A method and apparatus for CSI reporting in a node 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; the target CSI reporting configuration indicates a first resource set; 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 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 after a second time interval from 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.

Description

TECHNICAL FIELD

[0001] The present application relates to a transmission method and device in a wireless communication system, and in particular to a scheme and device for CSI (Channel State Information) reporting in a wireless communication system. BACKGROUND

[0002] 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, UE reporting can be used to optimize network parameters, such as better cell coverage, switching base stations according to UE location, etc.

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

[0004] Applicant found through research that when AI / ML function is 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 pointed out that in the description of the above problems, NR system is taken as an example, and the present application is also applicable to scenarios such as future 6G system, and achieves similar technical effects as 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 system, near distance communication system, NTN (NonTerrestrial Network), IoT (Internet of Things), URLLC (UltraReliable Low Latency Communication) network, 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.

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

[0006] The present application discloses a method used in a first node for wireless communication, characterized in that it comprises:

[0007] 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;

[0008] determining whether to transmit a target CSI on the first PUSCH; transmitting the target CSI on the first PUSCH only when a first condition is satisfied;

[0009] wherein 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 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 is a next uplink symbol whose CP starts 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 is a next uplink symbol whose CP starts 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.

[0010] In the present application, the AI (Artificial Intelligence) comprises ML (Machine Learning).

[0011] As one embodiment, the problem to be solved by the present application comprises: how to determine whether to transmit CSI on a PUSCH.

[0012] As one embodiment, the essence of the above method comprises: transmitting CSI on a PUSCH only when a first condition is satisfied.

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

[0014] As one embodiment, the essence of the above method comprises: whether to transmit CSI on a PUSCH depends on whether a generation manner of the CSI is based on AI.

[0015] As one embodiment, the benefit of the above method comprises: supporting AI-based CSI reporting.

[0016] As one embodiment, the benefit of the above method comprises: guaranteeing consistency of understanding of CSI reporting by a transmitting end and a receiving end.

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

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

[0019] As an embodiment, 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.

[0020] According to an 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.

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

[0022] As an embodiment, benefits of the above method include being compatible with existing system design and standards, and improving the forward and backward compatibility of the system.

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

[0024] According to an aspect of the present application, when the generation mode 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.

[0025] As an embodiment, benefits of the above method include being compatible with current system design and standards.

[0026] As an embodiment, benefits of the above method include enhancing the flexibility and robustness of the system, and adapting to different transmission scenarios and applications.

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

[0028] According to an aspect of the present application, 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.

[0029] As an embodiment, benefits of the above method include being compatible with current system design and standards.

[0030] According to an aspect of the present application, the generation manner of the target CSI is based on AI, including: 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 resource set, and the target CSI depends on an output of the first operation.

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

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

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

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

[0035] According to an aspect of the present application, the generation manner of the target CSI is based on AI, including: the generation manner of the target CSI is associated with a first type of identifier.

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

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

[0038] According to 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 of identifier associated with the generation manner of the target CSI.

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

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

[0041] According to an aspect of the present application, the generation manner of the target CSI is based on AI, including: 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.

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

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

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

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

[0046] ignoring the first DCI when the first condition is not satisfied;

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

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

[0049] 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.

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

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

[0052] sending at least one CSI reporting configuration; sending 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;

[0053] The target receiver of the first DCI determines whether to send 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 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 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 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 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 a generation manner of the target CSI is based on AI.

[0054] According to one aspect of the present application, it is characterized in that it comprises:

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

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

[0057] According to one 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; and the second time interval depends on at least one of the N time units.

[0058] According to one aspect of the present application, the first resource set is composed of one or more aperiodic RS resources, and the first RS is a latest RS in time in the first resource set triggered by the first DCI.

[0059] According to an aspect of the present application, the generation manner of the target CSI is based on AI, including: the generation manner of the target CSI includes the target receiver of the first DCI performing a first operation, an input of the first operation depends on a measurement based on the first resource set, and the target CSI depends on an output of the first operation.

[0060] According to an aspect of the present application, the generation manner of the target CSI is based on AI, including: the generation manner of the target CSI is associated to a first type identifier.

[0061] 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 associated to the generation manner of the target CSI.

[0062] According to an aspect of the present application, the generation manner of the target CSI is based on AI, including: 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.

[0063] According to an aspect of the present application, the method comprises:

[0064] When the first condition is not met, 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] The present application discloses a terminal, characterized in that the terminal comprises: one or more processors and a memory;

[0068] The memory is coupled to the one or more processors, and the memory is configured to store computer program code including computer instructions, and the one or more processors are configured to invoke the computer instructions to cause the terminal to execute the method in the first node.

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

[0070] The memory is coupled with the one or more processors, and is configured to store computer program codes including computer instructions, and the one or more processors are configured to invoke the computer instructions to cause the base station to perform the method in the second node.

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

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

[0073] 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.

[0074] 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 a first uplink symbol in the first PUSCH for carrying the at least one CSI, 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 the generation mode of the target CSI is based on AI.

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

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

[0077] The target receiver of the first DCI determines whether to send 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 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 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 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 whose CP starts 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.

[0078] As an embodiment, compared with the conventional scheme, the present application has the following advantages:

[0079] - guarantee consistency of CSI reporting understanding of the transceiver;

[0080] - support AI-based CSI reporting;

[0081] - reduce system measurement resources and overhead;

[0082] - improve forward and backward compatibility of the system;

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

[0084] - improve reliability and robustness of the system;

[0085] - simplify system design and reduce complexity of scheme implementation;

[0086] - improve accuracy and effectiveness of CSI reporting;

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

[0088] Other characteristics, objects and advantages of the present application will become more apparent from the following detailed description of non-restrictive embodiments, made with reference to the attached drawings:

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

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

[0091] Figure 3 A schematic diagram illustrating an embodiment of a radio protocol architecture for the user and control planes according to an embodiment of the application is shown;

[0092] Figure 4 A schematic diagram illustrating a first communication device and a second communication device according to an embodiment of the application is shown;

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

[0094] Figure 6 A schematic diagram illustrating a calculation formula of a second time interval according to an embodiment of the application is shown;

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

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

[0097] Figure 9 A schematic diagram illustrating a first operation according to an embodiment of the application is shown;

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

[0099] Figure 11 A schematic diagram illustrating a second time interval and a first type of identification relationship according to an embodiment of the application is shown;

[0100] Figure 12 A schematic diagram illustrating a second resource set according to an embodiment of the application is shown;

[0101] Figure 13 A schematic diagram illustrating a first condition not being met according to an embodiment of the application is shown;

[0102] Figure 14 A schematic diagram illustrating a second operation according to an embodiment of the application is shown;

[0103] Figure 15a schematic diagram showing a first operation according to another embodiment of the application is shown;

[0104] Figure 16 a schematic diagram showing a first operation according to another embodiment of the application is shown;

[0105] Figure 17 a schematic diagram showing a first operation according to another embodiment of the application is shown;

[0106] Figure 18 a schematic diagram showing a first operation according to another embodiment of the application is shown;

[0107] Figure 19 a schematic diagram showing a first operation according to another embodiment of the application is shown;

[0108] Figure 20 a schematic diagram showing a first operation according to another embodiment of the application is shown. DETAILED DESCRIPTION

[0109] 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, combining the embodiments in the drawings of Figure 1 the embodiments in the drawings of Figure 5 the embodiments in the drawings of Figure 20 the embodiments in the drawings of Figure 5 the embodiments in the drawings of Figure 6 the embodiments in the drawings of Figure 20 the embodiments in the drawings of

[0110] Example 1

[0111] Embodiment 1 illustrates a flowchart of a first DCI, at least one CSI reporting configuration and a target CSI according to an embodiment of the present application, as shown in FIG. 100. In FIG. 100, 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. Figure 1 Figure 1

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

[0113] Wherein, 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; a 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, 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 mode of the target CSI is based on AI.

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

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

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

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

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

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

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

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

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

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

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

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

[0126] As one embodiment, any of the at least one CSI reporting configuration configures a periodic CSI reporting.

[0127] As one embodiment, any of the at least one CSI reporting configuration configures a semi-persistent CSI reporting.

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

[0129] As one embodiment, any of the at least one CSI reporting configuration is one RRC IE.

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

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

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

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

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

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

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

[0137] As one embodiment, the first set of resources includes periodic RS resources.

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

[0139] As one embodiment, the first set of resources includes aperiodic RS resources.

[0140] As one embodiment, the first set of resources consists of one or more aperiodic RS resources.

[0141] As one embodiment, a resource in the first set of resources 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.

[0142] As one embodiment, the first set of resources includes one or more sets of RS (Reference Signal) resources, and one set of RS resources includes one or more RS resources.

[0143] As one embodiment, the first resource set comprises at least one of at least one CSI-RS resource set, at least one CSI-SSB (Channel State Information-Synchronization Signal Block) resource set, or at least one CSI-IM (Channel State Information-Interference Measurement) resource set.

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

[0145] 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.

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

[0147] As one subembodiment of the above embodiment, one RS resource set for channel measurement comprises one or more RS resources.

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

[0149] 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.

[0150] 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.

[0151] 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.

[0152] As one embodiment, the synchronization signal resource comprises at least a resource occupied by a synchronization signal.

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

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

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

[0156] As an 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.

[0157] As an 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.

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

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

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

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

[0162] As an embodiment, the benefits of the above method include: maintaining existing standards and system design, reducing complexity.

[0163] As an embodiment, the target CSI reporting configuration indicates at least one resource configuration, and the at least one resource configuration indicates the first resource set.

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

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

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

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

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

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

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

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

[0172] 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.

[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; the target CSI reporting configuration is used to configure reporting of the target CSI.

[0174] 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.

[0175] As an embodiment, the first PDCCH comprises a plurality of REs (Resource Elements).

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

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

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

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

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

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

[0182] As an embodiment, the symbol is an OFDM symbol generated after transform precoding.

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

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

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

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

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

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

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

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

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

[0192] As an embodiment, measurements based on the first set of resources are used for generating the target CSI.

[0193] As an embodiment, channel measurements and / or interference measurements based on the first set of resources are used for generating the target CSI.

[0194] As an embodiment, measurements based on one or more RS resources in the first set of resources are used for generating the target CSI.

[0195] As an 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.

[0196] As an embodiment, measurements based on no later than a latest 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.

[0197] As an 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.

[0198] As an embodiment, the channel measurements obtained based on the first set of resources refer to channel measurements obtained in the first set of resources.

[0199] As an embodiment, the channel measurements obtained based on the first set of resources include at least one of a channel matrix, a raw channel matrix, an eigenvector, and an eigenvalue.

[0200] As one embodiment, the channel measurements obtained based on the first set of resources comprise one or more of BLER, delay spread, Doppler spread, Doppler shift, average delay, average gain, path loss, and RSRP.

[0201] 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.

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

[0203] As one embodiment, the interference measurements obtained based on the first set of resources comprise at least one of interference power, interference variance, or interference power spectral density.

