Method and device for reporting information in node used for wireless communication

Abstract

The invention discloses a method and a device for reporting information in a node used for wireless communication. The first node receives the first CSI reporting configuration; the first CSI reporting configuration indicates a first RS resource; sending the target information in the first time-frequency resource block; the generation of the target information depends on a measurement based on a first set of opportunities; the first opportunity set comprises at least one transmission opportunity, which is not later than a reference resource, of the first RS resource; the first CSI reporting configuration comprises a first higher layer parameter, and the first opportunity set depends on a value of the first higher layer parameter; the interpretation for the first higher layer parameter depends on whether the generation of the target information is based on AI. According to the method and the device, AI-based information reporting is supported, the transmission opportunity of the RS resource set on which information reporting depends is determined, and the overall performance of the system is improved.

Description

Technical Field

[0001] The present application relates to a transmission method and device in a wireless communication system, and more particularly to a scheme and device for reporting information in a wireless communication system. Background Art

[0002] In traditional wireless communications, UE (User Equipment) obtains channel information by measuring downlink reference signals. 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) or CQI (Channel quality indicator).

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

[0004] The applicant has found through research that when the 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 response to the above problems, the present application discloses a solution. It should be noted that although a large number of embodiments of the present application are developed for AI / ML, the present application is also applicable to other solutions, such as traditional CSI (Channel State Information) reporting solutions. In addition, the use of a unified solution for different scenarios (including but not limited to AI / ML-based solutions and traditional CSI reporting solutions) also helps to reduce hardware complexity and cost. In the absence of conflict, the embodiments and features in any node of the present application can be applied to any other node. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be arbitrarily combined with each other.

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

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

[0007] receiving a first CSI reporting configuration; the first CSI reporting configuration indicating a first RS resource;

[0008] Sending target information in a first time-frequency resource block;

[0009] Among them, the generation of the target information depends on measurements based on a first timing set; the first timing set includes at least one transmission timing of the first RS resource that is no later than a reference resource; the first CSI reporting configuration includes a first higher-layer parameter, and the first timing set depends on the value of the first higher-layer parameter; the interpretation of the first higher-layer parameter depends on whether the generation of the target information is based on AI.

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

[0011] As an embodiment, the problem to be solved by the present application includes: how to determine the transmission timing of the RS resource set on which the generation of a piece of information depends.

[0012] As an embodiment, the essence of the above method includes: the transmission timing of the RS resource set on which the information is generated depends on whether the generation of the information is based on AI.

[0013] As an embodiment, the essence of the above method includes: whether the transmission opportunity set dependency information is generated based on AI.

[0014] As an embodiment, the benefits of the above method include: ensuring consistency in the understanding of the transmission opportunity set by the transmitting and receiving ends.

[0015] As an embodiment, the benefits of the above method include: improving the accuracy and real-time performance of information reporting.

[0016] As an embodiment, the benefits of the above method include: the transmission opportunity set depends on the value of the first higher-layer parameter, which simplifies system design and reduces solution complexity.

[0017] As an embodiment, the benefits of the above method include: enhancing the flexibility and robustness of the system, better adapting to various transmission conditions and application scenarios, and improving the overall performance of the system.

[0018] According to one aspect of the present application, it is characterized in that when the value of the first higher-layer parameter is a first candidate value, the interpretation of the first candidate value depends on whether the generation of the target information is based on AI.

[0019] As an embodiment, the essence of the above method includes: when the value of the first higher-layer parameter is a certain value, the transmission opportunity set depends on whether it is generated based on AI information.

[0020] As an embodiment, the benefits of the above method include: simplifying the design of the system and reducing the implementation complexity of the solution.

[0021] As an embodiment, the benefits of the above method include: being compatible with current system designs and standards, and improving the forward and backward compatibility of the system.

[0022] According to one aspect of the present application, it is characterized in that when the value of the first higher-layer parameter is a first candidate value and the generation of the target information is based on AI, the first timing set includes the M most recent transmission opportunities of the first RS resource that are no later than a reference resource, and M is a positive integer greater than 1; when the value of the first higher-layer parameter is the first candidate value and the generation of the target information is not based on AI, the first timing set includes the most recent transmission opportunity of the first RS resource that is no later than the reference resource.

[0023] As an embodiment, the essence of the above method includes: determining the number of transmission opportunities in the transmission opportunity set according to the fact that the generation of the target information is based on AI.

[0024] As an embodiment, the benefits of the above method include: being compatible with current system designs and standards, and improving the forward and backward compatibility of the system.

[0025] As an embodiment, the benefits of the above method include: supporting AI / ML-based information reporting solutions.

[0026] As an embodiment, the benefits of the above method include: enhancing the flexibility of the system and improving the overall performance of the system.

[0027] According to one aspect of the present application, it is characterized in that when the generation of the target information is based on AI, the candidate value range of the first higher-level parameter is a first candidate value range; when the generation of the target information is not based on AI, the candidate value range of the first higher-level parameter is a second candidate value range; and one candidate value in the first candidate value range does not belong to the second candidate value range.

[0028] As an embodiment, the essence of the above method includes: according to the fact that the generation of the target information is based on AI, determining the candidate value range of the first higher-layer parameter, and then determining the transmission opportunity set.

[0029] As an embodiment, the benefits of the above method include: supporting AI / ML-based information reporting solutions.

[0030] As an embodiment, the benefits of the above method include: being compatible with current system designs and standards, and improving the forward and backward compatibility of the system.

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

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

[0033] According to one aspect of the present application, it is characterized in that the second candidate value range consists of two candidate values, notConfigured and Configured; the first candidate value range includes a reference candidate value, and the reference candidate value is different from notConfigured and Configured.

[0034] As an embodiment, the benefits of the above method include: being compatible with current system designs and standards, and improving the forward and backward compatibility of the system.

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

[0036] According to one aspect of the present application, it is characterized in that the first candidate value range includes a reference candidate value; when the value of the first higher-layer parameter is the reference candidate value and the generation of the target information is based on AI, the first timing set includes the most recent M transmission opportunities of the first RS resource that are no later than the reference resource, and M is a positive integer greater than 1.

[0037] As an embodiment, the essence of the above method includes: the number of transmission opportunities in the transmission opportunity set depends on the value of the first higher layer parameter and the generation of the target information is based on AI.

[0038] As an embodiment, the benefits of the above method include: supporting AI / ML-based information reporting solutions.

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

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

[0041] According to one aspect of the present application, it is characterized in that the first CSI reporting configuration indicates a first resource set, the first resource set includes multiple RS resources, and the first RS resource is an RS resource in the first resource set; when the generation of the target information is based on AI, the target information indicates a resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

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

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

[0044] According to one aspect of the present application, it is characterized in that the reference resource depends on whether the generation of the target information is based on AI.

[0045] As an embodiment, the benefits of the above method include: simplifying system design, better adapting to various application scenarios, and having good flexibility.

[0046] According to one aspect of the present application, it is characterized in that only when the generation of the target information is based on AI, the generation of the target information is associated with a first identifier, and the first opportunity set depends on the first identifier.

[0047] 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.

[0048] As an embodiment, the benefits of the above method include: simplifying system design and having good flexibility.

[0049] According to one aspect of the present application, it is characterized in that only when the generation of the target information is based on AI, the generation of the target information includes the sender of the target information performing a first operation, the input of the first operation depends on the measurement based on the first resource set, and the target information depends on the output of the first operation.

[0050] As an embodiment, the first operation is training-based or AI-based.

[0051] As an embodiment, the benefits of the above method include: supporting AI / ML-based information reporting solutions.

[0052] As an embodiment, the benefits of the above method include: improving the accuracy and real-time performance of information reporting.

[0053] As an embodiment, the benefits of the above method include: improving the overall performance of the system.

[0054] The present application discloses a method used in a second node of wireless communication, characterized by comprising:

[0055] Sending a first CSI reporting configuration; the first CSI reporting configuration indicates a first RS resource; receiving target information in a first time-frequency resource block;

[0056] Among them, the generation of the target information depends on measurements based on a first timing set; the first timing set includes at least one transmission timing of the first RS resource that is no later than a reference resource; the first CSI reporting configuration includes a first higher-layer parameter, and the first timing set depends on the value of the first higher-layer parameter; the interpretation of the first higher-layer parameter depends on whether the generation of the target information is based on AI.

[0057] According to one aspect of the present application, it is characterized in that when the value of the first higher-layer parameter is a first candidate value, the interpretation of the first candidate value depends on whether the generation of the target information is based on AI.

[0058] According to one aspect of the present application, it is characterized in that when the value of the first higher-layer parameter is a first candidate value and the generation of the target information is based on AI, the first timing set includes the M most recent transmission opportunities of the first RS resource that are no later than a reference resource, and M is a positive integer greater than 1; when the value of the first higher-layer parameter is the first candidate value and the generation of the target information is not based on AI, the first timing set includes the most recent transmission opportunity of the first RS resource that is no later than the reference resource.

[0059] According to one aspect of the present application, it is characterized in that when the generation of the target information is based on AI, the candidate value range of the first higher-level parameter is a first candidate value range; when the generation of the target information is not based on AI, the candidate value range of the first higher-level parameter is a second candidate value range; and one candidate value in the first candidate value range does not belong to the second candidate value range.

[0060] According to one aspect of the present application, it is characterized in that the second candidate value range consists of two candidate values, notConfigured and Configured; the first candidate value range includes a reference candidate value, and the reference candidate value is different from notConfigured and Configured.

[0061] According to one aspect of the present application, it is characterized in that the first candidate value range includes a reference candidate value; when the value of the first higher-layer parameter is the reference candidate value and the generation of the target information is based on AI, the first timing set includes the most recent M transmission opportunities of the first RS resource that are no later than the reference resource, and M is a positive integer greater than 1.

[0062] According to one aspect of the present application, it is characterized in that the first CSI reporting configuration indicates a first resource set, the first resource set includes multiple RS resources, and the first RS resource is an RS resource in the first resource set; when the generation of the target information is based on AI, the target information indicates a resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

[0063] According to one aspect of the present application, it is characterized in that the reference resource depends on whether the generation of the target information is based on AI.

[0064] According to one aspect of the present application, it is characterized in that only when the generation of the target information is based on AI, the generation of the target information is associated with a first identifier, and the first opportunity set depends on the first identifier.

[0065] According to one aspect of the present application, it is characterized in that only when the generation of the target information is based on AI, the generation of the target information includes the sender of the target information performing a first operation, the input of the first operation depends on the measurement based on the first resource set, and the target information depends on the output of the first operation.

[0066] The present application discloses a terminal, characterized in that the terminal comprises: one or more processors and a memory;

[0067] The memory is coupled to the one or more processors, and is used to store computer program codes, where the computer program codes include computer instructions, and the one or more processors call the computer instructions to enable the terminal to execute the method in the first node.

[0068] The present application discloses a base station, characterized in that the base station comprises: one or more processors and a memory;

[0069] The memory is coupled to the one or more processors, and is used to store computer program codes, where the computer program codes include computer instructions, and the one or more processors call the computer instructions to cause the base station to perform the method in the second node.

[0070] The present application discloses a first node used for wireless communication, characterized in that it includes:

[0071] A first receiver receives a first CSI reporting configuration, wherein the first CSI reporting configuration indicates a first RS resource;

[0072] A first processor sends target information in a first time-frequency resource block;

[0073] Among them, the generation of the target information depends on measurements based on a first timing set; the first timing set includes at least one transmission timing of the first RS resource that is no later than a reference resource; the first CSI reporting configuration includes a first higher-layer parameter, and the first timing set depends on the value of the first higher-layer parameter; the interpretation of the first higher-layer parameter depends on whether the generation of the target information is based on AI.

[0074] The present application discloses a second node used for wireless communication, characterized in that it includes:

[0075] The second processor sends a first CSI reporting configuration; the first CSI reporting configuration indicates a first RS resource; and receives target information in a first time-frequency resource block;

[0076] Among them, the generation of the target information depends on measurements based on a first timing set; the first timing set includes at least one transmission timing of the first RS resource that is no later than a reference resource; the first CSI reporting configuration includes a first higher-layer parameter, and the first timing set depends on the value of the first higher-layer parameter; the interpretation of the first higher-layer parameter depends on whether the generation of the target information is based on AI.

[0077] As an embodiment, compared with the traditional solution, this application has the following advantages:

[0078] - Ensure the consistency of the understanding of the transmission opportunity set between the sender and the receiver;

[0079] -Support AI-based information reporting solutions;

[0080] -Reduce system measurement resources and overhead;

[0081] -Improve the system's forward and backward compatibility;

[0082] - Improve the flexibility and adaptability of the system;

[0083] - Improve system reliability and robustness;

[0084] -Simplify system design and reduce the complexity of solution implementation;

[0085] - Improve the accuracy and timeliness of information reporting;

[0086] - Enhance the overall performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0088] Figure 1 A flowchart showing a first CSI reporting configuration and target information according to an embodiment of the present application is shown;

[0089] Figure 2 A schematic diagram of a network architecture according to an embodiment of the present application is shown;

[0090] Figure 3 A schematic diagram showing an embodiment of a wireless protocol architecture of a user plane and a control plane according to an embodiment of the present application;

[0091] Figure 4 A schematic diagram showing a first communication device and a second communication device according to an embodiment of the present application is shown;

[0092] Figure 5 A flowchart of wireless transmission according to an embodiment of the present application is shown;

[0093] Figure 6 A schematic diagram showing that the interpretation of the first higher-level parameter depends on whether the generation of the target information is based on AI according to an embodiment of the present application;

[0094] Figure 7 A schematic diagram showing that the interpretation of the first higher-level parameter depends on whether the generation of the target information is based on AI according to another embodiment of the present application;

[0095] Figure 8 A schematic diagram showing that the interpretation of the first higher-level parameter depends on whether the generation of the target information is based on AI according to another embodiment of the present application;

[0096] Fig. 9 A schematic diagram showing a first candidate value range and a second candidate value range according to an embodiment of the present application is shown;

[0097] Fig.10A schematic diagram showing a relationship between a first opportunity set and a reference candidate value according to an embodiment of the present application;

[0098] Fig.11 A schematic diagram showing a first resource set and a second resource set according to an embodiment of the present application is shown;

[0099] Fig.12 A schematic diagram showing a reference resource according to an embodiment of the present application;

[0100] Fig.13 A schematic diagram showing a first identification according to an embodiment of the present application is shown;

[0101] Fig.14 A schematic diagram showing a first operation according to an embodiment of the present application;

[0102] Fig.15 A schematic diagram showing a first operation according to another embodiment of the present application;

[0103] Fig.16 A schematic diagram of deploying a first operation according to an embodiment of the present application is shown;

[0104] Fig.17 A schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of the present application is shown;

[0105] Fig.18 A schematic diagram based on artificial intelligence or machine learning according to an embodiment of the present application is shown;

[0106] Fig.19 A structural block diagram of a processing device used in a first node according to an embodiment of the present application is shown;

[0107] Fig. 20 A structural block diagram of a processing device used in a second node according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0108] The technical solution of the present application will be further described in detail below in conjunction with the accompanying drawings. It should be noted that the embodiments and features in the embodiments of the present application can be arbitrarily combined with each other without conflict. Based on considerations such as performance, flexibility, complexity, overhead, and compatibility, those skilled in the art are motivated to flexibly combine the embodiments in different drawings without conflict, such as but not limited to the attached drawings. Figure 1 The embodiments and appendix Figure 5 -Attached Fig. 20 The embodiments in Figure 5 The embodiments and appendix Figure 6 -Attached Fig. 20The embodiments in , etc.

[0109] Example 1

[0110] Embodiment 1 illustrates a flowchart of a first CSI reporting configuration and target information according to an embodiment of the present application, as shown in the attached Figure 1 Attached Figure 1 In the illustrated 100, each box represents a step. In particular, the order of the steps in the boxes does not represent a specific time sequence between the steps.

[0111] In Embodiment 1, the first node in the present application receives a first CSI reporting configuration in step 101; sends target information in a first time-frequency resource block in step 102;

[0112] Among them, the first CSI reporting configuration indicates a first RS resource; the generation of the target information depends on measurements based on a first timing set; the first timing set includes at least one transmission timing of the first RS resource that is no later than a reference resource; the first CSI reporting configuration includes a first higher-layer parameter, and the first timing set depends on the value of the first higher-layer parameter; the interpretation of the first higher-layer parameter depends on whether the generation of the target information is based on AI.

[0113] As an embodiment, the first CSI reporting configuration is transmitted on the PDSCH.

[0114] As an embodiment, the CSI reporting of the first CSI reporting configuration is periodic.

[0115] As an embodiment, the CSI reporting of the first CSI reporting configuration is semi-persistent.

[0116] As an embodiment, the CSI reporting of the first CSI reporting configuration is aperiodic.

[0117] As an embodiment, the first CSI reporting configuration is carried by higher layer signaling.

[0118] As an embodiment, the first CSI reporting configuration is carried by RRC (Radio Resource Control) signaling.

[0119] As an embodiment, the first CSI reporting configuration is carried by an RRC IE (Information Element).

[0120] As an embodiment, the first CSI reporting configuration is carried by at least one RRC IE.

[0121] As an embodiment, the first CSI reporting configuration includes information in one or more fields in at least one RRC IE.

[0122] As an embodiment, the first CSI reporting configuration includes information in one or more fields of each RRC IE in multiple RRC IEs.

[0123] As an embodiment, the first CSI reporting configuration is an RRC IE.

[0124] As an embodiment, the first CSI reporting configuration belongs to CSI-ReportConfig IE.

[0125] As an embodiment, the first CSI reporting configuration belongs to ServingCellConfig IE.

[0126] As an embodiment, the first CSI reporting configuration belongs to CSI-MeasConfig IE.

[0127] As an embodiment, the first CSI reporting configuration belongs to ServingCellConfigCommon IE.

[0128] As an embodiment, the first CSI reporting configuration belongs to ServingCellConfigCommonSIB IE.

[0129] As an embodiment, the first CSI reporting configuration includes part or all of the fields in the CSI-ReportConfig IE.

[0130] As an embodiment, the first CSI reporting configuration includes part or all of the fields in the ServingCellConfig IE.

[0131] As an embodiment, the first CSI reporting configuration includes part or all of the fields in the CSI-MeasConfig IE.

[0132] As an embodiment, the first CSI reporting configuration includes part or all of the fields in the ServingCellConfigCommon IE.

[0133] As an embodiment, the first CSI reporting configuration includes part or all of the fields in the ServingCellConfigCommonSIB IE.

[0134] As an embodiment, the first CSI reporting configuration indicates at least one resource configuration, and the at least one resource configuration indicates the first RS resource.

[0135] As an embodiment, the first CSI reporting configuration includes at least one resource configuration, and the at least one resource configuration indicates the first RS resource.

[0136] As an embodiment, a resource configuration is used to configure CSI resources.

[0137] As an embodiment, a resource configuration is an IE CSI-ResourceConfig.

[0138] As an embodiment, a resource configuration is carried by RRC IE.

[0139] As an embodiment, a resource configuration is carried by a CSI-ResourceConfig IE.

[0140] As an embodiment, the first CSI reporting configuration indicates configuration information of the first RS resource.

[0141] As an embodiment, the first CSI reporting configuration indicates an identifier of the first RS resource.

[0142] As an embodiment, the first RS resource is a periodic RS resource.

[0143] As an embodiment, the first RS resource is a semi-persistent RS resource.

[0144] As an embodiment, the first RS resource is an aperiodic RS resource.

[0145] As an embodiment, the first RS resource includes at least one of an antenna port, a TCI (Transmission Configuration Indication) status, a QCL (Quasi Co-Location) information, a time-frequency resource, a time-frequency code resource, a beam, an RS resource, a vector, or a matrix.

[0146] As an embodiment, the first RS resource is used for channel measurement.

[0147] As an embodiment, the first RS resource is used for interference measurement.

[0148] As an embodiment, the first RS resource is used for at least one of channel measurement or interference measurement.

[0149] As an embodiment, the first RS resources are used for channel measurement and interference measurement.

[0150] As an embodiment, the first RS resource is a downlink RS resource.

[0151] As an embodiment, the first RS resource is a CSI-RS (Channel State Information Reference Signal) resource.

[0152] As an embodiment, the first RS resource is a CSI-SSB (Channel State Information-Synchronization Signal Block) resource.

[0153] As an embodiment, the first RS resource is a CSI-IM (Channel State Information-Interference Measurement) resource.

[0154] As an embodiment, the first RS resource is a CSI-IM resource or an NZP (non-zero power) CSI-RS resource used for interference measurement.

[0155] As an embodiment, the first RS resource is a CSI-RS resource or a synchronization signal resource.

[0156] As an embodiment, the synchronization signal resources include at least resources occupied by the synchronization signal.

[0157] As an embodiment, the synchronization signal resource is SSB (Synchronization Signal Block).

[0158] As an embodiment, the synchronization signal resource is a SS / PBCH (synchronization signal / physical broadcast channel) block resource.

[0159] As an embodiment, the first time-frequency resource block includes multiple REs (Resource Element).

[0160] Typically, one RE occupies one symbol in the time domain and one subcarrier in the frequency domain.

[0161] As an embodiment, the first time-frequency resource block occupies at least one symbol in the time domain, and the first time-frequency resource block occupies at least one subcarrier in the time domain.

[0162] As an embodiment, the first time-frequency resource block occupies at least one symbol in the time domain, and the first time-frequency resource block occupies at least one RB (resource block) in the time domain.

[0163] As an embodiment, the symbol is a single carrier symbol.

[0164] As an embodiment, the symbol is a multi-carrier symbol.

[0165] As an embodiment, the symbol is a 6G single carrier symbol.

[0166] As an embodiment, the symbol is a 6G multi-carrier symbol.

[0167] As an embodiment, the multi-carrier symbol is an OFDM (Orthogonal Frequency Division Multiplexing) symbol.

[0168] As an embodiment, the symbol is obtained by performing OFDM symbol generation on the output of a transform precoding.

