CSI reporting method in node used for wireless communication, and apparatus

Abstract

Disclosed in the present application are a CSI reporting method in a node used for wireless communication, and an apparatus. The method comprises: a first node receiving a first information block, wherein the first information block indicates the reporting of a plurality of pieces of CSI; and sending target CSI in a first time-domain resource only when a first condition is satisfied, wherein the plurality of pieces of CSI comprise N1 pieces of CSI and the target CSI, N1 being a positive integer; the first condition comprises a first value being less than a first threshold; and the first value depends on the transmission and reception of the N1 pieces of CSI. The above method and apparatus improve the performance of CSI reporting, reduce the resource overhead of a system, and enhance the overall performance of the system.

Description

A CSI reporting method and apparatus used in a node for wireless communication

[0001] This application claims priority from the Chinese patent application No. 202411247758.5, filed on September 6, 2024, and entitled "A CSI reporting method and apparatus used 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 CSI reporting scheme and apparatus 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 select appropriate transmission parameters by itself using these information, or report these 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 CSI (Channel State Information) inference, system performance, and system design. Compared with the traditional processing method, AI / ML has the characteristics of being based on training and needing to be deployed. In addition, AI / ML is also a key candidate technology for future 6G communication. SUMMARY

[0005] Applicants have found through research that when inference-based CSI generation is introduced, the existing measurement mechanism, reporting mechanism, and related configuration signaling may not be able to meet the needs. 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 reporting schemes; further, a unified design scheme 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 the present application (if not specifically stated) can refer to the definitions in the specification protocols TS28 series, TS36 series, TS38 series, TS37 series of 3GPP. If necessary, the 3GPP standards TS38.211, TS38.212, TS38.213, TS38.214, TS38.215, TS38.321, TS38.331, TS38.305, TS38.304, TS37.355 can be referred to for the understanding of the present application.

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

[0008] receiving a first information block; the first information block indicates the reporting of a plurality of CSI (Channel State Information);

[0009] only when a first condition is met, transmitting a target CSI in a first time domain resource;

[0010] wherein the plurality of CSI includes N1 CSI and the target CSI, N1 is a positive integer; the first condition includes that the first value is less than a first threshold; the first value depends on the transmission and reception of the N1 CSI.

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

[0012] According to one aspect of the present application, the first node is a terminal.

[0013] According to one aspect of the present application, the first node is a relay node.

[0014] As an embodiment, the user equipment is a terminal.

[0015] As an embodiment, the problem to be solved by the present application includes how to handle the reporting of a target CSI in a plurality of CSI.

[0016] As an embodiment, the benefits of the above method include supporting the association between the reporting of a plurality of CSI.

[0017] As an embodiment, the benefits of the above method include minimizing the reporting of CSI, effectively reducing the reporting overhead of CSI.

[0018] As an embodiment, the benefits of the above method include improving the accuracy and real-time performance of channel information.

[0019] As an embodiment, benefits of the above method include: better adaptation to various application scenarios and terminals, and improvement of flexibility and adaptability of the system.

[0020] As an embodiment, benefits of the above method include: introduction of the first condition to assist in determining the reporting processing of the target CSI, simplification of system design, and reduction of implementation complexity of the scheme.

[0021] As an embodiment, benefits of the above method include: improvement of overall performance of the system.

[0022] According to an aspect of the present application, any CSI in the plurality of CSIs depends on an output of reasoning.

[0023] According to an aspect of the present application, the reasoning in the present application is based on training or AI.

[0024] As an embodiment, the AI (Artificial Intelligence) includes ML (Machine Learning).

[0025] As an embodiment, the first node deploys the reasoning in the present application.

[0026] As an embodiment, the reasoning in the present application is obtained by loading.

[0027] As an embodiment, benefits of the above method include: support of an AI / ML-based CSI reporting scheme.

[0028] As an embodiment, benefits of the above method include: better adaptation to various application scenarios and terminals, and improvement of flexibility and adaptability of the system.

[0029] As an embodiment, benefits of the above method include: improvement of accuracy of channel information reporting, reduction of reporting overhead, and improvement of overall performance of the system.

[0030] According to an aspect of the present application, a first CSI and the target CSI respectively depend on an output of a first reasoning and an output of a second reasoning, the first CSI being one of the N1 CSIs; the output of the first reasoning includes a first output and a second output, the first CSI depending on the first output, and an input of the second reasoning including the second output.

[0031] As an embodiment, benefits of the above method include: support of association between reasonings of multiple CSIs.

[0032] As an embodiment, benefits of the above method include: improving the performance of CSI reporting.

[0033] As an embodiment, benefits of the above method include: better adapting to various application scenarios and terminals, improving the flexibility and adaptability of the system.

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

[0035] According to an aspect of the present application, the transceiving condition of the N1 CSIs includes the number of CSIs in the N1 CSIs that are abandoned by the first node for transmission.

[0036] As an embodiment, the essence of the above method includes: the processing of the target CSI reporting in the multiple CSIs depends on the number of abandoned CSIs for transmission in the N1 CSIs.

[0037] As an embodiment, benefits of the above method include: simplifying system design and reducing the implementation complexity of the scheme.

[0038] As an embodiment, benefits of the above method include: improving the stability and robustness of the system and the scheme.

[0039] As an embodiment, benefits of the above method include: improving the flexibility and adaptability of the system.

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

[0041] According to an aspect of the present application, it includes:

[0042] When the second condition is met, abandon the transmission of the second CSI in the second time domain resource;

[0043] Wherein, the second CSI is one of the N1 CSIs; the second condition includes that the second time domain resource and the first signal overlap in time domain, or the second condition includes that the second time domain resource includes at least one DL (downlink) symbol.

[0044] As an embodiment, the essence of the above method includes: when the resource for transmitting a CSI reporting and the resource for transmitting other signals or the downlink transmission resource conflict, abandon the transmission of the CSI reporting.

[0045] As an embodiment, benefits of the above method include: effectively solving the conflict between multiple transmissions, reducing the implementation complexity of the system.

[0046] As an example, the above method has the benefit of enhancing the completeness and robustness of the system.

[0047] According to an aspect of the present application, the reception / transmission status of the N1 CSIs includes the number of CSIs in the N1 CSIs that are not successfully received by the transmitter of the first information block, or the reception / transmission status of the N1 CSIs includes the number of bits in the N1 CSIs that are not successfully received by the transmitter of the first information block.

[0048] As an example, the above method has the benefit of simplifying the system design and reducing the implementation complexity of the scheme.

[0049] As an example, the above method has the benefit of enhancing the stability and robustness of the system and the scheme.

[0050] As an example, the above method has the benefit of enhancing the stability and robustness of the system and the scheme.

[0051] As an example, the above method has the benefit of improving the flexibility of the system, adapting to different transmission environments and application scenarios.

[0052] As an example, the above method has the benefit of improving the performance of CSI reporting and the overall performance of the system.

[0053] According to an aspect of the present application, the method comprises:

[0054] receiving a second information block;

[0055] The second information block indicates that a second CSI is not successfully received by the transmitter of the first information block, and the second CSI is one of the N1 CSIs.

[0056] As an example, the above method has the benefit of simplifying the system design and reducing the implementation complexity of the scheme.

[0057] As an example, the above method has the benefit of enhancing the completeness and reliability of the system and the scheme.

[0058] As an example, the above method has the benefit of having small changes to the existing system and standard, and improving the forward and backward compatibility of the system.

[0059] According to an aspect of the present application, the reporting of the plurality of CSIs is a plurality of CSI reporting configured by the same CSI reporting configuration.

[0060] As an embodiment, benefits of the above method include: improving the estimation accuracy of CSI.

[0061] As an embodiment, benefits of the above method include: simplifying system design, and ensuring consistency of understanding of transmitted information by transmitters and receivers.

[0062] As an embodiment, benefits of the above method include: reducing the overhead of configuration information, and reducing the implementation complexity of the system.

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

[0064] transmitting a third information block;

[0065] The third information block indicates the first threshold.

[0066] As an embodiment, benefits of the above method include: simplifying system design, and reducing the implementation complexity of the scheme.

[0067] As an embodiment, benefits of the above method include: improving the flexibility of the system, and adapting to different transmission environments and application scenarios.

[0068] As an embodiment, benefits of the above method include: supporting terminals with different UE capabilities.

[0069] The present application discloses a method used in a second node for wireless communication, which features that the method comprises:

[0070] transmitting a first information block; the first information block indicates reporting of multiple CSIs;

[0071] receiving a target CSI on the first time domain resource only when a first condition is met;

[0072] The multiple CSIs include N1 CSIs and the target CSI, N1 is a positive integer; the first condition includes that the first value is less than a first threshold; and the first value depends on the transmission and reception of the N1 CSIs.

[0073] According to an aspect of the present application, the second node comprises a base station.

[0074] According to an aspect of the present application, the second node comprises a core network.

[0075] According to an aspect of the present application, the second node comprises a base station and a core network.

[0076] According to an aspect of the present application, the second node comprises a relay node.

[0077] According to an aspect of the present application, the second node comprises a user equipment.

[0078] According to an embodiment, the user equipment is a terminal.

[0079] According to an aspect of the present application, any of the plurality of CSI depends on an output of an inference.

[0080] According to an aspect of the present application, the inference in the present application is training-based or AI-based.

[0081] According to an embodiment, the AI (Artificial Intelligence) comprises ML (Machine Learning).

[0082] According to an embodiment, the receiver of the first information block deploys the inference in the present application.

[0083] According to an embodiment, the inference in the present application is obtained by loading.

[0084] According to an aspect of the present application, a first CSI and the target CSI depend on an output of a first inference and an output of a second inference respectively, the first CSI being one of the N1 CSIs; the output of the first inference comprising a first output and a second output, the first CSI depending on the first output, the input of the second inference comprising the second output.

[0085] According to an aspect of the present application, the transceiving condition of the N1 CSIs comprises a number of CSIs in the N1 CSIs that are dropped by the first node for transmission.

[0086] According to an aspect of the present application, when a second condition is satisfied, the receiver of the first information block drops a second CSI for transmission in a second time domain resource; wherein the second CSI is one of the N1 CSIs; the second condition comprises that the second time domain resource and a first signal overlap in time domain, or the second condition comprises that the second time domain resource comprises at least one DL (downlink) symbol.

[0087] According to an aspect of the present application, the transceiving condition of the N1 CSIs comprises a number of CSIs in the N1 CSIs that are not successfully received by the transmitter of the first information block, or the transceiving condition of the N1 CSIs comprises a number of bits in the N1 CSIs that are not successfully received by the transmitter of the first information block.

[0088] According to an aspect of the present application, it is characterized in that comprising:

[0089] sending a second information block;

[0090] wherein the second information block indicates that a second CSI is not successfully received by the second node, the second CSI being one of the N1 CSIs.

[0091] According to an aspect of the present application, it is characterized in that the reporting of the plurality of CSIs is a plurality of CSI reporting configured by a same CSI reporting configuration.

[0092] According to an aspect of the present application, it is characterized in that comprising:

[0093] receiving a third information block;

[0094] wherein the third information block indicates the first threshold.

[0095] The present application discloses a terminal, characterized in that the terminal comprises one or more processors and a memory;

[0096] 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, and the one or more processors are configured to invoke the computer instructions to cause the terminal to execute the method in the first node.

[0097] As an embodiment, the terminal is a user equipment.

[0098] The present application discloses a base station, characterized in that the base station comprises one or more processors and a memory;

[0099] 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, and the one or more processors are configured to invoke the computer instructions to cause the base station to execute the method in the second node.

[0100] The present application discloses a first node used for wireless communication, characterized in that comprising:

[0101] a first processor configured to receive a first information block; the first information block indicating reporting of a plurality of CSIs; and transmit a target CSI in a first time domain resource only when a first condition is satisfied;

[0102] wherein the plurality of CSIs comprises N1 CSIs and the target CSI, N1 being a positive integer; the first condition comprises that the first value is less than a first threshold; and the first value depends on a transceiving condition of the N1 CSIs.

[0103] A second node for wireless communication is disclosed, comprising:

[0104] a second processor configured to transmit a first information block; the first information block indicates reporting of a plurality of CSIs; the plurality of CSIs comprises N1 CSIs and a target CSI, N1 is a positive integer; and the first processor is configured to receive a first CSI on a first time domain resource when the target CSI is transmitted by a receiver of the first information block on the first time domain resource.

[0105] wherein the receiver of the first information block transmits the target CSI in a first time domain resource only when a first condition is satisfied; the first condition comprises that a first value is less than a first threshold; and the first value depends on a reception and transmission status of the N1 CSIs.

[0106] As an embodiment, compared with the conventional scheme, the present application has the following advantages:

[0107] -ensuring consistency of understanding of CSI reporting configuration and processing by the transmitter and receiver;

[0108] -supporting AI-based CSI reporting;

[0109] -supporting multiple associated CSI reporting;

[0110] -minimizing CSI reporting as much as possible to reduce the transmission load of the system;

[0111] -improving the accuracy of CSI reporting, reducing reporting delay and overhead;

[0112] -better adapting to various different application scenarios or terminals, improving the flexibility and adaptability of the system;

[0113] -enhancing the completeness, reliability and robustness of the system;

[0114] -improving the forward and backward compatibility of the system;

[0115] -simplifying system design and reducing the complexity of scheme implementation;

[0116] -improving the overall performance of the system. BRIEF DESCRIPTION OF DRAWINGS

[0117] Other features, objects and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments thereof, read in conjunction with the accompanying drawings:

[0118] Fig. 1 shows a flowchart of a first information block, a first condition and a target CSI according to an embodiment of the present application;

[0119] FIG. 2 illustrates a schematic diagram of a network architecture, according to one embodiment of the application;

[0120] FIG. 3 illustrates a schematic diagram of an embodiment of a wireless protocol architecture for a user plane and a control plane, according to one embodiment of the application;

[0121] FIG. 4 illustrates a schematic diagram of a first communication device and a second communication device, according to one embodiment of the application;

[0122] FIG. 5 illustrates a flow diagram of a wireless transmission, according to one embodiment of the application;

[0123] FIG. 6 illustrates a schematic diagram of a plurality of CSI, according to one embodiment of the application;

[0124] FIG. 7 illustrates a schematic diagram of a first CSI and a target CSI, according to one embodiment of the application;

[0125] FIG. 8 illustrates a schematic diagram of a transmission and reception of N1 CSI, according to one embodiment of the application;

[0126] FIG. 9 illustrates a schematic diagram of a relationship between a second condition and a second CSI, according to one embodiment of the application;

[0127] FIG. 10 illustrates a schematic diagram of a transmission and reception of N1 CSI, according to another embodiment of the application;

[0128] FIG. 11 illustrates a schematic diagram of a second information block, according to one embodiment of the application;

[0129] FIG. 12 illustrates a schematic diagram of a CSI reporting configuration, according to one embodiment of the application;

[0130] FIG. 13 illustrates a schematic diagram of a first given inference, according to one embodiment of the application;

[0131] FIG. 14A illustrates a schematic diagram of a RAN (Radio Access Network) domain AI / ML function deployment, according to one embodiment of the application;

[0132] FIG. 14B illustrates a schematic diagram of a UE AI / ML function deployment, according to one embodiment of the application;

[0133] FIGS. 15A-15B respectively illustrate a schematic diagram of a deployment of a first given inference, according to one embodiment of the application;

[0134] FIG. 16 illustrates a schematic diagram of an artificial intelligence or machine learning based processing system, according to one embodiment of the application;

[0135] FIG. 17 shows a structural block diagram of a processing device in a first node according to an embodiment of the present application;

[0136] FIG. 18 shows a structural block diagram of a processing device in a second node according to an embodiment of the present application. DETAILED DESCRIPTION

[0137] 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 combine the embodiments in different drawings flexibly without conflict, for example, but not limited to, the embodiments in FIG. 1 and the embodiments in FIGS. 5-18, the embodiments in FIG. 5 and the embodiments in FIGS. 6-18, etc.

[0138] Embodiment 1

[0139] Embodiment 1 illustrates a flowchart of a first information block, a first condition and a target CSI according to an embodiment of the present application, as shown in FIG. 1. In 100 shown in FIG. 1, each block represents a step. In particular, the order of the steps in the blocks does not represent a specific time sequence between the steps.

[0140] In embodiment 1, the first node in the present application receives a first information block in step 101; only when a first condition is met, a target CSI is sent in a first time domain resource in step 102;

[0141] Wherein, the first information block indicates the reporting of multiple CSIs; the multiple CSIs include N1 CSIs and the target CSI, N1 is a positive integer; the first condition includes that the first value is less than a first threshold; the first value depends on the reception and transmission of the N1 CSIs.

[0142] As an embodiment, the first information block is carried by a higher layer signaling.

[0143] As an embodiment, the first information block is carried by a Radio Resource Control (RRC) signaling.

[0144] As an embodiment, the first information block includes part or all of the fields in at least one RRC Information Element (IE).

[0145] As an embodiment, the first information block includes a MAC CE.

[0146] As an embodiment, the first information block is carried by RRC signaling and MAC (Medium Access Control) CE (Control Element) signaling.

[0147] As an embodiment, the first information block comprises DCI (Downlink Control Information).

[0148] As an embodiment, the first information block comprises at least one field in DCI (Downlink Control Information).

[0149] As an embodiment, the first information block comprises Time domain resource assignment field in DCI.

[0150] As an embodiment, the first information block comprises PUCCH resource indicator field in DCI.

[0151] As an embodiment, the first information block comprises one or more IE CSI-ReportConfig.

[0152] As an embodiment, the first information block comprises part or all fields in one or more IE CSI-ReportConfig.

[0153] As an embodiment, the first information block comprises part or all fields in IE ServingCellConfig.

[0154] As an embodiment, the first information block comprises part or all fields in IE CSI-MeasConfig IE.

[0155] As an embodiment, the first information block comprises part or all fields in IE ServingCellConfigCommon IE.

[0156] As an embodiment, the first information block comprises part or all fields in IE ServingCellConfig.

[0157] As an embodiment, the first information block indicating the reporting of multiple CSIs comprises: the first information block indicating an identity of the reporting of the multiple CSIs.

[0158] As an embodiment, the first information block indicating the reporting of the plurality of CSIs comprises: the first information block is used to configure the reporting of the plurality of CSIs.

[0159] As an embodiment, the first information block indicating the reporting of the plurality of CSIs comprises: the first information block comprises a first CSI reporting configuration, and the first CSI reporting configuration is used to configure the reporting of the plurality of CSIs.

[0160] As an embodiment, the first information block indicating the reporting of the plurality of CSIs comprises: the first information block indicates a first CSI reporting configuration, and the first CSI reporting configuration is used to configure the reporting of the plurality of CSIs.

[0161] As an embodiment, the first information block indicating the reporting of the plurality of CSIs comprises: the first information block comprises a plurality of CSI reporting configurations, and the plurality of CSI reporting configurations are respectively used to configure the reporting of the plurality of CSIs.

[0162] As an embodiment, the first information block indicating the reporting of the plurality of CSIs comprises: the first information block indicates a plurality of CSI reporting configurations, and the plurality of CSI reporting configurations are respectively used to configure the reporting of the plurality of CSIs.