[0204] As one embodiment, the interference measurements obtained based on the first set of resources comprise at least one of an interference channel matrix, an interference covariance matrix, an interference eigenvector, an interference eigenvalue, or an interference beam.

[0205] As one embodiment, the measurements based on the first set of resources comprise a channel matrix obtained based on measurements for the first set of resources.

[0206] As one embodiment, the measurements based on the first set of resources comprise a matrix or vector obtained by pre-processing a channel matrix obtained based on measurements for the first set of resources.

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

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

[0209] As one embodiment, the pre-processing comprises one or more of quantization, DFT (Discrete Fourier Transform), matrix decomposition, matrix transformation or projection, spatial-to-angle domain transformation, angle-to-spatial domain transformation, frequency-to-time domain transformation and time-to-frequency domain transformation, truncation, padding, mapping, labeling.

[0210] As one 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.

[0211] 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.

[0212] 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, wherein the plurality of CSI reporting configurations are respectively used to configure the plurality of CSIs.

[0213] 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 to configure the plurality of CSIs, and the target CSI is any one of the plurality of CSIs.

[0214] As an embodiment, the target CSI comprises at least one CSI reporting quantity.

[0215] As an embodiment, the target CSI comprises one or more of a precoding matrix indicator (PMI), a CSI-RS resource indicator (CRI), an SS / PBCH block resource indicator (SSBRI), a beam indication, a resource indication, a channel quality indicator (CQI), a rank indicator (RI), a layer indicator (LI), a reference signal received power (RSRP), a signal-to-noise and interference ratio (SINR), a capability index, or time domain channel properties (TDCP).

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

[0217] 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).

[0218] As an embodiment, the target CSI comprises one or more of a beam indication, a beam quantity, a CRI, an SS / PBCH Block Resource indicator, a quantity of CRIs or SSBRI, an RSRP, a differential RSRP, probability information, or confidence information.

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

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

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

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

[0223] As an embodiment, the method essentially comprises monitoring the AI model based on a performance parameter of an AI-based CSI reporting related.

[0224] As an embodiment, the method has the benefit of improving the performance of an AI-based CSI reporting scheme and improving the overall performance of the system.

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

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

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

[0228] As an embodiment, the target CSI comprises at least one resource indication, and one resource indication in the target CSI is used to indicate a beam or an RS resource.

[0229] 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 SSBRI (SS / PBCH Block Resource indicator).

[0230] 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 SSBRI (SS / PBCH Block Resource indicator).

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

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

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

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

[0235] 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.

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

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

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

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

[0240] As one embodiment, benefits of the above method include improving accuracy and real-time performance of CSI reporting, and enhancing overall system performance.

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

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

[0243] As an embodiment, the compressed CSI is neither defined in 3GPP Rel-18 nor defined in the versions before 3GPP Rel-18.

[0244] 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.

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

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

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

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

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

[0250] As an embodiment, whether the target CSI includes confidence information depends on whether the generation manner of the target CSI is AI-based; only when the generation manner of the target CSI is AI-based, the target CSI includes confidence information.

[0251] As an embodiment, whether the target CSI is non-codebook-based depends on whether the generation manner of the target CSI is AI-based; when the generation manner 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.

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

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

[0254] As an embodiment, when the generation manner of the target CSI is AI-based, the target CSI includes predicted CSI, or predicted beam information, or compressed CSI.

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

[0256] As an embodiment, the benefits of the above method include: small changes to existing systems and standards.

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

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

[0259] As an embodiment, the target CSI indicates at least one resource in a second resource set, which includes resources not belonging to the first resource set.

[0260] 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.

[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; 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.

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

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

[0264] As an embodiment, the benefits of the above method include: small changes to existing systems and standards.

[0265] 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:

[0266] The first node first performs measurements on the first resource set to obtain the channel parameter matrix H r×t , where r and t are the number of receive antennas and the number of antenna ports of the target CSI-RS resource, respectively; the channel parameter matrix H r×tThe power adjustment is performed, and the adjusted channel parameter matrix is where P is the assumed ratio of PDSCH EPRE to target CSI-RS EPRE (i.e., the first power control offset); and under the condition that a precoding matrix W t×l is used, the precoded channel parameter matrix is where 1 is the rank or the number of layers, in one case 1 is a positive integer not greater than t, and in another case the precoding matrix is an identity matrix, in which case t = 1; the equivalent channel capacity of H r×t · W t×l is 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 the CQI is determined from the equivalent channel capacity by, for example, table lookup. Generally, the calculation of the equivalent channel capacity requires the first node to estimate the interference (including noise), and the first node can obtain a more accurate measured interference 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 the CQI depends on the receiver performance or the modulation mode and other hardware-related factors.

[0267] 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 dependent. Without loss of generality, the AI model or parameters used to generate the target CSI are determined by the manufacturer of the first node.

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

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

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

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

[0272] As one embodiment, the target CSI being valid comprises that the target CSI is an updated CSI.

[0273] 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.

[0274] 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.

[0275] 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.

[0276] 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.

[0277] 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.

[0278] 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.

[0279] 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.

[0280] 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.

[0281] As an embodiment, the first PUSCH includes a plurality of REs (Resource Elements).

[0282] 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.

[0283] 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.

[0284] 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.

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

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

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

[0288] As an embodiment, the 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.

[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 and modulation.

[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 bit sequence generation, channel coding.

[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, and modulation.

[0292] As an 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.

[0293] As an 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.

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

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

[0296] As an embodiment, the first symbol is the first uplink symbol in the first PUSCH for carrying the at least one CSI.

[0297] As an embodiment, the first symbol is the first uplink symbol in the first PUSCH for carrying the target CSI.

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

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

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

[0301] Typically, the first reference symbol is the next uplink symbol after the end of the last symbol of the first PDCCH by a first time interval includes: the first reference symbol is the earliest uplink symbol later than the last symbol of the first PDCCH and satisfying a reference condition, the reference condition includes that the 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 first time interval is T proc,CSI , the specific meaning of T proc,CSI refers to the 5.4 section of 3GPP TS 38.214.

[0306] The first time interval is (Z) (2048 + 144 · κ2 -μ · T C + T switch ; wherein the specific meaning of Z, κ, μ, T C and T switch refers to the 5.4 section of 3GPP TS 38.214.

[0307] As an embodiment, the benefits of the above method include: maintaining the existing system design and standard, enhancing the consistency of the system.

[0308] Typically, the second symbol considers timing advance.

[0309] Typically, both the second symbol and the second reference symbol consider timing advance.

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

[0311] As an embodiment, the second symbol is the first uplink symbol in the first PUSCH for carrying the target CSI.

[0312] As an embodiment, the second reference symbol is Z' ref (n), the specific meaning of Z' ref (n) refers to the 5.4 section of 3GPP TS 38.214.

[0313] 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 end of the last symbol of the first RS in the first resource set and the second reference symbol is not less than the second time interval.

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

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

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

[0317] As an embodiment, the second time interval is T' proc,CSI , the specific meaning of T' proc,CSI refers to the 5.4 section of 3GPP TS 38.214.

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

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

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

[0321] As one embodiment, the first RS is the latest aperiodic RS in the first set of resources in time.

[0322] As one embodiment, the first RS is the earliest aperiodic RS in the first set of resources in time.

[0323] As one embodiment, the first RS is the latest or earliest aperiodic RS in the first set of resources in time.

[0324] As one embodiment, the first set of resources consists of one or more aperiodic RS resources, and the first RS is one RS in the first set of resources triggered by the first DCI.

[0325] As one embodiment, the first set of resources consists of one or more aperiodic RS resources, and the first RS is the latest RS in the first set of resources triggered by the first DCI in time.

[0326] As one embodiment, the first set of resources consists of one or more aperiodic RS resources, and the first RS is the latest or earliest RS in the first set of resources triggered by the first DCI in time.

[0327] As one embodiment, the benefits of the above method include: maintaining existing system design and standards, and enhancing system consistency.

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

[0329] As one embodiment, 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.

[0330] As one embodiment, the first set of resources consists of one or more aperiodic RS resources; 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.

[0331] As one embodiment, the benefits of the above method include: small changes to existing systems and standards.

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

[0333] 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.

[0334] 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.

[0335] 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.

[0336] 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.

[0337] 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: dropping the transmitting the target CSI on the first PUSCH when the first condition is not satisfied.

[0338] As one embodiment, 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 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.

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

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

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

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

[0343] As an embodiment, the generation manner of the target CSI is based on AI includes: the generation manner of the target CSI is based on training; the generation manner of the target CSI is not based on AI includes: the generation manner of the target CSI is not based on training.

[0344] As an embodiment, the generation manner of the target CSI is based on AI includes: the generation manner of the target CSI uses an AI model; the generation manner of the target CSI is not based on AI includes: the generation manner of the target CSI does not use an AI model.

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

[0346] As an embodiment, the generation manner of the target CSI is based on AI includes: the target CSI includes information generated based on a neural network; the generation manner of the target CSI is not based on AI includes: the target CSI does not include information generated based on a neural network.

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

[0348] As an embodiment, the generation manner of the target CSI is based on AI includes: the generation of the target CSI includes a first operation performed by a sender 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 generation manner of the target CSI is not based on AI includes: the generation of the target CSI does not include the first operation performed by the sender of the target CSI.

[0349] As an example, the target CSI generation manner is based on AI includes that: the target CSI reporting configuration indicates a first type of identity; the target CSI generation manner is not based on AI includes that: the target CSI reporting configuration does not indicate the first type of identity.

[0350] As an example, the target CSI generation manner is based on AI includes that: the target CSI generation is associated with a first type of identity; the target CSI generation manner is not based on AI includes that: the target CSI generation is not associated with the first type of identity.

[0351] As an example, the above method has the benefits of: simplifying system design, reducing implementation complexity.

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

[0353] As an example, when the target CSI generation manner is not based on AI, the second time interval is (Z'(2048+144)·κ2 -μ ·T c ; wherein the specific meaning of Z', κ, μ, T C refers to the 5.4 chapter of 3GPP TS 38.214.

[0354] As an example, when the target CSI generation manner is not based on AI, the second time interval is T' proc，CSI ; the specific meaning of T' proc，CSI refers to the 5.4 chapter of 3GPP TS 38.214.

[0355] As an example, the above method has the benefits of: small changes to existing standards and systems.

[0356] As an example, the second time interval depends on whether the target CSI generation manner is based on AI includes: the determination method of the second time interval depends on whether the target CSI generation manner is based on AI.

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

[0358] As an example, the second time interval depends on whether the target CSI generation manner is based on AI includes: in the case of the target CSI generation manner being based on AI and not being based on AI, the calculation formula of the second time interval is different.

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

[0360] As an embodiment, the calculation formula of the second time interval depends on whether the generation mode of the target CSI is based on AI, including: when the generation mode of the target CSI is based on AI, the second time interval is (Z') (2048+144)·κ2 -μ ·T C +W, wherein w is an integer or a real number greater than zero; when the generation mode of the target CSI is not based on AI, the second time interval is (Z') (2048+144·K2 -μ ·T C ; wherein the specific meanings of Z', κ, μ, T C refer to the 5.4 chapter of 3GPP TS 38.214.

