporting configuration indicates the first type of identifier, and the first operation is associated with the first type of identifier.

[0736] As an embodiment, the AI model used by the first operation is identified by the first type of identifier.

[0737] As an embodiment, the AI entity or AI function to which the first operation belongs is identified by the first type of identifier.

[0738] As an embodiment, the AI entity or AI function that performs the first operation is identified by the first type of identifier.

[0739] As an embodiment, the benefits of the above method include: identifying an AI model / entity / function through the first type of identifier, simplifying design and unifying understanding of different AI entities / functions among multiple nodes.

[0740] As an embodiment, the first type of identifier is used to identify or indicate a set of reference resources, and measurements on the set of reference resources are used to obtain a training data set for the first operation.

[0741] As an embodiment, the first type of identifier is used to identify configuration information of a set of reference resources, and measurements on the set of reference resources are used to obtain a training data set for the first operation.

[0742] As an embodiment, the training used to obtain the first operation is identified by the first type of identifier.

[0743] As an embodiment, the data set used for training of the first operation is identified by the first type of identifier.

[0744] As an embodiment, the target CSI reporting configuration indicates the first operation by indicating the first type of identifier.

[0745] As an embodiment, the benefits of the above method include: identifying the inference generated by an AI training or AI training data set by identifying the AI training or AI training data set, establishing consensus among different AI functions, and further simplifying design.

[0746] Embodiment 10

[0747] Embodiment 10 illustrates a schematic diagram of the first type of identification according to an embodiment of the present application; as shown in FIG. 10.

[0748] In Embodiment 10, the generation manner of the target CSI is based on AI, including that the generation manner of the target CSI is associated with the first type of identification.

[0749] As an embodiment, the first type of identification is a non-negative integer.

[0750] As an embodiment, the first type of identification is a string.

[0751] As an embodiment, the first type of identification is a model identification.

[0752] As an embodiment, the first type of identification is used to identify an AI model, an AI entity or an AI function.

[0753] As an embodiment, the first type of identification is used by the first node to determine an AI model, an AI entity or an AI function.

[0754] As an embodiment, the target CSI reporting configuration indicates the use of an AI model, an AI entity or an AI function by indicating the first type of identification.

[0755] As an embodiment, the benefits of the above method include identifying an AI model, an AI entity or an AI function through the first type of identification, simplifying system design, and establishing a consensus among multiple nodes on different AI models, AI entities or AI functions.

[0756] As an embodiment, the first type of identification is used to identify or indicate a resource set.

[0757] As an embodiment, the first type of identification is used to identify or indicate a resource set, and the measurement of the resource set is used to obtain a training data set.

[0758] As an embodiment, the first type of identification is used to identify or indicate a training data set.

[0759] As an embodiment, the benefits of the above method include identifying an AI training or an AI training data set by identifying an AI training or an AI training data set, establishing a consensus among different AI functions, and further simplifying system design.

[0760] As an embodiment, the generation manner of the target CSI is associated with the first type of identification includes that the generation of the target CSI is associated with the first type of identification through the target CSI reporting configuration.

[0761] As an embodiment, the target CSI generation manner being associated to the first type of identity comprises: the target CSI reporting configuration indicating the first type of identity; and the target CSI reporting configuration indicating the target CSI generation.

[0762] As an embodiment, the target CSI generation manner being associated to the first type of identity comprises: the target CSI generation manner comprising performing a first operation, an input of the first operation depending on measurement based on the first resource set, the target CSI depending on an output of the first operation, and the first operation being associated to the first type of identity.

[0763] As an embodiment, the above method has the benefit of supporting AI-based CSI reporting.

[0764] As an embodiment, the target CSI generation manner being associated to the first type of identity comprises: the target CSI generation manner using an AI model identified by the first type of identity.

[0765] As an embodiment, the target CSI generation manner being associated to the first type of identity comprises: an AI entity identified by the first type of identity generating the target CSI.

[0766] As an embodiment, the target CSI generation manner being associated to the first type of identity comprises: the target CSI being generated by an AI entity, and the first type of identity being used to identify the AI entity or a function.

[0767] As an embodiment, the target CSI generation manner being associated to the first type of identity comprises: the target CSI generation manner belonging to an AI function, and the first type of identity being used to identify the AI function.

[0768] As an embodiment, the target CSI generation manner not being associated to the first type of identity comprises: the target CSI generation manner comprising a first operation performed by a target receiver of the target CSI reporting configuration, an input of the first operation depending on measurement based on the first resource set, the target CSI depending on an output of the first operation, and the first operation not being associated to the first type of identity.

[0769] As an embodiment, the target CSI generation manner not being associated to the first type of identity comprises: the target CSI generation manner not using an AI model identified by the first type of identity.

[0770] As an embodiment, the target CSI generation manner not being associated to the first type of identity comprises: an AI entity identified by the first type of identity not being used to generate the target CSI.

[0771] As an embodiment, the first type of identifier not being associated to the generation manner of the target CSI comprises: the target CSI being generated by an AI entity, and the first type of identifier not being used to identify the AI entity or a function.

[0772] As an embodiment, the first type of identifier not being associated to the generation manner of the target CSI comprises: the generation manner of the target CSI belonging to an AI function, and the first type of identifier not being used to identify the AI function.

[0773] As an embodiment, the method has the advantages of simplifying system design and reducing implementation complexity of the scheme.

[0774] As an embodiment, the method has the advantages of improving flexibility of the system and adapting to transmission and application in different scenarios.

[0775] Embodiment 11

[0776] Embodiment 11 illustrates a schematic diagram of a second time interval and a first type of identifier relationship according to an embodiment of the present application; as shown in FIG. 11.

[0777] In embodiment 11, when the generation manner of the target CSI is based on AI, the second time interval depends on the first type of identifier associated to the generation manner of the target CSI.

[0778] As an embodiment, the first type of identifier is the first type of identifier associated to the generation manner of the target CSI, the first type of identifier belongs to one of V identifier sets, any identifier set of the V identifier sets includes one or more first type of identifiers, and V is a positive integer greater than 1; the first reference symbol depends on the identifier set to which the first type of identifier belongs.

