Information reporting method and apparatus in node used for wireless communication

Abstract

The present application discloses an information reporting method and apparatus in a node used for wireless communication. The method comprises: a first node receives a first information set, the first information set indicating M operations, and M being a positive integer greater than 1; and determines that M0 operations among the M operations are invalid, M0 being a non-negative integer. A target operation is invalid, the target operation being an operation among the M operations other than the M0 operations. The M0 operations depend on the order of the target operation among the M operations according to an execution sequence from first to last. The above-described method and apparatus support AI-based information reporting, determine an invalid operation among a plurality of operations, and determine whether to abandon or not update information reporting, thereby improving the accuracy of information reporting, reducing system resource overhead, and improving overall system performance.

Description

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

[0001] The present application claims priority from the Chinese Patent Application No. 202410960295.0 filed on July 17, 2024, and entitled "Method and apparatus for reporting information in a node for wireless communication", the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] The present application relates to a transmission method and apparatus in a wireless communication system, and in particular to a scheme and apparatus for reporting information in a wireless communication system. BACKGROUND

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

[0004] With the adoption of new technologies, the increase in the number of antennas, the diversification of application scenarios, and the improvement of system performance requirements, traditional measurement and reporting methods will bring a lot of redundant overhead. Therefore, in NR (New Radio) Rel-18 (Release-18), the research of AI (Artificial Intelligence) / ML (Machine Learning) technology is launched to explore its impact on system performance and system design. Compared with the traditional processing method, AI / ML has the characteristics of training and deployment. In addition, AI / ML is also a key candidate technology for future 6G communication. SUMMARY

[0005] The applicant found through research that when AI / ML functions are introduced, the existing measurement mechanism, reporting mechanism, and related configuration signaling may not be able to adapt to the needs of AI / ML. In view of the above problems, the present application discloses a solution. It should be noted that in the description of the above problems, the NR system is taken as an example, and the present application is also applicable to scenarios such as future 6G systems, achieving similar technical effects as the NR system; further, although the original intention of the present application is for AI / ML scenarios, the present application can also be applied to other non-AI / ML scenarios, such as traditional CSI (Channel State Information) reporting solutions; further, a unified design solution for different scenarios (such as other non-AI / ML scenarios, including but not limited to V2X (Vehicle to Everything), capacity enhancement systems, near distance communication systems, NTN (Non Terrestrial Network), IoT (Internet of Things), URLLC (Ultra Reliable Low Latency Communication) networks, etc.) can also help to reduce hardware complexity and cost. In the case of no conflict, the embodiments in any node of the present application and the features in the embodiments can be applied to any other node. In the case of no conflict, the embodiments of the present application and the features in the embodiments can be arbitrarily combined with each other.

[0006] In particular, the explanation of the terminology, nouns, functions, and variables in this application (if not specifically stated) can refer to the definitions in the 3GPP specification protocols TS28 series, TS36 series, TS38 series, TS37 series. If necessary, refer to 3GPP standards TS38.211, TS38.212, TS38.213, TS38.214, TS38.215, TS38.321, TS38.331, TS38.305, TS38.304, TS37.355 to assist in understanding this application.

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

[0008] receiving a first information set; the first information set indicates M operations, M is a positive integer greater than 1; determining that M0 operations in the M operations are invalid, M0 is a non-negative integer;

[0009] wherein the target operation is invalid, the target operation is an operation other than the M0 operations in the M operations; the M0 operations depend on the order of the M operations in the target operation according to the execution order from first to last.

[0010] According to one aspect of the present application, the first node is a user equipment.

[0011] According to one aspect of the present application, the first node is a relay node.

[0012] As an embodiment, the first node is a terminal.

[0013] As an embodiment, the user equipment is a terminal.

[0014] As an embodiment, the problem to be solved by the present application includes how to determine the invalid operation in the plurality of operations.

[0015] As an embodiment, the essence of the above method includes that the invalid operation in the plurality of operations depends on the order of the target operation in the plurality of operations.

[0016] As an embodiment, the benefits of the above method include ensuring consistent understanding of the invalid operation by the transceiver.

[0017] As an embodiment, the benefits of the above method include simplifying system design and reducing scheme complexity.

[0018] As an embodiment, the benefits of the above method include enhancing the flexibility of the system and better adapting to various transmission conditions and application scenarios.

[0019] As an embodiment, the method has the advantage of supporting processing based on multiple operations, improving robustness of the system.

[0020] As an embodiment, the method has the advantage of improving overall performance of the system.

[0021] According to an aspect of the present application, when the target operation is not the last operation in the M operations, M0 is a positive integer, and the M0 operations include operations after the target operation in the M operations.

[0022] As an embodiment, the method has the essence of including an invalid operation in multiple operations, which is an operation after the target operation in the multiple operations.

[0023] As an embodiment, the method has the advantage of ensuring consistency of understanding of the invalid operation by the transmitter and the receiver.

[0024] As an embodiment, the method has the advantage of avoiding the impact of the invalid operation on subsequent operations.

[0025] As an embodiment, the method has the advantage of simplifying system design and reducing complexity of the solution.

[0026] According to an aspect of the present application, it includes:

[0027] When an operation in the M operations is invalid, the first information report is abandoned or not updated; wherein the first information set is used to configure the first information report.

[0028] As an embodiment, the method has the essence of supporting information reporting based on multiple operations; when there is an invalid operation in the multiple operations, the information report is abandoned or not updated.

[0029] As an embodiment, the method has the advantage of ensuring consistency of understanding of the information report by the transmitter and the receiver.

[0030] As an embodiment, the method has the advantage of abandoning or not updating the invalid information report, reducing reporting overhead, and improving transmission efficiency of the system.

[0031] According to an aspect of the present application, it includes:

[0032] discard or not update M3 information reports in M2 information reports, wherein M2 is a positive integer greater than 1, M2 is not greater than M, M3 is a positive integer not greater than M2; the M2 information reports respectively depend on outputs of M2 operations, the M2 operations belong to the M operations, at least one of the M2 operations belongs to the M0 operations; the M3 information reports include information reports whose depended operations belong to the M0 operations.

[0033] As an embodiment, the first information report includes at least one information report.

[0034] As an embodiment, one information report depends on an output of at least one of the M operations.

[0035] As an embodiment, the essence of the above method includes that all information reports associated with invalid operations in the plurality of operations are discarded or not updated.

[0036] As an embodiment, the benefit of the above method includes that consistency of understanding of processing of information reports by a transceiver is ensured.

[0037] As an embodiment, the benefit of the above method includes that invalid information reports are discarded or not updated, which reduces reporting overhead and improves transmission efficiency of the system.

[0038] As an embodiment, the benefit of the above method includes that current standards and system designs are used as much as possible, which improves backward and forward compatibility of the system.

[0039] As an embodiment, the benefit of the above method includes that various application scenarios or terminals are better adapted, which has good flexibility and adaptability.

[0040] According to an aspect of the present application, the first information set indicates M configurations, M is a positive integer greater than 1, and the M configurations respectively indicate the M operations; the M configurations respectively include M first-type identifiers, and the M operations are respectively associated with the M first-type identifiers.

[0041] As an embodiment, the benefit of the above method includes that operations are associated by configured first-type identifiers, which simplifies system design and improves system flexibility.

[0042] According to an aspect of the present application, the first information set is also used to determine an order of the M first-type identifiers, and an execution order of the M operations from early to late is consistent with the order of the M first-type identifiers.

[0043] As an example, the essence of the above method includes: obtaining the execution order of the M operations from early to late by determining the order of the M first-type identifiers.

[0044] As an example, the benefits of the above method include: better adaptation to various application scenarios or terminals, good flexibility and adaptability.

[0045] As an example, the benefits of the above method include: the order of the M operations is consistent with the order of the M first-type identifiers, simplifying system design and reducing implementation complexity of the scheme.

[0046] According to an aspect of the present application, the first information set indicates the execution order of the M operations from early to late.

[0047] According to an aspect of the present application, the first information set indicates M configurations, M being a positive integer greater than 1, and the M configurations respectively indicating M operations; the first information set is used to determine the order of the M configurations, and the execution order of the M operations from early to late is consistent with the order of the M configurations.

[0048] As an example, the essence of the above method includes: obtaining the execution order of the M operations from early to late by determining the order of the M configurations.

[0049] As an example, the benefits of the above method include: little change to existing standards and system design.

[0050] As an example, the benefits of the above method include: better adaptation to various application scenarios or terminals, good flexibility and adaptability.

[0051] As an example, the benefits of the above method include: the order of the M operations is consistent with the order of the M configurations, simplifying system design and reducing implementation complexity of the scheme.

[0052] According to an aspect of the present application, at least two of the M operations correspond to different types, and the execution order of the M operations from early to late depends on the types corresponding to the M operations.

[0053] As an example, the benefits of the above method include: better adaptation to various application scenarios or terminals, good flexibility and adaptability.

[0054] As an example, the benefits of the above method include: determining the order of the operations according to the types corresponding to the operations, simplifying system design and reducing complexity of the scheme.

[0055] According to an aspect of the present application, the first node determines the target operation failure by itself.

[0056] As an example, benefits of the above method include better adaptation to various application scenarios or terminals, good flexibility and adaptability.

[0057] As an example, benefits of the above method include reduced signaling overhead and improved system transmission efficiency.

[0058] As an example, benefits of the above method include timely discovery of failed operations and improved overall system performance.

[0059] According to an aspect of the present application, comprising:

[0060] receiving first signaling; wherein,

[0061] The first signaling indicates that the target operation is invalid.

[0062] According to an aspect of the present application, comprising:

[0063] receiving first signaling; wherein,

[0064] The first signaling indicates a target identity, and the target operation is associated with the target identity.

[0065] As an example, benefits of the above method include reduced complexity of the first node and reduced energy consumption.

[0066] According to an aspect of the present application, comprising:

[0067] deploying at least one operation of the M operations.

[0068] As an example, the first node deploys the M operations.

[0069] As an example, the sender of the first information set deploys at least one operation of the M operations.

[0070] As an example, the first node deploys at least one operation of the M operations, and the sender of the first information set deploys the remaining operations of the M operations.

[0071] As an example, at least one operation of the M operations is based on training or AI.

[0072] As an example, at least one operation of the M operations is obtained by loading.

[0073] As an embodiment, the AI (Artificial Intelligence) comprises ML (Machine Learning).

[0074] As an embodiment, the method has the benefit of supporting AI-based information reporting.

[0075] As an embodiment, the method has the benefit of reserving sufficient freedom for the first node to adapt to various scenarios and terminals, and has adaptability and flexibility.

[0076] As an embodiment, the method has the benefit of reserving sufficient freedom for the sender of the first information set to adapt to various scenarios and terminals, and has adaptability and flexibility.

[0077] As an embodiment, the method has the benefit that the training for the M operations can not be performed at the first node, reducing the demand for processing capacity and power consumption of the first node.

[0078] The present application discloses a method in a second node used for wireless communication, comprising:

[0079] sending a first information set; the first information set indicates M operations, M is a positive integer greater than 1;

[0080] wherein the target receiver of the first information set determines that M0 operations in the M operations are invalid, M0 is a non-negative integer; the target operation is an operation other than the M0 operations in the M operations; the M0 operations depend on the order of the target operation in the M operations according to the execution order from first to last.

[0081] According to one aspect of the present application, when the target operation is not the last operation in the M operations, the M0 is a positive integer, and the M0 operations include the operations after the target operation in the M operations.

[0082] According to one aspect of the present application, comprising:

[0083] When one operation in the M operations is invalid, the target receiver of the first information set gives up or does not update the first information reporting; wherein the first information set is used to configure the first information reporting.

[0084] According to an aspect of the present application, the target receiver of the first information set gives up or does not update M3 information reports in M2 information reports, wherein M2 is a positive integer greater than 1, M2 is not greater than M, M3 is a positive integer not greater than M2; the M2 information reports respectively depend on the outputs of M2 operations, the M2 operations belong to the M operations, at least one of the M2 operations belongs to the M0 operations; the M3 information reports include the information reports whose depended operations belong to the M0 operations in the M2 information reports.

[0085] As an embodiment, the first information report includes at least one information report.

[0086] As an embodiment, one information report depends on the output of at least one of the M operations.

[0087] As an embodiment, the second node monitors whether the first information report is sent by the target receiver of the first information set.

[0088] As an embodiment, the second node determines by itself whether to give up receiving the first information report.

[0089] As an embodiment, the second processor receives the first information report or gives up receiving the first information report.

[0090] According to an aspect of the present application, the first information set indicates M configurations, M is a positive integer greater than 1, and the M configurations respectively indicate the M operations; the M configurations respectively include M first type identifiers, and the M operations are respectively associated to the M first type identifiers.

[0091] According to an aspect of the present application, the first information set is also used to determine the order of the M first type identifiers, and the execution order of the M operations from early to late is consistent with the order of the M first type identifiers.

[0092] According to an aspect of the present application, the first information set indicates the execution order of the M operations from early to late.

[0093] According to an aspect of the present application, the first information set indicates M configurations, M is a positive integer greater than 1, and the M configurations respectively indicate the M operations; the first information set is used to determine the order of the M configurations, and the execution order of the M operations from early to late is consistent with the order of the M configurations.

[0094] According to an aspect of the present application, at least two of the M operations correspond to different types, and the execution order of the M operations from early to late depends on the types to which the M operations correspond.

[0095] According to an aspect of the present application, the target receiver of the first information set determines the target operation invalidation by itself.

[0096] According to an aspect of the present application, comprising:

[0097] sending first signaling; wherein,

[0098] The first signaling indicates the target operation invalidation.

[0099] According to an aspect of the present application, comprising:

[0100] sending first signaling; wherein,

[0101] The first signaling indicates a target identity, and the target operation is associated to the target identity.

[0102] As an embodiment, the target receiver of the first information set deploys the M operations.

[0103] As an embodiment, the target receiver of the first information set deploys at least one of the M operations.

[0104] As an embodiment, the second node deploys at least one of the M operations.

[0105] As an embodiment, the second node deploys at least one of the M operations, and the target receiver of the first information set deploys the remaining operations of the M operations.

[0106] As an embodiment, at least one of the M operations is based on training or AI.

[0107] As an embodiment, the M operations are obtained by loading.

[0108] As an embodiment, the AI includes ML.

[0109] As an embodiment, the benefits of the above method include that the second node is reserved with sufficient degrees of freedom, suitable for various different scenarios and terminals, and has adaptability and flexibility.

[0110] As one embodiment, benefits of the above method include that training of one operation can not be performed at the second node, reducing the requirement of processing capability and power consumption of the second node.

[0111] The present application discloses a terminal, comprising one or more processors and a memory;

[0112] The memory is coupled with the one or more processors, and the memory is configured to store computer program codes, the computer program codes comprising computer instructions, the one or more processors invoking the computer instructions to cause the terminal to perform the method in the first node.

[0113] As one embodiment, the terminal is a user equipment.

[0114] The present application discloses a base station, comprising one or more processors and a memory;

[0115] The memory is coupled with the one or more processors, and the memory is configured to store computer program codes, the computer program codes comprising computer instructions, the one or more processors invoking the computer instructions to cause the base station to perform the method in the second node.

[0116] The present application discloses a first node used for wireless communication, comprising:

[0117] The first processor receives a first information set; the first information set indicates M operations, M being a positive integer greater than 1; determines that M0 operations in the M operations are invalid, M0 being a non-negative integer;

[0118] Wherein, a target operation is invalid, the target operation being one operation other than the M0 operations in the M operations; the M0 operations depend on the order of execution from early to late, and the target operation is in the M operations.

[0119] The present application discloses a second node used for wireless communication, comprising:

[0120] The second processor sends a first information set; the first information set indicates M operations, M being a positive integer greater than 1;

[0121] Wherein, a target receiver of the first information set determines that M0 operations in the M operations are invalid, M0 being a non-negative integer; a target operation is invalid, the target operation being one operation other than the M0 operations in the M operations; the M0 operations depend on the order of execution from early to late, and the target operation is in the M operations.

[0122] As an embodiment, compared with the conventional scheme, the present application has the following advantages:

[0123] - ensure the consistency of the understanding of the configuration and processing of the information reporting by the transceiver;

[0124] - support AI-based information reporting;

[0125] - support multiple operations for obtaining information reporting;

[0126] - improve the accuracy of information reporting, reduce reporting delay and overhead;

[0127] - better adapt to various different application scenarios or terminals, improve the flexibility and adaptability of the system;

[0128] - enhance the reliability and robustness of the system;

[0129] - improve the forward and backward compatibility of the system;

[0130] - simplify system design, reduce the complexity of scheme implementation;

[0131] - improve the overall performance of the system. BRIEF DESCRIPTION OF DRAWINGS

[0132] Other features, objects and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments with reference to the attached drawings:

[0133] Figure 1 shows a flowchart of a first information set and M operations according to an embodiment of the present application;

[0134] Figure 2 shows a schematic diagram of a network architecture according to an embodiment of the present application;

[0135] Figure 3 shows 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;

[0136] Figure 4 shows a schematic diagram of a first communication device and a second communication device according to an embodiment of the present application;

[0137] Figure 5 shows a flowchart of a wireless transmission according to an embodiment of the present application;

[0138] Figure 6 shows a schematic diagram of M0 operations according to an embodiment of the present application;

[0139] Figure 7 shows a schematic diagram of M configurations respectively indicating M operations according to an embodiment of the present application;

[0140] Figure 8 shows a schematic diagram of M first type identifiers according to an embodiment of the present application;

[0141] FIG. 9 shows a schematic diagram of the execution order of M operations from first to last, according to one embodiment of the present application;

[0142] FIG. 10 shows a schematic diagram of the execution order of M operations from first to last, according to another embodiment of the present application;

[0143] FIG. 11 shows a schematic diagram of determining a target operation failure, according to one embodiment of the present application;

[0144] FIG. 12 shows a schematic diagram of determining a target operation failure, according to another embodiment of the present application;

[0145] FIGS. 13A-13B show a schematic diagram of a first node deploying a first given operation, according to one embodiment of the present application;

[0146] FIG. 14 shows a schematic diagram of RAN (Radio Access Network) domain AI / ML function deployment, according to one embodiment of the present application;

[0147] FIG. 15 shows a schematic diagram of AI / ML function deployment of a UE, according to one embodiment of the present application;

[0148] FIG. 16 shows a schematic diagram of an artificial intelligence or machine learning based processing system, according to one embodiment of the present application;

[0149] FIG. 17 shows a schematic diagram of artificial intelligence or machine learning, according to one embodiment of the present application;

[0150] FIG. 18 shows a structural block diagram of a processing device in a first node, according to one embodiment of the present application;

[0151] FIG. 19 shows a structural block diagram of a processing device in a second node, according to one embodiment of the present application. DETAILED DESCRIPTION

[0152] The technical solutions of the present application will be further described in detail below with reference to the accompanying drawings. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other arbitrarily without conflict. Based on performance, flexibility, complexity, overhead and compatibility, etc., the person skilled in the art has the motivation to flexibly combine the embodiments in different drawings without conflict, for example, but not limited to, the embodiments in FIG. 1 and the embodiments in FIGS. 5-19, the embodiments in FIG. 5 and the embodiments in FIGS. 6-19, etc.