[0169] As an embodiment, the multi-carrier symbol is an SC-FDMA (Single Carrier-Frequency Division Multiple Access) symbol.

[0170] As an embodiment, the multi-carrier symbol is a DFT-S-OFDM (Discrete Fourier Transform Spread OFDM) symbol.

[0171] As an embodiment, the multi-carrier symbol is a FBMC (Filter Bank Multi Carrier) symbol.

[0172] As an embodiment, the multi-carrier symbol includes a CP (Cyclic Prefix).

[0173] As an embodiment, the first time-frequency resource block is configured or scheduled to a physical layer channel.

[0174] As an embodiment, the first time-frequency resource block is configured or scheduled to an uplink physical layer channel.

[0175] As an embodiment, the first time-frequency resource block is configured or indicated to PUSCH (Physical uplink shared channel).

[0176] As an embodiment, the first time-frequency resource block is configured or indicated to PUCCH (Physical uplink control channel).

[0177] As an embodiment, sending the target information in the first time-frequency resource block includes: sending a first signal in the first time-frequency resource block; wherein the first signal carries the target information.

[0178] As an embodiment, the first signal includes a baseband signal.

[0179] As an embodiment, the first signal includes a wireless signal.

[0180] As an embodiment, the first signal includes a radio frequency signal.

[0181] As an embodiment, the first signal is transmitted on an uplink physical channel.

[0182] As an embodiment, the first signal is transmitted on PUSCH.

[0183] As an embodiment, the first signal is transmitted on PUCCH.

[0184] As an embodiment, sending the target information in the first time-frequency resource block includes: the target information is used to generate a signal sent in the first time-frequency resource block after channel coding.

[0185] As an embodiment, sending the target information in the first time-frequency resource block includes: the target information is used to generate a signal sent in the first time-frequency resource block after channel coding and modulation.

[0186] As an embodiment, sending the target information in the first time-frequency resource block includes: the target information is used to generate a signal sent in the first time-frequency resource block after bit sequence generation and channel coding.

[0187] As an embodiment, sending the target information in the first time-frequency resource block includes: the target information is used to generate a signal sent in the first time-frequency resource block after bit sequence generation, channel coding, and modulation.

[0188] As an embodiment, sending the target information in the first time-frequency resource block includes: the target information is used to generate a signal sent in the first time-frequency resource block after bit sequence generation, code block segmentation and CRC attachment, channel coding, rate matching, and code block concatenation.

[0189] As an embodiment, sending the target information in the first time-frequency resource block includes: the target information is multiplexed into the first time-frequency resource block after bit sequence generation, code block segmentation and CRC addition, channel coding, rate matching, and code block connection.

[0190] As an embodiment, the target information includes CSI (channel state information) or beam information.

[0191] As an embodiment, the target information includes CSI.

[0192] As an embodiment, the target information is CSI.

[0193] As an embodiment, the target information includes at least one CSI reporting quantity.

[0194] As an embodiment, the target information includes a beam indication or an RS resource indication.

[0195] As an embodiment, the target information includes at least one resource indication and RSRP.

[0196] As an embodiment, the target information includes one or more of a beam indication, a CRI (CSI-RS Resource Indicator, channel state information reference signal resource indicator), a SS / PBCH block resource indicator (SS / PBCH Block Resource indicator, SSBRI), or a RSRP (reference signal received power, reference signal received power).

[0197] As an embodiment, the target information includes at least one resource indication.

[0198] As an embodiment, the target information includes at least one resource indication, and one resource indication in the target information is used to indicate a beam or RS resource.

[0199] As an embodiment, the target information includes at least one resource indication, and one resource indication in the target information is used to indicate a beam, a CSI-RS resource or a SS / PBCH block resource.

[0200] As an embodiment, the target information includes at least one resource indication; one resource indication in the target information is used to indicate a beam, or one resource indication in the target information is a CRI or SS / PBCH block resource indicator.

[0201] As an embodiment, the target information includes RSRP.

[0202] As an embodiment, the target information includes a first indication and RSRP, and the first indication is used to indicate a resource.

[0203] As an embodiment, the target information includes a first indication and RSRP, and the first indication is used to indicate an RS resource.

[0204] As an embodiment, the target information includes beam indication and RSRP.

[0205] As an embodiment, the target information includes RS resource indication and RSRP.

[0206] As an embodiment, the channel information in the present application includes at least one of a beam indication, a CRI, a SS / PBCH block resource indicator, or RSRP.

[0207] As an embodiment, the channel information described in the present application includes one of beam indication, beam number, CRI, SS / PBCH block resource indicator, number of CRI or SSBRI, RSRP, differential RSRP, probability information, or confidence information.

[0208] As an embodiment, the channel information described in the present application includes at least one of beam indication, number of beams, CRI, SS / PBCH block resource indicator, number of CRI or SSBRI, RSRP, differential RSRP, probability information, or confidence information.

[0209] As an embodiment, the channel information in the present application includes differential RSRP.

[0210] As an embodiment, the channel information in the present application includes one of differential RSRP, probability information, or confidence information.

[0211] As an embodiment, the channel information in the present application includes at least one of differential RSRP, probability information, or confidence information.

[0212] As an embodiment, the probability information indicates the probability that the corresponding beam is the optimal one or more beams.

[0213] As an embodiment, the probability information indicates the probability that the corresponding RS resource is the optimal one or more RS resources.

[0214] As an embodiment, the confidence information indicates the accuracy of RSRP.

[0215] As an embodiment, the confidence information indicates the accuracy of the differential RSRP.

[0216] As an embodiment, the essence of the above method includes: monitoring the AI ​​model based on performance parameters related to AI information reporting.

[0217] As an embodiment, the benefits of the above method include: improving the performance of the AI-based information reporting solution and improving the overall performance of the system.

[0218] As an embodiment, the target information includes predicted channel information.

[0219] As an embodiment, the target information includes channel information for a period of time in the future.

[0220] As an embodiment, the target information includes predicted beam information.

[0221] As an embodiment, the target information includes beam information for a period of time in the future.

[0222] As an embodiment, the target information includes a prediction of temporal channel information.

[0223] As a sub-embodiment of the above embodiment, the future period of time includes at least one time domain resource after the current time domain resource.

[0224] As a sub-embodiment of the above embodiment, the future period of time includes at least one time unit after the current time unit.

[0225] As a sub-embodiment of the above embodiment, the future period of time includes at least one time slot after the current time slot.

[0226] As a sub-embodiment of the above embodiment, the future period of time includes at least one symbol after the current symbol.

[0227] As an embodiment, the predicted channel information described in the present application includes at least one of a beam indication, a CRI, a SS / PBCH block resource indicator, or RSRP.

[0228] As an embodiment, the predicted channel information described in the present application includes one of beam indication, beam number, CRI, SS / PBCH block resource indicator, number of CRI or SSBRI, RSRP, differential RSRP, probability information, or confidence information.

[0229] As an embodiment, the predicted channel information described in the present application includes at least one of beam indication, number of beams, CRI, SS / PBCH block resource indicator, number of CRI or SSBRI, RSRP, differential RSRP, probability information, or confidence information.

[0230] As an embodiment, the predicted channel information described in the present application includes differential RSRP.

[0231] As an embodiment, the predicted channel information described in the present application includes one of differential RSRP, probability information, or confidence information.

[0232] As an embodiment, the predicted channel information described in the present application includes at least one of differential RSRP, probability information, or confidence information.

[0233] As an embodiment, the benefits of the above method include: reducing channel measurement overhead.

[0234] As an embodiment, the benefits of the above method include: improving the accuracy and real-time performance of channel information reporting and enhancing the overall performance of the system.

[0235] Typically, the first RS resource includes a plurality of transmission opportunities no later than the reference resource.

[0236] As an embodiment, the information recovered by the receiver of the target information based on the target information is unknown to the sender of the target information.

[0237] As an embodiment, the reporting amount included in the target information depends on whether the generation of the target information is based on AI.

[0238] As an embodiment, the target information is based on AI.

[0239] As an embodiment, the target information is not AI-based.

[0240] As an embodiment, whether the target information includes confidence information depends on whether the generation of the target information is based on AI; only when the generation of the target information is based on AI, the target information includes confidence information.

[0241] As an embodiment, whether the target information is based on a non-codebook depends on whether the generation of the target information is based on AI; when the generation of the target information is based on AI, the target information is based on a non-codebook; when the generation of the target information is not based on AI, the target information is based on a codebook.

[0242] As an embodiment, when the generation of the target information is not based on AI, the target information belongs to the CSI defined in 3GPP Rel-18.

[0243] As an embodiment, when the generation of the target information is based on AI, the target information does not belong to the CSI defined in 3GPP Rel-18 and earlier versions.

[0244] As an embodiment, when the generation of the target information is based on AI, the target information includes predicted channel or beam information, or compressed CSI.

[0245] As an embodiment, when the generation of the target information is based on AI, the target information includes CSI based on artificial intelligence or machine learning.

[0246] As an embodiment, when the target information is generated based on AI, the target information includes CSI generated based on a neural network.

[0247] As an embodiment, when the target information is generated based on AI, the target information includes CSI generated based on CNN (Conventional Neural Networks).

[0248] As an embodiment, the benefits of the above method include: supporting AI-based information reporting solutions.

[0249] As an embodiment, the advantages of the above method include: little modification to existing systems and standards.

[0250] As an embodiment, the reference resource is a CSI reference resource.

[0251] As an embodiment, the reference resource is a CSI reference resource of the target information.

[0252] As an embodiment, the reference resource depends on the time domain resources occupied by the first time-frequency resource block.

[0253] As an embodiment, the reference resource depends on the time unit where the first time-frequency resource block is located.

[0254] As an embodiment, the reference resource is earlier than the time domain resource occupied by the first time-frequency resource block.

[0255] As an embodiment, the reference resource is earlier than the time unit where the first time-frequency resource block is located.

[0256] As an embodiment, the time unit where the first time-frequency resource block is located is n', the reference resource belongs to the time unit f(n') in the time domain, and f(n') is n'+o', where o' is an integer.

[0257] As an embodiment, the time unit where the first time-frequency resource block is located is n', the reference resource belongs to the time unit f(n') in the time domain, and f(n') is n'+o', where o is an integer less than 0.

[0258] As an embodiment, the time unit where the first time-frequency resource block is located is n', the reference resource belongs to the time unit f(n') in the time domain, and f(n') is where μ DL and μ UL are the subcarrier spacings for downlink and uplink, respectively, o is an integer, Indicates a floor operation.

[0259] As an embodiment, the time unit where the first time-frequency resource block is located is n', the reference resource belongs to the time unit f(n') in the time domain, and f(n') is where μ DL and μ UL are the subcarrier spacings for downlink and uplink, respectively, and o is an integer less than 0. Indicates a floor operation.

[0260] As an embodiment, o is an integer.

[0261] As an embodiment, o is Where K offset It is configured by higher layer signaling, nCSI_ref is not less than The minimum value of and μ offset They are also configured by higher-layer signaling. For detailed introduction, refer to section 5.2.2.5 of 3GPP TS38.214.

[0262] As an embodiment, the reference resource depends on whether the generation of the target information is based on AI.

[0263] As an embodiment, the time unit where the first time-frequency resource block is located is n', the reference resource belongs to the time unit f(n') in the time domain, and f(n') is a function; the f(n') depends on a first parameter, and the first parameter depends on whether the generation of the target information is based on AI.

[0264] As an embodiment, the essence of the above method includes: when the generation of the target information is based on AI, setting the reference resources different from the two cases where the generation of the target information is not based on AI.

[0265] As an embodiment, the benefits of the above method include: fully considering the processing capabilities based on AI and not based on AI, and having greater flexibility and adaptability.

[0266] As an embodiment, the benefits of the above method include: improving the overall performance of the system.

[0267] As an embodiment, the first timing set includes at least one transmission timing of the first RS resource that is no later than the reference resource.

[0268] As an embodiment, the first timing set includes a most recent transmission timing of the first RS resource that is no later than the reference resource.

[0269] As an embodiment, the first timing set includes one or more most recent transmission timings of the first RS resource that are no later than the reference resource.

[0270] As an embodiment, the first timing set includes all transmission timings of the first RS resource that are no later than the reference resource.

[0271] As an embodiment, the first timing set only includes at least one transmission timing of the first RS resource that is no later than a reference resource.

[0272] As an embodiment, the first CSI reporting configuration indicates a first resource set, the first resource set includes at least one RS resource, and the first RS resource is an RS resource in the first resource set; the first timing set includes at least one transmission timing of each RS resource in the first resource set that is no later than the reference resource.

[0273] As an embodiment, the first CSI reporting configuration indicates a first resource set, the first resource set includes at least one RS resource, and the first RS resource is any RS resource in the first resource set; the first timing set includes at least one transmission timing of each RS resource in the first resource set that is no later than the reference resource.

[0274] As an embodiment, the first CSI reporting configuration indicates a first resource set, the first resource set includes at least one RS resource, and the first RS resource is an RS resource in the first resource set; the first timing set includes at least one transmission timing of each RS resource in at least one RS resource in the first resource set that is no later than the reference resource.

[0275] As an embodiment, the first CSI reporting configuration indicates a first resource set, the first resource set includes at least one RS resource, and the first RS resource is an RS resource in the first resource set; the first timing set includes at least one transmission timing of each RS resource in some RS resources in the first resource set that is no later than the reference resource.

[0276] As an embodiment, the first CSI reporting configuration indicates a first resource set, the first resource set includes at least one RS resource, and the first RS resource is an RS resource in the first resource set; the first timing set also includes at least one transmission timing of an RS resource other than the first RS resource in the first resource set that is no later than the reference resource.

[0277] As an embodiment, the first CSI reporting configuration indicates a first resource set, the first resource set includes at least one RS resource, and the first RS resource is an RS resource in the first resource set; the first timing set includes at least one transmission timing of at least one RS resource in the first resource set that is no later than a reference resource.

[0278] As an embodiment, the first CSI reporting configuration indicates a first resource set, the first resource set includes at least one RS resource, and the first RS resource is an RS resource in the first resource set; the first timing set includes a most recent transmission timing of at least one RS resource in the first resource set that is no later than a reference resource.

[0279] As an embodiment, the first CSI reporting configuration indicates a first resource set, the first resource set includes at least one RS resource, and the first RS resource is an RS resource in the first resource set; the first timing set includes one or more transmission timings of at least one RS resource in the first resource set that are no later than the reference resource.

[0280] As an embodiment, the first CSI reporting configuration indicates a first resource set, the first resource set includes at least one RS resource, and the first RS resource is an RS resource in the first resource set; the first timing set includes all transmission timings of at least one RS resource in the first resource set that are not later than a reference resource.

[0281] As an embodiment, the first CSI reporting configuration indicates a first resource set, the first resource set includes at least one RS resource, and the first RS resource is an RS resource in the first resource set; the first timing set includes the most recent transmission timing of each RS resource in some or all of the RS resources in the first resource set that is no later than a reference resource.

[0282] As an embodiment, the first CSI reporting configuration indicates a first resource set, the first resource set includes at least one RS resource, and the first RS resource is an RS resource in the first resource set; the first timing set includes one or more transmission timings of each RS resource in some or all of the RS resources in the first resource set that are no later than the most recent reference resource.

[0283] As an embodiment, the first CSI reporting configuration indicates a first resource set, the first resource set includes at least one RS resource, and the first RS resource is an RS resource in the first resource set; the first timing set includes all transmission timings of each RS resource in some or all of the RS resources in the first resource set that are no later than a reference resource.

[0284] As an embodiment, the first CSI reporting configuration indicates a first resource set, the first resource set includes multiple RS resources, and the first RS resource is an RS resource in the first resource set; when the generation of the target information is based on AI, the first timing set includes at least one transmission timing for each RS resource in some RS resources in the first resource set; when the generation of the target information is not based on AI, the first timing set includes at least one transmission timing for each RS resource in the first resource set.

[0285] As an embodiment, the first CSI reporting configuration indicates a first resource set, the first resource set includes multiple RS resources, and the first RS resource is an RS resource in the first resource set; when the generation of the target information is based on AI, the first timing set includes at least one transmission timing of only the first RS resource in the first resource set; when the generation of the target information is not based on AI, the first timing set includes at least one transmission timing for each RS resource in the first resource set.

[0286] As an embodiment, the essence of the above method includes: configuring different transmission opportunity sets for AI-based and non-AI-based solutions respectively.

[0287] As an embodiment, the benefits of the above method include: fully considering the processing capabilities based on AI and not based on AI, and having greater flexibility and adaptability.

[0288] As an embodiment, the benefits of the above method include: improving the accuracy and reliability of information reporting.

[0289] As an embodiment, the benefits of the above method include: improving the overall performance of the system.

[0290] As an embodiment, the first CSI reporting configuration indicates a first resource set, the first resource set includes multiple RS resources, and the first RS resource is an RS resource in the first resource set; when the generation of the target information is based on AI, the first timing set includes one or more transmission timings of at least one RS resource in the first resource set that are no later than the most recent reference resource; when the generation of the target information is not based on AI, the first timing set includes the most recent transmission timing of at least one RS resource in the first resource set that is no later than the most recent reference resource.

[0291] As an embodiment, when the generation of the target information is based on AI, the first timing set includes the most recent M transmission opportunities of the first RS resource that are no later than the reference resource, and M is a positive integer greater than 1; when the generation of the target information is not based on AI, the first timing set includes the most recent transmission opportunity of the first RS resource that is no later than the reference resource.

[0292] As an embodiment, the essence of the above method includes: for solutions that are not based on AI, the current system design and standards are used.

[0293] As an embodiment, the essence of the above method includes: configuring more transmission opportunities for the AI-based solution.

[0294] As an embodiment, the benefits of the above method include: enhancing the forward and backward compatibility of the system.

[0295] As an embodiment, the benefits of the above method include: little modification to existing systems and standards.

[0296] As an embodiment, the benefits of the above method include: enhancing the performance of the AI ​​solution and improving the accuracy of information reporting.

[0297] As an embodiment, the benefits of the above method include: improving the overall performance of the system.

[0298] As an embodiment, the first RS resource set is used for channel measurement; the generation of the target information depends on the measurement based on the first opportunity set, including: the generation of the target information depends on the channel measurement obtained based on the first opportunity set.

[0299] As an embodiment, the first RS resource set is used for interference measurement; the generation of the target information depends on the measurement based on the first opportunity set, including: the generation of the target information depends on the interference measurement obtained based on the first opportunity set.

[0300] As an embodiment, the first RS resource set is used for channel measurement and interference measurement; the generation of the target information depends on the measurement based on the first timing set, including: the generation of the target information depends on the channel measurement and interference measurement obtained based on the first timing set.

[0301] As an embodiment, the generation of the target information relies on measurements based on a first opportunity set, including: measurements based on the first opportunity set are used to generate the target information.

[0302] As an embodiment, the first RS resource set is used for channel measurement; the generation of the target information depends on the measurement based on the first timing set, including: the channel measurement obtained based on the first timing set is used to generate the target information.

[0303] As an embodiment, the first RS resource set is used for interference measurement; the generation of the target information depends on the measurement based on the first timing set, including: the interference measurement obtained based on the first timing set is used to generate the target information.

[0304] As an embodiment, the first RS resource set is used for channel measurement and interference measurement; the generation of the target information depends on the measurement based on the first timing set, including: the channel measurement and interference measurement obtained based on the first timing set are used to generate the target information.

[0305] As an embodiment, the first CSI reporting configuration indicates a first resource set; the first resource set includes at least one RS resource set for channel measurement, and an RS resource set for channel measurement includes one or more RS resources; the generation of the target information depends on the measurement based on the first timing set, including: the generation of the target information depends on the channel measurement obtained based on the first timing set.

[0306] As an embodiment, the first CSI reporting configuration indicates a first resource set; the first resource set includes at least one RS resource set for interference measurement, and an RS resource set for interference measurement includes one or more RS resources; the generation of the target information depends on the measurement based on the first timing set, including: the generation of the target information depends on the interference measurement obtained based on the first timing set.

[0307] As an embodiment, the first CSI reporting configuration indicates a first resource set; the first resource set includes at least one RS resource set for channel measurement, and at least one RS resource set for interference measurement; an RS resource set for channel measurement includes one or more RS resources, and an RS resource set for interference measurement includes one or more RS resources; the generation of the target information depends on the measurement based on the first timing set, including: the generation of the target information depends on the channel measurement and interference measurement obtained based on the first timing set.

[0308] As an embodiment, the first CSI reporting configuration indicates a first resource set; the first resource set includes at least one RS resource set for channel measurement, and an RS resource set for channel measurement includes one or more RS resources; the generation of the target information depends on the measurement based on the first timing set, including: the channel measurement obtained based on the first timing set is used to generate the target information.

[0309] As an embodiment, the first CSI reporting configuration indicates a first resource set; the first resource set includes at least one RS resource set for interference measurement, and an RS resource set for interference measurement includes one or more RS resources; the generation of the target information depends on the measurement based on the first timing set, including: the interference measurement obtained based on the first timing set is used to generate the target information.

[0310] As an embodiment, the first CSI reporting configuration indicates a first resource set; the first resource set includes at least one RS resource set for channel measurement, and at least one RS resource set for interference measurement; an RS resource set for channel measurement includes one or more RS resources, and an RS resource set for interference measurement includes one or more RS resources; the generation of the target information depends on the measurement based on the first timing set, including: the channel measurement and interference measurement obtained based on the first timing set are used to generate the target information.

[0311] As an embodiment, the channel measurement obtained based on the first timing set refers to: the channel measurement obtained based on at least one reference signal transmitted in the first timing set.

[0312] As an embodiment, the channel measurement obtained based on the first opportunity set refers to: the channel measurement obtained in the first opportunity set.

[0313] As an embodiment, the interference measurement obtained based on the first timing set refers to: the interference measurement obtained based on at least one reference signal transmitted in the first timing set.