[0163] As an embodiment, the one CSI reporting configuration in the present application comprises part or all of the fields in the CSI-ReportConfig IE.

[0164] As an embodiment, the one CSI reporting configuration in the present application comprises part or all of the fields in the ServingCellConfig IE.

[0165] As an embodiment, the one CSI reporting configuration in the present application comprises part or all of the fields in the CSI-MeasConfig IE.

[0166] As an embodiment, the one CSI reporting configuration in the present application comprises part or all of the fields in the ServingCellConfigCommon IE.

[0167] As an embodiment, the one CSI reporting configuration in the present application comprises part or all of the fields in the ServingCellConfigCommonSIB IE.

[0168] As an embodiment, the first information block indicating the reporting of the plurality of CSIs comprises: the first information block comprises a MAC CE, and the first information block activates the reporting of the plurality of CSIs.

[0169] As an embodiment, the first information block indicating the reporting of the multiple CSIs comprises: the first information block comprising at least one field in DCI, the first information block triggering the reporting of the multiple CSIs.

[0170] As an embodiment, the first information block indicating the reporting of the multiple CSIs comprises: the first information block comprising a CSIrequest field in DCI, the first information block triggering the reporting of the multiple CSIs.

[0171] As an embodiment, the above method has the benefits of: simplifying system design, improving system flexibility.

[0172] As an embodiment, the above method has the benefits of: improving system forward and backward compatibility.

[0173] As an embodiment, the first time-domain resource is indicated for uplink channel transmission.

[0174] As an embodiment, the first time-domain resource is indicated for PUSCH (Physical Uplink Shared Channel) transmission.

[0175] As an embodiment, the first time-domain resource is indicated for PUCCH (Physical Uplink Control Channel) transmission.

[0176] As an embodiment, the first time-domain resource occupies one or more symbols in time domain.

[0177] As an embodiment, the first time-domain resource occupies one or more symbols in a slot in time domain.

[0178] As an embodiment, the symbol is a single-carrier symbol.

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

[0180] As an embodiment, the multi-carrier symbol is an OFDM (Orthogonal Frequency Division Multiplexing) symbol.

[0181] As an embodiment, the symbol is obtained after OFDM symbol generation from the output of transform precoding.

[0182] As one embodiment, the multi-carrier symbol is a SC-FDMA (Single Carrier-Frequency Division Multiple Access) symbol.

[0183] As one embodiment, the multi-carrier symbol is a DFT-S-OFDM (Discrete Fourier Transform Spread OFDM) symbol.

[0184] As one embodiment, the multi-carrier symbol is a FBMC (Filter Bank Multi Carrier) symbol.

[0185] As one embodiment, the multi-carrier symbol comprises a CP (Cyclic Prefix).

[0186] As one embodiment, the above method has the benefit of reusing existing system design and standards.

[0187] As one embodiment, the first information block indicates the first time-domain resource.

[0188] As one embodiment, the first information block comprises at least one configuration, the at least one configuration indicating the first time-domain resource.

[0189] As one embodiment, the reporting configuration of the target CSI indicates the first time-domain resource.

[0190] As one embodiment, the plurality of CSIs are periodic.

[0191] As one embodiment, the plurality of CSIs are semi-persistent.

[0192] As one embodiment, the plurality of CSIs are aperiodic.

[0193] As one embodiment, the plurality of CSIs are event-triggered.

[0194] As one embodiment, the target CSI is periodic.

[0195] As one embodiment, the target CSI is semi-persistent.

[0196] As one embodiment, the target CSI is aperiodic.

[0197] As one embodiment, the target CSI is event triggered.

[0198] As one embodiment, transmitting one CSI on one time domain resource includes that the one CSI is used to generate a signal transmitted on the one time domain resource after channel coding.

[0199] As one embodiment, transmitting one CSI on one time domain resource includes that the one CSI is used to generate a signal transmitted on the one time domain resource after channel coding and modulation.

[0200] As one embodiment, transmitting one CSI on one time domain resource includes that the one CSI is used to generate a signal transmitted on the one time domain resource after bit sequence generation and channel coding.

[0201] As one embodiment, transmitting one CSI on one time domain resource includes that the one CSI is used to generate a signal transmitted on the one time domain resource after bit sequence generation, channel coding and modulation.

[0202] As one embodiment, transmitting one CSI on one time domain resource includes that the one CSI is used to generate a signal transmitted on the one time domain resource after bit sequence generation, code block segmentation and CRC attachment, channel coding, rate matching and code block concatenation.

[0203] As one embodiment, transmitting one CSI on one time domain resource includes that the one CSI is multiplexed to the one time domain resource.

[0204] As one embodiment, transmitting one CSI on one time domain resource includes that the one CSI is multiplexed to the one time domain resource after bit sequence generation, code block segmentation and CRC attachment, channel coding, rate matching and code block concatenation.

[0205] As one embodiment, the above method has the benefit of small change to current standards and system design, and improves the forward and backward compatibility of the system.

[0206] As one embodiment, the above method has the benefit of improving the flexibility of the scheme and system.

[0207] As an embodiment, the CSI described in this application comprises at least one of beam failure prediction and beam switch prediction.

[0208] As an embodiment, the CSI described in this application comprises at least one of predicted beam information, switched beam information, predicted CSI, estimated CSI, or compressed CSI.

[0209] As an embodiment, the CSI described in this application comprises at least one of predicted beam information, predicted CSI, estimated CSI, compressed CSI, confidence information, or performance monitoring result.

[0210] As an embodiment, the predicted beam information comprises beam indication or RS resource indication.

[0211] As an embodiment, the predicted beam information comprises beam indication and RSRP (reference signal received power).

[0212] As an embodiment, the predicted beam information comprises RS resource indication and RSRP.

[0213] As an embodiment, the predicted beam information comprises one or more of beam indication, CRI (CSI-RS Resource Indicator), SS / PBCH Block Resource indicator (SSBRI), and RSRP (reference signal received power).

[0214] As an embodiment, the benefit of the above method comprises reducing channel measurement overhead and improving overall system performance.

[0215] As an embodiment, the benefit of the above method comprises improving the accuracy and real-time performance of channel information reporting and improving the overall system performance.

[0216] As an embodiment, the CSI described in this application comprises channel impulse response.

[0217] As an embodiment, the CSI described in this application comprises small-scale characteristics.

[0218] As one embodiment, the CSI described in this application comprises one or more of a delay spread, a Doppler spread, a Doppler shift, a mean delay, or a mean gain.

[0219] As one embodiment, the CSI described in this application comprises a channel matrix.

[0220] As one embodiment, the channel matrix is in a spatial-frequency domain.

[0221] As one embodiment, the channel matrix is in an angular-delay domain projection.

[0222] As one embodiment, the CSI described in this application comprises at least one of an eigenvalue or an eigenvector of a channel.

[0223] As one embodiment, the CSI described in this application comprises a beam indication.

[0224] As one embodiment, the CSI described in this application comprises a beam indication, a CRI (CSI-RS resource indicator), and a L1-RSRP (Layer 1 Reference Signal Received Power).

[0225] As one embodiment, the CSI described in this application comprises one or more of a beam indication, a CRI (CSI-RS resource indicator), a L1-RSRP (Layer 1 Reference Signal Received Power), or a performance parameter related to AI-based CSI reporting.

[0226] As an embodiment, the CSI described in this application comprises one or more of CQI (Channel Quality Indicator), PMI (Precoding Matrix Indicator), CRI (CSI-RS resource indicator), SSBRI (SS / PBCH Block Resource Indicator), LI (Layer Indicator), RI (Rank Indicator), L1-RSRP (Layer 1 Reference Signal Received Power), L1-SINR (Layer 1 Signal-to-Interference and Noise Ratio), or beam information.

[0227] As an embodiment, the CSI described in this application comprises one or more of 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).

[0228] As an embodiment, the CSI described in this application comprises codebook-based channel information.

[0229] As an embodiment, the CSI described in this application comprises non-codebook-based channel information.

[0230] As an embodiment, the CSI described in this application comprises CSI defined by 3GPP Rel-18.

[0231] As an embodiment, the CSI described in this application comprises CSI belonging to 3GPP Rel-19.

[0232] As an embodiment, the CSI described in this application includes AI or machine learning based CSI.

[0233] As an embodiment, the CSI described in this application includes neural network based CSI.

[0234] As an embodiment, the CSI described in this application includes CNN (Conventional Neural Networks) based CSI.

[0235] As an embodiment, the CSI described in this application includes Transformer based CSI.

[0236] As an embodiment, the benefits of the above method include: supporting AI based CSI reporting scheme.

[0237] As an embodiment, the CSI described in this application includes one or more of CQI, PMI, CRI, SSBRI, LI (Layer Indicator), RI (Rank Indicator), L1-RSRP, L1-SINR, beam information, or performance parameters related to AI based CSI reporting.

[0238] As a sub-embodiment of the above embodiment, the performance parameters related to AI based CSI reporting include performance parameters predicted by the AI model.

[0239] As a sub-embodiment of the above embodiment, the performance parameters related to AI based CSI reporting include beam prediction accuracy.

[0240] As a sub-embodiment of the above embodiment, the performance parameters related to AI based CSI reporting include distribution characteristics of AI model input / output data.

[0241] As a sub-embodiment of the above embodiment, the performance parameters related to AI based CSI reporting include the difference between predicted and real L1-RSRP.

[0242] As a sub-embodiment of the above embodiment, the performance parameters related to AI based CSI reporting include the difference between predicted and real L1-SINR.

[0243] As a sub-embodiment of the above embodiment, the performance parameters related to AI based CSI reporting include link or system performance of the AI based CSI reporting scheme.

[0244] As a sub-embodiment of the above-mentioned embodiment, the performance parameter related to the AI-based CSI reporting comprises a link or system performance of the AI-based CSI reporting scheme estimated by the first node.

[0245] As an embodiment, the essence of the above-mentioned method comprises monitoring the AI model based on the performance parameter related to the AI-based CSI reporting.

[0246] As an embodiment, the benefit of the above-mentioned method comprises improving the performance of the AI-based CSI reporting scheme and improving the overall performance of the system.

[0247] As an embodiment, the plurality of time domain resources are respectively used for reporting the plurality of CSIs, and the plurality of time domain resources respectively belong to different time slots.

[0248] As an embodiment, the plurality of time domain resources are respectively used for reporting the plurality of CSIs, and the plurality of time domain resources are mutually orthogonal.

[0249] As an embodiment, the plurality of time domain resources are respectively used for reporting the plurality of CSIs, and any time domain resource of the plurality of time domain resources comprises one or more continuous symbols.

[0250] As an embodiment, the plurality of time domain resources are respectively used for reporting the plurality of CSIs, and the plurality of time domain resources are continuous.

[0251] As an embodiment, the plurality of time domain resources are respectively used for reporting the plurality of CSIs, and the plurality of time domain resources are periodic.

[0252] As an embodiment, the plurality of time domain resources are respectively used for reporting the plurality of CSIs, and the plurality of time domain resources are non-continuous.

[0253] As an embodiment, the plurality of time domain resources are respectively used for reporting the plurality of CSIs, and the plurality of time domain resources are equally spaced.

[0254] As an embodiment, the plurality of time domain resources are respectively used for reporting the plurality of CSIs, and the interval between any two adjacent time domain resources of the plurality of time domain resources is P time domain resources, and P is a positive integer.

[0255] As a sub-embodiment of the above-mentioned embodiment, the P is determined by the first node.

[0256] As a sub-embodiment of the above-mentioned embodiment, the P is reported by the first node to the sender of the first information block.

[0257] As a sub-example of the above embodiment, the P is sent by the sender of the first information block to the first node.

[0258] As a sub-example of the above embodiment, the first information block indicates the P.

[0259] As an example, benefits of the above method include: enhancing flexibility of system and scheme, adapting to different transmission environments and application scenarios.

[0260] As an example, the plurality of CSI includes N1 CSI and the target CSI, and the N1 CSI is a preceding CSI of the target CSI.

[0261] As an example, the plurality of CSI includes N1 CSI and the target CSI, and the target CSI is sent later than the N1 CSI.

[0262] As an example, the plurality of CSI includes N1 CSI and the target CSI, and the target CSI is a CSI after the N1 CSI.

[0263] Typically, the after refers to later in time domain.

[0264] As an example, the plurality of CSI includes N1 CSI and the target CSI, and a time domain resource corresponding to the target CSI is later than a time domain resource corresponding to any CSI in the N1 CSI.

[0265] As an example, the plurality of CSI includes N1 CSI and the target CSI, and a time slot corresponding to the target CSI is later than a time slot corresponding to any CSI in the N1 CSI.

[0266] As an example, benefits of the above method include: supporting multiple continuous CSI reporting.

[0267] As an example, benefits of the above method include: improving performance of CSI reporting, and improving overall performance of system.

[0268] As an example, the plurality of CSI includes N1 CSI and the target CSI, and generation of the target CSI is later than generation of the N1 CSI.

[0269] As an example, the plurality of CSI includes N1 CSI and the target CSI, and generation of the target CSI depends on the N1 CSI.

[0270] As one embodiment, the plurality of CSIs includes N1 CSIs and the target CSI, and the target CSI is generated depending on a last one of the N1 CSIs.

[0271] As one embodiment, the method has advantages including supporting a plurality of associated CSI reporting.

[0272] As one embodiment, the method has advantages including improving CSI reporting performance and improving overall system performance.

[0273] As one embodiment, a plurality of time domain resources are respectively indicated to the first node for reporting the plurality of CSIs, the first time domain resource is a time domain resource of the plurality of time domain resources indicated for reporting the target CSI, and the plurality of time domain resources are orthogonal to each other.

[0274] As one embodiment, a plurality of time domain resources are respectively indicated to the first node for reporting the plurality of CSIs, the first time domain resource is a time domain resource of the plurality of time domain resources indicated for reporting the target CSI, and the plurality of time domain resources are orthogonal to each other.

[0275] As one embodiment, a plurality of time domain resources are respectively indicated to the first node for reporting the plurality of CSIs, the plurality of time domain resources are orthogonal to each other, the first time domain resource is a time domain resource of the plurality of time domain resources indicated for reporting the target CSI, and the first time domain resource is later than a time domain resource of the plurality of time domain resources indicated for any one of the N1 CSIs.

[0276] Typically, the first time domain resource is a time domain resource of the plurality of time domain resources indicated for reporting the target CSI.

[0277] As one embodiment, the target CSI is any one of the non-first CSIs of the plurality of CSIs.

[0278] As one embodiment, the target CSI is any one of the non-first CSIs of the plurality of CSIs.

[0279] As one embodiment, the target CSI is a last one of the plurality of CSIs.

[0280] As one embodiment, the reporting of the N1 CSIs is earlier than the reporting of the target CSI.

[0281] As one embodiment, the plurality of CSIs consists of N1 CSIs and the target CSI.

[0282] As an example, the plurality of CSI includes at least N1 CSI and the target CSI.

[0283] As an example, the plurality of CSI includes at least N1 CSI and the target CSI, the N1 CSI is reported earlier than the target CSI.

[0284] As an example, the first value is an integer.

[0285] As an example, the first value is a non-negative integer.

[0286] As an example, the first value is a non-negative real number.

[0287] As an example, the first value is a value of a counter.

[0288] As an example, the first value is a value of a counter of the first node.

[0289] As an example, the benefit of the above method includes: improving the freedom of the first node.

[0290] As an example, the benefit of the above method includes: reducing the information reporting of the first node, reducing the system resource consumption.

[0291] As an example, the first value is a value of a counter of the second node.

[0292] As an example, the benefit of the above method includes: reducing the processing capability requirement and power consumption of the first node.

[0293] As an example, the first value is equal to the sum of two values.

[0294] As an example, the first value is equal to the sum of the values of two counters.

[0295] As an example, the first value is equal to the sum of the value of a counter of the first node and the value of a counter of the second node.

[0296] As an example, the benefit of the above method includes: improving the reliability and globality of the scheme and information.

[0297] As an example, the benefit of the above method includes: improving the completeness of the scheme.

[0298] As an example, the first value depends on the count of the transmission and reception of the N1 CSI.

[0299] As one embodiment, the first value is equal to a count of the N1 CSI transmissions.

[0300] As one embodiment, the first value is dependent on a change of the N1 CSI transmissions.

[0301] As one embodiment, the first value is dependent on an increment of the N1 CSI transmissions.

[0302] As one embodiment, the first value is dependent on the N1 CSI transmissions between the first node and the second node.

[0303] As one embodiment, the first value is dependent on a count of the N1 CSI transmissions between the first node and the second node.

[0304] As one embodiment, the first value is equal to a count of the N1 CSI transmissions between the first node and the second node.

[0305] As one embodiment, the first value is dependent on a change of the N1 CSI transmissions between the first node and the second node.

[0306] As one embodiment, the first value is dependent on an increment of the N1 CSI transmissions between the first node and the second node.

[0307] As one embodiment, the system design is simplified and the flexibility of the system and the scheme is improved.

[0308] As one embodiment, the N1 CSI transmissions include a number of the N1 CSI that are dropped by the first node.

[0309] As one embodiment, the N1 CSI transmissions include a number of bits of the N1 CSI that are dropped by the first node.

[0310] As one embodiment, the N1 CSI transmissions include a number of the N1 CSI that are not successfully received by a transmitter of the first information block.

[0311] As one embodiment, the N1 CSI transmissions include a number of bits of the N1 CSI that are not successfully received by a transmitter of the first information block.

[0312] As one embodiment, the N1 CSI transmissions include a number of the N1 CSI that are dropped by the first node and a number of the N1 CSI that are not successfully received by a transmitter of the first information block.

[0313] As an example, the transceiving status of the N1 CSIs includes a number of bits in the N1 CSIs that are dropped by the first node for transmission and a number of bits in the N1 CSIs that are not successfully received by a transmitter of the first information block.

[0314] As an example, the above method has the benefits of improving the reliability and robustness of the system and the scheme.

[0315] As an example, the first threshold is an integer.

[0316] As an example, the first threshold is a non-negative integer.

[0317] As an example, the first threshold is a non-negative real number.

[0318] As an example, the first threshold is predefined.

[0319] As an example, the first threshold is configurable.

[0320] As an example, the first threshold is configured by the second node.

[0321] As an example, the higher layer signaling indicates the first threshold.

[0322] As an example, the MAC CE signaling indicates the first threshold.

[0323] As an example, the transmitter of the first information block indicates the first threshold to the first node.

[0324] As an example, the first information block indicates the first threshold.

[0325] As an example, the first information block includes the first threshold.

[0326] As an example, the first threshold is reported depending on the capability of the UE (user equipment).

[0327] As an example, the first threshold belongs to the capability information of the first node.

[0328] As an example, the first node transmits a third information block; the third information block indicates the first threshold.

[0329] As an example, the above method has the benefits of simplifying the system design and reducing the implementation complexity of the scheme.

[0330] As an embodiment, benefits of the above method include: improving flexibility of the system, adapting to different transmission environments and application scenarios.

[0331] As an embodiment, benefits of the above method include: supporting terminals with different UE capabilities.

[0332] As an embodiment, the third information block is carried by higher layer signaling.

[0333] As an embodiment, the third information block includes one or more fields in one or more IEs (information elements).

[0334] As an embodiment, the third information block includes a MAC CE.