[0153] Embodiment 1

[0154] Embodiment 1 illustrates a flowchart of a first information set and M operations according to one embodiment of the present application, as shown in FIG. 1. In 100 shown in FIG. 1, each block represents a step. In particular, the order of the steps in the blocks does not represent a specific time sequence between the steps.

[0155] In Embodiment 1, the first node receives a first information set in step 101; determines that M0 operations of the M operations are invalid in step 102; wherein the first information set indicates M operations, M is a positive integer greater than 1; M0 is a non-negative integer; a target operation is invalid, the target operation is one operation other than the M0 operations in the M operations; the M0 operations depend on the order of the M operations according to the execution order from early to late.

[0156] As one embodiment, the first information set is carried by higher layer signaling.

[0157] As one embodiment, the first information set is carried by RRC (Radio Resource Control) signaling.

[0158] As one embodiment, the first information set is carried by RRC signaling and MAC CE signaling.

[0159] As one embodiment, the first information set includes one RRC IE (Information Element).

[0160] As one embodiment, the first information set includes multiple RRC IEs.

[0161] As one embodiment, the first information set includes part or all of the fields in one or more RRC IEs.

[0162] As one embodiment, the first information set includes one or more IEs CSI-ReportConfig.

[0163] As one embodiment, the first information set includes part or all of the fields in one or more IEs CSI-ReportConfig.

[0164] As one embodiment, the first information set includes N IEs CSI-ReportConfig, which are respectively used to configure N first information reports; N is a positive integer.

[0165] As one sub-embodiment of the above embodiment, N is equal to 1.

[0166] As one sub-example of the above embodiment, the N is equal to the M.

[0167] As one sub-example of the above embodiment, the N is less than the M.

[0168] As one sub-example of the above embodiment, the N is greater than 1 and less than the M.

[0169] As one embodiment, the first information set includes part or all of the fields in the IE ServingCellConfig.

[0170] As one embodiment, the first information set includes part or all of the fields in the IE CSI-MeasConfig IE.

[0171] As one embodiment, the first information set includes part or all of the fields in the IE ServingCellConfigCommon IE.

[0172] As one embodiment, the first information set includes part or all of the fields in the IE ServingCellConfig.

[0173] As one embodiment, at least one of the M operations is training-based or AI-based.

[0174] As one embodiment, the M operations include the target operation.

[0175] As one embodiment, the target operation is training-based or AI-based.

[0176] As one embodiment, the M operations include the target operation, which is training-based or AI-based.

[0177] As one embodiment, the benefits of the above method include supporting AI-based information reporting.

[0178] As one embodiment, the M operations include at least one training-based or AI-based operation and at least one operation that is not training-based or AI-based.

[0179] As one embodiment, the benefits of the above method include supporting joint processing between AI-based operations and non-AI-based operations, improving system flexibility, reducing scheme complexity, and improving information reporting accuracy.

[0180] As one embodiment, the benefits of the above method include enhancing system transmission efficiency and improving overall system performance.

[0181] In this application, the AI (Artificial Intelligence) includes ML (Machine Learning).

[0182] As one embodiment, a training-based or AI operation includes inference.

[0183] As one embodiment, a training-based or AI operation includes an AI entity.

[0184] As one embodiment, a training-based or AI operation includes an AI entity for inference.

[0185] As one embodiment, a training-based or AI operation includes a part of an AI entity.

[0186] As one embodiment, a training-based or AI operation includes a part of an AI entity for inference.

[0187] As one embodiment, a training-based or AI operation includes inference for obtaining the first information report.

[0188] As one embodiment, the inference includes AI (Artificial Intelligence) inference.

[0189] As one embodiment, a training-based or AI operation is based on artificial intelligence or machine learning.

[0190] As one embodiment, a training-based or AI operation is based on a neural network.

[0191] As one embodiment, a training-based or AI operation is based on a CNN (Conventional Neural Network).

[0192] As one embodiment, a model of a training-based or AI operation is obtained by training.

[0193] As one embodiment, a training-based or AI operation includes pre-processing.

[0194] As an embodiment, the pre-processing includes one or more of quantization, DFT (Discrete Fourier Transform), matrix decomposition, matrix transformation or projection, quantization, spatial-to-angle domain transformation, angle domain-to-spatial domain transformation, frequency domain-to-time domain transformation, time domain-to-frequency domain transformation, truncation, padding, mapping, or labeling.

[0195] As an embodiment, the labeling refers to labeling with labels.

[0196] As an embodiment, a training-based or AI-based operation includes post-processing.

[0197] As an embodiment, the post-processing includes one or more of DFT, quantization, angle domain-to-spatial domain transformation, spatial-to-angle domain transformation, time domain-to-frequency domain transformation, frequency domain-to-time domain transformation, truncation, and padding.

[0198] As an embodiment, a training-based or AI-based operation includes one or more of convolution, pooling, concatenation, and activation.

[0199] As an embodiment, a training-based or AI-based operation includes a fully connected layer.

[0200] As an embodiment, a training-based or AI-based operation includes a pooling layer.

[0201] As an embodiment, a training-based or AI-based operation includes at least one convolution layer.

[0202] As an embodiment, a training-based or AI-based operation includes at least one encoding layer.

[0203] As an embodiment, an encoding layer includes at least one convolution layer and a pooling layer.

[0204] As an embodiment, in a convolution layer, at least one convolution kernel is used to convolve an input to generate a corresponding feature map, at least one feature map output by the convolution layer is reshaped into a vector input to a fully connected layer; the fully connected layer converts the one vector into an output.

[0205] As an embodiment, some or all of the convolution kernel size, the number of convolution layers, the convolution step, the pooling kernel size, the pooling kernel step, the pooling function, the activation function, and the number of feature maps of a training-based or AI-based operation are obtained through training.

[0206] As an example, some or all of the convolution kernel, pooling kernel, pooling function, activation function, parameters of the pooling function, and parameters of the activation function are obtained through training or AI-based operations.

[0207] As an example, the M operations include AI inference for obtaining CSI.

[0208] As an example, the M operations include AI inference for obtaining channel information.

[0209] As an example, the M operations include AI inference for obtaining information other than channel information.

[0210] As an example, the M operations include AI inference for at least one of beam management, positioning or assisted positioning, CSI prediction, CSI estimation, or CSI compression.

[0211] As an example, the M operations include AI inference for at least one of performance monitoring, positioning, beam management, CSI prediction, CSI estimation, CSI compression, RLF (radio link failure) prediction, cell handover prediction, or serving cell prediction.

[0212] As an example, at least one of the M operations is used for an AI function.

[0213] As one example, the AI ​​function includes AI inference functionality.

[0214] As one example, the AI ​​functionality includes AI training functionality.

[0215] As one example, the AI ​​functionality includes AI management functionality.

[0216] As one example, the AI ​​function includes AI performance monitoring.

[0217] As an example, at least one of the M operations includes CSI compression based on artificial intelligence or machine learning.

[0218] As an example, at least one of the M operations includes an encoder for CSI compression based on artificial intelligence or machine learning.

[0219] As an example, at least one of the M operations includes CSI prediction or CSI estimation based on artificial intelligence or machine learning.

[0220] As an example, at least one of the M operations includes beam management based on artificial intelligence or machine learning.

[0221] As one embodiment, the beam management includes at least one of beam prediction, beam switching, beam failure prediction, or beam failure recovery.

[0222] As an example, at least one of the M operations includes positioning based on artificial intelligence or machine learning.

[0223] As an example, at least one of the M operations includes artificial intelligence or machine learning-assisted positioning.

[0224] As one example, the target operation includes CSI compression based on artificial intelligence or machine learning.

[0225] As one example, the target operation includes an encoder for CSI compression based on artificial intelligence or machine learning.

[0226] As one example, the target operation includes CSI prediction or CSI estimation based on artificial intelligence or machine learning.

[0227] As one example, the target operation includes beam management based on artificial intelligence or machine learning.

[0228] As one example, the target operation includes localization based on artificial intelligence or machine learning.

[0229] As one example, the target operation includes artificial intelligence or machine learning-assisted positioning.

[0230] As an example, the training of at least one of the M operations is performed by the first node.

[0231] As an example, the training of at least one of the M operations is performed by the sender of the first information set.

[0232] As an example, the training of at least one of the M operations is performed by the core network.

[0233] As an example, the training of at least one of the M operations is performed by an AI training producer.

[0234] As an example, the training of at least one of the M operations is performed by the MDA (Management Data Analytics Function).

[0235] As an example, the training of at least one of the M operations is performed by the MDA function located at the first node.

[0236] As an example, the training of at least one of the M operations is performed by the MDA function of the sender located in the first information set.

[0237] As an example, the training of at least one of the M operations is performed by NWDAF (Network Data Analytics Function).

[0238] As an example, the training of at least one of the M operations is performed by the MDAS (Management Data Analytics Service) producer.

[0239] As an example, the training of at least one of the M operations is performed by the MnS (Management Service) producer.

[0240] As an example, the training of the target operation is performed by the first node.

[0241] As an example, the training of the target operation is performed by the sender of the first information set.

[0242] As an example, the training of the target operation is performed by the core network.

[0243] As an example, the training of the target operation is performed by an AI training producer.

[0244] As an example, the training of the target operation is performed by the MDA (Management Data Analytics Function).

[0245] As an example, the training of the target operation is performed by the MDA function located at the first node.

[0246] As an example, the training of the target operation is performed by the MDA function of the sender located in the first information set.

[0247] As an example, the training of the target operation is performed by NWDAF (Network Data Analytics Function).

[0248] As an example, the training of the target operation is performed by the MDAS (Management Data Analytics Service) producer.

[0249] As an example, the training of the target operation is performed by the MnS (Management Service) producer.

[0250] As an example, the input to at least one of the M operations includes a measurement obtained based on at least one RS resource.

[0251] As an example, the input to at least one of the M operations includes channel measurements obtained based on CSI-RS resources or SS / PBCH block resources.

[0252] As an example, the input to at least one of the M operations includes interference measurements obtained based on CSI-RS resources or CSI-IM resources.

[0253] As an example, the input to at least one of the M operations includes the reception quality of at least one physical channel or physical signal.

[0254] As an example, the input to at least one of the M operations includes a matrix or vector obtained by preprocessing the channel matrix obtained from measurements based on at least one RS resource.

[0255] As an example, the output of at least one of the M operations includes channel information.

[0256] As an example, the output of at least one of the M operations includes information other than channel information.

[0257] As an example, the output of at least one of the M operations includes a channel matrix.

[0258] As an example, the output of at least one of the M operations includes CSI.

[0259] As an example, the output of at least one of the M operations includes compressed CSI.

[0260] As an example, the output of at least one of the M operations includes non-codebook-based CSI.

[0261] As an example, the output of at least one of the M operations includes a channel impulse response.

[0262] As an example, the output of at least one of the M operations includes small-scale characteristics.

[0263] As an example, the output of at least one of the M operations is used to determine one or more precoding matrices.

[0264] In one embodiment, the output of at least one of the M operations includes a predicted CSI.

[0265] In one embodiment, the output of at least one of the M operations includes predicted beam information.

[0266] In one embodiment, the output of at least one of the M operations includes location information.

[0267] As an example, the output of any of the M operations includes channel information, or at least one of the following: information other than channel information.

[0268] As one example, the channel information includes beam failure prediction.

[0269] As one example, the channel information includes beam switching prediction.

[0270] As one embodiment, the channel information includes at least one of predicted beam information, switched beam information, predicted CSI, estimated CSI, or compressed CSI.

[0271] As an example, the compressed CSI is based on a non-codebook.

[0272] As an example, the compressed CSI is not a reporting quantity defined by 3GPP Rel-18, nor is it a reporting quantity defined by versions prior to 3GPP Rel-18.

[0273] As an example, the compressed CSI is not a reporting quantity defined by 3GPP Rel-19, nor is it a reporting quantity defined by versions prior to 3GPP Rel-19.

[0274] As an example, the compressed CSI is not part of the reporting volume defined by the 5G standard.

[0275] As an example, the channel parameters recovered by the target receiver of the compressed CSI based on the compressed CSI are unknown to the sender of the compressed CSI.

[0276] As an example, the compressed CSI is based on artificial intelligence or machine learning.

[0277] As an example, the compressed CSI is based on a neural network.

[0278] As an example, the compressed CSI is based on CNN (Conventional Neural Networks).

[0279] As one example, the beam information includes the beam.

[0280] As an example, the beam information includes at least one of resource indication or RSRP (reference signal received power).

[0281] As one example, the beam information includes RSRP.

[0282] As an example, the resource indication in this application is used to indicate beam or RS resources.

[0283] As an example, the resource indication in this application is used to indicate at least one of beam, CSI-RS resource, or SS / PBCH block resource.

[0284] As an example, the resource indication in this application includes at least one of beam indication, CRI (CSI-RS Resource Indicator, Channel State Information Reference Signal Resource Indicator), or SS / PBCH Block Resource Indicator (SSBRI).

[0285] As one example, the channel information includes CSI (channel state information).

[0286] As one example, the channel information includes CSI or beam information.

[0287] As one example, the channel information includes the channel impulse response.

[0288] As one example, the channel information includes small-scale characteristics.

[0289] As one embodiment, the channel information includes one or more of delay spread, Doppler spread, Doppler shift, average delay, or average gain.

[0290] As one example, the channel information includes a channel matrix.

[0291] As an example, the channel matrix is ​​in the spatial-frequency domain.

[0292] As an example, the channel matrix is ​​in the angular-delay domain projection.

[0293] As one embodiment, the channel information includes at least one of the channel's feature values ​​or feature vectors.

[0294] As an example, the channel information includes one or more of the following: beam information, PMI (Precoding Matrix Indicator), CRI (CSI-RS Resource Indicator), CQI (Channel Quality Indicator), RI (Rank Indicator), LI (layer indicator), SSBRI (SS / PBCH Block Resource Indicator), RSRP (Reference Signal Received Power), SINR (Signal-to-Interference-plus-Noise Ratio), Capability Index, and TDCP (Time Domain Channel Properties).

[0295] As one example, the information other than the channel information includes performance monitoring results.

[0296] As an example, the performance monitoring includes performance monitoring for the AI ​​model.

[0297] As an example, the performance monitoring includes performance monitoring for AI functions.

[0298] As an example, the performance monitoring includes performance monitoring of the M operations, and the performance monitoring results include whether the M operations have failed.

[0299] As an example, the performance monitoring includes performance monitoring of at least one of the M operations, and the performance monitoring result includes whether at least one of the M operations has failed.

[0300] As an example, the performance monitoring results include whether the target operation has failed.

[0301] As an example, the performance monitoring results include whether the M operations failed.

[0302] As an example, the performance monitoring results include whether at least one of the M operations has failed.

[0303] As one example, the information other than the channel information includes RLF prediction.

[0304] As one example, the information other than the channel information includes cell handover prediction.

[0305] As one example, the information other than the channel information includes serving cell prediction.

[0306] As one example, the information other than the channel information includes positioning based on artificial intelligence or machine learning.

[0307] As one example, the information other than the channel information includes artificial intelligence or machine learning-assisted positioning.

[0308] As an example, the M operations are cascaded, and the execution order of the M operations is one after another; except for the last operation among the M operations, the output of one of the M operations is the input of the next operation.

[0309] As an example, the M operations are cascaded, and the execution order of the M operations is one after another; except for the last of the M operations, the output of one of the M operations is processed and used as the input of the next operation.

[0310] As an example, the advantages of the above method include: supporting multiple operations to cascade to complete a function, improving the accuracy of information reporting, and reducing the implementation complexity of the solution.

[0311] As an example, the M operations are performed in parallel and are executed together; the input of any one of the M operations does not depend on the output of any other one of the M operations.

[0312] As an example, the M operations are performed in parallel and are executed together; the output of any one of the M operations is not used as the input of any other one of the M operations.

[0313] As an example, the advantages of the above method include: supporting multiple operations to work together to complete a function, improving the accuracy of information reporting, and reducing the implementation complexity of the solution.

[0314] As an example, the advantages of the above method include: supporting redundant processing among multiple operations, improving the reliability and robustness of the system.

[0315] As an example, the M operations are divided into M1 parts, each of the M1 parts including at least one of the M operations, where M1 is a positive integer greater than 1 and less than M; the M1 parts are cascaded, and the execution order of the M1 parts is one after another; except for the first part of the M1 parts, the input of one part of the M1 parts depends on the output of the previous part; the operations in any part of the M1 parts are parallel, and the operations in any part of the M1 parts are executed together; the input of any operation in any part of the M1 parts does not depend on the output of any other operation in the M1 parts.

[0316] As an example, the M operations are divided into M1 parts, each of the M1 parts including at least one of the M operations, where M1 is a positive integer greater than 1 and less than M; the M1 parts are cascaded, and the execution order of the M1 parts is one after another; except for the last part of the M1 parts, the output of one part of the M1 parts is used as the input of the next part; the operations in any part of the M1 parts are parallel, and the operations in any part of the M1 parts are executed together; the output of any operation in any part of the M1 parts is not used as the input of any other operation in the N parts.

[0317] As an example, the advantages of the above method include: adapting to various different scenarios and terminals, and improving the adaptability and flexibility of the system.

[0318] As an example, the first information set explicitly indicates M operations.

[0319] As an example, the first information set implicitly indicates M operations.

[0320] As one example, the first information set indicates the indexes of M operations.

[0321] As one embodiment, the first information set includes indexes of M operations.

[0322] As one embodiment, the first information set indicates the configuration information for M operations.

[0323] As one embodiment, the first information set includes configuration information for M operations.

[0324] As one embodiment, the first information set indicates M configurations, and the M configurations respectively indicate the M operations.

[0325] As one embodiment, the first information set includes M configurations, each of which indicates one of the M operations.

[0326] As one embodiment, the first information set indicates M identifiers, and the M identifiers respectively indicate the M operations.

[0327] As one embodiment, the first information set includes M identifiers, each of which indicates one of the M operations.

[0328] As one embodiment, the first information set indicates M identifiers, the M identifiers respectively indicate M configurations, and the M configurations respectively indicate the M operations.

[0329] As one embodiment, the first information set includes M identifiers, each of which indicates one of the M configurations, and each of the M configurations indicates one of the M operations.

[0330] As one embodiment, the first information set indicates M identifiers, the M identifiers respectively indicate M configurations, and the M configurations are respectively used to configure the M operations.

[0331] As one embodiment, the first information set includes M identifiers, each of which indicates one of the M configurations, and each of the M configurations is used to configure the M operations.

[0332] As an example, the advantages of the above method include: adapting to various different scenarios and terminals, and improving the adaptability and flexibility of the system.

[0333] As one embodiment, the first node receives a first signaling message, which indicates whether an operation has failed.

[0334] As one embodiment, the first node receives a first signaling message indicating a target identifier and an operation associated with the target identifier failing.

[0335] As an example, the advantages of the above method include reducing the processing power requirements and power consumption of the first node.

[0336] As an example, the first node determines for itself whether an operation has failed.