[0314] As an embodiment, the interference measurement obtained based on the first opportunity set refers to: the interference measurement obtained in the first opportunity set.

[0315] As an embodiment, the channel measurement obtained based on the first opportunity set includes a channel matrix (channelmatrix).

[0316] As an embodiment, the channel measurement obtained based on the first opportunity set includes a raw channel matrix.

[0317] As an embodiment, the channel measurement obtained based on the first opportunity set includes an eigenvector.

[0318] As an embodiment, the channel measurement obtained based on the first opportunity set includes an eigenvector and an eigenvalue.

[0319] As an embodiment, the channel measurement obtained based on the first opportunity set includes one or more of BLER, delay spread, Doppler spread, Doppler shift, average delay, average gain, path loss and RSRP.

[0320] As an embodiment, the interference measurement obtained based on the first opportunity set includes at least one of interference power, interference variance or interference power spectral density.

[0321] As an embodiment, the interference measurement obtained based on the first opportunity set includes an interference channel matrix.

[0322] As an embodiment, the interference measurement obtained based on the first opportunity set includes an interference covariance matrix.

[0323] As an embodiment, the interference measurement obtained based on the first opportunity set includes an interference characteristic vector.

[0324] As an embodiment, the interference measurement obtained based on the first opportunity set includes an interference characteristic vector and an interference characteristic value.

[0325] As an embodiment, the interference measurement obtained based on the first opportunity set includes an interference beam.

[0326] Generally speaking, how the target information of the first node is generated based on the measurement of the first opportunity set is determined by the equipment manufacturer. Some non-limiting implementation methods are described below:

[0327] As an embodiment, the measurement based on the first opportunity set includes channel measurement obtained based on the first opportunity set.

[0328] As an embodiment, the measurement based on the first opportunity set includes interference measurement obtained based on the first opportunity set.

[0329] As an embodiment, the measurement based on the first opportunity set includes channel measurement and interference measurement obtained based on the first opportunity set.

[0330] As an embodiment, the interference measurement includes: one or more of interference power, interference variance or interference power spectrum density.

[0331] As an embodiment, the measurement based on the first opportunity set includes a channel impulse response obtained based on the measurement for the first opportunity set.

[0332] As an embodiment, the measurement based on the first opportunity set includes a channel matrix obtained based on the measurement for the first opportunity set.

[0333] As an embodiment, the measurement based on the first opportunity set includes an eigenvector and an eigenvalue of a channel matrix obtained based on the measurement for the first opportunity set.

[0334] As an embodiment, the measurement based on the first opportunity set includes a matrix or a vector obtained by preprocessing a channel matrix obtained based on the measurement for the first opportunity set.

[0335] As an embodiment, the channel matrix is ​​in spatial-frequency domain.

[0336] As an embodiment, the channel matrix is ​​in angular-delay domain projection.

[0337] As an embodiment, the preprocessing includes one or more of quantization, DFT (Discrete Fourier Transform), matrix decomposition, matrix transformation or projection, transformation from spatial domain to angular domain, transformation from angular domain to spatial domain, transformation from frequency domain to time domain and transformation from time domain to frequency domain, truncation, padding, mapping, and labeling.

[0338] As an embodiment, the preprocessing includes one or more of matrix decomposition, matrix transformation or projection.

[0339] As an embodiment, the preprocessing includes quantization.

[0340] As an embodiment, the preprocessing includes DFT.

[0341] As an embodiment, the preprocessing includes one or more of quantization, transformation from spatial domain to angular domain, transformation from angular domain to spatial domain, transformation from frequency domain to time domain, and transformation from time domain to frequency domain.

[0342] As an embodiment, the preprocessing includes truncation and / or padding.

[0343] As an embodiment, the preprocessing includes mapping.

[0344] As an embodiment, the preprocessing includes mapping to vectors.

[0345] As an embodiment, the preprocessing includes labeling.

[0346] As an embodiment, the label refers to marking with a label.

[0347] As an embodiment, the first RS resource is used for channel measurement, and the first higher layer parameter indicates a time domain limitation of the channel measurement.

[0348] As an embodiment, the first RS resource is used for channel measurement, and the first higher layer parameter is timeRestrictionForChannelMeasurements.

[0349] As an embodiment, the first RS resource is used for interference measurement, and the first higher layer parameter indicates a time domain limitation of the interference measurement.

[0350] As an embodiment, the first RS resource is used for interference measurement, and the first higher layer parameter is timeRestrictionForInterferenceMeasurements.

[0351] As an embodiment, the first RS resource is used for channel measurement and interference measurement, and the first higher layer parameter indicates time domain limitations of the channel measurement and interference measurement.

[0352] As an embodiment, the first RS resource is used for channel measurement and interference measurement, and the first higher layer parameter is timeRestrictionForInterferenceMeasurements.

[0353] As an embodiment, the first timing set depending on the value of the first higher layer parameter includes: the first timing set includes which transmission timing or timings of the first RS resource that are no later than the reference resource and depends on the value of the first higher layer parameter.

[0354] As an embodiment, the first timing set depends on the value of the first higher layer parameter, including: the at least one transmission timing of the first RS resource in the first timing set is no later than all transmission timings of the reference resource, or no later than the most recent transmission timing of the reference resource, depending on the value of the first higher layer parameter.

[0355] As an embodiment, the first timing set depends on the value of the first higher layer parameter, including: the at least one transmission timing of the first RS resource in the first timing set is no later than multiple transmission timings of the reference resource, or no later than the most recent transmission timing of the reference resource, depending on the value of the first higher layer parameter.

[0356] As an embodiment, the first timing set depends on the value of the first higher layer parameter, including: the at least one transmission timing of the first RS resource in the first timing set is no later than all transmission timings of the reference resource, no later than the most recent M transmission timings of the reference resource, or no later than the most recent transmission timing of the reference resource, depending on the value of the first higher layer parameter; M is a positive integer greater than 1.

[0357] As an embodiment, the essence of the above method includes: determining the transmission timing included in the first timing set through the value of the first higher-layer parameter.

[0358] As an embodiment, the benefits of the above method include: simplifying system design and reducing the implementation complexity of the solution.

[0359] As an embodiment, the benefits of the above method include: improving the flexibility and overall performance of the system.

[0360] As an embodiment, when the generation of the target information is not based on AI and the value of the first higher layer parameter is notConfigured, the first timing set includes all transmission timings of the first RS resource that are no later than a reference resource; when the generation of the target information is not based on AI and the value of the first higher layer parameter is Configured, the first timing set includes the most recent transmission timing of the first RS resource that is no later than the reference resource.

[0361] As an embodiment, the benefits of the above method include: little modification to existing systems and standards.

[0362] As an embodiment, when the generation of the target information is based on AI and the value of the first higher layer parameter is notConfigured, the first timing set includes all transmission timings of the first RS resource that are no later than a reference resource; when the generation of the target information is based on AI and the value of the first higher layer parameter is Configured, the first timing set includes the most recent M transmission timings of the first RS resource that are no later than the reference resource, where M is a positive integer greater than 1.

[0363] As an embodiment, the essence of the above method includes: for an AI-based solution and a non-AI-based solution, the value of the same first higher-layer parameter represents different meanings, and the transmission timings included in the first timing set determined thereby are different.

[0364] As an embodiment, the essence of the above method includes: configuring more transmission opportunities for the AI-based solution.

[0365] As an embodiment, the benefits of the above method include: little modification to existing systems and standards.

[0366] As an embodiment, the benefits of the above method include: enhancing the performance of the AI ​​solution and improving the accuracy of information reporting.

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

[0368] As an embodiment, the generation of the target information is based on AI, including: the generation of the target information uses an AI model; the generation of the target information is not based on AI, including: the generation of the target information does not use an AI model.

[0369] As an embodiment, the generation of the target information based on AI includes: the target information includes information based on artificial intelligence or machine learning; the generation of the target information not based on AI includes: the target information does not include information based on artificial intelligence or machine learning.

[0370] As an embodiment, the generation of the target information based on AI includes: the target information includes information generated based on a neural network (Neural Network); the generation of the target information not based on AI includes: the target information does not include information generated based on a neural network (Neural Network).

[0371] As an embodiment, the generation of the target information based on AI includes: the target information includes information generated based on CNN (Conventional Neural Networks); the generation of the target information not based on AI includes: the target information does not include information generated based on CNN.

[0372] As an embodiment, the generation of the target information is based on AI and includes: the generation of the target information includes the sender of the target information performing a first operation, the input of the first operation depends on the measurement based on the first timing set, and the target information depends on the output of the first operation; the generation of the target information is not based on AI and includes: the generation of the target information does not include the sender of the target information performing the first operation.

[0373] As an embodiment, the generation of the target information based on AI includes: the first CSI reporting configuration indicates a first type of identifier; the generation of the target information not based on AI includes: the first CSI reporting configuration does not indicate a first type of identifier.

[0374] As an embodiment, the generation of the target information is based on AI, including: the generation of the target information is associated with the first type of identification; the generation of the target information is not based on AI, including: the generation of the target information is not associated with the first type of identification.

[0375] As an example, in the case where the generation of the target information is not based on AI, how to generate the target information is determined by the manufacturer of the first node, or is implementation-dependent. A typical but non-limiting implementation is described below:

[0376] The first node first performs measurement on the first opportunity set to obtain a channel parameter matrix H r×t , where r and t are the number of receiving antennas and the number of antenna ports of the first CSI-RS resource respectively; for the channel parameter matrix H r×t After power adjustment, the adjusted channel parameter matrix is Where P is the ratio of the assumed PDSCH EPRE to the first CSI-RSEPRE (ie, the first power control offset); when the precoding matrix W is used t×l Under the condition of , the channel parameter matrix after precoding is Where l is the rank or the number of layers, in one case l is a positive integer not greater than t, in another case the precoding matrix is ​​the identity matrix, in which case t=l; H is calculated using criteria such as SINR (Signal Interference Noise Ratio), EESM (Exponential Effective SINR Mapping), or RBIR (Received Block mean mutual Information Ratio). r×t ·W t×l The equivalent channel capacity is then used to determine the CQI included in the first CSI report by looking up the equivalent channel capacity. Generally speaking, the calculation of the equivalent channel capacity requires the first node to estimate the interference (including noise), and the first node can obtain more accurate interference measurement by measuring the second opportunity set in the present application. Generally speaking, the direct mapping of the equivalent channel capacity to the CQI value depends on the receiver performance, or hardware-related factors such as the modulation method.

[0377] As an embodiment, in the case where the generation of the target information is based on AI, how to generate the target information is determined by the manufacturer of the first node, or is implementation-dependent. Without loss of generality, the AI ​​model or parameters used to generate the target information are determined by the manufacturer of the first node.

[0378] As an embodiment, the interpretation of the first higher-layer parameter depends on whether the generation of the target information is based on AI, including: the interpretation method of the first higher-layer parameter depends on whether the generation of the target information is based on AI.

[0379] As an embodiment, the interpretation of the first higher-layer parameter depends on whether the generation of the target information is based on AI, including: the specific meaning of the first higher-layer parameter depends on whether the generation of the target information is based on AI.

[0380] As an embodiment, the interpretation of the first higher-layer parameter depends on whether the generation of the target information is based on AI, including: the operation corresponding to the first higher-layer parameter depends on whether the generation of the target information is based on AI.

[0381] As an embodiment, the interpretation of the first higher-layer parameter depending on whether the generation of the target information is based on AI includes: the interpretation of the first higher-layer parameter for determining the first opportunity set depending on whether the generation of the target information is based on AI.

[0382] As an embodiment, the interpretation of the first higher-layer parameter depends on whether the generation of the target information is based on AI, including: the specific meaning of the first higher-layer parameter for determining the first opportunity set depends on whether the generation of the target information is based on AI.

[0383] As an embodiment, the interpretation of the first higher-layer parameter depends on whether the generation of the target information is based on AI, including: the method for determining the first opportunity set for the first higher-layer parameter depends on whether the generation of the target information is based on AI.

[0384] As an embodiment, the interpretation of the first higher-layer parameter depends on whether the generation of the target information is based on AI, including: the interpretation of a candidate value of the first higher-layer parameter depends on whether the generation of the target information is based on AI.

[0385] As an embodiment, the interpretation of the first higher-layer parameter depends on whether the generation of the target information is based on AI, including: the candidate value range of the first higher-layer parameter depends on whether the generation of the target information is based on AI.

[0386] As an embodiment, the interpretation of the first higher-layer parameter depends on whether the generation of the target information is based on AI, including: the candidate value range for the first higher-layer parameter includes a reference candidate value, and the interpretation of the reference candidate value depends on whether the generation of the target information is based on AI.

[0387] As an embodiment, the interpretation of the first higher-layer parameter depends on whether the generation of the target information is based on AI, including: when the value of the first higher-layer parameter is a first candidate value, the interpretation of the first candidate value depends on whether the generation of the target information is based on AI.

[0388] As an embodiment, the essence of the above method includes: for an AI-based solution and a non-AI-based solution, the value of the same first higher-layer parameter represents different meanings, and the transmission timings included in the first timing set determined thereby are different.

[0389] As an embodiment, the essence of the above method includes: determining the transmission timing included in the first timing set through different interpretations of the value of the first higher-layer parameter under an AI-based scheme and a non-AI-based scheme.

[0390] As an embodiment, the essence of the above method includes: jointly determining the transmission timing included in the first timing set by the value of the first higher-layer parameter and whether the generation of the target information is based on AI.

[0391] As an embodiment, the benefits of the above method include: little modification to existing systems and standards.

[0392] As an embodiment, the benefits of the above method include: enhancing the performance of the AI ​​solution and improving the accuracy of information reporting.

[0393] As an embodiment, the benefits of the above method include: improving the flexibility and overall performance of the system.

[0394] As an embodiment, the first opportunity set depends on whether the generation of the target information is based on AI.

[0395] As an embodiment, the benefits of the above method include: configuring a dedicated first opportunity set for the AI-based solution to improve the performance of the AI-based solution.

[0396] As an embodiment, the first opportunity set depends on whether the generation of the target information is based on AI, including: the first opportunity set includes which transmission opportunities in the first resource set that depend on whether the generation of the target information is based on AI.

[0397] As an embodiment, the first opportunity set depends on whether the generation of the target information is based on AI, including: the first opportunity set includes that the number of transmission opportunities in the first resource set depends on whether the generation of the target information is based on AI.

[0398] As an embodiment, the benefits of the above method include: simplifying system design and enhancing the degree of freedom of the system.

[0399] As an embodiment, the first timing set depends on whether the generation of the target information is based on AI, including: whether the first timing set includes part of the RS resources in the first resource set or the transmission timing of all RS resources depends on whether the generation of the target information is based on AI.

[0400] As an embodiment, the first timing set depends on whether the generation of the target information is based on AI, including: the first timing set includes an RS resource in the first resource set or the transmission timing of each RS resource depends on whether the generation of the target information is based on AI.

[0401] As an embodiment, the first timing set depends on whether the generation of the target information is based on AI, including: whether the first timing set must include the transmission timing of each RS resource in the first resource set depends on whether the generation of the target information is based on AI.

[0402] As an embodiment, the first opportunity set depends on a candidate value range of the first higher layer parameter;

[0403] As an embodiment, the first opportunity set depends on a candidate value range of the first higher-layer parameter; the candidate value range of the first higher-layer parameter depends on whether the generation of the target information is based on AI.

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

[0405] As an embodiment, the first opportunity set depends on whether the generation of the first higher layer parameter and the target information is based on AI.

[0406] As an embodiment, the first opportunity set depends on whether the interpretation of the first higher-layer parameter and the generation of the target information are based on AI.

[0407] As an embodiment, the first opportunity set depends on a candidate value range of the first higher-layer parameter and whether generation of the target information is based on AI.

[0408] As an embodiment, the essence of the above method includes: jointly determining the first opportunity set by determining whether the first higher-layer parameter and the generation of the target information are based on AI.

[0409] As an embodiment, the benefits of the above method include: more refined determination of the first opportunity set, adaptation to more different transmission conditions and scenarios, and enhancement of overall system performance.

[0410] As an embodiment, the benefits of the above method include: enhancing the reliability and robustness of the system.

[0411] As an embodiment, the benefits of the above method include: enhancing the flexibility of the system.

[0412] Example 2

[0413] Embodiment 2 illustrates a schematic diagram of a network architecture according to an embodiment of the present application, as shown in the attached Figure 2 shown.

[0414] Attached Figure 2 The network architecture 200 of LTE (Long-Term Evolution), LTE-A (Long-Term Evolution Advanced) and future 5G systems is illustrated. The network architecture 200 of LTE, LTE-A and future 5G systems is called EPS (Evolved Packet System) 200. The 5G NR or LTE network architecture 200 may be referred to as 5GS (5G System) / EPS (Evolved Packet System) 200 or some other suitable terminology. 5GS / EPS200 may include one or more UEs (User Equipment) 201, a UE 241 communicating with UE 201 via a sidelink, NG-RAN (Next Generation Radio Access Network) 202, 5GC (5G Core Network) / EPC (Evolved Packet Core) 210, HSS (Home Subscriber Server) / UDM (Unified Data Management) 220, and Internet service 230. 5GS / EPS200 may be interconnected with other access networks, but these entities / interfaces are not shown for simplicity. Figure 2As shown, 5GS / EPS200 provides packet switching services, however, those skilled in the art will readily appreciate that the various concepts presented throughout this application can be extended to networks that provide circuit switching services. NG-RAN202 includes NR (New Radio) Node B (gNB) 203 and other gNBs204. gNB203 provides user and control plane protocol terminations toward UE201. gNB203 can be connected to other gNBs204 via an Xn interface (e.g., backhaul). gNB203 may also be referred to as a base station, a base transceiver station, a radio base station, a radio transceiver, a transceiver function, a basic service set (BSS), an extended service set (ESS), a TRP (transmit receive point), or some other suitable term. gNB203 provides an access point to 5GC / EPC210 for UE201. Examples of UE 201 include cellular phones, smart phones, session initiation protocol (SIP) phones, laptop computers, personal digital assistants (PDAs), satellite radios, global positioning systems, multimedia devices, video devices, digital audio players (e.g., MP3 players), cameras, game consoles, drones, aircraft, narrowband physical network devices, machine type communication devices, land vehicles, cars, wearable devices, or any other similar functional devices. Those skilled in the art may also refer to UE 201 as a mobile station, a subscriber station, a mobile unit, a subscriber unit, a wireless unit, a remote unit, a mobile device, a wireless device, a wireless communication device, a remote device, a mobile subscriber station, an access terminal, a mobile terminal, a wireless terminal, a remote terminal, a handset, a user agent, a mobile client, a client, or some other suitable term. gNB203 is connected to 5GC / EPC210 via S1 / NG interface. 5GC / EPC210 includes MME (Mobility Management Entity) / AMF (Authentication Management Field) / SMF (Session Management Function) 211, other MME / AMF / SMF214, S-GW (Service Gateway) / UPF (User Plane Function) 212, and P-GW (Packet Data Network Gateway) / UPF213. MME / AMF / SMF211 is a control node that handles signaling between UE201 and 5GC / EPC210. ​​In general, MME / AMF / SMF211 provides bearer and connection management.All user IP (Internet Protocol) packets are transmitted through S-GW / UPF212, which is itself connected to P-GW / UPF213. P-GW provides UE IP address allocation and other functions. P-GW / UPF213 is connected to Internet service 230. Internet service 230 includes operator-corresponding Internet protocol services, which may specifically include Internet, Intranet, IMS (IP Multimedia Subsystem) and Packet switching services.

[0415] As an embodiment, the first node in the present application includes the UE201.

[0416] As an embodiment, the second node in the present application includes the gNB203.

[0417] As an embodiment, the UE 201 includes a mobile phone.

[0418] As an embodiment, the UE 201 includes a vehicle such as a car.

[0419] As an embodiment, the gNB203 is a macro cell base station.

[0420] As an embodiment, the gNB203 is a micro cell base station.

[0421] As an embodiment, the gNB203 is a pico cell base station.

[0422] As an embodiment, the gNB203 is a home base station (Femtocell).

[0423] As an embodiment, the gNB203 is a base station device that supports large delay difference.

[0424] As an embodiment, the gNB203 is a flying platform device.

[0425] As an embodiment, the gNB203 is a satellite device.

[0426] As an embodiment, the gNB203 is a testing device (e.g., a transceiver that simulates some functions of a base station, a signaling tester).

[0427] As an embodiment, the wireless link from the UE 201 to the gNB 203 is an uplink, and the uplink is used to perform uplink transmission.

[0428] As an embodiment, the wireless link from the gNB 203 to the UE 201 is a downlink, and the downlink is used to perform downlink transmission.

[0429] As an embodiment, the wireless link between the UE 201 and the gNB 203 includes a cellular network link.

[0430] As an embodiment, the UE 201 and the gNB 203 are connected via a Uu air interface.

[0431] As an embodiment, the sender of the first CSI reporting configuration includes the gNB203.

[0432] As an embodiment, the receiver of the first CSI reporting configuration includes the UE 201.

[0433] As an embodiment, the sender of the first RS resources includes the gNB203.

[0434] As an embodiment, the recipient of the first RS resource includes the UE201.

[0435] As an embodiment, the sender of the target information sent in the first time-frequency resource block includes the UE 201.

[0436] As an embodiment, the recipient of the target information sent in the first time-frequency resource block includes the gNB203.

[0437] As an embodiment, the sender of the first time-frequency resource block includes the UE 201.

[0438] As an embodiment, the receiver of the first time-frequency resource block includes the gNB203.

[0439] As an embodiment, the UE 201 supports the 6G system.

[0440] As an embodiment, the gNB203 supports the 6G system.

[0441] As an embodiment, the UE 201 at least supports the 5G system.

[0442] As an embodiment, the gNB203 at least supports the 5G system.

[0443] As an embodiment, the UE 201 supports AI.

[0444] As an embodiment, the gNB203 supports AI.

