Method and apparatus in node used for wireless communication

Abstract

The invention discloses a method and an apparatus in a node used for wireless communication. The first node receives a target CSI reporting configuration; the target CSI reporting configuration is used for configuring reporting of target CSI on a target cell, and the generation mode of the target CSI is based on AI; receiving a first information block, wherein the first information block indicates a target identifier; sending the target CSI; the generation mode of the target CSI is associated with a first type of identification; whether the first type of identifier associated with the generation mode of the target CSI is updated to the target identifier or not depends on whether the target cell belongs to a first cell set or not; only when the target cell belongs to the first cell set, the first type identifier associated with the generation mode of the target CSI is updated to the target identifier; the first cell is a service cell in the first cell set, and the first cell depends on the first information block; the target cell is different from the first cell.

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 channel 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 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) can also help reduce hardware complexity and cost. In the absence of conflict, the embodiments in the first node of the present application and the features in the embodiments can be applied to the second node, and vice versa. 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] As an example, the interpretation of the terms in the present application refers to the definitions of the TS38 series of specification protocols of 3GPP.

[0006] As an example, the interpretation of the terms in the present application refers to the definitions of the TS28 series of specification protocols of 3GPP.

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

[0008] receiving a target CSI reporting configuration; the target CSI reporting configuration is used to configure reporting of a target CSI on a target cell, and the target CSI is generated in an AI-based manner; receiving a first information block, the first information block indicating a target identifier;

[0009] Send target CSI;

[0010] Among them, the generation method of the target CSI is associated with a first type of identifier; whether the first type of identifier associated with the generation method of the target CSI is updated to the target identifier depends on whether the target cell belongs to a first cell set; only when the target cell belongs to the first cell set, the first type of identifier associated with the generation method of the target CSI is updated to the target identifier; the first cell set includes multiple service cells, the first cell is a service cell in the first cell set, and the first cell depends on the first information block; the target cell is different from the first cell.

[0011] As an embodiment, the problems to be solved by the present application include: when multiple cells adopt an AI-based CSI generation method, how to unify the understanding of different AI entities or functions, and report channel information.

[0012] As an embodiment, in the above method, the generation method of CSI in multiple cells is determined by the target identifier indicated by the first information block.

[0013] As an embodiment, the characteristic of the above method is that the understanding of different AI entities or functions is unified among multiple nodes.

[0014] As an embodiment, the benefits of the above method include: obtaining channel information of multiple time units based on the measurement of a resource set.

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

[0016] As an embodiment, the AI ​​(Artificial Intelligence) includes ML (Machine Learning).

[0017] As an embodiment, the benefits of the above method include: better adaptability to various application scenarios and terminals, and improved flexibility and adaptability.

[0018] As an embodiment, the benefits of the above method include: saving signaling overhead.

[0019] As an embodiment, the benefits of the above method include: improving the accuracy and real-time performance of channel information reporting.

[0020] According to one aspect of the present application, it is characterized in that the first node is a user equipment.

[0021] According to one aspect of the present application, it is characterized in that the first node is a relay node.

[0022] According to one aspect of the present application, it is characterized in that the first information block is applied to each serving cell in the first cell set.

[0023] As an embodiment, the benefits of the above method include: saving signaling overhead.

[0024] According to one aspect of the present application, it is characterized in that the first information block indicates the first cell.

[0025] As an embodiment, the above method has the following advantages: good flexibility.

[0026] According to one aspect of the present application, it is characterized in that the first cell is a service cell where the physical channel carrying the first information block is located.

[0027] As an embodiment, the benefits of the above method include: simplifying the design.

[0028] According to one aspect of the present application, it is characterized in that the target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of channel measurement or interference measurement of the target CSI, and the first resource set includes one or more RS resources on the target cell; the target CSI indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

[0029] As an embodiment, the benefits of the above method include: reducing the overhead required to obtain channel information.

[0030] As an embodiment, the benefits of the above method include: reducing the measurement resources required to obtain channel information.

[0031] According to one aspect of the present application, it is characterized in that the target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of channel measurement or interference measurement of the target CSI, and the first resource set includes one or more RS resources; the target CSI is generated by the first node or the receiver of the target CSI reporting configuration performing a first operation, the input of the first operation depends on the measurement based on the first resource set, and the target CSI depends on the output of the first operation.

[0032] As an embodiment, the benefits of the above method include: better adaptability to various application scenarios or terminals, and improved flexibility and adaptability.

[0033] As an embodiment, the benefits of the above method include: improving the accuracy of channel information reporting.

[0034] According to one aspect of the present application, it is characterized in that the target CSI generation method is associated with the first type of identifier including: the first operation is associated with the first type of identifier.

[0035] As an embodiment, the benefits of the above method include: identifying an AI entity or function through the first type of identifier simplifies the design and unifies the understanding of different AI entities or functions among multiple nodes.

[0036] According to one aspect of the present application, it is characterized in that the target CSI generation method is associated with a first type of identifier including: the target CSI reporting configuration indicates a first type of identifier, and the first type of identifier indicated by the target CSI reporting configuration is the first type of identifier associated with the target CSI generation method.

[0037] As an embodiment, the benefits of the above method include: the first type of identifier is indicated by the target CSI reporting configuration, which simplifies the design and is easy to implement.

[0038] According to one aspect of the present application, it is characterized in that the generation method of the target CSI is associated with the first type of identifier, including: the generation method of the target CSI uses the AI ​​model identified by the first type of identifier, or, the target CSI is generated by the AI ​​entity identified by the first type of identifier, or, the target CSI is used for the AI ​​function identified by the first type of identifier.

[0039] As an embodiment, the AI ​​(Artificial Intelligence) includes ML (Machine Learning).

[0040] As an embodiment, the benefits of the above method include: better adaptability to various application scenarios and terminals, and improved flexibility and adaptability.

[0041] As an embodiment, the benefits of the above method include: improving the accuracy and real-time performance of channel information reporting.

[0042] According to one aspect of the present application, it is characterized by comprising:

[0043] receiving a first higher layer parameter;

[0044] The first higher layer parameter indicates the first cell set.

[0045] As an embodiment, the benefits of the above method include: indicating multiple cells through the first higher-layer parameter simplifies the design and unifies the understanding of different AI entities or functions among multiple nodes.

[0046] According to one aspect of the present application, it is characterized by comprising:

[0047] sending a second information block;

[0048] The second information block indicates that the generation method of CSI on multiple serving cells is associated with the same first type identifier.

[0049] As an embodiment, the benefits of the above method include: the first node reports the terminal capability to help the base station select a more appropriate channel information measurement and reporting scheme, thereby improving transmission performance.

[0050] The present application discloses a method used in a second node of wireless communication, characterized by comprising:

[0051] Sending a target CSI reporting configuration; the target CSI reporting configuration is used to configure the reporting of the target CSI on the target cell, and the generation method of the target CSI is based on AI; sending a first information block, the first information block indicating a target identifier;

[0052] Receive target CSI;

[0053] Among them, the generation method of the target CSI is associated with a first type of identifier; whether the first type of identifier associated with the generation method of the target CSI is updated to the target identifier depends on whether the target cell belongs to a first cell set; only when the target cell belongs to the first cell set, the first type of identifier associated with the generation method of the target CSI is updated to the target identifier; the first cell set includes multiple service cells, the first cell is a service cell in the first cell set, and the first cell depends on the first information block; the target cell is different from the first cell.

[0054] According to one aspect of the present application, it is characterized in that the second node is a base station.

[0055] According to one aspect of the present application, it is characterized in that the second node is a user equipment.

[0056] According to one aspect of the present application, it is characterized in that the second node is a relay node.

[0057] According to one aspect of the present application, it is characterized in that the first information block is applied to each serving cell in the first cell set.

[0058] According to one aspect of the present application, it is characterized in that the first information block indicates the first cell.

[0059] According to one aspect of the present application, it is characterized in that the first cell is a service cell where the physical channel carrying the first information block is located.

[0060] According to one aspect of the present application, it is characterized in that the target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of channel measurement or interference measurement of the target CSI, and the first resource set includes one or more RS resources on the target cell; the target CSI indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

[0061] According to one aspect of the present application, it is characterized in that the target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of channel measurement or interference measurement of the target CSI, and the first resource set includes one or more RS resources; the method for generating the target CSI includes the receiver of the target CSI reporting configuration performing a first operation, the input of the first operation depends on the measurement based on the first resource set, and the target CSI depends on the output of the first operation.

[0062] According to one aspect of the present application, it is characterized in that the target CSI generation method is associated with the first type of identifier including: the first operation is associated with the first type of identifier.

[0063] According to one aspect of the present application, it is characterized in that the target CSI generation method is associated with a first type of identifier including: the target CSI reporting configuration indicates a first type of identifier, and the first type of identifier indicated by the target CSI reporting configuration is the first type of identifier associated with the target CSI generation method.

[0064] According to one aspect of the present application, it is characterized in that the generation method of the target CSI is associated with the first type of identifier, including: the generation method of the target CSI uses the AI ​​model identified by the first type of identifier, or, the target CSI is generated by the AI ​​entity identified by the first type of identifier, or, the target CSI is used for the AI ​​function identified by the first type of identifier.

[0065] According to one aspect of the present application, it is characterized by comprising:

[0066] sending a first higher layer parameter;

[0067] The first higher layer parameter indicates the first cell set.

[0068] According to one aspect of the present application, it is characterized by comprising:

[0069] receiving a second information block;

[0070] The second information block indicates that the generation method of CSI on multiple serving cells is associated with the same first type identifier.

[0071] The present application discloses a terminal, characterized in that the terminal comprises: one or more processors and a memory;

[0072] 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.

[0073] The present application discloses a base station, characterized in that the base station comprises: one or more processors and a memory;

[0074] 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.

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

[0076] A first receiver receives a target CSI reporting configuration, wherein the target CSI reporting configuration is used to configure reporting of a target CSI on a target cell, and a generation method of the target CSI is based on AI; receives a first information block, wherein the first information block indicates a target identifier;

[0077] A first processor sends a target CSI;

[0078] Among them, the generation method of the target CSI is associated with a first type of identifier; whether the first type of identifier associated with the generation method of the target CSI is updated to the target identifier depends on whether the target cell belongs to a first cell set; only when the target cell belongs to the first cell set, the first type of identifier associated with the generation method of the target CSI is updated to the target identifier; the first cell set includes multiple service cells, the first cell is a service cell in the first cell set, and the first cell depends on the first information block; the target cell is different from the first cell.

[0079] The present application discloses a second node used for wireless communication, characterized in that it includes:

[0080] The second processor sends a target CSI reporting configuration; the target CSI reporting configuration is used to configure the reporting of the target CSI on the target cell, and the generation method of the target CSI is based on AI; sends a first information block, the first information block indicates a target identifier; receives the target CSI;

[0081] Among them, the generation method of the target CSI is associated with a first type of identifier; whether the first type of identifier associated with the generation method of the target CSI is updated to the target identifier depends on whether the target cell belongs to a first cell set; only when the target cell belongs to the first cell set, the first type of identifier associated with the generation method of the target CSI is updated to the target identifier; the first cell set includes multiple service cells, the first cell is a service cell in the first cell set, and the first cell depends on the first information block; the target cell is different from the first cell.

[0082] As an embodiment, compared with the traditional solution, this application has the following advantages:

[0083] Higher channel information accuracy and real-time performance, enhanced overall system performance;

[0084] Saves signaling overhead;

[0085] Lower air interface overhead;

[0086] More flexible and diverse input information;

[0087] Greater flexibility and adaptability;

[0088] Enhanced reliability and robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] 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:

[0090] Figure 1 A flowchart of a target CSI reporting configuration, a first information block and a target CSI according to an embodiment of the present application is shown;

[0091] Figure 2 A schematic diagram of a network architecture according to an embodiment of the present application is shown;

[0092] 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;

[0093] 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;

[0094] Figure 5 A flow chart showing transmission between a first node and a second node according to an embodiment of the present application;

[0095] Figure 6 A schematic diagram showing a first information block according to an embodiment of the present application;

[0096] Figure 7 A schematic diagram showing a first cell relying on a first information block according to an embodiment of the present application is shown;

[0097] Figure 8 A schematic diagram showing a first cell relying on a first information block according to another embodiment of the present application is shown;

[0098] Fig. 9 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.10 A schematic diagram showing a relationship between a first operation and a target CSI according to an embodiment of the present application;

[0100] Fig.11A schematic diagram showing a method for generating target CSI according to an embodiment of the present application is associated with a first type of identifier;

[0101] Fig.12 A schematic diagram showing a first operation according to an embodiment of the present application;

[0102] Fig.13 A schematic diagram showing a method for generating target CSI according to another embodiment of the present application is associated with a first type of identifier;

[0103] Fig.14 A schematic diagram showing a method for generating target CSI according to another embodiment of the present application is associated with a first type of identifier;

[0104] Fig.15 A schematic diagram showing a first higher layer parameter according to an embodiment of the present application;

[0105] Fig.16 A schematic diagram showing a second information block according to an embodiment of the present application;

[0106] Fig.17 A schematic diagram showing a first operation according to another embodiment of the present application is shown.

[0107] Fig.18 A schematic diagram of a first node deploying a first operation according to an embodiment of the present application is shown.

[0108] Fig.19 A schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of the present application is shown;

[0109] Fig. 20 A schematic diagram based on artificial intelligence or machine learning according to an embodiment of the present application is shown;

[0110] Fig.21 A structural block diagram of a processing device used in a first node according to an embodiment of the present application is shown;

[0111] Fig. 22 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

[0112] 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. 22 The embodiments in Figure 5 The embodiments and appendix Figure 6 -Attached Fig. 22 The embodiments in , etc.

[0113] Example 1

[0114] Embodiment 1 illustrates a flow chart of a target CSI reporting configuration, a first information block, and a target CSI according to an embodiment of the present application, as shown in the attached figure. Figure 1 As shown in the 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.

[0115] In embodiment 1, the first node receives a target CSI reporting configuration in step 101; receives a first information block in step 102; and sends a target CSI in step 103; wherein the target CSI reporting configuration is used to configure the reporting of the target CSI on the target cell, and the generation method of the target CSI is based on AI; the first information block indicates a target identifier; the generation method of the target CSI is associated with a first type of identifier; whether the first type of identifier associated with the generation method of the target CSI is updated to the target identifier depends on whether the target cell belongs to a first cell set; only when the target cell belongs to the first cell set, the first type of identifier associated with the generation method of the target CSI is updated to the target identifier; the first cell set includes multiple service cells, the first cell is a service cell in the first cell set, and the first cell depends on the first information block; the target cell is different from the first cell.

[0116] As an embodiment, the target CSI reporting configuration is carried by higher layer signaling.

[0117] As an embodiment, the target CSI reporting configuration is carried by RRC (Radio Resource Control) signaling.

[0118] As an embodiment, the target CSI reporting configuration is carried by an RRC IE (Information Element).

[0119] As an embodiment, the target CSI reporting configuration is carried by at least one RRC IE.

[0120] As an embodiment, the target CSI reporting configuration includes information in one or more fields in at least one RRC IE.

[0121] As an embodiment, the target CSI reporting configuration includes information in one or more fields of each RRC IE in multiple RRC IEs.

[0122] As an embodiment, the target CSI reporting configuration is an RRC IE.

[0123] As an embodiment, the target CSI reporting configuration belongs to the CSI-ReportConfig IE.