[0337] As an example, the advantages of the above method include: providing sufficient degrees of freedom for the first node, adapting to various different scenarios and terminals, and having adaptability and flexibility.

[0338] Generally, how the first node determines whether an operation has failed is determined by the hardware manufacturer. Below are some non-limiting implementation methods:

[0339] As an example, the first node determines whether an operation has failed by monitoring whether an operation has achieved a given performance target.

[0340] As an example, the first node determines whether an operation has achieved the given performance target by monitoring the reception quality of the signal or channel generated based on the output of an operation.

[0341] As a sub-example of the above embodiments, the output of an operation includes one or more precoding matrices, and the signal or channel generated based on the output of this operation refers to a signal or channel precoded by the one or more precoding matrices.

[0342] As a sub-example of the above embodiments, the output of an operation includes one or more beams, and the signal or channel generated based on the output of this operation refers to a signal or channel employing the one or more beams.

[0343] As a sub-example of the above embodiments, the output of an operation includes one or more CQIs, which are used to determine one or more MCS (Modulation and Coding Schemes). The signal or channel generated based on the output of this operation refers to the signal or channel using the one or more MCSs.

[0344] As an example, the output of an operation includes reliability, and the first node determines whether the operation has achieved the given performance target by the relationship between the reliability and a certain threshold.

[0345] As an example, the output of an operation includes confidence information, and the first node determines whether the operation has achieved the given performance target by the relationship between the confidence information and a certain threshold.

[0346] As an example, the first node determines whether an operation meets the given performance target by performing ML testing or ML simulation on a model of an operation.

[0347] As an example, the first node recovers the input of an operation based on the output of the operation, and then determines whether the operation has achieved the given performance target by comparing the recovered input with the actual input.

[0348] As an example, one operation corresponds to one timer, and the first node determines whether the operation has failed based on whether the timer has expired.

[0349] As an example, an operation failure includes: an operation needs to be retrained.

[0350] As an example, an operation failure includes: an operation requiring redeployment or reloading.

[0351] As an example, if an operation requires retraining, the operation is invalidated.

[0352] As an example, if an operation needs to be redeployed or reloaded, the operation fails.

[0353] As an example, if the first node is retrained for an operation, that operation becomes invalid.

[0354] As an example, if the first node redeploys or reloads an operation, that operation fails.

[0355] As an example, the first node determines for itself whether an operation needs to be retrained.

[0356] As an example, the first node determines for itself whether an operation needs to be redeployed or reloaded.

[0357] As an example, if an operation fails to achieve the expected performance or performance target, the operation is considered to have failed.

[0358] As an example, the first node determines that an operation has failed based on the fact that an operation cannot achieve the expected performance or performance target.

[0359] As an example, if the timer corresponding to an operation expires, the operation becomes invalid.

[0360] As an example, a timer is started or restarted after the training of the corresponding operation is completed.

[0361] As an example, a timer is started or restarted after the deployment of the corresponding operation is completed.

[0362] As an example, a timer is started or restarted after the most recent training of the corresponding operation is completed.

[0363] As an example, a timer is started or restarted after the most recent deployment of the corresponding operation has been completed.

[0364] As an example, a timer is started or restarted after the corresponding operation is performed.

[0365] As an example, a timer is started or restarted after each output of the corresponding operation.

[0366] As an example, when a start instruction for a timer is received, the first node starts or restarts the timer.

[0367] As an example, if an operation is not updated within a time window, the operation fails.

[0368] As an example, updating an operation includes updating the input of the operation.

[0369] As an example, updating an operation includes updating the output of the operation.

[0370] As an example, updating an operation includes updating the AI ​​model associated with the operation.

[0371] As an example, updating an operation includes updating the AI ​​model used in the operation.

[0372] As an example, different operations correspond to their respective time windows.

[0373] As an example, different operations correspond to the same time window.

[0374] As an example, the time windows corresponding to different operations are configured separately.

[0375] As an example, a producer of an operation indicates whether the operation can achieve the expected performance or performance target.

[0376] As an example, an operation fails if any other operation associated with it fails.

[0377] As an example, an operation fails if any other operation cascaded with it fails.

[0378] As an example, an operation fails if any other operation that has an input-output relationship with an operation fails.

[0379] As an example, an operation fails if any other operation that has a direct or indirect input-output relationship with an operation fails.

[0380] As an example, if any other operation associated with an operation fails, the first node determines whether the first operation has failed based on the failed operation.

[0381] As an example, if any other operation cascaded with an operation fails, the first node determines whether the first operation has failed based on the failed operation.

[0382] As an example, if any other operation that has an input-output relationship with an operation fails, the first node determines whether the operation has failed based on the failed operation.

[0383] As an example, if any other operation that has a direct or indirect input-output relationship with an operation fails, the first node determines whether the operation has failed based on the failed operation.

[0384] As an example, if any other operation associated with an operation fails, the first node determines whether the operation has failed based on the relationship between the operation and the failed operation.

[0385] As an example, if any other operation cascaded with an operation fails, the first node determines whether the operation has failed based on the relationship between the operation and the failed operation.

[0386] As an example, if any other operation that has an input-output relationship with an operation fails, the first node determines whether the first operation has failed based on the failed operation. The first node determines whether the first operation has failed based on the relationship between the first operation and the failed operation.

[0387] As an example, if any other operation that has a direct or indirect input-output relationship with an operation fails, the first node determines whether the first operation has failed based on the failed operation. The first node determines whether the first operation has failed based on the relationship between the first operation and the failed operation.

[0388] As an example, if one operation fails, all other operations that take the output of said operation as input also fail.

[0389] As an example, if one operation fails, all other operations that preceded that operation also fail.

[0390] As an example, if one operation fails, all other operations based on that one operation also fail.

[0391] As an example, if one operation fails, all other operations included in that operation also fail.

[0392] As an example, if one operation fails, all other operations associated with that operation also fail.

[0393] As an example, when M equals 1, M0 equals 0.

[0394] As an example, the advantages of the above method include: enhancing the completeness of the system and the solution.

[0395] As an example, the execution order of the M operations is one after another; the M0 operations depend on the execution order from first to last, and the order of the target operation in the M operations refers to: the order in which the target operation is executed in the M operations depends on the order in which the target operation is executed in the M operations.

[0396] As an example, the M operations are cascaded, and except for the last operation among the M operations, the output of one of the M operations is the input of the next operation; the M0 operations depend on the execution order from first to last, and the order of the target operation among the M operations means that the M0 operations depend on the input-output order of the target operation among the M operations.

[0397] As an example, when the target operation is the last of the M operations, M0 equals 0.

[0398] As an example, when the target operation is not the last operation among the M operations, the M0 operations include the operations that follow the target operation among the M operations.

[0399] As an example, the advantages of the above method include: supporting multiple operations to cascade to complete a function, improving the accuracy of information reporting, and reducing the implementation complexity of the solution.

[0400] As an example, the M operations are parallel, and the input of any one of the M operations does not depend on the output of any other one of the M operations; the outputs of all the operations in the M operations are combined into the output of the M operations; the M0 operations depend on the execution order from first to last, and the order of the target operation in the M operations means that the order of the M0 operations depends on the output order of the target operation in the M operations.

[0401] As an example, the M operations are parallel, and the input of any one of the M operations does not depend on the output of any other one of the M operations; the inputs of all operations in the M operations are combined into the input of the M operations; the M0 operations depend on the execution order from first to last, and the order of the target operation in the M operations means that the M0 operations depend on the order of the input of the target operation in the M operations.

[0402] As an example, the M operations are parallel, and the input of any of the M operations does not depend on the output of any other of the M operations; when all the operations in the M operations reuse the same input and output, M0 equals 0.

[0403] As an example, the advantages of the above method include: supporting multiple operations to work together to complete a function, improving the accuracy of information reporting, and reducing the implementation complexity of the solution.

[0404] As an example, the advantages of the above method include: supporting redundant processing among multiple operations, improving the reliability and robustness of the system.

[0405] As an example, the M operations are divided into M1 parts, and any part of the M1 parts includes at least one operation from the M operations, where M1 is a positive integer greater than 1 and less than M; the execution order of the M1 parts is one after another; the M0 operations depend on the execution order from first to last, and the order of the target operation in the M operations refers to the order in which the part containing the target operation is executed in the M1 parts.

[0406] As an example, the M operations are divided into M1 parts, each of the M1 parts including at least one of the M operations, where M1 is a positive integer greater than 1 and less than M; the M1 parts are cascaded, and except for the last part of the M1 parts, the output of one part of the M1 parts is the input of the next part; the M0 operations depend on the execution order from first to last, and the order of the target operation in the M operations means that the M0 operations depend on the input-output order of the part containing the target operation in the M1 parts.

[0407] As a sub-example of the above embodiment, when the part where the target operation is located is the last part of the M1 parts, the M0 is equal to 0.

[0408] As a sub-example of the above embodiment, when the part where the target operation is located is not the last part among the M1 parts, the M0 operations include the operations among the M operations that are after the part where the target operation is located.

[0409] As an example, the M operations are divided into M1 parts, each of the M1 parts including at least one of the M operations, where M1 is a positive integer greater than 1 and less than M; the execution order of the M1 parts is one after another; the outputs of all operations in the part containing the target operation are merged into the output of the part containing the target operation; the M0 operations depend on the execution order from first to last, and the order of the target operation in the M operations refers to: the M0 operations jointly depend on which part of the M1 parts the target operation is executed in and the order of the outputs of the target operation in the part containing the target operation.

[0410] As an example, the M operations are divided into M1 parts, each of the M1 parts including at least one of the M operations, where M1 is a positive integer greater than 1 and less than M; the M1 parts are cascaded, and except for the last part of the M1 parts, the output of one part of the M1 parts is the input of the next part; the outputs of all operations in the part containing the target operation are merged into the output of the part containing the target operation; the M0 operations depend on the execution order from first to last, and the order of the target operation in the M operations means that the M0 operations jointly depend on the input-output order of the part containing the target operation in the M1 parts and the output order of the target operation in the part containing the target operation.

[0411] As an example, the M operations are divided into M1 parts, each of the M1 parts including at least one of the M operations, where M1 is a positive integer greater than 1 and less than M; the execution order of the M1 parts is one after another; the inputs of all operations in the part containing the target operation are merged into the input of the part containing the target operation; the M0 operations depend on the execution order from first to last, and the order of the target operation in the M operations refers to: the M0 operations jointly depend on which part of the M1 parts the target operation is executed in and the order of the inputs of the target operation in the part containing the target operation.

[0412] As an example, the M operations are divided into M1 parts, each of the M1 parts including at least one of the M operations, where M1 is a positive integer greater than 1 and less than M; the M1 parts are cascaded, and except for the last part of the M1 parts, the output of one part of the M1 parts is the input of the next part; the inputs of all operations in the part containing the target operation are merged into the input of the part containing the target operation; the M0 operations depend on the execution order from first to last, and the order of the target operation in the M operations means that the M0 operations jointly depend on the input-output order of the part containing the target operation in the M1 parts and the input order of the target operation in the part containing the target operation.

[0413] As an example, the advantages of the above method include: adapting to various different scenarios and terminals, and improving the adaptability and flexibility of the system.

[0414] Example 2

[0415] Example 2 illustrates a schematic diagram of a network architecture according to an embodiment of this application, as shown in Figure 2.

[0416] Figure 2 illustrates network architecture 200. Network architecture 200 is a 5G NR (New Radio) / LTE (Long-Term Evolution) / LTE-A (Long-Term Evolution Advanced) system, or a 5G+ network architecture, or a 6G network architecture, or a network architecture adopted in future evolutions by 3GPP; network architecture 200 may be referred to as 5GS (5G System) / EPS (Evolved Packet System), or 6GS (6G System); 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 can interconnect with other access networks, but these entities / interfaces are not shown for simplicity. As shown, the network architecture 200 provides packet-switched services; however, those skilled in the art will readily understand that the various concepts presented throughout this application can be extended to networks providing circuit-switched services or other cellular networks. The RAN includes node 203. The RAN may also include other nodes 204. Node 203 provides user and control plane protocol termination toward UE 201. Node 203 may be connected to other nodes 204 via an Xn interface (e.g., backhaul) / X2 interface. Node 203 may also be referred to as a base station, base transceiver station, radio base station, radio transceiver, transceiver function, basic service set (BSS), extended service set (ESS), TRP (transmitter-receiver node), or some other suitable term. The core network 210 is a 5GC (5G Core Network) / EPC (Evolved Packet Core), or the core network 210 is a 6GC; node 203 provides UE 201 with an access point to the core network 210.Examples of UE201 include cellular phones, smartphones, Session Initiation Protocol (SIP) phones, laptops, 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 IoT devices, machine-type communication devices, land vehicles, automobiles, wearable devices, or any other similar functional devices. Those skilled in the art may also refer to UE201 as a mobile station, subscriber station, mobile unit, subscriber unit, radio unit, remote unit, mobile device, radio device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handheld device, user agent, mobile client, client, or any other suitable term. Node 203 is connected to the core network 210 via an S1 / NG interface. The core network 210 includes an MME (Mobility Management Entity) / AMF (Authentication Management Field) / SMF (Session Management Function) 211, other MMEs / AMFs / SMFs 214, an S-GW (Service Gateway) / UPF (User Plane Function) 212, and a P-GW (Packet Data Network Gateway) / UPF 213. The MME / AMF / SMF 211 is the control node that handles signaling between the UE 201 and the core network 210. Generally, the MME / AMF / SMF 211 provides bearer and connection management. All user IP (Internet Protocol) packets are transmitted through the S-GW / UPF 212, which is itself connected to the P-GW / UPF 213. The P-GW provides UE IP address allocation and other functions. The P-GW / UPF 213 is connected to the Internet service 230. Internet services 230 include operator-compliant Internet protocol services, which may specifically include the Internet, intranet, IMS (IP Multimedia Subsystem), and packet switching services.

[0417] As an example, the first node includes the UE201.

[0418] As one embodiment, the second node includes the node 203.

[0419] As an example, the wireless link between the UE201 and the node203 includes a cellular link.

[0420] As an example, the sender of the first information set includes the node 203.

[0421] As an example, the recipient of the first information set includes the UE201.

[0422] As an example, the sender of the first information report includes the UE201.

[0423] As an example, the recipient of the first information report includes the node 203.

[0424] As an example, the sender of the first signaling includes the node 203.

[0425] As an example, the recipient of the first signaling includes the UE201.

[0426] As an example, the executor of the target operation includes the UE201.

[0427] As an example, the executors of the M operations include the UE201 and the node203.

[0428] As an example, the executors of the M operations include at least one of the UE201 and the node203.

[0429] Example 3

[0430] Example 3 illustrates a schematic diagram of an embodiment of a wireless protocol architecture for the user plane and control plane according to an embodiment of this application, as shown in Figure 3.

[0431] Example 3 illustrates a schematic diagram of an embodiment of a wireless protocol architecture for a user plane and control plane according to this application, as shown in Figure 3. Figure 3 is a schematic diagram illustrating an embodiment of a radio protocol architecture for a user plane 350 and a control plane 300. Figure 3 shows the radio protocol architecture for the control plane 300 between a first communication node device (UE, gNB, or RSU in V2X) and a second communication node device (gNB, UE, or RSU in V2X), or between two UEs, using 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. Layer 1 will be referred to herein as PHY 301. Layer 2 (L2 layer) 305 is above PHY 301 and is responsible for the link between the first communication node device and the second communication node device, or between two UEs. Layer L2 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. It also provides security through encrypted data packets and supports cross-cell mobility between the second communication node devices and the first communication node device. The RLC sublayer 303 provides upper-layer packet segmentation and reassembly, retransmission of lost packets, and packet reordering to compensate for out-of-order reception due to HARQ. The MAC sublayer 302 provides multiplexing between logical and transport channels. It is also responsible for allocating various radio resources (e.g., resource blocks) within a cell among the first communication node devices. Furthermore, the MAC sublayer 302 handles HARQ operations. In the control plane 300, the Radio Resource Control (RRC) sublayer 306 of Layer 3 (L3) is responsible for acquiring radio resources (i.e., radio bearers) and configuring the lower layers using RRC signaling between the second and first communication node devices. The user plane 350's radio protocol architecture includes Layer 1 (L1) and Layer 2 (L2). The radio protocol architecture for the first and second communication node devices in the user plane 350 is largely the same as the corresponding layers and sublayers in the control plane 300 for Physical Layer 351, PDCP sublayer 354 in L2 Layer 355, RLC sublayer 353 in L2 Layer 355, and MAC sublayer 352 in L2 Layer 355. However, 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 an SDAP (Service Data Adaptation Protocol) sublayer 356, which is responsible for mapping between QoS streams and data radio bearers (DRBs) to support service diversity. Although not illustrated, the first communication node device may have several upper layers above the L2 layer 355, including a network layer (e.g., IP layer) terminating at the P-GW on the network side and an application layer terminating at the other end of the connection (e.g., a remote UE, server, etc.).

[0432] As an example, the wireless protocol architecture in Figure 3 is applicable to the first node.

[0433] As an example, the wireless protocol architecture in Figure 3 is applicable to the second node.

[0434] As an example, the higher layer mentioned in this application refers to the layer above the physical layer.

[0435] As an example, the first information set is generated in the RRC sublayer 306.

[0436] As an example, the first information set is generated in the MAC sublayer 302 or the MAC sublayer 352.

[0437] As an example, the first information report is generated in the MAC sublayer 302 or the MAC sublayer 352.

[0438] As an example, the first information report is generated in the PHY301 or the PHY351.

[0439] As an example, the first signaling is generated in the RRC sublayer 306.

[0440] As an example, the first signaling is generated in the MAC sublayer 302 or the MAC sublayer 352.

[0441] As an example, the first signaling is generated in the PHY301 or the PHY351.

[0442] Example 4

[0443] Example 4 illustrates a schematic diagram of a first communication device and a second communication device according to an embodiment of this application, as shown in Figure 4. 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.

[0444] The first communication device 410 includes a controller / processor 475, a memory 476, a receiver processor 470, a transmitter processor 416, a multi-antenna receiver processor 472, a multi-antenna transmitter processor 471, a transmitter / receiver 418, and an antenna 420.

[0445] The second communication device 450 includes a controller / processor 459, a memory 460, a data source 467, a transmitting processor 468, a receiving processor 456, a multi-antenna transmitting processor 457, a multi-antenna receiving processor 458, a transmitter / receiver 454, and an antenna 452.

[0446] In the transmission from the first communication device 410 to the second communication device 450, at the first communication device 410, upper-layer data packets from the core network are provided to the controller / processor 475. The controller / processor 475 implements L2 layer functionality. 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 operation, 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 L1 layer (i.e., physical layer). Transmit processor 416 performs encoding and interleaving to facilitate forward error correction (FEC) at the second communication device 450, and constellation mapping based on various modulation schemes (e.g., binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), M-phase shift keying (M-PSK), and M-quadrature amplitude modulation (M-QAM). Multi-antenna transmit processor 471 performs digital spatial precoding on the encoded and modulated symbols, including codebook-based precoding and non-codebook-based precoding, and beamforming processing, generating 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 and / or frequency domains, and then uses an inverse fast Fourier transform (IFFT) to generate a physical channel carrying the time-domain multicarrier symbol stream. The multi-antenna transmit processor 471 then performs transmit analog precoding / beamforming operations on the time-domain multicarrier symbol stream. Each transmitter 418 converts the baseband multicarrier symbol stream provided by the multi-antenna transmit processor 471 into an RF stream, which is then provided to a different antenna 420.