[0361] As an embodiment, the calculation formula of the second time interval depends on whether the generation mode of the target CSI is based on AI, including: when the generation mode of the target CSI is based on AI, the second time interval is a·[(Z') (2048+144)·κ2 -μ ·T C ], wherein a is an integer or a real number greater than 1; when the generation mode of the target CSI is not based on AI, the second time interval is (Z') (2048+144·K2 -μ ·T C ; wherein the specific meanings of Z', κ, μ, T C refer to the 5.4 chapter of 3GPP TS 38.214.

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

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

[0364] As an embodiment, the second time interval depends on whether the generation manner of the target CSI is based on AI, including: 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.

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

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

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

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

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

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

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

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

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

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

[0375] As an embodiment, the generation of the target CSI uses an AI model; and the first reference interval depends on the AI model.

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

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

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

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

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

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

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

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

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

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

[0386] As an embodiment, the second reference interval is (Z') (2048 + 144·κ2 -μ ·T C ; where the specific meanings of Z', κ, μ, T C refer to the 5.4 section of 3GPP TS 38.214.

[0387] As an embodiment, the second reference interval is T' proc ; the specific meaning of T' proc refer to the 5.4 section of 3GPP TS 38.214.

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

[0389] As an embodiment, the benefits of the above method include: small changes to existing standards and systems.

[0390] As an embodiment, the benefits of the above method include: improving the flexibility of the system, adapting to different scenarios and demand transmission and application.

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

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

[0393] As one embodiment, the second time interval depending on whether the generation manner of the target CSI is based on AI comprises: a calculation formula of the second time interval depending on a first parameter, the first parameter depending on whether the generation manner of the target CSI is based on AI.

[0394] As one embodiment, the second time interval depending on whether the generation manner of the target CSI is based on AI comprises: whether a calculation formula of the second time interval depending on a second parameter depends on whether the generation manner of the target CSI is based on AI; only when the generation manner of the target CSI is based on AI, the calculation formula of the second time interval depending on the second parameter.

[0395] As one embodiment, the calculation formula of the second time interval depending on the second parameter comprises: the second time interval and the second parameter being in a linear relationship.

[0396] As one embodiment, the calculation formula of the second time interval depending on the second parameter comprises: the second time interval and the second parameter being in a nonlinear relationship.

[0397] As one embodiment, the calculation formula of the second time interval depending on the second parameter comprises: when the generation manner of the target CSI is based on AI, the second time interval being (Z')(2048+144)·κ2 -μ ·T C + W, wherein specific meanings of the Z', the κ, the μ, the T C refer to 5.4 section of 3GPP TS 38.214; and W is the second parameter.

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

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

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

[0401] As one embodiment, the calculation formula of the second time interval depending on the second parameter comprises: when the generation manner of the target CSI is based on AI, the second time interval being a·[(Z')(2048+144)·K2 -μ ·T C ], wherein specific meanings of the Z', the K, the μ, the T C refer to 5.4 section of 3GPP TS 38.214; and a is the second parameter.

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

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

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

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

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

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

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

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

[0410] As an embodiment, the second capability parameter represents an AI-unrelated capability parameter reported by the first node.

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

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

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

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

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

[0416] 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.

[0417] As an embodiment, the method has the advantage of enhancing the reliability and robustness of the system.

[0418] 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.

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

[0420] As an embodiment, the second time interval depending on the first capability parameter comprises: the second time interval and the first capability parameter are in a non-linear relationship.

[0421] 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.

[0422] 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.

[0423] 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.

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

[0425] As one embodiment, the second time interval depending on the second capability parameter comprises: the second time interval and the second capability parameter are in a non-linear relationship

[0426] As one embodiment, the second time interval depending on the second capability parameter comprises: the second time interval depending on a first parameter, the first parameter depending on the second capability parameter.

[0427] 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, a value of the first parameter depending on the second capability parameter.

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

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

[0430] Example 2

[0431] Embodiment 2 illustrates a schematic diagram of a network architecture according to one embodiment of the present application, as shown in FIG. 2. Figure 2

[0432] FIG. 1 illustrates a schematic diagram of a network architecture according to one embodiment of the present application, as shown in FIG. 1. Figure 2 ​This describes the 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 / EPS200 may include one or more UEs (User Equipment) 201, a UE 241 communicating with UE 201 via a sidelink, NG-RAN (Next Generation Radio Access Network) 202, 5GC (5G Core Network) / EPC (Evolved Packet Core) 210, HSS (Home Subscriber Server) / UDM (Unified Data Management) 220, and Internet services 230. The 5GS / EPS200 can interconnect with other access networks, but these entities / interfaces are not shown for simplicity. (See attached...) Figure 2As shown, 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 networks providing circuit-switched services. The NG-RAN 202 includes a NR (New Radio) NodeB (gNB) 203 and other gNBs 204. The gNB 203 provides user and control plane protocol terminations towards the UE 201. The gNB 203 can be connected to the other gNBs 204 via an Xn interface (e.g., backhaul). The gNB 203 can also be referred to as a base station, a base transceiver station, a radio base station, a radio transceiver, a transceiver function, a basic service set (BSS), an extended service set (ESS), a TRP (Transmit Receive Point), or some other suitable terminology. The gNB 203 provides access to the 5GC / EPC 210 for the UE 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, a car, a wearable device, or any other similar functional device. Those skilled in the art will also recognize that a UE 201 can be referred to 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 wirelessAll user Internet Protocal (IP) packets are transferred through the S-GW / UPF 212, which is connected to the P-GW / UPF 213. The P-GW provides UE IP address allocation in addition to other functions. The P-GW / UPF 213 is connected to the Internet services 230. The Internet services 230 include operator corresponding Internet protocol services, which can include the Internet, intranet, IMS (IP Multimedia Subsystem), and packet switching services, in particular.

[0433] As an embodiment, the first node in the present application comprises the UE 201.

[0434] As an embodiment, the second node in the present application comprises the gNB 203.

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

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

[0437] As an embodiment, the gNB 203 is a macro cell base station.

[0438] As an embodiment, the gNB 203 is a micro cell base station.

[0439] As an embodiment, the gNB 203 is a pico cell base station.

[0440] As an embodiment, the gNB 203 is a femto cell base station.

[0441] As an embodiment, the gNB 203 is a base station device supporting large latency difference.

[0442] As an embodiment, the gNB 203 is a flying platform device.

[0443] As an embodiment, the gNB 203 is a satellite device.

[0444] As an embodiment, the gNB 203 is a test device (e.g. a transceiver simulating part of the functions of a base station, a signaling tester).

[0445] As an embodiment, the wireless link from the UE 201 to the gNB 203 is an uplink, which is used to perform uplink transmission.

[0446] As one embodiment, a wireless link from the gNB 203 to the UE 201 is a downlink, which is used to perform downlink transmission.

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

[0448] As one embodiment, a connection between the UE 201 and the gNB 203 is through a Uu air interface.

[0449] As one embodiment, a sender of the at least one CSI reporting configuration comprises the gNB 203.

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

[0451] As one embodiment, a sender of the target CSI reporting configuration comprises the gNB 203.

[0452] As one embodiment, a receiver of the target CSI reporting configuration comprises the UE 201.

[0453] As one embodiment, a sender of the first DCI comprises the gNB 203.

[0454] As one embodiment, a receiver of the first DCI comprises the UE 201.

[0455] As one embodiment, a sender of the first resource set comprises the gNB 203.

[0456] As one embodiment, a receiver of the first resource set comprises the UE 201.

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

[0458] As one embodiment, a receiver of the at least one CSI comprises the gNB 203.

[0459] As one embodiment, a sender of the target CSI comprises the UE 201.

[0460] As one embodiment, a receiver of the target CSI comprises the gNB 203.

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

[0462] As one embodiment, the gNB 203 supports a 6G system.

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

[0464] As one embodiment, the gNB 203 supports at least a 5G system.

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

[0466] As one embodiment, the gNB 203 supports AI.

[0467] Example 3

[0468] Embodiment 3 illustrates a diagram of an embodiment of a radio protocol architecture for the user plane and control plane, according to one embodiment of the application, as shown in FIG. 3. Figure 3

[0469] Embodiment 3 illustrates a diagram of an embodiment of a radio protocol architecture for the user plane and control plane, according to one embodiment of the application, as shown in FIG. 3. Figure 3 Figure 3 is a diagram illustrating an embodiment of a radio protocol architecture for the user plane 350 and control plane 300, Figure 3 ​​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, is shown with three layers: Layer 1, Layer 2, and Layer 3. Layer 1 (L1 layer), which is the lowest layer, implements various PHY (Physical layer) signal processing functions. The L1 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 are terminated at the second communication node device. The PDCP sublayer 304 provides multiplexing between different radio bearers and logical channels. The PDCP sublayer 304 also provides security functions, such as ciphering / de-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 configuring the lower layers using RRC signaling between the second communication node device and the first communication node device. The radio protocol architecture for the user plane 350 includes Layer 1 (L1 layer) and Layer 2 (L2 layer), which are substantially the same as the corresponding layers and sublayers in the control plane 300 for the PHY 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.The L2 layer 355 in the user plane 350 also includes a SDAP (Service Data Adaptation Protocol) sublayer 356, which is responsible for the mapping between a QoS flow and a data radio bearer (DRB) to support the diversity of services. Although not illustrated, 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.).

[0470] As one embodiment, the wireless protocol architecture in FIG. 1A is applicable to the first node in the present application. Figure 3

[0471] As one embodiment, the wireless protocol architecture in FIG. 1A is applicable to the second node in the present application. Figure 3

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

[0473] As one embodiment, the at least one CSI is generated at the RRC 306.

[0474] As one embodiment, the target CSI is generated at the RRC 306.

[0475] As one embodiment, the reference signal in the first resource set is generated at the PHY 301 or the PHY 351.

[0476] As one embodiment, the at least one CSI is generated at the PHY 301 or the PHY 351.

[0477] As one embodiment, the at least one CSI is generated at the MAC 302 or the MAC 352.

[0478] As one embodiment, the target CSI is generated at the PHY 301 or the PHY 351.

[0479] As one embodiment, the target CSI is generated at the MAC 302 or the MAC 352.

[0480] Example 4

[0481] 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. 1C. The first communication device and the second communication device in FIG. 1C are similar to the first communication device and the second communication device in FIG. 1A, respectively. Figure 4 Figure 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.

[0482] The first communication device 410 includes a controller / processor 475, a memory 476, a receive processor 470, a transmit processor 416, a multi-antenna receive processor 472, a multi-antenna transmit processor 471, a transmitter / receiver 418, and antennas 420.

[0483] The second communication device 450 includes a controller / processor 459, a memory 460, a data source 467, a transmit processor 468, a receive processor 456, a multi-antenna transmit processor 457, a multi-antenna receive processor 458, a transmitter / receiver 454, and antennas 452.

[0484] In transmissions from the first communication device 410 to the second communication device 450, at the first communication device 410, upper layer data 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 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, which are then provided to the various antennas 420.