[0445] Example 3

[0446] Embodiment 3 illustrates a schematic diagram of an embodiment of a wireless protocol architecture of a user plane and a control plane according to an embodiment of the present application, as shown in the attached figure. Figure 3 shown.

[0447] Embodiment 3 shows a schematic diagram of an embodiment of a wireless protocol architecture of a user plane and a control plane according to the present application, as shown in the attached Figure 3 shown. Figure 3 is a schematic diagram illustrating an embodiment of a radio protocol architecture for a user plane 350 and a control plane 300, Figure 3The radio protocol architecture of the control plane 300 for a first communication node device (UE, gNB or RSU in V2X) and a second communication node device (gNB, UE or RSU in V2X), or between two UEs is shown in three layers: Layer 1, Layer 2 and Layer 3. Layer 1 (L1 layer) is the lowest layer and implements various PHY (physical layer) signal processing functions. The L1 layer will be referred to as PHY301 in this article. Layer 2 (L2 layer) 305 is above PHY301 and is responsible for the link between the first communication node device and the second communication node device, or between two UEs. The L2 layer 305 includes a MAC (Medium Access Control) sublayer 302, an RLC (Radio Link Control) sublayer 303, and a PDCP (Packet Data Convergence Protocol) sublayer 304, which terminate at the second communication node device. The PDCP sublayer 304 provides multiplexing between different radio bearers and logical channels. The PDCP sublayer 304 also provides security by encrypting data packets, and provides support for inter-zone mobility of the first communication node device between the second communication node device. The RLC sublayer 303 provides segmentation and reassembly of upper layer data packets, retransmission of lost data packets, and reordering of data packets to compensate for out-of-order reception due to HARQ. The MAC sublayer 302 provides multiplexing between logical and transport channels. The MAC sublayer 302 is also responsible for allocating various radio resources (e.g., resource blocks) in a cell between the first communication node devices. The MAC sublayer 302 is also responsible for HARQ operations. The RRC (Radio Resource Control) sublayer 306 in layer 3 (L3 layer) in the control plane 300 is responsible for obtaining radio resources (i.e., radio bearers) and configuring the lower layer using RRC signaling between the second communication node device and the first communication node device. The radio protocol architecture of the user plane 350 includes layer 1 (L1 layer) and layer 2 (L2 layer). The radio protocol architecture used for the first communication node device and the second communication node device in the user plane 350 is substantially the same as the corresponding layers and sublayers in the control plane 300 for the physical layer 351, the PDCP sublayer 354 in the L2 layer 355, the RLC sublayer 353 in the L2 layer 355, and the MAC sublayer 352 in the L2 layer 355, but the PDCP sublayer 354 also provides header compression for upper layer data packets to reduce radio transmission overhead.The L2 layer 355 in the user plane 350 also includes a SDAP (Service Data Adaptation Protocol) sublayer 356, which is responsible for mapping between QoS flows and data radio bearers (DRBs) to support the diversity of services. Although not shown in the figure, the first communication node device may have several upper layers above the L2 layer 355, including a network layer (e.g., an IP layer) terminated at the P-GW on the network side and an application layer terminated at the other end of the connection (e.g., a remote UE, a server, etc.).

[0448] As an example, Figure 3 The wireless protocol architecture in is applicable to the first node in this application.

[0449] As an example, Figure 3 The wireless protocol architecture in is applicable to the second node in this application.

[0450] As an embodiment, the higher layer in the present application refers to a layer above the physical layer.

[0451] As an embodiment, the first CSI reporting configuration is generated in the RRC306.

[0452] As an embodiment, the reference signal in the first RS resource is generated in the PHY301 or the PHY351.

[0453] As an embodiment, the target information is generated by the PHY301 or the PHY351.

[0454] As an embodiment, the target information is generated by the MAC302 or the MAC352.

[0455] Example 4

[0456] Embodiment 4 illustrates a schematic diagram of a first communication device and a second communication device according to an embodiment of the present application, as shown in the attached figure. Figure 4 Attached Figure 4 is a block diagram of a first communication device 410 and a second communication device 450 communicating with each other in an access network.

[0457] The first communication device 410 includes a controller / processor 475 , a memory 476 , a receive processor 470 , a transmit processor 416 , a multi-antenna receive processor 472 , a multi-antenna transmit processor 471 , a transmitter / receiver 418 and an antenna 420 .

[0458] The second communication device 450 includes a controller / processor 459, a memory 460, a data source 467, a transmit processor 468, a receive processor 456, a multi-antenna transmit processor 457, a multi-antenna receive processor 458, a transmitter / receiver 454 and an antenna 452.

[0459] In transmission from the first communication device 410 to the second communication device 450, at the first communication device 410, upper layer data packets from the core network are provided to the controller / processor 475. The controller / processor 475 implements the functionality of the L2 layer. In the DL, the controller / processor 475 provides header compression, encryption, packet segmentation and reordering, multiplexing between logical and transport channels, and allocation of radio resources to the second communication device 450 based on various priority metrics. The controller / processor 475 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the second communication device 450. The transmit processor 416 and the multi-antenna transmit processor 471 implement various signal processing functions for the L1 layer (i.e., the physical layer). The transmit processor 416 implements coding and interleaving to facilitate forward error correction (FEC) at the second communication device 450, as well as constellation mapping based on various modulation schemes (e.g., binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), M-phase shift keying (M-PSK), M-quadrature amplitude modulation (M-QAM)). The multi-antenna transmit processor 471 performs digital spatial precoding, including codebook-based precoding and non-codebook-based precoding, and beamforming processing on the coded and modulated symbols to generate one or more parallel streams. The transmit processor 416 then maps each parallel stream to a subcarrier, multiplexes the modulated symbols with a reference signal (e.g., a pilot) in the time domain and / or frequency domain, and then uses an inverse fast Fourier transform (IFFT) to generate a physical channel carrying a time-domain multi-carrier symbol stream. The multi-antenna transmit processor 471 then performs a transmit analog precoding / beamforming operation on the time-domain multi-carrier symbol stream. Each transmitter 418 converts the baseband multi-carrier symbol stream provided by the multi-antenna transmit processor 471 into a radio frequency stream, and then provides it to a different antenna 420.

[0460] In the transmission from the first communication device 410 to the second communication device 450, at the second communication device 450, each receiver 454 receives a signal through its corresponding antenna 452. Each receiver 454 recovers the information modulated onto the RF carrier and converts the RF stream into a baseband multi-carrier symbol stream and provides it to the receiving processor 456. The receiving processor 456 and the multi-antenna receiving processor 458 implement various signal processing functions of the L1 layer. The multi-antenna receiving processor 458 performs a receiving analog precoding / beamforming operation on the baseband multi-carrier symbol stream from the receiver 454. The receiving processor 456 uses a fast Fourier transform (FFT) to convert the baseband multi-carrier symbol stream after the receiving analog precoding / beamforming operation from the time domain to the frequency domain. In the frequency domain, the physical layer data signal and the reference signal are demultiplexed by the receiving processor 456, wherein the reference signal will be used for channel estimation, and the data signal is recovered after multi-antenna detection in the multi-antenna receiving processor 458 to any parallel stream destined for the second communication device 450. The symbols on each parallel stream are demodulated and recovered in the receiving processor 456, and soft decisions are generated. The receiving processor 456 then decodes and deinterleaves the soft decisions to recover the upper layer data and control signals transmitted by the first communication device 410 on the physical channel. The upper layer data and control signals are then provided to the controller / processor 459. The controller / processor 459 implements the functions of the L2 layer. The controller / processor 459 may be associated with a memory 460 storing program codes and data. The memory 460 may be referred to as a computer-readable medium. In DL (DownLink, downlink), the controller / processor 459 provides multiplexing, packet reassembly, decryption, header decompression, and control signal processing between the transmission and logical channels to recover the upper layer data packets from the core network. The upper layer data packets are then provided to all protocol layers above the L2 layer. Various control signals may also be provided to L3 for L3 processing. The controller / processor 459 is also responsible for error detection using confirmation (ACK) and / or negative confirmation (NACK) protocols to support HARQ operations.

[0461] In the transmission from the second communication device 450 to the first communication device 410, at the second communication device 450, a data source 467 is used to provide upper layer data packets to the controller / processor 459. The data source 467 represents all protocol layers above the L2 layer. Similar to the transmission function at the first communication device 410 described in DL, the controller / processor 459 implements header compression, encryption, packet segmentation and reordering, and multiplexing between logical and transport channels based on the radio resource allocation of the first communication device 410, and implements L2 layer functions for the user plane and the control plane. The controller / processor 459 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the first communication device 410. The transmit processor 468 performs modulation mapping and channel coding processing, and the multi-antenna transmit processor 457 performs digital multi-antenna spatial precoding, including codebook-based precoding and non-codebook-based precoding, and beamforming processing. Then, the transmit processor 468 modulates the generated parallel stream into a multi-carrier / single-carrier symbol stream, which is then provided to different antennas 452 via the transmitter 454 after analog precoding / beamforming operations in the multi-antenna transmit processor 457. Each transmitter 454 first converts the baseband symbol stream provided by the multi-antenna transmit processor 457 into a radio frequency symbol stream, and then provides it to the antenna 452.

[0462] In the transmission from the second communication device 450 to the first communication device 410, the function at the first communication device 410 is similar to the reception function at the second communication device 450 described in the transmission from the first communication device 410 to the second communication device 450. Each receiver 418 receives a radio frequency signal through its corresponding antenna 420, converts the received radio frequency signal into a baseband signal, and provides the baseband signal to the multi-antenna reception processor 472 and the reception processor 470. The reception processor 470 and the multi-antenna reception processor 472 jointly implement the functions of the L1 layer. The controller / processor 475 implements the L2 layer functions. The controller / processor 475 can be associated with a memory 476 that stores program codes and data. The memory 476 can be referred to as a computer-readable medium. The controller / processor 475 provides demultiplexing between transmission and logical channels, packet reassembly, decryption, header decompression, control signal processing to recover the upper layer data packets from the second communication device 450. The upper layer data packets from the controller / processor 475 can be provided to the core network. The controller / processor 475 is also responsible for error detection using an ACK and / or NACK protocol to support HARQ operations.

[0463] As an embodiment, the second communication device 450 includes: at least one processor and at least one memory, the at least one memory including computer program code; the at least one memory and the computer program code are configured to be used together with the at least one processor. The second communication device 450 device at least: receives a first CSI reporting configuration; the first CSI reporting configuration indicates a first RS resource; sends target information in a first time-frequency resource block; wherein the generation of the target information depends on a measurement based on a first timing set; the first timing set includes at least one transmission timing of the first RS resource that is no later than a reference resource; the first CSI reporting configuration includes a first higher-layer parameter, the first timing set depends on the value of the first higher-layer parameter; the interpretation of the first higher-layer parameter depends on whether the generation of the target information is based on AI.

[0464] As an embodiment, the second communication device 450 includes: a memory storing a computer-readable instruction program, wherein the computer-readable instruction program generates actions when executed by at least one processor, and the actions include: receiving a first CSI reporting configuration; the first CSI reporting configuration indicates a first RS resource; sending target information in a first time-frequency resource block; wherein generation of the target information depends on measurement based on a first timing set; the first timing set includes at least one transmission timing of the first RS resource that is no later than a reference resource; the first CSI reporting configuration includes a first higher-layer parameter, and the first timing set depends on the value of the first higher-layer parameter; and interpretation of the first higher-layer parameter depends on whether generation of the target information is based on AI.

[0465] As an embodiment, the first communication device 410 includes: at least one processor and at least one memory, the at least one memory including computer program code; the at least one memory and the computer program code are configured to be used together with the at least one processor. The first communication device 410 device at least: sends a first CSI reporting configuration; the first CSI reporting configuration indicates a first RS resource; receives target information in a first time-frequency resource block; wherein the generation of the target information depends on a measurement based on a first timing set; the first timing set includes at least one transmission timing of the first RS resource that is no later than a reference resource; the first CSI reporting configuration includes a first higher-layer parameter, the first timing set depends on the value of the first higher-layer parameter; the interpretation of the first higher-layer parameter depends on whether the generation of the target information is based on AI.

[0466] As an embodiment, the first communication device 410 includes: a memory storing a computer-readable instruction program, wherein the computer-readable instruction program generates actions when executed by at least one processor, and the actions include: sending a first CSI reporting configuration; the first CSI reporting configuration indicates a first RS resource; receiving target information in a first time-frequency resource block; wherein generation of the target information depends on measurement based on a first timing set; the first timing set includes at least one transmission timing of the first RS resource that is no later than a reference resource; the first CSI reporting configuration includes a first higher-layer parameter, and the first timing set depends on the value of the first higher-layer parameter; and interpretation of the first higher-layer parameter depends on whether generation of the target information is based on AI.

[0467] As an embodiment, the first node in the present application includes the second communication device 450.

[0468] As an embodiment, the second node in the present application includes the first communication device 410.

[0469] As an embodiment, at least one of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, and the data source 467} is used to receive the first CSI reporting configuration; and at least one of {the antenna 420, the transmitter 418, the transmitting processor 416, the multi-antenna transmitting processor 471, the controller / processor 475, and the memory 476} is used to send the first CSI reporting configuration.

[0470] As an embodiment, at least one of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, and the data source 467} is used to receive the reference signal in the first RS resource; and at least one of {the antenna 420, the transmitter 418, the transmitting processor 416, the multi-antenna transmitting processor 471, the controller / processor 475, and the memory 476} is used to send the reference signal in the first RS resource.

[0471] As an embodiment, at least one of {the antenna 420, the receiver 418, the receiving processor 470, the multi-antenna receiving processor 472, the controller / processor 475, and the memory 476} is used to receive the target information in the first time-frequency resource block; at least one of {the antenna 452, the transmitter 454, the transmitting processor 468, the multi-antenna transmitting processor 457, the controller / processor 459, the memory 460, and the data source 467} is used to send the target information in the first time-frequency resource block.

[0472] Example 5

[0473] Embodiment 5 illustrates a flowchart of wireless transmission according to an embodiment of the present application, as shown in the attached Figure 5 As shown in the attached Figure 5 In the embodiment, the second node U1 and the first node U2 are communication nodes transmitted via the air interface. Figure 5 In the process, the steps in box F51 to box F53 are respectively optional.

[0474] For the second node U1, a first CSI reporting configuration is sent in step S511; a signal is sent in a first RS resource in step S512; and target information is received in a first time-frequency resource block in step S513.

[0475] For the first node U2, a first operation is deployed in step S521; a first CSI reporting configuration is received in step S522; a signal is received in a first RS resource in step S523; a first operation is performed in step S524; and target information is sent in a first time-frequency resource block in step S525.

[0476] In Example 5, the first CSI reporting configuration indicates a first RS resource; the generation of the target information depends on measurements based on a first timing set; the first timing set includes at least one transmission timing of the first RS resource that is no later than a reference resource; the first CSI reporting configuration includes a first higher-layer parameter, and the first timing set depends on the value of the first higher-layer parameter; the interpretation of the first higher-layer parameter depends on whether the generation of the target information is based on AI.

[0477] As an embodiment, the first node U2 is the first node in this application.

[0478] As an embodiment, the second node U1 is the second node in this application.

[0479] As an embodiment, the air interface between the second node U1 and the first node U2 includes a wireless interface between a base station device and a user equipment.

[0480] As an embodiment, the air interface between the second node U1 and the first node U2 includes a wireless interface between a relay node device and a user equipment.

[0481] As an embodiment, the air interface between the second node U1 and the first node U2 includes a wireless interface between user equipments.

[0482] As an embodiment, the second node U1 is a base station maintaining a service cell of the first node U2.

[0483] As an example, Figure 5 The steps in box F51 exist.

[0484] As an embodiment, the first node adopts a single side AI model.

[0485] As an embodiment, when the generation of the target information is based on AI, Figure 5 The steps in box F53 exist.

[0486] As an embodiment, when the generation of the target information is based on AI, Figure 5 The step in box F53 exists, and the first node adopts a single side AI model.

[0487] As an embodiment, when the generation of the target information is based on AI, Figure 5 The steps in box F53 exist, the first operation is used for beam prediction, and the first node adopts a single side AI model.

[0488] As an embodiment, sending a signal in the first RS resource means: sending a wireless signal in the first RS resource.

[0489] As an embodiment, sending a signal in the first RS resource means: sending a reference signal in the first RS resource.

[0490] As an embodiment, receiving a signal in the first RS resource means: receiving a wireless signal in the first RS resource.

[0491] As an embodiment, receiving a signal in the first RS resource means: receiving a reference signal in the first RS resource.

[0492] As an embodiment, the deployment of the first operation is earlier than the reception of the first CSI reporting configuration.

[0493] As an embodiment, the deployment of the first operation is later than the reception of the first CSI reporting configuration.

[0494] As an example, Figure 5 The step in box F52 exists; the method in the first node used for wireless communication includes: receiving a signal in the first RS resource set.

[0495] As an example, Figure 5 The step in box F52 exists; the method in the second node used for wireless communication includes: sending a signal in the first RS resource set.

[0496] As an embodiment, the first node is a consumer.

[0497] As an embodiment, the first node is a consumer of an AI function.

[0498] As an embodiment, the first node is a user of AI inference.

[0499] As an embodiment, the first node is an AI trained user.

[0500] As an embodiment, the first node is a MnS (Management Service) user.

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

[0502] As an embodiment, the first node is a producer of AI training.

[0503] As an embodiment, the input of the first operation depends on measurements based on the first opportunity set.

[0504] As an embodiment, the generation of the target information is based on AI, and the input of the first operation depends on the measurement based on the first opportunity set.

[0505] As an embodiment, the input of the first operation depends on the measurement based on the first opportunity set, including: the measurement based on the first opportunity set is used to generate the input of the first operation.

[0506] As an embodiment, the input of the first operation depends on the measurement based on the first opportunity set, including: the channel measurement obtained based on the first opportunity set is used to generate the input of the first operation.

[0507] As an embodiment, the input of the first operation depends on the measurement based on the first opportunity set, including: the interference measurement obtained based on the first opportunity set is used to generate the input of the first operation.

[0508] As an embodiment, the input of the first operation depends on the measurement based on the first opportunity set, including: channel measurement and interference measurement obtained based on the first opportunity set are used to generate the input of the first operation.

[0509] As an embodiment, the target information depends on the output of the first operation.

[0510] As an embodiment, the generation of the target information is based on AI, and the target information depends on the output of the first operation.

[0511] As an embodiment, the target information being dependent on the output of the first operation includes: the target information includes the output of the first operation.

[0512] As an embodiment, the target information being dependent on the output of the first operation includes: the target information includes the post-processed output of the first operation.

[0513] As an embodiment, the target information being dependent on the output of the first operation includes: the target information includes a truncated and / or quantized output of the first operation.

[0514] As an embodiment, the target information being dependent on the output of the first operation includes: the output of the first operation being used to generate the target information.

[0515] As an embodiment, the target information being dependent on the output of the first operation includes: the output of the first operation is used to generate the target information after post-processing.

[0516] As an embodiment, the target information being dependent on the output of the first operation includes: the output of the first operation being truncated and / or quantized and then used to generate the target information.

[0517] As an embodiment, the target information being dependent on the output of the first operation includes: part or all of the output of the first operation is used to generate the target information after post-processing.

[0518] As an embodiment, the target information being dependent on the output of the first operation includes: part or all of the output of the first operation being truncated and / or quantized and then used to generate the target information.

[0519] As an embodiment, the input of the first operation depends on the measurement based on the first opportunity set, and the target information depends on the output of the first operation.

[0520] As an embodiment, the generation of the target information includes performing a first operation, an input of the first operation depends on a measurement based on the first opportunity set, and the target information depends on an output of the first operation.

[0521] As an embodiment, the benefits of the above method include: supporting AI-based solutions and improving the accuracy and real-time nature of information reporting.

[0522] As an embodiment, the benefits of the above method include: improving the overall performance of the system.

[0523] As an embodiment, the generation of the target information is not based on AI, and the target information includes N information blocks, and the N information blocks respectively include channel information of N time units, where N is a positive integer greater than 1; the generation of any information block among the N information blocks depends on the measurement based on the first opportunity set.

[0524] As an embodiment, the generation of the target information is based on AI, and the target information includes N information blocks, and the N information blocks respectively include channel information of N time units, where N is a positive integer greater than 1; the generation of any information block among the N information blocks depends on the measurement based on the first opportunity set.

[0525] As an embodiment, the generation of the target information is not based on AI, and the target information includes N information blocks, and the N information blocks respectively include channel information of N time units, where N is a positive integer greater than 1; the generation of the N information blocks respectively depends on measurements based on N opportunity sets, and the first opportunity set is one of the N opportunity sets. The generation of the first information block depends on measurements based on the first opportunity set, and the first information block is one of the N information blocks.

[0526] As an embodiment, the generation of the target information is based on AI, and the target information includes N information blocks, and the N information blocks respectively include channel information of N time units, where N is a positive integer greater than 1; the generation of the N information blocks respectively depends on measurements based on N opportunity sets, and the first opportunity set is one of the N opportunity sets. The generation of the first information block depends on measurements based on the first opportunity set, and the first information block is one of the N information blocks.

[0527] As an embodiment, the N information blocks respectively include predicted channel information of N time units.

[0528] As an embodiment, the N information blocks respectively include predicted beam information of N time units.

[0529] As an embodiment, any information block among the N information blocks indicates at least one resource in the first resource set.

[0530] As an embodiment, the essence of the above method includes: supporting joint reporting of information of multiple time units.

[0531] As an embodiment, the essence of the above method includes: supporting an AI-based channel information prediction scheme.

[0532] As an embodiment, the benefits of the above method include: reducing system overhead and enhancing the transmission efficiency of the system.

[0533] As an embodiment, the benefits of the above method include: improving the accuracy and real-time performance of channel information and improving the overall performance of the system.

[0534] As an embodiment, the first CSI reporting configuration indicates a first resource set, the first resource set includes at least one RS resource, and the first RS resource is an RS resource in the first resource set.