[0124] As an embodiment, the target CSI reporting configuration belongs to ServingCellConfig IE.

[0125] As an embodiment, the target CSI reporting configuration belongs to the CSI-MeasConfig IE.

[0126] As an embodiment, the target CSI reporting configuration belongs to ServingCellConfigCommon IE.

[0127] As an embodiment, the target CSI reporting configuration belongs to ServingCellConfigCommonSIB IE.

[0128] As an embodiment, the target CSI reporting configuration includes part or all of the fields in the CSI-ReportConfig IE.

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

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

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

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

[0133] As an embodiment, the target CSI reporting configuration includes a CSI resource configuration, and the one CSI resource configuration is used to indicate a first resource set, and the first resource set is used for at least one of channel measurement or interference measurement of the target CSI.

[0134] As a sub-embodiment of the above embodiment, the CSI resource configuration is an IE CSI-ResourceConfig.

[0135] As a sub-embodiment of the above embodiment, the target CSI reporting configuration includes a resourcesForChannelMeasurement domain, and the resourcesForChannelMeasurement domain included in the target CSI reporting configuration indicates the one CSI resource configuration.

[0136] As an embodiment, the target CSI reporting configuration includes multiple CSI resource configurations, and one CSI resource configuration among the multiple CSI resource configurations is used to configure the first RS resource set.

[0137] As an embodiment, the target CSI reporting configuration includes a reportConfigType (reporting configuration type) field; the reportConfigType (reporting configuration type) field in the target CSI reporting configuration indicates whether the target CSI is periodic (periodic), semi-persistent on PUSCH (semi Persistent On PUSCH), semi-persistent on PUCCH (semi Persistent On PUCCH), or aperiodic (aperiodic).

[0138] As an embodiment, the target CSI is periodic, semi-persistent or non-periodic.

[0139] As an embodiment, the target CSI reporting configuration further indicates the reporting amount included in the target CSI.

[0140] As an embodiment, the target CSI reporting configuration includes a reportQuantity field, and the field in the target CSI reporting configuration indicates a reporting quantity (report quantity) included in the target CSI.

[0141] As an embodiment, the reported amount included in the target CSI includes at least one of CQI (Channel quality indicator), PMI (Precoding Matrix Indicator), CRI (CSI-RS Resource Indicator), SS / PBCH block resource indicator (SS / PBCH Block Resource indicator, SSBRI), layer indication (Layer Indicator, LI), RI (Rank Indicator, rank indication), L1-RSRP (Layer 1 reference signal received power) or L1-SINR (Layer 1 signal-to-noise and interference ratio, layer 1 signal-to-noise ratio).

[0142] As an embodiment, the target CSI reporting configuration is used to configure the reporting of the target CSI on the target cell, including: RS resources used for the reporting of the target CSI are transmitted on the target cell.

[0143] As an embodiment, the target CSI reporting configuration is used to configure the reporting of the target CSI on the target cell, including: the time-frequency domain resources occupied by the RS resources used for the reporting of the target CSI belong to the target cell.

[0144] As an embodiment, the target CSI reporting configuration is used to configure the reporting of the target CSI on the target cell, including: the target CSI reporting configuration indicates at least one resource set, the at least one resource set indicated by the target CSI reporting configuration is used for reporting the target CSI, and the at least one resource set used for reporting the target CSI belongs to the target cell.

[0145] As an embodiment, the target CSI reporting configuration is used to configure the reporting of the target CSI on the target cell, including: the reporting of the target CSI is transmitted on the target cell.

[0146] As an embodiment, the target CSI reporting configuration is used to configure the reporting of the target CSI on the target cell, including: the time-frequency domain resources carrying the reporting of the target CSI belong to the target cell.

[0147] As an embodiment, the first information block is carried by physical layer signaling.

[0148] As an embodiment, the first information block includes DCI.

[0149] As an embodiment, the first information block includes part or all of the fields in the DCI.

[0150] As an embodiment, the first information block includes a MAC CE.

[0151] As an embodiment, the first information block is cell common.

[0152] As an embodiment, the first information block is cell specific.

[0153] As an embodiment, the first information block is common to a user equipment group (UE group common).

[0154] As an embodiment, the first information block is UE group specific.

[0155] As an embodiment, the first information block is user equipment specific (UE specific).

[0156] As an embodiment, the first information block explicitly indicates the target identifier.

[0157] As an embodiment, the first information block implicitly indicates the target identifier.

[0158] As an embodiment, the first information block includes a field, and the field included in the first information block indicates the target identifier.

[0159] Typically, the target cell is a serving cell of the first node, and all serving cells of the first node include the first cell set and at least one serving cell outside the first cell set.

[0160] Typically, the target cell is a serving cell of the first node, and the target cell is a serving cell in the first cell set or a serving cell outside the first cell set.

[0161] Typically, only when the target cell belongs to the first cell set, the first type of identifier associated with the method of generating the target CSI is updated to the target identifier, including: when the target cell does not belong to the first cell set, the first type of identifier associated with the method of generating the target CSI is not updated to the target identifier.

[0162] As an embodiment, the first category identifier being updated to the target identifier includes: the first category identifier is equal to the target identifier.

[0163] As an embodiment, the first-category identifier being updated to the target identifier includes: the first-category identifier being set as the target identifier.

[0164] As an embodiment, the first type of identifier is updated to the target identifier, including: the first node assumes that the first type of identifier is the target identifier.

[0165] As an embodiment, the first cell set is the cell set to which the first cell belongs.

[0166] As an embodiment, the first cell set is a cell set among multiple cell sets of the first node that includes the first cell; any cell set among the multiple cell sets includes one or more service cells.

[0167] As an embodiment, the multiple service cells included in the first cell set belong to the same cell group, and the first cell belongs to the one cell group.

[0168] As an embodiment, the multiple service cells included in the first cell set belong to the same cell group, and the first cell is a SpCell (Special Cell) or a SCell (Secondary Cell) in the cell group.

[0169] As an embodiment, the multiple service cells included in the first cell set belong to the same cell group, the cell group is a PCG (Primary Cell Group), and the first cell is a PCell (Primary Cell) in the cell group.

[0170] As an embodiment, the multiple service cells included in the first cell set belong to the same cell group, the cell group is a PCG (Primary Cell Group), and the first cell is a SCell (Secondary Cell) in the cell group.

[0171] As an embodiment, the multiple service cells included in the first cell set belong to the same cell group, the cell group is an SCG (Secondary Cell Group), and the first cell is a PSCell (Primary Secondary Cell) in the cell group.

[0172] As an embodiment, the multiple service cells included in the first cell set belong to the same cell group, the cell group is an SCG (Secondary Cell Group), and the first cell is an SCell (Secondary Cell) in the cell group.

[0173] As an embodiment, the target identifier is a first-category identifier.

[0174] As an embodiment, the target identifier is a non-negative integer.

[0175] As an embodiment, the target identifier is a character string.

[0176] As an embodiment, the target identifier is used to identify the AI ​​model.

[0177] As an embodiment, the target identifier is used to identify the AI ​​entity.

[0178] As an embodiment, the target identifier is used to identify the AI ​​function.

[0179] As an embodiment, the benefits of the above method include that an AI entity or function is identified by the target identifier, which simplifies the design and unifies the understanding of different AI entities or functions among multiple nodes.

[0180] As an embodiment, the target identifier is a model identifier.

[0181] As an embodiment, the target identifier is used to identify an AI model.

[0182] As an embodiment, the target identifier is used by the first node to determine an AI model.

[0183] As an embodiment, the target identifier is used by the first node to determine the AI ​​model adopted by the first operation.

[0184] As an embodiment, the benefits of the above method include identifying an AI model / entity / function through the target identifier, simplifying the design and unifying the understanding of different AI entities / functions among multiple nodes.

[0185] As an embodiment, the benefits of the above method include that all serving cells in the first cell set can use the same AI model.

[0186] As an embodiment, the benefits of the above method include that the CSI on all serving cells in the first cell set can be generated by the same AI entity.

[0187] As an embodiment, the target identifier is used to identify or indicate a resource set.

[0188] As an embodiment, the target identifier is used to identify or indicate a resource set, and the resource set identified or indicated by the target identifier includes resources on each serving cell in the first cell set, and the resources include at least one of time-frequency resources or RS resources.

[0189] As an embodiment, the target identifier is used to identify or indicate a resource set, and the measurement of the resource set identified or indicated by the target identifier is used to obtain a training data set.

[0190] As an embodiment, the target identifier is used to identify or indicate a resource set, and the resource set identified or indicated by the target identifier includes resources on each serving cell in the first cell set, and the resources include at least one of time-frequency resources or RS resources; the measurement of the resource set identified or indicated by the target identifier is used to obtain a training data set.

[0191] As an embodiment, the target identifier is used to identify or indicate a resource set.

[0192] As an embodiment, the target identifier is used to identify or indicate a training data set.

[0193] As an embodiment, the target identifier is used to identify or indicate a training data set, and the training data set identified or indicated by the target identifier includes training data on each serving cell in the first cell set.

[0194] As an embodiment, the benefits of the above method include that 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, further simplifying the design.

[0195] As an embodiment, the benefits of the above method include that all service cells in the first cell set can use the same AI model, and the training data for training the same AI model comes from all service cells in the first cell set, so that more accurate training data can be obtained, thereby improving the reliability and accuracy of the AI ​​model.

[0196] As an embodiment, the target CSI is generated based on AI.

[0197] As an embodiment, the method for generating the target CSI is based on AI, including: the method for generating the target CSI is based on training.

[0198] As an embodiment, the target CSI is generated based on AI, including: the target CSI is generated using an AI model.

[0199] As an embodiment, the target CSI is generated in an AI-based manner, including: the target CSI includes information based on artificial intelligence or machine learning.

[0200] As an embodiment, the target CSI is generated based on AI, including: the target CSI includes information generated based on a neural network.

[0201] As an embodiment, the target CSI is generated based on AI, including: the target CSI includes information generated based on CNN (Conventional Neural Networks, convolutional neural network).

[0202] As an embodiment, the target CSI generation method is based on AI and includes: the target CSI reporting configuration indicates a first type of identifier.

[0203] As an embodiment, the method for generating the target CSI is based on AI and includes: the target CSI reporting configuration indicates a first type of identifier, and the first operation is associated with the first type of identifier indicated by the target CSI reporting configuration.

[0204] As an embodiment, the method for generating the target CSI is based on AI and includes: the method for generating the target CSI is associated with a first type of identifier.

[0205] As an embodiment, the target CSI generation method is associated with the first type of identifier, including: the target CSI generation method belongs to an AI function, and the first type of identifier is used to identify the AI ​​function.

[0206] As an embodiment, the first type identifier is a non-negative integer.

[0207] As an embodiment, the first type of identifier is a character string.

[0208] As an embodiment, the first type of identification is used to identify the AI ​​model.

[0209] As an embodiment, the first type of identification is used to identify an AI entity.

[0210] As an embodiment, the first type of identification is used to identify the AI ​​function.

[0211] As an embodiment, the benefits of the above method include that an AI entity or function is identified by the first type of identifier, which simplifies the design and unifies the understanding of different AI entities or functions among multiple nodes.

[0212] As an embodiment, the first type of identifier is a model identifier.

[0213] As an embodiment, the first type of identifier is used to identify an AI model.

[0214] As an embodiment, the first type of identifier is used by the first node to determine an AI model.

[0215] As an embodiment, the first type of identifier is used by the first node to determine the AI ​​model adopted by the first operation.

[0216] As an embodiment, the benefits of the above method include identifying an AI model / entity / function through the first type of identifier, simplifying the design and unifying the understanding of different AI entities / functions among multiple nodes.

[0217] As an embodiment, the first type of identifier is used to identify or indicate a resource set.

[0218] As an embodiment, the first type of identifier is used to identify or indicate a resource set, and the measurement of the resource set is used to obtain a training data set.

[0219] As an embodiment, the first type of identifier is used to identify or indicate a resource set.

[0220] As an embodiment, the first type of identifier is used to identify or indicate a training data set.

[0221] As an embodiment, the benefits of the above method include that 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, further simplifying the design.

[0222] As an embodiment, the target CSI is sent later than the first moment.

[0223] As an embodiment, only when the target cell belongs to the first cell set, starting from the first moment, the first type of identifier associated with the generation method of the target CSI is updated to the target identifier, and the first moment is later than the reception of the first information block.

[0224] As an embodiment, the first moment is the moment when the first information block becomes effective.

[0225] As an embodiment, a HARQ-ACK (Hybrid Automatic Repeat request-Acknowledgement) corresponding to the first information block is sent in a first physical channel.

[0226] As an embodiment, the first physical channel includes transmission on a PUSCH (Physical uplink shared channel).

[0227] As an embodiment, the first physical channel includes transmission on PUCCH (Physical uplink control channel).

[0228] Typically, the HARQ-ACK corresponding to the first information block is a positive HARQ-ACK corresponding to the first information block.

[0229] As an embodiment, the HARQ-ACK corresponding to the first information block indicates that the first information block is correctly received.

[0230] As an embodiment, the HARQ-ACK corresponding to the first information block indicates that the PDSCH scheduled by the first information block is correctly received.

[0231] As an embodiment, the HARQ-ACK corresponding to the first information block indicates that an activation command carried by the first information block is correctly received.

[0232] As an embodiment, the positive HARQ-ACK corresponding to the first information block indicates that the first information block is correctly received.

[0233] As an embodiment, the positive HARQ-ACK corresponding to the first information block indicates that the PDSCH scheduled by the first information block is correctly received.

[0234] As an embodiment, the positive HARQ-ACK corresponding to the first information block indicates that an activation command carried by the first information block is correctly received.

[0235] As an embodiment, the first moment is later than the first physical channel in the time domain.

[0236] As an embodiment, the first moment is a first time slot that is later than the first physical channel by at least a first reference time interval.

[0237] As an embodiment, the first moment is the first time slot after the first reference time interval after the last symbol occupied by the first physical channel.

[0238] As an embodiment, the first moment is a first time slot that is later than the first physical channel by at least a first reference time interval.

[0239] As an embodiment, the first moment is the first time slot after the first reference time interval time slots after the last symbol occupied by the first physical channel.

[0240] As an embodiment, the first moment is the first time slot after the first reference time interval symbols after the last symbol occupied by the first physical channel.

[0241] As an embodiment, the first physical channel is transmitted on time slot n, the first moment is the first time slot after time slot m, and m is equal to n+first reference time interval.

[0242] As an embodiment, the first reference time interval is a positive real number.

[0243] As an embodiment, the first reference time interval is a time length of a positive integer number of symbols.

[0244] As an embodiment, the unit of the first reference time interval is symbol.

[0245] As an embodiment, the unit of the first reference time interval is milliseconds.

[0246] As an embodiment, the unit of the first reference time interval is a time slot.

[0247] As an embodiment, the first reference time interval is a time length of a positive integer number of time slots.

[0248] As an embodiment, the first reference time interval is beamAppTime.

[0249] As an embodiment, the first reference time interval is

[0250] As a sub-embodiment of the above embodiment, the μ is the subcarrier spacing configuration of the first physical channel.

[0251] As a sub-embodiment of the above embodiment, the The number of time slots included in a subframe.

[0252] As a sub-embodiment of the above embodiment, the k mac is equal to 0, or, the k mac Equal to the value of parameter K-Mac.