[0335] As an embodiment, the third information block includes control information.

[0336] As an embodiment, the third information block includes UCI (uplink control information).

[0337] As an embodiment, the third information block is carried by physical layer signaling.

[0338] As an embodiment, the third information block is carried by physical layer uplink signaling.

[0339] As an embodiment, the third information block is transmitted on a physical layer channel.

[0340] As an embodiment, the third information block is transmitted on a physical layer uplink channel.

[0341] As an embodiment, the third information block is transmitted on a PUCCH (Physical Uplink Control Channel).

[0342] As an embodiment, the third information block is transmitted on a PUSCH (Physical Uplink Shared Channel).

[0343] As an embodiment, the third information block belongs to capability information of the first node.

[0344] As an embodiment, the third information block includes capability information of the first node.

[0345] As an embodiment, the third information block includes one or more capability parameters of the first node.

[0346] As an embodiment, the third information block comprises one or more fields in one UE (user equipment) capability IE (information element).

[0347] As an embodiment, the third information block comprises one or more fields in one or more UE (user equipment) capability IE (information element).

[0348] As an embodiment, the third information block comprises one or more parameters in one or more UE (user equipment) capability IE.

[0349] As an embodiment, the first node transmits the capability information of the first node after receiving a UECapabilityEnquiry from the network, and the third information block belongs to the capability information of the first node.

[0350] As an embodiment, the capability information of the first node comprises a UECapabilityInformation.

[0351] As an embodiment, the capability information of the first node comprises a radio access capability of the first node.

[0352] As an embodiment, the benefits of the above method comprise: supporting terminals with different UE capabilities.

[0353] As an embodiment, the benefits of the above method comprise: improving the stability and robustness of the system.

[0354] As an embodiment, the third information block explicitly indicates the first threshold.

[0355] As an embodiment, the third information block implicitly indicates the first threshold.

[0356] As an embodiment, the third information block directly indicates the first threshold.

[0357] As an embodiment, the third information block indirectly indicates the first threshold.

[0358] As an embodiment, the third information block comprises the first threshold.

[0359] As an embodiment, the third information block indicates the first threshold from candidate values of the first threshold.

[0360] As an embodiment, the third information block indicates an index of the first threshold in candidate values of the first threshold.

[0361] As an embodiment, the third information block indicates an identity of the first threshold in candidate values of the first threshold.

[0362] As an embodiment, the third information block indicates a sequence number of the first threshold in candidate values of the first threshold.

[0363] As an embodiment, the above method has the advantages of simplifying system design and reducing system complexity.

[0364] As an embodiment, the first condition is that the first value is less than the first threshold.

[0365] As an embodiment, the first condition includes that the first value is equal to or less than the first threshold.

[0366] As an embodiment, the first condition further includes that the first value is equal to the first threshold.

[0367] As an embodiment, the first condition includes a plurality of sub-conditions, and the first sub-condition is one of the plurality of sub-conditions, the first sub-condition includes that the first value is less than the first threshold; when one of the plurality of sub-conditions is not satisfied, the first condition is not satisfied; when all of the plurality of sub-conditions are satisfied, the first condition is satisfied.

[0368] As a sub-embodiment of the above embodiment, the first sub-condition includes that the first value is equal to or less than the first threshold.

[0369] As a sub-embodiment of the above embodiment, the second sub-condition is one of the plurality of sub-conditions, and the second sub-condition includes that the first time domain resource is not disabled.

[0370] As a sub-embodiment of the above embodiment, the second sub-condition is one of the plurality of sub-conditions, and the second sub-condition is that the first time domain resource is used for uplink (UL) transmission.

[0371] As a sub-embodiment of the above embodiment, the second sub-condition is one of the plurality of sub-conditions, and the second sub-condition includes that the first time domain resource and other signals do not overlap in time domain.

[0372] As one sub-example of the above embodiment, the second sub-condition is one of the multiple sub-conditions, and the second sub-condition comprises that the first time-domain resource does not include any DL (downlink) symbol.

[0373] As one sub-example of the above embodiment, the second sub-condition is one of the multiple sub-conditions, and the second sub-condition comprises that the first time-domain resource does not include any DL (downlink) symbol.

[0374] As one embodiment, the above method has the benefits of improving the stability and robustness of the system and the scheme.

[0375] As one embodiment, the above method has the benefits of improving the overall performance of the system.

[0376] Embodiment 2

[0377] Embodiment 2 illustrates a schematic diagram of a network architecture according to one embodiment of the present application, as shown in FIG. 2.

[0378] FIG. 2 illustrates a network architecture 200. The network architecture 200 is a 5G NR (New Radio) / LTE (Long-Term Evolution) / LTE-A (Long-Term Evolution Advanced) system, or the network architecture 200 is a 5G+ network architecture, or the network architecture 200 is a 6G network architecture, or the network architecture 200 is a network architecture adopted in 3GPP future continued evolution; the network architecture 200 can be referred to as 5GS (5G System) / EPS (Evolved Packet System), or the network architecture 200 can be referred to as 6GS (6G System); the network architecture 200 includes a UE (User Equipment) 201, a RAN (Radio Access Network) 202, a core network 210, a HSS (Home Subscriber Server) / UDM (Unified Data Management) 220, and at least one of an Internet service 230. The network architecture 200 can be interconnected with other access networks, but these entities / interfaces are not shown for simplicity. As illustrated, the network architecture 200 provides packet-switched services, however, those skilled in the art will readily appreciate that the various concepts presented throughout this application are amenable to use with networked systems including, but not limited to, other cellular systems, wireless or wired packet-switched network systems, or other mobile communication systems. The RAN includes a node 203. The RAN can also include other nodes 204. The node 203 provides user and control plane protocol terminations toward the UE 201. The node 203 can be connected to the other nodes 204 via an Xn interface (e.g., backhaul) / X2 interface. The node 203 can also be referred to as a base station, a base transceiver station, a radio base station, a radio transceiver, a transceiver function, a basic service set (BSS), an extended service set (ESS), a TRP (Transmit Receive Point), or some other suitable terminology. The core network 210 is a 5GC (5G Core Network) / EPC (Evolved Packet Core), or the core network 210 is a 6GC; the node 203 provides an access point to the core network 210 for the UE 201.Examples of a UE 201 include a cellular phone, a smart phone, a session initiation protocol (SIP) phone, a laptop, a personal digital assistant (PDA), a satellite radio, a non-tethered base station communication, a satellite mobile communication, a global positioning system, a multimedia device, a video device, a digital audio player (e.g., MP3 player), a camera, a game console, a drone, a flying vehicle, a narrowband internet of things device, a machine type communication device, a land vehicle, a car, a wearable device, or any other similar functional device. Those skilled in the art will also The node 203 is connected by an S1 / NG interface to the core network 210. The core network 210 includes a MME (Mobility Management Entity) / AMF (Authentication Management Field) / SMF (Session Management Function) 211, other MME / AMF / SMF 214, a S-GW (Service Gateway) / UPF (User Plane Function) 212, and a P-GW (Packet Date Network Gateway) / UPF 213. The MME / AMF / SMF 211 is a control node that handles signaling between the UE 201 and the core network 210. Generally, the MME / AMF / SMF 211 provides bearer and connection management. All user IP (Internet Protocal) packets are transferred through the S-GW / UPF 212, which itself is connected to the P-GW / UPF 213. The P-GW provides UE IP address allocation as well as other functions. The P-GW / UPF 213 is connected to the Internet services 230. The Internet services 230 include operator corresponding Internet protocol services, which can specifically include the Internet, an intranet, an IMS (IP Multimedia Subsystem), and a packet switching service.

[0379] As one embodiment, the first node comprises the UE 201.

[0380] As one embodiment, the second node comprises the node 203.

[0381] As one embodiment, the wireless link between the UE 201 and the node 203 comprises a cellular network link.

[0382] As one embodiment, the sender of the first information block comprises the node 203.

[0383] As one embodiment, the receiver of the first information block comprises the UE 201.

[0384] As one embodiment, the sender of the target CSI comprises the UE 201.

[0385] As one embodiment, the receiver of the target CSI comprises the node 203.

[0386] As one embodiment, the sender of the first CSI comprises the UE 201.

[0387] As one embodiment, the receiver of the first CSI comprises the node 203.

[0388] As one embodiment, the sender of the second CSI comprises the UE 201.

[0389] As one embodiment, the receiver of the second CSI comprises the node 203.

[0390] As one embodiment, the sender of the N1 CSIs comprises the UE 201.

[0391] As one embodiment, the receiver of the N1 CSIs comprises the node 203.

[0392] As one embodiment, the sender of the plurality of CSIs comprises the UE 201.

[0393] As one embodiment, the receiver of the plurality of CSIs comprises the node 203.

[0394] As one embodiment, the performer of the first inference comprises the UE 201.

[0395] As one embodiment, the performer of the second inference comprises the UE 201.

[0396] Embodiment 3

[0397] Embodiment 3 illustrates a diagram of an embodiment of a radio protocol architecture for the user and control planes according to one embodiment of the application, as shown in FIG. 3.

[0398] Figure 3 is a schematic diagram illustrating an embodiment of a radio protocol architecture for a user plane 350 and a control plane 300, Figure 3 showing three layers of the radio protocol architecture for the control plane 300 between a first communication node device (UE, gNB or RSU in V2X) and a second communication node device (gNB, UE or RSU in V2X), or between two UEs: Layer 1, Layer 2, and Layer 3. Layer 1 (LI layer) is the lowest layer and implements various PHY (Physical layer) signal processing functions. The LI layer will be referred to as the PHY 301 herein. Layer 2 (L2 layer) 305 is above the PHY 301 and is responsible for the link between the first communication node device and the second communication node device, or between two UEs. The L2 layer 305 includes a MAC (Medium Access Control) sublayer 302, a RLC (Radio Link Control) sublayer 303, and a PDCP (Packet Data Convergence Protocol) sublayer 304, which terminate the functions of the second communication node device. The PDCP sublayer 304 provides multiplexing between different radio bearers and logical channels. The PDCP sublayer 304 also provides security functions, such as ciphering of the data packets, and header compression. The RLC sublayer 303 provides segmentation and reassembly of upper layer data packets, retransmission of lost data packets, and reordering of data packets to compensate for out-of-order reception due to HARQ. The MAC sublayer 302 provides multiplexing between logical and transport channels. The MAC sublayer 302 is also responsible for allocating the various radio resources (e.g., resource blocks) in one cell among the UEs. The MAC sublayer 302 is also responsible for HARQ operations. The RRC (Radio Resource Control) sublayer 306 in Layer 3 (L3 layer) in the control plane 300 is responsible for obtaining radio resources (i.e., radio bearers) and the use of RRC signaling between the second communication node device and the first communication node device for configuring the lower layers. The radio protocol architecture for the user plane 350 includes Layer 1 (LI layer) and Layer 2 (L2 layer), which are substantially the same as the corresponding layers and sublayers in the control plane 300 for the physical layer 351, the PDCP sublayer 354 in the L2 layer 355, the RLC sublayer 353 in the L2 layer 355, and the MAC sublayer 352 in the L2 layer 355 for the first communication node device and the second communication node device, but the PDCP sublayer 354 also provides header compression for upper layer data packets to reduce radio transmission overhead.The L2 layer 355 in the user plane 350 also includes a SDAP (Service Data Adaptation Protocol) sublayer 356, which is responsible for the mapping between a QoS flow and a data radio bearer (DRB) to support the diversity of services. Although not illustrated, the first communication node device can have several upper layers above the L2 layer 355, including a network layer (e.g., IP layer) that terminates at a P-GW on the network side and an application layer that terminates at the other end of the connection (e.g., a remote UE, a server, etc.).

[0399] As one embodiment, the wireless protocol architecture in FIG. 3 is applicable to the first node in the present application.

[0400] As one embodiment, the wireless protocol architecture in FIG. 3 is applicable to the second node in the present application.

[0401] As one embodiment, the higher layer in the present application refers to a layer above the physical layer.

[0402] As one embodiment, the first information block is generated at the RRC 306.

[0403] As one embodiment, the first information block is generated at the MAC sublayer 302 or the MAC sublayer 352.

[0404] As one embodiment, the target CSI is generated at the MAC 302 or the MAC 352.

[0405] As one embodiment, the target CSI is generated at the PHY 301 or the PHY 351.

[0406] As one embodiment, the first CSI is generated at the MAC 302 or the MAC 352.

[0407] As one embodiment, the first CSI is generated at the PHY 301 or the PHY 351.

[0408] As one embodiment, the second CSI is generated at the MAC 302 or the MAC 352.

[0409] As one embodiment, the second CSI is generated at the PHY 301 or the PHY 351.

[0410] As one embodiment, the N1 CSIs are generated at the MAC 302 or the MAC 352.

[0411] As one embodiment, the N1 CSIs are generated at the PHY 301 or the PHY 351.

[0412] As one embodiment, the multiple CSIs are generated at the MAC 302 or the MAC 352.

[0413] As one embodiment, the multiple CSIs are generated at the PHY 301 or the PHY 351.

[0414] Embodiment 4

[0415] Embodiment 4 illustrates a schematic diagram of a first communication device and a second communication device according to one embodiment of the application, as shown in FIG. 4. FIG. 4 is a block diagram of a first communication device 410 and a second communication device 450 in communication with each other in an access network.

[0416] The first communication device 410 includes a controller / processor 475, a memory 476, a receive processor 470, a transmit processor 416, a multiple antenna receive processor 472, a multiple antenna transmit processor 471, a transmitter / receiver 418, and an antenna 420.

[0417] The second communication device 450 includes a controller / processor 459, a memory 460, a data source 467, a transmit processor 468, a receive processor 456, a multiple antenna transmit processor 457, a multiple antenna receive processor 458, a transmitter / receiver 454, and an antenna 452.

[0418] In transmissions from the first communication device 410 to the second communication device 450, at the first communication device 410, upper layer packets from the core network are provided to the controller / processor 475. The controller / processor 475 implements functionality of the L2 layer. In the DL, the controller / processor 475 provides header compression, ciphering, packet segmentation and reordering, multiplexing between logical and transport channels, and radio resource allocations for the second communication device 450 based on various priority metrics. The controller / processor 475 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the second communication device 450. The transmit processor 416 and the multiple antenna transmit processor 471 implement various signal processing functions for the LI layer (i.e., physical layer). The transmit processor 416 implements coding and interleaving to facilitate forward error correction (FEC) at the second communication device 450 and maps the coded and interleaved data to modulation symbols based on various modulation schemes (e.g., binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), M-phase shift keying (M-PSK), M-quadrature amplitude modulation (M-QAM)). The multiple antenna transmit processor 471 performs digital spatial pre-coding on the modulated symbols, including codebook-based and non-codebook-based pre-coding, and beamforming processing, generating one or more parallel streams. The transmit processor 416 then maps each parallel stream to a subcarrier, multiplexes the modulated symbols in the time and / or frequency domain with reference signals (e.g., pilot), and then performs a fast Fourier transform (FFT) to generate a time-domain multicarrier symbol stream. The multiple antenna transmit processor 471 then performs transmit analog pre-coding / beamforming operations on the time-domain multicarrier symbol stream. Each transmitter 418 converts the baseband multicarrier symbol stream provided by the multiple antenna transmit processor 471 into a radio frequency signal that is transmitted via a respective antenna 420.

[0419] In transmission from the first communication device 410 to the second communication device 450, at the second communication device 450, each receiver 454 receives a signal through its respective antenna 452. Each receiver 454 recovers information modulated onto an RF carrier and converts the RF stream into a baseband, multicarrier symbol stream to receive processor 456. The receive processor 456 and the multi-antenna receive processor 458 implement various signal processing functions of the Ll layer. The multi-antenna receive processor 458 performs receive analog precoding / beamforming operation on the baseband, multicarrier symbol stream from the receivers 454. The receive processor 456 converts the baseband, multicarrier symbol stream from the receive analog precoding / beamforming operation into the time domain using a Fast Fourier Transform (FFT). In the time domain, a physical layer data signal and the reference signal are demultiplexed from the baseband, multicarrier symbol stream by the receive processor 456, where the reference signal will be used for channel estimation and the data signal is recovered after multi-antenna detection in the multi-antenna receive processor 458 for any parallel streams destined for the second communication device 450. The symbols on each parallel stream are demodulated and recovered in the receive processor 456 and generate soft decisions. The receive processor 456 then decodes and de-interleaves the soft decisions to recover the upper layer data and control signals transmitted by the first communication device 410 on the physical channels. The upper layer data and control signals are then provided to the controller / processor 459. The controller / processor 459 implements the functions of the L2 layer. The controller / processor 459 can be associated with a memory 460 that stores program codes and data. The memory 460 can be referred to as a computer readable medium. In the DL (DownLink), the controller / processor 459 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, control signal processing to recover upper layer data packets from the core network. The upper layer data packets are then provided to all protocol layers above the L2 layer. Various control signals can also be provided to the L3 for L3 processing. The controller / processor 459 is also responsible for error detection using an acknowledgement (ACK) and / or negative acknowledgement (NACK) protocol to support HARQ operations.

[0420] In the transmission from the second communication device 450 to the first communication device 410, at the second communication device 450, a data source 467 is used to provide upper layer packets to a controller / processor 459. The data source 467 represents all protocol layers above the L2 layer. Similar to the transmit function described at the first communication device 410 in the DL, the controller / processor 459 implements header compression, ciphering, packet segmentation and reordering, and multiplexing between logical and transport channels based on radio resource allocations for the first communication device 410, implements L2 layer functionality for the user plane and control plane. The controller / processor 459 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the first communication device 410. A transmit processor 468, in conjunction with a multi-antenna transmit processor 457, performs modulation mapping, channel coding processing, digital multi-antenna spatial processing, including codebook-based and non-codebook-based precoding, and beamforming processing, and then the transmit processor 468 generates parallel streams of symbols that are modulated onto different carriers, and the modulated symbol streams are then provided to different antennas 452 via transmitters 454 after analog precoding / beamforming at the multi-antenna transmit processor 457. Each transmitter 454 modulates a respective symbol stream, converts the modulated symbol stream from digital form to analog form, and transmits the analog signal via the corresponding antenna 452.

[0421] In the transmission from the second communication device 450 to the first communication device 410, the functionality at the first communication device 410 is similar to the functionality described in connection with the reception at the second communication device 450 in the transmission from the first communication device 410 to the second communication device 450. Each receiver 418 receives a signal from its respective antenna 420, converts the received signal to a baseband signal, and provides the baseband signal to a multi-antenna receive processor 472 and a receive processor 470. The receive processor 470 and the multi-antenna receive processor 472, in conjunction with the controller / processor 475, implement the L1 layer functions. The controller / processor 475 implements L2 layer functionality. The controller / processor 475 can be associated with a memory 476 that stores program codes and data. The memory 476 can be referred to as a computer-readable medium. The controller / processor 475 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, control signal processing to recover upper layer packets from the second communication device 450. Upper layer packets from the controller / processor 475 can be provided to a core network. The controller / processor 475 is also responsible for error detection using an ACK and / or NACK protocol to support HARQ operations.