[0779] As an embodiment, the first type of identifier is the first type of identifier associated to the generation manner of the target CSI, the first type of identifier belongs to one of V identifier sets, any identifier set of the V identifier sets includes one or more first type of identifiers, and V is a positive integer greater than 1; the second time interval depends on the identifier set to which the first type of identifier belongs.

[0780] As an embodiment, the calculation formula of the second time interval depends on the first type of identifier associated to the generation manner of the target CSI.

[0781] As an embodiment, the first type of identifier is the first type of identifier associated to the generation manner of the target CSI, the first type of identifier belongs to one of V identifier sets, any identifier set of the V identifier sets includes one or more first type of 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 type of identifier belongs.

[0782] As an embodiment, the first type of identifier is a first type of identifier associated with a generation manner of the target CSI, the first type of identifier belongs to one of V identifier sets, any identifier set of the V identifier sets includes one or more first type of identifiers, and V is a positive integer greater than 1; the calculation formula of the second time interval includes V formulas, the V formulas and the V identifier sets correspond one by one, and the calculation formula of the second time interval is the calculation formula corresponding to the identifier set to which the first type of identifier belongs.

[0783] 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 manner of the target CSI.

[0784] 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 generation manner of the target CSI.

[0785] 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 of the V candidate value ranges includes one or more integers or real numbers, V is a positive integer greater than 1; the first type of identifier belongs to one of V identifier sets, any identifier set of the V identifier sets includes one or more first type of identifiers, V is a positive integer greater than 1; the V candidate value ranges and the V identifier sets correspond one by one, and the first parameter is a candidate value in the candidate value range corresponding to the identifier set to which the first type of identifier belongs.

[0786] As an embodiment, the essence of the above method includes: setting different CSI calculation times for CSI reporting associated with different first type of identifiers.

[0787] As an embodiment, the essence of the above method includes: considering the influence of different AI models, AI entities or AI functions when setting the CSI calculation time.

[0788] 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.

[0789] As an embodiment, the benefits of the above method include: improving the overall performance of the system.

[0790] Embodiment 12

[0791] Embodiment 12 illustrates a schematic diagram of a second resource set according to an embodiment of the present application; as shown in FIG. 12. In FIG. 12, resource #1, …, resource #m, … is at least one resource in the second resource set.

[0792] In embodiment 12, the generation manner of the target CSI based on AI includes that the target CSI indicates at least one resource in a second resource set, and the second resource set includes resources not belonging to the first resource set.

[0793] As an embodiment, the second resource set includes the first resource set and resources outside the first resource set.

[0794] 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 RS resources in the first resource set and RS resources outside the first resource set.

[0795] 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.

[0796] As an embodiment, the second resource set includes resources not belonging to the first resource set, and the resources in the second resource set include at least one of antenna ports, TCI states, QCL information, frequency resources, time-frequency code resources, beams, RS resources, vectors, or matrices.

[0797] As an embodiment, the second resource set includes at least one training data set.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] As an embodiment, the target CSI reporting configuration indicates one resource configuration, and the one resource configuration indicates the first resource set and the second resource set.

[0803] 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.

[0804] As an embodiment, the target CSI reporting configuration indicates configuration information of the second resource set.

[0805] As an embodiment, the target CSI reporting configuration indicates an identity of the second resource set.

[0806] As an embodiment, the target CSI reporting configuration indicates a first type of identity, and the second resource set depends on the first type of identity.

[0807] As an embodiment, the second resource set depending on the first type of identity comprises that the first type of identity is used to identify the second resource set.

[0808] As an embodiment, the second resource set depending on the first type of identity comprises that the first type of identity is used to identify a reference resource set, and the reference resource set comprises the second resource set.

[0809] As an embodiment, the second resource set depending on the first type of identity comprises that the first type of identity is used to identify a reference resource set, and the reference resource set comprises the second resource set, and the target CSI reporting configuration is used to indicate the second resource set from the reference resource set.

[0810] As an embodiment, information other than the target CSI reporting configuration indicates the second resource set.

[0811] As an embodiment, the information other than the target CSI reporting configuration indicating the second resource set comprises a higher layer parameter.

[0812] As an embodiment, the information other than the target CSI reporting configuration indicating the second resource set comprises an RRC parameter.

[0813] As an embodiment, the information other than the target CSI reporting configuration indicating the second resource set comprises a MAC CE.

[0814] As one embodiment, the information other than the target CSI reporting configuration of the second set of resources comprises DCI (downlink control information).

[0815] As one embodiment, the target CSI is generated based on AI, and the first node is not required to measure the second set of resources.

[0816] As one embodiment, the target CSI is generated based on AI, and the first set of resources is used for measurement and the second set of resources is used for prediction.

[0817] As one embodiment, the target CSI is generated based on AI, and the first set of resources is used for measurement and the second set of resources is used for prediction.

[0818] As one embodiment, the target CSI is generated based on AI, and only the first set of resources among the first set of resources and the second set of resources is used for measurement.

[0819] As one embodiment, only the first set of resources among the first set of resources and the second set of resources is used for measurement comprises that only the first set of resources among the first set of resources and the second set of resources is used for measurement by the first node.

[0820] As one embodiment, only the first set of resources among the first set of resources and the second set of resources is used for measurement comprises that the first set of resources is used for measurement by the first node, and the first node is not required to measure part or all of the second set of resources.

[0821] As one embodiment, the first node is not required to measure the second set of resources comprises that the first node does not measure part or all of the second set of resources.

[0822] As one embodiment, the first node is not required to measure the second set of resources comprises that whether the first node measures part or all of the second set of resources is related to implementation of the first node or determined by the first node.

[0823] Embodiment 13

[0824] Embodiment 13 illustrates a schematic diagram of the first condition not being met according to one embodiment of the present application; as shown in FIG. 13.