[0485] 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, multicarrier symbol stream to receive processor 456. 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, multicarrier symbol stream from the receivers 454. The receive processor 456 converts the baseband, multicarrier symbol stream from the receive analog precoding / beamforming operation into the time domain using a Fast Fourier Transform (FFT). In the time domain, a physical layer data signal and the reference signal are demultiplexed from the baseband, multicarrier symbol stream by the receive processor 456, where the reference signal will be used for channel estimation and the data signal is 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.

[0486] In the transmission from the second communication device 450 to the first communication device 410, at the second communication device 450, a data source 467 is used to provide upper layer 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 creates parallel streams of coded and modulated symbols for the different antenna ports, which are provided to different antennas 452 via separate transmitters 454 after analog precoding / beamforming at the multi-antenna transmit processor 457. Each transmitter 454 then converts the baseband streams into radio frequency signals and transmits the radio frequency signals via the antennas 452.

[0487] 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 radio frequency signal through its respective antenna 420, converts the received radio frequency signal into 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 functions. The controller / processor 475 can be associated with a memory 476 that stores program codes and data. The memory 476 can be referred to as a computer-readable medium. The controller / processor 475 provides demultiplexing between 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.

[0488] 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.

[0489] 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.

[0490] 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.

[0491] 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.

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

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

[0494] As an embodiment, at least one of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, 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.

[0495] 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.

[0496] 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.

[0497] 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.

[0498] Example 5

[0499] 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 over-the-air interface transmission. In FIG. 5, the steps in block F51 to block F55 are optional, respectively. Figure 5 Figure 5 In FIG. 5, the second node U1 and the first node U2 are communication nodes for over-the-air interface transmission. In FIG. 5, the steps in block F51 to block F55 are optional, respectively. Figure 5

[0500] ​​For the second node U1, deploying a second operation in step S511; sending at least one CSI reporting configuration in step S512; sending a first DCI on a first PDCCH in step S513; sending a signal in a first resource set in step S514; receiving a target CSI on a first PUSCH only when a first condition is satisfied in step S515; performing the second operation in step S516.

[0501] For the first node U2, deploying a first operation in step S521; receiving at least one CSI reporting configuration in step S522; receiving a first DCI on a first PDCCH in step S523; receiving a signal in a first resource set in step S524; performing the first operation in step S525; determining whether to send a target CSI on a first PUSCH in step S526; sending the target CSI on the first PUSCH only when the first condition is satisfied in step S527.

[0502] In embodiment 5, the first DCI triggers reporting of at least one CSI on a 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 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 is a next uplink symbol whose CP starts 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.

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

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

[0505] As one embodiment, the air interface between the second node U1 and the first node U2 includes a wireless interface between the base station equipment and the user equipment.

[0506] As one embodiment, the air interface between the second node U1 and the first node U2 includes a wireless interface between the relay node device and the user equipment.

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

[0508] In one embodiment, the second node U1 is the serving cell sustaining base station of the first node U2.

[0509] As an example, Appendix Figure 5 The steps in block F53 are present; the method used in the first node for wireless communication includes: receiving a signal in the first resource set.

[0510] As an example, Appendix Figure 5 The steps in block F53 are present; the method in the second node used for wireless communication includes: transmitting a signal in the first resource set.

[0511] As an example, sending a signal in the first resource set means sending a wireless signal in the first resource set.

[0512] As an example, sending a signal in the first resource set means sending a reference signal in the first resource set.

[0513] As an example, receiving a signal in the first resource set means receiving a wireless signal in the first resource set.

[0514] As an example, receiving a signal in the first resource set means receiving a reference signal in the first resource set.

[0515] As an example, Appendix Figure 5 In the context of generating the target CSI using AI, the steps in block F54 are present.

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

[0517] As an example, Appendix Figure 5 In the process, when the target CSI is generated based on AI, the steps in box F54 and box F55 are present, and the first node and the second node adopt a two-sided AI model.

[0518] As one embodiment, the step in block F54 exists, the step in block F55 does not exist, and the first node employs a single side AI model. Figure 5 As one embodiment, the step in block F51 exists, and the method in the second node for wireless communication comprises deploying the second operation.

[0519] Figure 5 As one embodiment, the step in block F51 exists, and the method in the second node for wireless communication comprises deploying the second operation.

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

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

[0522] As one embodiment, the step in block F55 exists, and the method in the second node for wireless communication comprises performing the second operation. Figure 5 As one embodiment, the step in block F55 exists, and the method in the second node for wireless communication comprises performing the second operation.

[0523] Figure 5 As one embodiment, 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 employ a two-sided AI model.

[0524] As one embodiment, 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 employs a single side AI model. Figure 5 As one embodiment, the deployment of the first operation is earlier than the receiving of the at least one CSI reporting configuration.

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

[0526] As one embodiment, an output of the first operation comprises the target CSI; and an input of the second operation comprises the target CSI.

[0527] As one embodiment, the first node is a consumer.

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

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

[0530] As one embodiment, the first node is a consumer of AI inference.

[0531] As one embodiment, the first node is a consumer of AI training.

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

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

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

[0535] Example 6

[0536] Embodiment 6 illustrates a diagram of a formula of the second time interval according to one embodiment of the present application; as shown in FIG. 6. Figure 6

[0537] 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 or not.

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

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

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

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

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

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

[0544] As one embodiment, the unit of the first parameter is slot.

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

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

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

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

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

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

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

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

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

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

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

[0556] As an embodiment, the specific meaning of the Z′, the Z′(m), the Z′1, the Z′2, the Z′3 refers to the 5.4 section of 3GPP TS 38.214.

[0557] As an embodiment, the benefit of the above method comprises: using existing standards and system design, reducing implementation complexity.

[0558] As an embodiment, the second time interval and the first parameter are a functional relationship.

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

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

[0561] As an embodiment, the second time interval and the first parameter are a nonlinear relationship.

[0562] As an embodiment, the second time interval is (Z')(2048+144·K2 -μ ·T C wherein the Z', the K, the m, the T C have the specific meaning referred in section 5.4 of 3GPP TS 38.214; the first parameter is the Z'.

[0563] As an embodiment, the second time interval is (Z')(2048+144)·K2 -μ ·T C +W, wherein the Z', the K, the m, the T C have the specific meaning referred in section 5.4 of 3GPP TS 38.214; the W is an integer or real number greater than 0; the first parameter is the Z'.

[0564] As an embodiment, the second time interval is a·[(Z')(2048+144)·K2 -μ ·T C ], wherein the Z', the K, the m, the T C have the specific meaning referred in section 5.4 of 3GPP TS 38.214; the a is an integer or real number greater than 1; the first parameter is the Z'.

[0565] As an embodiment, the second time interval is wherein the Z'(m), the K, the m, the T C have the specific meaning referred in section 5.4 of 3GPP TS 38.214; the first parameter is the Z'(m).

[0566] As an embodiment, the second time interval is (Z1')(2048+144·K2 -μ ·T C wherein the Z1', the K, the m, the T C have the specific meaning referred in section 5.4 of 3GPP TS 38.214; the first parameter is the Z1'.

[0567] As an embodiment, the second time interval is (Z'2)(2048+144·K2 -μ ·T C wherein the Z'2, the K, the m, the T C have the specific meaning referred in section 5.4 of 3GPP TS 38.214; the first parameter is the Z'2.

[0568] As an embodiment, the second time interval is (Z'3)(2048+144·K2 -μ ·T C wherein the Z'3, the K, the mu, the T C have the specific meanings referred to in section 5.4 of 3GPP TS 38.214; the first parameter is the Z'3.

[0569] As an embodiment, the second time interval is (2Z'2)(2048+144·K2 -μ ·T C wherein the Z'2, the K, the mu, the T C have the specific meanings referred to in section 5.4 of 3GPP TS 38.214; the first parameter is the Z'2.

[0570] As an embodiment, the above method has the benefits including: following the existing standards and system design, reducing the implementation complexity.

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

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

[0573] As an embodiment, when the generation mode of the target CSI is not based on AI, the first parameter is Z'; when the generation mode of the target CSI is based on AI, the first parameter is not Z'.

[0574] As an embodiment, when the generation mode of the target CSI is not based on AI, the first parameter is Z'1; when the generation mode of the target CSI is based on AI, the first parameter is not Z'1.

[0575] As an embodiment, when the generation mode of the target CSI is not based on AI, the first parameter is Z'2; when the generation mode of the target CSI is based on AI, the first parameter is not Z'2.

[0576] 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 Z'3; when the generation mode of the target CSI is based on AI, the first parameter is not Z'3.

[0577] As an embodiment, when the generation mode of the target CSI is based on AI, the first parameter is not any one of Z'1, Z'2, Z'3.

[0578] As an embodiment, when the generation manner of the target CSI is based on AI, the first parameter is not Z'(m) defined in 3GPP TS 38.214 before version 18.

[0579] As an embodiment, the essence of the above method includes: processing the AI-based and non-AI-based schemes on a case-by-case basis.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0594] As an embodiment, the second candidate value range includes X0, X1, X2, X3, X5, X6; wherein the specific meaning of X0, X1, X2, X3, X5, X6 refers to chapter 5.4 of 3GPP TS 38.214.

[0595] As an embodiment, the essence of the above method includes: processing AI-based and non-AI-based schemes on a case-by-case basis.

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

[0597] As an embodiment, the first candidate value range and the second candidate value range are not the same.

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

[0599] As an embodiment, the first candidate value range is sorted 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.

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

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

[0602] As an embodiment, whether the first parameter depends on the first capability parameter depends on whether the generation method of the target CSI is based on AI; only when the generation method of the target CSI is based on AI, the first parameter depends on the first capability parameter.

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

[0604] As an embodiment, when the 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 capability parameters reported by the first node.

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

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

[0607] As an embodiment, the second capability parameter represents an AI-unrelated capability parameter reported by the first node.

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

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

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

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

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

[0613] 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.

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

[0615] Example 7

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

[0617] In the embodiment 7, 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.

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

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

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

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

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

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

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

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

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

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

[0628] As one embodiment, the target CSI comprises a plurality of information blocks, and the target CSI comprises the plurality of information blocks including the N information blocks.

[0629] As one embodiment, the above method has the benefit of enhancing flexibility and robustness of the system.

[0630] As one embodiment, one time unit comprises one or more slots.

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

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

[0633] As one embodiment, the N time units are orthogonal to each other.

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

[0635] As one embodiment, there are two time units in the N time units that overlap.

[0636] As one embodiment, the N time units are consecutive.

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

[0638] As one embodiment, the interval between any two adjacent time units in the N time units is P time units, and P is a positive integer.

[0639] As one embodiment, the above method has the benefit of maintaining existing system design and standards.

[0640] As one embodiment, the above method has the benefit of enhancing flexibility and robustness of the system.

[0641] Generally, how the first node determines the N or at least one of the N time units is determined by the hardware manufacturer, and some non-limiting embodiments are described as follows:

[0642] As one embodiment, the first node determines the N time units based on variation of the channel.