[0422] As one embodiment, the second communication device 450 comprises: at least one processor and at least one memory including a computer program code; the at least one memory and the computer program code are configured to, with the at least one processor, cause the second communication device 450 to perform at least the following: receive a first information block; the first information block indicates reporting of a plurality of CSIs; transmit a target CSI in a first time domain resource only when a first condition is met; wherein the plurality of CSIs comprises N1 CSIs and the target CSI, N1 is a positive integer; the first condition comprises that the first number is less than a first threshold; the first number depends on a reception / transmission situation of the N1 CSIs.

[0423] As one embodiment, the second communication device 450 comprises: a memory storing a computer readable program of instructions which, when executed by at least one processor, causes the second communication device 450 to perform at least the following: receive a first information block; the first information block indicates reporting of a plurality of CSIs; transmit a target CSI in a first time domain resource only when a first condition is met; wherein the plurality of CSIs comprises N1 CSIs and the target CSI, N1 is a positive integer; the first condition comprises that the first number is less than a first threshold; the first number depends on a reception / transmission situation of the N1 CSIs.

[0424] As one embodiment, the first communication device 410 comprises: at least one processor and at least one memory including a computer program code; the at least one memory and the computer program code are configured to, with the at least one processor, cause the first communication device 410 to perform at least the following: transmit a first information block; the first information block indicates reporting of a plurality of CSIs; receive a target CSI on the first time domain resource only when a first condition is met; wherein the plurality of CSIs comprises N1 CSIs and the target CSI, N1 is a positive integer; the first condition comprises that the first number is less than a first threshold; the first number depends on a reception / transmission situation of the N1 CSIs.

[0425] As one embodiment, the first communication device 410 comprises: a memory storing a computer readable program of instructions which, when executed by at least one processor, causes the first communication device 410 to perform at least the following: transmit a first information block; the first information block indicates reporting of a plurality of CSIs; receive a target CSI on the first time domain resource only when a first condition is met; wherein the plurality of CSIs comprises N1 CSIs and the target CSI, N1 is a positive integer; the first condition comprises that the first number is less than a first threshold; the first number depends on a reception / transmission situation of the N1 CSIs.

[0426] As an embodiment, the first node in the present application comprises the second communication device 450.

[0427] As an embodiment, the second node in the present application comprises the first communication device 410.

[0428] As an embodiment, at least one of {the antenna 452, the receiver 454, the receive processor 456, the multi-antenna receive processor 458, the controller / processor 459, the memory 460, the data source 467} is configured to receive the first information block in the present application.

[0429] As an embodiment, at least one of {the antenna 420, the transmitter 418, the transmit processor 416, the multi-antenna transmit processor 471, the controller / processor 475, the memory 476} is configured to transmit the first information block in the present application.

[0430] As an embodiment, at least one of {the antenna 452, the transmitter / receiver 454, the transmit processor 468, the multi-antenna transmit processor 457, the receive processor 456, the multi-antenna receive processor 458, the controller / processor 459, the memory 460, the data source 467} is configured to transmit the target CSI in the present application.

[0431] As an embodiment, at least one of {the antenna 420, the transmitter / receiver 418, the receive processor 470, the multi-antenna receive processor 472, the transmit processor 416, the multi-antenna transmit processor 471, the controller / processor 475, the memory 476} is configured to receive the target CSI in the present application.

[0432] As an embodiment, at least one of {the antenna 452, the transmitter / receiver 454, the transmit processor 468, the multi-antenna transmit processor 457, the receive processor 456, the multi-antenna receive processor 458, the controller / processor 459, the memory 460, the data source 467} is configured to transmit the N1 CSI in the present application.

[0433] As an embodiment, at least one of {the antenna 420, the transmitter / receiver 418, the receive processor 470, the multi-antenna receive processor 472, the transmit processor 416, the multi-antenna transmit processor 471, the controller / processor 475, the memory 476} is configured to receive the N1 CSI in the present application.

[0434] As an embodiment, at least one of {the antenna 452, the transmitter / receiver 454, the transmit processor 468, the multi-antenna transmit processor 457, the receive processor 456, the multi-antenna receive processor 458, the controller / processor 459, the memory 460, the data source 467} is configured to transmit the plurality of CSIs in the present application.

[0435] As an embodiment, at least one of {the antenna 420, the transmitter / receiver 418, the receive processor 470, the multi-antenna receive processor 472, the transmit processor 416, the multi-antenna transmit processor 471, the controller / processor 475, the memory 476} is configured to receive the plurality of CSIs in the present application.

[0436] As an embodiment, at least one of {the antenna 452, the receiver / transmitter 454, the receive processor 456, the transmit processor 468, the multi-antenna receive processor 458, the multi-antenna transmit processor 457, the controller / processor 459, the memory 460, the data source 467} is configured to perform the first inference in the present application.

[0437] As an embodiment, at least one of {the antenna 452, the receiver / transmitter 454, the receive processor 456, the transmit processor 468, the multi-antenna receive processor 458, the multi-antenna transmit processor 457, the controller / processor 459, the memory 460, the data source 467} is configured to perform the second inference in the present application.

[0438] Embodiment 5

[0439] Embodiment 5 illustrates a flowchart of wireless transmission according to an embodiment of the present application, as shown in FIG. 5. In FIG. 5, the second node N1 and the first node U1 are communication nodes for transmission over an air interface. In FIG. 5, the steps in blocks F51 to F53 are optional, respectively.

[0440] For the second node N1, a first information block is transmitted in step S511; a second information block is transmitted in step S512; a third information block is received in step S513; and a target CSI is received in a first time domain resource only when a first condition is satisfied in step S514.

[0441] For the first node U1, a first information block is received in step S521; a second information block is received in step S522; a third information block is transmitted in step S523; and a target CSI is transmitted in a first time domain resource only when a first condition is satisfied in step S524.

[0442] In embodiment 5, the first information block indicates reporting of multiple CSIs; the multiple CSIs include N1 CSIs and the target CSI, N1 is a positive integer; the first condition includes that the first value is less than a first threshold; the first value depends on the reception of the N1 CSIs.

[0443] As an embodiment, the step in block F53 exists only when the first condition is satisfied.

[0444] As an embodiment, the first node U1 is the first node in the present application.

[0445] As an embodiment, the second node N1 is the second node in the present application.

[0446] As an embodiment, the air interface between the second node N1 and the first node U1 includes a wireless interface between a base station device and a user equipment.

[0447] As an embodiment, the air interface between the second node N1 and the first node U1 includes a wireless interface between a relay node device and a user equipment.

[0448] As an embodiment, the air interface between the second node N1 and the first node U1 includes a wireless interface between a user equipment and a user equipment.

[0449] As an embodiment, the second node N1 is a serving cell maintaining base station of the first node U1.

[0450] As an embodiment, the first node transmits the multiple CSIs; the second node receives the multiple CSIs.

[0451] As an embodiment, the first node transmits at least one CSI of the multiple CSIs; the second node receives at least one CSI of the multiple CSIs.

[0452] As an embodiment, the first node transmits the N1 CSIs; the second node receives the N1 CSIs.

[0453] As an embodiment, the first node transmits at least one CSI of the N1 CSIs; the second node receives at least one CSI of the N1 CSIs.

[0454] As an embodiment, the second node transmits RS in at least one RS resource; the first node receives RS in at least one RS resource; measurement based on the at least one RS resource is used to generate the multiple CSIs.

[0455] As one embodiment, the second node transmits RS in at least one RS resource; the first node receives RS in at least one RS resource; measurement based on the at least one RS resource is used to generate at least one CSI of the plurality of CSI.

[0456] As one embodiment, the second node transmits RS in at least one RS resource; the first node receives RS in at least one RS resource; measurement based on the at least one RS resource is used to generate the N1 CSI.

[0457] As one embodiment, the second node transmits RS in at least one RS resource; the first node receives RS in at least one RS resource; measurement based on the at least one RS resource is used to generate the target CSI.

[0458] As one embodiment, the second node transmits RS in a first set of RS resources; the first node receives RS in the first set of RS resources; wherein the first set of RS resources comprises one or more RS resources, generation of any CSI of the plurality of CSI is based on measurement of the first set of RS resources.

[0459] As one embodiment, the second node transmits RS in a plurality of sets of RS resources; the first node receives RS in the plurality of sets of RS resources; wherein any set of RS resources of the plurality of sets of RS resources comprises one or more RS resources, measurement of the plurality of sets of RS resources is respectively used to generate the plurality of CSI.

[0460] As one embodiment, the second node transmits RS in a plurality of RS resources; the first node receives RS in the plurality of RS resources; measurement of the plurality of RS resources is respectively used to generate the plurality of CSI.

[0461] As one embodiment, when the first condition is not satisfied, the first node refrains from transmitting the target CSI.

[0462] As one embodiment, when the first condition is not satisfied, the first node refrains from transmitting the target CSI in the first time-domain resource.

[0463] As one embodiment, when the first condition is not satisfied, the second node refrains from receiving the target CSI.

[0464] As one embodiment, the first node refrains from transmitting the target CSI in the first time-domain resource comprises that the first node refrains from transmitting the target CSI.

[0465] As an embodiment, when the first condition is not satisfied, the second node monitors whether the target CSI is transmitted by the first node on the first time domain resource.

[0466] As an embodiment, when the first condition is not satisfied, the second node gives up receiving the target CSI in the first time domain resource.

[0467] As an embodiment, the second node giving up receiving the target CSI in the first time domain resource comprises: the second node giving up receiving the target CSI.

[0468] As an embodiment, the above method has the benefits of simplifying design and reducing implementation complexity.

[0469] As an embodiment, the first node giving up transmitting the target CSI in the first time domain resource comprises: the first node transmitting the target CSI in a target time domain resource, the target time domain resource being different from the first time domain resource.

[0470] As an embodiment, when the first condition is not satisfied, the first node transmits the target CSI in a target time domain resource, the target time domain resource being different from the first time domain resource.

[0471] As an embodiment, when the first condition is not satisfied, the first node transmits part or all of the information of the target CSI in a target time domain resource, the target time domain resource being different from the first time domain resource.

[0472] As an embodiment, the second node giving up receiving the target CSI in the first time domain resource comprises: the second node receiving the target CSI in a target time domain resource, the target time domain resource being different from the first time domain resource.

[0473] As an embodiment, when the first condition is not satisfied, the second node receives the target CSI in a target time domain resource, the target time domain resource being different from the first time domain resource.

[0474] As an embodiment, when the first condition is not satisfied, the second node receives part or all of the information of the target CSI in a target time domain resource, the target time domain resource being different from the first time domain resource.

[0475] As an embodiment, the above method has the benefits of guaranteeing the reporting of CSI as much as possible.

[0476] As an embodiment, the above method has the benefits of improving the accuracy and real-time performance of channel information.

[0477] As an embodiment, the above method has the advantage of improving the overall performance of the system.

[0478] As an embodiment, the first node transmits the target CSI on the first time domain resource, or the first node abandons transmitting the target CSI on the first time domain resource.

[0479] As an embodiment, when the target CSI is transmitted by the first node on the first time domain resource, the second node receives the target CSI on the first time domain resource.

[0480] As an embodiment, when the target CSI is abandoned by the first node to transmit on the first time domain resource, the second node abandons receiving the target CSI on the first time domain resource.

[0481] As an embodiment, the second node monitors whether the target CSI is transmitted by the first node on the first time domain resource.

[0482] As an embodiment, the second node determines by itself whether to receive the target CSI on the first time domain resource.

[0483] As an embodiment, the second node determines by itself whether to abandon receiving the target CSI on the first time domain resource.

[0484] As an embodiment, the above method has the advantage of enhancing the completeness of the system and the scheme.

[0485] As an embodiment, when the first node transmits the target CSI on the first time domain resource, the code rate of the CSI on the first time domain resource is less than or equal to the maximum code rate configured by the higher layer parameter.

[0486] As an embodiment, when the first node transmits the target CSI on the first time domain resource, the value of the first function is less than or equal to the first threshold, and the first function depends on the total number of bits transmitted on the first time domain resource.

[0487] As an embodiment, the value of the first function represents the total number of encoded bits, and the first threshold represents the maximum total number of encoded bits that can be carried on the first time domain resource.

[0488] As an embodiment, the first function is The first threshold is Or

[0489] As one embodiment, the first function is The first threshold is

[0490] As one embodiment, the first function is The first threshold is Or

[0491] As one embodiment, the first function is (O CSI-2 + L CSI-2 ) / (N L · Q' CSI,2 · Q m ), the first threshold is

[0492] As one embodiment, C UL-SCH , K r , Q' CSI-1 , Q'ACK / CG-UCI, Q'ACK / UTO-UCI, a, N L , Q' CSI,2 , and Q m See Section 6.3.2.4 of 3GPP TS 38.212 for the specific definition of C, K, Q', Q'ACK / CG-UCI, Q'ACK / UTO-UCI, a, N, Q', and Q.

[0493] As one embodiment, is a CSI offset value, see Table 9.3-2 of 3GPP TS 38.213 for the specific definition of.

[0494] As one embodiment, R is a signal code rate in DCI (Downlink Control Information).

[0495] As one embodiment, the benefits of the above method include: improving the flexibility and robustness of the system, adapting to different transmission conditions and scene applications.

[0496] As one embodiment, the benefits of the above method include: following the current system design and standards.

[0497] As one embodiment, the benefits of the above method include: enhancing the overall performance of the system.

[0498] Embodiment 6

[0499] Embodiment 6 illustrates a schematic diagram of multiple CSIs according to one embodiment of the present application; as shown in FIG. 6.

[0500] In Embodiment 6, any of the plurality of CSIs depends on an output of inference.

[0501] As one embodiment, any of the plurality of CSIs depends on all or part of information of an output of inference.

[0502] As one embodiment, the plurality of CSIs depends on an output of inference.

[0503] As one embodiment, the plurality of CSIs respectively depends on outputs of a plurality of inferences.

[0504] As one embodiment, the plurality of CSIs depends on an output of the same inference.

[0505] As one embodiment, the plurality of CSIs respectively depends on a plurality of outputs of the same inference.

[0506] As one embodiment, the plurality of CSIs depends on an output of AI inference.

[0507] As one embodiment, the plurality of CSIs respectively depends on outputs of a plurality of AI inferences.

[0508] As one embodiment, the plurality of CSIs depends on an output of the same AI inference.

[0509] As one embodiment, the plurality of CSIs respectively depends on a plurality of outputs of the same AI inference.

[0510] As one embodiment, the benefits of the above method include supporting AI-based CSI reporting.

[0511] As one embodiment, the benefits of the above method include improving the performance of CSI reporting and reducing system reporting overhead.

[0512] As one embodiment, any of the plurality of CSIs depends on an output of inference, and the inference depended on by any of the plurality of CSIs corresponds to a first identifier.

[0513] As one embodiment, any of the plurality of CSIs depends on an output of inference, and the inference depended on by any of the plurality of CSIs is identified by a first identifier.

[0514] As one embodiment, the benefits of the above method include simplifying system design and reducing the implementation complexity of the scheme.

[0515] As one embodiment, the benefits of the above method include supporting multiple inferences of one AI model or entity.

[0516] As one embodiment, the benefits of the above method include improving the performance of AI-based CSI reporting.

[0517] As an embodiment, the first identifier is a non-negative integer.

[0518] As an embodiment, the first identifier is a string.

[0519] As an embodiment, the first identifier is used to identify an AI model.

[0520] As an embodiment, the first identifier is used to identify an AI entity.

[0521] As an embodiment, the first identifier is used to identify an AI function.

[0522] As an embodiment, the first identifier is used to identify an inference.

[0523] As an embodiment, the first identifier is used to identify an AI inference.

[0524] As an embodiment, the first identifier is a model identifier.

[0525] As an embodiment, the first identifier is used by the first node to determine an AI model.

[0526] As an embodiment, the benefits of the above method include: identifying an AI model / entity / function / inference through the first identifier, simplifying system design, and unifying the understanding of different AI models / entities / functions / inferences among multiple nodes.

[0527] As an embodiment, the first identifier is used to identify or indicate a resource set.

[0528] As an embodiment, the first identifier is used to identify or indicate a resource set, and the measurement of the resource set is used to obtain a training data set.

[0529] As an embodiment, the first identifier is used to identify or indicate a training data set.

[0530] As an embodiment, the training of an AI model / entity / function / inference is identified by the first identifier.

[0531] As an embodiment, the benefits of the above method include: identifying an AI training or AI training data set to identify the inference generated by this AI training or AI training data set, establishing consensus among different AI functions, and further simplifying system design.

[0532] As an embodiment, the first identifier is used to identify or indicate a system configuration.

[0533] As an embodiment, the first indication is used to identify or indicate a system resource configuration.

[0534] As an embodiment, the first indication is used to identify or indicate a CSI reporting configuration.

[0535] As an embodiment, the first indication is used to identify configuration information of a set of reference resources, measurements on the set of reference resources are used to obtain a training dataset for an AI model / entity / function / inference.

[0536] As an embodiment, the above method has the benefit of simplifying system design and improving system flexibility by identifying an AI model / entity / function / inference through identifying a configuration information.

[0537] As an embodiment, a CSI depends on an output includes that the output is used to generate the CSI.

[0538] As an embodiment, a CSI depends on an output includes that all or part of information of the output is used to generate the CSI.

[0539] As an embodiment, a CSI depends on an output includes that the output is used to generate the CSI after post-processing.

[0540] As an embodiment, a CSI depends on an output includes that the CSI includes the output.

[0541] As an embodiment, a CSI depends on an output includes that the CSI includes all or part of the output.

[0542] As an embodiment, a CSI depends on an output includes that the CSI includes the result of post-processing of the output.

[0543] As an embodiment, the post-processing includes one or more of sampling, quantization, truncation, DFT (Discrete Fourier Transform), angle domain to spatial domain transformation, spatial domain to angle domain transformation, time domain to frequency domain transformation, and frequency domain to time domain transformation.

[0544] As an embodiment, a CSI depends on an output includes that the CSI includes the result of truncation and / or quantization of the output.

[0545] As an embodiment, a CSI depends on an output includes that the output is used to generate the CSI after truncation and / or quantization.

[0546] As one embodiment, the one CSI is any of the multiple CSIs, and the one output is an output of the inference.

[0547] As one embodiment, the one CSI is the first CSI, and the one output is an output of the first inference.

[0548] As one embodiment, the one CSI is the first CSI, and the one output is the first output.

[0549] As one embodiment, the one CSI is the target CSI, and the one output is an output of performing the second inference.

[0550] As one embodiment, the above method has the benefit of enhancing the flexibility of the system, better adapting to various transmission conditions and application scenarios.

[0551] As one embodiment, the above method has the benefit of enhancing the backward compatibility of the system.

[0552] As one embodiment, the generating the one CSI comprises calculating the CSI.