[0825] In embodiment 13, the first DCI is ignored when the first condition is not satisfied; wherein no HARQ-ACK or transport block is multiplexed on the first PUSCH.

[0826] As one embodiment, the when the first condition is not satisfied includes: when the first symbol is earlier than the first reference symbol.

[0827] As one embodiment, the when the first condition is not satisfied includes: when the second symbol is earlier than the second reference symbol.

[0828] As one embodiment, the 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.

[0829] As one embodiment, no HARQ-ACK or transport block is multiplexed on the first PUSCH; the first DCI is ignored when the first condition is not satisfied.

[0830] As one embodiment, the first resource set consists of one or more aperiodic RS resources; no HARQ-ACK or transport block is multiplexed on the first PUSCH; the first node ignores the first DCI when the first condition is not satisfied.

[0831] As one embodiment, the first resource set consists of one or more periodic or semi-persistent RS resources; the first node ignores the first DCI when the first condition is not satisfied; wherein no HARQ-ACK or transport block is multiplexed on the first PUSCH.

[0832] As one embodiment, the first resource set consists of one or more periodic or semi-persistent RS resources; the first processor ignores the first DCI when the first condition is not satisfied; wherein no HARQ-ACK or transport block is multiplexed on the first PUSCH.

[0833] As one embodiment, HARQ-ACK or transport block is multiplexed on the first PUSCH; the target CSI is sent on the first PUSCH and is not updated when the first condition is not satisfied.

[0834] As one embodiment, the first set of resources consists of one or more aperiodic RS resources; a HARQ-ACK or a transport block is multiplexed on the first PUSCH; the target CSI is transmitted on the first PUSCH and the target CSI is not updated when the first condition is not met.

[0835] As one embodiment, the first node ignores the first DCI or transmits the target CSI on the first PUSCH and the target CSI is not updated when the first condition is not met.

[0836] As one embodiment, the first processor ignores the first DCI or transmits the target CSI on the first PUSCH and the target CSI is not updated when the first condition is not met.

[0837] As one embodiment, the ignoring the first DCI comprises dropping transmitting a signal on the first PUSCH.

[0838] As one embodiment, the ignoring the first DCI comprises dropping transmitting the target CSI on the first PUSCH.

[0839] As one embodiment, the ignoring the first DCI comprises dropping transmitting the at least one CSI on the first PUSCH.

[0840] As one embodiment, the transmitting the target CSI on the first PUSCH and the target CSI is not updated comprises the first node not being expected to transmit the target CSI on the first PUSCH and the target CSI being valid.

[0841] As one embodiment, the transmitting the target CSI on the first PUSCH and the target CSI is not updated comprises the first node not being expected to transmit the target CSI on the first PUSCH and the target CSI being updated.

[0842] As one embodiment, the target CSI not being updated comprises the first node not being expected to update the target CSI.

[0843] As one embodiment, the target CSI not being updated comprises whether the target CSI is actually updated being implementation dependent or self-determined by the first node.

[0844] As one embodiment, the target CSI not being updated comprises the target CSI not being valid.

[0845] As one embodiment, the target CSI not being updated comprises: the target CSI being the same as a latest one of the CSI reported on the first PUSCH based on the CSI reporting configuration.

[0846] As one embodiment, the target CSI not being updated comprises: the target CSI being irrelevant to a measurement based on a latest RS occasion of a CSI reference resource of the target CSI in the first resource set.

[0847] As one embodiment, the target CSI not being updated comprises: the target CSI being irrelevant to a measurement based on a latest RS occasion of a CSI reference resource of the target CSI in the first resource set.

[0848] As one embodiment, the target CSI not being updated comprises: the target CSI not being updated based on a measurement of at least a latest RS occasion of a CSI reference resource of the target CSI in the first resource set.

[0849] As one embodiment, the first resource set consists of one or more aperiodic RS resources; the target CSI not being updated comprises: the target CSI being irrelevant to a measurement based on an aperiodic RS resource triggered by the first DCI in the first resource set.

[0850] As one embodiment, the first resource set consists of one or more aperiodic RS resources; the target CSI not being updated comprises: the target CSI not being generated based on a measurement of an aperiodic RS resource triggered by the first DCI in the first resource set.

[0851] As one embodiment, the first resource set consists of one or more aperiodic RS resources; the target CSI not being updated comprises: the target CSI not being updated based on a measurement of an aperiodic RS resource triggered by the first DCI in the first resource set.

[0852] Embodiment 14

[0853] Embodiment 14 illustrates a schematic diagram of a second operation according to an embodiment of the present application; as shown in FIG. 14.

[0854] In embodiment 14, the output of the first operation comprises a first CSI, the target CSI carries the first CSI, and the first CSI is used by a target receiver of the target CSI as an input of the second operation to generate a second CSI.

[0855] As one embodiment, the first operation is for CSI compression, the second operation is for CSI recovery, and the first node and the second node employ a two-sided AI model.

[0856] As one embodiment, the target CSI comprises the first CSI.

[0857] As one embodiment, the first CSI is post-processed to generate the target CSI.

[0858] As one embodiment, the first CSI comprises N sub-CSIs, and the N information blocks respectively carry the N sub-CSIs.

[0859] As one embodiment, the first CSI comprises an output of the first operation.

[0860] As one embodiment, the second CSI comprises a recovery of at least part of an input to the first operation.

[0861] As one embodiment, the second CSI comprises one or more of a PMI (Precoding Matrix Indicator), a CRI (CSI-RS Resource Indicator), an SS / PBCH Block Resource indicator (SSBRI), a beam indication, a resource indication, a CQI (Channel Quality Indicator), an RI (Rank Indicator), a LI (Layer Indicator), an RSRP (reference signal received power), a SINR (signal-to-noise and interference ratio), a Capability Index, or a TDCP (Time Domain Channel Properties).

[0862] As one embodiment, the second CSI comprises one or more of a channel matrix, an eigenvector, an eigenvalue, or a precoding matrix.

[0863] As one embodiment, the second operation is an inverse operation of the first operation.