[0447] 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 radio frequency carrier and converts the radio frequency stream into a baseband multicarrier symbol stream, which is then provided to the receiver processor 456. The receiver processor 456 and the multi-antenna receiver processor 458 implement various signal processing functions of the L1 layer. The multi-antenna receiver processor 458 performs receive analog precoding / beamforming operations on the baseband multicarrier symbol stream from the receiver 454. The receiver processor 456 uses a Fast Fourier Transform (FFT) to convert the baseband multicarrier symbol stream after the receive analog precoding / beamforming operations 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 receiver processor 456, where the reference signal is used for channel estimation, and the data signal is recovered in the multi-antenna receiver processor 458 after multi-antenna detection to recover any parallel stream destined for the second communication device 450. Symbols on each parallel stream are demodulated and recovered in the receive processor 456, generating soft decisions. The receive processor 456 then decodes and deinterleaves the soft decisions to recover the upper-layer data and control signals transmitted over the physical channel by the first communication device 410. The upper-layer data and control signals are then provided to the controller / processor 459. The controller / processor 459 implements the functions of Layer 2 (L2). The controller / processor 459 may be associated with a memory 460 storing program code and data. The memory 460 may be referred to as computer-readable media. In the DL (Layered Logic), the controller / processor 459 provides multiplexing, packet reassembly, decryption, header decompression, and control signal processing between the transmission and logical channels to recover upper-layer packets from the core network. The upper-layer packets are then provided to all protocol layers above Layer 2. Various control signals may also be provided to Layer 3 (L3) for L3 processing. The controller / processor 459 is also responsible for error detection using ACK and / or NACK protocols to support HARQ operation.

[0448] 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 functions at the first communication device 410 described in the 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, implementing L2 layer functions for the user plane and control plane. The controller / processor 459 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the first communication device 410. Transmit processor 468 performs modulation mapping and channel coding processing, while multi-antenna transmit processor 457 performs digital multi-antenna spatial precoding, including codebook-based and non-codebook-based precoding, and beamforming processing. Subsequently, transmit processor 468 modulates the generated parallel stream into a multi-carrier / single-carrier symbol stream. After analog precoding / beamforming operations in multi-antenna transmit processor 457, the stream is provided to different antennas 452 via transmitter 454. Each transmitter 454 first converts the baseband symbol stream provided by multi-antenna transmit processor 457 into a radio frequency symbol stream before providing it to antenna 452.

[0449] 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 receiving 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 radio frequency signals through its corresponding antenna 420, converts the received radio frequency signals into baseband signals, and provides the baseband signals to the multi-antenna receiving processor 472 and the receiving processor 470. The receiving processor 470 and the multi-antenna receiving processor 472 jointly implement the L1 layer functions. The controller / processor 475 implements the L2 layer functions. The controller / processor 475 may be associated with a memory 476 that stores program code and data. The memory 476 may be referred to as computer-readable media. The controller / processor 475 provides multiplexing, packet reassembly, decryption, header decompression, and control signal processing between the transmission and logical channels to recover upper-layer data packets from the second communication device 450. The upper-layer data packets from the controller / processor 475 may be provided to the core network. The controller / processor 475 is also responsible for error detection using ACK and / or NACK protocols to support HARQ operation.

[0450] As one 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 means at least: receiving a first information set; the first information set indicating M operations, M being a positive integer greater than 1; determining that M0 of the M operations have failed, M0 being a non-negative integer; wherein a target operation has failed, the target operation being an operation other than the M0 operations among the M operations; the M0 operations depend on the order of execution from first to last, and the target operation is ordered among the M operations.

[0451] As one embodiment, the second communication device 450 includes: a memory storing a computer-readable instruction program that generates actions when executed by at least one processor, the actions including: receiving a first information set; the first information set indicating M operations, M being a positive integer greater than 1; determining that M0 of the M operations are invalid, M0 being a non-negative integer; wherein a target operation is invalidated, the target operation being an operation other than the M0 operations among the M operations; the M0 operations depend on the order of execution from first to last, and the order of the target operation among the M operations.

[0452] As one 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 means at least: transmitting a first information set; the first information set indicating M operations, M being a positive integer greater than 1; wherein a target recipient of the first information set determines that M0 of the M operations are invalid, M0 being a non-negative integer; the target operation is invalid, the target operation being an operation other than the M0 operations among the M operations; the M0 operations depend on the order of execution from first to last, and the target operation is ordered among the M operations.

[0453] As one embodiment, the first communication device 410 includes: a memory storing a computer-readable instruction program that generates actions when executed by at least one processor, the actions including: sending a first information set; the first information set indicating M operations, M being a positive integer greater than 1; wherein a target recipient of the first information set determines that M0 of the M operations are invalid, M0 being a non-negative integer; the target operation is invalid, the target operation being an operation other than the M0 operations among the M operations; the M0 operations depend on the order of execution from first to last, and the target operation is ordered among the M operations.

[0454] As an example, the first node in this application includes the second communication device 450.

[0455] As an example, the second node in this application includes the first communication device 410.

[0456] As an example, 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 set in this application; 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 transmit the first information set in this application.

[0457] As an example, at least one of the following is used to send the first information report in this application, or to abandon sending the first information report: {the antenna 452, the transmitter / receiver 454, the transmission processor 468, the multi-antenna transmission processor 457, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, and the data source 467}.

[0458] As an example, at least one of the following is used to receive the first information report in this application, or to waive the right to receive the first information report: {the antenna 420, the transmitter / receiver 418, the receiving processor 470, the multi-antenna receiving processor 472, the transmitting processor 416, the multi-antenna transmitting processor 471, the controller / processor 475, and the memory 476}.

[0459] As an example, 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 signaling in this application; 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 transmit the first signaling in this application.

[0460] As an example, at least one of the following is used to perform the target operation described in this application: {the antenna 452, the receiver / transmitter 454, the receiving processor 456, the transmitting processor 468, the multi-antenna receiving processor 458, the multi-antenna transmitting processor 457, the controller / processor 459, the memory 460, and the data source 467}.

[0461] As an example, at least one of the following is used to perform the M operations in this application: {the antenna 452, the receiver / transmitter 454, the receiving processor 456, the transmitting processor 468, the multi-antenna receiving processor 458, the multi-antenna transmitting processor 457, the controller / processor 459, the memory 460, and the data source 467}.

[0462] As an example, at least one of {the antenna 452, the receiver / transmitter 454, the receiving processor 456, the transmitting processor 468, the multi-antenna receiving processor 458, the multi-antenna transmitting processor 457, the controller / processor 459, the memory 460, and the data source 467} is used to perform at least one of the M operations in this application.

[0463] As an example, at least one of the following {antenna 420, transmitter / receiver 418, receiver processor 470, transmitter processor 416, multi-antenna receiver processor 472, multi-antenna transmitter processor 471, controller / processor 475, memory 476} is used to perform at least one of the M operations in this application.

[0464] Example 5

[0465] Example 5 illustrates a flowchart of wireless transmission according to an embodiment of this application; as shown in Figure 5. In Figure 5, the second node U1 and the first node U2 are communication nodes transmitted via an air interface. In Figure 5, the steps in blocks F51 to F57 are optional, and the steps in blocks F54, F55, F56, and F57 are one of four choices.

[0466] For the second node U1, in step S511, a first information set is sent; in step S512, a first signaling is sent; in step S513, (M2-M3) information reports are received; in step S514, M2 information reports are received; and in step S515, a first information report is received.

[0467] For the first node U2, in step S521, at least one of the M operations is deployed; in step S522, a first information set is received; in step S523, a first signaling is received; in step S524, it is determined that M0 of the M operations have failed; in step S525, at least one of the M operations is executed; in step S526, M3 of the M2 information reports are abandoned; in step S527, information reports other than the M3 of the M2 information reports are sent; in step S528, the M3 of the M2 information reports are not updated; in step S529, the M2 information reports are sent; in step S5210, the first information report is not updated; in step S5211, the first information report is sent; and in step S5212, the first information report is abandoned.

[0468] In embodiment 5, the first information set indicates M operations, where M is a positive integer greater than 1; M0 is a non-negative integer; the target operation is invalid, and the target operation is an operation other than the M0 operations among the M operations; the M0 operations depend on the order of execution from first to last, and the target operation is ordered among the M operations.

[0469] As an example, the first node U2 is the first node in this application.

[0470] As an example, the second node U1 is the second node in this application.

[0471] As one embodiment, the air interface between the second node U1 and the first node U2 includes a wireless interface between the base station equipment and the user equipment.

[0472] As one embodiment, the air interface between the second node U1 and the first node U2 includes a wireless interface between the relay node device and the user equipment.

[0473] As one embodiment, the air interface between the second node U1 and the first node U2 includes a wireless interface between user equipment.

[0474] As one example, the second node U1 is the serving cell sustaining base station of the first node U2.

[0475] As an example, step S521 is not present, and the M operations do not require deployment.

[0476] As an example, at least one of the M operations does not need to be deployed.

[0477] As an example, the M operations need to be deployed.

[0478] As an example, at least one of the M operations needs to be deployed.

[0479] As an example, the target operation needs to be deployed.

[0480] As an example, the first node deploys the M operations.

[0481] As an example, the first node deploys at least one of the M operations.

[0482] As an example, deploying at least one of the M operations includes deploying each of the M operations.

[0483] As an example, deploying at least one of the M operations includes deploying a portion of the M operations.

[0484] As an example, deploying at least one of the M operations includes: deploying the target operation.

[0485] As an example, the first node deploys the target operation.

[0486] As an example, deploying an operation includes: obtaining an operation.

[0487] As an example, deploying an operation includes: loading an operation.

[0488] As one example, deploying an operation includes: submitting a request to load an operation.

[0489] As one example, the advantages include: providing sufficient degrees of freedom for the first node to adapt to various scenarios and terminals, thus exhibiting adaptability and flexibility.

[0490] As an example, the deployment of at least one of the M operations precedes the reception of the first information set.

[0491] As an example, the deployment of at least one of the M operations is later than the reception of the first information set.

[0492] As an example, the M operations are obtained through loading.

[0493] As an example, at least one of the M operations is obtained by loading.

[0494] As an example, at least one of the M operations is not obtained by loading.

[0495] As an example, the target operation is obtained by loading.

[0496] As one embodiment, the loading includes loading from the serving cell of the first node.

[0497] As one embodiment, the loading includes loading from the sustaining base station of the serving cell of the first node.

[0498] As one example, the loading includes loading from the core network.

[0499] As an example, the advantages of the above method include reducing the processing power requirements and power consumption of the first node.

[0500] As an example, at least one of the M operations deployed in the first node independently performs at least one function, without requiring the second node to deploy any of the operations.

[0501] As an example, at least one of the M operations deployed in the first node performs at least one function without requiring the participation of any operation deployed in the second node.

[0502] As an example, at least one of the M operations deployed in the first node performs at least one function without requiring the cooperation of any operation deployed in the second node.

[0503] As an example, at least one of the M operations deployed in the first node performs at least one function without requiring further processing from any of the operations deployed in the second node.

[0504] As an example, the second node deploys at least one operation; the at least one operation deployed in the second node, together with at least one of the M operations deployed in the first node, performs at least one function.

[0505] As an example, the second node deploys at least one operation; the at least one operation deployed in the second node cooperates with at least one of the M operations deployed in the first node to complete at least one function.

[0506] As one embodiment, one of the functions includes: performance monitoring, positioning, beam management, CSI prediction, CSI estimation, CSI compression, RLF (radio link failure) prediction, cell handover prediction, or serving cell prediction.

[0507] As an example, at least one of the M operations deployed by the first node is used for information compression, and at least one operation other than the M operations deployed by the second node is used for information recovery.

[0508] As an example, at least one of the M operations deployed by the first node is used for channel information compression, and at least one operation other than the M operations deployed by the second node is used for channel information recovery.

[0509] As an example, at least one of the M operations deployed by the first node is used for CSI compression, and at least one operation other than the M operations deployed by the second node is used for CSI recovery.

[0510] As an example, the M operations are performed by the physical layer of the first node.

[0511] As an example, the M operations are performed at a higher level than the first node.

[0512] As an example, at least one of the M operations is performed by the physical layer of the first node.

[0513] As an example, at least one of the M operations is performed at a higher level than the first node.

[0514] As an example, the target operation is performed by the physical layer of the first node.

[0515] As an example, the target operation is performed at a higher level than the first node.

[0516] As an example, if the M operations fail, block F53 does not exist, and the first node does not execute the M operations.

[0517] As an example, if at least one of the M operations fails, block F53 is not present, and the first node does not execute at least one of the M operations.

[0518] As an example, if the target operation fails, box F53 does not exist, and the first node does not perform the target operation.

[0519] As an example, block F53 exists, and the execution of at least one of the M operations in step S525 does not include the target operation.

[0520] As an example, block F53 exists, and the execution of at least one of the M operations in step S525 includes all operations of the M operations except for the target operation and the M0 operations.

[0521] As an example, block F54 exists, where information reporting other than M3 of the M2 information reports depends on the output of at least one of the M operations.

[0522] As an example, block F55 exists, where the M2 information reports depend on the output of at least one of the M operations.

[0523] As an example, block F56 exists, where the first information reporting depends on the output of at least one of the M operations.

[0524] As an example, the first information reporting depends on the output of the M operations.

[0525] As an example, when a two-sided AI model is adopted, the first node performs at least one of the M operations, and the second node performs at least one operation other than the M operations.

[0526] As an example, when a two-sided AI model is adopted, the first node performs at least one of the M operations, and the second node performs at least one operation other than the M operations; the operation jointly performed by the first node and the second node constitutes at least one function.

[0527] As an example, the first node performs at least one of the M operations to compress information, and the second node performs at least one operation other than the M operations to restore information.

[0528] As an example, the first node performs at least one of the M operations to compress channel information, and the second node performs at least one operation other than the M operations to recover channel information.

[0529] As an example, the first node performs at least one of the M operations to perform CSI compression, and the second node performs at least one operation other than the M operations to perform CSI recovery.

[0530] As an example, when a single-side AI model is adopted, the first node performs at least one of the M operations to complete at least one function.

[0531] As an example, the AI ​​training function in the RAN (Radio Access Network) domain is located in the RAN domain-specific management function, while the AI ​​inference function is located in the UE.

[0532] As an example, RAN domain-specific management functions provide AI training function management capabilities and AI inference function management capabilities.

[0533] As an example, the AI ​​training function is located in the RAN domain-specific management function, while the AI ​​inference function is located locally in the gNB.

[0534] As an example, the management capability of AI training is provided by RAN domain-specific management functions, while the management capability of AI inference is provided locally by the gNB.

[0535] As an example, MnF refers to Management Function.

[0536] As an example, both the AI ​​training function and the AI ​​inference function are located in the UE, wherein the UE provides the ability to train and infer.

[0537] As an example, RAN domain-specific management functions provide management capabilities for AI training and AI inference functions.

[0538] As an example, both the AI ​​training function and the AI ​​inference function are located in the gNB.

[0539] As an example, the management capabilities for both AI training and AI inference are provided locally by gNB.

[0540] As an example, the first node determines on its own that the target operation has failed.

[0541] As an example, the first node relies on the first signaling to determine whether the target operation has failed.

[0542] As an example, the first signaling indicates that the target operation has failed.

[0543] As one embodiment, the first signaling indicates a target identifier, and the target operation is associated with the target identifier.

[0544] As an example, when one of the M operations fails, block F54 is present, and the first node abandons the M3 information reports out of the M2 information reports; wherein, M2 is a positive integer greater than 1, M2 is not greater than M, and M3 is a positive integer not greater than M2; the M2 information reports depend on the output of the M2 operations respectively, the M2 operations belong to the M operations, and at least one of the M2 operations belongs to the M0 operations; the M3 information reports include information reports in which the operations depended on by the M2 information reports belong to the M0 operations.

[0545] As an example, when one of the M operations fails, box F55 exists, and the first node does not update the M3 information report out of the M2 information reports; wherein, M2 is a positive integer greater than 1, M2 is not greater than M, and M3 is a positive integer not greater than M2; the M2 information reports depend on the output of the M2 operations respectively, the M2 operations belong to the M operations, and at least one of the M2 operations belongs to the M0 operations; the M3 information report includes information reports in the M2 information reports whose dependent operations belong to the M0 operations.

[0546] As an example, the first information set is used to configure the reporting of the M2 information items.

[0547] As an example, the first information set is used to configure the information reporting of the M2 information reports other than the M3 information reports.

[0548] As an example, the first signaling indicates that the target operation has failed; the first information set is used to configure the information reporting of the M2 information reports other than the M3 information reports.

[0549] As an example, the essence of the above method includes: configuring only the reporting of information that has not failed.

[0550] As an example, the advantages of the above method include: reducing system resource overhead and improving system transmission efficiency.

[0551] As an example, when one of the M operations fails, box F56 exists, and the first node does not update the first information report; wherein, the first information set is used to configure the first information report.

[0552] As an example, when one of the M operations fails, block F57 exists, and the first node abandons the reporting of the first information; wherein, the first information set is used to configure the reporting of the first information.

[0553] As an example, the M3 information reports include information reports on all the operations that the M2 information reports depend on, which belong to the M0 operations.

[0554] As an example, the M3 information reports include the information reports that depend on the target operation from the M2 information reports.

[0555] As an example, the M3 information reports include information reports in the M2 information reports that depend on the M0 operations; the M3 information reports also include information reports in the M2 information reports that depend on the target operation.

[0556] As an example, M3 is equal to M2.

[0557] As an example, M3 is equal to M2; the first node abandons or does not update the reporting of M2 pieces of information.

[0558] As an example, the M2 operations belong to the M0 operations.

[0559] As an example, the M2 operations belong to the M0 operations; the first node abandons or does not update the M2 information reports.

[0560] As an example, the essence of the above method includes enhancing the completeness of the system and solution.

[0561] As an example, when any of the M operations fails, the first node abandons or does not update the first information report.

[0562] As an example, when none of the M operations fail, the first node sends the first information report.

[0563] As an example, when none of the M operations fail, the first node sends M2 information reports.

[0564] As an example, the advantages of the above method include: if the operation fails, the information report that depends on the output of the operation will not be sent or updated, saving system resources and computing overhead, and saving energy consumption.

[0565] As an example, an information report includes UCI (Uplink Control Information).

[0566] As an example, a message is reported and transmitted on PUSCH (Physical Uplink Shared Channel).

[0567] As an example, a message is reported and transmitted on the PUCCH (Physical Uplink Control Channel).

[0568] As an example, an information report includes at least one of the following: predicted beam information, predicted CSI, estimated CSI, compressed CSI, confidence information, or performance monitoring results.

[0569] As an example, an information report is used for at least one of performance monitoring, positioning, beam management, CSI prediction, CSI estimation, CSI compression, RLF (radio link failure) prediction, cell handover prediction, or serving cell prediction.