[0553] As one embodiment, how the one output is used to generate the one CSI is determined by the manufacturer of the first node, or is implementation dependent. Some typical but non-limiting embodiments are described below:

[0554] As one embodiment, the one output comprises a channel parameter matrix H r×t , where r, t are the number of receive antennas and the number of antenna ports of the CSI-RS resource, respectively; the first node performs power adjustment on the channel parameter matrix H r×t , and the adjusted channel parameter matrix is , where P is the assumed ratio of PDSCH EPRE to CSI-RS EPRE (i.e., the first power control offset); under the condition of using a precoding matrix W t×l , the precoded channel parameter matrix is where l is the rank or the number of layers, in one case l is a positive integer no greater than t, in another case the precoding matrix is an identity matrix, in which case t = l; H is calculated using, for example, a SINR (Signal Interference Noise Ratio), EESM (Exponential Effective SINR Mapping), or RBIR (Received Block mean mutual Information Ratio) criterion r×t • W t×l the equivalent channel capacity, and then determining the CQI included in the first CSI report from the equivalent channel capacity by, for example, a table lookup. Generally, the calculation of the equivalent channel capacity requires the first node to estimate the interference (including noise), which the first node can obtain a more accurate measurement of the interference using the measurement of the second set of occasions in the present application. Generally, the direct mapping of the equivalent channel capacity to the value of CQI depends on the receiver performance, or the modulation scheme, and other hardware-related factors.

[0555] As an embodiment, the inference in the present application is training-based or AI-based.

[0556] As an embodiment, the inference in the present application is AI inference.

[0557] In the present application, the AI (Artificial Intelligence) includes ML (Machine Learning).

[0558] As an embodiment, the benefits of the above method include supporting AI-based CSI reporting.

[0559] As an embodiment, a training-based or AI-based inference includes an AI entity.

[0560] As an embodiment, a training-based or AI-based inference includes a part of an AI entity.

[0561] As an embodiment, a training-based or AI-based inference includes a part of an AI entity for inference.

[0562] As an embodiment, a training-based or AI-based inference is based on a neural network.

[0563] As one embodiment, a training-based or AI inference is based on CNN (Conventional Neural Networks).

[0564] As one embodiment, a training-based or AI inference is based on Transformer.

[0565] As one embodiment, a model of a training-based or AI inference is obtained by training.

[0566] As one embodiment, a training-based or AI inference includes pre-processing.

[0567] As one 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-to-spatial domain transformation, frequency-to-time domain transformation, time-to-frequency domain transformation, truncation, padding, mapping, or labeling.

[0568] As one embodiment, the labeling refers to labeling with labels.

[0569] As one embodiment, a training-based or AI inference includes post-processing.

[0570] As one embodiment, the post-processing includes one or more of DFT, quantization, angle-to-spatial domain transformation, spatial-to-angle domain transformation, time-to-frequency domain transformation, frequency-to-time domain transformation, truncation, and padding.

[0571] As one embodiment, a training-based or AI inference includes one or more of convolution, pooling, concatenation, and activation.

[0572] As one embodiment, a training-based or AI inference includes a fully connected layer.

[0573] As one embodiment, a training-based or AI inference includes a pooling layer.

[0574] As one embodiment, a training-based or AI inference includes at least one convolution layer.

[0575] As one embodiment, a training-based or AI inference includes at least one encoding layer.

[0576] As an embodiment, one encoding layer comprises at least one convolution layer and one pooling layer.

[0577] As an embodiment, in the convolution layer, at least one convolution kernel is used to convolve the input to generate a corresponding feature map, and at least one feature map output by the convolution layer is reshaped into a vector input to the fully connected layer; the fully connected layer converts the one vector into an output.

[0578] As an embodiment, part 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 in the inference based on training or AI are obtained through training.

[0579] As an embodiment, part or all of the convolution kernel, the pooling kernel, the pooling function, the activation function, the parameters of the pooling function, and the parameters of the activation function in the inference based on training or AI are obtained through training.

[0580] As an embodiment, the inference in the present application does not require deployment.

[0581] As an embodiment, the inference in the present application requires deployment.

[0582] As an embodiment, the first node deploys the inference in the present application.

[0583] As an embodiment, deploying an inference comprises obtaining an inference.

[0584] As an embodiment, deploying an inference comprises loading an inference.

[0585] As an embodiment, deploying an inference comprises submitting a request to load an inference.

[0586] As an embodiment, the benefits include reserving sufficient degrees of freedom for the first node to adapt to various different scenarios and terminals, and having adaptability and flexibility.

[0587] As an embodiment, the model of the inference in the present application is obtained through training.

[0588] As an embodiment, the inference in the present application is not obtained through loading.

[0589] As an embodiment, the inference in the present application is obtained through loading.

[0590] As one embodiment, the loading comprises loading from a serving cell of the first node.

[0591] As one embodiment, the loading comprises loading from a maintenance base station of a serving cell of the first node.

[0592] As one embodiment, the loading comprises loading from a core network.

[0593] As one embodiment, the benefits of the above method comprise reducing the demand for processing capacity and power consumption of the first node.

[0594] As one embodiment, the AI training function of the RAN domain is located in the 3GPP RAN domain-specific management function, and the AI inference function is located in the UE.

[0595] As one embodiment, the RAN domain-specific management function provides the AI training function management capability and the AI inference function management capability.

[0596] As one embodiment, the AI training function is located in the RAN domain-specific management function, and the AI inference function is located locally in the gNB.

[0597] As one embodiment, the management capability of the AI training function is provided by the RAN domain-specific management function, and the management capability of the AI inference is provided locally by the gNB.

[0598] As one embodiment, MnF refers to Management Function.

[0599] As one embodiment, the AI training function and the AI inference function are both located in the UE, wherein the UE provides the capability of training and inference.

[0600] As one embodiment, the RAN domain-specific management function provides the management capability of the AI training function and the management capability of the AI inference function.

[0601] As one embodiment, the AI training function and the AI inference function are both located in the gNB.

[0602] As one embodiment, the management capability of the AI training function and the management capability of the AI inference function are both provided locally by the gNB.

[0603] Embodiment 7

[0604] Embodiment 7 illustrates a schematic diagram of a first CSI and a target CSI according to an embodiment of the present disclosure; as shown in FIG. 7.

[0605] In embodiment 7, a first CSI and the target CSI are dependent on an output of a first inference and an output of a second inference respectively, the first CSI being one of the N1 CSIs; the output of the first inference including a first output and a second output, the first CSI being dependent on the first output, the input of the second inference including the second output.

[0606] As an embodiment, the first CSI is the last one of the N1 CSIs.

[0607] As an embodiment, the first CSI is a preceding CSI of the target CSI.

[0608] As an embodiment, the target CSI is sent later than the first CSI.

[0609] As an embodiment, the target CSI is a CSI after the first CSI.

[0610] As an embodiment, a time domain resource corresponding to the target CSI is later than a time domain resource corresponding to the first CSI.

[0611] As an embodiment, a time slot corresponding to the target CSI is later than a time slot corresponding to the first CSI.

[0612] As an embodiment, the first inference and the second inference are AI inferences for obtaining CSI.

[0613] As an embodiment, the first inference and the second inference are AI inferences for obtaining channel information.

[0614] As an embodiment, the first inference and the second inference are AI inferences for at least one of beam management, positioning or assistance positioning, CSI prediction, CSI estimation, or CSI compression.

[0615] As an embodiment, the first inference and the second inference are AI inferences 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.

[0616] As an embodiment, the first inference and the second inference include CSI compression based on artificial intelligence or machine learning.

[0617] As one embodiment, the first inference and the second inference comprise an AI or ML based CSI compression encoder.

[0618] As one embodiment, the first inference and the second inference comprise an AI or ML based CSI prediction or CSI estimation.

[0619] As one embodiment, the first inference and the second inference comprise an AI or ML based CSI prediction and compression.

[0620] As one embodiment, the first inference and the second inference comprise an AI or ML based beam management.

[0621] As one embodiment, the beam management comprises at least one of beam prediction, beam switching, beam failure prediction, or beam failure recovery.

[0622] As one embodiment, the first inference and the second inference are AI functions.

[0623] As one embodiment, the first inference and the second inference are executed by the first node.

[0624] As one embodiment, the training of the first inference and the second inference is executed by a sender of the first information block.

[0625] As one embodiment, the training of the first inference and the second inference is executed by a core network.

[0626] As one embodiment, the training of the first inference and the second inference is executed by an AI training producer.

[0627] As one embodiment, the training of the first inference and the second inference is executed by an MDA function.

[0628] As one embodiment, the training of the first inference and the second inference is executed by an MDA function located at the first node.

[0629] As one embodiment, the training of the first inference and the second inference is executed by an MDA function located at a sender of the first information set.

[0630] As one embodiment, the training of the first inference and the second inference is executed by a NWDAF.

[0631] As an embodiment, the training of the first inference and the second inference is performed by a MDAS (Management Data Analytics Service) producer.

[0632] As an embodiment, the training of the first inference and the second inference is performed by a MnS (Management Service) producer.

[0633] As an embodiment, the first inference and the second inference are both inferences with the same AI model.

[0634] As an embodiment, the benefits of the above method include supporting multiple executions and outputs of an AI model.

[0635] As an embodiment, the benefits of the above method include improving the performance of AI-based CSI reporting.

[0636] As an embodiment, the first inference and the second inference are inferences with different AI models respectively.

[0637] As an embodiment, the benefits of the above method include supporting joint processing of multiple AI models.

[0638] As an embodiment, the benefits of the above method include improving the performance of AI-based CSI reporting.

[0639] As an embodiment, the first inference and the second inference are both inferences corresponding to a first identity.

[0640] As an embodiment, the first inference and the second inference correspond to different identities respectively.

[0641] As an embodiment, the first inference is an inference corresponding to a first identity, and the second inference is an inference corresponding to a second identity.

[0642] As a sub-embodiment of the above embodiment, the first identity and the second identity are respectively used to identify different AI functions.

[0643] As a sub-embodiment of the above embodiment, the first identity and the second identity are respectively used to identify different AI models.

[0644] As a sub-embodiment of the above embodiment, the first identity and the second identity are respectively used to identify different CSI reporting configurations.

[0645] As a sub-example of the above embodiment, the first and second identities are respectively used to identify different sets of RS resources.

[0646] As a sub-example of the above embodiment, the first and second identities are respectively used to identify different sets of training data.

[0647] As an example, benefits of the above method include enhancing completeness and flexibility of the system.

[0648] As an example, the first node employs a single side AI model.

[0649] As an example, the first and second inferences are for beam prediction, and the first node employs a single side AI model.

[0650] As an example, the first and second inferences are for CSI prediction, and the first node employs a single side AI model.

[0651] As an example, the first and second nodes employ a two-sided AI model.

[0652] As an example, the first and second inferences are for CSI compression, the first and second nodes employ a two-sided AI model, and the second node performs a third inference for CSI recovery.

[0653] As an example, the first and second inferences are for CSI prediction and compression, the first and second nodes employ a two-sided AI model, and the second node performs a third inference for CSI recovery.

[0654] As an example, the third inference is based on training or AI.

[0655] As an example, the third inference is an AI inference.

[0656] As an example, the third inference is deployment free.

[0657] As an example, the third inference is deployment required.

[0658] As an example, the second node deploys the third inference.

[0659] As one embodiment, the third inference is obtained by training.

[0660] As one embodiment, the third inference is not obtained by loading.

[0661] As one embodiment, the third inference is obtained by loading.

[0662] As one embodiment, the third inference is associated with the first inference and the second inference.

[0663] As one embodiment, the third inference is associated with the first inference.

[0664] As one embodiment, the third inference is associated with the second inference.

[0665] As one embodiment, the method has the benefit of supporting multiple inferences to accomplish a function.

[0666] As one embodiment, the method has the benefit of improving the performance of AI-based CSI reporting.

[0667] As one embodiment, the third inference corresponds to a first identity with the first inference and the second inference.

[0668] As one embodiment, the third inference corresponds to a first identity with the first inference.

[0669] As one embodiment, the third inference corresponds to a first identity with the second inference.

[0670] As one embodiment, the method has the benefit of simplifying system design and reducing system implementation complexity.

[0671] As one embodiment, the output of the first inference and the second inference includes channel information.

[0672] As one embodiment, the output of the first inference and the second inference includes a channel matrix.

[0673] As one embodiment, the output of the first inference and the second inference includes CSI.

[0674] As one embodiment, the output of the first inference and the second inference includes compressed CSI.

[0675] As one embodiment, the output of the first inference and the second inference includes predicted CSI.

[0676] In one embodiment, the output of the first and second inferences includes predicted and compressed CSI.

[0677] In one embodiment, the output of the first and second inferences includes non-codebook based CSI.

[0678] In one embodiment, the output of the first and second inferences includes channel impulse response.

[0679] In one embodiment, the output of the first and second inferences includes small scale properties.

[0680] In one embodiment, the output of the first and second inferences is used to determine one or more precoding matrices.

[0681] In one embodiment, the output of the first and second inferences includes predicted beam information.

[0682] In one embodiment, the output of the first and second inferences includes information other than channel information.

[0683] In one embodiment, the output of the first and second inferences includes at least one of channel information or information other than channel information.

[0684] In one embodiment, the output of the first and second inferences includes previous channel information to current channel information.

[0685] In one embodiment, the output of the first and second inferences includes accumulated channel information.

[0686] In one sub-embodiment of the above embodiment, the accumulated channel information is all channel information previous to current channel information.

[0687] In one sub-embodiment of the above embodiment, the accumulated channel information is at least one channel information previous to current channel information.

[0688] In one sub-embodiment of the above embodiment, the accumulated channel information characterizes all channel information previous to current channel information.

[0689] In one sub-embodiment of the above embodiment, the accumulated channel information characterizes at least one channel information previous to current channel information.

[0690] As one embodiment, the output of the first inference and the second inference comprises accumulated CSI.

[0691] As one embodiment, the output of the first inference and the second inference respectively comprises all or part of parameters of the first inference and the second inference.

[0692] As one embodiment, the output of the first inference and the second inference respectively comprises all or part of output of intermediate process of the first inference and the second inference.

[0693] As one embodiment, the output of the first inference and the second inference respectively comprises state information of the first inference and the second inference.

[0694] As one sub-embodiment of the above-mentioned embodiment, the state information is state information of an AI model corresponding to the first inference or the second inference.

[0695] As one sub-embodiment of the above-mentioned embodiment, the state information is all or part of parameters of an AI model corresponding to the first inference or the second inference.

[0696] As one sub-embodiment of the above-mentioned embodiment, the state information is intermediate output of an AI model corresponding to the first inference or the second inference.

[0697] As one sub-embodiment of the above-mentioned embodiment, the state information is parameters of all or part of layers of a neural network of an AI model corresponding to the first inference or the second inference.

[0698] As one sub-embodiment of the above-mentioned embodiment, the state information is output of all or part of layers of a neural network of an AI model corresponding to the first inference or the second inference.

[0699] As one sub-embodiment of the above-mentioned embodiment, the state information is output of all or part of hidden layers of a neural network of an AI model corresponding to the first inference or the second inference.

[0700] As one sub-embodiment of the above-mentioned embodiment, the state information is all or part of output of hidden layers of a neural network of an AI model corresponding to the first inference or the second inference.

[0701] As one embodiment, the input of the first inference and the second inference comprises measurement obtained based on at least one RS resource.

[0702] As one embodiment, the input of the first inference and the second inference comprises channel measurement obtained based on CSI-RS resource or SS / PBCH block resource.

[0703] As an embodiment, the input of the first and second inferences comprises interference measurements obtained based on CSI-RS resources or CSI-IM resources.

[0704] As an embodiment, the input of the first and second inferences comprises a matrix or vector obtained by pre-processing a channel matrix based on measurements of at least one RS resource.

[0705] As an embodiment, the benefits of the above method include: small changes to existing standards and system design.

[0706] As an embodiment, the input of the first and second inferences comprises accumulated channel information.

[0707] As an embodiment, the input of the first and second inferences comprises accumulated CSI.

[0708] As an embodiment, the benefits of the above method include: improving the accuracy of CSI reporting and the effectiveness of CSI compression, and improving the overall performance of the system.

[0709] As an embodiment, the input of the first and second inferences respectively comprises state information of the first and second inferences.

[0710] As an embodiment, the input of the first and second inferences comprises measurements obtained based on at least one RS resource and accumulated channel information.

[0711] As an embodiment, the input of the first and second inferences comprises measurements obtained based on at least one RS resource and accumulated CSI.

[0712] As an embodiment, the input of the first inference comprises measurements obtained based on at least one RS resource and state information of the first inference.

[0713] As an embodiment, the input of the first inference comprises measurements obtained based on at least one RS resource and state information of an AI model corresponding to the first inference.

[0714] As an embodiment, the input of the second inference comprises measurements obtained based on at least one RS resource and state information of the second inference.

[0715] As an embodiment, the input of the second inference comprises measurements obtained based on at least one RS resource and state information of an AI model corresponding to the second inference.

[0716] As an embodiment, the input of the second inference comprises measurements obtained based on at least one RS resource and state information of the first inference.

[0717] As an embodiment, the input of the second inference comprises measurements obtained based on at least one RS resource and state information of the AI model corresponding to the first inference.

[0718] As an embodiment, the benefits of the above method include introducing relevance between multiple inferences, and improving the performance of AI-based CSI reporting.

[0719] As an embodiment, the benefits of the above method include supporting joint operation of multiple inferences, improving the accuracy of CSI reporting, and reducing reporting overhead.

[0720] As an embodiment, the output of the first inference comprises the first output and the second output.

[0721] As an embodiment, the output of the first inference comprises the first output and the second output; the first output comprises the first CSI, and the second output comprises accumulated channel information.

[0722] As an embodiment, the output of the first inference comprises the first output and the second output; the first output comprises the first CSI, and the second output comprises accumulated CSI.

[0723] As an embodiment, the output of the first inference comprises the first output and the second output; the first output comprises the first CSI, and the second output comprises state information of the first inference.

[0724] As an embodiment, the output of the first inference comprises the first output and the second output; the first output comprises the first CSI, and the second output comprises state information of the AI model corresponding to the first inference.

[0725] As an embodiment, the output of the first inference comprises the first output and the second output; the first output is used to generate the first CSI, and the second output comprises accumulated channel information.

[0726] As an embodiment, the output of the first inference comprises the first output and the second output; the first output is used to generate the first CSI, and the second output comprises accumulated CSI.

[0727] As an embodiment, the output of the first inference comprises the first output and the second output; the first output is used to generate the first CSI, and the second output comprises state information of the first inference.

[0728] As an embodiment, the output of the first inference comprises the first output and the second output; the first output is used to generate the first CSI, and the second output comprises state information of the AI model corresponding to the first inference.

[0729] As an embodiment, the output of the second inference comprises the first output and the second output.

[0730] As an embodiment, the output of the second inference comprises the first output and the second output; the first output comprises the first CSI, and the second output comprises accumulated channel information.

[0731] As an embodiment, the output of the second inference comprises the first output and the second output; the first output comprises the first CSI, and the second output comprises accumulated CSI.

[0732] As an embodiment, the output of the second inference comprises the first output and the second output; the first output comprises the first CSI, and the second output comprises state information of the second inference.

[0733] As an embodiment, the output of the second inference comprises the first output and the second output; the first output comprises the first CSI, and the second output comprises state information of the AI model corresponding to the second inference.

[0734] As an embodiment, the output of the second inference comprises the first output and the second output; the first output is used to generate the first CSI, and the second output comprises accumulated channel information.

[0735] As an embodiment, the output of the second inference comprises the first output and the second output; the first output is used to generate the first CSI, and the second output comprises accumulated CSI.