[0253] As an embodiment, the first reference time interval is reported by the first node.

[0254] As an embodiment, the first reference time interval is indicated by a capability report of the first node.

[0255] As an embodiment, the symbol is a single carrier symbol.

[0256] As an embodiment, the symbol is a multi-carrier symbol.

[0257] As an embodiment, the symbol is an OFDM (Orthogonal Frequency Division Multiplexing) symbol.

[0258] As an embodiment, the symbol is obtained by performing OFDM symbol generation on the output of a transform precoding.

[0259] As an embodiment, the symbol is a DFT-S-OFDM (Discrete Fourier Transform Spread OFDM) symbol.

[0260] As an embodiment, the multi-carrier symbol is an OFDM (Orthogonal Frequency Division Multiplexing) symbol.

[0261] As an embodiment, the symbol is obtained by performing OFDM symbol generation on the output of a transform precoding.

[0262] As an embodiment, the symbol is an SC-FDMA (Single Carrier-Frequency Division Multiple Access) symbol.

[0263] As an embodiment, the symbol is a DFT-S-OFDM (Discrete Fourier Transform Spread OFDM) symbol.

[0264] As an embodiment, the multi-carrier symbol is a FBMC (Filter Bank Multi Carrier) symbol.

[0265] As an embodiment, the multi-carrier symbol includes a CP (Cyclic Prefix).

[0266] Example 2

[0267] 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.

[0268] Attached Figure 2The network architecture 200 is described. The network architecture 200 is a 5G NR (New Radio) / LTE (Long-Term Evolution) / LTE-A (Long-Term Evolution Advanced) system, or the network architecture 200 is a 5G+ network architecture, or the network architecture 200 is a 6G network architecture, or the network architecture 200 is a network architecture adopted by 3GPP in the future evolution; the network architecture 200 may be called 5GS (5G System) / EPS (Evolved Packet System), or the network architecture 200 may be called 6GS (6G System); the network architecture 200 includes at least one of UE (User Equipment) 201, RAN (Radio Access Network) 202, core network 210, HSS (Home Subscriber Server) / UDM (Unified Data Management) 220 and Internet service 230. The network architecture 200 may be interconnected with other access networks, but these entities / interfaces are not shown for simplicity. As shown, the network architecture 200 provides packet switching services, but it will be readily understood by those skilled in the art that the various concepts presented throughout this application can be extended to networks or other cellular networks that provide circuit switching services. The RAN includes a node 203. The RAN may also include other nodes 204. The node 203 provides user and control plane protocol terminations toward the UE 201. The node 203 may be connected to the other nodes 204 via an Xn interface (e.g., backhaul) / X2 interface. The node 203 may also be referred to as a base station, a base transceiver station, a radio base station, a radio transceiver, a transceiver function, a basic service set (BSS), an extended service set (ESS), a TRP (transmitting receiving node) or some other suitable terminology. The core network 210 is 5GC (5G Core Network) / EPC (Evolved Packet Core), or the core network 210 is 6GC; the node 203 provides an access point to the core network 210 for the UE 201.Examples of UE201 include cellular phones, smart phones, session initiation protocol (SIP) phones, laptop computers, personal digital assistants (PDAs), satellite radios, non-terrestrial base station communications, satellite mobile communications, global positioning systems, multimedia devices, video devices, digital audio players (e.g., MP3 players), cameras, game consoles, drones, aircraft, narrowband Internet of Things devices, machine type communication devices, land vehicles, cars, wearable devices, or any other similar functional devices. Those skilled in the art may also refer to UE201 as a mobile station, a subscriber station, a mobile unit, a subscriber unit, a wireless unit, a remote unit, a mobile device, a wireless device, a wireless communication device, a remote device, a mobile subscriber station, an access terminal, a mobile terminal, a wireless terminal, a remote terminal, a handset, a user agent, a mobile client, a client, or some other suitable term. Node 203 is connected to the core network 210 via an S1 / NG interface. The core network 210 includes MME (Mobility Management Entity) / AMF (Authentication Management Field) / SMF (Session Management Function) 211, other MME / AMF / 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 the core network 210. Generally, MME / AMF / SMF211 provides bearer and connection management. All user IP (Internet Protocol) packets are transmitted through S-GW / UPF212, and S-GW / UPF212 itself is connected to P-GW / UPF213. P-GW provides UE IP address allocation and other functions. P-GW / UPF213 is connected to Internet service 230. The Internet service 230 includes the operator's corresponding Internet protocol service, which may specifically include the Internet, intranet, IMS (IP Multimedia Subsystem) and packet switching services.

[0269] As an embodiment, the first node includes the UE201.

[0270] As an embodiment, the second node includes the node 203.

[0271] As an embodiment, the wireless link between the UE 201 and the node 203 includes a cellular network link.

[0272] As an embodiment, the sender of the target CSI reporting configuration includes the node 203.

[0273] As an embodiment, the receiver of the target CSI reporting configuration includes the UE201.

[0274] As an embodiment, the sender of the first information block includes the node 203.

[0275] As an embodiment, the receiver of the first information block includes the UE201.

[0276] As an embodiment, the sender of the target CSI includes the UE201.

[0277] As an embodiment, the receiver of the target CSI includes the node 203.

[0278] As an embodiment, the sender of the first higher layer parameter includes the node 203.

[0279] As an embodiment, the recipient of the first higher layer parameter includes the UE201.

[0280] As an embodiment, the sender of the second information block includes the UE201.

[0281] As an embodiment, the receiver of the second information block includes the node 203.

[0282] Example 3

[0283] 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.

[0284] Embodiment 3 shows a schematic diagram of an embodiment of a wireless protocol architecture of a user plane and a control plane according to the present application, as shown in the attached Figure 3 shown. Figure 3 is a schematic diagram illustrating an embodiment of a radio protocol architecture for a user plane 350 and a control plane 300, Figure 3The radio protocol architecture of the control plane 300 between a first communication node device (UE, gNB or RSU in V2X) and a second communication node device (gNB, UE or RSU in V2X), or between two UEs is shown with three layers: Layer 1, Layer 2 and Layer 3. Layer 1 (L1 layer) is the lowest layer and implements various PHY (physical layer) signal processing functions. The L1 layer will be referred to as PHY301 in this article. Layer 2 (L2 layer) 305 is above PHY301 and is responsible for the link between the first communication node device and the second communication node device, or between two UEs. The L2 layer 305 includes a MAC (Medium Access Control) sublayer 302, an RLC (Radio Link Control) sublayer 303, and a PDCP (Packet Data Convergence Protocol) sublayer 304, which terminate at the second communication node device. The PDCP sublayer 304 provides multiplexing between different radio bearers and logical channels. The PDCP sublayer 304 also provides security by encrypting data packets, and provides support for inter-zone mobility of the first communication node device between the second communication node device. The RLC sublayer 303 provides segmentation and reassembly of upper layer data packets, retransmission of lost data packets, and reordering of data packets to compensate for out-of-order reception due to HARQ. The MAC sublayer 302 provides multiplexing between logical and transport channels. The MAC sublayer 302 is also responsible for allocating various radio resources (e.g., resource blocks) in a cell between the first communication node devices. The MAC sublayer 302 is also responsible for HARQ operations. The RRC (Radio Resource Control) sublayer 306 in layer 3 (L3 layer) in the control plane 300 is responsible for obtaining radio resources (i.e., radio bearers) and configuring the lower layer using RRC signaling between the second communication node device and the first communication node device. The radio protocol architecture of the user plane 350 includes layer 1 (L1 layer) and layer 2 (L2 layer). The radio protocol architecture used for the first communication node device and the second communication node device in the user plane 350 is substantially the same as the corresponding layers and sublayers in the control plane 300 for the physical layer 351, the PDCP sublayer 354 in the L2 layer 355, the RLC sublayer 353 in the L2 layer 355, and the MAC sublayer 352 in the L2 layer 355, but the PDCP sublayer 354 also provides header compression for upper layer data packets to reduce radio transmission overhead.The L2 layer 355 in the user plane 350 also includes a SDAP (Service Data Adaptation Protocol) sublayer 356, which is responsible for mapping between QoS flows and data radio bearers (DRBs) to support the diversity of services. Although not shown in the figure, the first communication node device may have several upper layers above the L2 layer 355, including a network layer (e.g., an IP layer) terminated at the P-GW on the network side and an application layer terminated at the other end of the connection (e.g., a remote UE, a server, etc.).

[0285] As an example, Figure 3 The wireless protocol architecture is applicable to the first node.

[0286] As an example, Figure 3 The wireless protocol architecture is applicable to the second node.

[0287] As an embodiment, the higher layer in the present application refers to a layer above the physical layer.

[0288] As an embodiment, the target CSI reporting configuration is generated in the RRC sublayer 306.

[0289] As an embodiment, the first information block is generated in the PHY301 or the PHY351.

[0290] As an embodiment, the first information block is generated in the MAC sublayer 302 or the MAC sublayer 352.

[0291] As an embodiment, the target CSI is generated by the PHY301 or the PHY351.

[0292] As an embodiment, the target CSI is generated in the MAC sublayer 302 or the MAC sublayer 352.

[0293] As an embodiment, the first higher layer parameter is generated in the RRC sublayer 306.

[0294] As an embodiment, the first higher layer parameter is generated in the MAC sublayer 302 or the MAC sublayer 352 .

[0295] As an embodiment, the second information block is generated in the RRC sublayer 306.

[0296] As an embodiment, the second information block is generated in the MAC sublayer 302 or the MAC sublayer 352.

[0297] As an embodiment, the second information block is generated in the PHY301 or the PHY351.

[0298] Example 4

[0299] 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 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.

[0300] 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 .

[0301] 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.

[0302] In the transmission from the first communication device 410 to the second communication device 450, at the first communication device 410, the upper layer data packets from the core network are provided to the controller / processor 475. The controller / processor 475 implements the functionality of the L2 layer. In DL (DownLink), the controller / processor 475 provides header compression, encryption, packet segmentation and reordering, multiplexing between logical and transport channels, and radio resource allocation to the second communication device 450 based on various priority metrics. The controller / processor 475 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the second communication device 450. The transmit processor 416 and the multi-antenna transmit processor 471 implement various signal processing functions for the L1 layer (i.e., the physical layer). The transmit processor 416 implements coding and interleaving to facilitate forward error correction (FEC) at the second communication device 450, as well as constellation mapping based on various modulation schemes (e.g., binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), M-phase shift keying (M-PSK), M-quadrature amplitude modulation (M-QAM). The multi-antenna transmit processor 471 performs digital spatial precoding on the coded and modulated symbols, including codebook-based precoding and non-codebook-based precoding, and beamforming processing to generate one or more parallel 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 different antennas 420.

[0303] In the transmission from the first communication device 410 to the second communication device 450, at the second communication device 450, each receiver 454 receives a signal through its corresponding antenna 452. Each receiver 454 recovers the information modulated onto the RF carrier and converts the RF stream into a baseband multi-carrier symbol stream and provides it to the receiving processor 456. The receiving processor 456 and the multi-antenna receiving processor 458 implement various signal processing functions of the L1 layer. The multi-antenna receiving processor 458 performs a receiving analog precoding / beamforming operation on the baseband multi-carrier symbol stream from the receiver 454. The receiving processor 456 uses a fast Fourier transform (FFT) to convert the baseband multi-carrier symbol stream after the receiving analog precoding / beamforming operation from the time domain to the frequency domain. In the frequency domain, the physical layer data signal and the reference signal are demultiplexed by the receiving processor 456, wherein the reference signal will be used for channel estimation, and the data signal is recovered after multi-antenna detection in the multi-antenna receiving processor 458 to any parallel stream destined for the second communication device 450. The symbols on each parallel stream are demodulated and recovered in the receiving processor 456, and soft decisions are generated. The receiving processor 456 then decodes and deinterleaves the soft decisions to recover the upper layer data and control signals transmitted by the first communication device 410 on the physical channel. The upper layer data and control signals are then provided to the controller / processor 459. The controller / processor 459 implements the functions of the L2 layer. The controller / processor 459 may be associated with a memory 460 storing program codes and data. The memory 460 may be referred to as a computer-readable medium. In DL, the controller / processor 459 provides multiplexing, packet reassembly, decryption, header decompression, and control signal processing between the transmission and logical channels to recover the upper layer data packets from the core network. The upper layer data packets are then provided to all protocol layers above the L2 layer. Various control signals may also be provided to L3 for L3 processing. The controller / processor 459 is also responsible for error detection using confirmation (ACK) and / or negative confirmation (NACK) protocols to support HARQ operations.

[0304] 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.

[0305] 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.

[0306] 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 with the at least one processor. The second communication device 450 device at least: receives a target CSI reporting configuration; the target CSI reporting configuration is used to configure the reporting of the target CSI on the target cell, and the generation method of the target CSI is based on AI; receives a first information block, the first information block indicates a target identifier; sends a target CSI; wherein the generation method of the target CSI is associated with a first type of identifier; whether the first type of identifier associated with the generation method of the target CSI is updated to the target identifier depends on whether the target cell belongs to the first cell set; only when the target cell belongs to the first cell set, the first type of identifier associated with the generation method of the target CSI is updated to the target identifier; the first cell set includes multiple service cells, the first cell is a service cell in the first cell set, and the first cell depends on the first information block; the target cell is different from the first cell.

[0307] As an embodiment, the second communication device 450 includes: a memory storing a computer-readable instruction program, the computer-readable instruction program generates an action when executed by at least one processor, the action including: receiving a target CSI reporting configuration; the target CSI reporting configuration is used to configure the reporting of the target CSI on the target cell, and the target CSI generation method is based on AI; receiving a first information block, the first information block indicating a target identifier; sending a target CSI; wherein the target CSI generation method is associated with a first type of identifier; whether the first type of identifier associated with the generation method of the target CSI is updated to the target identifier depends on whether the target cell belongs to a first cell set; only when the target cell belongs to the first cell set, the first type of identifier associated with the generation method of the target CSI is updated to the target identifier; the first cell set includes multiple service cells, the first cell is a service cell in the first cell set, and the first cell depends on the first information block; the target cell is different from the first cell.

[0308] As an embodiment, the first communication device 410 includes: at least one processor and at least one memory, the at least one memory including computer program code; the at least one memory and the computer program code are configured to be used with the at least one processor. The first communication device 410 device at least: sends a target CSI reporting configuration; the target CSI reporting configuration is used to configure the reporting of the target CSI on the target cell, and the generation method of the target CSI is based on AI; sends a first information block, the first information block indicates a target identifier; receives a target CSI; wherein the generation method of the target CSI is associated with a first type of identifier; whether the first type of identifier associated with the generation method of the target CSI is updated to the target identifier depends on whether the target cell belongs to the first cell set; only when the target cell belongs to the first cell set, the first type of identifier associated with the generation method of the target CSI is updated to the target identifier; the first cell set includes multiple service cells, the first cell is a service cell in the first cell set, and the first cell depends on the first information block; the target cell is different from the first cell.

[0309] As an embodiment, the first communication device 410 includes: a memory storing a computer-readable instruction program, the computer-readable instruction program generates actions when executed by at least one processor, the actions including: sending a target CSI reporting configuration; the target CSI reporting configuration is used to configure the reporting of the target CSI on the target cell, and the target CSI generation method is based on AI; sending a first information block, the first information block indicating a target identifier; receiving a target CSI; wherein the target CSI generation method is associated with a first type of identifier; whether the first type of identifier associated with the target CSI generation method is updated to the target identifier depends on whether the target cell belongs to a first cell set; only when the target cell belongs to the first cell set, the first type of identifier associated with the target CSI generation method is updated to the target identifier; the first cell set includes multiple service cells, the first cell is a service cell in the first cell set, and the first cell depends on the first information block; the target cell is different from the first cell.