[0570] As an example, an information report includes at least one of performance monitoring results, location information, RLF prediction, beam failure prediction, cell handover prediction, beam handover prediction, or serving cell prediction.

[0571] As one embodiment, the beam management includes at least one of beam prediction, beam switching, beam failure prediction, or beam failure recovery.

[0572] As an example, an information report includes a reporting volume that does not belong to 3GPP Rel-18 and earlier versions.

[0573] As an example, an information report may include a reporting quantity that is not defined in the 5G standard.

[0574] As an example, an information report includes the reporting volume defined in the 6G standard.

[0575] As an example, an information report may include information generated based on artificial intelligence or machine learning.

[0576] As an example, an information report includes information generated based on a neural network.

[0577] As an example, an information report includes information generated based on CNN (Conventional Neural Networks).

[0578] As an example, an information report may include channel information, or one of the following: information other than channel information.

[0579] As an example, an information report includes at least one of channel information or information other than channel information.

[0580] As an example, the first information reporting depends on the output of the last of the M operations.

[0581] As an example, the first information reporting depends on the output of the M operations.

[0582] As an example, the first information reporting depends on the output of at least one of the M operations.

[0583] As an example, the M operations are cascaded, and the output of one of the M operations is the input of the next operation, except for the last operation among the M operations; the first information reporting depends on the output of the last operation among the M operations.

[0584] As an example, the M operations are divided into M1 parts, each of the M1 parts including at least one of the M operations, where M1 is a positive integer greater than 1 and less than M; the M1 parts are cascaded, and except for the last part of the M1 parts, the output of one part of the M1 parts is the input of the next part; the first information reporting depends on the output of the last part of the M1 parts.

[0585] As one embodiment, a given information report that depends on the output of at least one operation includes: the output of the at least one operation being used to generate the given information report; wherein,

[0586] The given information report is the first information report, and the at least one operation includes some or all of the M operations;

[0587] or,

[0588] The given information report is any one of the M2 information reports, and the at least one operation is one of the M2 operations.

[0589] As an example, one of the M2 information reports depends on the output of the target operation.

[0590] As an example, an information report depends on the output of at least one operation, which includes: the output of the at least one operation being post-processed and used to generate the information report.

[0591] As an example, an information report that depends on the output of at least one operation includes: all or part of the output of the at least one operation being post-processed and used to generate the information report.

[0592] As an example, the output of an information report that depends on at least one operation includes: the information report includes the output of the at least one operation.

[0593] As an example, the output of an information report that depends on at least one operation includes: the information report includes all or part of the output of the at least one operation.

[0594] As an example, the output of an information report that depends on at least one operation includes: the information report includes the post-processed output of the at least one operation.

[0595] As an example, the post-processing includes one or more of the following: sampling, quantization, truncation, DFT (Discrete Fourier Transform), angular domain to spatial domain transformation, spatial domain to angular domain transformation, time domain to frequency domain transformation, and frequency domain to time domain transformation.

[0596] As an example, the information report includes a truncated and / or quantized output of the at least one operation.

[0597] As an example, the output of the at least one operation, after being truncated and / or quantized, is used to generate the information report.

[0598] As an example, some or all of the output of the at least one operation is truncated and / or quantized and used to generate the information report.

[0599] As an example, an information report that depends on the output of at least one operation includes: measurements obtained based on at least one RS resource and the output of the at least one operation being used to generate the information report.

[0600] As an example, an information report depends on the output of at least one operation, which includes: the reception quality of at least one physical channel or physical signal and the output of the at least one operation being used to generate the information report.

[0601] As an example, the advantages of the above method include: enhancing the flexibility of the system and better adapting to various transmission conditions and application scenarios.

[0602] As an example, the advantages of the above method include: enhanced backward compatibility of the system.

[0603] As one embodiment, generating the information report includes: calculating the information report.

[0604] As one example, how the output of the at least one operation is used to generate the information report is determined by the manufacturer of the first node, or is implementation-dependent. Some typical but non-limiting implementations are described below:

[0605] As an example, the output of the at least one operation includes predicted channel information, and a comparison between the channel information obtained from measurements based on at least one RS resource and the predicted channel information is used to generate the information report.

[0606] As an example, the output of the at least one operation includes predicted beam information, and a comparison between the beam information obtained from measurements based on at least one RS resource and the predicted beam information is used to generate the information report.

[0607] As an example, the output of the at least one operation includes predicted beam information, and whether the beam information obtained from the measurement of at least one RS resource is the same as the predicted beam information is used to generate the information report.

[0608] As an example, the output of the at least one operation includes predicted beam information, and the information report includes CSI.

[0609] As an example, the output of the at least one operation includes predicted beam information; when the comparison result between the beam information obtained from the measurement based on at least one RS resource and the predicted beam information is greater than a certain threshold, the information report indicates that the at least one operation has failed.

[0610] As an example, the output of the at least one operation includes predicted beam information; when the predicted beam information is worse than a certain threshold, the information report indicates that the at least one operation has failed.

[0611] As an example, the output of the at least one operation includes predicted beam information; when the predicted beam information is worse than a certain threshold, the information report indicates beam failure.

[0612] As an example, the output of the at least one operation includes a predicted RSRP; when the difference between the RSRP measured based on at least one RS resource and the predicted RSRP is greater than a certain threshold, the information report indicates that the at least one operation has failed.

[0613] As an example, the output of the at least one operation includes a predicted link quality; when the difference between the link quality measured based on at least one RS resource and the predicted link quality is greater than a certain threshold, the information report indicates that the at least one operation has failed.

[0614] As an example, the output of the at least one operation includes a predicted link quality; when the predicted link quality is worse than a certain threshold, the information report indicates an RLF or beam failure.

[0615] As an example, the output of the at least one operation includes predicted beam information, and the information report includes one of performance monitoring results, RLF prediction, cell handover prediction, or serving cell prediction.

[0616] As an example, the output of the at least one operation includes predicted channel information, and the information report includes one of performance monitoring results, RLF prediction, cell handover prediction, or serving cell prediction.

[0617] As an example, the output of the at least one operation includes location information, and the information report includes location information.

[0618] As an example, the method for generating the information report based on the output of the at least one operation is not based on artificial intelligence or machine learning.

[0619] As an example, the updating of an information report includes: the information report being valid.

[0620] As one example, the updating of an information report includes: the information report being different from the most recent report.

[0621] As one example, the updating of an information report includes the following: the information report may be different from the most recent report.

[0622] As one example, the updating of an information report includes the following: the information report is not necessarily the same as the most recent report.

[0623] As an example, the updating of an information report includes: the information report is based on measurements of at least one RS resource.

[0624] As an example, the updating of an information report includes: the information report is generated based on a measurement of at least the most recent RS timing of at least one RS resource no later than the CSI reference resource of the information report.

[0625] As an example, the updating of an information report includes: the information report being updated based on a measurement of at least the most recent RS timing of at least one RS resource no later than the CSI reference resource of the information report.

[0626] As an example, the information report not being updated includes: the first node is not expected to update the information report.

[0627] As an example, the first node is not expected to update the information report, including whether the information report is actually updated is determined by the first node itself or is implementation-related.

[0628] As an example, the information report not being updated includes: the information report being invalid.

[0629] As an example, the statement that an information report is not updated includes: the information report is not updated based on the most recent report.

[0630] As one example, the information report not being updated includes: the information report being the same as the most recent report.

[0631] As one embodiment, the statement that an information report is not updated includes: the first information report must be the same as the most recent report.

[0632] As an example, the fact that an information report is not updated includes: the information report is unrelated to the measurement of the RS resource used for the measurement of the information report at a time no later than the most recent RS timing of the CSI reference resource for the information report.

[0633] As an example, the information report not being updated includes: the information report includes a default value.

[0634] As an example, the fact that an information report is not updated includes: the information report is unrelated to the measurement of RS resources.

[0635] As an example, the information report not being updated includes: the information report is not generated based on measurements of RS resources.

[0636] Example 6

[0637] Example 6 illustrates a schematic diagram of M0 operations according to an embodiment of the present application; as shown in Figure 6; in Figure 6, the M operations include operation #1, operation #2, ..., operation #M.

[0638] In Example 6, when the target operation is not the last operation among the M operations, M0 is a positive integer, and the M0 operations include the operations that follow the target operation among the M operations.

[0639] As an example, the M operations are cascaded, and the execution order of the M operations is one after another; when the target operation is not the last operation among the M operations, the M0 operations include the operations among the M operations that follow the target operation.

[0640] As an example, the M operations are cascaded, and except for the last operation among the M operations, the output of one of the M operations is the input of the next operation; the M0 operations include the operations among the M operations that follow the output of the target operation.

[0641] As an example, the target operation is the first operation among the M operations, and the M0 operations include all operations among the M operations except for the target operation.

[0642] As an example, the target operation is the first of the M operations, and M0 is equal to M minus 1.

[0643] As an example, the target operation is the last of the M operations, where M0 equals 0.

[0644] As an example, the advantages of the above method include: supporting multiple operations to cascade to complete a function, improving the accuracy of information reporting, and reducing the implementation complexity of the solution.

[0645] As an example, the benefits of the above method include: enhanced system stability and reliability.

[0646] As an example, the M operations are divided into M1 parts, each of the M1 parts including at least one of the M operations, where M1 is a positive integer greater than 1 and less than M; the M1 parts are cascaded, and the execution order of the M1 parts is one after another; the M0 operations include operations in all parts following the part containing the target operation.

[0647] As an example, the M operations are divided into M1 parts, each of the M1 parts including at least one of the M operations, where M1 is a positive integer greater than 1 and less than M; the M1 parts are cascaded, and the execution order of the M1 parts is one after another; the M0 operations include the operations in the part containing the target operation excluding the target operation and the operations in all parts after the part containing the target operation.

[0648] As an example, the M operations are divided into M1 parts, each of the M1 parts including at least one of the M operations, where M1 is a positive integer greater than 1 and less than M; the M1 parts are cascaded, and the execution order of the M1 parts is one after another; the outputs of all operations in the part containing the target operation are merged into the output of the part containing the target operation; the M0 operations include the operations in the part containing the target operation except for the target operation and the operations in all parts after the part containing the target operation.

[0649] As an example, the M operations are divided into M1 parts, each of the M1 parts including at least one of the M operations, where M1 is a positive integer greater than 1 and less than M; the M1 parts are cascaded, and the execution order of the M1 parts is one after another; the inputs of all operations in the part containing the target operation are merged into the input of the part containing the target operation; the M0 operations include the operations in the part containing the target operation except for the target operation and the operations in all parts after the part containing the target operation.

[0650] As an example, the advantages of the above method include: adapting to various different scenarios and terminals, and improving the adaptability and flexibility of the system.

[0651] As an example, determining that M0 out of the M operations have failed includes: determining that in addition to the target operation failing, M0 out of the M operations have also failed.

[0652] As an example, determining that M0 out of the M operations are invalid includes: determining that, in the order of execution from first to last, the operations among the M operations that precede the target operation are valid.

[0653] As an example, determining that M0 out of the M operations are invalid includes: all operations other than the target operation and the M0 operations are valid.

[0654] As an example, determining that M0 out of the M operations are invalid includes: determining that in addition to the target operation being invalid, M0 out of the M operations are also invalid; and that the operations other than the target operation and the M0 operations are valid.

[0655] Example 7

[0656] Example 7 illustrates a schematic diagram of M configurations indicating M operations according to an embodiment of this application, as shown in Figure 7. In Figure 7, the M operations include operation #1, operation #2, ..., operation #M; the M configurations include configuration #1, configuration #2, ..., configuration #M; and the M first-class identifiers include identifier #1, identifier #2, ..., identifier #M.

[0657] In embodiment 7, the first information set indicates M configurations, where M is a positive integer greater than 1, and the M configurations respectively indicate the M operations; the M configurations each include M first-class identifiers, and the M operations are respectively associated with the M first-class identifiers.

[0658] As one embodiment, the first information set indicating M configurations includes: the first information set indicating the identifier of each of the M configurations.

[0659] As one embodiment, the first information set indicates that the M configurations include: the first information set includes M configurations.

[0660] As one embodiment, the first information set indicates M configurations including: the first information set includes at least one RRC IE, and the M configurations are carried by the at least one RRC IE.

[0661] As one embodiment, the first information set indicates that the M configurations include: the first information set includes M RRC IEs, and the M configurations are carried by the M RRC IEs respectively, or the M configurations each include the M RRC IEs.

[0662] As an example, the M configurations each include M RRC IEs.

[0663] As an example, the M configurations each include M IE CSI-ReportConfigs.

[0664] As an example, the M configurations are each carried by M RRC IEs.

[0665] As an example, the M configurations are each carried by M IE CSI-ReportConfigs.

[0666] As an example, the M configurations are carried by at least one RRC IE.

[0667] As an example, the M configurations are carried by at least one IE CSI-ReportConfig.

[0668] As one embodiment, the M configurations include some or all of the domains in at least one RRC IE.

[0669] As an example, the M configurations include some or all of the domains in at least one IE CSI-ReportConfig.

[0670] As an example, the M configurations respectively indicating the M operations include: the M configurations respectively directly indicating the M operations.

[0671] As an example, the M configurations respectively indicating the M operations include: the M configurations respectively explicitly indicating the M operations.

[0672] As an example, the M configurations respectively indicating the M operations include: the M configurations respectively implicitly indicating the M operations.

[0673] As an example, the M configurations respectively indicating the M operations include: the M configurations respectively indicating the indexes of the M operations.

[0674] As an example, the M configurations respectively indicate the M operations, including: the M configurations each include an index of the M operations.

[0675] As an example, the M configurations respectively indicate the M operations, including: the M configurations each include identification information for the M operations.

[0676] As one embodiment, the M configurations respectively indicating the M operations include: the M configurations respectively indicating M first-class identifiers, and the M first-class identifiers respectively indicating the M operations.

[0677] As an example, the M configurations respectively indicating the M operations include: the M configurations each include M first-class identifiers, and the M first-class identifiers respectively indicate the M operations.

[0678] As an example, the M configurations respectively indicate the M operations, including: the M configurations each include M first-class identifiers, and the M operations are respectively associated with the M first-class identifiers.

[0679] As an example, the advantages of the above method include: simplifying system design and reducing the complexity of solution implementation.

[0680] As an example, the advantages of the above method include: improving system flexibility and adapting to different terminals and transmission environments.

[0681] As an example, the first type of identifier is a non-negative integer.

[0682] As an example, the first type of identifier is a string.

[0683] As an example, the first type of identifier is used to identify AI models.

[0684] As an example, the first type of identifier is used to identify AI entities.

[0685] As an example, the first type of identifier is used to identify AI functions.

[0686] As an example, the advantages of the above method include: identifying an AI entity or function through the first type of identifier, simplifying system design, and unifying the understanding of different AI entities or functions across multiple nodes.

[0687] As an example, the first type of identifier is a model identifier.

[0688] As an example, the first type of identifier is used to identify an AI model.

[0689] As an example, the first type of identifier is used by the first node to identify an AI model.

[0690] As an example, the first type of identifier is used by the first node to determine the AI ​​model adopted by the first reference operation.

[0691] As an example, the advantages of the above method include: using the first type of identifier to identify an AI model / entity / function simplifies system design and unifies the understanding of different AI entities / functions across multiple nodes.

[0692] As an example, the first type of identifier is used to identify or indicate a set of resources.

[0693] As one embodiment, the first type of identifier is used to identify or indicate a set of resources, the measurement of which is used to obtain a training dataset.

[0694] As an example, the first type of identifier is used to identify or indicate a set of resources.

[0695] As an example, the first type of identifier is used to identify or indicate the training dataset.

[0696] As an example, the benefits of the above method include: identifying the inferences generated by an AI training or AI training dataset by identifying the AI ​​training or AI training dataset, establishing consensus among different AI functions, and further simplifying system design.

[0697] As an example, associating a given operation with a given identifier includes: the given operation being identified by the given identifier; wherein the given operation is any one of the M operations, and the given identifier is one of the M first-class identifiers.

[0698] As an example, associating a given operation with a given identifier includes: the AI ​​model used by the given operation is identified by the given identifier; wherein, the given operation is any one of the M operations, and the given identifier is one of the M first-class identifiers.

[0699] As an example, associating a given operation with a given identifier includes: the AI ​​entity included in the given operation is identified by the given identifier; wherein, the given operation is any one of the M operations, and the given identifier is one of the M first-class identifiers.

[0700] As an example, associating a given operation with a given identifier includes: the AI ​​function to which the given operation is used is identified by the given identifier; wherein, the given operation is any one of the M operations, and the given identifier is one of the M first-class identifiers.

[0701] As an example, associating a given operation with a given identifier includes: the AI ​​entity performing the given operation being identified by the given identifier; wherein the given operation is any one of the M operations, and the given identifier is one of the M first-class identifiers.

[0702] As an example, associating a given operation with a given identifier includes: the given identifier being used by the first node to determine the AI ​​model adopted by the given operation; wherein, the given operation is any one of the M operations, and the given identifier is one of the M first-class identifiers.

[0703] As an example, the advantages 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 models / entities / functions across multiple nodes.

[0704] As one embodiment, associating a given operation with a given identifier includes: the given identifier being used to identify or indicate a set of RS resources, and a measurement of the set of RS resources being used to obtain a training dataset for the given operation; wherein the given operation is any one of the M operations, and the given identifier is one of the M first-class identifiers.

[0705] As an example, associating a given operation with a given identifier includes: obtaining training for the given operation is identified by the given identifier; wherein the given operation is any one of the M operations, and the given identifier is one of the M first-class identifiers.

[0706] As an example, associating a given operation with a given identifier includes: the dataset used for training the given operation is identified by the given identifier; wherein the given operation is any one of the M operations, and the given identifier is one of the M first-class identifiers.

[0707] As an example, the benefits of the above method include: identifying the inferences generated by an AI training or AI training dataset by identifying the AI ​​training or AI training dataset, establishing consensus among different AI functions, and further simplifying the design.

[0708] As one embodiment, the given operation associated with a given identifier includes: the given operation performing spatial beam prediction for a second type of resource set based on measurements of a first type of resource set, the second type of resource set depending on the given identifier; wherein, the given operation is any one of the M operations, and the given identifier is one of the M first type identifiers.

[0709] As an example, the advantages of the above method include: reducing RS overhead and reducing feedback latency.

[0710] As one embodiment, the given operation associated with a given identifier includes: the given operation performing channel information prediction for a second type of resource set based on measurements of the first type of resource set, the second type of resource set depending on the given identifier; wherein, the given operation is any one of the M operations, and the given identifier is one of the M first type identifiers.

[0711] As one embodiment, the association of a given operation with a given identifier includes: the given operation performing temporal beam prediction for a second type of resource set based on historical measurements of a first type of resource set, the second type of resource set depending on the given identifier; wherein the given operation is any one of the M operations, and the given identifier is one of the M first type identifiers.

[0712] As an example, the advantages of the above method include: reducing beam feedback delay and improving the real-time performance of beam acquisition.