[0864] As one embodiment, the second operation is training-based.

[0865] As one embodiment, the training for obtaining the second operation is performed by the target receiver of the target CSI.

[0866] As one embodiment, the training for obtaining the second operation is performed by an MDA function.

[0867] As one embodiment, the training for obtaining the second operation is performed by an MDA producer.

[0868] As one embodiment, the training for obtaining the second operation is performed by a NWDAF.

[0869] As one embodiment, the training for obtaining the second operation is performed by a core network.

[0870] As one embodiment, the training for obtaining the second operation is performed by an AI (Artificial Intelligence) training producer.

[0871] As one embodiment, the first operation and the second operation are obtained through different training.

[0872] As one embodiment, the first operation and the second operation are obtained through independent training.

[0873] As one embodiment, the benefits of the above method include saving air interface overhead, better flexibility, adapting to different terminals, and better forward compatibility.

[0874] As one embodiment, the first operation and the second operation are obtained through joint training.

[0875] As one embodiment, the benefits of the above method include optimizing system performance.

[0876] As one embodiment, the training of the second operation depends on the first operation.

[0877] As one embodiment, the producer of the second operation trains the second operation according to the output of the first operation.

[0878] Embodiment 15

[0879] Embodiment 15 illustrates a schematic diagram of a first operation according to another embodiment of the present application; as shown in FIG. 15. In embodiment 15, the first operation includes K1 sub-operations, and K1 is a positive integer not greater than 1.

[0880] In embodiment 15, the K1 sub-operations are denoted as sub-operation #0, …, sub-operation #(K1-1), respectively.

[0881] As one embodiment, each of the K1 sub-operations is training-based.

[0882] As one embodiment, at least one of the K1 sub-operations is training-based.

[0883] As one embodiment, each training-based sub-operation of the K1 sub-operations is based on the same training performer.

[0884] As one embodiment, two sub-operations of the K1 sub-operations are based on different training performers.

[0885] As one embodiment, at least one of the K1 sub-operations is deployment- required.

[0886] As one embodiment, at least one of the K1 sub-operations is loading- required.

[0887] As one embodiment, all loading-required sub-operations of the K1 sub-operations are loaded from the same producer.

[0888] As one embodiment, two loading-required sub-operations of the K1 sub-operations are loaded from different producers.

[0889] As one embodiment, at least one of the K1 sub-operations is not training-based.

[0890] As one embodiment, at least one of the K1 sub-operations is based on a codebook for precoding defined in 3GPP R18 or a version before 3GPP R18.

[0891] As one embodiment, one or more of the K1 sub-operations is AI-based.

[0892] As one embodiment, one or more of the K1 sub-operations includes inference.

[0893] As one embodiment, one or more of the K1 sub-operations includes AI inference.

[0894] As one embodiment, one or more of the K1 sub-operations includes AI inference for CSI.

[0895] As one embodiment, one or more of the K1 sub-operations comprises pre-processing.

[0896] As one embodiment, one or more of the K1 sub-operations comprises post-processing.

[0897] As one embodiment, two of the K1 sub-operations are serial, such as all sub-operations in FIG. 15(a), sub-operation #2 to sub-operation #(K1-1) in FIG. 15(b), and sub-operation #0 to sub-operation #(K1-4) in FIG. 15(c).

[0898] As one embodiment, two sub-operations being serial means that the output of one of the two sub-operations is used as the input of the other of the two sub-operations.

[0899] As one embodiment, two of the K1 sub-operations are parallel, such as sub-operation #0 and sub-operation #1 in FIG. 15(b), sub-operation #(K1-3) and sub-operation #(K1-2) in FIG. 15(c).

[0900] As one embodiment, two sub-operations being parallel means that the outputs of the two sub-operations are collectively used as the input of another sub-operation.

[0901] As one embodiment, the K1 sub-operations comprise one or more of convolution, pooling, concatenation, or activation.

[0902] As one embodiment, one of the K1 sub-operations comprises a fully connected layer.

[0903] As one embodiment, one of the K1 sub-operations comprises a pooling layer.

[0904] As one embodiment, one of the K1 sub-operations comprises at least one convolution layer.

[0905] As one embodiment, one of the K1 sub-operations comprises at least one encoding layer.

[0906] As one embodiment, two of the K1 sub-operations respectively comprise a fully connected layer and at least one encoding layer.

[0907] As one embodiment, one encoding layer comprises at least one convolution layer and one pooling layer.

[0908] Embodiment 16

[0909] Embodiment 16 illustrates a first operation deployment scenario according to an embodiment of the present application; as shown in FIG. 16.

[0910] In Embodiment 16, the first processor deploys the first operation.

[0911] As one embodiment, the deployment includes obtaining the first operation.

[0912] As one embodiment, the deployment includes obtaining an AI entity.

[0913] As one embodiment, the deployment includes obtaining an AI entity that performs the first operation.

[0914] As one embodiment, the deployment includes obtaining an AI entity that includes an AI function that performs the first operation.

[0915] As one embodiment, the deployment includes loading the first operation.

[0916] As one embodiment, the deployment includes making a request to load the first operation.

[0917] As one embodiment, the request in FIG. 16 is a request to load the first operation made by the first node.

[0918] As one embodiment, the response in FIG. 16 is a response to the request to load the first operation made by the first node.

[0919] As one embodiment, the first node obtains the first operation through the response in FIG. 16.

[0920] As one embodiment, the first operation is obtained from a serving cell of the first node.

[0921] As one embodiment, the first operation is obtained from a maintaining base station of the serving cell of the first node.

[0922] As one embodiment, the first operation is obtained from a core network.

[0923] As one embodiment, the first operation is obtained from a first producer.

[0924] As one embodiment, the first producer provides the first operation to the first node through the response in FIG. 16.

[0925] As one embodiment, the deployment is done by an AI function.

[0926] As one embodiment, the deploying is done by an AI function deployed at the first node.

[0927] As one embodiment, the deploying is done by an AI deployment function.