[0535] As an embodiment, the resources in the first resource set include at least one of antenna ports, TCI (Transmission Configuration Indication) status, QCL (Quasi Co-Location) information, time-frequency resources, time-frequency code resources, beams, RS resources, vectors, or matrices.

[0536] As an embodiment, the first resource set includes one or more RS (Reference Signal) resource sets, and an RS resource set includes one or more RS resources.

[0537] As an embodiment, the first resource set includes at least one of at least one CSI-RS resource set, at least one CSI-SSB (Channel State Information-Synchronization Signal Block) resource set, or at least one CSI-IM (Channel State Information-Interference Measurement) resource set.

[0538] As an embodiment, the first resource set includes one or more RS resources, and any RS resource in the first RS resource set is a CSI-RS (Channel State Information Reference Signal) resource or a synchronization signal resource.

[0539] As an embodiment, the target information indicates at least one RS resource in the first resource set.

[0540] As an embodiment, the target information indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

[0541] As an embodiment, the target information is generated based on AI, and the target information indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

[0542] As an embodiment, whether the resources indicated by the target information belong to the first resource set depends on whether the generation of the target information is based on AI; when the generation of the target information is based on AI, the resources indicated by the target information belong to the second resource set, and the second resource set includes resources that do not belong to the first resource set; when the generation of the target information is not based on AI, the resources indicated by the target information belong to the first resource set.

[0543] As an embodiment, whether the RS resources indicated by the target information belong to the first resource set depends on whether the generation of the target information is based on AI; when the generation of the target information is based on AI, the resources indicated by the target information belong to the second resource set, and the second resource set includes resources that do not belong to the first resource set; only when the generation of the target information is not based on AI, the resources indicated by the target information belong to the first resource set.

[0544] As an embodiment, the benefits of the above method include: supporting AI-based solutions and improving system flexibility while being compatible with existing systems and standards.

[0545] As an embodiment, the advantages of the above method include: little modification to existing systems and standards.

[0546] As an embodiment, the target information includes N information blocks, and the N information blocks respectively include channel information of N time units, where N is a positive integer greater than 1; any one of the N information blocks indicates at least one RS resource in the first resource set.

[0547] As an embodiment, the target information includes N information blocks, and the N information blocks respectively include channel information of N time units, where N is a positive integer greater than 1; any one of the N information blocks indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

[0548] As an embodiment, the target information is generated based on AI, and the target information includes N information blocks, and the N information blocks respectively include channel information of N time units, where N is a positive integer greater than 1; any one of the N information blocks indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

[0549] As an embodiment, the number of information blocks included in the target information is equal to the N.

[0550] As an embodiment, the number of information blocks included in the target information is greater than the N.

[0551] As an embodiment, the target information includes multiple information blocks, and the number of information blocks included in the target information is not less than the N.

[0552] As an embodiment, the target information includes multiple information blocks, and the multiple information blocks included in the target information include the N information blocks.

[0553] As an embodiment, the benefits of the above method include: enhancing the flexibility and robustness of the system.

[0554] As an embodiment, the N time units are mutually orthogonal.

[0555] As an embodiment, the N time units are different from each other.

[0556] As an embodiment, there are two overlapping time units among the N time units.

[0557] As an embodiment, any time unit among the N time units includes a time slot.

[0558] As an embodiment, any time unit among the N time units includes one or more time slots.

[0559] As an embodiment, any time unit among the N time units includes a subframe.

[0560] As an embodiment, any time unit among the N time units includes one or more subframes.

[0561] As an embodiment, any time unit among the N time units includes multiple consecutive symbols.

[0562] As an embodiment, at least one time unit among the N time units is no earlier than the time domain resources occupied by the first time-frequency resource block.

[0563] As an embodiment, at least one time unit among the N time units is later than the time domain resources occupied by the first time-frequency resource block.

[0564] As an embodiment, the N time units are continuous.

[0565] As an embodiment, the N time units are periodic.

[0566] As an embodiment, the N time units are non-continuous.

[0567] As an embodiment, the N time units are equally spaced.

[0568] As an embodiment, the interval between any two adjacent time units among the N time units is P time units, where P is a positive integer.

[0569] As an embodiment, the N time units are not earlier than or later than the reference resource.

[0570] As an embodiment, the N time units are no earlier or later than the time domain resources occupied by the first time-frequency resource block.

[0571] As an embodiment, the N time units are no earlier than or later than the time unit to which the first time-frequency resource block belongs in the time domain.

[0572] As an embodiment, any time unit among the N time units includes a period of time.

[0573] As an embodiment, the durations of the N time units are the same.

[0574] As an embodiment, there are two time units among the N time units whose durations are different.

[0575] As an embodiment, the benefits of the above method include: using existing system designs and standards.

[0576] As an embodiment, the benefits of the above method include: enhancing the flexibility and robustness of the system.

[0577] Example 6

[0578] Embodiment 6 illustrates a schematic diagram of how the interpretation of the first higher-level parameter depends on whether the generation of the target information is based on AI according to an embodiment of the present application; Figure 6 shown.

[0579] In Embodiment 6, when the value of the first higher-layer parameter is a first candidate value, interpretation of the first candidate value depends on whether generation of the target information is based on AI.

[0580] As an embodiment, when the value of the first higher-layer parameter is a first candidate value, the interpretation of the first candidate value depends on whether the generation of the target information is based on AI, including: the same first candidate value has different meanings in AI-based and non-AI-based scenarios.

[0581] As an embodiment, when the value of the first higher-layer parameter is a first candidate value, the interpretation of the first candidate value depends on whether the generation of the target information is based on AI, including: the same first candidate value has different meanings for determining the first timing set in AI-based and non-AI-based scenarios.

[0582] As an embodiment, when the value of the first higher-layer parameter is a first candidate value, the interpretation of the first candidate value depends on whether the generation of the target information is based on AI, including: the same first candidate value has different meanings for the number of transmission opportunities included in the first opportunity set in AI-based and non-AI-based scenarios.

[0583] As an embodiment, when the value of the first higher-layer parameter is a first candidate value, the interpretation of the first candidate value depends on whether the generation of the target information is based on AI, including: the same first candidate value has different meanings for the transmission timing included in the first timing set in AI-based and non-AI-based scenarios.

[0584] As an embodiment, when the value of the first higher-layer parameter is a first candidate value, the interpretation of the first candidate value is different in AI-based and non-AI-based scenarios.

[0585] As an embodiment, when the value of the first higher-layer parameter is not the first candidate value, the interpretation of the first candidate value does not depend on whether the generation of the target information is based on AI.

[0586] As an embodiment, when the value of the first higher-layer parameter is not the first candidate value, the interpretation of the first candidate value is consistent in AI-based and non-AI-based scenarios.

[0587] As an embodiment, the first candidate value is Configured.

[0588] As an embodiment, the first candidate value includes one or more characters.

[0589] As an embodiment, the first candidate value is an integer.

[0590] As an embodiment, the first candidate value is a positive integer.

[0591] As an embodiment, the essence of the above method includes: the interpretation of the first candidate value depends on whether the generation of the target information is based on an AI method and is only limited to certain specific cases, and the value of the first higher-level parameter corresponding to the certain specific cases is the first candidate value.

[0592] As an embodiment, the essence of the above method includes: processing AI-based and non-AI-based solutions according to different situations.

[0593] As an embodiment, the benefits of the above method include: simplifying system design and reducing the implementation complexity of the solution.

[0594] As an embodiment, the benefits of the above method include: improving the flexibility and robustness of the system.

[0595] As an embodiment, the benefits of the above method include: reducing the impact on existing systems and standards.

[0596] As an embodiment, when the value of the first higher layer parameter is a first candidate value, the first opportunity set depends on whether the generation of the target information is based on AI.

[0597] As an embodiment, when the value of the first higher layer parameter is a first candidate value, the transmission timings included in the first timing set depend on whether the generation of the target information is based on AI.

[0598] As an embodiment, when the value of the first higher layer parameter is a first candidate value, the number of transmission opportunities included in the first opportunity set depends on whether the generation of the target information is based on AI.

[0599] As an embodiment, when the value of the first higher-layer parameter is a first candidate value and the generation of the target information is based on AI, the first timing set includes at least one transmission timing of at least one RS resource in the first resource set; when the value of the first higher-layer parameter is a first candidate value and the generation of the target information is not based on AI, the first timing set includes at least one transmission timing of each RS resource in the first resource set.

[0600] As an embodiment, when the value of the first higher-layer parameter is a first candidate value and the generation of the target information is based on AI, the first timing set includes at least one transmission timing of at least one RS resource in the first resource set that is no later than a reference resource; when the value of the first higher-layer parameter is a first candidate value and the generation of the target information is not based on AI, the first timing set includes the most recent transmission timing of each RS resource in the first resource set that is no later than the reference resource.

[0601] As an embodiment, when the value of the first higher-layer parameter is a first candidate value and the generation of the target information is based on AI, the first timing set includes at least one transmission timing of at least one RS resource in the first resource set that is no later than a reference resource; when the value of the first higher-layer parameter is a first candidate value and the generation of the target information is not based on AI, the first timing set includes all transmission timings of each RS resource in the first resource set that are no later than the reference resource.

[0602] As an embodiment, when the value of the first higher-layer parameter is a first candidate value and the generation of the target information is based on AI, the first timing set includes at least one transmission timing of at least one RS resource in the first resource set that is no later than the most recent transmission timing of the target information; when the value of the first higher-layer parameter is a first candidate value and the generation of the target information is not based on AI, the first timing set includes each RS resource in the first resource set that is no later than the most recent transmission timing of the target information.

[0603] As an embodiment, when the value of the first higher-layer parameter is a first candidate value and the generation of the target information is based on AI, the first timing set includes at least one transmission opportunity of at least one RS resource in the first resource set that is no later than the first time-frequency resource block; when the value of the first higher-layer parameter is a first candidate value and the generation of the target information is not based on AI, the first timing set includes the most recent transmission opportunity of each RS resource in the first resource set that is no later than the first time-frequency resource block.

[0604] As an embodiment, the benefits of the above method include: better adaptability to various application scenarios and good flexibility.

[0605] As an embodiment, the benefits of the above method include: improving the accuracy and real-time performance of channel information reporting.

[0606] Example 7

[0607] Embodiment 7 illustrates a schematic diagram of how the interpretation of the first higher-level parameter depends on whether the generation of the target information is based on AI according to another embodiment of the present application; Figure 7 As shown in the attached Figure 7 In the figure, transmission timing #1, ..., transmission timing #n, transmission timing #n+1, ..., transmission timing #m, transmission timing #m+1, ..., represent multiple transmission timings.

[0608] In Example 7, when the value of the first higher-layer parameter is the first candidate value and the generation of the target information is based on AI, the first timing set includes the M most recent transmission opportunities of the first RS resource that are no later than the reference resource, and M is a positive integer greater than 1; when the value of the first higher-layer parameter is the first candidate value and the generation of the target information is not based on AI, the first timing set includes the most recent transmission opportunity of the first RS resource that is no later than the reference resource.

[0609] As an embodiment, the first candidate value is Configured.

[0610] As an embodiment, the first candidate value includes one or more characters.

[0611] As an embodiment, the first candidate value is an integer.

[0612] As an embodiment, the M is predefined.

[0613] As an embodiment, the M is configurable.

[0614] As an embodiment, the first CSI configuration information indicates the M.

[0615] As an embodiment, a second higher layer parameter indicates said M, and said second higher layer parameter is different from said first higher layer parameter.

[0616] As an embodiment, a second higher layer parameter indicates the M, the first CSI configuration information includes the second higher layer parameter, and the second higher layer parameter is different from the first higher layer parameter.

[0617] As an embodiment, a second higher layer parameter indicates the M, and the second higher layer parameter does not belong to the first CSI configuration information.

[0618] As an embodiment, the benefits of the above method include: enhancing the flexibility of the system.

[0619] As an embodiment, the M is reported by the first node.

[0620] As an embodiment, the benefits of the above method include: reducing the complexity of system design.

[0621] As an embodiment, the M is indicated by a capability report of the first node.

[0622] As an embodiment, the benefits of the above method include: taking UE capability factors into consideration, enhancing system robustness, and improving UE and system performance.

[0623] As an embodiment, said M is equal to the value of said first higher layer parameter.

[0624] As an embodiment, the benefits of the above method include: simplifying system design and reducing the complexity of system implementation.

[0625] As an embodiment, the benefits of the above method include: improving the flexibility of the system.

[0626] Example 8

[0627] Embodiment 8 A schematic diagram showing whether the interpretation of the first higher-level parameter depends on whether the generation of the target information is based on AI according to another embodiment of the present application; as shown in the attached Figure 8 shown.

[0628] In Example 8, when the generation of the target information is based on AI, the candidate value range of the first higher-level parameter is a first candidate value range; when the generation of the target information is not based on AI, the candidate value range of the first higher-level parameter is a second candidate value range; and one candidate value in the first candidate value range does not belong to the second candidate value range.

[0629] As an embodiment, the number of candidate values ​​included in the first candidate value range is the same as the number of candidate values ​​included in the second candidate value range.

[0630] As an embodiment, the number of candidate values ​​included in the first candidate value range is different from the number of candidate values ​​included in the second candidate value range.

[0631] As an embodiment, the number of candidate values ​​included in the first candidate value range is 1, and the number of candidate values ​​included in the second candidate value range is 2.

[0632] As an embodiment, the number of candidate values ​​included in the first candidate value range is 3, and the number of candidate values ​​included in the second candidate value range is 2.

[0633] As an embodiment, the benefits of the above method include: little modification to existing systems and standards.

[0634] As an embodiment, the number of candidate values ​​included in the first candidate value range is the same as the number of candidate values ​​included in the second candidate value range, and one candidate value in the first candidate value range does not belong to the second candidate value range.

[0635] As an embodiment, the number of candidate values ​​included in the first candidate value range and the number of candidate values ​​included in the second candidate value range are both 2, and one candidate value in the first candidate value range does not belong to the second candidate value range.

[0636] As an embodiment, the benefits of the above method include: enhancing the flexibility of the system.

[0637] As an embodiment, the first candidate value range and the second candidate value range do not overlap.

[0638] As an embodiment, the number of candidate values ​​included in the first candidate value range is different from the number of candidate values ​​included in the second candidate value range, and the first candidate value range and the second candidate value range do not overlap.

[0639] As an embodiment, the number of candidate values ​​included in the first candidate value range is the same as the number of candidate values ​​included in the second candidate value range, and the first candidate value range and the second candidate value range do not overlap.

[0640] As an embodiment, there is at least one candidate value that belongs to both the first candidate value range and the second candidate value range.

[0641] As an embodiment, the second candidate value range belongs to the first candidate value range.

[0642] As an embodiment, the first candidate value range includes the second candidate value range.

[0643] As an embodiment, the first candidate value range includes at least one candidate value that does not belong to the second candidate value range.

[0644] As an embodiment, the first candidate value range includes the second candidate value range, and the first candidate value range includes at least one candidate value that does not belong to the second candidate value range.

[0645] As an embodiment, the second candidate value range only includes notConfigured.

[0646] As an embodiment, the second candidate value range only includes Configured.

[0647] As an embodiment, the second candidate value range consists of two candidate values: notConfigured and Configured.

[0648] As an embodiment, the benefits of the above method include: using existing system designs and standards.

[0649] As an embodiment, the first candidate value range includes a reference candidate value, and the reference candidate value is different from notConfigured and Configured.

[0650] As an embodiment, the first candidate value range includes at least one integer.

[0651] As an embodiment, the first candidate value range includes at least one positive integer.

[0652] As an embodiment, the benefits of the above method include: supporting AI-based solutions and improving the accuracy and reliability of information reporting.

[0653] As an embodiment, the benefits of the above method include: simplifying system design and reducing implementation complexity.

[0654] As an embodiment, the first candidate value range includes notConfigured; the second candidate value range includes notConfigured.

[0655] As an embodiment, the first candidate value range includes Configured; the second candidate value range includes Configured.

[0656] As an embodiment, the first candidate value range includes notConfigured and Configured; the second candidate value range includes notConfigured and Configured.

[0657] As an embodiment, the benefits of the above method include: enhancing the consistency of the system.

[0658] Example 9

[0659] Embodiment 9 illustrates a schematic diagram of a first candidate value range and a second candidate value range according to an embodiment of the present application; Fig. 9 shown.

[0660] In Embodiment 9, the second candidate value range consists of two candidate values, notConfigured and Configured; the first candidate value range includes a reference candidate value, and the reference candidate value is different from notConfigured and Configured.

[0661] As an embodiment, the first candidate value range is a reference candidate value, and the reference candidate value is different from notConfigured and Configured.

[0662] As an embodiment, the first candidate value range includes notConfigured and reference candidate values.

[0663] As an embodiment, the first candidate value range includes notConfigured, Configured and reference candidate values.

[0664] As an embodiment, the first candidate value range consists of notConfigured and reference candidate values.

[0665] As an embodiment, the first candidate value range consists of notConfigured, Configured and reference candidate values.

[0666] As an embodiment, the first candidate value range does not include Configured.

[0667] As an embodiment, the reference candidate value includes one or more characters.

[0668] As an embodiment, the reference candidate value is an integer.

[0669] As an embodiment, the reference candidate value is a positive integer.

[0670] As an embodiment, the reference candidate value indicates the M.

[0671] As an embodiment, the reference candidate value is predefined.

[0672] As an embodiment, the reference candidate value is configurable.

[0673] As an embodiment, the first CSI configuration information indicates the reference candidate value.

[0674] As an embodiment, a second higher layer parameter indicates the reference candidate value, and the second higher layer parameter is different from the first higher layer parameter.

[0675] As an embodiment, a second higher layer parameter indicates the reference candidate value, the first CSI configuration information includes the second higher layer parameter, and the second higher layer parameter is different from the first higher layer parameter.

[0676] As an embodiment, a second higher layer parameter indicates the reference candidate value, and the second higher layer parameter does not belong to the first CSI configuration information.

[0677] As an embodiment, the benefits of the above method include: enhancing the flexibility of the system.

[0678] As an embodiment, the reference candidate value is reported by the first node.

[0679] As an embodiment, the benefits of the above method include: reducing the complexity of system design.

[0680] As an embodiment, the reference candidate value is indicated by a capability report of the first node.

[0681] As an embodiment, the benefits of the above method include: taking UE capability factors into consideration, enhancing system robustness, and improving UE and system performance.

[0682] Example 10

[0683] Embodiment 10 illustrates a schematic diagram of the relationship between the first timing set and the reference candidate value according to an embodiment of the present application; Fig.10 As shown in the attached Fig.10 In the figure, transmission timing #1, ..., transmission timing #n, transmission timing #n+1, ..., transmission timing #m, transmission timing #m+1, ..., represent multiple transmission timings.

[0684] In embodiment 10, the first candidate value range includes a reference candidate value; when the value of the first higher layer parameter is the reference candidate value and the generation of the target information is based on AI, the first timing set includes the most recent M transmission opportunities of the first RS resource that are no later than the reference resource, and M is a positive integer greater than 1.

[0685] As an embodiment, the second candidate value range does not include a reference candidate value; when the generation of the target information is not based on AI, the value of the first higher layer parameter belongs to the second candidate value range, and the first timing set includes the most recent transmission timing of the first RS resource that is no later than the reference resource.

[0686] As an embodiment, the first candidate value range includes a reference candidate value; when the target information is generated based on AI and the value of the first higher layer parameter is not the reference candidate value, the first timing set includes the most recent transmission timing of the first RS resource that is no later than the reference resource.

[0687] As an embodiment, the M is predefined.

[0688] As an embodiment, the M is configurable.

[0689] As an embodiment, the first CSI configuration information indicates the M.

[0690] As an embodiment, a second higher layer parameter indicates said M, and said second higher layer parameter is different from said first higher layer parameter.

[0691] As an embodiment, a second higher layer parameter indicates the M, the first CSI configuration information includes the second higher layer parameter, and the second higher layer parameter is different from the first higher layer parameter.

[0692] As an embodiment, a second higher layer parameter indicates the M, and the second higher layer parameter does not belong to the first CSI configuration information.

[0693] As an embodiment, the benefits of the above method include: enhancing the flexibility of the system.

[0694] As an embodiment, the M is reported by the first node.

[0695] As an embodiment, the benefits of the above method include: reducing the complexity of system design.

[0696] As an embodiment, the M is indicated by a capability report of the first node.

[0697] As an embodiment, the benefits of the above method include: taking UE capability factors into consideration, enhancing system robustness, and improving UE and system performance.

[0698] As an embodiment, said M is equal to the value of said first higher layer parameter.

[0699] As an embodiment, the benefits of the above method include: simplifying system design and reducing the complexity of system implementation.

[0700] As an embodiment, the benefits of the above method include: improving the flexibility of the system.

[0701] Embodiment 11

[0702] Embodiment 11 illustrates a schematic diagram of a first resource set and a second resource set according to an embodiment of the present application; Fig.11 As shown in FIG. 1 , resource #1, ..., resource #n, ... represent resources in the first resource set; resource #1, ..., resource #m, ... represent resources in the second resource set.

[0703] In Example 11, the first CSI reporting configuration indicates a first resource set, the first resource set includes multiple RS resources, and the first RS resource is an RS resource in the first resource set; when the generation of the target information is based on AI, the target information indicates a resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

[0704] As an embodiment, the second resource set includes the first resource set and resources outside the first resource set.

[0705] As an embodiment, the first resource set includes one or more RS resources, the second resource set includes one or more RS resources, and the second resource set includes the first resource set and RS resources outside the first resource set.

[0706] As an embodiment, the number of resources included in the first resource set is less than the number of resources included in the second resource set.

[0707] As an embodiment, the number of RS resources included in the first resource set is less than the number of RS resources included in the second resource set.

[0708] As an embodiment, the second resource set includes resources that do not belong to the first resource set.

[0709] As an embodiment, the second resource set includes antenna ports that do not belong to the first resource set.