[0713] As an example, the association of a given operation with a given identifier includes: the given operation performing a temporal channel information prediction for a second type of resource set based on historical measurements of a first type of resource set, the second type of resource set depending on the given identifier; wherein the given operation is any one of the M operations, and the given identifier is one of the M first type identifiers.

[0714] As an example, the advantages of the above method include: reducing channel information feedback delay and improving the real-time performance of channel information acquisition.

[0715] Example 8

[0716] Example 8 illustrates a schematic diagram of M first-class identifiers according to an embodiment of this application; as shown in Figure 8. In Figure 8, the M operations include operation #1, operation #2, ..., operation #M; the M first-class identifiers include identifier #1, identifier #2, ..., identifier #M.

[0717] In embodiment 8, the first information set is also used to determine the order of the M first-class identifiers, and the execution order of the M operations is consistent with the order of the M first-class identifiers.

[0718] As an example, the M operations are each associated with one of the M first-class identifiers; the execution order of the M operations is consistent with the sorting of the M first-class identifiers.

[0719] As an example, the essence of the above method includes: determining the execution order of the M operations from first to last by sorting the M first-class identifiers.

[0720] As an example, the advantages of the above method include: simplifying system design and reducing the complexity of solution implementation.

[0721] As an example, the advantages of the above method include: improving system flexibility and adapting to different terminals and transmission environments.

[0722] As one embodiment, the first information set includes the sorting of the M first-class identifiers.

[0723] As an example, the first information set is used to indicate the order of the M first-class identifiers.

[0724] As an example, the first information set explicitly indicates the order of the M first-class identifiers.

[0725] As an example, the first information set implicitly indicates the order of the M first-class identifiers.

[0726] As an example, the first information set directly indicates the order of the M first-class identifiers.

[0727] As an example, the first information set indirectly indicates the order of the M first-class identifiers.

[0728] As one embodiment, the first information set includes the M first-class identifiers; the M first-class identifiers have a sorting relationship.

[0729] As an example, the first information set includes the M first-class identifiers; the M first-class identifiers have a sorting relationship, and the sorting relationship is default.

[0730] As an example, the first information set includes an information block, which indicates the order of the M first-class identifiers.

[0731] As an example, the first information set indicates an information block, and the information block indicates the order of the M first-class identifiers.

[0732] As one embodiment, the first information set includes a first identifier set, which includes the M first-class identifiers.

[0733] As an example, the first information set indicates a first identifier set, which includes the M first-class identifiers.

[0734] As a sub-implementation of the above embodiment, the order of the M first-class identifiers is the order of the M first-class identifiers in the first identifier set from first to last.

[0735] As a sub-implementation of the above embodiment, the order of the M first-class identifiers is the order of indication of the M first-class identifiers in the first information set from first to last.

[0736] As a sub-implementation of the above embodiment, the order of the M first-class identifiers is consistent with the order of indication of the M first-class identifiers in the first information set.

[0737] As an example, the advantages of the above method include: simplifying system design and reducing the complexity of solution implementation.

[0738] As one embodiment, the first information set includes at least one configuration used to determine the order of the M first-class identifiers.

[0739] As an example, the first information set includes M configurations, any one of the M configurations indicating a first-class identifier among the M first-class identifiers and its preceding first-class identifier.

[0740] As an example, the first information set includes M configurations, any one of the M configurations indicating a first-class identifier among the M first-class identifiers and its subsequent first-class identifiers.

[0741] As an example, the first information set includes M configurations, any one of the M configurations indicating a first-class identifier, its preceding first-class identifier, and its following first-class identifier among the M first-class identifiers.

[0742] As a sub-implementation of the above embodiment, if a configuration indicates that a first-class identifier among the M first-class identifiers has no preceding first-class identifier, the configuration indicates that the preceding first-class identifier is invalid (null).

[0743] As a sub-example of the above embodiment, if a configuration indicates that a first-class identifier among the M first-class identifiers has no preceding first-class identifier, the configuration does not indicate the preceding first-class identifier.

[0744] As a sub-implementation of the above embodiment, if a configuration indicates that a first-class identifier among the M first-class identifiers has no subsequent first-class identifier, the configuration indicates that the subsequent first-class identifier is invalid (null).

[0745] As a sub-implementation of the above embodiments, if a configuration indicates that there is no subsequent first-class identifier among the M first-class identifiers, the configuration does not indicate the subsequent first-class identifier.

[0746] As an example, the advantages of the above method include: improving system flexibility and adapting to different terminals and transmission environments.

[0747] Example 9

[0748] Example 9 illustrates a schematic diagram of the execution order of M operations according to an embodiment of this application, as shown in Figure 9. In Figure 9, the M operations include operation #1, operation #2, ..., operation #M; the M configurations include configuration #1, configuration #2, ..., configuration #M.

[0749] In embodiment 9, the first information set indicates the execution order of the M operations from first to last; or, the first information set indicates M configurations, where M is a positive integer greater than 1, and the M configurations each indicate M operations; the first information set is used to determine the order of the M configurations, and the execution order of the M operations from first to last is consistent with the order of the M configurations.

[0750] As an example, the first information set indicates the execution order of the M operations from first to last.

[0751] As one embodiment, the first information set indicates M configurations, where M is a positive integer greater than 1, and the M configurations respectively indicate M operations; the first information set is used to determine the order of the M configurations, and the execution order of the M operations is consistent with the order of the M configurations.

[0752] As one embodiment, the first information set includes the execution order of the M operations from first to last.

[0753] As an example, the first information set explicitly indicates the execution order of the M operations from first to last.

[0754] As an example, the first information set implicitly indicates the execution order of the M operations from first to last.

[0755] As an example, the first information set directly indicates the execution order of the M operations from first to last.

[0756] As an example, the first information set indirectly indicates the execution order of the M operations from first to last.

[0757] As an example, the advantages of the above method include: improving system flexibility and adapting to different terminals and transmission environments.

[0758] As an example, any one of the M operations has a subsequent operation that is executed after it, and the subsequent operation is another operation among the M operations; the first information set indicates the operation executed first among the M operations.

[0759] As an example, any one of the M operations has a preceding operation that was executed before it, and the preceding operation is another operation among the M operations; the first information set indicates the last operation executed among the M operations.

[0760] As an example, the advantages of the above method include: simplifying system design and reducing the complexity of solution implementation.

[0761] As one embodiment, the first information set includes the sorting of the M configurations.

[0762] As an example, the first information set is used to indicate the order of the M configurations.

[0763] As an example, the first information set explicitly indicates the order of the M configurations.

[0764] As an example, the first information set implicitly indicates the order of the M configurations.

[0765] As an example, the first information set directly indicates the order of the M configurations.

[0766] As an example, the first information set indirectly indicates the order of the M configurations.

[0767] As an example, the advantages of the above method include: improving system flexibility and adapting to different terminals and transmission environments.

[0768] As one embodiment, the first information set includes the M configurations; the M configurations themselves carry sorting information.

[0769] As one embodiment, the first information set indicates the M configurations; the M configurations each include M indexes; the order of the M configurations is consistent with the order of the M indexes.

[0770] As one embodiment, the first information set indicates the M configurations; the M configurations each include M identifiers; the order of the M configurations is consistent with the order of the M identifiers.

[0771] As an example, the first information set indicates the M configurations; any one of the M configurations indicates a preceding configuration, which is another configuration among the M configurations.

[0772] As an example, the first information set indicates the M configurations; any one of the M configurations indicates a subsequent configuration, which is another configuration among the M configurations.

[0773] As an example, the first information set indicates the M configurations; any one of the M configurations indicates a preceding configuration and a following configuration, wherein the preceding configuration is another configuration that is different from any one of the M configurations and the following configuration is another configuration.

[0774] As a sub-example of the above embodiments, if a configuration has no preceding configuration, the configuration indicates that the preceding configuration is invalid (null).

[0775] As a sub-example of the above embodiments, if a configuration has no preceding configuration, the configuration does not indicate the preceding configuration.

[0776] As a sub-example of the above embodiments, if a configuration has no subsequent configuration, the configuration indicates that the subsequent configuration is invalid (null).

[0777] As a sub-example of the above embodiments, if a configuration has no subsequent configuration, the configuration does not indicate the subsequent configuration.

[0778] As an example, the advantages of the above method include: simplifying system design and reducing the complexity of solution implementation.

[0779] As an example, the order of the M configurations is the order in which the M configurations are indicated in the first information set from first to last.

[0780] As an example, the order of the M configurations is consistent with the order in which the M configurations are indicated in the first information set.

[0781] As an example, the advantages of the above method include: simplifying system design and reducing the complexity of solution implementation.

[0782] Example 10

[0783] Example 10 illustrates a schematic diagram of the execution order of M operations according to another embodiment of this application, as shown in Figure 10. In Figure 10, the M operations include operation #1, operation #2, ..., operation #M; the types corresponding to the M operations include type #1, type #2, ..., type #M.

[0784] In Example 10, at least two of the M operations correspond to different types, and the execution order of the M operations depends on the types corresponding to the M operations.

[0785] As an example, the type corresponding to an operation includes: the type of the output of the operation.

[0786] As an example, the type corresponding to an operation includes: the type of input to the operation.

[0787] As an example, the type corresponding to an operation includes: the types of the input and output of the operation.

[0788] As an example, the type corresponding to an operation includes: the data types of the input and output of the operation.

[0789] As an example, the type corresponding to an operation includes: the analytic data type of the operation.

[0790] As an example, the type corresponding to an operation includes: the purpose or function of the operation.

[0791] As an example, the type corresponding to an operation includes: the AI ​​model type associated with the operation.

[0792] As an example, the type corresponding to an operation includes: the AI ​​entity type associated with the operation.

[0793] As an example, the type corresponding to an operation includes: the AI ​​function type associated with the operation.

[0794] As an example, the type corresponding to an operation includes: the AI ​​neural network model type associated with the operation.

[0795] As an example, the advantages of the above method include: improving system flexibility and adapting to different terminals and transmission environments.

[0796] As an example, the advantages of the above method include: supporting AI-based information reporting.

[0797] As an example, the execution order of the M operations depends on the types of the M operations, including: the execution order of the M operations depends on the sorting of the types of the M operations.

[0798] As an example, the execution order of the M operations depends on the types of the M operations, including: the execution order of the M operations is consistent with the sorting of the types of the M operations.

[0799] As an example, the order of the types corresponding to the M operations is predefined; the execution order of the M operations from first to last is consistent with the order of the types corresponding to the M operations.

[0800] As an example, the first information set indicates the sorting of the types corresponding to the M operations; the execution order of the M operations from first to last is consistent with the sorting of the types corresponding to the M operations.

[0801] As an example, the first information set includes the M configurations, wherein the M configurations indicate the sorting of the types corresponding to the M operations; the execution order of the M operations from first to last is consistent with the sorting of the types corresponding to the M operations.

[0802] As an example, the first information set includes the M configurations, wherein the M configurations include information on the sorting of the types corresponding to the M operations; the execution order of the M operations from first to last is consistent with the sorting of the types corresponding to the M operations.

[0803] As an example, the advantages of the above method include: reducing the complexity of the first node and reducing energy consumption.

[0804] As an example, the order of the types corresponding to the M operations is determined by the first node itself; the execution order of the M operations from first to last is consistent with the order of the types corresponding to the M operations.

[0805] As an example, the advantages of the above method include: providing sufficient degrees of freedom for the first node, adapting to various different scenarios and terminals, and having adaptability and flexibility.

[0806] As an example, the execution order of the M operations depends on the types corresponding to the M operations, including: the first given operation and the second given operation are two operations among the M operations; when the type corresponding to the first given operation belongs to a first type set and the type corresponding to the second given operation belongs to a second type set, the first given operation is executed before the second given operation; when the type corresponding to the first given operation belongs to the second type set and the type corresponding to the second given operation belongs to the first type set, the second given operation is executed before the first given operation; the first type set and the second type set are different, the first type set includes at least one type, and the second type set includes at least one type.

[0807] As an example, the advantages of the above method include: improving the flexibility of the system.

[0808] As an example, the benefits of the above method include: improved system stability and robustness.

[0809] As a sub-implementation of the above embodiments, the first type set includes encoders, and the second type set includes decoders.

[0810] As a sub-implementation of the above embodiments, the first type set includes AI encoders, and the second type set includes AI decoders.

[0811] As a sub-implementation of the above embodiments, the first type set includes models for information prediction, and the second type set includes models for information compression.

[0812] As a sub-implementation of the above embodiments, the first type set includes models for information prediction, and the second type set includes models for information verification.

[0813] As a sub-implementation of the above embodiments, the first type set includes models for information prediction, and the second type set includes models for performance evaluation.

[0814] As a sub-implementation of the above embodiments, the first type set includes models for information compression, and the second type set includes models for information decompression.

[0815] As a sub-implementation of the above embodiments, the first type set includes at least one of performance monitoring, positioning, beam management, CSI prediction, CSI estimation, CSI compression, RLF (radio link failure) prediction, cell handover prediction, and serving cell prediction; the second type set includes at least one of performance monitoring, positioning, beam management, CSI prediction, CSI estimation, CSI compression, RLF (radio link failure) prediction, cell handover prediction, and serving cell prediction.

[0816] As a sub-implementation of the above embodiments, the first type set includes channel information, and the second type set includes information other than channel information.

[0817] As a sub-implementation of the above embodiments, the first type set includes channel information, and the second type set includes at least one of performance monitoring results, RLF prediction, cell handover prediction, serving cell prediction, or location.

[0818] As a sub-implementation of the above embodiments, the first type set includes beam information, and the second type set includes at least one of predicted CSI, estimated CSI, or compressed CSI.

[0819] As a sub-implementation of the above embodiments, the first type set includes beam information or switched beam information, and the second type set includes at least one of predicted CSI, estimated CSI, or compressed CSI.

[0820] As a sub-implementation of the above embodiments, the first type set includes predicted CSI, and the second type set includes compressed CSI obtained by compressing the predicted CSI.

[0821] As a sub-implementation of the above embodiments, the first type set includes location information, and the second type set includes channel information.

[0822] As a sub-implementation of the above embodiments, the first type set includes positioning information, and the second type set includes beam information.

[0823] As a sub-implementation of the above embodiments, the first type set includes location information, and the second type set includes at least one of predicted CSI, estimated CSI, or compressed CSI.

[0824] As a sub-implementation of the above embodiments, the first type set includes location information, and the second type set includes at least one of performance monitoring results, RLF prediction, cell handover prediction, or serving cell prediction.

[0825] As an example, the execution order of the M operations depends on the types corresponding to the M operations, including: the first given operation and the second given operation are two operations among the M operations; when the type corresponding to the first given operation is a first type and the type corresponding to the second given operation is a second type, the first given operation is executed before the second given operation; when the type corresponding to the first given operation is the second type and the type corresponding to the second given operation is the first type, the second given operation is executed before the first given operation; the first type and the second type are different.

[0826] As an example, the advantages of the above method include: improving system flexibility and adapting to different terminals and transmission environments.

[0827] As an example, the advantages of the above method include: simplifying system design and reducing the complexity of solution implementation.

[0828] As a sub-implementation of the above embodiments, the first type belongs to the first type set, and the second type belongs to the second type set.

[0829] As an example, the execution order of the M operations depends on the type of the M operations: the M operations include three sequentially performed operations, and the outputs of the three sequentially performed operations include information other than RSRP, beam prediction, and channel information.

[0830] As an example, the execution order of the M operations depends on the type of the M operations: the M operations include three sequentially performed operations, and the outputs of the three sequentially performed operations include RSRP, beam prediction, and RLF failure prediction, respectively.

[0831] Example 11

[0832] Example 11 illustrates a schematic diagram of determining the failure of a target operation according to an embodiment of this application; as shown in Figure 11.

[0833] In Example 11, the first node determines on its own that the target operation has failed.

[0834] As an example, the first node determines the target identifier itself; the target identifier is associated with the target operation.

[0835] As an example, the first node determines the target identifier itself in the first processor.

[0836] As an example, the first node determines that the target operation failure occurs in the first processor.

[0837] Generally, how the first node determines the failure of the target operation is determined by the hardware equipment manufacturer. Below are some non-limiting implementation methods:

[0838] As an example, the first node determines that the target operation has failed by monitoring that the target operation has not achieved a given performance target.

[0839] As an example, the first node determines that the target operation has not achieved the given performance target by monitoring the reception quality of the signal or channel generated based on the output of the target operation.

[0840] As a sub-example of the above embodiments, the output of the target operation includes one or more precoding matrices, and the signal or channel generated based on the output of the target operation refers to a signal or channel precoded by the one or more precoding matrices.

[0841] As a sub-example of the above embodiments, the output of the target operation includes one or more beams, and the signal or channel generated based on the output of the target operation refers to a signal or channel using the one or more beams.

[0842] As a sub-example of the above embodiment, the output of the target operation includes one or more CQIs, which are used to determine one or more MCS (Modulation and Coding Schemes). The signal or channel generated based on the output of the target operation refers to the signal or channel using the one or more MCSs.

[0843] As a sub-example of the above embodiment, the output of the target operation includes reliability, and the first node determines that the target operation has not achieved the given performance target by the reliability being lower than a certain threshold.

[0844] As a sub-example of the above embodiment, the output of the target operation includes confidence information, and the first node determines that the target operation has not achieved the given performance target by the confidence information being lower than a certain threshold.

[0845] As an example, the output of the target operation includes reliability, and the first node determines that the target operation has failed if the reliability is lower than a certain threshold.

[0846] As an example, the output of the target operation includes confidence information, and the first node determines that the target operation has failed when the confidence information is lower than a certain threshold.

[0847] As an example, the first node determines that the target operation has not achieved the given performance target by performing ML testing or ML simulation on the model of the target operation.

[0848] As an example, the first node recovers the input of the target operation based on the output of the target operation, and then determines that the target operation has not achieved the given performance target by comparing the recovered input of the target operation with the actual input of the target operation.

[0849] As an example, the target operation corresponds to a timer, and the first node determines that the target operation has failed based on the expiration of the timer.

[0850] As an example, the first node retrains the target operation, and the target operation becomes invalid.

[0851] As an example, the first node determines on its own that the target operation needs to be retrained.

[0852] As an example, the first node determines on its own whether the target operation needs to be redeployed or reloaded.

[0853] As an example, if the target operation fails to achieve the expected performance or performance target, the target operation fails.

[0854] As an example, the first node determines that the target operation has failed based on the fact that the target operation cannot achieve the expected performance or performance target.

[0855] Example 12

[0856] Example 12 illustrates a schematic diagram of determining the failure of a target operation according to another embodiment of this application; as shown in Figure 12.

[0857] In embodiment 12, the first node receives a first signaling; wherein the first signaling indicates that the target operation has failed; or, the first signaling indicates a target identifier, and the target operation is associated with the target identifier.

[0858] As an example, the first node receives a first signaling message indicating that the target operation has failed.

[0859] As one embodiment, the first node receives a first signaling, the first signaling indicating a target identifier, and the target operation is associated with the target identifier.

[0860] As one embodiment, the first signaling includes higher layer signaling.

[0861] As an example, the first signaling includes RRC (Radio Resource Control) signaling.