[0928] As one embodiment, the deploying is done by an AI deployment function deployed at the first node.

[0929] As one embodiment, the deploying is done by an AI inference function.

[0930] As one embodiment, the deploying is done by an AI inference function deployed at the first node.

[0931] As one embodiment, the deploying is done by an AI entity.

[0932] As one embodiment, the deploying is done by an AI entity deployed at the first node.

[0933] As one embodiment, the deploying is done by an AI entity having a deployment function.

[0934] As one embodiment, the deploying is done by an AI entity having a deployment function deployed at the first node.

[0935] As one embodiment, the deploying is done by an AI entity having an inference function.

[0936] As one embodiment, the deploying is done by an AI entity having an inference function deployed at the first node.

[0937] As one embodiment, the deploying includes obtaining the first operation from a first producer.

[0938] As one embodiment, the deploying includes making a request to a first producer to load the first operation.

[0939] As one embodiment, the deploying includes loading the first operation from a first producer.

[0940] As one embodiment, the first producer generates and provides at least one of an AL entity and an AL function.

[0941] As one embodiment, the first producer is a producer of the first operation.

[0942] As one embodiment, the first producer comprises an AL entity producer.

[0943] As one embodiment, the first producer comprises an AL function producer.

[0944] As one embodiment, the first producer comprises an AL deployment producer.

[0945] As one embodiment, the first producer comprises an AL loading producer.

[0946] As one embodiment, the first producer comprises an AL training producer.

[0947] As one embodiment, the first producer comprises an AL inference producer.

[0948] As one embodiment, the first producer comprises a producer of deployment of AL entity.

[0949] As one embodiment, the first producer comprises a producer of loading of AL entity.

[0950] As one embodiment, the first producer comprises a MnS (Management Service) producer.

[0951] As one embodiment, the sender of the target CSI reporting configuration is the first producer.

[0952] As one embodiment, the sender of the target CSI reporting configuration is different from the first producer.

[0953] As one embodiment, the training for obtaining the first operation is performed by the first producer.

[0954] As one embodiment, the performer of the training for obtaining the first operation is different from the first producer.

[0955] Embodiment 17

[0956] Embodiment 17 illustrates a schematic diagram of an artificial intelligence or machine learning based processing system according to one embodiment of the present application; as shown in FIG. 17. FIG. 17(a) comprises a third processing machine, a fourth processing machine and a fifth processing machine, and FIG. 17(b) comprises a third processing machine, a fourth processing machine, a fifth processing machine and a sixth processing machine.

[0957] In embodiment 17(a), the third processor sends a first data set to the fourth processor, and sends a second data set to the fifth processor; the fourth processor generates a target first-type parameter group according to the first data set, and sends the generated target first-type parameter group to the fifth processor; the fifth processor processes the second data set using the target first-type parameter group to obtain a first-type output. In FIG. 17(a), the first-type feedback is optional.

[0958] In embodiment 17(b), the third processor sends a first data set to the fourth processor, and sends a second data set to the fifth processor; the fourth processor generates a target first-type parameter group according to the first data set, and sends the generated target first-type parameter group to the fifth processor; the fifth processor processes the second data set using the target first-type parameter group to obtain a first-type output, and sends the first-type output to the sixth processor. In FIG. 17(b), the first-type feedback and the second-type feedback are optional.

[0959] As an embodiment, in FIG. 17(a), the fifth processor sends the first-type output to the second node in the present application.

[0960] As an embodiment, FIG. 17(a) uses a single-sided AI model for beam prediction or channel information prediction, and the fifth processor performs the first operation for beam prediction or channel information prediction.

[0961] As an embodiment, FIG. 17(b) uses a two-sided AI model for CSI compression, and the first operation is for compressing CSI, and the second operation is for recovering CSI, and the fifth processor performs the first operation, and the sixth processor includes the second operation.

[0962] As an embodiment, the AI includes ML (Machine Learning) inference.

[0963] As an embodiment, the fifth processor performs the first operation.

[0964] As an embodiment, the sixth processor includes the second operation.

[0965] As an embodiment, the fifth processor sends the first-type feedback to the fourth processor, and the first-type feedback is used to trigger recalculation or update of the target first-type parameter group.

[0966] As an embodiment, the sixth processor sends second type feedback to the third processor, the second type feedback is used to generate the first data set or the second data set, or the second type feedback is used to trigger sending of the first data set or sending of the second data set.

[0967] As an embodiment, the third processor generates the first data set and the second data set according to measurement of first type wireless signals, the first type wireless signals include downlink RS.

[0968] As an embodiment, the fifth processor belongs to the first node, and the sixth processor belongs to the second node.

[0969] As an embodiment, the target CSI belongs to the first type output.

[0970] As an embodiment, the second data set includes the input of the first operation.

[0971] As an embodiment, the second data set includes information obtained based on the target CSI reporting configuration and the M1 configurations.

[0972] As an embodiment, the first data set includes training data.

[0973] As an embodiment, the fourth processor belongs to a producer of the first operation.

[0974] As an embodiment, the fourth processor includes an AI training producer.

[0975] As an embodiment, the fourth processor includes an AI training function.

[0976] As an embodiment, the fourth processor is used for model training, and a trained model is described by the target first type parameter group.

[0977] As an embodiment, the fourth processor belongs to the first node.

[0978] The above embodiment avoids passing the first data set to the second node.

[0979] As an embodiment, the fourth processor belongs to the second node.

[0980] The above embodiment supports joint training and optimizes system performance.

[0981] As an embodiment, the fourth processor belongs to a core network.

[0982] The above embodiments support joint training across the whole network, further optimizing system performance.

[0983] As one embodiment, the second dataset comprises inference data.

[0984] As one embodiment, the fifth processor comprises an AI inference producer.

[0985] As one embodiment, the fifth processor comprises an AI inference function.

[0986] As one embodiment, the fifth processor belongs to the first node.

[0987] As one embodiment, the fifth processor constructs a model according to the target first-type parameter group, and then inputs the second dataset into the constructed model to obtain the first-type output.