[0710] As an embodiment, the second resource set includes resources that do not belong to the first resource set, and the resources in the second resource set include at least one of antenna ports, TCI status, QCL information, frequency resources, time-frequency code resources, beams, RS resources, vectors, or matrices.

[0711] As an embodiment, the second resource set includes at least one training data set.

[0712] As an embodiment, the second resource set is used to train an AI model.

[0713] As an embodiment, the second resource set includes one or more RS (Reference Signal) resource sets, and one RS resource set includes one or more RS resources.

[0714] As an embodiment, the second resource set includes at least one of at least one CSI-RS resource set, at least one CSI-SSB (Channel State Information-Synchronization Signal Block) resource set, or at least one CSI-IM (Channel State Information-Interference Measurement) resource set.

[0715] As an embodiment, the second resource set includes at least one RS resource set for channel measurement, and an RS resource set for channel measurement includes one or more RS resources.

[0716] As an embodiment, the second resource set includes at least one RS resource set for channel measurement, and at least one RS resource set for interference measurement; an RS resource set for channel measurement includes one or more RS resources, and an RS resource set for interference measurement includes one or more RS resources.

[0717] As an embodiment, the second resource set includes at least one RS resource set for interference measurement; an RS resource set for interference measurement includes one or more RS resources.

[0718] As an embodiment, the second resource set includes one or more RS resources.

[0719] As an embodiment, the second resource set includes one or more downlink RS resources.

[0720] As an embodiment, the second resource set includes one or more RS resources, and any RS resource in the second resource set is a CSI-RS (Channel State Information Reference Signal) resource or a synchronization signal resource.

[0721] As an embodiment, the first CSI reporting configuration indicates at least one resource configuration, and the at least one resource configuration indicates the second resource set.

[0722] As an embodiment, the first CSI reporting configuration includes at least one resource configuration, and the at least one resource configuration indicates the second resource set.

[0723] As an embodiment, the first CSI reporting configuration indicates at least one resource configuration, and the at least one resource configuration indicates the first resource set and the second resource set.

[0724] As an embodiment, the first CSI reporting configuration includes at least one resource configuration, and the at least one resource configuration indicates the first resource set and the second resource set.

[0725] As an embodiment, the first CSI reporting configuration indicates a resource configuration, and the one resource configuration indicates the first resource set and the second resource set.

[0726] As an embodiment, the first CSI reporting configuration indicates two resource configurations, and the two resource configurations respectively indicate the first resource set and the second resource set.

[0727] As an embodiment, the first CSI reporting configuration indicates configuration information of the second resource set.

[0728] As an embodiment, the first CSI reporting configuration indicates an identifier of the second resource set.

[0729] As an embodiment, the first CSI reporting configuration is used to indicate the second resource set from a reference resource set.

[0730] As an embodiment, the first CSI reporting configuration indicates a first identifier, and the second resource set depends on the first identifier.

[0731] As an embodiment, the second resource set being dependent on the first identifier includes: the first identifier is used to identify the second resource set.

[0732] As an embodiment, the second resource set depends on the first identifier, including: the first identifier is used to identify a reference resource set, and the reference resource set includes the second resource set.

[0733] As an embodiment, the second resource set depends on the first identifier, including: the first identifier is used to identify a reference resource set, the reference resource set includes the second resource set, and the first CSI reporting configuration is used to indicate the second resource set from the reference resource set.

[0734] As an embodiment, the information other than the first CSI reporting configuration indicates the second resource set.

[0735] As an embodiment, the information indicating other than the first CSI reporting configuration of the second resource set includes higher layer parameters.

[0736] As an embodiment, the information indicating other than the first CSI reporting configuration of the second resource set includes RRC parameters.

[0737] As an embodiment, the information indicating the second resource set other than the first CSI reporting configuration includes a partial or entire field of an RRC IE.

[0738] As an embodiment, the information other than the first CSI reporting configuration indicating the second resource set includes MAC CE.

[0739] As an embodiment, the information indicating the second resource set other than the first CSI reporting configuration includes DCI (downlink control information).

[0740] As an embodiment, the second resource set includes at least one RS resource; any one of the N information blocks indicates at least one RS resource in the second resource set.

[0741] As an embodiment, the second resource set includes at least one beam; any one of the N information blocks indicates at least one resource in the second resource set, including: any one of the N information blocks indicates at least one beam in the second resource set.

[0742] As an embodiment, the second resource set includes one or more beams; any one of the N information blocks indicates at least one resource in the second resource set, including: any one of the N information blocks indicates at least one beam in the second resource set.

[0743] As an embodiment, the second resource set includes one or more vectors; any one of the N information blocks indicates at least one resource in the second resource set, including: any one of the N information blocks indicates at least one vector in the second resource set.

[0744] As an embodiment, the second resource set includes one or more matrices; any one of the N information blocks indicates at least one resource in the second resource set, including: any one of the N information blocks indicates at least one matrix in the second resource set.

[0745] As an embodiment, the second resource set includes one or more DFT vectors; any one of the N information blocks indicates at least one resource in the second resource set, including: any one of the N information blocks indicates at least one DFT vector in the second resource set.

[0746] As an embodiment, the second resource set includes one or more DFT codebooks; any one of the N information blocks indicates at least one resource in the second resource set, including: any one of the N information blocks indicates at least one codebook in the second resource set.

[0747] As an embodiment, the second resource set includes one or more antenna ports; any one of the N information blocks indicates at least one resource in the second resource set, including: any one of the N information blocks indicates at least one antenna port in the second resource set.

[0748] As an embodiment, when the generation of the target information is based on AI, the target information includes N information blocks, and the N information blocks respectively include channel information of N time units, each of the N information blocks indicates a resource in the second resource set, and N is a positive integer greater than 1.

[0749] As an embodiment, when the generation of the target information is based on AI, the target information includes N information blocks, and the N information blocks respectively include channel information of N time units, each of the N information blocks indicates a resource in the second resource set and RSRP, and N is a positive integer greater than 1.

[0750] As an embodiment, the first CSI reporting configuration indicates a first resource set, the first resource set includes multiple RS resources, and the first RS resource is an RS resource in the first resource set; when the generation of the target information is based on AI, the target information indicates a resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set; when the generation of the target information is not based on AI, the target information indicates an RS resource in the first resource set.

[0751] As an embodiment, the first CSI reporting configuration indicates a first resource set, the first resource set includes multiple RS resources, and the first RS resource is an RS resource in the first resource set; the target information includes a first indication; when the generation of the target information is based on AI, the first indication is used to indicate a resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

[0752] As an embodiment, the first CSI reporting configuration indicates a first resource set, the first resource set includes multiple RS resources, and the first RS resource is an RS resource in the first resource set; the target information includes a first indication; when the generation of the target information is based on AI, the first indication is used to indicate a resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set; when the generation of the target information is not based on AI, the first indication is used to indicate an RS resource in the first resource set.

[0753] As an embodiment, the first indication is CRI or SSBRI.

[0754] As an embodiment, the first indication is one of CRI, SSBRI or beam indication.

[0755] As an embodiment, the first indication is one of CRI, SSBRI, and data set indication.

[0756] As an embodiment, the generation of the target information is based on AI, and the first node is not required to measure the second resource set.

[0757] As an embodiment, the generation of the target information is based on AI, the first resource set is used for measurement, and the second resource set is used for prediction.

[0758] As an embodiment, the generation of the target information is based on AI, the first resource set is used for measurement, and the second resource set is used for prediction.

[0759] As an embodiment, the generation of the target information is based on AI, and only the first resource set of the first resource set and the second resource set is used for measurement.

[0760] As an embodiment, that only the first resource set among the first resource set and the second resource set is used for measurement includes: that only the first resource set among the first resource set and the second resource set is used for measurement by the first node.

[0761] As an embodiment, the fact that only the first resource set among the first resource set and the second resource set is used for measurement includes: the first resource set is used by the first node for measurement, and the first node is not required to measure part or all of the resources in the second resource set.

[0762] As an embodiment, the first node is not required to measure the second resource set, including: the first node does not measure part or all of the resources in the second resource set.

[0763] As an embodiment, the first node is not required to measure the second resource set including: whether the first node measures part or all of the resources in the second resource set is implementation-related or independently determined by the first node.

[0764] Example 12

[0765] Embodiment 12 illustrates a schematic diagram of a reference resource according to an embodiment of the present application; Fig.12 shown.

[0766] In Example 12, the reference resource depends on whether the generation of the target information is based on AI.

[0767] As an embodiment, the reference resource depends on whether the generation of the target information is based on AI, including: the method for determining the reference resource depends on whether the generation of the target information is based on AI.

[0768] As an embodiment, the reference resource is determined by a function, and the function of determining the reference resource depends on whether generation of the target information is based on AI.

[0769] As an embodiment, the timing unit where the reference resource is located is determined by a function, and the function depends on whether the generation of the target information is based on AI.

[0770] As an embodiment, the reference resource is determined by a mapping, and determining the mapping of the reference resource depends on whether generation of the target information is based on AI.

[0771] As an embodiment, the timing unit where the reference resource is located is determined by a mapping, and the mapping depends on whether the generation of the target information is based on AI.

[0772] As an embodiment, when the generation of the target information is based on AI, the timing unit where the reference resource is located is the result of a first mapping; when the generation of the target information is not based on AI, the timing unit where the reference resource is located is the result of a second mapping; the first mapping and the second mapping are different.

[0773] As an embodiment, the timing unit where the reference resource is located is determined by a function, the function depends on a first parameter, and the first parameter depends on whether the generation of the target information is based on AI.

[0774] As a sub-embodiment of the above embodiment, the function dependence on the first parameter includes: the function and the first parameter are in a linear relationship.

[0775] As a sub-embodiment of the above embodiment, the function dependence on the first parameter includes: the function and the first parameter are in a non-linear relationship.

[0776] As an embodiment, when the generation of the target information is based on AI, the timing unit where the reference resource is located is the output of the first function; when the generation of the target information is not based on AI, the timing unit where the reference resource is located is the output of the second function; the first function and the second function are different, or the values ​​of the same parameter in the first function and the second function are different.

[0777] As an embodiment, the time unit where the first time-frequency resource block is located is n', the reference resource belongs to the time unit f(n') in the time domain, and f(n') is a function; the f(n') depends on a first parameter, and the first parameter depends on whether the generation of the target information is based on AI.

[0778] As an embodiment, the first parameter is o'.

[0779] As an embodiment, the first parameter is o.

[0780] As an embodiment, the first parameter is K offset .

[0781] As an embodiment, the first parameter is nCSI_ref

[0782] As an embodiment, the first parameter is

[0783] As an embodiment, the first parameter is

[0784] As an embodiment, the first parameter is μ offset .

[0785] As an embodiment, the o′, the o, the Ko ffset , the nCSI_ref, the Said and the μ offset For a detailed introduction, refer to section 5.2.2.5 of 3GPP TS38.214.

[0786] As an embodiment, the reference resource depends on whether the generation of the target information is based on AI, including: the reference resource depends on a first parameter, and the first parameter depends on whether the generation of the target information is based on AI.

[0787] As an embodiment, the first parameter depends on whether the generation of the target information is based on AI, including: the value range of the first parameter depends on whether the generation of the target information is based on AI.

[0788] As an embodiment, the first parameter depends on whether the generation of the target information is based on AI, including: the value of the first parameter depends on whether the generation of the target information is based on AI.

[0789] As an embodiment, the first parameter depends on whether the generation of the target information is based on AI, including: whether the first parameter is predefined or configurable depends on whether the generation of the target information is based on AI.

[0790] As an embodiment, when the generation of the target information is based on AI, the value of the first parameter is predefined; when the generation of the target information is not based on AI, the value of the first parameter is configurable.

[0791] As an embodiment, the first parameter dependence on whether the generation of the target information is based on AI includes: whether the value of the first parameter is configured by a first higher-layer parameter or a second higher-layer parameter configuration and whether the generation of the target information is based on AI.

[0792] As an embodiment, when the generation of the target information is based on AI, the value of the first parameter is configured by a first higher-layer parameter; when the generation of the target information is not based on AI, the value of the first parameter is configured by a second higher-layer parameter.

[0793] As an embodiment, whether the reference resource depends on the generation of the target information based on AI includes: a parameter for determining whether the reference resource depends on the generation of the target information based on AI.

[0794] As an embodiment, whether the reference resource depends on whether the generation of the target information is based on AI includes: whether the reference resource is earlier in the time domain than the first time-frequency resource block and whether the generation of the target information is based on AI.

[0795] As an embodiment, whether the reference resource depends on whether the generation of the target information is based on AI includes: whether the interval between the reference resource and the first time-frequency resource block in the time domain depends on whether the generation of the target information is based on AI.

[0796] As an embodiment, whether the reference resource depends on whether the generation of the target information is based on AI includes: whether the reference resource is a CSI reference resource depends on whether the generation of the target information is based on AI; when the generation of the target information is based on AI, the reference resource is not a CSI reference resource; when the generation of the target information is not based on AI, the reference resource is a CSI reference resource.

[0797] As an embodiment, whether the reference resource depends on whether the generation of the target information is based on AI includes: whether the reference resource is a first reference resource or a second reference resource depends on whether the generation of the target information is based on AI.

[0798] As an embodiment, when the generation of the target information is based on AI, the reference resource is a first reference resource; when the generation of the target information is not based on AI, the reference resource is a second reference resource; the first reference resource and the second reference resource are different.

[0799] As an embodiment, the reference resource includes a CSI reference resource (referenceresource) of the target information.

[0800] As an embodiment, the essence of the above method includes: when the generation of the target information is based on AI and when the generation of the target information is not based on AI, the reference resources are different.

[0801] As an embodiment, the benefits of the above method include: fully considering the processing capabilities based on AI and not based on AI, and having greater flexibility and adaptability.

[0802] As an embodiment, whether the reference resource depends on the generation of the target information based on AI includes: the reference resource depends on the first identifier.

[0803] As an embodiment, the reference resource being dependent on the first identifier includes: a method for determining the reference resource being dependent on the first identifier.

[0804] As an embodiment, the reference resource dependence on the first identifier includes: whether the reference resource is earlier than the first time-frequency resource block dependence on the first identifier in the time domain.

[0805] As an embodiment, the reference resource dependence on the first identifier includes: an interval between the reference resource and the first time-frequency resource block in the time domain depends on the first identifier.

[0806] As an embodiment, the reference resource dependence on the first identifier includes: whether the reference resource is a CSI reference resource dependence on the first identifier.

[0807] As an embodiment, the reference resource dependency on the first identifier includes: whether the reference resource dependency on the first identifier belongs to a first identifier set or a second identifier set, the first identifier set includes one or more first-category identifiers, the second identifier set includes one or more first-category identifiers, and the first identifier set and the second identifier set are different.

[0808] As an embodiment, the reference resource being dependent on the first identifier includes: whether the reference resource is a first reference resource or a second reference resource being dependent on the first identifier.

[0809] As an embodiment, the essence of the above method includes: the reference resource is related to the first identifier.

[0810] As an embodiment, the benefits of the above method include: fully considering that different AI models or entities or functions may have different processing capabilities, and having greater flexibility and adaptability.

[0811] Example 13

[0812] Embodiment 13 illustrates a schematic diagram of a first identification according to an embodiment of the present application; Fig.13 shown.

[0813] In Embodiment 13, only when the generation of the target information is based on AI, the generation of the target information is associated with the first identifier, and the first opportunity set depends on the first identifier.

[0814] As an embodiment, the first identifier is a non-negative integer.

[0815] As an embodiment, the first identifier is a character string.

[0816] As an embodiment, the first identifier is a model identifier.

[0817] As an embodiment, the first identifier is used to identify an AI model.

[0818] As an embodiment, the first identifier is used by the first node to determine an AI model.

[0819] As an embodiment, the first CSI reporting configuration indicates the use of the AI ​​model by indicating the first identifier.

[0820] As an embodiment, the first CSI reporting configuration obtains input of an AI entity / function / inference associated with the first identifier by indicating the first identifier.

[0821] As an embodiment, the benefits of the above method include: identifying an AI entity or function through the first identifier, simplifying system design, and unifying the understanding of different AI entities or functions among multiple nodes.

[0822] As an embodiment, the generation of the target information is associated with the first identifier and includes: the generation of the target information includes the sender of the target information performing a first operation, the input of the first operation depends on the measurement based on the first resource set, and the target information depends on the output of the first operation.

[0823] As an embodiment, the generation of the target information associated with the first identifier includes: the generation of the target information includes the sender of the target information performing a first operation, the input of the first operation depends on the measurement based on the first resource set, the target information depends on the output of the first operation, and the first operation is associated with the first identifier.

[0824] As an embodiment, the benefits of the above method include: supporting AI-based information reporting.

[0825] As an embodiment, the first CSI reporting configuration indicates the first identifier, and the first operation is associated with the first identifier.

[0826] As an embodiment, the generation of the target information is associated with the first identifier, including: the generation of the target information is associated with the first identifier through the first CSI reporting configuration.

[0827] As an embodiment, the generation of the target information is associated with the first identifier, including: the first CSI reporting configuration indicates the first identifier; and the first CSI reporting configuration indicates the generation of the target information.

[0828] As an embodiment, the generation of the target information is associated with the first identifier, including: the generation of the target information depends on an AI entity or function, and the AI ​​entity or function is associated with the first identifier.

[0829] As an embodiment, the generation of the target information is associated with the first identifier, including: the generation of the target information depends on an AI entity or function, and the first identifier is used to identify the AI ​​entity or function.

[0830] As an embodiment, the generation of the target information is associated with the first identifier, including: the generation of the target information depends on an AI model, and the AI ​​model is associated with the first identifier.

[0831] As an embodiment, the generation of the target information is associated with the first identifier, including: the generation of the target information depends on an AI model, and the first identifier is used to identify the AI ​​model.

[0832] As an embodiment, the benefits of the above method include: simplifying system design and reducing system implementation complexity.

[0833] As an embodiment, the first opportunity set being dependent on the first identifier includes: the number of transmission opportunities included in the first opportunity set being dependent on the first identifier.

[0834] As an embodiment, the first timing set being dependent on the first identifier includes: the first timing set includes which RS resources in the first resource set whose transmission timing is dependent on the first identifier.

[0835] As an embodiment, the first timing set being dependent on the first identifier includes: the first timing set includes transmission timings of several RS resources in the first resource set being dependent on the first identifier.

[0836] As an embodiment, the first timing set depends on the first identifier, including: the first timing set depends on which of T1 identifiers the first identifier is, the T1 identifiers are different from each other, any identifier among the T1 identifiers is a first-category identifier, and T1 is a positive integer greater than 1.

[0837] As an embodiment, the first timing set depends on the first identifier, including: the first timing set depends on whether the first identifier belongs to a first identifier set or a second identifier set, the first identifier set includes one or more first-category identifiers, the second identifier set includes one or more first-category identifiers, and the first identifier set and the second identifier set are different.

[0838] As an embodiment, the benefits of the above method include: enhancing the flexibility of the system and adapting to applications in different scenarios.

[0839] As an embodiment, the first opportunity set depends on whether the first identifier belongs to the first identifier set or the second identifier set, including: the number of transmission opportunities included in the first opportunity set depends on whether the first identifier belongs to the first identifier set or the second identifier set.

[0840] As an embodiment, the first timing set depends on whether the first identifier belongs to the first identifier set or the second identifier set, including: the first timing set includes which RS resources in the first resource set, whose transmission timing depends on whether the first identifier belongs to the first identifier set or the second identifier set.

[0841] As an embodiment, the first timing set depends on whether the first identifier belongs to the first identifier set or the second identifier set, including: the first timing set includes transmission timings of several RS resources in the first resource set, which depends on whether the first identifier belongs to the first identifier set or the second identifier set.

[0842] As an embodiment, when the first identifier belongs to a first identifier set, the first timing set includes at least one transmission timing of at least one RS resource in the first resource set; when the first identifier belongs to a second identifier set, the first timing set includes at least one transmission timing of each RS resource in the first resource set.

[0843] As an embodiment, when the first identifier belongs to a first identifier set, the first timing set includes at least one transmission timing of at least one RS resource in the first resource set that is no later than a reference resource; when the first identifier belongs to a second identifier set, the first timing set includes at least one transmission timing of each RS resource in the first resource set that is no later than the reference resource.

[0844] As an embodiment, when the first identifier belongs to a first identifier set, the first timing set includes at least one transmission timing of at least one RS resource in the first resource set that is no later than a reference resource; when the first identifier belongs to a second identifier set, the first timing set includes at least one transmission timing of at least one RS resource in the first resource set.

[0845] As an embodiment, when the first identifier belongs to a first identifier set, the first timing set includes at least one transmission timing of at least one RS resource in the first resource set that is no later than a reference resource; when the first identifier belongs to a second identifier set, the first timing set includes at least one transmission timing of at least one RS resource in the first resource set that is no later than the first time-frequency resource block.

[0846] Embodiment 14

[0847] Embodiment 14 illustrates a schematic diagram of a first operation according to an embodiment of the present application; Fig.14 shown.

[0848] In Embodiment 14, only when the generation of the target information is based on AI, the generation of the target information includes the sender of the target information performing a first operation, the input of the first operation depends on the measurement based on the first resource set, and the target information depends on the output of the first operation.

[0849] 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.

[0850] As an embodiment, the above method has the following benefits: it is suitable for channel compression and saves feedback overhead.

[0851] As an embodiment, the measurement based on the first resource set includes channel information obtained by measurement, and the output of the first operation includes predicted channel information.

[0852] As an embodiment, the measurement based on the first resource set includes channel information obtained by measurement, and the output of the first operation includes spatial beam prediction.

[0853] As an embodiment, the measurement based on the first resource set includes channel information obtained by measurement, and the output of the first operation includes spatial beam prediction for the second resource set.

[0854] As an embodiment, the benefits of the above method include: reducing RS resource overhead and reducing feedback delay.