[0736] As an embodiment, the output of the second inference comprises the first output and the second output; the first output is used to generate the first CSI, and the second output comprises state information of the second inference.

[0737] As an example, the output of the second inference includes the first output and the second output; the first output is used to generate the first CSI, and the second output includes state information of an AI model corresponding to the second inference.

[0738] As an example, the above method has the benefit of enhancing the flexibility of the system, better adapting to various transmission conditions and application scenarios.

[0739] As an example, the above method has the benefit of improving the performance of AI-based CSI prediction and compression.

[0740] As an example, the above method has the benefit of improving the accuracy of CSI reporting, reducing the reporting overhead, and improving the overall performance of the system.

[0741] As an example, the output of the first inference includes a first output and a second output, the first CSI depends on the first output, and the input of the second inference includes the second output.

[0742] As an example, the output of the first inference includes a first output and a second output, the first CSI depends on the first output; the input of the second inference includes a first input and a second input, the first input depends on measurements obtained based on at least one RS resource, and the second input depends on the second output.

[0743] As an example, one input depending on one output includes one input depending on all or part of the content of one output.

[0744] As an example, one input depending on one output includes the one output being used to generate the one input.

[0745] As an example, one input depending on one output includes the one output being used to generate the one input after post-processing.

[0746] As an example, one input depending on one output includes the one input including the one output.

[0747] As an example, one input depending on one output includes the one input including all or part of the content of the one output.

[0748] As an example, one input depending on one output includes the one input including the result of post-processing of the one output.

[0749] As an example, in one input depending on one output, the one input is the second input, and the one output is the second output.

[0750] As one embodiment, benefits of the above method include: enhancing flexibility of the system, better adapting to various different transmission conditions and application scenarios.

[0751] As one embodiment, benefits of the above method include: enhancing backward compatibility of the system.

[0752] As one embodiment, the output of the first inference includes a first output and a second output, the first CSI depends on the first output; the input of the second inference includes a first input and a second input, the first input includes measurement obtained based on at least one RS resource, and the second input includes the second output.

[0753] As one embodiment, the output of the first inference includes a first output and a second output, the first CSI depends on the first output; the input of the second inference includes a first input and a second input, the first input includes measurement obtained based on at least one RS resource, and the second input is the second output.

[0754] As one sub-embodiment of the above embodiment, the second output of the first inference includes historical channel information.

[0755] As one sub-embodiment of the above embodiment, the second output of the first inference includes historical CSI.

[0756] As one sub-embodiment of the above embodiment, the second output of the first inference includes accumulated channel information.

[0757] As one sub-embodiment of the above embodiment, the second output of the first inference includes accumulated CSI.

[0758] As one sub-embodiment of the above embodiment, the second output of the first inference represents historical channel information.

[0759] As one sub-embodiment of the above embodiment, the second output of the first inference represents accumulated channel information.

[0760] As one sub-embodiment of the above embodiment, the second output of the first inference includes all or part of parameters of the first inference.

[0761] As one sub-embodiment of the above embodiment, the second output of the first inference includes all or part of outputs of intermediate processes of the first inference.

[0762] As one sub-example of the above embodiment, the second output of the first inference comprises state information of the first inference.

[0763] As one sub-example of the above embodiment, the second output of the first inference comprises state information of the AI model corresponding to the first inference.

[0764] As one sub-example of the above embodiment, the second output of the first inference comprises all or part of parameters of the AI model corresponding to the first inference.

[0765] As one sub-example of the above embodiment, the second output of the first inference comprises intermediate output of the AI model corresponding to the first inference.

[0766] As one sub-example of the above embodiment, the second output of the first inference comprises parameters of all or part of layers of the neural network of the AI model corresponding to the first inference.

[0767] As one sub-example of the above embodiment, the second output of the first inference comprises output of all or part of layers of the neural network of the AI model corresponding to the first inference.

[0768] As one sub-example of the above embodiment, the second output of the first inference comprises output of all or part of hidden layers of the neural network of the AI model corresponding to the first inference.

[0769] As one sub-example of the above embodiment, the second output of the first inference comprises all or part of output of hidden layers of the neural network of the AI model corresponding to the first inference.

[0770] As one embodiment, the above method has the benefits of introducing relevance between multiple inferences, and improving the performance of AI-based CSI reporting.

[0771] As one embodiment, the above method has the benefits of supporting joint operation of multiple inferences, improving the accuracy of CSI reporting, and reducing reporting overhead.

[0772] As one embodiment, the above method has the benefits of improving the flexibility of the system.

[0773] Embodiment 8

[0774] Embodiment 8 illustrates a schematic diagram of the transmission and reception of N1 CSI according to one embodiment of the present application; as shown in FIG. 8.

[0775] In embodiment 8, the transceiving status of the N1 CSIs includes a number of CSIs in the N1 CSIs that are dropped by the first node.

[0776] As one embodiment, the transceiving status of the N1 CSIs includes a number of bits in the N1 CSIs that are dropped by the first node.

[0777] As one embodiment, the transceiving status of the N1 CSIs is a number of CSIs in the N1 CSIs that are dropped by the first node.

[0778] As one embodiment, the transceiving status of the N1 CSIs is a number of bits in the N1 CSIs that are dropped by the first node.

[0779] As one embodiment, the transceiving status of the N1 CSIs includes a number of CSIs in the N1 CSIs that are consecutively dropped by the first node.

[0780] As one embodiment, the transceiving status of the N1 CSIs includes a number of bits in the N1 CSIs that are consecutively dropped by the first node.

[0781] As one embodiment, the transceiving status of the N1 CSIs is a number of CSIs in the N1 CSIs that are consecutively dropped by the first node.

[0782] As one embodiment, the transceiving status of the N1 CSIs is a number of bits in the N1 CSIs that are consecutively dropped by the first node.

[0783] As one embodiment, the first value and the number of CSIs in the N1 CSIs that are dropped by the first node are linearly related.

[0784] As one embodiment, the first value and the number of CSIs in the N1 CSIs that are dropped by the first node are non-linearly related.

[0785] As one embodiment, the first value is the number of CSIs in the N1 CSIs that are dropped by the first node.

[0786] As one embodiment, the first value is the number of CSIs in the N1 CSIs that are consecutively dropped by the first node.

[0787] As one embodiment, the first value is equal to the number of CSIs in the N1 CSIs that are dropped by the first node divided by a total number of CSIs in the plurality of CSIs.

[0788] As an embodiment, the first value equals to the number of CSIs in the N1 CSIs that are dropped by the first node divided by (N1+1).

[0789] As an embodiment, the first value and the number of bits in the N1 CSIs that are dropped by the first node are in a linear relationship.

[0790] As an embodiment, the first value and the number of bits in the N1 CSIs that are dropped by the first node are in a non-linear relationship.

[0791] As an embodiment, the first value is the number of bits in the N1 CSIs that are dropped by the first node.

[0792] As an embodiment, the first value is the number of bits in the N1 CSIs that are consecutively dropped by the first node.

[0793] As an embodiment, the first value equals to the number of bits in the N1 CSIs that are dropped by the first node divided by the total number of bits in the plurality of CSIs.

[0794] As an embodiment, the initial value of the first value equals to 0; when the first node drops one CSI in the N1 CSIs, the first value increases by 1.

[0795] As an embodiment, the initial value of the first value equals to 0; when the first node drops one bit in the N1 CSIs, the first value increases by 1.

[0796] As an embodiment, the above method has the advantages of simplifying system design and reducing implementation complexity.

[0797] As an embodiment, the above method has the advantages of improving system flexibility and adaptability.

[0798] As an embodiment, the above method has the advantages of improving CSI reporting performance and overall system performance.

[0799] As an embodiment, the first value depending on the transmission of the N1 CSIs includes that when the first node transmits one CSI in the N1 CSIs, the first value is initialized to 0.

[0800] As an embodiment, the first value depending on the transmission of the N1 CSIs includes that when the first node transmits one bit in the N1 CSIs, the first value is initialized to 0.

[0801] As an embodiment, benefits of the above method include: supporting statistics of transceiving cases of continuous multiple CSIs.

[0802] As an embodiment, benefits of the above method include: supporting statistics of transceiving cases of continuous multiple bits.

[0803] As an embodiment, benefits of the above method include: improving performance of CSI reporting and overall performance of the system.

[0804] Embodiment 9

[0805] Embodiment 9 illustrates a schematic diagram of a relationship between a second condition and a second CSI according to an embodiment of the present application; as shown in FIG. 9.

[0806] In embodiment 9, when the second condition is satisfied, the first node in the present application gives up transmitting the second CSI in the second time domain resource; wherein the second CSI is one of the N1 CSIs; the second condition includes that the second time domain resource and the first signal overlap in time domain, or the second condition includes that the second time domain resource includes at least one DL (downlink) symbol.

[0807] Typically, whether the first node gives up transmitting the second CSI depends on whether the second condition is satisfied.

[0808] As an embodiment, when the second condition is satisfied, the second CSI is given up transmitting on the second time domain resource; the second condition includes that the second time domain resource and the resource carrying the first signal overlap in time domain or / and frequency domain, or the second condition includes that the second time domain resource includes at least one DL (downlink) symbol.

[0809] As an embodiment, when the second condition is satisfied, the second CSI is given up transmitting on the second time domain resource; the second condition includes that the second time domain resource and the first signal overlap in time domain.

[0810] As an embodiment, when the second condition is satisfied, the second CSI is given up transmitting on the second time domain resource; the second condition includes that the second time domain resource includes at least one DL (downlink) symbol.

[0811] As an embodiment, when the second condition is satisfied, the first CSI is given up transmitting on the second time domain resource; the second condition includes that the second time domain resource is used for DL (downlink) transmission.

[0812] As an embodiment, the second CSI is dropped on the second time-domain resource when a second condition is satisfied; the second condition comprises that the second time-domain resource is disabled.

[0813] As an embodiment, the second CSI is dropped on the second time-domain resource when a second condition is satisfied; the second condition comprises that the second time-domain resource and the first signal overlap in time domain, or the second time-domain resource comprises at least one DL(downlink) symbol.

[0814] As an embodiment, the second condition comprises at least one sub-condition; the second condition is satisfied when any one of the at least one sub-condition is satisfied; the second condition is not satisfied when all of the at least one sub-condition are not satisfied.

[0815] As a sub-embodiment of the above embodiment, one of the at least one sub-condition is that the second time-domain resource and the first signal overlap in time domain.

[0816] As a sub-embodiment of the above embodiment, one of the at least one sub-condition is that the second time-domain resource comprises at least one DL(downlink) symbol.

[0817] As a sub-embodiment of the above embodiment, one of the at least one sub-condition is that the second time-domain resource is used for DL(downlink) transmission.

[0818] As a sub-embodiment of the above embodiment, one of the at least one sub-condition is that the second time-domain resource is disabled.

[0819] As an embodiment, the essence of the above method comprises avoiding transmission conflict between CSI reporting and other signals.

[0820] As an embodiment, the benefit of the above method comprises improving the stability and robustness of the system.

[0821] As an embodiment, the benefit of the above method comprises improving the overall performance of the system.

[0822] As an embodiment, the first signal comprises a synchronization signal.

[0823] As an embodiment, the first signal comprises an SS / PBCH block.

[0824] As an embodiment, the first signal comprises a PDCCH(Physical Downlink Control Channel).

[0825] As an example, the first signal comprises a PDSCH (Physical Downlink Shared Channel).

[0826] As an example, the first signal carries a HARQ-ACK.

[0827] As an example, the first signal carries a DCI.

[0828] As an example, the first signal comprises a PUCCH (Physical Uplink Control Channel).

[0829] As an example, the first signal comprises a PUSCH (Physical Uplink Shared Channel).

[0830] As an example, the first signal has a higher priority than the first CSI.

[0831] As an example, the first signal comprises a retransmitted CSI report.

[0832] As an example, the first signal comprises a CSI report that is reported multiple times.

[0833] As an example, the first signal is a blank signal.

[0834] As an example, the first signal is a predefined signal.

[0835] As an example, the above method has the benefit of improving the stability and completeness of the system.

[0836] As an example, the above method has the benefit of better adapting to different transmission conditions and application scenarios, and improving the flexibility of the system.

[0837] Embodiment 10

[0838] Embodiment 10 illustrates a schematic diagram of the transceiving of N1 CSIs according to another embodiment of the present application; as shown in FIG. 10.

[0839] In embodiment 10, the transceiving of the N1 CSIs comprises the number of CSIs in the N1 CSIs that are not successfully received by the sender of the first information block, or the transceiving of the N1 CSIs comprises the number of bits in the N1 CSIs that are not successfully received by the sender of the first information block.

[0840] As one embodiment, the transceiving status of the N1 CSIs includes a number of CSIs in the N1 CSIs that are not successfully received by the transmitter of the first information block.

[0841] As one embodiment, the transceiving status of the N1 CSIs includes a number of bits in the N1 CSIs that are not successfully received by the transmitter of the first information block.

[0842] As one embodiment, the transceiving status of the N1 CSIs includes a number of CSIs in the N1 CSIs that are continuously unsuccessfully received by the transmitter of the first information block.

[0843] As one embodiment, the transceiving status of the N1 CSIs includes a number of bits in the N1 CSIs that are continuously unsuccessfully received by the transmitter of the first information block.

[0844] As one embodiment, the transceiving status of the N1 CSIs is a number of CSIs in the N1 CSIs that are continuously unsuccessfully received by the transmitter of the first information block.

[0845] As one embodiment, the transceiving status of the N1 CSIs is a number of bits in the N1 CSIs that are continuously unsuccessfully received by the transmitter of the first information block.

[0846] As one embodiment, the transceiving status of the N1 CSIs includes a number of CSIs in the N1 CSIs that are dropped by the first node and a number of CSIs in the N1 CSIs that are not successfully received by the transmitter of the first information block.

[0847] As one embodiment, the transceiving status of the N1 CSIs includes a number of bits in the N1 CSIs that are dropped by the first node and a number of bits in the N1 CSIs that are not successfully received by the transmitter of the first information block.

[0848] As one embodiment, the transceiving status of the N1 CSIs includes a number of CSIs in the N1 CSIs that are continuously dropped by the first node and a number of CSIs in the N1 CSIs that are continuously unsuccessfully received by the transmitter of the first information block.

[0849] As one embodiment, the transceiving status of the N1 CSIs includes a number of bits in the N1 CSIs that are continuously dropped by the first node and a number of bits in the N1 CSIs that are continuously unsuccessfully received by the transmitter of the first information block.

[0850] As an embodiment, benefits of the above method include: improving stability and robustness of the system and the scheme.

[0851] As an embodiment, benefits of the above method include: improving flexibility and adaptability of the system.

[0852] Typically, the sender of the first information block indicates the CSI that is not successfully received by the sender of the first information block.

[0853] Typically, the sender of the first information block indicates to the first node the CSI that is not successfully received by the sender of the first information block among the N1 CSI.

[0854] As an embodiment, benefits of the above method include: enhancing forward and backward compatibility of the system.

[0855] As an embodiment, the first value and the number of CSI that is not successfully received by the sender of the first information block among the N1 CSI are in a linear relationship.

[0856] As an embodiment, the first value and the number of CSI that is not successfully received by the sender of the first information block among the N1 CSI are in a nonlinear relationship.

[0857] As an embodiment, the first value is the number of CSI that is not successfully received by the sender of the first information block among the N1 CSI.

[0858] As an embodiment, the first value is the number of CSI that is not continuously successfully received by the sender of the first information block among the N1 CSI.

[0859] As an embodiment, the first value is equal to the number of CSI that is not successfully received by the sender of the first information block among the N1 CSI divided by the total number of CSI in the plurality of CSI.

[0860] As an embodiment, the first value is equal to the number of CSI that is not successfully received by the sender of the first information block among the N1 CSI divided by (N1+1).

[0861] As an embodiment, the first value and the number of bits that is not successfully received by the sender of the first information block among the N1 CSI are in a linear relationship.

[0862] As an embodiment, the first value and the number of bits that is not successfully received by the sender of the first information block among the N1 CSI are in a nonlinear relationship.

[0863] As an embodiment, the first value is equal to the number of bits in the N1 CSIs that are not successfully received by the transmitter of the first information block divided by the total number of bits in the plurality of CSIs.

[0864] As an embodiment, the first value is equal to the number of bits in the N1 CSIs that are not successfully received by the transmitter of the first information block divided by the total number of bits in the plurality of CSIs.

[0865] As an embodiment, the first value is equal to the number of bits in the N1 CSIs that are not successfully received by the transmitter of the first information block divided by the total number of bits in the plurality of CSIs.

[0866] As an embodiment, the first value is equal to the number of bits in the N1 CSIs that are not successfully received by the transmitter of the first information block divided by the total number of bits in the plurality of CSIs.

[0867] As an embodiment, the first value is equal to the number of bits in the N1 CSIs that are not successfully received by the transmitter of the first information block divided by the total number of bits in the plurality of CSIs.

[0868] As an embodiment, the first value is equal to the number of bits in the N1 CSIs that are not successfully received by the transmitter of the first information block divided by the total number of bits in the plurality of CSIs.

[0869] As an embodiment, the first value is equal to the number of bits in the N1 CSIs that are not successfully received by the transmitter of the first information block divided by the total number of bits in the plurality of CSIs.

[0870] As an embodiment, the initial value of the first value is equal to 0; the first value is increased by 1 when the transmitter of the first information block does not successfully receive one of the N1 CSIs.

[0871] As an embodiment, the initial value of the first value is equal to 0; the first value is increased by 1 when the transmitter of the first information block does not successfully receive one of the N1 CSIs.

[0872] As an embodiment, the initial value of the first value is equal to 0; the first value is increased by 1 when the transmitter of the first information block does not successfully receive one of the N1 CSIs.

[0873] As an embodiment, the initial value of the first number is equal to 0; the first number is increased by 1 when the first node gives up sending one bit of the N1 CSI, or the sender of the first information block fails to successfully receive one bit of the N1 CSI.

[0874] As an embodiment, the above method has the benefits of simplifying system design and reducing implementation complexity.

[0875] As an embodiment, the above method has the benefits of improving system flexibility and adaptability.

[0876] As an embodiment, the above method has the benefits of improving CSI reporting performance and overall system performance.

[0877] As an embodiment, the first number is initialized to 0 when the sender of the first information block successfully receives one CSI of the N1 CSI.

[0878] As an embodiment, the first number is initialized to 0 when the sender of the first information block successfully receives one bit of the N1 CSI.

[0879] As an embodiment, the first number is initialized to 0 when the first node sends one CSI of the N1 CSI, or the sender of the first information block successfully receives one CSI of the N1 CSI.

[0880] As an embodiment, the first number is initialized to 0 when the first node sends one bit of the N1 CSI, or the sender of the first information block successfully receives one bit of the N1 CSI.

[0881] As an embodiment, the above method has the benefits of supporting statistics of continuous multiple CSI transceiving cases.

[0882] As an embodiment, the above method has the benefits of supporting statistics of continuous multiple bit transceiving cases.

[0883] As an embodiment, the above method has the benefits of improving CSI reporting performance and overall system performance.

[0884] Embodiment 11

[0885] Embodiment 11 shows a schematic diagram of a second information block according to an embodiment of the present application; as shown in FIG. 11.