[0988] As one embodiment, the first operation is described by the target first-type parameter group.

[0989] As one embodiment, the target first-type parameter group is used to construct the first operation.

[0990] As one embodiment, the fifth processor comprises the second operation.

[0991] As one embodiment, the fifth processor generates a recovery dataset according to the first-type output, and the error of the recovery dataset and the second dataset is used to generate the first-type feedback.

[0992] As one sub-embodiment of the above embodiment, the generation of the recovery dataset adopts a similar second operation.

[0993] As one embodiment, the first-type 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 recalculates the target first-type parameter group.

[0994] As one embodiment, when the error is too large or the update time is too long, the performance of the trained model is considered to be unable to meet the requirements.

[0995] As one embodiment, the target first-type parameter group comprises one or more of the following: convolution kernel size, convolution layer number, convolution step length, pooling kernel size, pooling kernel step length, pooling function, activation function, or feature map number.

[0996] As an example, the target first-type parameter group comprises one or more of a convolution kernel, a pooling kernel, a pooling function, an activation function, a parameter of the pooling function, or a parameter of the activation function.

[0997] Embodiment 18

[0998] Embodiment 18 illustrates a schematic diagram based on artificial intelligence or machine learning according to an embodiment of the present application; as shown in FIG. 18. FIG. 18 comprises a third operation, a fourth operation, a fifth operation, a sixth operation, and a seventh operation; the arrowed line represents the order of the flow.

[0999] In embodiment 18, the third operation and the fourth operation belong to a first phase, the fifth operation belongs to a second phase, the sixth operation belongs to a third phase, and the seventh operation belongs to a fourth phase.

[1000] As an example, the third operation comprises AI training, the fourth operation comprises AI testing, the fifth operation comprises AI emulation, the sixth operation comprises AI entity loading, and the seventh operation comprises AI inference.

[1001] As an example, the first phase comprises a training phase, the second phase comprises an emulation phase, the third phase comprises a deployment phase, and the fourth phase comprises an inference phase.

[1002] As an example, the first phase comprises at least one of model training and testing.

[1003] As an example, the AI model training comprises initial training and re-training of one or a group of AI entities.

[1004] As an example, the AI model training comprises AI entity validation.

[1005] As an example, the AI entity validation is used to evaluate the performance of the AI entity.

[1006] As an example, if the result of AI entity validation does not meet the expectation, the AI model will be re-trained.

[1007] As one embodiment, the AI testing includes testing the validated AI entity to estimate the performance of the trained AI model.

[1008] As one embodiment, if the result of the AI testing meets the expectation, the AI entity proceeds to the next stage; otherwise, the AI model will be retrained.

[1009] As one embodiment, the second stage includes AI simulation, which simulates the inference of the AI entity in a simulation environment.

[1010] As one embodiment, the AI simulation estimates the performance of the inference of the AI entity in a simulation environment before using the AI entity.

[1011] As one embodiment, the second stage is optional.

[1012] As one embodiment, the third stage includes AI entity loading, which is to obtain the trained AI entity to obtain the desired AI inference function.

[1013] As one embodiment, the third stage is optional.

[1014] As one embodiment, the third stage is no longer needed when the training function and the inference function are co-located.

[1015] As one embodiment, the fourth stage includes AI inference.

[1016] As one embodiment, the seventh operation includes the first operation.

[1017] As one embodiment, the seventh operation includes the second operation.

[1018] Embodiment 19

[1019] Embodiment 19 illustrates a structural block diagram of a processing device in a first node according to one embodiment of the present application; as shown in FIG. 19. In FIG. 19, the processing device 1900 in the first node includes a first receiver 1901 and a first processor 1902.

[1020] The first receiver 1901 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, the at least one CSI reporting configuration is used to configure the reporting of the at least one CSI;

[1021] The first processor 1902 determines whether to transmit a target CSI on the first PUSCH; transmits the target CSI on the first PUSCH only when a first condition is satisfied;

[1022] 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 configuration, 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, the first resource set includes one or more RS resources; the first condition includes that a first symbol is not earlier than a first reference symbol and a second symbol is not earlier than a second reference symbol; the first symbol is a first uplink symbol in the first PUSCH for carrying the at least one CSI, the first reference symbol is a next uplink symbol whose CP starts at a first time interval after the end of the last symbol of the first PDCCH; the second symbol is a first uplink symbol in the first PUSCH for carrying the target CSI, the second reference symbol is a next uplink symbol whose CP starts at a second time interval after the end of the last symbol of a first RS in the first resource set; the second time interval depends on whether the generation manner of the target CSI is based on AI.

[1023] As an embodiment, the calculation formula of the second time interval depends on a first parameter, the first parameter depends on whether the generation manner of the target CSI is based on AI.

[1024] As an embodiment, when the generation manner of the target CSI is based on AI, the target CSI includes N information blocks, the N information blocks respectively include channel information of N time units, N is a positive integer greater than 1; the second time interval depends on at least one of the N time units.

[1025] As an embodiment, the first resource set is composed of one or more aperiodic RS resources, the first RS is the latest RS in time in the first resource set triggered by the first DCI.

[1026] As an embodiment, the generation manner of the target CSI based on AI includes that the generation manner of the target CSI includes performing a first operation, the input of the first operation depends on measurement based on the first resource set, and the target CSI depends on the output of the first operation.

[1027] As an embodiment, the generation manner of the target CSI based on AI includes that the generation manner of the target CSI is associated with a first type identifier.

[1028] As an embodiment, when the generation manner of the target CSI is based on AI, the second time interval is associated with the first type identifier to which the generation manner of the target CSI is dependent.

[1029] As an embodiment, the generation manner of the target CSI being based on AI comprises that the target CSI indicates at least one resource in a second resource set, the second resource set comprising resources not belonging to the first resource set.

[1030] As an embodiment, any information block in the N information blocks indicates at least one resource in a second resource set, the second resource set comprising resources not belonging to the first resource set.