[0310] As an embodiment, the first node in the present application includes the second communication device 450.

[0311] As an embodiment, the second node in the present application includes the first communication device 410.

[0312] As an embodiment, at least one of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, and the data source 467} is used to receive the target CSI reporting configuration; and at least one of {the antenna 420, the transmitter 418, the transmitting processor 416, the multi-antenna transmitting processor 471, the controller / processor 475, and the memory 476} is used to send the target CSI reporting configuration.

[0313] 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 information block; 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 information block.

[0314] As an embodiment, at least one of {the antenna 420, the receiver 418, the receive processor 470, the multi-antenna receive processor 472, the controller / processor 475, and the memory 476} is used to receive the target CSI; and at least one of {the antenna 452, the transmitter 454, the transmit processor 468, the multi-antenna transmit processor 457, the controller / processor 459, the memory 460, and the data source 467} is used to send the target CSI.

[0315] 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 higher layer parameters; 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 higher layer parameters.

[0316] 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 second information block; and at least one of {the antenna 452, the transmitter 454, the transmitting processor 468, the multi-antenna transmitting processor 457, the controller / processor 459, the memory 460, and the data source 467} is used to send the second information block.

[0317] Example 5

[0318] Embodiment 5 illustrates a flow chart of transmission between a first node and a second node according to an embodiment of the present application; 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.

[0319] For the second node U1, the second information block is received in step S511; the first higher layer parameter is sent in step S512; the target CSI reporting configuration is sent in step S5101; the first information block is sent in step S5102; and the target CSI is received in step S5103.

[0320] For the first node U2, the second information block is sent in step S521; the first higher layer parameter is received in step S522; the target CSI reporting configuration is received in step S5201; the first information block is received in step S5202; the first operation is performed in step S523; and the target CSI is sent in step S5203.

[0321] In Example 5, the target CSI reporting configuration is used to configure the reporting of the target CSI on the target cell, and the generation method of the target CSI is based on AI; the first information block indicates the target identifier; the generation method of the target CSI is associated with a first type of identifier; whether the first type of identifier associated with the generation method of the target CSI is updated to the target identifier depends on whether the target cell belongs to the first cell set; only when the target cell belongs to the first cell set, the first type of identifier associated with the generation method of the target CSI is updated to the target identifier; the first cell set includes multiple service cells, the first cell is a service cell in the first cell set, and the first cell depends on the first information block; the target cell is different from the first cell.

[0322] As an embodiment, the first node U2 is the first node in this application.

[0323] As an embodiment, the second node U1 is the second node in this application.

[0324] 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.

[0325] 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.

[0326] As an embodiment, the air interface between the second node U1 and the first node U2 includes a wireless interface between user equipments.

[0327] As an embodiment, the second node U1 is a base station maintaining a service cell of the first node U2.

[0328] As an example, Figure 5 The steps in box F51 in exist; the method in the first node used for wireless communication includes: sending a second information block.

[0329] As an example, Figure 5 The steps in box F51 in exist; the method in the second node used for wireless communication includes: receiving a second information block.

[0330] As an example, Figure 5 The step in box F52 in exists; the method in a first node used for wireless communication comprises: receiving a first higher layer parameter.

[0331] As an example, Figure 5 The step in box F52 exists; the method in the second node used for wireless communication includes: sending a first higher layer parameter.

[0332] As an example, Figure 5 The steps in box F53 exist; the method in the first node used for wireless communication includes: performing a first operation.

[0333] As an embodiment, the target CSI reporting configuration is transmitted on a PDSCH (Physical Downlink Shared Channel).

[0334] As an embodiment, the first information block is transmitted on a PDSCH (Physical Downlink Shared Channel).

[0335] As an embodiment, the first information block is transmitted on PDCCH (Physical Downlink Control Channel).

[0336] As an embodiment, the target CSI is transmitted on PUCCH (Physical Uplink Control Channel).

[0337] As an embodiment, the target CSI is transmitted on a PUSCH (Physical Uplink Shared Channel).

[0338] As an embodiment, the first higher layer parameter is transmitted on PDSCH (Physical Downlink Shared Channel).

[0339] As an embodiment, the second information block is transmitted on PUCCH (Physical Uplink Control Channel).

[0340] As an embodiment, the second information block is transmitted on PUSCH (Physical Uplink Shared Channel).

[0341] As an embodiment, the second information block is received earlier than the first information block is sent.

[0342] As an embodiment, the reception of the second information block is earlier than the sending of the first higher layer parameter.

[0343] As an embodiment, the reception of the second information block is earlier than the sending of the target CSI reporting configuration.

[0344] As an embodiment, the reception of the second information block is no earlier than the sending of the target CSI reporting configuration.

[0345] As an embodiment, the first higher layer parameter is earlier than the target CSI reporting configuration.

[0346] As an embodiment, the first higher layer parameter is no earlier than the target CSI reporting configuration.

[0347] As an embodiment, the first information block is received earlier than the first node performs the first operation.

[0348] Example 6

[0349] Embodiment 6 illustrates a schematic diagram of a first information block according to an embodiment of the present application; Figure 6 In embodiment 6, the first information block is applied to each serving cell in the first cell set.

[0350] As an embodiment, the first information block is used to update the first type of identification in each serving cell in the first cell set.

[0351] As an embodiment, starting from a first moment, the first information block is applied to each serving cell in the first cell set; the first moment is later than a reception moment of the first information block.

[0352] As an embodiment, the first information block indicates a target identifier, and the target identifier indicated by the first information block is used to update the first type of identifier in each serving cell in the first cell set.

[0353] As an embodiment, starting from a first moment, the first type of identifier in each serving cell in the first cell set is updated to the target identifier.

[0354] Example 7

[0355] Embodiment 7 illustrates a schematic diagram of a first cell relying on a first information block according to an embodiment of the present application; Figure 7 In Embodiment 7, the first information block indicates the first cell.

[0356] As an embodiment, the first information block explicitly indicates the first cell.

[0357] As an embodiment, the first information block implicitly indicates the first cell.

[0358] As an embodiment, the cells indicated by the first information block include only the first cell.

[0359] As an embodiment, the first information block includes a first field, the first field includes at least one bit, and the first field in the first information block indicates the first cell.

[0360] As an embodiment, the first information block includes a first field, the first field in the first information block indicates a cell index, and the first cell is identified by the cell index.

[0361] As an embodiment, the first information block includes a first field, the first field in the first information block includes a cell index, and the first cell is identified by the cell index.

[0362] As an embodiment, the cell index includes at least one of ServCellIndex or SCellIndex.

[0363] As an embodiment, the first information block includes a first field, and the first field in the first information block is used to indicate the first cell from a plurality of cell candidates.

[0364] As an embodiment, the first information block indicating the first cell includes: the first information block indicating whether the first cell is activated.

[0365] As an embodiment, the first information block includes a first field, the first field includes at least one bit, each bit in the first field in the first information block corresponds to a cell, and each bit in the first field in the first information block indicates whether the corresponding cell is activated.

[0366] As a sub-embodiment of the above embodiment, when a bit of the first field in the first information block corresponding to the first cell is set to 1, the first cell is activated.

[0367] As a sub-embodiment of the above embodiment, when a bit of the first field in the first information block corresponding to the first cell is set to 0, the first cell is deactivated.

[0368] As an embodiment, the first information block indicates that the first cell includes: the first information block indicates whether the first cell is activated, and the first cell is a serving cell in a first cell set; when the first cell is activated, the first type of identifier associated with the method for generating CSI on each cell in the first cell set is updated to the target identifier; when the first cell is deactivated, the first type of identifier associated with the method for generating CSI on each cell in the first cell set is not updated to the target identifier.

[0369] As an embodiment, the first information block indicates the first cell, which is a serving cell in the first cell set; the first type of identifier associated with the generation method of CSI on each cell in the first cell set is updated to the target identifier.

[0370] As an embodiment, the first information block indicates the first cell, which is a service cell in the first cell set; whether the first type of identifier associated with the generation method of the target CSI is updated to the target identifier depends on whether the target cell belongs to the first cell set.

[0371] Example 8

[0372] Embodiment 8 illustrates a schematic diagram of a first cell relying on a first information block according to another embodiment of the present application; Figure 8 In Embodiment 8, the first cell is a serving cell where the physical channel carrying the first information block is located.

[0373] As an embodiment, the first cell is a service cell where a physical channel carrying the first information block is located, including: the first information block is transmitted on the first cell.

[0374] As an embodiment, the first cell is a service cell where a physical channel carrying the first information block is located, including: the physical channel carrying the first information block belongs to the first cell.

[0375] As an embodiment, the first cell is a service cell where the physical channel carrying the first information block is located, including: the RB (Resource Block) occupied by the physical channel carrying the first information block belongs to the first cell.

[0376] As an embodiment, the first cell is a service cell where the physical channel carrying the first information block is located, including: the BWP (Bandwidth Part) occupied by the physical channel carrying the first information block belongs to the first cell.

[0377] As an embodiment, the physical channel carrying the first information block is PDCCH.

[0378] As an embodiment, the physical channel carrying the first information block is PDSCH.

[0379] Example 9

[0380] Embodiment 9 illustrates a schematic diagram of a first resource set and a second resource set according to an embodiment of the present application; Fig. 9In Embodiment 9, the target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of channel measurement or interference measurement of the target CSI, and the first resource set includes one or more RS resources on the target cell; the target CSI indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

[0381] As an embodiment, the target CSI reporting configuration includes a CSI resource configuration, and the one CSI resource configuration is used to indicate the first resource set.

[0382] As a sub-embodiment of the above embodiment, the CSI resource configuration is an IE CSI-ResourceConfig.

[0383] As a sub-embodiment of the above embodiment, the target CSI reporting configuration includes a resourcesForChannelMeasurement domain, and the resourcesForChannelMeasurement domain included in the target CSI reporting configuration indicates the one CSI resource configuration.

[0384] As an embodiment, the target CSI reporting configuration includes multiple CSI resource configurations, and one CSI resource configuration among the multiple CSI resource configurations is used to indicate the first resource set.

[0385] As an embodiment, a CSI resource configuration used to configure the first resource set includes an identifier or index of each RS resource in the first resource set.

[0386] As an embodiment, a CSI resource configuration used to configure the first resource set includes an identifier of each RS resource in the first resource set.

[0387] As an embodiment, a CSI resource configuration used to configure the first resource set is used to configure each RS resource in the first resource set.

[0388] As an embodiment, a CSI resource configuration used to configure the first resource set includes configuration information of each RS resource in the first resource set.

[0389] As an embodiment, the target CSI reporting configuration includes a resourcesForChannelMeasurement domain, and the resourcesForChannelMeasurement domain included in the target CSI reporting configuration is used to indicate the first resource set.

[0390] 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.

[0391] 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.

[0392] 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.

[0393] As an embodiment, 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.

[0394] As an embodiment, the first resource set includes at least one RS resource set for channel measurement, and at least one RS resource set for interference measurement; 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.

[0395] As an embodiment, the first 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.

[0396] As an embodiment, an RS resource set for channel measurement includes one or more RS resources, and any RS resource in the RS resource set for channel measurement is a CSI-RS resource or a synchronization signal resource.

[0397] As an embodiment, an RS resource set for interference measurement includes one or more RS resources.

[0398] As an embodiment, an RS resource set for interference measurement includes one or more RS resources, and any RS resource in the RS resource set for interference measurement is a CSI-IM resource or an NZP (non-zero power) CSI-RS resource for interference measurement.

[0399] As an embodiment, the first resource set includes one or more RS resources.

[0400] As an embodiment, the first resource set includes one or more downlink RS resources.

[0401] As an embodiment, the first resource set includes one or more RS resources, and any RS resource in the first resource set is a CSI-RS (Channel State Information Reference Signal) resource or a synchronization signal resource.

[0402] As an embodiment, the synchronization signal resources include at least resources occupied by the synchronization signal.

[0403] As an embodiment, the synchronization signal resource is SSB (Synchronization Signal Block).

[0404] As an embodiment, the synchronization signal resource is a SS / PBCH (synchronization signal / physical broadcast channel) block resource.

[0405] As an embodiment, the target CSI reporting configuration indicates at least one resource configuration, and the at least one resource configuration indicates the first resource set.

[0406] As an embodiment, the target CSI reporting configuration includes at least one resource configuration, and the at least one resource configuration indicates the first resource set.

[0407] As an embodiment, a resource configuration is used to configure CSI resources.

[0408] As an embodiment, a resource configuration is an IE CSI-ResourceConfig.

[0409] As an embodiment, a resource configuration is carried by RRC IE.

[0410] As an embodiment, a resource configuration is carried by a CSI-ResourceConfig IE.

[0411] As an embodiment, the target CSI reporting configuration indicates configuration information of the first resource set.

[0412] As an embodiment, the target CSI reporting configuration indicates an identifier of the first resource set.

[0413] As an embodiment, the first resource set is used for channel measurement of the target CSI, and any RS resource in the first resource set used for the channel measurement of the target CSI is a CSI-RS resource or a synchronization signal resource.

[0414] As an embodiment, the first resource set is used for interference measurement of the target CSI, and any RS resource in the first resource set used for the interference measurement of the target CSI is a CSI-IM resource or an NZP (non-zero power) CSI-RS resource for interference measurement.

[0415] As an embodiment, the resources included in the first resource set belong to the target cell.

[0416] As an embodiment, the RS resources included in the first resource set are transmitted on the target cell.

[0417] As an embodiment, the first resource set only includes one or more RS resources on the target cell.

[0418] As an embodiment, the first resource set includes at least one RS resource that does not belong to the target cell.

[0419] As an embodiment, the target CSI reporting configuration indicates a first resource set, the first resource set is used for channel measurement of the target CSI, and the target CSI includes at least RSRP.

[0420] As an embodiment, the target CSI reporting configuration includes a resourcesForChannelMeasurement domain, and the resourcesForChannelMeasurement domain included in the target CSI reporting configuration is used to indicate the first resource set, and the first resource set is used for channel measurement of the target CSI, and the target CSI includes at least RSRP.

[0421] As an embodiment, the target CSI includes at least one CSI report.

[0422] As an embodiment, the target CSI includes predicted channel information.

[0423] As an embodiment, the target CSI includes predicted beam information.

[0424] As an embodiment, the target CSI includes at least one of CQI (Channel quality indicator), PMI (Precoding Matrix Indicator), CRI (CSI-RS Resource Indicator), SS / PBCH Block Resource Indicator (SS / PBCH Block Resource indicator, SSBRI), Layer Indicator (Layer Indicator, LI), RI (Rank Indicator, rank indication), L1-RSRP (Layer 1 reference signal received power, Layer 1 reference signal received power) or L1-SINR (Layer 1 signal-to-noise and interference ratio, Layer 1 signal-to-noise ratio)

[0425] As an embodiment, the target CSI includes one or more of PMI, CRI, CQI, RI, LI, SSBRI, RSRP, SINR, capability index and TDCP.

[0426] As an embodiment, the target CSI includes a channel matrix.