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

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

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

[0646] As one embodiment, the first node determines the N time units to be time units with faster channel variation (e.g., variation greater than a threshold).

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

[0648] As one embodiment, the first node determines the N time units to be time units with lower time correlation (e.g., lower than a threshold).

[0649] As one embodiment, the second time interval depending on at least one of the N time units includes the second time interval depending on a target time unit of the N time units.

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

[0651] As one embodiment, the target time unit is a latest one of the N time units.

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

[0653] As one embodiment, the second time interval depending on a target time unit of the N time units includes the second time interval depending on a time interval between the target time unit and a last symbol of the first PDCCH.

[0654] 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 includes the second time interval depending on a time interval between a starting symbol of the target time unit and the last symbol of the first PDCCH.

[0655] As one embodiment, the second time interval depending on at least one of the N time units includes the second time interval depending on a target symbol of the N time units.

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

[0657] As one embodiment, the target symbol is a latest one of the N time units.

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

[0659] As one embodiment, the second time interval depending on the target symbol in the N time units includes that the second time interval depends on a time interval between the target symbol and a last symbol of the first PDCCH.

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

[0661] As one embodiment, the benefits of the above method include small changes to existing standards and system design.

[0662] As one embodiment, the second time interval depending on at least one of the N time units includes that the second time interval depends on the N.

[0663] As one embodiment, the second time interval depending on the N includes that the N belongs to one of V1 candidate value ranges, any candidate value range in the V1 candidate value ranges includes one or more positive integers, 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 N belongs.

[0664] As one embodiment, the second time interval depending on the N includes that the second time interval depends on a first parameter, and the first parameter depends on the N.

[0665] As one embodiment, the second time interval depending on the N includes that the second time interval and the first parameter are in a linear relationship, and the first parameter depends on the N.

[0666] As one embodiment, the second time interval depending on the N includes that the second time interval and the first parameter are in a linear relationship, and the first parameter and the N are in a linear relationship.

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

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

[0669] As an example, the advantages of the above method include: more precise setting of CSI calculation time, effective utilization of system resources, and improved accuracy and real-time performance of CSI reporting.

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

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

[0672] As one embodiment, the second time interval depending on the length of the N time units includes: the second time interval and the length of the N time units are linearly related.

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

[0674] As one embodiment, the second time interval depending on the length of the N time units includes: the second time interval depending on a first parameter, wherein the first parameter depends on the length of the N time units.

[0675] As one embodiment, the second time interval depending on the length of the N time units includes: 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.

[0676] As an example, the essence of the above method includes: CSI calculation time depends on the length of the time unit to which CSI reporting is targeted.

[0677] As an example, the advantages of the above method include: more precise setting of CSI calculation time, effective utilization of system resources, and improved accuracy and real-time performance of CSI reporting.

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

[0679] Example 8

[0680] FIG. 8 illustrates a schematic diagram of a first RS according to one embodiment of the present application; as shown in FIG. 8. Figure 8 In some embodiments, RS#1, …, RS#n, …, RS#M are one or more aperiodic RS resources in the first resource set. Figure 8

[0681] In embodiment 8, the first resource set consists of one or more aperiodic RS resources, and the first RS is the last RS in time in the first resource set triggered by the first DCI.

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

[0683] As one embodiment, the first RS is the last aperiodic RS in time in the first resource set.

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

[0685] As one embodiment, the first RS is the last aperiodic RS in the first resource set triggered by the first DCI.

[0686] As one embodiment, the above method has the benefits of: maintaining the existing system design and standard, and enhancing the consistency of the system.

[0687] Example 9

[0688] FIG. 9 illustrates a schematic diagram of a first operation according to one embodiment of the present application; as shown in FIG. 9. Figure 9 In some embodiments, the target CSI is generated based on AI, including: the target CSI is generated based on performing a first operation, the input of the first operation depends on the measurement based on the first resource set, and the target CSI depends on the output of the first operation.

[0689] As one embodiment, the first operation is based on training or AI.

[0690] As one embodiment, the first operation is obtained by training.

[0691] As one embodiment, the first operation is based on a neural network (NN).

[0692] As one embodiment, the first operation is based on a neural network (NN).

[0693] ​As one embodiment, the first operation comprises an AI entity.

[0694] As one embodiment, the first operation comprises a part of an AI entity.

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

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

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

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

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

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

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

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

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

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

[0705] As one embodiment, the first operation comprises inference.

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

[0707] As one embodiment, the first operation comprises AI inference for CSI.

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

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

[0710] As one embodiment, benefits of the above method include improved performance of measurement and reporting of CSI (including beam), including more accurate CSI, lower reference signal overhead and reporting overhead, thus improving overall system performance.

[0711] As one embodiment, benefits of the above method include more accurate and complete CSI, lower reference signal overhead, improved real-time performance of CSI.

[0712] As one embodiment, the first operation is deployment-agnostic.

[0713] As one embodiment, the first operation is obtained by load.

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

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

[0716] As one embodiment, the first operation is obtained by load from a core network.

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

[0718] As one embodiment, the first operation includes at least one of a fully connected layer, a pooling layer, at least one convolution layer, at least one encoding layer.

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

[0720] As one embodiment, at a convolution layer, at least one convolution kernel is used to convolve an input to generate a corresponding feature map, 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.

[0721] As an embodiment, part or all of the kernel size, the number of convolution layers, the convolution step, the kernel size, the kernel step, the pooling function, the activation function and the number of feature maps of the first operation are obtained by training.

[0722] As an embodiment, part or all of the 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.

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

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

[0725] As an embodiment, the preprocessing includes one or more of quantization, spatial-to-angle domain transformation, angle-to-spatial domain transformation, frequency-to-time domain transformation and time-to-frequency domain transformation.

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

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

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

[0729] As an embodiment, the post-processing includes one or more of angle-to-spatial domain transformation, spatial-to-angle domain transformation, time-to-frequency domain transformation and frequency-to-time domain transformation.

[0730] As an embodiment, the measurement based on the first resource set includes channel information before compression, and the output of the first operation includes channel information after compression.

[0731] As an embodiment, the benefits of the above method include: suitable for channel compression, saving feedback overhead.

[0732] As an embodiment, the measurement based on the first resource set includes measured channel information, and the output of the first operation includes predicted channel information.

[0733] As an embodiment, the measurement based on the first resource set includes measured channel information, and the output of the first operation includes spatial beam prediction.

[0734] As an example, the advantages of the above method include: reducing RS resource overhead and reducing feedback latency.

[0735] As an example, the measurement based on the first resource set includes historical channel information, and the output of the first operation includes predicted channel information.

[0736] As one embodiment, the measurement based on the first resource set includes historical channel information, and the output of the first operation includes temporal beam prediction.

[0737] As an example, the advantages of the above method include: reducing channel information feedback delay and improving the real-time performance of channel information acquisition.

[0738] As one embodiment, the measurement based on the first resource set includes current channel information, and the output of the first operation includes channel information after a period of time.

[0739] As an example, the benefits of the above method include: improved CSI accuracy and real-time performance, and reduced RS overhead.

[0740] As one embodiment, the measurement based on the first resource set includes incomplete channel information, while the output of the first operation includes complete channel information.

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

[0742] As an example, the measurement based on the first resource set includes channel information of P1 antenna ports, and the output of the first operation includes channel information of P2 antenna ports, where P1 and P2 are positive integers greater than 1, and P1 is less than P2.

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

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

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

[0746] As an embodiment, the output of the first operation comprises one or more of a beam indication, a CRI (CSI-RS Resource Indicator), a SS / PBCH Block Resource indicator (SSBRI), or a RSRP (Reference Signal Received Power).

[0747] As an embodiment, the output of the first operation comprises one or more of a PMI, a CRI, a CQI, a RI, a LI, a SSBRI, a RSRP, a SINR, a capability index, and a TDCP.

[0748] As an embodiment, the output of the first operation comprises one or more of a channel impulse response, a small scale property, a channel matrix.

[0749] As an embodiment, the output of the first operation comprises one or more of a delay spread, a Doppler spread, a Doppler shift, a mean delay, and a mean gain.

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

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

[0752] As an embodiment, the generation of the target CSI is based on AI, and the input of the first operation is dependent on measurements based on the first set of resources.

[0753] 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.

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

[0755] 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.

[0756] 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.

[0757] 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.

[0758] As an embodiment, the target CSI depends on the output of the first operation includes that the output of the first operation is used to generate the target CSI after post-processing.

[0759] As an embodiment, the input of the first operation depends on measurements based on the first resource set, and the target CSI depends on the output of the first operation.

[0760] As an embodiment, the generation of the target CSI includes performing a first operation, and the input of the first operation depends on measurements based on the first resource set, and the target CSI depends on the output of the first operation.

[0761] As an embodiment, the benefits of the above method include supporting AI-based solutions and improving the accuracy and real-time performance of information reporting.

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

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

[0764] As an embodiment, the target CSI reporting configuration indicates a first type of identifier, and the first operation is associated with the first type of identifier.

[0765] As an embodiment, an AI model used by the first operation is identified by the first type of identifier.

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

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

[0768] As an embodiment, the benefits of the above method include identifying an AI model / entity / function through the first type of identifier, simplifying design, and unifying understanding of different AI entities / functions among multiple nodes.

[0769] As an embodiment, the first type of identifier is used to identify or indicate a reference resource set, and measurements for the reference resource set are used to obtain a training data set for the first operation.

[0770] As an embodiment, the first type of identifier is used to identify configuration information of a reference resource set, and measurements for the reference resource set are used to obtain a training data set for the first operation.

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

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

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

[0774] As an embodiment, the benefits of the above method include: identifying the inference generated by an AI training or an AI training data set by identifying the AI training or the AI training data set, establishing consensus between different AI functions, and further simplifying design.

[0775] Example 10

[0776] Embodiment 10 illustrates a schematic diagram of a first type identifier according to an embodiment of the present application; as shown in FIG. 10. Figure 10

[0777] In embodiment 10, the generation mode of the target CSI based on AI includes that the generation mode of the target CSI is associated with the first type identifier.

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

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

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

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

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

[0783] 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 identifier.

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

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

[0786] As an embodiment, the first type of identification is used to identify or indicate a resource set, and measurements of the resource set are used to obtain a training dataset.

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

[0788] As an embodiment, the above method has the benefit of identifying an AI training or AI training dataset by identifying the AI training or AI training dataset, establishing consensus between different AI functions, and further simplifying system design.

[0789] As an embodiment, the first type of identification is associated with the generation of the target CSI includes that the generation of the target CSI is associated with the first type of identification through the target CSI reporting configuration.

[0790] As an embodiment, the first type of identification is associated with the generation of the target CSI includes that the target CSI reporting configuration indicates the first type of identification; and the target CSI reporting configuration indicates the generation of the target CSI.

[0791] As an embodiment, the first type of identification is associated with the generation of the target CSI includes that the generation of the target CSI includes performing a first operation, the input of the first operation depends on measurements based on the first resource set, and the target CSI depends on the output of the first operation, and the first operation is associated with the first type of identification.