[1031] As an embodiment, the first processor 1902, when the first condition is not met, ignores the first DCI; wherein no HARQ-ACK or transport block is multiplexed on the first PUSCH.

[1032] As an embodiment, the output of the first operation comprises a first CSI, the target CSI carries the first CSI, and the first CSI is used by a target receiver of the target CSI as input of a second operation to generate a second CSI.

[1033] As an embodiment, the first processor 1902 deploys the first operation.

[1034] As an embodiment, the first operation is associated with the first type identifier.

[1035] As an embodiment, the first operation is based on training or based on AI.

[1036] As an embodiment, the second operation is based on training or based on AI.

[1037] As an embodiment, the first node is a user equipment.

[1038] As an embodiment, the first node is a relay node device.

[1039] As an embodiment, the first receiver 1901 comprises 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.

[1040] As one example, the first processor 1902 includes at least one of {antenna 452, receiver / transmitter 454, receive processor 456, transmit processor 468, multi-antenna receive processor 458, multi-antenna transmit processor 457, controller / processor 459, memory 460, data source 467} in embodiment 4.

[1041] Embodiment 20

[1042] Embodiment 20 illustrates a structural block diagram of a processing apparatus in a second node according to an embodiment of the present application; as FIG. 20 shows. In FIG. 20, the processing apparatus 2000 in the second node includes a second processor 2001.

[1043] The second processor 2001 transmits at least one CSI reporting configuration; transmits a first DCI on a first PDCCH, the first DCI triggers reporting of at least one CSI on a first PUSCH, the at least one CSI reporting configuration is used to configure reporting of the at least one CSI;

[1044] In embodiment 20, a target receiver of the first DCI determines whether to transmit a target CSI on the first PUSCH; the target receiver of the first DCI transmits the target CSI on the first PUSCH only when a first condition is met; 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 configuration, 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, the first resource set includes one or more RS resources; the first condition includes that a first symbol is not earlier than a first reference symbol and a second symbol is not earlier than a second reference symbol; the first symbol is a first uplink symbol in the first PUSCH for carrying the at least one CSI, the first reference symbol is a next uplink symbol whose CP starts at a first time interval after the end of a last symbol of the first PDCCH; the second symbol is a first uplink symbol in the first PUSCH for carrying the target CSI, the second reference symbol is a next uplink symbol whose CP starts at a second time interval after the end of a last symbol of a first RS in the first resource set; the second time interval depends on whether the generation manner of the target CSI is based on AI.

[1045] As one example, the second processor 2001 determines whether to receive a target CSI on the first PUSCH; receives the target CSI on the first PUSCH only when the first condition is met;

[1046] As an embodiment, the calculation formula of the second time interval depends on a first parameter, the first parameter depends on whether the generation manner of the target CSI is based on AI.

[1047] As an embodiment, when the generation manner of the target CSI is based on AI, the target CSI includes N information blocks, the N information blocks respectively include channel information of N time units, N is a positive integer greater than 1; the second time interval depends on at least one of the N time units.

[1048] As an embodiment, the first resource set is composed of one or more aperiodic RS resources, and the first RS is the latest RS in time in the first resource set triggered by the first DCI.

[1049] As an embodiment, the generation manner of the target CSI based on AI includes that the generation manner of the target CSI includes that the target receiver of the first DCI performs a first operation, the input of the first operation depends on measurement based on the first resource set, and the target CSI depends on the output of the first operation.

[1050] As an embodiment, the generation manner of the target CSI based on AI includes that the generation manner of the target CSI is associated with a first type identifier.

[1051] As an embodiment, when the generation manner of the target CSI is based on AI, the second time interval depends on the first type identifier associated with the generation manner of the target CSI.

[1052] As an embodiment, the generation manner of the target CSI based on AI includes that the target CSI indicates at least one resource in a second resource set, and the second resource set includes resources not belonging to the first resource set.

[1053] As an embodiment, any information block in the N information blocks indicates at least one resource in a second resource set, and the second resource set includes resources not belonging to the first resource set.

[1054] As an embodiment, the second processor 2001, when the first condition is not met, gives up receiving a signal on the first PUSCH or gives up receiving the target CSI on the first PUSCH;

[1055] As one embodiment, the target receiver of the first DCI ignores the first DCI when the first condition is not satisfied; wherein no HARQ-ACK or transport block is multiplexed on the first PUSCH.

[1056] As one embodiment, the output of the first operation comprises first CSI, the target CSI carries the first CSI, and the first CSI is used by a target receiver of the target CSI as input of a second operation for generating second CSI.

[1057] As one embodiment, the second processor 2001 performs a second operation; wherein the output of the first operation comprises first CSI, the target CSI carries the first CSI, and the first CSI is used by a target receiver of the target CSI as input of the second operation for generating second CSI.

[1058] As one embodiment, the second processor 2001 deploys the second operation.

[1059] As one embodiment, the first operation is associated to the first type of identity.

[1060] As one embodiment, the first operation is training-based or AI-based.

[1061] As one embodiment, the second operation is training-based or AI-based.

[1062] As one embodiment, the second node is a base station device.

[1063] As one embodiment, the second node is a user equipment.

[1064] As one embodiment, the second node is a relay node device.

[1065] As one embodiment, the second processor 2001 comprises at least one of {antennas 420, receivers / transmitters 418, receive processor 470, transmit processor 416, multi-antenna receive processor 472, multi-antenna transmit processor 471, controller / processor 475, memory 476} in embodiment 4.