[0855] As an embodiment, the measurement based on the first RS resource set includes current channel information, and the output of the first operation includes predicted channel information.

[0856] As an embodiment, the measurement based on the first resource set includes historical channel information, and the output of the first operation includes predicted channel information.

[0857] As an embodiment, the measurement based on the first resource set includes historical channel information, and the output of the first operation includes temporal beam prediction.

[0858] As an embodiment, the measurement based on the first resource set includes historical channel information, and the output of the first operation includes temporal beam prediction for the second resource set.

[0859] As an embodiment, the benefits of the above method include: reducing the channel information feedback delay and improving the real-time performance of channel information acquisition.

[0860] As an embodiment, the measurement based on the first resource set includes current channel information, and the output of the first operation includes channel information after a period of time.

[0861] As an embodiment, the measurement based on the first resource set includes current channel information, and the output of the first operation includes future channel information.

[0862] As an embodiment, the measurement based on the first resource set includes historical channel information, and the output of the first operation includes future channel information.

[0863] As an embodiment, the benefits of the above method include: improving CSI accuracy and real-time performance, and reducing RS overhead.

[0864] As an embodiment, the measurement based on the first resource set includes incomplete channel information, and the output of the first operation includes complete channel information.

[0865] As an embodiment, the benefits of the above method include: reducing RS overhead and improving the accuracy and completeness of CSI.

[0866] As an embodiment, the measurement based on the first resource set includes channel information of P1 antenna ports, and the output of the first operation includes channel information of P2 antenna ports, and the P1 and the P2 are positive integers greater than 1 respectively, and the P1 is less than the P2.

[0867] As a sub-embodiment of the above embodiment, the P1 antenna ports are a proper subset of the P2 antenna ports.

[0868] As a sub-embodiment of the above embodiment, the P2 antenna ports belong to the second resource set.

[0869] As an embodiment, the measurement based on the first resource set includes channel information of first frequency domain resources, and the output of the first operation includes channel information of second frequency domain resources, and the second frequency domain resources include frequency domain resources that do not belong to the first frequency domain resources.

[0870] As a sub-embodiment of the above embodiment, the first frequency domain resources are a proper subset of the second frequency domain resources.

[0871] As an embodiment, the input of the first operation also includes the second resource set.

[0872] As an embodiment, the first operation is training-based or AI-based.

[0873] As an embodiment, the first operation is obtained through training.

[0874] As an embodiment, the training for obtaining the first operation is performed by the first node.

[0875] As an embodiment, the training for obtaining the first operation is performed by the sender of the first CSI reporting configuration.

[0876] As an embodiment, the training for obtaining the first operation is performed by a sender of the first resource set.

[0877] As an embodiment, the training for obtaining the first operation is performed by an MDA function (Management Data Analytics Function).

[0878] As an embodiment, the training for obtaining the first operation is performed by a MDAS (Management Data Analytics Service) producer.

[0879] As an embodiment, the training for obtaining the first operation is performed by NWDAF (Network Data Analytics Function).

[0880] As an embodiment, the training for obtaining the first operation is performed by a core network.

[0881] As an embodiment, the training for obtaining the first operation is performed by an AI training producer.

[0882] As an embodiment, an executor of the training for obtaining the first operation is different from a sender of the first CSI reporting configuration.

[0883] As an embodiment, a performer of training for obtaining the first operation is different from a sender of the first RS resource set.

[0884] As an embodiment, the first operation includes inference.

[0885] As an embodiment, the first operation is inference.

[0886] As an embodiment, the first operation includes AI (Artificial Intelligence).

[0887] As an embodiment, the first operation is AI inference.

[0888] As an embodiment, the first operation includes AI inference for CSI.

[0889] As an embodiment, the first operation is AI inference for CSI.

[0890] As an embodiment, the first operation includes AI inference for beam prediction.

[0891] As an embodiment, the first operation is AI inference for beam prediction.

[0892] As an embodiment, the first operation includes AI inference for at least one of beam prediction, CSI prediction, CSI estimation, or CSI compression.

[0893] As an embodiment, the CSI prediction includes beam prediction.

[0894] As an embodiment, the benefits of the above method include: improving the performance of CSI (including beam) measurement and reporting, including more accurate CSI, lower reference signal overhead and reporting overhead, thereby improving the overall system performance.

[0895] As an embodiment, the benefits of the above method include: more accurate and complete CSI, lower reference signal overhead, and improved real-time performance of CSI.

[0896] As an embodiment, the first operation is based on an AI model.

[0897] As an embodiment, the first operation is based on a neural network.

[0898] As an embodiment, the first operation includes an AI entity.

[0899] As an embodiment, the first operation includes AI inferring an entity.

[0900] As an embodiment, the first operation includes an AI entity for inference.

[0901] As an embodiment, the first operation includes a portion of an AI entity.

[0902] As an embodiment, the first operation includes a part of an AI entity for inference.

[0903] As an embodiment, the first operation includes an AI entity for CSI.

[0904] As an embodiment, the first operation includes an AI entity for beam prediction.

[0905] As an embodiment, the first operation includes an AI entity for CSI prediction, estimation or compression.

[0906] As an embodiment, the first operation includes inference of an AI entity for CSI.

[0907] As an embodiment, the first operation includes inference of an AI entity for CSI prediction, estimation, or compression.

[0908] As an embodiment, the first operation is performed by an AI entity.

[0909] As an embodiment, the first operation is performed by an AI entity deployed on the first node.

[0910] As an embodiment, the first operation is performed by an AI function.

[0911] As an embodiment, the first operation is performed by an AI function deployed on the first node.

[0912] As an embodiment, the AI ​​function includes an AI inference function.

[0913] As an embodiment, the AI ​​function includes an AI training function.

[0914] As an embodiment, the AI ​​function includes an AI management function.

[0915] As an embodiment, the first operation is performed by the physical layer of the first node.

[0916] As an embodiment, the first operation is performed by a higher layer of the first node.

[0917] As an embodiment, the first operation requires deployment.

[0918] As an embodiment, the first operation is obtained by loading.

[0919] As an embodiment, the first operation is obtained by loading from a serving cell of the first node.

[0920] As an embodiment, the first operation is obtained from maintaining base station loading of a serving cell of the first node.

[0921] As an embodiment, the first operation is obtained by loading from the core network.

[0922] As an embodiment, the output of the first operation does not belong to the CSI defined in 3GPP Rel-18, nor does it belong to the CSI defined in versions before 3GPP Rel-18.

[0923] As an embodiment, the output of the first operation is based on artificial intelligence or machine learning.

[0924] As an embodiment, the output of the first operation is based on a neural network.

[0925] As an embodiment, the output of the first operation is based on CNN.

[0926] As an embodiment, the output of the first operation includes CSI.

[0927] As an embodiment, the output of the first operation includes predicted beam information.

[0928] As an embodiment, the output of the first operation includes beam indication and RSRP.

[0929] As an embodiment, the output of the first operation includes RS resource indication and RSRP.

[0930] As an embodiment, the output of the first operation includes resource indication and RSRP.

[0931] As an embodiment, the output of the first operation includes one or more of a beam indication, a CRI (CSI-RS Resource Indicator, channel state information reference signal resource indicator), a SS / PBCH block resource indicator (SS / PBCH Block Resource indicator, SSBRI), or a RSRP (reference signal received power, reference signal received power).

[0932] As an embodiment, the output of the first operation includes one or more of PMI, CRI, CQI, RI, LI, SSBRI, RSRP, SINR, capability index and TDCP.

[0933] As an embodiment, the output of the first operation includes a channel impulse response.

[0934] As an embodiment, the output of the first operation includes small-scale characteristics.

[0935] As an embodiment, the output of the first operation includes one or more of delay spread, Doppler spread, Doppler shift, average delay and average gain.

[0936] As an embodiment, the output of the first operation includes a channel matrix.

[0937] As an embodiment, the output of the first operation includes the target information.

[0938] As an embodiment, in the above method, the first operation is used for beam prediction, CSI prediction or estimation to reduce RS overhead and / or improve CSI accuracy / completeness.

[0939] As an embodiment, the first operation includes preprocessing.

[0940] As an embodiment, the preprocessing includes DFT (Discrete Fourier Transform).

[0941] As an embodiment, the preprocessing includes one or more of matrix decomposition, matrix transformation and projection.

[0942] As an embodiment, the preprocessing includes one or more of quantization, transformation from spatial domain to angular domain, transformation from angular domain to spatial domain, transformation from frequency domain to time domain, and transformation from time domain to frequency domain.

[0943] As an embodiment, the preprocessing includes truncation and / or padding.

[0944] As an embodiment, the preprocessing includes mapping.

[0945] As an embodiment, the preprocessing includes mapping to vectors.

[0946] As an embodiment, the preprocessing includes labeling.

[0947] As an embodiment, the label refers to marking with a label.

[0948] As an embodiment, the first operation includes post-processing.

[0949] As an embodiment, the post-processing includes DFT.

[0950] As an embodiment, the post-processing includes quantization.

[0951] As an embodiment, the post-processing includes one or more of a transformation from the angle domain to the spatial domain, a transformation from the spatial domain to the angle domain, a transformation from the time domain to the frequency domain, and a transformation from the frequency domain to the time domain.

[0952] As an embodiment, the post-processing includes truncation and / or padding.

[0953] As an embodiment, the first operation includes one or more of convolution, pooling, cascading and activation.

[0954] As an embodiment, the first operation includes a fully connected layer.

[0955] As an embodiment, the first operation includes a pooling layer.

[0956] As an embodiment, the first operation includes at least one convolutional layer.

[0957] As an embodiment, the first operation includes at least one encoding layer.

[0958] As an embodiment, a coding layer includes at least one convolutional layer and one pooling layer.

[0959] As an embodiment, in the convolution layer, at least one convolution kernel is used to convolve the input to generate a corresponding feature map, and at least one feature map output by the convolution layer is reshaped into a vector input to the fully connected layer; the fully connected layer converts the one vector into an output.

[0960] As an embodiment, part or all of the convolution kernel size, number of convolution layers, convolution step size, pooling kernel size, pooling kernel step size, pooling function, activation function and number of feature maps of the first operation are obtained through training.

[0961] As an embodiment, part or all of the convolution kernel, pooling kernel, pooling function, activation function, parameters of the pooling function and parameters of the activation function of the first operation are obtained through training.

[0962] As an embodiment, the first operation is associated with the first identifier.

[0963] As an embodiment, the first CSI reporting configuration indicates a first identifier, and the first operation is associated with the first identifier.

[0964] As an embodiment, the first operation is identified by the first identifier.

[0965] As an embodiment, the AI ​​model adopted by the first operation is identified by the first identifier.

[0966] As an embodiment, the AI ​​entity to which the first operation belongs is identified by the first identifier.

[0967] As an embodiment, the AI ​​function to which the first operation belongs is identified by the first identifier.

[0968] As an embodiment, the AI ​​entity or AI function to which the first operation belongs is identified by the first identifier.

[0969] As an embodiment, the AI ​​function that performs the first operation is identified by the first identifier.

[0970] As an embodiment, the AI ​​entity that performs the first operation is identified by the first identifier.

[0971] As an embodiment, the AI ​​entity or AI function that performs the first operation is identified by the first identifier.

[0972] As an embodiment, the first identifier is used by the first node to determine the AI ​​model adopted by the first operation.

[0973] As an embodiment, the benefits of the above method include: identifying an AI model / entity / function through the first identifier, simplifying the design and unifying the understanding of different AI entities / functions among multiple nodes.

[0974] As an embodiment, the first identifier is used to identify or indicate a reference resource set, and measurements on the reference resource set are used to obtain a training data set for the first operation.

[0975] As an embodiment, the first identifier is used to identify configuration information of a reference resource set, and measurements of the reference resource set are used to obtain a training data set for the first operation.

[0976] As an embodiment, the training for obtaining the first operation is identified by the first identifier.

[0977] As an embodiment, the training data set used for the first operation is identified by the first identifier.

[0978] As an embodiment, the benefits of the above method include: by identifying an AI training or an AI training data set to identify the inferences generated by this AI training or AI training data set, a consensus is established between different AI functions, and the design is further simplified.

[0979] As an embodiment, the first CSI reporting configuration indicates the first operation by indicating the first identifier.

[0980] As an embodiment, the first operation performs spatial beam prediction for a second resource set based on measurement of the first resource set, and the second resource set depends on the first identifier.

[0981] As an embodiment, the benefits of the above method include: reducing RS overhead and reducing feedback delay.

[0982] As an embodiment, the first operation predicts channel information for a second resource set based on measurement of the first resource set, and the second resource set depends on the first identifier.

[0983] As an embodiment, the first operation performs temporal beam prediction for a second resource set based on historical measurements of the first resource set, and the second resource set depends on the first identifier.

[0984] As an embodiment, the benefits of the above method include: reducing beam feedback delay and improving the real-time performance of beam acquisition.

[0985] As an embodiment, the first operation performs temporal channel information prediction for a second resource set based on historical measurements of the first resource set, and the second resource set depends on the first identifier.

[0986] As an embodiment, the benefits of the above method include: reducing the channel information feedback delay and improving the real-time performance of channel information acquisition.

[0987] Embodiment 15

[0988] Embodiment 15 illustrates a schematic diagram of a first operation according to another embodiment of the present application; Fig.15 In Example 15, the first operation includes K1 sub-operations, and K1 is a positive integer not greater than 1.

[0989] In Example 15, the K1 sub-operations are respectively represented as sub-operation #0, ..., sub-operation #(K1-1).

[0990] As an embodiment, each of the K1 sub-operations is based on training.

[0991] As an embodiment, at least one of the K1 sub-operations is based on training.

[0992] As an embodiment, each training-based sub-operation in the K1 sub-operations is based on the same executor of the training.

[0993] As an embodiment, two of the K1 sub-operations are based on different executors of the training.

[0994] As an embodiment, at least one sub-operation among the K1 sub-operations needs to be deployed.

[0995] As an embodiment, at least one of the K1 sub-operations needs to be loaded.

[0996] As an embodiment, all the sub-operations that need to be loaded in the K1 sub-operations are loaded from the same producer.

[0997] As an embodiment, two of the K1 sub-operations that need to be loaded are loaded from different producers.

[0998] As an embodiment, at least one of the K1 sub-operations is not based on training.

[0999] As an embodiment, at least one of the K1 sub-operations is based on a codebook for precoding defined in 3GPP R18 or a version prior to 3GPP R18.

[1000] As an embodiment, one or more of the K1 sub-operations are AI-based.

[1001] As an embodiment, one or more of the K1 sub-operations include inference.

[1002] As an embodiment, one or more of the K1 sub-operations include AI inference.

[1003] As an embodiment, one or more of the K1 sub-operations include AI inference for CSI.

[1004] As an embodiment, the AI ​​(Artificial Intelligence) includes ML (Machine Learning).

[1005] As an embodiment, one or more of the K1 sub-operations include preprocessing.

[1006] As an embodiment, one or more of the K1 sub-operations include post-processing.

[1007] As an embodiment, there are two sub-operations in the K1 sub-operations that are serial, as shown in the attached Fig.15 All sub-operations in (a), sub-operation #2 to sub-operation #(K1-1) in 15(b), and sub-operation #0 to sub-operation #(K1-4) in 15(c).

[1008] As an embodiment, two sub-operations are serial, which means that the output of one of the two sub-operations is used as the input of the other sub-operation of the two sub-operations.

[1009] As an embodiment, there are two sub-operations in the K1 sub-operations that are parallel, as shown in the attached Fig.15 (b) Sub-operation #0 and sub-operation #1, attached Fig.15 Sub-operation #(K1-3) and sub-operation #(K1-2) in (c).

[1010] As an embodiment, two sub-operations are parallel means that the outputs of the two sub-operations are used together as the input of another sub-operation.

[1011] As an embodiment, the K1 sub-operations include one or more of convolution, pooling, cascading or activation.

[1012] As an embodiment, there is a sub-operation among the K1 sub-operations including a fully connected layer.

[1013] As an embodiment, there is a sub-operation among the K1 sub-operations including a pooling layer.

[1014] As an embodiment, there is a sub-operation among the K1 sub-operations including at least one convolutional layer.

[1015] As an embodiment, there is a sub-operation among the K1 sub-operations that includes at least one encoding layer.

[1016] As an embodiment, there are two sub-operations among the K1 sub-operations, each of which includes a fully connected layer and at least one encoding layer.

[1017] As an embodiment, a coding layer includes at least one convolutional layer and one pooling layer.

[1018] Example 16

[1019] Embodiment 16 illustrates a schematic diagram of a first operation of deploying according to an embodiment of the present application; Fig.16 shown.

[1020] In embodiment 16, the first processor deploys the first operation.

[1021] As an embodiment, the deployment includes obtaining the first operation.

[1022] As an embodiment, the deploying includes obtaining an AI entity.

[1023] As an embodiment, the deploying includes obtaining an AI entity that performs the first operation.

[1024] As an embodiment, the deploying includes obtaining an AI entity including an AI function for performing the first operation.

[1025] As an embodiment, the deploying includes loading the first operation.

[1026] As an embodiment, the deploying includes making a request to load the first operation.

[1027] As an example, Fig.16 The request in is a request made by the first node to load the first operation.

[1028] As an example, Fig.16 The response in is a response to the request for loading the first operation made by the first node.

[1029] As an embodiment, the first node is connected Fig.16 The first operation is obtained in response to the above.

[1030] As an embodiment, the first operation is obtained by loading from a serving cell of the first node.

[1031] As an embodiment, the first operation is obtained by loading from a base station maintaining a service cell of the first node.

[1032] As an embodiment, the first operation is obtained by loading from the core network.

[1033] As an embodiment, the first operation is obtained by loading from a first producer.

[1034] As an embodiment, the first producer attaches Fig.16 The response in provides the first operation to the first node.

[1035] As an embodiment, the deployment is performed by an AI function.

[1036] As an embodiment, the deployment is completed by an AI function deployed on the first node.

[1037] As an embodiment, the deployment is performed by an AI deployment function.

[1038] As an embodiment, the deployment is completed by an AI deployment function deployed on the first node.

[1039] As an embodiment, the deployment is performed by AI inference function.

[1040] As an embodiment, the deployment is accomplished by an AI inference function deployed on the first node.

[1041] As an embodiment, the deployment is performed by an AI entity.

[1042] As an embodiment, the deployment is completed by an AI entity deployed on the first node.

[1043] As an embodiment, the deployment is completed by an AI entity having a deployment function.

[1044] As an embodiment, the deployment is completed by an AI entity with deployment function deployed on the first node.

[1045] As an embodiment, the deployment is performed by an AI entity having an inference function.

[1046] As an embodiment, the deployment is completed by an AI entity with inference function deployed on the first node.

[1047] As an embodiment, the deploying includes obtaining the first operation from a first producer.

[1048] As an embodiment, the deploying includes making a request to a first producer to load the first operation.

[1049] As an embodiment, the deploying includes loading the first operation from a first producer.

[1050] As an embodiment, the first producer generates and provides an AL entity.

[1051] As an embodiment, the first producer generates and provides AL functionality.

[1052] As an embodiment, the first producer is the producer of the first operation.

[1053] As an embodiment, the first producer includes an AL entity producer.

[1054] As an embodiment, the first producer includes an AL function producer.

[1055] As an embodiment, the first producer includes an AL deployment producer.

[1056] As an embodiment, the first producer includes an AL loading producer.

[1057] As an embodiment, the first producer includes an AL training producer.

[1058] As an embodiment, the first producer includes an AL inference producer.

[1059] As an embodiment, the first producer includes a producer of deployment of an AL entity.

[1060] As an embodiment, the first producer includes a loaded producer of an AL entity.

[1061] As an embodiment, the first producer includes a MnS (Management Service) producer.

[1062] As an embodiment, the sender of the first CSI reporting configuration is the first producer.

[1063] As an embodiment, the sender of the first CSI reporting configuration is different from the first producer.

[1064] As an embodiment, the training for obtaining the first operation is performed by the first producer.

[1065] As an embodiment, a performer of the training for obtaining the first operation is different from the first producer.

[1066] As an embodiment, the AI ​​includes ML (Machine Learning).

[1067] Embodiment 17

[1068] Embodiment 17 illustrates a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of the present application; Fig.17 Attached Fig.17 (a) comprising a third processor, a fourth processor and a fifth processor, Fig.17 (b) includes a third processor, a fourth processor, a fifth processor and a sixth processor.

[1069] In Example 17(a), the third processor sends a first data set to the fourth processor and a second data set to the fifth processor; the fourth processor generates a target first-category parameter group based on the first data set, and the fourth processor sends the generated target first-category parameter group to the fifth processor; the fifth processor processes the second data set using the target first-category parameter group to obtain a first-category output. Fig.17(a),The first type of feedback is optional.

[1070] In Example 17(b), the third processor sends a first data set to the fourth processor and a second data set to the fifth processor; the fourth processor generates a target first-category parameter group based on the first data set, and the fourth processor sends the generated target first-category parameter group to the fifth processor; the fifth processor processes the second data set using the target first-category parameter group to obtain a first-category output, and the fifth processor sends the first-category output to the sixth processor. Fig.17 (b), the first type of feedback and the second type of feedback are optional.

[1071] As an example, Fig.17 In (a), the fifth processor sends the first type of output to the second node in the present application.

[1072] As an example, Fig.17 (a) A single-side AI model is used for beam prediction or channel information prediction, and the fifth processor performs the first operation, where the first operation is used for beam prediction or channel information prediction.

[1073] As an embodiment, the AI ​​includes ML (Machine Learning) inference.

[1074] As an embodiment, the fifth processor performs the first operation.

[1075] As an embodiment, the fifth processor sends the first type of feedback to the fourth processor, and the first type of feedback is used to trigger recalculation or update of the target first type parameter group.

[1076] As an embodiment, the sixth processor sends a second type of feedback to the third processor, and the second type of feedback is used to generate the first data set or the second data set, or the second type of feedback is used to trigger the sending of the first data set or the sending of the second data set.