[0792] As an embodiment, the above method has the benefit of supporting AI-based CSI reporting.

[0793] As an embodiment, the first type of identification is associated with the generation of the target CSI includes that the generation of the target CSI uses an AI model identified by the first type of identification.

[0794] As an embodiment, the first type of identification is associated with the generation of the target CSI includes that an AI entity identified by the first type of identification generates the target CSI.

[0795] As an embodiment, the first type of identification is associated with the generation of the target CSI includes that the target CSI is generated by an AI entity, and the first type of identification is used to identify the AI entity or function.

[0796] As an embodiment, the first type of identification is associated with the generation of the target CSI includes that the generation of the target CSI belongs to an AI function, and the first type of identification is used to identify the AI function.

[0797] As an embodiment, the target CSI generation manner not being associated to the first type of identification comprises: the target CSI generation manner comprising a target receiver performing a first operation on a target CSI reporting configuration, an input of the first operation depending on a measurement based on the first resource set, the target CSI depending on an output of the first operation, the first operation not being associated to the first type of identification.

[0798] As an embodiment, the target CSI generation manner not being associated to the first type of identification comprises: the target CSI generation manner not using an AI model identified by the first type of identification.

[0799] As an embodiment, the target CSI generation manner not being associated to the first type of identification comprises: an AI entity identified by the first type of identification not being used to generate the target CSI.

[0800] As an embodiment, the target CSI generation manner not being associated to the first type of identification comprises: the target CSI being generated by an AI entity, the first type of identification not being used to identify the AI entity or function.

[0801] As an embodiment, the target CSI generation manner not being associated to the first type of identification comprises: the target CSI generation manner belonging to an AI function, the first type of identification not being used to identify the AI function.

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

[0803] As an embodiment, the above method has the benefits of: improving the flexibility of the system, adapting to different scenarios of transmission and application.

[0804] Example 11

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

[0806] In embodiment 11, when the target CSI generation manner is based on AI, the second time interval depends on the first type of identification to which the target CSI generation manner is associated.

[0807] As an embodiment, the first type of identification is the first type of identification to which the target CSI generation manner is associated, the first type of identification belonging to one of V identification sets, any identification set of the V identification sets comprising one or more first type of identification, V being a positive integer greater than 1; the first reference symbol depending on the identification set to which the first type of identification belongs. ​

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

[0809] 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.

[0810] As an embodiment, the first type of identifier is a first type of identifier associated to the generation manner of the target CSI, the first type of identifier belongs to one of V sets of identifiers, any set of identifiers of the V sets of identifiers comprises 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 set of identifiers to which the first type of identifier belongs.

[0811] As an embodiment, the first type of identifier is a first type of identifier associated to the generation manner of the target CSI, the first type of identifier belongs to one of V sets of identifiers, any set of identifiers of the V sets of identifiers comprises one or more first type of identifiers, and V is a positive integer greater than 1; the calculation formula of the second time interval comprises V formulas, the V formulas and the V sets of identifiers correspond to each other, and the calculation formula of the second time interval is the calculation formula corresponding to the set of identifiers to which the first type of identifier belongs.

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

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

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

[0815] As an embodiment, the second time interval is (Z')(2048+144)·κ2 -μ ·T C wherein the Z', the κ, the μ, the T C have the specific meanings referred to in section 5.4 of 3GPP TS 38.214; the Z' depends on the first type identifier to which the generation manner of the target CSI is associated.

[0816] As an embodiment, the second time interval is (Z')(2048+144)·κ2 -μ ·T C +W, wherein the Z', the κ, the μ, the T C have the specific meanings referred to in section 5.4 of 3GPP TS 38.214; the W depends on the first type identifier to which the generation manner of the target CSI is associated.

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

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

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

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

[0821] Example 12

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

[0823] In embodiment 12, the generation manner of the target CSI based on AI includes: the target CSI indicates at least one resource in the second resource set, and the second resource set includes resources that do not belong to the first resource set.

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

[0825] As an embodiment, the first set of resources includes one or more RS resources, and the second set of resources includes one or more RS resources.

[0826] As an embodiment, the first set of resources includes fewer resources than the second set of resources.

[0827] As an embodiment, the second set of resources includes resources not included in the first set of resources, and the resources in the second set of resources include at least one of antenna ports, TCI states, QCL information, frequency resources, time-frequency code resources, beams, RS resources, vectors, or matrices.

[0828] As an embodiment, the second set of resources includes at least one training dataset.

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

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

[0831] As an embodiment, the second set of resources includes one or more RS resources, and any RS resource in the second set of resources is a CSI-RS resource or a synchronization signal resource.

[0832] As an embodiment, the target CSI reporting configuration includes at least one resource configuration, and the at least one resource configuration indicates the first set of resources and the second set of resources.

[0833] As an embodiment, the target CSI reporting configuration indicates one resource configuration, and the one resource configuration indicates the first set of resources and the second set of resources.

[0834] As an embodiment, the target CSI reporting configuration indicates two resource configurations, and the two resource configurations respectively indicate the first set of resources and the second set of resources.

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

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

[0837] As an embodiment, the target CSI reporting configuration indicates a first type identifier, and the second resource set depends on the first type identifier.

[0838] As an embodiment, the second resource set depending on the first type identifier comprises that the first type identifier is used to identify the second resource set.

[0839] As an embodiment, the second resource set depending on the first type identifier comprises that the first type identifier is used to identify a reference resource set, and the reference resource set comprises the second resource set.

[0840] As an embodiment, the second resource set depending on the first type identifier comprises that the first type identifier 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.

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

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

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

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

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

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

[0847] As an embodiment, the target CSI is generated in an AI-based manner, the first resource set is used for measurement, and the second resource set is used for prediction.

[0848] As an embodiment, the target CSI is generated in an AI-based manner, the first resource set is used for measurement, and the second resource set is used for prediction.

[0849] As one embodiment, the target CSI is generated based on AI, and only the first set of resources among the first and second sets of resources is used for measurement.

[0850] As one embodiment, only the first set of resources among the first and second sets of resources being used for measurement includes only the first set of resources among the first and second sets of resources being used for measurement by the first node.

[0851] As one embodiment, only the first set of resources among the first and second sets of resources being used for measurement includes the first set of resources being used for measurement by the first node, the first node not being required to measure part or all of the second set of resources.

[0852] As one embodiment, the first node not being required to measure the second set of resources includes the first node not measuring part or all of the second set of resources.

[0853] As one embodiment, the first node not being required to measure the second set of resources includes whether the first node measures part or all of the second set of resources being implementation dependent or self-determined by the first node.

[0854] Example 13

[0855] Embodiment 13 illustrates a diagram of the first condition not being satisfied according to one embodiment of the application; as shown in FIG. 13. Figure 13

[0856] 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.

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

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

[0859] 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.

[0860] ​As one embodiment, 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.

[0861] As one embodiment, the first set of resources 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.

[0862] As one embodiment, the first set of resources 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.

[0863] As one embodiment, the first set of resources 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.

[0864] As one embodiment, HARQ-ACK or 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 satisfied.

[0865] As one embodiment, the first set of resources consists of one or more aperiodic RS resources; HARQ-ACK or 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 satisfied.

[0866] As one embodiment, the first node ignores the first DCI or the target CSI is transmitted on the first PUSCH and the target CSI is not updated when the first condition is not satisfied.

[0867] As one embodiment, the first processor ignores the first DCI or the target CSI is transmitted on the first PUSCH and the target CSI is not updated when the first condition is not satisfied.

[0868] As one embodiment, the ignoring the first DCI comprises dropping transmitting a signal on the first PUSCH.

[0869] As one embodiment, the ignoring the first DCI comprises dropping transmitting the target CSI on the first PUSCH.

[0870] As one embodiment, the ignoring the first DCI comprises dropping transmitting the at least one CSI on the first PUSCH.

[0871] As one embodiment, the transmitting the target CSI on the first PUSCH and the target CSI being not updated comprises the first node not being expected to transmit the target CSI on the first PUSCH and the target CSI being valid.

[0872] As one embodiment, the transmitting the target CSI on the first PUSCH and the target CSI being not updated comprises the first node not being expected to transmit the target CSI on the first PUSCH and the target CSI being updated.

[0873] As one embodiment, the target CSI being not updated comprises the first node not being expected to update the target CSI.

[0874] As one embodiment, the target CSI being not updated comprises whether the target CSI is actually updated being implementation dependent or self-determined by the first node.

[0875] As one embodiment, the target CSI being not updated comprises the target CSI not being valid.

[0876] As one embodiment, the target CSI being not updated comprises the target CSI being identical to a latest one of the CSI reporting configurations on the target CSI earlier than the first PUSCH.

[0877] As one embodiment, the target CSI being not 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.

[0878] As one embodiment, the target CSI being not 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.

[0879] As one embodiment, the target CSI is not updated including: the target CSI is not updated based on measurements of at least the latest RS occasion in the first set of resources no later than a CSI reference resource of the target CSI.

[0880] As one embodiment, the first set of resources consists of one or more aperiodic RS resources; the target CSI is not updated including: the target CSI is independent of measurements based on aperiodic RS resources in the first set of resources triggered by the first DCI.

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

[0882] As one embodiment, the first set of resources consists of one or more aperiodic RS resources; the target CSI is not updated including: the target CSI is not updated based on measurements of aperiodic RS resources in the first set of resources triggered by the first DCI.

[0883] Example 14

[0884] Embodiment 14 illustrates a schematic diagram of a second operation according to one embodiment of the application; as shown in FIG. 14. Figure 14

[0885] In Embodiment 14, the output of the first operation includes a first CSI, the target CSI carries the first CSI, and the first CSI is used by a target receiver of the target CSI as input of the second operation to generate a second CSI.

[0886] 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.

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

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

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

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

[0891] As one embodiment, the second CSI comprises a recovery of at least part of an input to the first operation.

[0892] As one embodiment, the second CSI comprises one or more of a PMI (Precoding Matrix Indicator), a CRI (CSI-RS Resource Indicator), a SS / PBCH Block Resource indicator (SSBRI), a beam indication, a resource indication, a CQI (Channel Quality Indicator), a RI (Rank Indicator), a LI (Layer Indicator), a RSRP (reference signal received power), a SINR (signal-to-noise and interference ratio), a Capability Index, or a TDCP (Time Domain Channel Properties).

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

[0894] As one embodiment, the second operation is an inverse operation of the first operation.

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

[0896] As one embodiment, the training to obtain the second operation is performed by the target receiver of the target CSI.

[0897] As one embodiment, the training to obtain the second operation is performed by an MDA function.

[0898] As one embodiment, the training to obtain the second operation is performed by an MDAS producer.

[0899] As one embodiment, the training to obtain the second operation is performed by a NWDAF.

[0900] As one embodiment, the training to obtain the second operation is performed by a core network.

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

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

[0903] As an embodiment, the first operation and the second operation are obtained by training independently from each other.

[0904] As an embodiment, the above method has benefits including saving air interface overhead, having better flexibility, being able to adapt to different terminals, and having better forward compatibility.