[0886] In Embodiment 11, the first node receives a second information block; wherein the second information block indicates that a second CSI, which is one of the N1 CSIs, is not successfully received by the sender of the first information block.

[0887] As an embodiment, the second information block is carried by a higher layer signaling.

[0888] As an embodiment, the second information block comprises a MAC CE.

[0889] As an embodiment, the second information block is carried by a MAC CE.

[0890] As an embodiment, the second information block comprises a DCI (Downlink Control Information).

[0891] As an embodiment, the second information block comprises at least one field in a DCI (Downlink Control Information).

[0892] As an embodiment, the second information block is carried by a DCI (Downlink Control Information).

[0893] As an embodiment, the second information block comprises control information.

[0894] As an embodiment, the second information block is carried by a physical layer signaling.

[0895] As an embodiment, the second information block is carried by a physical layer downlink signaling.

[0896] As an embodiment, the second information block is transmitted on a physical layer channel.

[0897] As an embodiment, the second information block is transmitted on a physical layer downlink channel.

[0898] As an embodiment, the second information block is transmitted on a PDCCH.

[0899] As an embodiment, the second information block is transmitted on a PDSCH.

[0900] As an embodiment, the second information block carries ACK information.

[0901] As an embodiment, the second information block carries NACK information.

[0902] As one embodiment, the second information block carries ACK or NACK information.

[0903] As one embodiment, the second information block carries ACK information corresponding to the second CSI.

[0904] As one embodiment, the second information block carries NACK information corresponding to the second CSI.

[0905] As one embodiment, the second information block carries ACK or NACK information corresponding to the second CSI.

[0906] As one embodiment, the second information block explicitly indicates that the second CSI is not successfully received by the sender of the first information block.

[0907] As one embodiment, the second information block implicitly indicates that the second CSI is not successfully received by the sender of the first information block.

[0908] As one embodiment, the second information block indirectly indicates that the second CSI is not successfully received by the sender of the first information block.

[0909] As one embodiment, the second information block indicates that the second CSI is successfully received by the sender of the first information block.

[0910] As one embodiment, the second information block indicates whether the second CSI is successfully received by the sender of the first information block.

[0911] As one embodiment, the second information block indicates whether the second CSI is successfully received by the sender of the first information block; when the second CSI is successfully received by the sender of the first information block, the second information block carries ACK information; when the second CSI is not successfully received by the sender of the first information block, the second information block carries NACK information.

[0912] As one embodiment, the second CSI is not successfully received by the sender of the first information block includes that the sender of the first information block fails to recover all information in the second CSI.

[0913] As one embodiment, the second CSI is not successfully received by the sender of the first information block includes that information in the second CSI is not recovered by the sender of the first information block.

[0914] As one embodiment, the second CSI not being successfully received by the sender of the first information block includes the information in the second CSI not being recoverable by the sender of the first information block.

[0915] As one embodiment, the second CSI not being successfully received by the sender of the first information block includes the sender of the first information block not receiving any signal.

[0916] As one embodiment, the second CSI not being successfully received by the sender of the first information block includes the sender of the first information block not receiving any signal on the resource on which the second CSI is sent.

[0917] As one embodiment, the second CSI not being successfully received by the sender of the first information block includes the sender of the first information block not receiving the signal carrying the second CSI.

[0918] As one embodiment, the second CSI not being successfully received by the sender of the first information block includes the sender of the first information block receiving the signal carrying the second CSI, but failing to demodulate the signal carrying the second CSI.

[0919] As one embodiment, the second CSI not being successfully received by the sender of the first information block includes the sender of the first information block receiving the signal carrying the second CSI, but failing to obtain the information bits in the signal carrying the second CSI.

[0920] As one embodiment, the second CSI not being successfully received by the sender of the first information block includes the sender of the first information block receiving the signal carrying the second CSI, demodulating the signal carrying the second CSI to obtain information bits, but failing a reception information check.

[0921] As one embodiment, the second CSI not being successfully received by the sender of the first information block includes the sender of the first information block receiving the signal carrying the second CSI, demodulating the signal carrying the second CSI to obtain information bits, but failing a reception cyclic redundancy check.

[0922] As one embodiment, the second CSI is not successfully received by the sender of the first information block includes that the sender of the first information block receives a signal carrying the second CSI, demodulates the signal carrying the second CSI to obtain information bits, but fails a reception information CRC (Cyclic Redundancy Checksum) check.

[0923] As one embodiment, the second CSI is successfully received by the sender of the first information block includes that the sender of the first information block successfully recovers all information in the second CSI.

[0924] As one embodiment, the second CSI is successfully received by the sender of the first information block includes that information in the second CSI is recovered by the sender of the first information block.

[0925] As one embodiment, the second CSI is successfully received by the sender of the first information block includes that the sender of the first information block receives a signal carrying the second CSI, demodulates the signal carrying the second CSI to obtain information bits, and passes a reception information check.

[0926] As one embodiment, the second CSI is successfully received by the sender of the first information block includes that the sender of the first information block receives a signal carrying the second CSI, demodulates the signal carrying the second CSI to obtain information bits, and passes a reception information cyclic redundancy check.

[0927] As one embodiment, the second CSI is successfully received by the sender of the first information block includes that the sender of the first information block receives a signal carrying the second CSI, demodulates the signal carrying the second CSI to obtain information bits, and passes a reception information CRC (Cyclic Redundancy Checksum) check.

[0928] As one embodiment, the above method has the benefit of enhancing the completeness and robustness of the system.

[0929] As one embodiment, the above method has the benefit of having small changes to existing systems and standards, and enhancing the forward and backward compatibility of the system.

[0930] Embodiment 12

[0931] Embodiment 12 illustrates a schematic diagram of CSI reporting configuration according to one embodiment of the present application; as shown in FIG. 12.

[0932] In embodiment 12, the multiple CSI reporting is multiple CSI reporting configured by one CSI reporting configuration.

[0933] As an embodiment, the multiple CSI reporting is multiple consecutive CSI reporting configured by one CSI reporting configuration.

[0934] As an embodiment, the multiple CSI reporting is multiple associated CSI reporting configured by one CSI reporting configuration.

[0935] As an embodiment, the method has the benefit of supporting multiple consecutive or associated CSI reporting.

[0936] As an embodiment, the method has the benefit of improving the performance of system CSI reporting.

[0937] As an embodiment, the first information block includes a first CSI reporting configuration used to configure the multiple CSI reporting.

[0938] As an embodiment, the first information block indicates a first CSI reporting configuration used to configure the multiple CSI reporting.

[0939] As an embodiment, the first information block includes an identity of a first CSI reporting configuration used to configure the multiple CSI reporting.

[0940] As an embodiment, the first information block indicates an identity of a first CSI reporting configuration used to configure the multiple CSI reporting.

[0941] As an embodiment, the first information block includes a first identity indicating a first CSI reporting configuration used to configure the multiple CSI reporting.

[0942] As an embodiment, the method has the benefit of better adapting to different transmission conditions and application scenarios, and improving the flexibility of the system.

[0943] As an embodiment, the first CSI reporting configuration indicates at least one of at least one RS resource used for measurement of the first CSI reporting and the second CSI reporting, a reporting type of the first CSI reporting and the second CSI reporting, or a reporting quantity of the first CSI reporting and the second CSI reporting.

[0944] As an embodiment, the first CSI reporting configuration indicates at least one of at least one RS resource for measurement of the reporting of the first CSI and the reporting of the second CSI, at least one RS resource for which the reporting of the first CSI and the reporting of the second CSI are, a reporting type of the reporting of the first CSI and the reporting of the second CSI, or a reporting quantity of the reporting of the first CSI and the reporting of the second CSI.

[0945] As a sub-embodiment of the above-mentioned embodiment, the measurement comprises at least one of channel measurement and interference measurement.

[0946] As a sub-embodiment of the above-mentioned embodiment, the reporting type indicates at least one of periodic reporting, semi-persistent reporting, or aperiodic reporting.

[0947] As a sub-embodiment of the above-mentioned embodiment, the reporting type indicates at least one of periodic reporting, semi-persistent reporting, aperiodic reporting, or event triggered reporting.

[0948] As an embodiment, the first information block indicates a third configuration, the third configuration is used to configure one inference, and the plurality of CSIs depend on an output of the one inference.

[0949] As an embodiment, the first information block comprises a first identifier, the first identifier indicates a third configuration, the third configuration is used to configure one inference, and the plurality of CSIs depend on an output of the one inference.

[0950] As an embodiment, the above-mentioned method has the benefits of simplifying the design of the system and improving the flexibility of the system.

[0951] As an embodiment, the third configuration comprises at least one RRC IE.

[0952] As an embodiment, the third configuration comprises at least one IE CSI-ReportConfig.

[0953] As an embodiment, the third configuration comprises part or all of the fields in at least one RRC IE.

[0954] As an embodiment, the third configuration comprises part or all of the fields in at least one IE CSI-ReportConfig.

[0955] As an embodiment, the third configuration is carried by at least one RRC IE.

[0956] As an embodiment, the third configuration is carried by at least one IE CSI-ReportConfig.

[0957] As one example, benefits of the above-described method include: using current standards and system designs.

[0958] Embodiment 13

[0959] Embodiment 13 illustrates a diagram of a first given inference according to one embodiment of the application; as shown in Figure 13. In Embodiment 13, the first given inference includes K1 sub-operations, the K1 being a positive integer not greater than 1.

[0960] In Embodiment 13, the K1 sub-operations are denoted as sub-operation #0, …, sub-operation #(K1-1), respectively.

[0961] As one example, the first given inference is the first inference.

[0962] As one example, the first given inference is the second inference.

[0963] As one example, the first given inference is the third inference.

[0964] As one example, each of the K1 sub-operations is trained-based.

[0965] As one example, at least one of the K1 sub-operations is trained-based.

[0966] As one example, each trained-based sub-operation of the K1 sub-operations is based on the same trained performer.

[0967] As one example, two sub-operations of the K1 sub-operations are based on different trained performers.

[0968] As one example, at least one of the K1 sub-operations is deployment- required.

[0969] As one example, at least one of the K1 sub-operations is loading- required.

[0970] As one example, all loading-required sub-operations of the K1 sub-operations are loaded from the same producer.

[0971] As one example, two loading-required sub-operations of the K1 sub-operations are loaded from different producers.

[0972] As one example, at least one of the K1 sub-operations is not trained-based.

[0973] As one embodiment, at least one of the K1 sub-operations is based on a codebook defined for precoding by 3GPP R18 or a version before 3GPP R18.

[0974] As one embodiment, one or more of the K1 sub-operations is based on AI.

[0975] As one embodiment, one or more of the K1 sub-operations includes inference.

[0976] As one embodiment, one or more of the K1 sub-operations includes AI inference.

[0977] As one embodiment, one or more of the K1 sub-operations includes AI inference for CSI.

[0978] As one embodiment, the AI (Artificial Intelligence) includes ML (Machine Learning).

[0979] As one embodiment, one or more of the K1 sub-operations includes pre-processing.

[0980] As one embodiment, one or more of the K1 sub-operations includes post-processing.

[0981] As one embodiment, two of the K1 sub-operations are serial, such as all sub-operations in FIG. 13(a), sub-operation #2 to sub-operation #(K1-1) in FIG. 13(b), and sub-operation #0 to sub-operation #(K1-4) in FIG. 13(c).

[0982] As one embodiment, two sub-operations being serial means that the output of one of the two sub-operations is used for the input of the other of the two sub-operations.

[0983] As one embodiment, two of the K1 sub-operations are parallel, such as sub-operation #0 and sub-operation #1 in FIG. 13(b), sub-operation #(K1-3) and sub-operation #(K1-2) in FIG. 13(c).

[0984] As one embodiment, two sub-operations being parallel means that the outputs of the two sub-operations are collectively used for the input of another sub-operation.

[0985] As one embodiment, the K1 sub-operations include one or more of convolution, pooling, concatenation, or activation.

[0986] As one embodiment, one of the K1 sub-operations includes a fully connected layer.

[0987] As one embodiment, one of the K1 sub-operations includes a pooling layer.

[0988] As one embodiment, one of the K1 sub-operations includes at least one convolution layer.

[0989] As one embodiment, one of the K1 sub-operations includes at least one encoding layer.

[0990] As one embodiment, two of the K1 sub-operations respectively include a fully connected layer and at least one encoding layer.

[0991] As one embodiment, an encoding layer includes at least one convolution layer and a pooling layer.

[0992] Embodiment 14A

[0993] Embodiment 14A illustrates a schematic diagram of RAN (Radio Access Network) domain AI / ML function deployment according to one embodiment of the present application; as shown in FIG. 14A. The gNB in Embodiment 14A can be replaced by, for example, an eNB, or a 6G base station, and the like network device.

[0994] AI / ML related functions include ML training function (also referred to as AI training, or AI / ML training), ML testing function, ML inference function (also referred to as AI inference, or AI / ML inference), and the like. The ML training function, the ML testing function, and the ML inference function can be deployed independently, or can be co-located deployed. The deployment of AI / ML related functions can be implemented through software, such as the download and / or running of executable files; or can be implemented through a combination of software and hardware, such as accelerating specific computing units through hardware to improve operation speed or save power consumption.

[0995] For ML training function, it can be deployed in cross-domain management system, or domain-specific management system, which is used to manage RAN domain or CN (Core Network) domain. For example, for MDA (Management Data Analytics) ML training function can be deployed in MDAF (MDA Function); for network data analytics ML training can be deployed in NWDAF (Network Data Analytics Function), i.e. ML training function is MTLF (Model Training logical function).

[0996] For ML inference function, it can also be deployed in cross-domain management system, or domain-specific management system; for example, ML inference function is MDAF, or ML inference function is AnLF (Analytics logical function) in NWDAF.

[0997] Similarly, ML testing function can also be deployed in cross-domain management system, or domain-specific management system.

[0998] In embodiment 14A, RAN domain ML training function 1402 is located in RAN domain management function 1403; and ML inference function is located in base station, i.e. AI / ML inference function 1404 is located in gNB 1405, and AI / ML inference function 1406 is located in gNB 1407.

[0999] In FIG. 14A, the management of ML inference function of multiple base stations is completed by RAN domain management function 1403, i.e. data interaction is performed with RAN domain MnS (Management Service) consumer / cross-domain management 1401 (as shown by the dashed arrow in FIG. 14A).

[1000] Optionally, the management of ML inference function can also be completed by the base station itself, i.e. each base station can independently perform data interaction with RAN domain MnS consumer / cross-domain management 1401.

[1001] It should be noted that embodiment 14A is only a non-limiting implementation; optionally, RAN domain ML training function can also be deployed in base station; or optionally, part of base stations deploy ML inference function and RAN domain ML training function, and part of base stations only deploy ML inference function.

[1002] As one embodiment, one gNB (or base station) in embodiment 14A is the second node in the present application.

[1003] As one embodiment, the second processor in the present application comprises an AL / ML inference function in FIG. 14A, i.e., 1404 or 1406.

[1004] Embodiment 14B

[1005] Embodiment 14B illustrates a schematic diagram of AI / ML function deployment of a UE according to one embodiment of the present application; as shown in FIG. 14B. The RAN domain ML training function 1505 in FIG. 14B is optional.

[1006] The UE function 1504 is deployed in the first node in the present application, and the UE function 1504 comprises an AI / ML inference function 1506; the AI / ML inference function 1506 uses a ML model (also referred to as an AI model) for inference; one ML model is usually trained before being used for AI / ML inference.

[1007] As one embodiment, the plurality of CSIs in the present application are obtained through inference of the AI / ML inference function 1506.

[1008] As one embodiment, the target CSI in the present application is obtained through inference of the AI / ML inference function 1506.

[1009] As one embodiment, the first processor in the present application comprises an AL / ML inference function 1506 in FIG. 14B.

[1010] As one embodiment, the UE function 1504 comprises a RAN domain ML training function 1505, which runs training data through a ML model, derives a related loss, adjusts parameters of the ML model based on the calculated loss; the ML training comprises at least one of ML initial training, ML re-training, and reinforcement learning.

[1011] The above embodiments can reduce the complexity of the base station, or save the air interface resources caused by reporting training data; however, the above embodiments put higher requirements on the processing capability of the UE side.

[1012] Optionally, the UE function 1504 further comprises a CN domain ML training function (not included in FIG. 14B).

[1013] Optionally, the UE function 1504 further comprises an AI / ML deployment function (not included in FIG. 14B) for loading ML models and data.

[1014] As one embodiment, the first node indicates whether the ML training function (RAN domain or CN domain) is supported through capability reporting, which is RRC signaling or NAS (Non-Access Stratum) signaling.

[1015] As one embodiment, the ML model, and related metadata, is loaded by the first node from a network device or a remote server.

[1016] Optionally, the UE function 1504 is a 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 indicated by double-headed arrow 1507).

[1017] Optionally, the UE function 1504 is a 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 management data requests, ML model activation, and / or ML training, etc. (as indicated by double-headed arrow 1507).

[1018] As one embodiment, the ML model is based on a neural network.

[1019] As one embodiment, the ML model is based on a CNN (Conventional Neural Networks).

[1020] As one embodiment, the ML model is based on a Transformer architecture.

[1021] Embodiments 15A-15B

[1022] Embodiments 15A-15B respectively illustrate a schematic diagram of the first node deploying a first given inference according to one embodiment of the present application; as respectively shown in FIGS. 15A-15B.

[1023] In embodiment 15A, the first node makes a request to a first producer to load a first given inference, and obtains the first given inference from the first producer; the first given inference is the first inference or the second inference.

[1024] As one embodiment, the deployment comprises obtaining the first given inference.

[1025] As one embodiment, the deployment comprises obtaining an AI entity.

[1026] As one embodiment, the deployment comprises obtaining an AI entity that executes the first given inference.

[1027] As one embodiment, the deployment comprises obtaining an AI entity that comprises an AI function that executes the first given inference.

[1028] As one embodiment, the deployment comprises loading the first given inference.

[1029] As one embodiment, the deployment comprises making a request to load the first given inference.

[1030] As one embodiment, the first given inference is obtained from loading at a serving cell of the first node.

[1031] As one embodiment, the first given inference is obtained from loading at a maintaining base station of a serving cell of the first node.

[1032] As one embodiment, the first given inference is obtained from loading at a core network.

[1033] As one embodiment, the first given inference is obtained from loading at a first producer.

[1034] As one embodiment, the deployment is done by an AI function.

[1035] As one embodiment, the deployment is done by an AI function deployed at the first node.

[1036] As one embodiment, the deployment is done by an AI deployment function.

[1037] As one embodiment, the deployment is done by an AI deployment function deployed at the first node.

[1038] As one embodiment, the deployment is done by an AI inference function.

[1039] As one embodiment, the deploying is done by an AI inference function deployed at the first node.

[1040] As one embodiment, the deploying is done by an AI entity.

[1041] As one embodiment, the deploying is done by an AI entity deployed at the first node.

[1042] As one embodiment, the deploying is done by an AI entity having a deployment function.

[1043] As one embodiment, the deploying is done by an AI entity having a deployment function deployed at the first node.

[1044] As one embodiment, the deploying is done by an AI entity having an inference function.

[1045] As one embodiment, the deploying is done by an AI entity having an inference function deployed at the first node.