[0427] As an embodiment, the target CSI includes a feature vector.

[0428] As an embodiment, the target CSI includes a eigenvector and an eigenvalue.

[0429] As an embodiment, the target CSI includes precoding information.

[0430] As an embodiment, the target CSI includes non-codebook based precoding information.

[0431] As an embodiment, the target CSI is used to determine at least one precoding matrix.

[0432] As an embodiment, the target CSI indicates at least one precoding matrix.

[0433] As an embodiment, the precoding matrix is ​​in spatial-frequency domain.

[0434] As an embodiment, the precoding matrix is ​​an angular-delay domain projection.

[0435] As an embodiment, the target CSI includes information about relative phases, amplitudes and / or coefficients between multiple antenna ports.

[0436] As an embodiment, the target CSI includes compressed CSI.

[0437] As an embodiment, the target CSI includes predicted / estimated CSI.

[0438] As an embodiment, the first node is not required to measure the second set of resources.

[0439] As an embodiment, the first resource set is used for measurement, and the second resource set is used for prediction.

[0440] As an embodiment, only the first resource set among the first resource set and the second resource set is used for measurement.

[0441] 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.

[0442] 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 the second resource set.

[0443] As an embodiment, the target CSI is generated based on AI, and the first node is not required to measure the second resource set.

[0444] As an embodiment, the target CSI is generated based on AI, the first resource set is used for measurement, and the second resource set is used for prediction.

[0445] As an embodiment, the target CSI is generated based on AI, the first resource set is used for measurement, and the second resource set is used for prediction.

[0446] As an embodiment, the target CSI is generated based on AI, and only the first resource set of the first resource set and the second resource set is used for measurement.

[0447] 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.

[0448] 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.

[0449] 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.

[0450] 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.

[0451] As an embodiment, the second resource set includes the first resource set and resources outside the first resource set.

[0452] 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.

[0453] 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.

[0454] 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.

[0455] As an embodiment, the second resource set includes resources that do not belong to the first resource set.

[0456] As an embodiment, the second resource set includes antenna ports that do not belong to the first resource set.

[0457] 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.

[0458] As an embodiment, the second resource set includes at least one RS resource.

[0459] As an embodiment, the second resource set includes at least one beam.

[0460] As an embodiment, the second resource set includes one or more beams.

[0461] As an embodiment, the second resource set includes one or more vectors.

[0462] As an embodiment, the second resource set includes one or more matrices.

[0463] As an embodiment, the second resource set includes one or more codebooks.

[0464] As an embodiment, the second resource set includes one or more DFT vectors.

[0465] As an embodiment, the second resource set includes one or more DFT codebooks.

[0466] As an embodiment, the second resource set includes one or more antenna ports.

[0467] As an embodiment, the second resource set includes at least one training data set.

[0468] As an embodiment, the second resource set is used to train an AI model.

[0469] 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.

[0470] 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.

[0471] 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.

[0472] 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.

[0473] 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.

[0474] As an embodiment, the second resource set includes one or more RS resources.

[0475] As an embodiment, the second resource set includes one or more downlink RS resources.

[0476] 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.

[0477] As an embodiment, the target CSI reporting configuration indicates at least one resource configuration, and the at least one resource configuration indicates the second resource set.

[0478] As an embodiment, the target CSI reporting configuration includes at least one resource configuration, and the at least one resource configuration indicates the second resource set.

[0479] As an embodiment, the target CSI reporting configuration indicates at least one resource configuration, and the at least one resource configuration indicates the first resource set and the second resource set.

[0480] As an embodiment, the target CSI reporting configuration includes at least one resource configuration, and the at least one resource configuration indicates the first resource set and the second resource set.

[0481] As an embodiment, the target CSI reporting configuration indicates a resource configuration, and the one resource configuration indicates the first resource set and the second resource set.

[0482] As an embodiment, the target CSI reporting configuration indicates two resource configurations, and the two resource configurations respectively indicate the first resource set and the second resource set.

[0483] As an embodiment, the target CSI reporting configuration indicates configuration information of the second resource set.

[0484] As an embodiment, the target CSI reporting configuration indicates an identifier of the second resource set.

[0485] As an embodiment, the target CSI reporting configuration is used to indicate the second resource set from a reference resource set.

[0486] As an embodiment, the target CSI reporting configuration indicates a first type of identifier, and the second resource set depends on the first type of identifier.

[0487] As an embodiment, the second resource set being dependent on the first type of identifier includes: the first type of identifier is used to identify the second resource set.

[0488] As an embodiment, the second resource set depends on the first type of identifier, including: the first type of identifier is used to identify a reference resource set, and the reference resource set includes the second resource set.

[0489] As an embodiment, the second resource set depends on the first type of identifier, including: the first type of identifier is used to identify a reference resource set, the reference resource set includes the second resource set, and the target CSI reporting configuration is used to indicate the second resource set from the reference resource set.

[0490] As an embodiment, the information other than the target CSI reporting configuration indicating the second resource set includes higher layer parameters.

[0491] As an embodiment, the information other than the target CSI reporting configuration indicating the second resource set includes RRC parameters.

[0492] As an embodiment, the information other than the target CSI reporting configuration indicating the second resource set includes a partial or entire field of an RRC IE.

[0493] As an embodiment, the information other than the target CSI reporting configuration indicating the second resource set includes MAC CE.

[0494] As an embodiment, the information other than the target CSI reporting configuration indicating the second resource set includes DCI (downlink control information).

[0495] As an embodiment, the target CSI indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

[0496] As an embodiment, the target CSI is generated based on AI, and the target CSI indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

[0497] As an embodiment, whether the resources indicated by the target CSI belong to the first resource set depends on whether the generation method of the target CSI is based on AI; when the generation method of the target CSI is based on AI, the resources indicated by the target CSI belong to the second resource set, and the second resource set includes resources that do not belong to the first resource set; when the generation method of the target CSI is not based on AI, the resources indicated by the target CSI belong to the first resource set.

[0498] As an embodiment, whether the RS resources indicated by the target CSI belong to the first resource set depends on whether the generation method of the target CSI is based on AI; when the generation method of the target CSI is based on AI, the RS resources indicated by the target CSI belong to the second resource set, and the second resource set includes RS resources that do not belong to the first resource set; only when the generation method of the target CSI is not based on AI, the RS resources indicated by the target CSI belong to the first resource set.

[0499] As an embodiment, the target CSI indicates at least one resource in the second resource set.

[0500] As an embodiment, the second resource set includes at least one RS resource; the target CSI indicating at least one resource in the second resource set includes: the target CSI indicating at least one RS resource in the second resource set.

[0501] As an embodiment, the second resource set includes at least one beam; the target CSI indicating at least one resource in the second resource set includes: the target CSI indicating at least one beam in the second resource set.

[0502] As an embodiment, the second resource set includes one or more beams; the target CSI indicating at least one resource in the second resource set includes: the target CSI indicating at least one beam in the second resource set.

[0503] As an embodiment, the second resource set includes one or more vectors; the target CSI indicating at least one resource in the second resource set includes: the target CSI indicating at least one vector in the second resource set.

[0504] As an embodiment, the second resource set includes one or more matrices; the target CSI indicating at least one resource in the second resource set includes: the target CSI indicating at least one matrix in the second resource set.

[0505] As an embodiment, the second resource set includes one or more DFT vectors; the target CSI indicating at least one resource in the second resource set includes: the target CSI indicating at least one DFT vector in the second resource set.

[0506] As an embodiment, the second resource set includes one or more codebooks; the target CSI indicating at least one resource in the second resource set includes: the target CSI indicating at least one codebook in the second resource set.

[0507] As an embodiment, the second resource set includes one or more antenna ports; the target CSI indicating at least one resource in the second resource set includes: the target CSI indicating at least one antenna port in the second resource set.

[0508] Example 10

[0509] Embodiment 10 illustrates a schematic diagram of the relationship between the first operation and the target CSI according to an embodiment of the present application; Fig.10 In Embodiment 10, the target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of channel measurement or interference measurement of the target CSI, and the first resource set includes one or more RS resources; the target CSI generation method includes the first node or the receiver of the target CSI reporting configuration performing a first operation, the input of the first operation depends on the measurement based on the first resource set, and the target CSI depends on the output of the first operation.

[0510] Typically, the first processor performs the first operation.

[0511] As an embodiment, the first operation is used for beam prediction, and the first node adopts a singleside AI model.

[0512] As an embodiment, 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 input of the first operation depends on the measurement based on the first resource set, including: the input of the first operation depends on the channel measurement obtained based on the first resource set.

[0513] As an embodiment, 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 input of the first operation depends on the measurement based on the first resource set, including: the input of the first operation depends on the interference measurement obtained based on the first resource set.

[0514] As an embodiment, the first resource set includes at least one RS resource set for channel measurement, and at least one RS resource set for interference measurement; 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 input of the first operation depends on the measurement based on the first resource set, including: the input of the first operation depends on the channel measurement and interference measurement obtained based on the first resource set.

[0515] As an embodiment, the input of the first operation depends on the measurement based on the first resource set, including: the measurement based on the first resource set is used to generate the input of the first operation.

[0516] As an embodiment, 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 input of the first operation depends on the measurement based on the first resource set, including: the channel measurement obtained based on the first resource set is used to generate the input of the first operation.

[0517] As an embodiment, 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 input of the first operation depends on the measurement based on the first resource set, including: the interference measurement obtained based on the first resource set is used to generate the input of the first operation.

[0518] As an embodiment, the first resource set includes at least one RS resource set for channel measurement, and at least one RS resource set for interference measurement; 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 input of the first operation depends on the measurement based on the first resource set, including: the channel measurement and interference measurement obtained based on the first resource set are used to generate the input of the first operation.

[0519] As an embodiment, the channel measurement obtained based on the first resource set refers to: the channel measurement obtained based on at least one reference signal transmitted in the first resource set.

[0520] As an embodiment, the channel measurement obtained based on the first resource set refers to: the channel measurement obtained in the first resource set.

[0521] As an embodiment, the interference measurement obtained based on the first resource set refers to: interference measurement obtained based on at least one reference signal transmitted in the first resource set.

[0522] As an embodiment, the interference measurement obtained based on the first resource set refers to: interference measurement obtained in the first resource set.

[0523] As an embodiment, the channel measurement obtained based on the first resource set includes a channel matrix (channelmatrix).

[0524] As an embodiment, the channel measurement obtained based on the first resource set includes a raw channel matrix.

[0525] As an embodiment, the channel measurement obtained based on the first resource set includes an eigenvector.

[0526] As an embodiment, the channel measurement obtained based on the first resource set includes an eigenvector and an eigenvalue.

[0527] As an embodiment, the channel measurement obtained based on the first resource set includes one or more of BLER, delay spread, Doppler spread, Doppler shift, average delay, average gain, path loss and RSRP.

[0528] As an embodiment, the interference measurement obtained based on the first resource set includes at least one of interference power, interference variance or interference power spectral density.

[0529] As an embodiment, the interference measurement obtained based on the first resource set includes an interference channel matrix.

[0530] As an embodiment, the interference measurement obtained based on the first resource set includes an interference covariance matrix.

[0531] As an embodiment, the interference measurement obtained based on the first resource set includes an interference characteristic vector.

[0532] As an embodiment, the interference measurement obtained based on the first resource set includes an interference characteristic vector and an interference characteristic value.

[0533] As an embodiment, the interference measurement obtained based on the first resource set includes an interference beam.

[0534] Generally speaking, how the first node determines the input of the first operation based on the measurement of the first resource set is determined by the equipment manufacturer. Some non-limiting implementation methods are described below:

[0535] As an embodiment, the input of the first operation includes channel measurements obtained based on the first set of resources.

[0536] As an embodiment, the input of the first operation includes channel measurement and interference measurement obtained based on the first resource set.

[0537] As an embodiment, the input of the first operation includes interference measurement obtained based on the first set of resources.

[0538] As an embodiment, the interference measurement includes: one or more of interference power, interference variance or interference power spectrum density.

[0539] As an embodiment, the input of the first operation includes a channel impulse response obtained based on measurement of the first set of resources.

[0540] As an embodiment, the input of the first operation includes a channel matrix obtained based on measurement of the first set of resources.

[0541] As an embodiment, the input of the first operation includes eigenvectors and eigenvalues ​​of a channel matrix obtained based on measurements of the first set of resources.

[0542] As an embodiment, the input of the first operation includes a matrix or a vector obtained by preprocessing a channel matrix obtained based on measurements of the first resource set.

[0543] As an embodiment, the channel matrix is ​​in spatial-frequency domain.

[0544] As an embodiment, the channel matrix is ​​in angular-delay domain projection.

[0545] 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.

[0546] As an embodiment, the preprocessing includes one or more of matrix decomposition, matrix transformation or projection.

[0547] As an embodiment, the preprocessing includes quantization.

[0548] As an embodiment, the preprocessing includes DFT.

[0549] 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.

[0550] As an embodiment, the preprocessing includes truncation and / or padding.

[0551] As an embodiment, the preprocessing includes mapping.

[0552] As an embodiment, the preprocessing includes mapping to vectors.

[0553] As an embodiment, the preprocessing includes labeling.

[0554] As an embodiment, the label refers to marking with a label.

[0555] As an embodiment, the target CSI includes the output of the first operation.

[0556] As an embodiment, the target CSI includes a post-processed output of the first operation.

[0557] As an embodiment, the target CSI includes a truncated and / or quantized output of the first operation.

[0558] As an embodiment, the output of the first operation is used to generate the target CSI.

[0559] As an embodiment, the output of the first operation is used to generate the target CSI after post-processing.

[0560] As an embodiment, the output of the first operation is truncated and / or quantized and then used to generate the target CSI.

[0561] As an embodiment, part or all of the output of the first operation is used to generate the target CSI after post-processing.

[0562] As an embodiment, part or all of the output of the first operation is used to generate the target CSI after being truncated and / or quantized.

[0563] As an embodiment, the target CSI indicates at least one resource in the first resource set.

[0564] As an embodiment, the first resource set includes at least one RS resource; the target CSI indicating at least one resource in the first resource set includes: the target CSI indicating at least one RS resource in the first resource set.

[0565] As an embodiment, the first resource set includes at least one beam; the target CSI indicating at least one resource in the first resource set includes: the target CSI indicating at least one beam in the first resource set.

[0566] As an embodiment, the first resource set includes one or more beams; the target CSI indicating at least one resource in the first resource set includes: the target CSI indicating at least one beam in the first resource set.

[0567] As an embodiment, the first resource set includes one or more vectors; the target CSI indicating at least one resource in the first resource set includes: the target CSI indicating at least one vector in the first resource set.

[0568] As an embodiment, the first resource set includes one or more matrices; the target CSI indicating at least one resource in the first resource set includes: the target CSI indicating at least one matrix in the first resource set.

[0569] As an embodiment, the first resource set includes one or more DFT vectors; the target CSI indicating at least one resource in the first resource set includes: the target CSI indicating at least one DFT vector in the first resource set.

[0570] As an embodiment, the first resource set includes one or more codebooks; the target CSI indicating at least one resource in the first resource set includes: the target CSI indicating at least one codebook in the first resource set.

[0571] As an embodiment, the first resource set includes one or more antenna ports; the target CSI indicating at least one resource in the first resource set includes: the target CSI indicating at least one antenna port in the first resource set.