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

[0906] As an embodiment, the above method has benefits including optimizing system performance.

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

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

[0909] Example 15

[0910] 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, where K1 is a positive integer not greater than 1. Figure 15

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

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

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

[0914] As an embodiment, each training-based sub-operation among the K1 sub-operations is based on training performed by the same performer.

[0915] As an embodiment, two training-based sub-operations among the K1 sub-operations are based on training performed by different performers.

[0916] ​As one embodiment, at least one of the K1 sub-operations is a deployment requiring operation.

[0917] As one embodiment, at least one of the K1 sub-operations is a loading requiring operation.

[0918] As one embodiment, all loading requiring sub-operations of the K1 sub-operations are loaded from the same producer.

[0919] As one embodiment, two loading requiring sub-operations of the K1 sub-operations are loaded from different producers.

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

[0921] 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.

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

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

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

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

[0926] As one embodiment, one or more of the K1 sub-operations includes pre-processing.

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

[0928] As one embodiment, two of the K1 sub-operations are serial, as in Figure 15 all sub-operations in 15(a), sub-operation #2 to sub-operation #(K1-1) in 15(b), and sub-operation #0 to sub-operation #(K1-4) in 15(c).

[0929] As 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.

[0930] As one example, two of the K1 sub-operations are parallel, such as sub-operation #0 and sub-operation #1 in (b), FIG. 10A, Figure 15 sub-operation #(K1-3) and sub-operation #(K1-2) in (c), FIG. 10A, Figure 15

[0931] As one example, two sub-operations being parallel means that the outputs of the two sub-operations are collectively used as inputs to another sub-operation.

[0932] As one example, the K1 sub-operations include one or more of convolution, pooling, concatenation, or activation.

[0933] As one example, one of the K1 sub-operations includes a fully connected layer.

[0934] As one example, one of the K1 sub-operations includes a pooling layer.

[0935] As one example, one of the K1 sub-operations includes at least one convolution layer.

[0936] As one example, one of the K1 sub-operations includes at least one encoding layer.

[0937] As one example, two of the K1 sub-operations each include a fully connected layer and at least one encoding layer.

[0938] As one example, an encoding layer includes at least one convolution layer and a pooling layer.

[0939] Example 16

[0940] Embodiment 16 illustrates a diagram of deploying a first operation, according to an embodiment of the application; as shown in Figure 16 FIG. 10B.

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

[0942] As one example, the deployment includes obtaining the first operation.

[0943] As one example, the deployment includes obtaining an AI entity.

[0944] As one example, the deployment includes obtaining an AI entity that performs the first operation.

[0945] ​As one embodiment, the deploying comprises obtaining an AI entity comprising an AI function to perform the first operation.

[0946] As one embodiment, the deploying comprises loading the first operation.

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

[0948] As one embodiment, the request in the Figure 16 is the request to load the first operation made by the first node.

[0949] As one embodiment, the response in the Figure 16 is the response to the request to load the first operation made by the first node.

[0950] As one embodiment, the first node obtains the first operation through the response in the Figure 16 .

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

[0952] As one embodiment, the first operation is obtained from a maintaining base station of a serving cell of the first node.

[0953] As one embodiment, the first operation is obtained from a core network.

[0954] As one embodiment, the first operation is obtained from a first producer.

[0955] As one embodiment, the first producer provides the first operation to the first node through the response in the Figure 16 .

[0956] As one embodiment, the deploying is done by an AI function.

[0957] As one embodiment, the deploying is done by an AI function deployed at the first node.

[0958] As one embodiment, the deploying is done by an AI deployment function.

[0959] As one embodiment, the deploying is done by an AI deployment function deployed at the first node.

[0960] As one embodiment, the deploying is done by an AI inference function.

[0961] As one embodiment, the deploying is done by an AI inference function deployed at the first node.

[0962] As one embodiment, the deploying is done by an AI entity.

[0963] As one embodiment, the deploying is done by an AI entity deployed at the first node.

[0964] As one embodiment, the deploying is done by an AI entity having a deployment function.

[0965] As one embodiment, the deploying is done by an AI entity having a deployment function deployed at the first node.

[0966] As one embodiment, the deploying is done by an AI entity having an inference function.

[0967] As one embodiment, the deploying is done by an AI entity having an inference function deployed at the first node.

[0968] As one embodiment, the deploying includes obtaining the first operation from a first producer.

[0969] As one embodiment, the deploying includes making a request to a first producer to load the first operation.

[0970] As one embodiment, the deploying includes loading the first operation from a first producer.

[0971] As one embodiment, the first producer generates and provides at least one of an AL entity and an AL function.

[0972] As one embodiment, the first producer is a producer of the first operation.

[0973] As one embodiment, the first producer includes an AL entity producer.

[0974] As one embodiment, the first producer includes an AL function producer.

[0975] As one embodiment, the first producer includes an AL deployment producer.

[0976] As one embodiment, the first producer includes an AL load producer.

[0977] As one embodiment, the first producer comprises an AL training producer.

[0978] As one embodiment, the first producer comprises an AL inference producer.

[0979] As one embodiment, the first producer comprises an AL entity deployment producer.

[0980] As one embodiment, the first producer comprises an AL entity loading producer.

[0981] As one embodiment, the first producer comprises an MnS (Management Service) producer.

[0982] As one embodiment, the sender of the target CSI reporting configuration is the first producer.

[0983] As one embodiment, the sender of the target CSI reporting configuration is different from the first producer.

[0984] As one embodiment, the training for obtaining the first operation is performed by the first producer.

[0985] As one embodiment, the performer of the training for obtaining the first operation is different from the first producer.

[0986] Example 17

[0987] 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. Figure 17 As shown in FIG. 17, the processing system comprises a first processing machine, a second processing machine, a third processing machine, a fourth processing machine and a fifth processing machine. Figure 17 (a) comprises the third processing machine, the fourth processing machine and the fifth processing machine, as shown in FIG. 17(a). Figure 17 (b) comprises the third processing machine, the fourth processing machine, the fifth processing machine and a sixth processing machine.

[0988] In Embodiment 17(a), the third processing machine sends a first data set to the fourth processing machine, and sends a second data set to the fifth processing machine; the fourth processing machine generates a target first type parameter set according to the first data set, and sends the generated target first type parameter set to the fifth processing machine; the fifth processing machine processes the second data set using the target first type parameter set to obtain a first type output. In the attached Figure 17 (a), the first type feedback is optional.

[0989] 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 the accompanying Figure 17 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 the accompanying

[0990] 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 the accompanying Figure 17 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 the accompanying

[0991] 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 the accompanying Figure 17 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 the accompanying

[0992] 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 the accompanying Figure 17 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 the accompanying

[0993] 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 the accompanying

[0994] 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 the accompanying

[0995] 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 the accompanying

[0996] 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 the accompanying

[0997] 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 the accompanying

[0998] As an embodiment, the third processor generates the first data set and the second data set according to the measurement of the first type of wireless signal, the first type of wireless signal including downlink RS.

[0999] As an embodiment, the fifth processor belongs to the first node, and the sixth processor belongs to the second node.

[1000] As an embodiment, the target CSI belongs to the first type of output.

[1001] As an embodiment, the second data set includes the input of the first operation.

[1002] As an embodiment, the second data set includes information obtained based on the target CSI reporting configuration and the M1 configurations.

[1003] As an embodiment, the first data set includes training data.

[1004] As an embodiment, the fourth processor belongs to a producer of the first operation.

[1005] As an embodiment, the fourth processor includes an AI training producer.

[1006] As an embodiment, the fourth processor includes an AI training function.

[1007] As an embodiment, the fourth processor is used for model training, and the trained model is described by the target first type of parameter group.

[1008] As an embodiment, the fourth processor belongs to the first node.

[1009] The above embodiment avoids passing the first data set to the second node.

[1010] As an embodiment, the fourth processor belongs to the second node.

[1011] The above embodiment supports joint training and optimizes system performance.

[1012] As an embodiment, the fourth processor belongs to a core network.

[1013] The above embodiment supports full-network joint training and further optimizes system performance.

[1014] As an embodiment, the second data set includes inference data.

[1015] As one embodiment, the fifth handler comprises an AI inference producer.

[1016] As one embodiment, the fifth handler comprises an AI inference function.

[1017] As one embodiment, the fifth handler belongs to the first node.

[1018] As one embodiment, the fifth handler constructs a model according to the target first-type parameter group, and then inputs the second data set into the constructed model to obtain the first-type output.

[1019] As one embodiment, the first operation is described by the target first-type parameter group.

[1020] As one embodiment, the target first-type parameter group is used to construct the first operation.

[1021] As one embodiment, the fifth handler comprises the second operation.

[1022] As one embodiment, the fifth handler generates a recovery data set according to the first-type output, and an error of the recovery data set and the second data set is used to generate the first-type feedback.

[1023] As one sub-embodiment of the above embodiment, the generation of the recovery data set adopts the second operation.

[1024] 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 handler recalculates the target first-type parameter group.

[1025] As one embodiment, when the error is too large or the update is not performed for too long a time, the performance of the trained model is considered to be unable to meet the requirements.

[1026] As one embodiment, the target first-type parameter group comprises one or more of a convolution kernel size, a convolution layer number, a convolution step, a pooling kernel size, a pooling kernel step, a pooling function, an activation function, or a feature map number.

[1027] As one embodiment, 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.

[1028] Example 18

[1029] Embodiment 18 illustrates an AI or machine learning based schematic diagram according to an embodiment of the present application; as shown in FIG. 18. FIG. 18 is a flowchart illustrating an exemplary process for AI or machine learning based schematic diagram according to an embodiment of the present application. The process includes a third operation, a fourth operation, a fifth operation, a sixth operation, and a seventh operation; the arrowed lines represent the order of the flow. Figure 18 Figure 18 The process includes a third operation, a fourth operation, a fifth operation, a sixth operation, and a seventh operation; the arrowed lines represent the order of the flow.

[1030] In Embodiment 18, the third and fourth operations 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.

[1031] As an embodiment, the third operation includes AI training, the fourth operation includes AI testing, the fifth operation includes AI emulation, the sixth operation includes AI entity loading, and the seventh operation includes AI inference.

[1032] As an embodiment, the first phase includes a training phase, the second phase includes an emulation phase, the third phase includes a deployment phase, and the fourth phase includes an inference phase.

[1033] As an embodiment, the first phase includes at least one of model training and testing.

[1034] As an embodiment, the AI model training includes initial training and re-training of one or a set of AI entities.

[1035] As an embodiment, the AI model training includes AI entity validation.

[1036] As an embodiment, the AI entity validation is used to evaluate the performance of the AI entity.

[1037] As an embodiment, if the result of AI entity validation does not meet the expectation, the AI model will be re-trained.

[1038] As an embodiment, the AI testing includes testing the validated AI entity to evaluate the performance of the trained AI model.

[1039] ​As one example, if the result of the AI testing meets expectations, the AI entity proceeds to the next stage; otherwise, the AI model is retrained.