[0862] As an example, the first signaling includes MAC CE signaling.

[0863] As one example, the first signaling includes DCI.

[0864] As one example, the first signaling includes RRC signaling and MAC CE.

[0865] As one embodiment, the first signaling includes higher-layer signaling and DCI.

[0866] As an example, the first signaling is RRC signaling.

[0867] As an example, the first signaling is MAC CE signaling.

[0868] As an example, the first signaling is DCI.

[0869] As an example, the first signaling indicates whether the target operation has failed.

[0870] As an example, the first signaling explicitly indicates whether the target operation has failed.

[0871] As an example, the first signaling implicitly indicates whether the target operation has failed.

[0872] As an example, the first signaling indicates a failed operation.

[0873] As an example, an operation that was not indicated as invalid by the first signaling was not invalid.

[0874] As an example, the first signaling indicates an operation that has not failed.

[0875] As an example, an operation that was not indicated as not invalid by the first signaling failed.

[0876] As an example, the first signaling explicitly indicates the target identifier.

[0877] As an example, the first signaling implicitly indicates the target identifier.

[0878] As one embodiment, the first signaling includes a target identifier.

[0879] As one embodiment, the first signaling indicates a target identifier, which is associated with a first type of identifier associated with the target operation.

[0880] As one embodiment, the first signaling includes a target identifier, which is associated with a first type of identifier associated with the target operation.

[0881] As one embodiment, the first signaling indicates a target identifier, which is the same as a first-class identifier associated with the target operation.

[0882] As one embodiment, the first signaling includes a target identifier, which is the same as a first type of identifier associated with the target operation.

[0883] As an example, when the target operation is associated with the target identifier, the target operation fails.

[0884] As an example, the operation associated with the target identifier fails.

[0885] As an example, the target identifier is a first-type identifier.

[0886] As an example, the target identifier is associated with a first type of identifier.

[0887] Generally, how the sender of the first signaling determines that the target operation has failed is determined by the hardware equipment manufacturer. Below are some non-limiting implementation methods:

[0888] As an example, the sender of the first signaling generates and sends a signal based on the output of the target operation that is fed back, and determines that the target operation has failed based on the signal reception quality fed back by the receiver of this signal.

[0889] As an example, the sender of the first signaling receives the input and output of the target operation as feedback. The sender of the first signaling recovers the input based on the output. If the comparison result between the recovered input and the feedback input is greater than a certain threshold, the sender determines that the target operation has failed.

[0890] As an example, the executor of the target operation recovers the input of the target operation based on the output of the target operation, and feeds back the comparison result between the recovered input and the actual input to the sender of the first signaling. The sender of the first signaling determines that the target operation has failed based on the feedback comparison result being greater than a certain threshold.

[0891] As an example, the sender of the first signaling determines that the target operation has failed based on the expiration of a timer.

[0892] As an example, the sender of the first signaling determines that the target operation has failed by performing ML testing or ML evaluation on the model of the target operation.

[0893] As an example, the sender of the first signaling determines that the target operation has failed based on the instructions of the core network.

[0894] As an example, if the sender of the first signaling indicates that the target operation needs to be retrained, the target operation becomes invalid.

[0895] As an example, if the sender of the first signaling indicates that the target operation needs to be redeployed or reloaded, the target operation fails.

[0896] Examples 13A-13B

[0897] Examples 13A-13B illustrate schematic diagrams of deploying a first given operation on a first node according to an embodiment of this application, as shown in Figures 13A-13B respectively.

[0898] In embodiment 13A, the first node requests the first producer to load a first given operation and obtains the first given operation from the first producer. The first given operation is one of the M operations described in this application.

[0899] As one example, the deployment includes obtaining the first given operation.

[0900] As one example, the deployment includes obtaining an AI entity.

[0901] As one example, the deployment includes obtaining an AI entity that performs the first given operation.

[0902] As one example, the deployment includes obtaining an AI entity that includes AI functionality to perform the first given operation.

[0903] As one example, the deployment includes loading the first given operation.

[0904] As one example, the deployment includes submitting a request to load the first given operation.

[0905] As an example, the first given operation is obtained from the serving cell of the first node.

[0906] As an example, the first given operation is loaded from the sustaining base station of the serving cell of the first node.

[0907] As an example, the first given operation is obtained from the core network.

[0908] As an example, the first given operation is obtained from the first producer.

[0909] As an example, the deployment is accomplished by an AI function.

[0910] As an example, the deployment is accomplished by AI functionality deployed on the first node.

[0911] As an example, the deployment is accomplished by an AI deployment function.

[0912] As an example, the deployment is accomplished by the AI ​​deployment function deployed on the first node.

[0913] As an example, the deployment is accomplished using AI inference functionality.

[0914] As an example, the deployment is accomplished by an AI inference function deployed on the first node.

[0915] As an example, the deployment is performed by an AI entity.

[0916] As an example, the deployment is performed by an AI entity deployed on the first node.

[0917] As an example, the deployment is performed by an AI entity with a deployment function.

[0918] As an example, the deployment is performed by an AI entity with deployment capabilities deployed on the first node.

[0919] As an example, the deployment is accomplished by an AI entity with an inference function.

[0920] As an example, the deployment is performed by an AI entity with inference capabilities deployed on the first node.

[0921] As one embodiment, the deployment includes obtaining the first given operation from a first producer.

[0922] As one embodiment, the deployment includes making a request to the first producer to load the first given operation.

[0923] As one embodiment, the deployment includes loading the first given operation from the first producer.

[0924] As an example, the first producer generates and provides the AL entity.

[0925] As an example, the first producer generates and provides AL functionality.

[0926] As an example, the first producer is the producer of the first given operation.

[0927] As an example, the first producer includes an AL entity producer.

[0928] As one example, the first producer includes an AL function producer.

[0929] As one example, the first producer includes an AL deployment producer.

[0930] As one example, the first producer includes an AL loading producer.

[0931] As one example, the first producer includes an AL-trained producer.

[0932] As an example, the first producer includes an AL inference producer.

[0933] As an example, the first producer includes the producer of the AL entity deployment.

[0934] As one example, the first producer includes the producer that loads the AL entity.

[0935] As an example, the first producer includes an MnS (Management Service) producer.

[0936] As an example, the training for obtaining the first given operation is performed by the first producer.

[0937] As an example, the executor used to obtain the training for the first given operation is different from the first producer.

[0938] In embodiment 13B, the first node requests the second producer to load a first given operation and obtains the first given operation from the first producer. The first given operation is one of the M operations described in this application.

[0939] As one example, the deployment includes obtaining the first given operation.

[0940] As one example, the deployment includes obtaining an AI entity or AI function that performs the first given operation.

[0941] As one example, the deployment includes loading the first given operation.

[0942] As one example, the deployment includes submitting a request to load the first given operation.

[0943] As an example, the deployment is accomplished by AI functionality deployed on the first node.

[0944] As an example, the deployment is accomplished by the AI ​​deployment function deployed on the first node.

[0945] As an example, the deployment is performed by an AI entity with a deployment function.

[0946] As one example, the second producer generates and provides AI entities or AI functions.

[0947] As one example, the second producer includes an MnS (Management Service) producer.

[0948] As an example, the second producer includes the producer of the AI ​​model training.

[0949] As one example, the second producer is the sender of the first information set.

[0950] As one example, the second producer is different from the sender of the first information set.

[0951] As one example, the second producer is the serving cell of the first node.

[0952] As one example, the second producer is the maintenance base station of the serving cell of the first node.

[0953] As one example, the second producer is the core network.

[0954] As an example, the first given operation is obtained from the serving cell of the first node.

[0955] As an example, the first given operation is loaded from the sustaining base station of the serving cell of the first node.

[0956] As an example, the first given operation is obtained from the core network.

[0957] As an example, the training for obtaining the first given operation is performed by the second producer.

[0958] As an example, the second producer is different from the first producer.

[0959] As an example, the first producer generates and provides the AL entity.

[0960] As an example, the first producer generates and provides AL functionality.

[0961] As an example, the first producer is the producer of the first given operation.

[0962] As an example, the first producer includes an AL entity producer.

[0963] As one example, the first producer includes an AL function producer.

[0964] As one example, the first producer includes an AL deployment producer.

[0965] As one example, the first producer includes an AL loading producer.

[0966] As one example, the first producer includes an AL-trained producer.

[0967] As an example, the first producer includes an AL inference producer.

[0968] As an example, the first producer includes the producer of the AL entity deployment.

[0969] As one example, the first producer includes the producer that loads the AL entity.

[0970] As an example, the first producer includes an MnS (Management Service) producer.

[0971] Example 14

[0972] Example 14 illustrates a schematic diagram of RAN (Radio Access Network) domain AI / ML function deployment according to one embodiment of this application; as shown in Figure 14. The gNB in ​​Example 14 can be replaced with, for example, an eNB, or a network device such as a 6G base station.

[0973] AI / ML related functions include ML training (also known as AI training, or AI / ML training), ML testing, and ML inference (also known as AI inference, or AI / ML inference), etc. ML training, ML testing, and ML inference functions can be deployed independently or co-located. Deployment of AI / ML related functions can be implemented through software, such as downloading and / or running executable files; or it can be implemented through a combination of software and hardware, such as accelerating specific computing units through hardware to improve computing speed or save power.

[0974] ML training functionality can be deployed in a cross-domain management system or a domain-specific management system; the domain-specific management system is used to manage the RAN domain or the CN (Core Network) domain. For example, ML training functionality for MDA (Management Data Analytics) can be deployed on MDAF (MDA Function); ML training for network data analytics can be deployed on NWDAF (Network Data Analytics Function), meaning the ML training functionality is an MTLF (Model Training Logical Function).

[0975] The ML inference function can also be deployed in a cross-domain management system or a domain-specific management system; for example, the ML inference function is MDAF, or the ML inference function is AnLF (Analytics logical function) located in NWDAF.

[0976] Similarly, ML testing functionality can also be deployed in cross-domain management systems or domain-specific management systems.

[0977] In Example 14, the RAN domain ML training function 1402 is located in the RAN domain management function 1403; while the ML inference function is located in the base station, that is, the AI / ML inference function 1404 is located in gNB 1405, and the AI / ML inference function 1406 is located in gNB 1407.

[0978] In Figure 14, the management of ML inference functions of multiple base stations is completed by RAN domain management function 1403, that is, data interaction with RAN domain MnS (Management Service) consumer / cross-domain management 1401 (as shown by the dashed arrow in Figure 14).

[0979] Optionally, the management of ML inference function can also be completed by the base station itself, that is, each base station can independently interact with the RAN domain MnS consumer / cross-domain management 1401.

[0980] It should be noted that Example 14 is merely a non-limiting implementation; optionally, the ML training function of the RAN domain may also be deployed at the base station; or optionally, some base stations may deploy both the ML inference function and the ML training function of the RAN domain, while some base stations may only deploy the ML inference function.

[0981] As an example, one of the gNBs (or base stations) in Example 14 is the second node of this application.

[0982] As an example, the second processor in this application includes an AL / ML inference function, namely 1404 or 1406, as shown in Figure 14.

[0983] Example 15

[0984] Example 15 illustrates a schematic diagram of the deployment of AI / ML functionality in a UE according to one embodiment of this application; as shown in Figure 15. The RAN domain ML training function 1505 in Figure 15 is optional.

[0985] UE function 1504 is deployed in the first node of this application, and the UE function 1504 includes AI / ML inference function 1506; the AI / ML inference function 1506 uses an ML model (also called an AI model) for inference; an ML model is typically trained before being used for AI / ML inference.

[0986] As an example, the first information report in this application is obtained through inference by the AI / ML inference function 1506.

[0987] As an example, the first processor in this application includes an AL / ML inference function 1506 in Figure 15.

[0988] As an example, the UE function 1504 includes a RAN domain ML training function 1505, which runs training data through an ML model to obtain a relevant loss and adjusts the parameters of the ML model based on the calculated loss; the ML training includes at least one of ML initial training, ML re-training, and reinforcement learning.

[0989] The above embodiments can reduce the complexity of the base station, or save air interface resources caused by reporting training data; however, the above embodiments place high demands on the processing capabilities of the UE side.

[0990] Optionally, the UE function 1504 also includes a CN domain ML training function (not shown in Figure 15).

[0991] Optionally, the UE function 1504 also includes an AI / ML deployment function (not shown in Figure 15) for loading ML models and data.

[0992] As an example, the first node indicates whether it supports ML training function (RAN domain or CN domain) through capability reporting. The capability reporting is RRC signaling or NAS (Non-Access Stratum) signaling.

[0993] As an example, the ML model and the associated metadata are loaded by the first node from a network device or a remote server.

[0994] Optionally, the UE function 1504 is an MnS (Management Service) producer that provides data to the CN domain MnF (Management Function) 1501, and / or the RAN domain MnF 1502, and / or the cross-domain management system 1503 for management or analysis (as shown by double arrow 1507).

[0995] Optionally, the UE function 1504 is an MnS consumer that loads data from the CN domain MnF (Management Function) 1501, and / or the RAN domain MnF 1502, and / or the cross-domain management system 1503 for AI / ML-related management, such as managing data requests, ML model activation, and / or ML training (as shown by double arrow 1507).

[0996] As an example, the ML model is based on a neural network.

[0997] As an example, the ML model is based on CNN (Conventional Neural Networks).

[0998] As an example, the ML model is based on the Transformer architecture.

[0999] Example 16

[1000] Example 16 illustrates a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of this application; as shown in Figure 16. Figure 16(a) includes a third processor, a fourth processor, and a fifth processor, and Figure 16(b) includes a third processor, a fourth processor, a fifth processor, and a sixth processor.

[1001] In Example 16(a), the third processor sends a first dataset to the fourth processor and a second dataset to the fifth processor; the fourth processor generates a target first-type parameter set based on the first dataset, and sends the generated target first-type parameter set to the fifth processor; the fifth processor processes the second dataset using the target first-type parameter set to obtain a first-type output. In Figure 16(a), the first-type feedback is optional.

[1002] In Example 16(b), the third processor sends a first dataset to the fourth processor and a second dataset to the fifth processor; the fourth processor generates a target first-type parameter set based on the first dataset, and sends the generated target first-type parameter set to the fifth processor; the fifth processor processes the second dataset using the target first-type parameter set to obtain a first-type output, and sends the first-type output to the sixth processor. In Figure 16(b), the first-type feedback and the second-type feedback are optional.

[1003] In this application, the first given operation is one of the M operations.

[1004] As an example, in Figure 16(a), the fifth processor sends the first type of output to the second node in this application.

[1005] As an example, Figure 16(a) employs a single-side AI model, in which the fifth processor performs the M operations described in this application.

[1006] As an example, Figure 16(a) employs a single-side AI model, in which the fifth processor executes the first given operation in this application, which is one of the M operations.

[1007] As an example, Figure 16(a) employs a single-side AI model, in which the fifth processor performs the target operation described in this application.

[1008] As an example, Figure 16(a) employs a single-side AI model, in which the fifth processor executes at least one of the M operations deployed on the first node in this application.

[1009] As an example, Figure 16(b) employs a two-sided AI model, in which the fifth processor performs the target operation described in this application, and the sixth processor performs at least one operation in addition to the M operations deployed on the second node in this application.

[1010] As an example, Figure 16(b) employs a two-sided AI model, where the fifth processor executes the first given operation in this application, and the sixth processor executes at least one operation other than the M operations deployed on the second node in this application; the first given operation is one of the M operations.

[1011] As an example, Figure 16(b) employs a two-sided AI model, in which the fifth processor executes at least one of the M operations deployed on the first node in this application, and the sixth processor executes at least one operation other than the M operations deployed on the second node in this application.

[1012] As an example, the fifth processor sends a first type of feedback to the fourth processor, and the first type of feedback is used to trigger a recalculation or update of the target first type of parameter group.

[1013] As one embodiment, the sixth processor sends a second type of feedback to the third processor, the second type of feedback being used to generate the first dataset or the second dataset, or the second type of feedback being used to trigger the sending of the first dataset or the second dataset.

[1014] As one embodiment, the third processor generates the first dataset and the second dataset based on measurements of a first type of wireless signal, the first type of wireless signal including downlink RS.

[1015] As one embodiment, the fifth processor belongs to the first node, and the sixth processor belongs to the second node.

[1016] As an example, the given information report belongs to the first type of output; wherein, the given information report is the first information report; or, the given information report is any one of the M2 information reports.

[1017] As an example, the second dataset includes the inputs of the M operations.

[1018] As an example, the second dataset includes the input of the first given operation.

[1019] As an example, the second dataset includes the input of the target operation.

[1020] As an example, the second dataset includes the input of at least one of the M operations deployed on the first node.

[1021] As an example, the second dataset includes information obtained based on the M configurations.

[1022] As an example, the second dataset includes information obtained based on at least one of the M configurations.

[1023] As an example, the second dataset includes information obtained based on the M configurations and information obtained based on the outputs of the M operations.

[1024] As an example, the second dataset includes information obtained based on the M configurations and information obtained based on the output of at least one of the M operations.

[1025] As an example, the second dataset includes information obtained based on at least one of the M configurations and information obtained based on the output of at least one of the M operations.

[1026] As an example, the first dataset includes training data.

[1027] As an example, the fourth processor belongs to the producer of the target operation.

[1028] As an example, the fourth processor belongs to the producer of the first given operation.

[1029] As one embodiment, the fourth processor includes an AI training producer.

[1030] As one embodiment, the fourth processor includes an AI training function.

[1031] As an example, the fourth processor is used for model training, and the trained model is described by the target first class of parameter sets.

[1032] As one embodiment, the fourth processor belongs to the first node.

[1033] As an example, the advantages of the above method include avoiding passing the first dataset to the second node.

[1034] As one example, the fourth processor belongs to the second node.

[1035] As an example, the advantages of the above method include: supporting joint training and optimizing system performance.

[1036] As an example, the fourth processor belongs to the core network.

[1037] As an example, the advantages of the above method include: supporting joint training across the entire network and further optimizing system performance.

[1038] As an example, the second dataset includes inference data.

[1039] As one embodiment, the fifth processor includes an AI inference producer.

[1040] As one embodiment, the fifth processor includes an AI inference function.

[1041] As an example, the fifth processor belongs to the first node.

[1042] As an example, the fifth processor constructs a model based on the target first type of parameter group, and then inputs the second dataset into the constructed model to obtain the first type of output.

[1043] As an example, the M operations are described by the target first type of parameter group.

[1044] As an example, at least one of the M operations is described by the target first type of parameter group.

[1045] As an example, the first given operation is described by the target first type of parameter group.

[1046] As an example, the target operation is described by the target first type of parameter group.

[1047] As an example, the target first type of parameter group is used to construct the M operations.

[1048] As an example, the target first type of parameter group is used to construct at least one of the M operations.

[1049] As an example, the target first type of parameter group is used to construct the first given operation.

[1050] As an example, the first set of target parameters is used to construct the target operation.

[1051] As an example, the fifth processor generates a recovery dataset based on the first type of output, and the error between the recovery dataset and the second dataset is used to generate the first type of feedback.