[0572] As an embodiment, the first operation performs spatial beam prediction for the second resource set based on the measurement of the first resource set.

[0573] As an embodiment, the benefits of the above method include reducing RS overhead and reducing feedback delay.

[0574] As an embodiment, the first operation predicts channel information for the second resource set based on measurement of the first resource set.

[0575] As an embodiment, the channel information in the present application includes beam information.

[0576] As an embodiment, the first operation performs temporal beam prediction for the second resource set based on historical measurements of the first resource set.

[0577] As an embodiment, the benefits of the above method include reducing beam feedback delay and improving the real-time performance of beam acquisition.

[0578] As an embodiment, the first operation performs temporal channel information prediction for the second resource set based on historical measurements of the first resource set.

[0579] 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.

[0580] As an embodiment, the input of the first operation also includes the second resource set.

[0581] As an embodiment, the first operation is associated with a first type of identification.

[0582] As an embodiment, the first operation is associated with a first-category identifier, and the target identifier is a first-category identifier.

[0583] As an embodiment, the target CSI reporting configuration indicates a first type of identifier, and the first operation is associated with the first type of identifier indicated by the target CSI reporting configuration.

[0584] Embodiment 11

[0585] Embodiment 11 illustrates a schematic diagram of associating a target CSI generation method according to an embodiment of the present application with a first type of identifier; Fig.11 As shown. In embodiment 11, the target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of the channel measurement or interference measurement of the target CSI, and the first resource set includes one or more RS resources; the target CSI generation method includes the first node or the receiver of the target CSI reporting configuration performing a first operation, the input of the first operation depends on the measurement based on the first resource set, and the target CSI depends on the output of the first operation; the target CSI generation method is associated with the first type of identifier, including: the first operation is associated with the first type of identifier.

[0586] As an embodiment, the first type identifier is a non-negative integer.

[0587] As an embodiment, the first type of identifier is a character string.

[0588] As an embodiment, the first operation is identified by the first type identifier.

[0589] As an embodiment, the AI ​​model adopted by the first operation is identified by the first category identifier.

[0590] As an embodiment, the AI ​​entity to which the first operation belongs is identified by the first type identifier.

[0591] As an embodiment, the AI ​​function to which the first operation belongs is identified by the first category identifier.

[0592] As an embodiment, the AI ​​entity or AI function to which the first operation belongs is identified by the first type identifier.

[0593] As an embodiment, the benefits of the above method include: identifying an AI entity or function through the first type of identifier simplifies the design and unifies the understanding of different AI entities or functions among multiple nodes.

[0594] As an embodiment, the AI ​​function that performs the first operation is identified by the first category identifier.

[0595] As an embodiment, the AI ​​entity that performs the first operation is identified by the first type identifier.

[0596] As an embodiment, the AI ​​entity or AI function that performs the first operation is identified by the first type identifier.

[0597] As an embodiment, the first type of identifier is a model identifier.

[0598] As an embodiment, the first type of identifier is used to identify an AI model.

[0599] As an embodiment, the first type of identifier is used by the first node to determine an AI model.

[0600] As an embodiment, the first type of identifier is used by the first node to determine the AI ​​model adopted by the first operation.

[0601] As an embodiment, the benefits of the above method include: identifying an AI model / entity / function through the first type of identifier simplifies the design and unifies the understanding of different AI entities / functions among multiple nodes.

[0602] As an embodiment, the first type of identifier is used to identify or indicate a first resource set, and measurements of the first resource set are used to obtain a training data set for the first operation.

[0603] As an embodiment, the first type of identifier is used to identify configuration information of a first resource set, and measurements of the first resource set are used to obtain a training data set for the first operation.

[0604] As an embodiment, the training for obtaining the first operation is identified by the first category identifier.

[0605] As an embodiment, the data set used for training the first operation is identified by the first category identifier.

[0606] 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, further simplifying the design.

[0607] As an embodiment, the target CSI reporting configuration indicates the first operation by indicating the first type of identifier.

[0608] As an embodiment, the target CSI reporting configuration indicates the use of the AI ​​model by indicating the first type of identifier.

[0609] As an embodiment, the target CSI reporting configuration obtains the input of the AI ​​entity / function / inference associated with the first type of identifier by indicating the first type of identifier.

[0610] As an embodiment, the first operation performs spatial beam prediction for a second resource set based on the measurement of the first resource set, and the second resource set depends on the first type of identifier.

[0611] As an embodiment, the benefits of the above method include: reducing RS overhead and reducing feedback delay.

[0612] As an embodiment, the first operation predicts channel information for a second resource set based on measurement of the first resource set, and the second resource set depends on the first type of identifier.

[0613] As an embodiment, the channel information in the present application includes beam information.

[0614] As an embodiment, the first operation performs temporal beam prediction for a second resource set based on historical measurements of the first resource set, and the second resource set depends on the first type of identifier.

[0615] As an embodiment, the benefits of the above method include: reducing beam feedback delay and improving the real-time performance of beam acquisition.

[0616] As an embodiment, the first operation performs temporal channel information prediction for a second resource set based on historical measurements of the first resource set, and the second resource set depends on the first type of identifier.

[0617] 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.

[0618] As an embodiment, the first type of identifier is used to indicate the second resource set.

[0619] Example 12

[0620] Embodiment 12 illustrates a schematic diagram of a first operation according to another embodiment of the present application; Fig.12 In Example 12, the first operation is based on training or AI.

[0621] As an embodiment, the first operation is based on training.

[0622] As an embodiment, the first operation is based on AI.

[0623] 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.

[0624] As an embodiment, the above method has the following benefits: it is suitable for channel compression and saves feedback overhead.

[0625] 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.

[0626] 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.

[0627] 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.

[0628] As an embodiment, the resources in the second resource set include at least one of antenna ports, time-frequency resources, time-frequency code resources, beams, RS resources, vectors, or matrices.

[0629] As an embodiment, the benefits of the above method include: reducing RS overhead and reducing feedback delay.

[0630] As an embodiment, the channel information in the present application includes beam information.

[0631] As an embodiment, the measurement based on the first resource set includes current channel information, and the output of the first operation includes predicted channel information.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] 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.

[0637] 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.

[0638] 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.

[0639] As an embodiment, the benefits of the above method include: improving CSI accuracy and real-time performance, and reducing RS overhead.

[0640] 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.

[0641] As an embodiment, the benefits of the above method include: reducing RS overhead and improving the accuracy and completeness of CSI.

[0642] 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.

[0643] As a sub-embodiment of the above embodiment, the P1 antenna ports are a proper subset of the P2 antenna ports.

[0644] As a sub-embodiment of the above embodiment, the P2 antenna ports belong to the second resource set.

[0645] 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.

[0646] As a sub-embodiment of the above embodiment, the first frequency domain resources are a proper subset of the second frequency domain resources.

[0647] As an embodiment, the first operation is based on training.

[0648] As an embodiment, the first operation is obtained through training.

[0649] As an embodiment, the training for obtaining the first operation is performed by the first node.

[0650] As an embodiment, the training for obtaining the first operation is performed by the sender of the target CSI reporting configuration.

[0651] As an embodiment, the training for obtaining the first operation is performed by a sender of the first resource set.

[0652] As an embodiment, the training for obtaining the first operation is performed by an MDA function (Management Data Analytics Function).

[0653] As an embodiment, the training for obtaining the first operation is performed by a MDAS (Management Data Analytics Service) producer.

[0654] As an embodiment, the training for obtaining the first operation is performed by NWDAF (Network Data Analytics Function).

[0655] As an embodiment, the training for obtaining the first operation is performed by a core network.

[0656] As an embodiment, the training for obtaining the first operation is performed by an AI training producer.

[0657] As an embodiment, an executor of the training for obtaining the first operation is different from a sender of the target CSI reporting configuration.

[0658] As an embodiment, a performer of training for obtaining the first operation is different from a sender of the first set of resources.

[0659] As an embodiment, the first operation includes inference.

[0660] As an embodiment, the first operation includes AI (Artificial Intelligence).

[0661] As an embodiment, the first operation is inference.

[0662] As an embodiment, the first operation is AI inference.

[0663] As an embodiment, the first operation includes AI inference for CSI.

[0664] As an embodiment, the first operation includes AI inference for beam prediction.

[0665] 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.

[0666] As an embodiment, the first operation is AI inference for CSI.

[0667] As an embodiment, the first operation includes AI inference for at least one of beam prediction, CSI prediction, CSI estimation, or CSI compression.

[0668] As an embodiment, the CSI prediction includes beam prediction.

[0669] 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.

[0670] As an embodiment, the first operation is based on an AI model.

[0671] As an embodiment, the first operation includes an AI entity.

[0672] As an embodiment, the first operation includes AI inferring an entity.

[0673] As an embodiment, the first operation includes an AI entity for inference.

[0674] As an embodiment, the first operation includes a portion of an AI entity.

[0675] As an embodiment, the first operation includes a part of an AI entity for inference.

[0676] As an embodiment, the first operation includes an AI entity for CSI.

[0677] As an embodiment, the first operation includes an AI entity for beam prediction.

[0678] As an embodiment, the first operation includes an AI entity for CSI prediction, estimation or compression.

[0679] As an embodiment, the first operation includes inference of an AI entity for CSI.

[0680] As an embodiment, the first operation includes inference of an AI entity for CSI prediction, estimation, or compression.

[0681] As an embodiment, the first operation is performed by an AI entity.

[0682] As an embodiment, the first operation is performed by an AI entity deployed on the first node.

[0683] As an embodiment, the first operation is performed by an AI function.

[0684] As an embodiment, the first operation is performed by an AI function deployed on the first node.

[0685] As an embodiment, the AI ​​function includes an AI inference function.

[0686] As an embodiment, the AI ​​function includes an AI training function.

[0687] As an embodiment, the AI ​​function includes an AI management function.

[0688] As an embodiment, the first operation is performed by the physical layer of the first node.

[0689] As an embodiment, the first operation is performed by a higher layer of the first node.

[0690] As an embodiment, the first operation requires deployment.

[0691] As an embodiment, the first operation is obtained by loading.

[0692] As an embodiment, the first operation is obtained by loading from a serving cell of the first node.

[0693] As an embodiment, the first operation is obtained from maintaining base station loading of a serving cell of the first node.

[0694] As an embodiment, the first operation is obtained by loading from the core network.

[0695] As an embodiment, the first operation is based on artificial intelligence or machine learning.

[0696] As an embodiment, the first operation is based on a neural network.

[0697] As an embodiment, the first operation includes neural network-based CSI compression.

[0698] As an embodiment, the first operation includes an encoder for CSI compression based on a neural network.

[0699] As an embodiment, the first operation includes CNN-based CSI compression.

[0700] As an embodiment, the first operation includes a CNN-based CSI compression encoder.

[0701] As an embodiment, the output of the first operation is non-codebook based.

[0702] 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.

[0703] As an embodiment, the output of the first operation is based on artificial intelligence or machine learning.

[0704] As an embodiment, the output of the first operation is based on a neural network.

[0705] As an embodiment, the output of the first operation is based on CNN.

[0706] As an embodiment, the output of the first operation includes CSI.

[0707] As an embodiment, the output of the first operation includes predicted beam information.

[0708] As an embodiment, the output of the first operation includes beam indication and RSRP.

[0709] As an embodiment, the output of the first operation includes RS resource indication and RSRP.

[0710] As an embodiment, the output of the first operation includes resource indication and RSRP.

[0711] 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).

[0712] 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.

[0713] As an embodiment, the output of the first operation includes a channel impulse response.

[0714] As an embodiment, the output of the first operation includes small-scale characteristics.

[0715] 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.

[0716] As an embodiment, the output of the first operation includes a channel matrix.

[0717] As an embodiment, the output of the first operation includes the target CSI.

[0718] As an embodiment, the target CSI includes predicted or estimated CSI.

[0719] As an embodiment, the target CSI includes predicted beam information.

[0720] 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.

[0721] As an embodiment, the first node is a consumer.

[0722] As an embodiment, the first node is a consumer of an AI function.

[0723] As an embodiment, the first node is a user of AI inference.

[0724] As an embodiment, the first node is an AI trained user.

[0725] As an embodiment, the first node is a MnS (Management Service) user.

[0726] As an embodiment, the first node is a producer of AI inference.

[0727] As an embodiment, the first node is a producer of AI training.

[0728] As an embodiment, the first operation includes preprocessing.

[0729] As an embodiment, the preprocessing includes DFT (Discrete Fourier Transform).

[0730] As an embodiment, the preprocessing includes one or more of matrix decomposition, matrix transformation and projection.

[0731] 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.

[0732] As an embodiment, the preprocessing includes truncation and / or padding.

[0733] As an embodiment, the preprocessing includes mapping.

[0734] As an embodiment, the preprocessing includes mapping to vectors.

[0735] As an embodiment, the preprocessing includes labeling.

[0736] As an embodiment, the label refers to marking with a label.

[0737] As an embodiment, the first operation includes post-processing.

[0738] As an embodiment, the post-processing includes DFT.

[0739] As an embodiment, the post-processing includes quantization.

[0740] 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.

[0741] As an embodiment, the post-processing includes truncation and / or padding.

[0742] As an embodiment, the first operation includes one or more of convolution, pooling, cascading and activation.

[0743] As an embodiment, the first operation includes a fully connected layer.

[0744] As an embodiment, the first operation includes a pooling layer.

[0745] As an embodiment, the first operation includes at least one convolutional layer.

[0746] As an embodiment, the first operation includes at least one encoding layer.

[0747] As an embodiment, a coding layer includes at least one convolutional layer and one pooling layer.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] Example 13

[0752] Embodiment 13 illustrates a schematic diagram of associating a target CSI generation method with a first type of identifier according to another embodiment of the present application; Fig.13 In embodiment 13, the target CSI generation method is associated with the first type of identifier, including: the target CSI reporting configuration indicates the first type of identifier, and the first type of identifier indicated by the target CSI reporting configuration is the first type of identifier associated with the target CSI generation method.

[0753] As an embodiment, the target CSI reporting configuration explicitly indicates the first type of identifier.

[0754] As an embodiment, the target CSI reporting configuration implicitly indicates the first type of identifier.

[0755] As an embodiment, the target CSI reporting configuration includes a reference domain, and the reference domain included in the target CSI reporting configuration indicates the first type of identifier.

[0756] As an embodiment, the target CSI reporting configuration indicates the first operation by indicating the first type of identifier.

[0757] As an embodiment, the target CSI reporting configuration indicates the use of the AI ​​model adopted by the first operation by indicating the first type identifier.

[0758] As an embodiment, the target CSI reporting configuration obtains the input of the AI ​​entity / function / inference associated with the first type of identifier by indicating the first type of identifier, and the first operation is associated with the first type of identifier.

[0759] Embodiment 14

[0760] Embodiment 14 illustrates a schematic diagram of associating a target CSI generation method with a first type of identifier according to another embodiment of the present application; Fig.14 In Embodiment 14, the generation method of the target CSI is associated with the first type of identifier, including: the generation method of the target CSI uses the AI ​​model identified by the first type of identifier, or the target CSI is generated by the AI ​​entity identified by the first type of identifier, or the target CSI is used for the AI ​​function identified by the first type of identifier.

[0761] As an embodiment, the method for generating the target CSI uses an AI model identified by the first type of identifier.

[0762] As an embodiment, the target CSI is generated by an AI entity identified by the first type identifier.