[1066] Those skilled in the art can understand that all or part of the steps in the foregoing method can be instructed by programs to complete the related hardware, and the programs can be stored in a computer readable storage medium, such as a read-only memory, a hard disk, an optical disk or the like. Alternatively, all or part of the steps of the foregoing embodiments can also be implemented using one or more integrated circuits. Correspondingly, each module unit in the foregoing embodiments 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 combination of software and hardware. The user equipment, terminal and UE in the present application include but are not limited to unmanned aerial vehicles, communication modules on unmanned aerial vehicles, remote control aircraft, aircraft, small aircraft, mobile phones, tablet computers, notebooks, vehicle-mounted communication devices, wireless sensors, network cards, Internet of Things terminals, RFID terminals, NB-IOT terminals, MTC (Machine Type Communication) terminals, eMTC (enhanced MTC) terminals, data cards, network cards, vehicle-mounted communication devices, low-cost mobile phones, low-cost tablet computers and other wireless communication devices. The base station or system device in the present application includes but is not limited to macro cellular base stations, micro cellular base stations, home base stations, relay base stations, gNB (NR NodeB) NR NodeB, TRP (Transmitter Receiver Point) and other wireless communication devices.

[1067] The above only describes the preferred embodiments of the present application, and is not intended to limit the protection scope of the present application. Any changes and modifications made on the basis of the embodiments described in the specification, if they can obtain similar partial or overall technical effects, should be considered as obvious and belong to the protection scope of the present application.

Claims

1. A method in a first node for wireless communication, characterized by, Comprising: receiving at least one CSI reporting configuration; receiving a first DCI on a first PDCCH, the first DCI triggering reporting of at least one CSI on a first PUSCH, the at least one CSI reporting configuration being used to configure 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 a target CSI reporting configuration is used to configure reporting of the target CSI, the target CSI reporting configuration being one of the at least one CSI reporting configuration, the target CSI being one of the at least one CSI; the target CSI reporting configuration indicating a first resource set, the first resource set being used for at least one of channel measurement or interference resource measurement of the target CSI, the first resource set including one or more RS resources; the first condition including a first symbol not earlier than a first reference symbol and a second symbol not earlier than a second reference symbol; the first symbol being a first uplink symbol in the first PUSCH for carrying the at least one CSI, the first reference symbol being a next uplink symbol whose CP starts a first time interval after an end of a last symbol of the first PDCCH; the second symbol being a first uplink symbol in the first PUSCH for carrying the target CSI, the second reference symbol being a next uplink symbol whose CP starts a second time interval after an end of a last symbol of a first RS in the first resource set; the second time interval depending on whether a generation manner of the target CSI is based on AI.

2. The method of claim 1, wherein, A calculation formula of the second time interval depends on a first parameter, the first parameter depending on whether the generation manner of the target CSI is based on AI.

3. The method according to claim 1 or 2, characterized in that, When the generation manner of the target CSI is based on AI, the target CSI includes N information blocks, the N information blocks respectively including channel information of N time units, N being a positive integer greater than 1; the second time interval depending 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 aperiodic RS resources, the first RS being a 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 generation manner of the target CSI being based on AI includes: the generation manner of the target CSI including performing a first operation, an input of the first operation depending on measurement based on the first resource set, the target CSI depending on an output of the first operation.

6. The method according to any one of claims 1 to 5, characterized in that, The generation manner of the target CSI being based on AI includes: the generation manner of the target CSI being associated to a first type of identifier.

7. The method of claim 6, wherein, When the generation manner of the target CSI is based on AI, the second time interval depending on the first type of identifier to which the generation manner of the target CSI is associated.

8. The method according to any one of claims 1 to 7, characterized in that, The generation manner of the target CSI is based on AI, and the target CSI indicates at least one resource in a second resource set, the second resource set including resources not belonging to the first resource set.

9. The method according to any one of claims 1 to 8, characterized in that, Comprise: 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.

10. A terminal, characterized by comprising: The terminal comprises 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 code, the computer program code comprising computer instructions, and the one or more processors invoke the computer instructions to make the terminal execute the method of any one of claims 1-9.

11. A method in a second node for wireless communication, the method comprising: Comprise: Send at least one CSI reporting configuration; Send the first DCI on the first PDCCH, 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; Wherein, the target receiver of the first DCI determines whether to send the target CSI on the first PUSCH; only when the first condition is met, the target receiver 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 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 a first symbol is not earlier than a first reference symbol and a second symbol is not earlier than a 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 a 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 a 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 manner of the target CSI is based on AI.

12. The method of claim 11, wherein, Comprise: Determine whether to receive the target CSI on the first PUSCH; only when the first condition is met, receive the target CSI on the first PUSCH.

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 manner of the target CSI is based on AI.

14. The method of any one of claims 11-13, wherein, When the generation manner of the target CSI is based on AI, the target CSI comprises N information blocks, the N information blocks respectively comprise channel information of N time units, N is a positive integer greater than 1; 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 is composed of one or more aperiodic RS resources, and the first RS is the latest RS in the first resource set in time triggered by the first DCI.

16. The method according to any one of claims 11 to 15, characterized in that, The generation manner of the target CSI based on AI comprises that the generation manner of the target CSI comprises that the target receiver of the first DCI performs a first operation, an input of the first operation depends on measurement based on the first resource set, and the target CSI depends on an output of the first operation.

17. The method of any one of claims 11 to 16, wherein, The generation manner of the target CSI based on AI comprises that the generation manner of the target CSI is associated to a first type identifier.

18. The method of claim 17, wherein, When the generation manner of the target CSI is based on AI, the second time interval depends on the first type identifier associated to the generation manner of the target CSI.

19. The method of any one of claims 11-18, wherein, The generation manner of the target CSI based on AI comprises that the target CSI indicates at least one resource in a second resource set, and the second resource set comprises resources not belonging to the first resource set.

20. The method of any one of claims 11-19, wherein, Comprise: When the first condition is not met, abandon receiving a signal on the first PUSCH, or abandon receiving the target CSI on the first PUSCH; Wherein, no HARQ-ACK or transport block is multiplexed on the first PUSCH.

21. The method of any one of claims 11-19, wherein, When the first condition is not met, the target receiver 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, comprising: The base station comprises one or more processors and a memory; The memory is coupled with the one or more processors, and the memory is used to store computer program code, the computer program code comprises computer instructions, and the one or more processors invoke the computer instructions to enable the base station to perform the method in any one of claims 11-21.

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