[1077] As an embodiment, the third processor generates the first data set and the second data set based on measurements of a first type of wireless signal, where the first type of wireless signal includes a downlink RS.

[1078] As an embodiment, the fifth processor belongs to the first node, and the sixth processor belongs to the second node.

[1079] As an embodiment, the target information belongs to the first type of output.

[1080] As an embodiment, the second data set includes the input of the first operation.

[1081] As an embodiment, the second data set includes information obtained based on the first CSI reporting configuration and the M1 configurations.

[1082] As an embodiment, the first data set includes training data (Training Data).

[1083] As an embodiment, the fourth processor is a producer of the first operation.

[1084] As an embodiment, the fourth processor includes an AI training producer.

[1085] As an embodiment, the fourth processor includes an AI training function.

[1086] As an embodiment, the fourth processor is used for model training, and the trained model is described by the target first type parameter group.

[1087] As an embodiment, the fourth processor belongs to the first node.

[1088] The above embodiment avoids transferring the first data set to the second node.

[1089] As an embodiment, the fourth processor belongs to the second node.

[1090] The above embodiments support joint training and optimize system performance.

[1091] As an embodiment, the fourth processor belongs to the core network.

[1092] The above embodiments support joint training of the entire network to further optimize system performance.

[1093] As an embodiment, the second data set includes inference data.

[1094] As an embodiment, the fifth processor includes an AI inference producer.

[1095] As an embodiment, the fifth processor includes an AI inference function.

[1096] As an embodiment, the fifth processor belongs to the first node.

[1097] As an embodiment, the fifth processing machine constructs a model according to the target first-category parameter group, and then inputs the second data set into the constructed model to obtain the first-category output.

[1098] As an embodiment, the first operation is described by the target first type parameter group.

[1099] As an embodiment, the target first-category parameter group is used to construct the first operation.

[1100] As an embodiment, the fifth processing machine generates a restored data set according to the first type of output, and an error between the restored data set and the second data set is used to generate the first type of feedback.

[1101] As an embodiment, the first type of feedback is used to reflect the performance of the trained model; when the performance of the trained model cannot meet the requirements, the fourth processor will recalculate the target first type parameter group.

[1102] As an embodiment, when the error is too large or has not been updated for too long, the performance of the trained model is considered to be unable to meet the requirements.

[1103] As an embodiment, the target first category parameter group includes: one or more of: convolution kernel size, number of convolution layers, convolution step size, pooling kernel size, pooling kernel step size, pooling function, activation function, or number of feature maps.

[1104] As an embodiment, the target first-category parameter group includes: one or more of a convolution kernel, a pooling kernel, a pooling function, an activation function, parameters of a pooling function, or parameters of an activation function.

[1105] Embodiment 18

[1106] Embodiment 18 illustrates a schematic diagram based on artificial intelligence or machine learning according to an embodiment of the present application; Fig.18 Attached Fig.18 It includes a third operation, a fourth operation, a fifth operation, a sixth operation and a seventh operation; the lines with arrows indicate the order of the process.

[1107] In Example 18, the third operation and the fourth operation belong to the first stage, the fifth operation belongs to the second stage, the sixth operation belongs to the third stage, and the seventh operation belongs to the fourth stage.

[1108] 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.

[1109] As an embodiment, the first phase includes a training phase, the second phase includes an emulation phase, the third phase includes a deployment phase, and the fourth phase includes an emulation phase.

[1110] As an embodiment, the first stage includes AI model training.

[1111] As an embodiment, the first stage includes AI model training and AI testing.

[1112] As an embodiment, the AI ​​includes ML (Machine Learning) inference.

[1113] As an embodiment, the AI ​​model training includes initial training and re-training of one or a group of AI entities.

[1114] As an embodiment, the AI ​​model training relies on training data.

[1115] As an embodiment, the AI ​​model training includes AI entity validation.

[1116] As an embodiment, the AI ​​entity verification is used to evaluate the performance of the AI ​​entity.

[1117] As an embodiment, the AI ​​entity verification relies on verification data.

[1118] As an embodiment, if the result of the AI ​​entity verification does not meet expectations, the AI ​​model will be retrained.

[1119] As an embodiment, the AI ​​testing includes testing the verified AI entity to estimate the performance of the trained AI model.

[1120] As an embodiment, if the result of the AI ​​test meets expectations, the AI ​​entity proceeds to the next stage; otherwise the AI ​​model will be retrained.

[1121] As an embodiment, the AI ​​test relies on test data.

[1122] As an embodiment, the second stage includes AI simulation, which performs inference of AI entities in a simulation environment.

[1123] As an embodiment, the AI ​​simulation is to estimate the performance of AI entity inference in a simulation environment before using the AI ​​entity.

[1124] As an embodiment, the second stage is optional.

[1125] As an embodiment, the third stage includes AI entity loading, and the AI ​​entity loading is to obtain the trained AI entity to obtain the desired AI inference function.

[1126] As an embodiment, the third stage is optional.

[1127] As an example, when the training function and the inference function are co-located, the third stage is no longer needed.

[1128] As an embodiment, the fourth stage includes AI inference.

[1129] As an embodiment, the seventh operation includes the first operation.

[1130] Embodiment 19

[1131] Embodiment 19 illustrates a structural block diagram of a processing device used in a first node according to an embodiment of the present application; Fig.19 As shown in the attached Fig.19 In the embodiment, the processing device 1900 in the first node includes a first receiver 1901 and a first processor 1902.

[1132] A first receiver 1901 receives a first CSI reporting configuration, wherein the first CSI reporting configuration indicates a first RS resource;

[1133] The first processor 1902 sends target information in a first time-frequency resource block;

[1134] In Example 19, the generation of the target information depends on measurements based on a first timing set; the first timing set includes at least one transmission timing of the first RS resource that is no later than a reference resource; the first CSI reporting configuration includes a first higher layer parameter, and the first timing set depends on the value of the first higher layer parameter; the interpretation of the first higher layer parameter depends on whether the generation of the target information is based on AI.

[1135] As an embodiment, when the value of the first higher-layer parameter is a first candidate value, the interpretation of the first candidate value depends on whether the generation of the target information is based on AI.

[1136] As an embodiment, when the value of the first higher-layer parameter is the first candidate value and the generation of the target information is based on AI, the first timing set includes the M most recent transmission opportunities of the first RS resource that are no later than the reference resource, and M is a positive integer greater than 1; when the value of the first higher-layer parameter is the first candidate value and the generation of the target information is not based on AI, the first timing set includes the most recent transmission opportunity of the first RS resource that is no later than the reference resource.

[1137] As an embodiment, when the generation of the target information is based on AI, the candidate value range of the first higher-level parameter is a first candidate value range; when the generation of the target information is not based on AI, the candidate value range of the first higher-level parameter is a second candidate value range; one candidate value in the first candidate value range does not belong to the second candidate value range.

[1138] As an embodiment, the second candidate value range consists of two candidate values, notConfigured and Configured; the first candidate value range includes a reference candidate value, and the reference candidate value is different from notConfigured and Configured.

[1139] As an embodiment, the first candidate value range includes a reference candidate value; when the value of the first higher layer parameter is the reference candidate value and the generation of the target information is based on AI, the first timing set includes the most recent M transmission opportunities of the first RS resource that are no later than the reference resource, and M is a positive integer greater than 1.

[1140] As an embodiment, the first CSI reporting configuration indicates a first resource set, the first resource set includes multiple RS resources, and the first RS resource is an RS resource in the first resource set; when the generation of the target information is based on AI, the target information indicates a resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

[1141] As an embodiment, the reference resource depends on whether the generation of the target information is based on AI.

[1142] As an embodiment, only when the generation of the target information is based on AI, the generation of the target information is associated with the first identifier, and the first opportunity set depends on the first identifier.

[1143] As an embodiment, only when the generation of the target information is based on AI, the generation of the target information includes the sender of the target information performing a first operation, the input of the first operation depends on the measurement based on the first resource set, and the target information depends on the output of the first operation.

[1144] As an embodiment, the first timing set depends on the first identifier and includes: the first timing set includes at least one transmission timing of at least one RS resource in the first resource set that is no later than a reference resource, and the reference resource depends on the first identifier.

[1145] As an embodiment, the target information includes N information blocks, and the N information blocks respectively include channel information of N time units, where N is a positive integer greater than 1; generation of the N information blocks respectively depends on measurements based on N timing sets, and the first timing set is one of the N timing sets; generation of the first information block depends on measurements based on the first timing set, and the first information block is one of the N information blocks.

[1146] As an embodiment, any one of the N information blocks indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

[1147] As an embodiment, the first processor 1902 deploys the first operation.

[1148] As an embodiment, the first operation is associated with the first identifier.

[1149] As an embodiment, the first CSI reporting configuration indicates a first identifier.

[1150] As an embodiment, the information block outside the first CSI reporting configuration indicates the first identifier.

[1151] As an embodiment, the first receiver 1901 receives a signal in the first resource set.

[1152] As an embodiment, the first receiver 1901 receives a reference signal in the first resource set, and the first resource set includes one or more RS resources.

[1153] As an embodiment, the first operation is training-based or AI-based.

[1154] As an embodiment, the first operation requires deployment.

[1155] As an embodiment, the first operation is obtained by loading.

[1156] As an embodiment, the first receiver receives a signal in the second resource set.

[1157] As an embodiment, the first receiver receives a reference signal in the second resource set, and the second resource set includes one or more RS resources.

[1158] As an embodiment, the first receiver does not receive a signal in the second resource set.

[1159] As an embodiment, the first node is user equipment.

[1160] As an embodiment, the first node is a relay node device.

[1161] As an embodiment, the first receiver 1901 includes at least one of {antenna 452, receiver 454, receiving processor 456, multi-antenna receiving processor 458, controller / processor 459, memory 460, data source 467} in Embodiment 4.

[1162] As an embodiment, the first processor 1902 includes at least one of {antenna 452, receiver / transmitter 454, receiving processor 456, transmitting processor 468, multi-antenna receiving processor 458, multi-antenna transmitting processor 457, controller / processor 459, memory 460, data source 467} in Example 4.

[1163] Embodiment 20

[1164] Embodiment 20 illustrates a structural block diagram of a processing device used in a second node according to an embodiment of the present application; Fig. 20 In the attached Fig. 20 In the example, the processing device 2000 in the second node includes a second processor 2001.

[1165] The second processor 2001 sends a first CSI reporting configuration; the first CSI reporting configuration indicates a first RS resource; and receives target information in a first time-frequency resource block;

[1166] In Example 20, the generation of the target information depends on measurements based on a first timing set; the first timing set includes at least one transmission timing of the first RS resource that is no later than a reference resource; the first CSI reporting configuration includes a first higher layer parameter, and the first timing set depends on the value of the first higher layer parameter; the interpretation of the first higher layer parameter depends on whether the generation of the target information is based on AI.

[1167] As an embodiment, when the value of the first higher-layer parameter is a first candidate value, the interpretation of the first candidate value depends on whether the generation of the target information is based on AI.

[1168] As an embodiment, when the value of the first higher-layer parameter is the first candidate value and the generation of the target information is based on AI, the first timing set includes the M most recent transmission opportunities of the first RS resource that are no later than the reference resource, and M is a positive integer greater than 1; when the value of the first higher-layer parameter is the first candidate value and the generation of the target information is not based on AI, the first timing set includes the most recent transmission opportunity of the first RS resource that is no later than the reference resource.

[1169] As an embodiment, when the generation of the target information is based on AI, the candidate value range of the first higher-level parameter is a first candidate value range; when the generation of the target information is not based on AI, the candidate value range of the first higher-level parameter is a second candidate value range; one candidate value in the first candidate value range does not belong to the second candidate value range.

[1170] As an embodiment, the second candidate value range consists of two candidate values, notConfigured and Configured; the first candidate value range includes a reference candidate value, and the reference candidate value is different from notConfigured and Configured.

[1171] As an embodiment, the first candidate value range includes a reference candidate value; when the value of the first higher layer parameter is the reference candidate value and the generation of the target information is based on AI, the first timing set includes the most recent M transmission opportunities of the first RS resource that are no later than the reference resource, and M is a positive integer greater than 1.

[1172] As an embodiment, the first CSI reporting configuration indicates a first resource set, the first resource set includes multiple RS resources, and the first RS resource is an RS resource in the first resource set; when the generation of the target information is based on AI, the target information indicates a resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

[1173] As an embodiment, the reference resource depends on whether the generation of the target information is based on AI.

[1174] As an embodiment, only when the generation of the target information is based on AI, the generation of the target information is associated with the first identifier, and the first opportunity set depends on the first identifier.

[1175] As an embodiment, only when the generation of the target information is based on AI, the generation of the target information includes the sender of the target information performing a first operation, the input of the first operation depends on the measurement based on the first resource set, and the target information depends on the output of the first operation.

[1176] As an embodiment, the first timing set depends on the first identifier and includes: the first timing set includes at least one transmission timing of at least one RS resource in the first resource set that is no later than a reference resource, and the reference resource depends on the first identifier.

[1177] As an embodiment, the target information includes N information blocks, and the N information blocks respectively include channel information of N time units, where N is a positive integer greater than 1; generation of the N information blocks respectively depends on measurements based on N timing sets, and the first timing set is one of the N timing sets; generation of the first information block depends on measurements based on the first timing set, and the first information block is one of the N information blocks.

[1178] As an embodiment, any one of the N information blocks indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

[1179] As an embodiment, the first processor 1902 deploys the first operation.

[1180] As an embodiment, the first operation is associated with the first identifier.

[1181] As an embodiment, the first CSI reporting configuration indicates a first identifier.

[1182] As an embodiment, the information block outside the first CSI reporting configuration indicates the first identifier.

[1183] As an embodiment, the second processor 2001 sends a signal in the first resource set.

[1184] As an embodiment, the first operation is training-based or AI-based.

[1185] As an embodiment, the first operation requires deployment.

[1186] As an embodiment, the first operation is obtained by loading.

[1187] As an embodiment, the second processor 2001 sends a signal in the second resource set.

[1188] As an embodiment, the second processor 2001 sends a reference signal in the second resource set, and the second resource set includes one or more RS resources.

[1189] As an embodiment, the second processor 2001 does not send a signal in the second resource set.

[1190] As an embodiment, the second node is a base station device.

[1191] As an embodiment, the second node is user equipment.

[1192] As an embodiment, the second node is a relay node device.

[1193] As an embodiment, the second processor 2001 includes at least one of {antenna 420, receiver / transmitter 418, receiving processor 470, transmitting processor 416, multi-antenna receiving processor 472, multi-antenna transmitting processor 471, controller / processor 475, memory 476} in Example 4.

[1194] A person of ordinary skill in the art can understand that all or part of the steps in the above method can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a hard disk or an optical disk. Optionally, all or part of the steps in the above embodiment can also be implemented using one or more integrated circuits. Accordingly, each module unit in the above embodiment can be implemented in the form of hardware or in the form of a software function module, and the present application is not limited to any specific form of software and hardware combination. The user equipment, terminal and UE in the present application include but are not limited to drones, communication modules on drones, remote-controlled aircraft, aircraft, small aircraft, mobile phones, tablet computers, notebooks, vehicle-mounted communication equipment, wireless sensors, Internet cards, Internet of Things terminals, RFID terminals, NB-IOT terminals, MTC (Machine Type Communication) terminals, eMTC (enhanced MTC) terminals, data cards, Internet cards, vehicle-mounted communication equipment, low-cost mobile phones, low-cost tablet computers and other wireless communication devices. The base stations or system devices in this application include but are not limited to macrocell base stations, microcell base stations, home base stations, relay base stations, gNB (NR Node B) NR Node B, TRP (Transmitter Receiver Point) and other wireless communication devices.

[1195] The above is only a preferred embodiment of the present application and is not intended to limit the scope of protection of the present application. Any changes and modifications made based on the embodiments described in the specification, if similar partial or complete technical effects can be obtained, should be considered obvious and fall within the scope of protection of the present invention.

Claims

1. A method in a first node for wireless communication, characterized in that include: receiving a first CSI reporting configuration; the first CSI reporting configuration indicating a first RS resource; Sending target information in a first time-frequency resource block; Among them, the generation of the target information depends on measurements based on a first timing set; the first timing set includes at least one transmission timing of the first RS resource that is no later than a reference resource; the first CSI reporting configuration includes a first higher-layer parameter, and the first timing set depends on the value of the first higher-layer parameter; the interpretation of the first higher-layer parameter depends on whether the generation of the target information is based on AI.

2. The method according to claim 1, characterized in that When the value of the first higher-layer parameter is a first candidate value, interpretation of the first candidate value depends on whether generation of the target information is based on AI.

3. The method according to claim 1 or 2, characterized in that: When the value of the first higher-layer parameter is the first candidate value and the generation of the target information is based on AI, the first timing set includes the M most recent transmission opportunities of the first RS resource that are no later than the reference resource, where M is a positive integer greater than 1; when the value of the first higher-layer parameter is the first candidate value and the generation of the target information is not based on AI, the first timing set includes the most recent transmission opportunity of the first RS resource that is no later than the reference resource.

4. The method according to claim 1, characterized in that: When the generation of the target information is based on AI, the candidate value range of the first higher-level parameter is a first candidate value range; when the generation of the target information is not based on AI, the candidate value range of the first higher-level parameter is a second candidate value range; one candidate value in the first candidate value range does not belong to the second candidate value range.

5. The method according to claim 4, characterized in that The second candidate value range consists of two candidate values, notConfigured and Configured; the first candidate value range includes a reference candidate value, and the reference candidate value is different from notConfigured and Configured.

6. The method according to claim 4 or 5, characterized in that: The first candidate value range includes a reference candidate value; when the value of the first higher layer parameter is the reference candidate value and the generation of the target information is based on AI, the first timing set includes the most recent M transmission opportunities of the first RS resource that are no later than the reference resource, where M is a positive integer greater than 1.

7. The method according to any one of claims 1 to 6, characterized in that: The first CSI reporting configuration indicates a first resource set, the first resource set includes multiple RS resources, and the first RS resource is an RS resource in the first resource set; when the generation of the target information is based on AI, the target information indicates a resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

8. The method according to any one of claims 1 to 7, characterized in that: The reference resource depends on whether the generation of the target information is based on AI.

9. The method according to any one of claims 1 to 8, characterized in that: Only when the generation of the target information is based on AI, the generation of the target information is associated with the first identifier, and the first opportunity set depends on the first identifier.

10. The method according to any one of claims 1 to 9, characterized in that: Only when the generation of the target information is based on AI, the generation of the target information includes the sender of the target information performing a first operation, the input of the first operation depends on the measurement based on the first resource set, and the target information depends on the output of the first operation.

11. A terminal, characterized in that: The terminal includes: one or more processors and a memory; The memory is coupled to the one or more processors, and the memory is used to store computer program codes, where the computer program codes include computer instructions. The one or more processors call the computer instructions to enable the terminal to execute the method according to any one of claims 1 to 10.

12. A method in a second node for wireless communication, characterized in that: include: Sending a first CSI reporting configuration; the first CSI reporting configuration indicates a first RS resource; Receiving target information in a first time-frequency resource block; Among them, the generation of the target information depends on measurements based on a first timing set; the first timing set includes at least one transmission timing of the first RS resource that is no later than a reference resource; the first CSI reporting configuration includes a first higher-layer parameter, and the first timing set depends on the value of the first higher-layer parameter; the interpretation of the first higher-layer parameter depends on whether the generation of the target information is based on AI.

13. The method according to claim 12, characterized in that When the value of the first higher-layer parameter is a first candidate value, interpretation of the first candidate value depends on whether generation of the target information is based on AI.

14. The method according to claim 12 or 13, characterized in that When the value of the first higher-layer parameter is the first candidate value and the generation of the target information is based on AI, the first timing set includes the M most recent transmission opportunities of the first RS resource that are no later than the reference resource, where M is a positive integer greater than 1; when the value of the first higher-layer parameter is the first candidate value and the generation of the target information is not based on AI, the first timing set includes the most recent transmission opportunity of the first RS resource that is no later than the reference resource.

15. The method according to claim 12, characterized in that When the generation of the target information is based on AI, the candidate value range of the first higher-level parameter is a first candidate value range; when the generation of the target information is not based on AI, the candidate value range of the first higher-level parameter is a second candidate value range; one candidate value in the first candidate value range does not belong to the second candidate value range.

16. The method according to claim 15, characterized in that The second candidate value range consists of two candidate values, notConfigured and Configured; the first candidate value range includes a reference candidate value, and the reference candidate value is different from notConfigured and Configured.

17. The method according to claim 15 or 16, characterized in that The first candidate value range includes a reference candidate value; when the value of the first higher layer parameter is the reference candidate value and the generation of the target information is based on AI, the first timing set includes the most recent M transmission opportunities of the first RS resource that are no later than the reference resource, where M is a positive integer greater than 1.

18. The method according to any one of claims 12 to 17, characterized in that The first CSI reporting configuration indicates a first resource set, the first resource set includes multiple RS resources, and the first RS resource is an RS resource in the first resource set; when the generation of the target information is based on AI, the target information indicates a resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

19. The method according to any one of claims 12 to 18, characterized in that The reference resource depends on whether the generation of the target information is based on AI.

20. The method according to any one of claims 12 to 19, characterized in that Only when the generation of the target information is based on AI, the generation of the target information is associated with the first identifier, and the first opportunity set depends on the first identifier.

21. The method according to any one of claims 12 to 20, characterized in that Only when the generation of the target information is based on AI, the generation of the target information includes the sender of the target information performing a first operation, the input of the first operation depends on the measurement based on the first resource set, and the target information depends on the output of the first operation.

22. A base station, characterized in that: The base station includes: one or more processors and a memory; The memory is coupled to the one or more processors, and the memory is used to store computer program codes, where the computer program codes include computer instructions, and the one or more processors call the computer instructions to enable the base station to perform the method according to any one of claims 12 to 21.

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