[0763] As an embodiment, the AI ​​model adopted by the first operation is identified by the first category identifier, and the target CSI depends on the output of the first operation.

[0764] As an embodiment, the AI ​​entity or AI function to which the first operation belongs is identified by the first category identifier, and the target CSI depends on the output of the first operation.

[0765] As an embodiment, the AI ​​entity or AI function that performs the first operation is identified by the first type identifier, and the target CSI depends on the output of the first operation.

[0766] As an embodiment, the target CSI is used for the AI ​​function identified by the first type identifier.

[0767] As an embodiment, the target CSI is used to obtain a training data set for the AI ​​function identified by the first type identifier.

[0768] Embodiment 15

[0769] Embodiment 15 illustrates a schematic diagram of a first higher layer parameter according to an embodiment of the present application; Fig.15 In embodiment 15, the first receiver receives a first higher-layer parameter, wherein the first higher-layer parameter indicates the first cell set.

[0770] As an embodiment, the first higher layer parameter is carried by higher layer signaling.

[0771] As an embodiment, the first higher layer parameter is carried by RRC (Radio Resource Control) signaling.

[0772] As an embodiment, the first higher layer parameter is carried by an RRC IE (Information Element).

[0773] As an embodiment, the first higher layer parameter is carried by at least one RRC IE.

[0774] As an embodiment, the first higher layer parameter includes information in one or more fields in at least one RRC IE.

[0775] As an embodiment, the first higher layer parameter includes information in one or more fields of each RRC IE in a plurality of RRC IEs.

[0776] As an embodiment, the first higher layer parameter is an RRC IE.

[0777] As an embodiment, the first higher layer parameter includes MAC CE.

[0778] As an embodiment, the first higher layer parameter includes sCellToAddModList or sCellToAddModListSCG.

[0779] As an embodiment, the name of the first higher-layer parameter includes: Cell.

[0780] As an embodiment, the name of the first higher-layer parameter includes: List.

[0781] As an embodiment, the name of the first higher-layer parameter includes: CellToAddModList.

[0782] As an embodiment, the name of the first higher-layer parameter includes: Set of Cells To Add Mod List.

[0783] As an embodiment, the first higher layer parameter indicates an index of each cell in the first cell set.

[0784] As an embodiment, the first higher layer parameter indicates an index of each cell in the first cell set, and the index indicated by the first higher layer parameter is SCellIndex or ServCellIndex.

[0785] As an embodiment, the first higher layer parameter indicates multiple cell sets, the first cell set is one cell set among the multiple cell sets indicated by the first higher layer parameter, and any cell set among the multiple cell sets includes at least one serving cell.

[0786] As an embodiment, each cell in the first cell set is a service cell of the first node.

[0787] As an embodiment, the first node adds a secondary serving cell for each cell in the first cell set.

[0788] As an embodiment, the sCellToAddModList or sCellToAddModListSCG most recently received by the first node includes each cell in the first cell set.

[0789] As an embodiment, for each cell in the first cell set, the first node is allocated an SCellIndex or ServCellIndex for this cell.

[0790] As an embodiment, an RRC connection has been established between the first node and each cell in the first cell set.

[0791] As an embodiment, the C-RNTI of the first node is allocated by a cell in the first cell set.

[0792] As an embodiment, the C-RNTI of the first node is allocated by a cell that does not belong to the first cell set.

[0793] As an embodiment, the first cell set includes the SpCell (SpecialCell) of the first node.

[0794] As an embodiment, the first cell set includes the SCell (Secondary Cell) of the first node.

[0795] As an embodiment, any cell in the first cell set is the SpCell or SCell of the first node.

[0796] As an embodiment, the definition of the serving cell refers to 3GPP TS 38.331.

[0797] As an embodiment, the first cell set belongs to the same cell group.

[0798] As an embodiment, the first cell sets all belong to MCG (Master Cell Group) or all belong to SCG (Secondary Cell Group).

[0799] As an embodiment, the first cell set belongs to the same PUCCH (Physical Uplink Control Channel) group.

[0800] As an embodiment, a PUCCH group includes a group of cells, and the PUCCH signaling of the group of cells is associated with the PUCCH of the SpCell (Special Cell) or the PUCCH of the PUCCH SCell (Secondary Cell); a PUCCH SCell is an SCell configured with PUCCH.

[0801] As an embodiment, a PUCCH group includes a group of cells, and the PUCCH signaling of the group of cells is associated with the PUCCH of the same cell.

[0802] As an embodiment, the cells in the first cell set have the same numerology.

[0803] As an embodiment, the cells in the first cell set have the same subcarrier spacing configuration.

[0804] Example 16

[0805] Embodiment 16 illustrates a schematic diagram of a second information block according to an embodiment of the present application; Fig.16In embodiment 16, the first processor sends a second information block, wherein the second information block indicates that the generation method of CSI on multiple serving cells is associated with the same first type identifier.

[0806] As an embodiment, the second information block is carried by RRC (Radio Resource Control) signaling.

[0807] As an embodiment, the second information block is carried by an RRC IE (Information Element).

[0808] As an embodiment, the second information block is carried by at least one RRC IE.

[0809] As an embodiment, the second information block includes information in one or more fields in at least one RRC IE.

[0810] As an embodiment, the second information block includes information in one or more fields of each RRC IE in multiple RRC IEs.

[0811] As an embodiment, the second information block is an RRC IE.

[0812] As an embodiment, the second information block includes IE UECapabilityInformation.

[0813] As an embodiment, the second information block is IE UECapabilityInformation.

[0814] As an embodiment, the second information block includes information in one or more fields in IE UECapabilityInformation.

[0815] As an embodiment, the second information block is carried by physical layer signaling.

[0816] As an embodiment, the second information block includes a MAC CE.

[0817] As an embodiment, the second information block indicates that the generation method of CSI on the first cell set is associated with the same first type identifier.

[0818] As an embodiment, the second information block indicates that a generation method of CSI on a cell set where the first cell is located is associated with the same first type identifier.

[0819] As an embodiment, the second information block indicates that the generation method of CSI on multiple service cells is associated with the same first type identifier, including: the second information block indicates that the generation method of at least one CSI on each of the multiple service cells is associated with the same first type identifier.

[0820] As an embodiment, the second information block indicates that the generation method of CSI on multiple service cells is associated with the same first-class identifier, including: the second information block indicates that the sender of the target CSI reporting configuration can configure the generation method of CSI on multiple service cells to be associated with the same first-class identifier.

[0821] As an embodiment, the second information block belongs to the capability report of the first node.

[0822] Embodiment 17

[0823] Embodiment 17 illustrates a schematic diagram of a first operation according to another embodiment of the present application; Fig.17 In Embodiment 17, the first operation includes K1 sub-operations, and K1 is a positive integer not greater than 1. Fig.17 In the example, the K1 sub-operations are respectively represented as sub-operation #0, ..., sub-operation #(K1-1).

[0824] As an embodiment, each of the K1 sub-operations is based on training.

[0825] As an embodiment, at least one of the K1 sub-operations is based on training.

[0826] As an embodiment, each training-based sub-operation in the K1 sub-operations is based on the same executor of the training.

[0827] As an embodiment, two of the K1 sub-operations are based on different executors of the training.

[0828] As an embodiment, at least one sub-operation among the K1 sub-operations needs to be deployed.

[0829] As an embodiment, at least one of the K1 sub-operations needs to be loaded.

[0830] As an embodiment, all the sub-operations that need to be loaded in the K1 sub-operations are loaded from the same producer.

[0831] As an embodiment, two of the K1 sub-operations that need to be loaded are loaded from different producers.

[0832] As an embodiment, at least one of the K1 sub-operations is not based on training.

[0833] 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.

[0834] As an embodiment, one or more of the K1 sub-operations are AI-based.

[0835] As an embodiment, one or more of the K1 sub-operations include inference.

[0836] As an embodiment, one or more of the K1 sub-operations include AI inference.

[0837] As an embodiment, one or more of the K1 sub-operations include AI inference for CSI.

[0838] As an embodiment, the AI ​​(Artificial Intelligence) includes ML (Machine Learning).

[0839] As an embodiment, one or more of the K1 sub-operations include preprocessing.

[0840] As an embodiment, one or more of the K1 sub-operations include post-processing.

[0841] As an embodiment, there are two sub-operations in the K1 sub-operations that are serial, as shown in the attached Fig.17 All sub-operations in (a), sub-operation #2 to sub-operation #(K1-1) in 17(b), and sub-operation #0 to sub-operation #(K1-4) in 17(c).

[0842] 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.

[0843] As an embodiment, there are two sub-operations in the K1 sub-operations that are parallel, as shown in the attached Fig.17 (b) Sub-operation #0 and sub-operation #1, attached Fig.17 Sub-operation #(K1-3) and sub-operation #(K1-2) in c).

[0844] As an embodiment, two sub-operations are parallel, which means that the outputs of the two sub-operations are commonly used as the input of another sub-operation.

[0845] As an embodiment, the K1 sub-operations include one or more of convolution, pooling, cascading or activation.

[0846] As an embodiment, there is a sub-operation among the K1 sub-operations including a fully connected layer.

[0847] As an embodiment, there is a sub-operation among the K1 sub-operations including a pooling layer.

[0848] As an embodiment, there is a sub-operation among the K1 sub-operations including at least one convolutional layer.

[0849] As an embodiment, there is a sub-operation among the K1 sub-operations that includes at least one encoding layer.

[0850] 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.

[0851] As an embodiment, a coding layer includes at least one convolutional layer and one pooling layer.

[0852] Embodiment 18

[0853] Embodiment 18 illustrates a schematic diagram of a first node deploying a first operation according to an embodiment of the present application; Fig.18 In Embodiment 18, the first processor deploys the first operation.

[0854] As an embodiment, the deployment of the first operation is earlier than the reception of the target CSI reporting configuration.

[0855] As an embodiment, the deployment of the first operation is later than the reception of the target CSI reporting configuration.

[0856] As an embodiment, the deployment includes obtaining the first operation.

[0857] As an embodiment, the deploying includes obtaining an AI entity.

[0858] As an embodiment, the deploying includes obtaining an AI entity that performs the first operation.

[0859] As an embodiment, the deploying includes obtaining an AI entity including an AI function for performing the first operation.

[0860] As an embodiment, the deploying includes loading the first operation.

[0861] As an embodiment, the deploying includes making a request to load the first operation.

[0862] As an example, Fig. 9 The request in is a request made by the first node to load the first operation.

[0863] As an example, Fig. 9 The response in is a response to the request for loading the first operation made by the first node.

[0864] As an embodiment, the first node is connected Fig. 9 The first operation is obtained in response to the above.

[0865] As an embodiment, the first operation is obtained by loading from a serving cell of the first node.

[0866] As an embodiment, the first operation is obtained by loading from a base station maintaining a service cell of the first node.

[0867] As an embodiment, the first operation is obtained by loading from the core network.

[0868] As an embodiment, the first operation is obtained by loading from a first producer.

[0869] As an embodiment, the first producer attaches Fig. 9 The response in provides the first operation to the first node.

[0870] As an embodiment, the deployment is performed by an AI function.

[0871] As an embodiment, the deployment is completed by an AI function deployed on the first node.

[0872] As an embodiment, the deployment is performed by an AI deployment function.

[0873] As an embodiment, the deployment is completed by an AI deployment function deployed on the first node.

[0874] As an embodiment, the deployment is performed by AI inference function.

[0875] As an embodiment, the deployment is accomplished by an AI inference function deployed on the first node.

[0876] As an embodiment, the deployment is performed by an AI entity.

[0877] As an embodiment, the deployment is completed by an AI entity deployed on the first node.

[0878] As an embodiment, the deployment is completed by an AI entity having a deployment function.

[0879] As an embodiment, the deployment is completed by an AI entity with deployment function deployed on the first node.

[0880] As an embodiment, the deployment is performed by an AI entity having an inference function.

[0881] As an embodiment, the deployment is completed by an AI entity with inference function deployed on the first node.

[0882] As an embodiment, the deploying includes obtaining the first operation from a first producer.

[0883] As an embodiment, the deploying includes making a request to a first producer to load the first operation.

[0884] As an embodiment, the deploying includes loading the first operation from a first producer.

[0885] As an embodiment, the first producer generates and provides an AL entity.

[0886] As an embodiment, the first producer generates and provides AL functionality.

[0887] As an embodiment, the first producer is the producer of the first operation.

[0888] As an embodiment, the first producer includes an AL entity producer.

[0889] As an embodiment, the first producer includes an AL function producer.

[0890] As an embodiment, the first producer includes an AL deployment producer.

[0891] As an embodiment, the first producer includes an AL loading producer.

[0892] As an embodiment, the first producer includes an AL training producer.

[0893] As an embodiment, the first producer includes an AL inference producer.

[0894] As an embodiment, the first producer includes a producer of deployment of an AL entity.

[0895] As an embodiment, the first producer includes a loaded producer of an AL entity.

[0896] As an embodiment, the first producer includes a MnS (Management Service) producer.

[0897] As an embodiment, the sender of the target CSI reporting configuration is the first producer.

[0898] As an embodiment, the sender of the target CSI reporting configuration is different from the first producer.

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

[0900] As an embodiment, a performer of the training for obtaining the first operation is different from the first producer.

[0901] As an embodiment, the AI ​​includes ML (Machine Learning).

[0902] Embodiment 19

[0903] Embodiment 19 illustrates a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of the present application; Fig.19 Attached Fig.19 (a) comprising a third processor, a fourth processor and a fifth processor, Fig.19 (b) includes a third processor, a fourth processor, a fifth processor and a sixth processor.

[0904] In Example 19(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.19 (a),The first type of feedback is optional.

[0905] In Example 19(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.19 (b), the first type of feedback and the second type of feedback are optional.

[0906] As an example, Fig.19 In (a), the fifth processor sends the first type of output to the second node in the present application.

[0907] As an example, Fig.19 (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.

[0908] As an embodiment, the AI ​​includes ML (Machine Learning) inference.

[0909] As an embodiment, the fifth processor performs the first operation.

[0910] 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.

[0911] 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.

[0912] 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.

[0913] As an embodiment, the fifth processor belongs to the first node, and the sixth processor belongs to the second node.

[0914] As an embodiment, the target CSI belongs to the first type of output.

[0915] As an embodiment, the second data set includes the input of the first operation.

[0916] As an embodiment, the second data set includes information obtained based on the first configuration and the M1 configurations.

[0917] As an embodiment, the first data set includes training data (Training Data).

[0918] As an embodiment, the fourth processor is a producer of the first operation.

[0919] As an embodiment, the fourth processor includes an AI training producer.

[0920] As an embodiment, the fourth processor includes an AI training function.

[0921] As an embodiment, the fourth processor is used for model training, and the trained model is described by the target first type parameter group.

[0922] As an embodiment, the fourth processor belongs to the first node.

[0923] The above embodiment avoids transferring the first data set to the second node.

[0924] As an embodiment, the fourth processor belongs to the second node.

[0925] The above embodiments support joint training and optimize system performance.

[0926] As an embodiment, the fourth processor belongs to the core network.

[0927] The above embodiments support joint training of the entire network, further optimizing system performance.

[0928] As an embodiment, the second data set includes inference data.

[0929] As an embodiment, the fifth processor includes an AI inference producer.

[0930] As an embodiment, the fifth processor includes an AI inference function.

[0931] As an embodiment, the fifth processor belongs to the first node.