[1052] As an example, the first type of feedback is used to reflect the performance of the trained model; when the performance of the trained model fails to meet the requirements, the fourth processing opportunity recalculates the target first type of parameter set.

[1053] As an example, when the error is too large or the update has not been performed for too long, the performance of the trained model is considered to be unsatisfactory.

[1054] As an example, the target first type of parameter group includes one or more of the following: convolution kernel size, number of convolution layers, convolution stride, pooling kernel size, pooling kernel stride, pooling function, activation function, or number of feature maps.

[1055] As an example, the target first type of parameter group includes one or more of the following: convolution kernel, pooling kernel, pooling function, activation function, parameters of pooling function, or parameters of activation function.

[1056] As an example, the AI ​​includes machine learning (ML) inference.

[1057] Example 17

[1058] Example 17 illustrates a schematic diagram based on artificial intelligence or machine learning according to an embodiment of this application; as shown in Figure 17. Figure 17 includes a third operation, a fourth operation, a fifth operation, a sixth operation, and a seventh operation. In Figure 17, the arrowed lines indicate the sequence of processes.

[1059] In Example 17, the third and fourth operations 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.

[1060] As an example, 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.

[1061] As an example, the first stage includes a training phase, the second stage includes an emulation phase, the third stage includes a deployment phase, and the fourth stage includes an emulation phase.

[1062] As an example, the first stage includes AI model training.

[1063] As an example, the first stage includes AI model training and AI testing.

[1064] As an example, the AI ​​includes machine learning (ML) inference.

[1065] As an example, the AI ​​model training includes initial training and re-training of one or a group of AI entities.

[1066] As an example, the training of the AI ​​model depends on training data.

[1067] As an example, the AI ​​model training includes AI entity validation.

[1068] As an example, the AI ​​entity verification is used to evaluate the performance of the AI ​​entity.

[1069] As an example, the AI ​​entity verification relies on verification data.

[1070] As an example, if the AI ​​entity verification results do not meet expectations, the AI ​​model will be retrained.

[1071] As an example, the AI ​​testing includes testing the validated AI entity to estimate the performance of the trained AI model.

[1072] As an example, if the AI ​​test results meet expectations, the AI ​​entity proceeds to the next stage; otherwise, the AI ​​model will be retrained.

[1073] As an example, the AI ​​test relies on test data.

[1074] As an example, the second stage includes AI simulation, which performs inference of AI entities in a simulation environment.

[1075] As an example, the AI ​​simulation estimates the performance of AI entity inference in a simulation environment before using the AI ​​entity.

[1076] As one embodiment, the second stage is optional.

[1077] As an example, the third stage includes AI entity loading, which is to obtain trained AI entities to obtain the desired AI inference capabilities.

[1078] As an example, the third stage is optional.

[1079] As an example, the third stage is no longer needed when the training and inference functions are co-located.

[1080] As an example, the fourth stage includes AI inference.

[1081] As an example, the seventh operation includes the M operations.

[1082] As an example, the seventh operation includes the first given operation.

[1083] As an example, the seventh operation includes the target operation.

[1084] As an example, the seventh operation includes at least one of the M operations.

[1085] Example 18

[1086] Example 18 illustrates a structural block diagram of a processing apparatus for a first node according to an embodiment of the present application; as shown in Figure 18. In Figure 18, the processing apparatus 1800 in the first node includes a first processor 1801.

[1087] The first processor 1801 receives a first information set; the first information set indicates M operations, where M is a positive integer greater than 1;

[1088] The first processor 1801 determines that M0 of the M operations are invalid, where M0 is a non-negative integer;

[1089] In Example 18, the target operation fails. The target operation is an operation other than the M0 operations among the M operations. The M0 operations depend on the order of execution from first to last, and the target operation is ordered among the M operations.

[1090] As an example, when the target operation is not the last operation among the M operations, M0 is a positive integer, and the M0 operations include the operations that follow the target operation among the M operations.

[1091] As an example, the M operations are divided into M1 parts, and any part of the M1 parts includes at least one operation from the M operations, where M1 is a positive integer greater than 1 and less than M; when the part where the target operation is located is not the last part of the M1 parts, the M0 operations include the operations from the M operations that follow the part where the target operation is located.

[1092] As an example, when one of the M operations fails, the first processor 1801 abandons the first information reporting; wherein, the first information set is used to configure the first information reporting.

[1093] As an example, when one of the M operations fails, the first processor 1801 does not update the first information report; wherein, the first information set is used to configure the first information report.

[1094] As an example, when one of the M operations fails, the first processor 1801 abandons the M3 information reports out of the M2 information reports; wherein, M2 is a positive integer greater than 1, M2 is not greater than M, and M3 is a positive integer not greater than M2; the M2 information reports depend on the output of the M2 operations respectively, the M2 operations belong to the M operations, and at least one of the M2 operations belongs to the M0 operations; the M3 information reports include information reports in which the operations depended on by the M2 information reports belong to the M0 operations.

[1095] As an example, when one of the M operations fails, the first processor 1801 does not update the M3 information report out of the M2 information reports; wherein, M2 is a positive integer greater than 1, M2 is not greater than M, and M3 is a positive integer not greater than M2; the M2 information reports depend on the output of the M2 operations respectively, the M2 operations belong to the M operations, and at least one of the M2 operations belongs to the M0 operations; the M3 information report includes information reports in the M2 information reports whose dependent operations belong to the M0 operations.

[1096] As an example, a message reporting depends on the output of at least one of the M operations.

[1097] As an example, the first information set indicates M configurations, where M is a positive integer greater than 1, and the M configurations respectively indicate the M operations; the M configurations each include M first-type identifiers, and the M operations are respectively associated with the M first-type identifiers.

[1098] As an example, the first information set is also used to determine the order of the M first-class identifiers, and the execution order of the M operations is consistent with the order of the M first-class identifiers.

[1099] As an example, the first information set indicates the execution order of the M operations from first to last.

[1100] As one embodiment, the first information set indicates M configurations, where M is a positive integer greater than 1, and the M configurations respectively indicate M operations; the first information set is used to determine the order of the M configurations, and the execution order of the M operations is consistent with the order of the M configurations.

[1101] As an example, at least two of the M operations correspond to different types, and the execution order of the M operations depends on the types corresponding to the M operations.

[1102] As an example, the first node determines on its own that the target operation has failed.

[1103] As one embodiment, the first processor 1801 receives a first signaling; wherein the first signaling indicates that the target operation has failed.

[1104] As one embodiment, the first processor 1801 receives a first signaling; wherein the first signaling indicates a target identifier, and the target operation is associated with the target identifier.

[1105] As an example, the M operations are based on training or AI.

[1106] As an example, at least one of the M operations is based on training or AI.

[1107] As an example, the target operation is based on training or AI.

[1108] As an example, the M operations do not require deployment.

[1109] As an example, the first processor 1801 deploys the M operations.

[1110] As an example, the first processor 1801 deploys at least one of the M operations.

[1111] As an example, the first processor 1801 deploys the target operation.

[1112] As an example, the M operations are obtained through loading.

[1113] As an example, at least one of the M operations is obtained by loading.

[1114] As an example, the target operation is obtained by loading.

[1115] As one example, the first node is a user equipment.

[1116] As one example, the first node is a terminal.

[1117] As one example, the user equipment is a terminal.

[1118] As an example, the first node is a relay node device.

[1119] As an example, the first processor 1801 includes at least one of the following in embodiment 4: {antenna 452, receiver / transmitter 454, receiving processor 456, transmitting processor 468, multi-antenna receiving processor 458, multi-antenna transmitting processor 457, controller / processor 459, memory 460, data source 467}.

[1120] Example 19

[1121] Example 19 illustrates a structural block diagram of a processing apparatus for a second node according to an embodiment of the present application; as shown in Figure 19. In Figure 19, the processing apparatus 1900 in the second node includes a second processor 1901.

[1122] The second processor 1901 sends a first information set; the first information set indicates M operations, where M is a positive integer greater than 1.

[1123] In Example 19, the target recipient of the first information set determines that M0 of the M operations are invalid, where M0 is a non-negative integer; the target operation is invalid, and the target operation is an operation other than the M0 operations among the M operations; the M0 operations depend on the order of execution from first to last, and the target operation is ordered among the M operations.

[1124] As an example, when the target operation is not the last operation among the M operations, M0 is a positive integer, and the M0 operations include the operations that follow the target operation among the M operations.

[1125] As an example, the M operations are divided into M1 parts, and any part of the M1 parts includes at least one operation from the M operations, where M1 is a positive integer greater than 1 and less than M; when the part where the target operation is located is not the last part of the M1 parts, the M0 operations include the operations from the M operations that follow the part where the target operation is located.

[1126] As an example, when one of the M operations fails, the target receiver of the first information set abandons the first information reporting; wherein, the first information set is used to configure the first information reporting.

[1127] As an example, when one of the M operations fails, the target receiver of the first information set does not update the first information report; wherein, the first information set is used to configure the first information report.

[1128] As an example, when one of the M operations fails, the target receiver of the first information set abandons the M3 information reports out of the M2 information reports; wherein, M2 is a positive integer greater than 1, M2 is not greater than M, and M3 is a positive integer not greater than M2; the M2 information reports depend on the output of the M2 operations respectively, the M2 operations belong to the M operations, and at least one of the M2 operations belongs to the M0 operations; the M3 information reports include information reports in which the operations depended on by the M2 information reports belong to the M0 operations.

[1129] As an example, when one of the M operations fails, the target receiver of the first information set does not update the M3 information report out of the M2 information reports; wherein, M2 is a positive integer greater than 1, M2 is not greater than M, and M3 is a positive integer not greater than M2; the M2 information reports depend on the output of the M2 operations respectively, the M2 operations belong to the M operations, and at least one of the M2 operations belongs to the M0 operations; the M3 information report includes information reports in the M2 information reports whose dependent operations belong to the M0 operations.

[1130] As an example, a message reporting depends on the output of at least one of the M operations.

[1131] As one embodiment, the second node monitors whether the first information report is sent by the target recipient of the first information set.

[1132] As an example, the second node monitors whether the M2 information reports are sent by the target recipient of the first information set.

[1133] As one embodiment, the second node determines on its own whether to give up receiving the first information report.

[1134] As one embodiment, the second processor 1901 receives the first information report.

[1135] As one embodiment, the second processor 1901 abandons receiving the first information report.

[1136] As an example, the second processor 1901 receives the M2 information reports.

[1137] As an example, the second processor 1901 receives information reports other than the M3 information reports from the M2 information reports.

[1138] As an example, the first information set indicates M configurations, where M is a positive integer greater than 1, and the M configurations respectively indicate the M operations; the M configurations each include M first-type identifiers, and the M operations are respectively associated with the M first-type identifiers.

[1139] As an example, the first information set is also used to determine the order of the M first-class identifiers, and the execution order of the M operations is consistent with the order of the M first-class identifiers.

[1140] As an example, the first information set indicates the execution order of the M operations from first to last.

[1141] As one embodiment, the first information set indicates M configurations, where M is a positive integer greater than 1, and the M configurations respectively indicate M operations; the first information set is used to determine the order of the M configurations, and the execution order of the M operations is consistent with the order of the M configurations.

[1142] As an example, at least two of the M operations correspond to different types, and the execution order of the M operations depends on the types corresponding to the M operations.

[1143] As an example, the target recipient of the first information set determines that the target operation has failed.

[1144] As one embodiment, the second processor 1901 sends a first signaling message; wherein the first signaling message indicates that the target operation has failed.

[1145] As one embodiment, the second processor 1901 sends a first signaling; wherein the first signaling indicates a target identifier, and the target operation is associated with the target identifier.

[1146] As an example, the M operations are based on training or AI.

[1147] As an example, at least one of the M operations is based on training or AI.

[1148] As an example, the target operation is based on training or AI.

[1149] As an example, the M operations do not require deployment.

[1150] As an example, the M operations need to be deployed.

[1151] As an example, at least one of the M operations needs to be deployed.

[1152] As an example, the target operation needs to be deployed.

[1153] As an example, the M operations are obtained through loading.

[1154] As an example, at least one of the M operations is obtained by loading.

[1155] As an example, the target operation is obtained by loading.

[1156] In one embodiment, the second node is a base station.

[1157] In one embodiment, the second node is a user equipment.

[1158] As one embodiment, the second node is a relay node device.

[1159] In one embodiment, the second node is a terminal.

[1160] As an example, the second processor 1901 includes at least one of the following in embodiment 4: {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}.

[1161] Those skilled in the art will understand that all or part of the steps in the above methods can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium, such as a read-only memory, hard disk, or optical disk. Optionally, all or part of the steps in the above embodiments can also be implemented using one or more integrated circuits. Accordingly, each module unit in the above embodiments can be implemented in hardware or in the form of software functional modules. This application is not limited to any specific combination of software and hardware. The user equipment, terminal, and UE in this application include, but are not limited to, drones, communication modules on drones, remote-controlled aircraft, aircraft, small aircraft, mobile phones, tablets, laptops, vehicle-mounted communication equipment, vehicles, RSUs, wireless sensors, internet access cards, IoT terminals, RFID terminals, NB-IoT terminals, MTC (Machine Type Communication) terminals, eMTC (enhanced MTC) terminals, data cards, internet access cards, vehicle-mounted communication equipment, low-cost mobile phones, low-cost tablets, 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, eNBs, gNBs, TRPs (Transmitter Receiver Points), GNSS, relay satellites, satellite base stations, airborne base stations, RSUs (Road Side Units), drones, and testing equipment, such as transceivers or signaling testers that simulate some functions of a base station, and other wireless communication equipment.

[1162] Those skilled in the art will understand that the present invention can be practiced in other specified forms without departing from its core or essential characteristics. Therefore, the embodiments disclosed herein should in any way be considered descriptive rather than restrictive. The scope of the invention is defined by the appended claims rather than the foregoing description, and all modifications within their equivalent meaning and scope are considered to be included therein.

Claims

1. A method in a first node for wireless communication, characterized by, The method comprises: receiving a first information set; the first information set indicates M operations, M being a positive integer greater than 1; determining that M0 operations in the M operations are invalid, M0 being a non-negative integer; wherein a target operation is invalid, the target operation being one operation in the M operations other than the M0 operations; the M0 operations are dependent on the ordering of the M operations according to the execution order from early to late, and the target operation is in the ordering of the M operations.

2. The method of claim 1, wherein, When the target operation is not the last operation in the M operations, the M0 is a positive integer, and the M0 operations include the operations in the M operations after the target operation.

3. The method according to claim 1 or 2, characterized in that, The method comprises: when one operation in the M operations is invalid, abandoning or not updating a first information report; wherein the first information set is used to configure the first information report; or, the first processor abandons or does not update M3 information reports in M2 information reports; wherein M2 is a positive integer greater than 1, M2 is not greater than M, M3 is a positive integer not greater than M2; the M2 information reports are respectively dependent on the outputs of M2 operations, the M2 operations belong to the M operations, and at least one operation in the M2 operations belongs to the M0 operations; the M3 information reports include the information reports in the M2 information reports whose dependent operations belong to the M0 operations.

4. The method according to any one of claims 1 to 3, characterized in that, The first information set indicates M configurations, M being a positive integer greater than 1, and the M configurations respectively indicating the M operations; the M configurations respectively include M first type identifiers, and the M operations are respectively associated with the M first type identifiers.

5. The method of claim 4, wherein, The first information set is also used to determine the ordering of the M first type identifiers, which is consistent with the execution order from early to late of the M operations and the ordering of the M first type identifiers.

6. The method according to any one of claims 1 to 4, characterized in that, The first information set indicates the execution order from early to late of the M operations; or, The first information set indicates M configurations, M being a positive integer greater than 1, and the M configurations respectively indicating the M operations; the first information set is used to determine the ordering of the M configurations, which is consistent with the execution order from early to late of the M operations and the ordering of the M configurations.

7. The method according to any one of claims 1 to 6, characterized in that, At least two operations in the M operations correspond to different types, and the execution order from early to late of the M operations is dependent on the types corresponding to the M operations.

8. The method according to any one of claims 1 to 7, characterized in that, The first node determines the target operation invalid by itself.

9. The method according to any one of claims 1 to 7, characterized in that, The method comprises: receiving first signaling; wherein the first signaling indicates that the target operation is invalid; or, the first signaling indicates a target identifier, and the target operation is associated with the target identifier.

10. A terminal, characterized by comprising: The terminal comprises one or more processors and a memory; the memory is coupled with the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, and the one or more processors invoke the computer instructions to enable the terminal to execute the method in any one of claims 1-9.

11. A method in a second node for wireless communication, the method comprising: The method comprises: transmitting a first information set; the first information set indicates M operations, M is a positive integer greater than 1; wherein a target receiver of the first information set determines that M0 operations in the M operations are invalid, M0 is a non-negative integer; a target operation is invalid, the target operation is one operation in the M operations other than the M0 operations; the M0 operations depend on a sorting of the M operations according to an execution order from early to late.

12. The method of claim 11, wherein, When the target operation is not the last operation in the M operations, the M0 is a positive integer, and the M0 operations include operations in the M operations after the target operation.

13. The method according to claim 11 or 12, characterized in that, When one operation in the M operations is invalid, the target receiver of the first information set gives up or does not update a first information report; wherein the first information set is used to configure the first information report; or, the target receiver of the first information set gives up or does not update M3 information reports in M2 information reports; wherein M2 is a positive integer greater than 1, M2 is not greater than M, M3 is a positive integer not greater than M2; the M2 information reports respectively depend on the outputs of M2 operations, the M2 operations belong to the M operations, and at least one operation in the M2 operations belongs to the M0 operations; the M3 information reports include information reports in the M2 information reports whose dependent operations belong to the M0 operations.

14. The method of any one of claims 11-13, wherein, The first information set indicates M configurations, M is a positive integer greater than 1, and the M configurations respectively indicate the M operations; the M configurations respectively include M first type identifiers, and the M operations are respectively associated with the M first type identifiers.

15. The method of claim 14, wherein, The first information set is also used to determine the sorting of the M first type identifiers, and the execution order from early to late of the M operations is consistent with the sorting of the M first type identifiers.

16. The method of any one of claims 11-14, wherein, The first information set indicates the execution order from early to late of the M operations; or, The first information set indicates M configurations, M is a positive integer greater than 1, and the M configurations respectively indicate the M operations; the first information set is used to determine the sorting of the M configurations, and the execution order from early to late of the M operations is consistent with the sorting of the M configurations.

17. The method of any one of claims 11 to 16, wherein, At least two operations in the M operations correspond to different types, and the execution order from early to late of the M operations depends on the types corresponding to the M operations.

18. The method of any one of claims 11-17, wherein, The target receiver of the first information set determines the target operation invalid by itself.

19. The method of any one of claims 11-17, wherein, Comprising: transmitting first signaling; wherein The first signaling indicates that the target operation is invalid; or, The first signaling indicates a target identifier, and the target operation is associated with the target identifier.

20. A base station, comprising: The base station comprises one or more processors and a memory; The memory is coupled with the one or more processors, and is configured to store computer program codes including computer instructions, and the one or more processors are configured to invoke the computer instructions to cause the base station to perform the method according to any one of claims 11-19.

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