[1040] As one example, the second stage includes AI simulation, which simulates inference of the AI entity in a simulation environment.

[1041] As one example, the AI simulation estimates performance of inference of the AI entity in the simulation environment before the AI entity is used.

[1042] As one example, the second stage is optional.

[1043] As one example, the third stage includes AI entity loading, which is to obtain the trained AI entity to obtain desired AI inference functionality.

[1044] As one example, the third stage is optional.

[1045] As one example, the third stage is not needed when the training functionality and the inference functionality are co-located.

[1046] As one example, the fourth stage includes AI inference.

[1047] As one example, the seventh operation includes the first operation.

[1048] As one example, the seventh operation includes the second operation.

[1049] Example 19

[1050] Embodiment 19 illustrates a structural block diagram of a processing apparatus in a first node according to an embodiment of the present application; as shown in FIG. 19. In FIG. 19, the processing apparatus 1900 in the first node includes a first receiver 1901 and a first processor 1902. Figure 19 As shown in FIG. 19, the first receiver 1901 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, the at least one CSI reporting configuration being used to configure reporting of the at least one CSI. Figure 19 As shown in FIG. 19, the first processor 1902 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.

[1051] As shown in FIG. 19, the first processor 1902 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.

[1052] As shown in FIG. 19, the first processor 1902 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.

[1053] In Example 19, 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 includes 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 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, 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; the second time interval depends on whether a generation manner of the target CSI is based on AI.

[1054] As one embodiment, a 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.

[1055] As one 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.

[1056] As one embodiment, the first resource set is composed of one or more aperiodic RS resources, the first RS is a latest RS in time in the first resource set triggered by the first DCI.

[1057] As one embodiment, 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 resource set, the target CSI depends on an output of the first operation.

[1058] As one embodiment, the generation manner of the target CSI based on AI includes: the generation manner of the target CSI is associated to a first type of identifier.

[1059] 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.

[1060] 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.

[1061] 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.

[1062] 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.

[1063] 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.

[1064] As an embodiment, the first processor 1902 deploys the first operation.

[1065] As an embodiment, the first operation is associated with the first type identifier.

[1066] As an embodiment, the first operation is based on training or based on AI.

[1067] As an embodiment, the second operation is based on training or based on AI.

[1068] As an embodiment, the first node is a user equipment.

[1069] As an embodiment, the first node is a relay node equipment.

[1070] As an embodiment, the first receiver 1901 comprises at least one of {antenna 452, receiver 454, reception processor 456, multi-antenna reception processor 458, controller / processor 459, memory 460, data source 467} in embodiment 4.

[1071] As an example, the first processor 1902 includes at least one of the following in embodiment 4: {antenna 452, receiver / transmitter 454, receiving processor 456, transmitting processor 468, multi-antenna receiving processor 458, multi-antenna transmitting processor 457, controller / processor 459, memory 460, data source 467}.

[1072] Example 20

[1073] Example 20 illustrates a structural block diagram of a processing apparatus for a second node according to an embodiment of this application; as shown in the appendix. Figure 20 Shown. (In the appendix) Figure 20 In the second node, the processing device 2000 includes a second processor 2001.

[1074] The second processor 2001 sends at least one CSI reporting configuration; sends a 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;

[1075] In embodiment 20, the 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 the reporting of the target CSI, the target CSI reporting configuration being one of at least one CSI reporting configurations, and the target CSI being one of at least one CSIs; 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 the first symbol is not earlier than the first reference symbol and the second symbol is not earlier than the second reference symbol; the first symbol is the first uplink symbol in the first PUSCH used to carry the at least one CSI, and the first reference symbol is the uplink symbol after a first time interval following the end of the last symbol of the first PDCCH, where the CP begins; the second symbol is the first uplink symbol in the first PUSCH used to carry the target CSI, and the second reference symbol is the uplink symbol after a second time interval following the end of the last symbol of the first RS in the first resource set, where the CP begins; the second time interval depends on whether the target CSI is generated based on AI.

[1076] As an embodiment, the second processor 2001 determines whether a target CSI is received on the first PUSCH; receives the target CSI on the first PUSCH only when the first condition is satisfied.

[1077] As an embodiment, a calculation formula of the second time interval depends on a first parameter, the first parameter depends on whether a generation manner of the target CSI is based on AI.

[1078] 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.

[1079] 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.

[1080] 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, 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.

[1081] As an embodiment, the generation manner of the target CSI based on AI includes that the generation manner of the target CSI is associated to a first type identifier.

[1082] 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 to the generation manner of the target CSI.

[1083] 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, the second resource set includes resources not belonging to the first resource set.

[1084] 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 includes resources not belonging to the first resource set.

[1085] As an embodiment, the second processor 2001, when the first condition is not satisfied, gives up receiving a signal on the first PUSCH, or gives up receiving the target CSI on the first PUSCH;

[1086] 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.

[1087] As one 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 for generating a second CSI.

[1088] As one embodiment, the second processor 2001 performs a second operation; wherein 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 the second operation for generating a second CSI.

[1089] As one embodiment, the second processor 2001 deploys the second operation.

[1090] As one embodiment, the first operation is associated to the first type of identity.

[1091] As one embodiment, the first operation is training-based or AI-based.

[1092] As one embodiment, the second operation is training-based or AI-based.

[1093] As one embodiment, the second node is a base station device.

[1094] As one embodiment, the second node is a user equipment.

[1095] As one embodiment, the second node is a relay node device.

[1096] 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.

[1097] 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.

[1098] 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 technical effects, should be considered as obvious and belong to the protection scope of the present application.

Claims

1. A method for a first node in wireless communication, characterized in that, include: Receive at least one CSI reported configuration; A first DCI is received on a first PDCCH, and the first DCI triggers the reporting of at least one CSI on a first PUSCH, wherein the at least one CSI reporting configuration is used to configure the reporting of the at least one CSI; Determine whether to send the target CSI on the first PUSCH; send the target CSI on the first PUSCH only if the first condition is met; The target CSI reporting configuration is used to configure the reporting of the target CSI, which is one of the at least one CSI reporting configurations, and the target CSI is one of the at least one CSIs. The target CSI reporting configuration indicates a first resource set, which is used for at least one of channel measurement or interference resource measurement of the target CSI, and the first resource set includes one or more RS resources. The 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 the first uplink symbol in the first PUSCH used to carry the at least one CSI, and the first reference symbol is the uplink symbol after a first time interval following the end of the last symbol of the first PDCCH, where the CP starts. The second symbol is the first uplink symbol in the first PUSCH used to carry the target CSI, and the second reference symbol is the uplink symbol after a second time interval following the end of the last symbol of the first RS in the first resource set, where the CP starts. The second time interval depends on whether the target CSI is generated based on AI.

2. The method according to claim 1, characterized in that, The calculation formula for the second time interval depends on the first parameter, which depends on whether the target CSI is generated based on AI.

3. The method according to claim 1 or 2, characterized in that, When the target CSI is generated based on AI, the target CSI includes N information blocks, each of which includes channel information for N time units, where N is a positive integer greater than 1; the second time interval depends on at least one of the N time units.

4. The method according to any one of claims 1 to 3, characterized in that, The first resource set consists of one or more aperiodic RS resources, where the first RS is the latest RS in time in the first resource set triggered by the first DCI.

5. The method according to any one of claims 1 to 4, characterized in that, The generation method of the target CSI based on AI includes: the generation method of the target CSI includes performing a first operation, the input of the first operation depends on the measurement of the first resource set, and the target CSI depends on the output of the first operation.

6. The method according to any one of claims 1 to 5, characterized in that, The generation method of the target CSI is based on AI and includes: the generation method of the target CSI is associated with the first type of identifier.

7. The method according to claim 6, characterized in that, When the target CSI is generated using AI, the second time interval depends on the first type of identifier associated with the target CSI generation method.

8. The method according to any one of claims 1 to 7, characterized in that, The generation method of the target CSI is based on AI and includes: the target CSI indicates at least one resource in a second resource set, the second resource set including resources that do not belong to the first resource set.

9. The method according to any one of claims 1 to 8, characterized in that, include: If the first condition is not met, the first DCI is ignored; In this case, no HARQ-ACK or transport block is reused on the first PUSCH.

10. A terminal, characterized in that, The terminal includes: one or more processors and memory; The memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the terminal to perform the method as described in any one of claims 1-9.

11. A method for a second node in wireless communication, characterized in that, include: Send at least one CSI reporting configuration; A first DCI is sent on the first PDCCH, and the first DCI triggers the reporting of at least one CSI on the first PUSCH, wherein 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 transmit the 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 the reporting of the target CSI, the target CSI reporting configuration is one of at least one CSI reporting configurations, and the target CSI is one of 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 The conditions include that the first symbol is not earlier than the first reference symbol and the second symbol is not earlier than the second reference symbol; the first symbol is the first uplink symbol in the first PUSCH used to carry the at least one CSI, and the first reference symbol is the uplink symbol after a first time interval following the end of the last symbol of the first PDCCH; the second symbol is the first uplink symbol in the first PUSCH used to carry the target CSI, and the second reference symbol is the uplink symbol after a second time interval following the end of the last symbol of the first RS in the first resource set; the second time interval depends on whether the target CSI is generated based on AI.

12. The method according to claim 11, characterized in that, include: Determine whether to receive the target CSI on the first PUSCH; receive the target CSI on the first PUSCH only if the first condition is met.

13. The method according to claim 11 or 12, characterized in that, The calculation formula for the second time interval depends on the first parameter, which depends on whether the target CSI is generated based on AI.

14. The method according to any one of claims 11 to 13, characterized in that, When the target CSI is generated based on AI, the target CSI includes N information blocks, each of which includes channel information for N time units, where 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 consists of one or more aperiodic RS resources, where the first RS is the latest RS in time in the first resource set triggered by the first DCI.

16. The method according to any one of claims 11 to 15, characterized in that, The generation method of the target CSI based on AI includes: the generation method of the target CSI includes the target receiver of the first DCI performing a first operation, the input of the first operation depending on the measurement based on the first resource set, and the target CSI depending on the output of the first operation.

17. The method according to any one of claims 11 to 16, characterized in that, The generation method of the target CSI is based on AI and includes: the generation method of the target CSI is associated with the first type of identifier.

18. The method according to claim 17, characterized in that, When the target CSI is generated using AI, the second time interval depends on the first type of identifier associated with the target CSI generation method.

19. The method according to any one of claims 11 to 18, characterized in that, The generation method of the target CSI is based on AI and includes: the target CSI indicates at least one resource in a second resource set, the second resource set including resources that do not belong to the first resource set.

20. The method according to any one of claims 11 to 19, characterized in that, include: If the first condition is not met, either the reception of signals on the first PUSCH is abandoned, or the reception of the target CSI on the first PUSCH is abandoned. No HARQ-ACK or transport block is reused on the first PUSCH.

21. The method according to any one of claims 11 to 19, characterized in that, When the first condition is not met, the target 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, characterized in that, The base station includes: one or more processors and a memory; The memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the base station to perform the method as described in any one of claims 11-21.

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