[0932] 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.

[0933] As an embodiment, the first operation is described by the target first type parameter group.

[0934] As an embodiment, the target first-category parameter group is used to construct the first operation.

[0935] 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.

[0936] 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.

[0937] 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.

[0938] 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.

[0939] 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.

[0940] Embodiment 20

[0941] Embodiment 20 illustrates a schematic diagram based on artificial intelligence or machine learning according to an embodiment of the present application; Fig. 20 Attached Fig. 20 Including a third operation, a fourth operation, a fifth operation, a sixth operation and a seventh operation. In Example 20, 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. Fig. 20 In the diagram, the lines with arrows indicate the sequence of the processes.

[0942] 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.

[0943] 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.

[0944] As an embodiment, the first stage includes AI model training.

[0945] As an embodiment, the first stage includes AI model training and AI testing.

[0946] As an embodiment, the AI ​​includes ML (Machine Learning) inference.

[0947] As an embodiment, the AI ​​model training includes initial training and re-training of one or a group of AI entities.

[0948] As an embodiment, the AI ​​model training relies on training data.

[0949] As an embodiment, the AI ​​model training includes AI entity validation.

[0950] As an embodiment, the AI ​​entity verification is used to evaluate the performance of the AI ​​entity.

[0951] As an embodiment, the AI ​​entity verification relies on verification data.

[0952] As an embodiment, if the result of the AI ​​entity verification does not meet expectations, the AI ​​model will be retrained.

[0953] As an embodiment, the AI ​​testing includes testing the verified AI entity to estimate the performance of the trained AI model.

[0954] 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.

[0955] As an embodiment, the AI ​​test relies on test data.

[0956] As an embodiment, the second stage includes AI simulation, which performs inference of AI entities in a simulation environment.

[0957] As an embodiment, the AI ​​simulation is to estimate the performance of AI entity inference in a simulation environment before using the AI ​​entity.

[0958] As an embodiment, the second stage is optional.

[0959] 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.

[0960] As an embodiment, the third stage is optional.

[0961] As an example, when the training function and the inference function are co-located, the third stage is no longer needed.

[0962] As an embodiment, the fourth stage includes AI inference.

[0963] As an embodiment, the seventh operation includes the first operation.

[0964] Embodiment 21

[0965] Embodiment 21 illustrates a structural block diagram of a processing device used in a first node according to an embodiment of the present application; Fig.21 As shown in the attached Fig.21 In the embodiment, the processing device 2100 in the first node includes a first receiver 2101 and a first processor 2102.

[0966] As an embodiment, the first node is user equipment.

[0967] As an embodiment, the first node is a relay node device.

[0968] As an embodiment, the first receiver 2101 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.

[0969] As an embodiment, the first processor 2102 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.

[0970] The first receiver 2101 receives a target CSI reporting configuration; the target CSI reporting configuration is used to configure the reporting of the target CSI on the target cell, and the target CSI is generated based on AI; and receives a first information block, wherein the first information block indicates a target identifier.

[0971] The first processor 2102 sends target CSI.

[0972] In Example 21, the method for generating the target CSI is associated with a first type of identifier; whether the first type of identifier associated with the method for generating the target CSI is updated to the target identifier depends on whether the target cell belongs to a first cell set; only when the target cell belongs to the first cell set, the first type of identifier associated with the method for generating the target CSI is updated to the target identifier; the first cell set includes multiple service cells, a first cell is a service cell in the first cell set, and the first cell depends on the first information block; the target cell is different from the first cell.

[0973] As an embodiment, the first information block is applied to each serving cell in the first cell set.

[0974] As an embodiment, the first information block indicates the first cell.

[0975] As an embodiment, the first cell is a service cell where the physical channel carrying the first information block is located.

[0976] As an embodiment, the target CSI reporting configuration indicates a first resource set, which is used for at least one of channel measurement or interference measurement of the target CSI, and the first resource set includes one or more RS resources on the target cell; the target CSI indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

[0977] As an embodiment, the target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of channel measurement or interference measurement of the target CSI, and the first resource set includes one or more RS resources; the method for generating the target CSI includes the first node or the receiver of the target CSI reporting configuration performing a first operation, the input of the first operation depends on measurement based on the first resource set, and the target CSI depends on the output of the first operation.

[0978] As an embodiment, the target CSI generation method is associated with the first type of identifier, including: the first operation is associated with the first type of identifier.

[0979] As an embodiment, the target CSI generation method is associated with a first type of identifier, including: the target CSI reporting configuration indicates a first type of identifier, and the first type of identifier indicated by the target CSI reporting configuration is the first type of identifier associated with the target CSI generation method.

[0980] As an embodiment, the generation method of the target CSI is associated with the first category identifier, including: the generation method of the target CSI uses the AI ​​model identified by the first category identifier, or the target CSI is generated by the AI ​​entity identified by the first category identifier, or the target CSI is used for the AI ​​function identified by the first category identifier.

[0981] As an embodiment, it includes:

[0982] The first receiver 2101 receives a first higher layer parameter;

[0983] The first higher layer parameter indicates the first cell set.

[0984] As an embodiment, it includes:

[0985] The first processor 2102 sends a second information block;

[0986] The second information block indicates that the generation method of CSI on multiple serving cells is associated with the same first type identifier.

[0987] As an embodiment, the first receiver 2101 receives a signal in the first resource set.

[0988] As an embodiment, the first receiver 2101 receives a reference signal in the first resource set, and the first resource set includes one or more RS resources.

[0989] As an embodiment, the first operation is training-based or AI-based.

[0990] As an embodiment, the first operation requires deployment.

[0991] As an embodiment, the first operation is obtained by loading.

[0992] Embodiment 22

[0993] Embodiment 22 illustrates a structural block diagram of a processing device used in a second node according to an embodiment of the present application; Fig. 22 As shown in the attached Fig. 22 In the example, the processing device 2200 in the second node includes a second processor 2201.

[0994] As an embodiment, the second node is a base station device.

[0995] As an embodiment, the second node is user equipment.

[0996] As an embodiment, the second node is a relay node device.

[0997] As an embodiment, the second processor 2201 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.

[0998] The second processor 2201 sends a target CSI reporting configuration; the target CSI reporting configuration is used to configure the reporting of the target CSI on the target cell, and the target CSI is generated based on AI; sends a first information block, the first information block indicates a target identifier; receives the target CSI.

[0999] In Example 22, the method for generating the target CSI is associated with a first type of identifier; whether the first type of identifier associated with the method for generating the target CSI is updated to the target identifier depends on whether the target cell belongs to a first cell set; only when the target cell belongs to the first cell set, the first type of identifier associated with the method for generating the target CSI is updated to the target identifier; the first cell set includes multiple service cells, a first cell is a service cell in the first cell set, and the first cell depends on the first information block; the target cell is different from the first cell.

[1000] As an embodiment, the first information block is applied to each serving cell in the first cell set.

[1001] As an embodiment, the first information block indicates the first cell.

[1002] As an embodiment, the first cell is a service cell where the physical channel carrying the first information block is located.

[1003] As an embodiment, the target CSI reporting configuration indicates a first resource set, which is used for at least one of channel measurement or interference measurement of the target CSI, and the first resource set includes one or more RS resources on the target cell; the target CSI indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

[1004] As an embodiment, the target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of channel measurement or interference measurement of the target CSI, and the first resource set includes one or more RS resources; the method for generating the target CSI includes the receiver of the target CSI reporting configuration performing a first operation, the input of the first operation depends on the measurement based on the first resource set, and the target CSI depends on the output of the first operation.

[1005] As an embodiment, the target CSI generation method is associated with the first type of identifier, including: the first operation is associated with the first type of identifier.

[1006] As an embodiment, the target CSI generation method is associated with a first type of identifier, including: the target CSI reporting configuration indicates a first type of identifier, and the first type of identifier indicated by the target CSI reporting configuration is the first type of identifier associated with the target CSI generation method.

[1007] As an embodiment, the generation method of the target CSI is associated with the first category identifier, including: the generation method of the target CSI uses the AI ​​model identified by the first category identifier, or the target CSI is generated by the AI ​​entity identified by the first category identifier, or the target CSI is used for the AI ​​function identified by the first category identifier.

[1008] As an embodiment, it includes:

[1009] The second processor 2201 sends a first higher layer parameter;

[1010] The first higher layer parameter indicates the first cell set.

[1011] As an embodiment, it includes:

[1012] The second processor 2201 receives a second information block;

[1013] The second information block indicates that the generation method of CSI on multiple serving cells is associated with the same first type identifier.

[1014] As an embodiment, the second processor 2201 sends a reference signal in the first resource set.

[1015] As an embodiment, the first operation is training-based or AI-based.

[1016] As an embodiment, the first operation requires deployment.

[1017] As an embodiment, the first operation is obtained by loading.

[1018] 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, transportation tools, vehicles, RSUs, wireless sensors, Internet cards, Internet of Things terminals, RFID terminals, NB-IOT terminals, MTC (Machine Type Communication, Machine Type Communication) terminals, eMTC (enhanced MTC, enhanced MTC) terminals, data cards, Internet cards, vehicle-mounted communication equipment, low-cost mobile phones, low-cost tablet computers and other wireless communication devices. The base stations or system equipment in this application include but are not limited to macrocell base stations, microcell base stations, small cell base stations, home base stations, relay base stations, eNB, gNB, TRP (Transmitter Receiver Point), GNSS, relay satellites, satellite base stations, aerial base stations, RSU (Road Side Unit), drones, test equipment, such as wireless communication equipment such as transceivers or signaling testers that simulate some functions of base stations.

[1019] It should be understood by those skilled in the art that the present invention may be implemented in other specified forms without departing from its core or essential features. Therefore, the embodiments disclosed herein should be considered illustrative rather than restrictive in any way. The scope of the invention is determined by the appended claims rather than the preceding description, and all modifications within their equivalent meanings and regions are considered to be included therein.

Claims

1. A method in a first node for wireless communication, characterized in that: include: receiving a target CSI reporting configuration; the target CSI reporting configuration is used to configure reporting of a target CSI on a target cell, and the target CSI is generated in an AI-based manner; receiving a first information block, the first information block indicating a target identifier; Send target CSI; Among them, the generation method of the target CSI is associated with a first type of identifier; whether the first type of identifier associated with the generation method of the target CSI is updated to the target identifier depends on whether the target cell belongs to a first cell set; only when the target cell belongs to the first cell set, the first type of identifier associated with the generation method of the target CSI is updated to the target identifier; the first cell set includes multiple service cells, the first cell is a service cell in the first cell set, and the first cell depends on the first information block; the target cell is different from the first cell.

2. The method in the first node according to claim 1, characterized in that: The first information block is applied to each serving cell in the first set of cells.

3. The method in the first node according to claim 1 or 2, characterized in that: The first information block indicates the first cell.

4. The method in the first node according to claim 1 or 2, characterized in that: The first cell is a serving cell where the physical channel carrying the first information block is located.

5. The method in the first node according to any one of claims 1 to 4, characterized in that: The target CSI reporting configuration indicates a first resource set, which is used for at least one of channel measurement or interference measurement of the target CSI, and the first resource set includes one or more RS resources on the target cell; the target CSI indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

6. The method in the first node according to any one of claims 1 to 5, characterized in that: The target CSI reporting configuration indicates a first resource set, which is used for at least one of channel measurement or interference measurement of the target CSI, and the first resource set includes one or more RS resources; the method for generating the target CSI includes the first node or the receiver of the target CSI reporting configuration performing a first operation, the input of the first operation depends on measurement based on the first resource set, and the target CSI depends on the output of the first operation.

7. The method in the first node according to claim 6, characterized in that: The target CSI generation method is associated with the first type of identifier, including: the first operation is associated with the first type of identifier.

8. The method in the first node according to any one of claims 1 to 7, characterized in that: The target CSI generation method being associated with a first type of identifier includes: the target CSI reporting configuration indicating a first type of identifier, and the first type of identifier indicated by the target CSI reporting configuration is the first type of identifier associated with the target CSI generation method.

9. The method in the first node according to any one of claims 1 to 8, characterized in that: The generation method of the target CSI is associated with the first type of identifier, including: the generation method of the target CSI uses the AI ​​model identified by the first type of identifier, or the target CSI is generated by the AI ​​entity identified by the first type of identifier, or the target CSI is used for the AI ​​function identified by the first type of identifier.

10. A terminal, characterized in that: The terminal includes: one or more processors and a memory; The memory is coupled to the one or more processors, and the memory is used to store computer program codes, where the computer program codes include computer instructions. The one or more processors call the computer instructions to enable the terminal to execute the method according to any one of claims 1 to 9.

11. A method in a second node for wireless communication, characterized in that: include: Sending a target CSI reporting configuration; the target CSI reporting configuration is used to configure the reporting of the target CSI on the target cell, and the generation method of the target CSI is based on AI; sending a first information block, the first information block indicating a target identifier; Receive target CSI; Among them, the generation method of the target CSI is associated with a first type of identifier; whether the first type of identifier associated with the generation method of the target CSI is updated to the target identifier depends on whether the target cell belongs to a first cell set; only when the target cell belongs to the first cell set, the first type of identifier associated with the generation method of the target CSI is updated to the target identifier; the first cell set includes multiple service cells, the first cell is a service cell in the first cell set, and the first cell depends on the first information block; the target cell is different from the first cell.

12. The method in the second node according to claim 11, characterized in that: The first information block is applied to each serving cell in the first set of cells.

13. The method in the second node according to claim 11 or 12, characterized in that: The first information block indicates the first cell.

14. The method in the second node according to claim 11 or 12, characterized in that: The first cell is a serving cell where the physical channel carrying the first information block is located.

15. The method in the second node according to any one of claims 11 to 14, characterized in that: The target CSI reporting configuration indicates a first resource set, which is used for at least one of channel measurement or interference measurement of the target CSI, and the first resource set includes one or more RS resources on the target cell; the target CSI indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

16. The method in the second node according to any one of claims 11 to 15, characterized in that: The target CSI reporting configuration indicates a first resource set, which is used for at least one of channel measurement or interference measurement of the target CSI, and the first resource set includes one or more RS resources; the method for generating the target CSI includes the receiver of the target CSI reporting configuration performing a first operation, the input of the first operation depends on the measurement based on the first resource set, and the target CSI depends on the output of the first operation.

17. The method in the second node according to claim 16, characterized in that: The target CSI generation method is associated with the first type of identifier, including: the first operation is associated with the first type of identifier.

18. The method in the second node according to any one of claims 11 to 17, characterized in that: The target CSI generation method being associated with a first type of identifier includes: the target CSI reporting configuration indicating a first type of identifier, and the first type of identifier indicated by the target CSI reporting configuration is the first type of identifier associated with the target CSI generation method.

19. The method in the second node according to any one of claims 11 to 18, characterized in that: The generation method of the target CSI is associated with the first type of identifier, including: the generation method of the target CSI uses the AI ​​model identified by the first type of identifier, or the target CSI is generated by the AI ​​entity identified by the first type of identifier, or the target CSI is used for the AI ​​function identified by the first type of identifier.

20. A base station, characterized in that: The base station includes: one or more processors and a memory; The memory is coupled to the one or more processors, and the memory is used to store computer program codes, where the computer program codes include computer instructions, and the one or more processors call the computer instructions to enable the base station to perform the method according to any one of claims 11 to 19.

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