[1046] As one embodiment, the deploying includes obtaining the first given inference from a first producer.

[1047] As one embodiment, the deploying includes making a request to a first producer to load the first given inference.

[1048] As one embodiment, the deploying includes loading the first given inference from a first producer.

[1049] As one embodiment, the first producer generates and provides an AL entity.

[1050] As one embodiment, the first producer generates and provides an AL function.

[1051] As one embodiment, the first producer is a producer of the first given inference.

[1052] As one embodiment, the first producer includes an AL entity producer.

[1053] As one embodiment, the first producer includes an AL function producer.

[1054] As one embodiment, the first producer includes an AL deployment producer.

[1055] As one embodiment, the first producer comprises an AL training producer.

[1056] As one embodiment, the first producer comprises an AL training producer.

[1057] As one embodiment, the first producer comprises an AL inference producer.

[1058] As one embodiment, the first producer comprises a producer of deployment of AL entity.

[1059] As one embodiment, the first producer comprises a producer of loading of AL entity.

[1060] As one embodiment, the first producer comprises an MnS (Management Service) producer.

[1061] As one embodiment, the sender of the first information block is the first producer.

[1062] As one embodiment, the sender of the first information block is different from the first producer.

[1063] As one embodiment, the training for obtaining the first given inference is performed by the first producer.

[1064] As one embodiment, the performer of the training for obtaining the first given inference is different from the first producer.

[1065] As one embodiment, the AI comprises ML (Machine Learning).

[1066] In embodiment 15B, the first node makes a request to a second producer for loading a first given inference, and obtains the first given inference from the first producer; the first given inference is the first inference or the second inference.

[1067] As one embodiment, the deployment comprises obtaining the first given inference.

[1068] As one embodiment, the deployment comprises obtaining an AI entity or AI function that performs the first given inference.

[1069] As one embodiment, the deployment comprises loading the first given inference.

[1070] As one embodiment, the deployment comprises making a request for loading the first given inference.

[1071] As one embodiment, the deployment is done by an AI function deployed at the first node.

[1072] As one embodiment, the deployment is done by an AI deployment function deployed at the first node.

[1073] As one embodiment, the deployment is done by an AI entity with a deployment function.

[1074] As one embodiment, the second producer generates and provides an AI entity or AI function.

[1075] As one embodiment, the second producer comprises a MnS (Management Service) producer.

[1076] As one embodiment, the second producer comprises a producer of training of AI models.

[1077] As one embodiment, the second producer is a target receiver of the first channel information reporting.

[1078] As one embodiment, the second producer is different from the target receiver of the first channel information reporting.

[1079] As one embodiment, the second producer is a serving cell of the first node.

[1080] As one embodiment, the second producer is a maintaining base station of the serving cell of the first node.

[1081] As one embodiment, the second producer is a core network.

[1082] As one embodiment, the first given inference is obtained from a serving cell of the first node.

[1083] As one embodiment, the first given inference is obtained from a maintaining base station of the serving cell of the first node.

[1084] As one embodiment, the first given inference is obtained from a core network.

[1085] As one embodiment, training for obtaining the first given inference is performed by the second producer.

[1086] As one embodiment, the second producer is different from the first producer.

[1087] As one embodiment, the first producer generates and provides an AL entity.

[1088] As one embodiment, the first producer generates and provides an AL function.

[1089] As one embodiment, the first producer is a producer of the first given inference.

[1090] As one embodiment, the first producer comprises an AL entity producer.

[1091] As one embodiment, the first producer comprises an AL function producer.

[1092] As one embodiment, the first producer comprises an AL deployment producer.

[1093] As one embodiment, the first producer comprises an AL loading producer.

[1094] As one embodiment, the first producer comprises an AL training producer.

[1095] As one embodiment, the first producer comprises an AL inference producer.

[1096] As one embodiment, the first producer comprises a producer of deployment of an AL entity.

[1097] As one embodiment, the first producer comprises a producer of loading of an AL entity.

[1098] As one embodiment, the first producer comprises a MnS (Management Service) producer.

[1099] Embodiment 16

[1100] Embodiment 16 illustrates a schematic diagram of an artificial intelligence or machine learning based processing system according to one embodiment of the present application; as shown in FIG. 16. FIG. 16(a) comprises a third processor, a fourth processor and a fifth processor, and FIG. 16(b) comprises a third processor, a fourth processor, a fifth processor and a sixth processor.

[1101] In embodiment 16(a), the third processor sends a first data set to the fourth processor, and a second data set to the fifth processor; the fourth processor generates a target first group of parameters according to the first data set, and the fourth processor sends the generated target first group of parameters to the fifth processor; the fifth processor processes the second data set using the target first group of parameters to obtain a first output. In FIG. 16(a), a first feedback is optional.

[1102] In embodiment 16(b), the third handler sends a first data set to the fourth handler, and a second data set to the fifth handler; the fourth handler generates a target first-type parameter group according to the first data set, and sends the generated target first-type parameter group to the fifth handler; the fifth handler processes the second data set using the target first-type parameter group to obtain a first-type output, and sends the first-type output to the sixth handler. In FIG. 16(b), the first-type feedback and the second-type feedback are optional.

[1103] As an embodiment, in FIG. 16(a), the fifth handler sends the first-type output to the second node in the present application.

[1104] As an embodiment, in FIG. 16(a), a single-sided AI model is used for beam prediction or channel information prediction, and the fifth handler performs at least one of the first inference and the second inference, at least one of which is used for beam prediction or channel information prediction.

[1105] As an embodiment, in FIG. 16(b), a two-sided AI model is used for CSI compression, at least one of the first inference and the second inference is used for compressing CSI, the third inference is used for recovering CSI, the fifth handler performs at least one of the first inference and the second inference, and the sixth handler includes the third inference.

[1106] As an embodiment, in FIG. 16(b), a two-sided AI model is used for CSI prediction and compression, at least one of the first inference and the second inference is used for predicting and compressing CSI, the third inference is used for recovering CSI, the fifth handler performs at least one of the first inference and the second inference, and the sixth handler includes the third inference.

[1107] As an embodiment, the fifth handler performs at least one of the first inference and the second inference.

[1108] As an embodiment, the sixth handler includes the third inference.

[1109] As an embodiment, the fifth handler sends a first-type feedback to the fourth handler, and the first-type feedback is used to trigger recalculation or update of the target first-type parameter group.

[1110] As an embodiment, the sixth processor sends second type feedback to the third processor, the second type feedback is used to generate the first data set or the second data set, or the second type feedback is used to trigger the sending of the first data set or the sending of the second data set.

[1111] As an embodiment, the third processor generates the first data set and the second data set according to the measurement of the first type wireless signal, the first type wireless signal includes downlink RS.

[1112] As an embodiment, the fifth processor belongs to the first node, and the sixth processor belongs to the second node.

[1113] As an embodiment, the target CSI belongs to the first type output.

[1114] As an embodiment, the plurality of CSIs belong to the first type output.

[1115] As an embodiment, the second data set includes the input of the first inference.

[1116] As an embodiment, the second data set includes the input of the second inference.

[1117] As an embodiment, the second data set includes the input of the first inference and the second inference.

[1118] As an embodiment, the second data set includes information obtained based on the first information block.

[1119] As an embodiment, the first data set includes training data.

[1120] As an embodiment, the fourth processor belongs to the producer of the first inference.

[1121] As an embodiment, the fourth processor belongs to the producer of the second inference.

[1122] As an embodiment, the fourth processor includes an AI training producer.

[1123] As an embodiment, the fourth processor includes an AI training function.

[1124] As an embodiment, the fourth processor is used for model training, and the trained model is described by the target first type parameter group.

[1125] As an embodiment, the fourth processor belongs to the first node.

[1126] The above embodiments avoid passing the first dataset to the second node.

[1127] As one embodiment, the fourth processor belongs to the second node.

[1128] The above embodiments support joint training, optimizing system performance.

[1129] As one embodiment, the fourth processor belongs to the core network.

[1130] The above embodiments support network-wide joint training, further optimizing system performance.

[1131] As one embodiment, the second dataset includes inference data.

[1132] As one embodiment, the fifth processor includes an AI inference producer.

[1133] As one embodiment, the fifth processor includes an AI inference function.

[1134] As one embodiment, the fifth processor belongs to the first node.

[1135] As one embodiment, the fifth processor constructs a model according to the target first-type parameter group, and then inputs the second dataset into the constructed model to obtain the first-type output.

[1136] As one embodiment, the first inference is described by the target first-type parameter group.

[1137] As one embodiment, the second inference is described by the target first-type parameter group.

[1138] As one embodiment, the first inference and the second inference are described by the target first-type parameter group.

[1139] As one embodiment, the target first-type parameter group is used to construct the first inference.

[1140] As one embodiment, the target first-type parameter group is used to construct the second inference.

[1141] As one embodiment, the target first-type parameter group is used to construct the first inference and the second inference.

[1142] As one embodiment, the fifth processor includes the third inference.

[1143] As an embodiment, the fifth processor generates a recovery data set according to the first type of output, and an error of the recovery data set and the second data set is used to generate the first type of feedback.

[1144] As a sub-embodiment of the above embodiment, the generation of the recovery data set employs a similar third inference.

[1145] As an embodiment, the first type of feedback is used to reflect the performance of the trained model; when the performance of the trained model cannot meet the requirement, the fourth processor can recalculate the target first type of parameter group.

[1146] As an embodiment, when the error is too large or the update is not performed for too long a time, the performance of the trained model is considered to be unable to meet the requirement.

[1147] As an embodiment, the target first type of parameter group includes one or more of a convolution kernel size, a convolution layer number, a convolution step, a pooling kernel size, a pooling kernel step, a pooling function, an activation function, or a feature map number.

[1148] As an embodiment, the target first type of parameter group includes one or more of a convolution kernel, a pooling kernel, a pooling function, an activation function, a parameter of the pooling function, or a parameter of the activation function.

[1149] Embodiment 17

[1150] Embodiment 17 illustrates a structural block diagram of a processing apparatus in a first node according to an embodiment of the present application; as shown in FIG. 17. In FIG. 17, the processing apparatus 1700 in the first node includes a first processor 1701.

[1151] The first processor 1701 receives a first information block; and transmits a target CSI in a first time domain resource only when a first condition is met.

[1152] In embodiment 17, the first information block indicates reporting of a plurality of CSIs; the plurality of CSIs include N1 CSIs and the target CSI, N1 being a positive integer; the first condition includes that the first value is less than a first threshold; and the first value depends on a transmission and reception situation of the N1 CSIs.

[1153] As an embodiment, any CSI in the plurality of CSIs depends on an output of an inference.

[1154] As an embodiment, the first CSI and the target CSI depend on an output of a first inference and an output of a second inference respectively, the first CSI being one of the N1 CSIs; the output of the first inference including a first output and a second output, the first CSI depending on the first output, the input of the second inference including the second output.

[1155] As an embodiment, the transceiving status of the N1 CSIs includes a number of CSIs of the N1 CSIs that are dropped by the first node for transmission.

[1156] As an embodiment, the first processor 1701 drops transmission of a second CSI in a second time domain resource when a second condition is met; wherein the second CSI is one of the N1 CSIs; the second condition including that the second time domain resource and a first signal overlap in time domain, or the second condition including that the second time domain resource includes at least one DL (downlink) symbol.

[1157] As an embodiment, the transceiving status of the N1 CSIs includes a number of CSIs of the N1 CSIs that are not successfully received by a transmitter of the first information block, or the transceiving status of the N1 CSIs includes a number of bits of the N1 CSIs that are not successfully received by the transmitter of the first information block.

[1158] As an embodiment, the first processor 1701 receives a second information block; wherein the second information block indicates that a second CSI is not successfully received by the transmitter of the first information block, the second CSI being one of the N1 CSIs.

[1159] As an embodiment, the reporting of the plurality of CSIs is a plurality of CSI reporting configured by a same CSI reporting configuration.

[1160] As an embodiment, the first processor 1701 transmits a third information block; wherein the third information block indicates the first threshold.

[1161] As an embodiment, the first inference and the second inference are inferences corresponding to a first identity.

[1162] As an embodiment, the first information block indicates a first RS resource set, the first RS resource set including one or more RS resources; at least one of the input of the first inference and the input of the second inference depending on measurement based on the first RS resource set.

[1163] As an embodiment, the first inference and the second inference are training-based or AI-based.

[1164] As an example, the inference in this application is training based or AI based.

[1165] As an example, the AI (Artificial Intelligence) includes ML (Machine Learning).

[1166] As an example, the first processor 1701 deploys the first inference.

[1167] As an example, the first processor 1701 deploys the second inference.

[1168] As an example, the first inference and the second inference are obtained by loading.

[1169] As an example, the first processor 1701 receives a signal in the first RS resource set.

[1170] As an example, the first processor 1701 receives a reference signal in the first RS resource set, the first RS resource set including one or more RS resources.

[1171] As an example, the first node is a user equipment.

[1172] As an example, the first node is a terminal.

[1173] As an example, the first node is a relay node.

[1174] As an example, the user equipment is a terminal.

[1175] As an example, the first processor 1701 includes at least one of {antenna 452, receiver / transmitter 454, receiving processor 456, transmitting processor 468, multi-antenna receiving processor 458, multi-antenna transmitting processor 457, controller / processor 459, memory 460, data source 467} in embodiment 4.

[1176] Embodiment 18

[1177] Embodiment 18 illustrates a structural block diagram of a processing apparatus in a second node according to an embodiment of the present application; as shown in FIG. 18. In FIG. 18, the processing apparatus 1800 in the second node includes a second processor 1801.

[1178] The second processor 1801 transmits a first information block; receives a target CSI on the first time domain resource only when a first condition is met;

[1179] In Embodiment 18, the first information block indicates reporting of multiple CSIs; the multiple CSIs include N1 CSIs and the target CSI, N1 being a positive integer; the first condition includes that the first value is less than a first threshold; and the first value depends on a transmission / reception condition of the N1 CSIs.

[1180] As an embodiment, the second processor 1801 monitors whether the target CSI is transmitted by the receiver of the first information block on the first time domain resource.

[1181] As an embodiment, any CSI in the multiple CSIs depends on an output of reasoning.

[1182] As an embodiment, a first CSI and the target CSI respectively depend on an output of a first reasoning and an output of a second reasoning, the first CSI being one of the N1 CSIs; the output of the first reasoning includes a first output and a second output, the first CSI depending on the first output, and the input of the second reasoning including the second output.

[1183] As an embodiment, the transmission / reception condition of the N1 CSIs includes a number of CSIs in the N1 CSIs that are abandoned by the first node for transmission.

[1184] As an embodiment, when a second condition is satisfied, the receiver of the first information block abandons transmitting a second CSI in a second time domain resource; the second CSI being one of the N1 CSIs; the second condition including that the second time domain resource and a first signal overlap in time domain, or the second condition including that the second time domain resource includes at least one DL (downlink) symbol.

[1185] As an embodiment, when a second condition is satisfied, the second processor 1801 abandons receiving a second CSI in a second time domain resource; the second CSI being one of the N1 CSIs; the second condition including that the second time domain resource and a first signal overlap in time domain, or the second condition including that the second time domain resource includes at least one DL (downlink) symbol.

[1186] As an embodiment, the second processor 1801 monitors whether the second CSI is transmitted by the receiver of the first information block on the second time domain resource.

[1187] As an embodiment, the transceiving status of the N1 CSIs comprises a number of CSIs in the N1 CSIs that are not successfully received by a transmitter of the first information block, or the transceiving status of the N1 CSIs comprises a number of bits in the N1 CSIs that are not successfully received by the transmitter of the first information block.

[1188] As an embodiment, the second processor 1801 transmits a second information block; wherein the second information block indicates that a second CSI is not successfully received by the second processor 1801, the second CSI being one of the N1 CSIs.

[1189] As an embodiment, the reporting of the plurality of CSIs is a plurality of CSI reporting configured by a same CSI reporting configuration.

[1190] As an embodiment, the second processor 1801 receives a third information block; wherein the third information block indicates the first threshold.

[1191] As an embodiment, the first inference and the second inference are both inferences corresponding to a first identity.

[1192] As an embodiment, the first information block indicates a first set of RS resources; at least one of an input of the first inference and an input of the second inference depends on a measurement based on the first set of RS resources.

[1193] As an embodiment, the first inference and the second inference are training-based or AI-based.

[1194] As an embodiment, the inference in the present application is training-based or AI-based.

[1195] As an embodiment, the AI (Artificial Intelligence) comprises ML (Machine Learning).

[1196] As an embodiment, the first inference and the second inference are obtained by loading.

[1197] As an embodiment, the second processor 1801 transmits a signal in the first set of RS resources.

[1198] As an embodiment, the second processor 1801 transmits a reference signal in the first set of RS resources, the first set of RS resources comprising one or more RS resources.

[1199] As an embodiment, the second node comprises a base station.

[1200] As one embodiment, the second node comprises a core network.

[1201] As one embodiment, the second node comprises a base station and a core network.

[1202] As one embodiment, the second node comprises a relay node.

[1203] As one embodiment, the second node comprises a user equipment.

[1204] As one embodiment, the user equipment is a terminal.

[1205] As one embodiment, the second processor 1801 comprises at least one of {antenna 420, receiver / transmitter 418, receive processor 470, transmit processor 416, multi-antenna receive processor 472, multi-antenna transmit processor 471, controller / processor 475, memory 476} in embodiment 4.

[1206] Those skilled in the art can understand that all or part of the steps of the above method can be instructed by a program to complete the relevant hardware, and the program can be stored in a computer readable storage medium, such as a read-only memory, a hard disk, or an optical disk, etc. Alternatively, all or part of the steps of the above embodiment can also be implemented using one or more integrated circuits. Correspondingly, each module unit in the above embodiment can be implemented in the form of hardware or in the form of a software function module, and the present application is not limited to any specific form of combination of software and hardware. The user equipment, terminal and UE in the present application include but are not limited to unmanned aerial vehicles, communication modules on unmanned aerial vehicles, remote control aircrafts, aircrafts, small aircrafts, mobile phones, tablet computers, notebooks, vehicle-mounted communication devices, wireless sensors, network cards, Internet of Things terminals, RFID terminals, NB-IOT terminals, MTC (Machine Type Communication) terminals, eMTC (enhanced MTC) terminals, data cards, network cards, vehicle-mounted communication devices, low-cost mobile phones, low-cost tablet computers, and other wireless communication devices. The base station or system device in the present application includes but is not limited to macro cellular base stations, micro cellular base stations, home base stations, relay base stations, gNB (NR Node B) NR Node B, TRP (Transmitter Receiver Point) and other wireless communication devices.

[1207] The above is only a preferred embodiment of the present application, and is not intended to limit the protection scope of the present application. Any changes and modifications made on the basis of the embodiments described in the specification, if they can obtain similar partial or overall technical effects, should be considered as obvious and belong to the protection scope of the present application.

Claims

1. A method in a first node for wireless communication, characterized by, The method comprises: receiving a first information block; the first information block indicates reporting of a plurality of CSI; only when a first condition is met, transmitting a target CSI in a first time domain resource; wherein the plurality of CSI comprises N1 CSI and the target CSI, N1 being a positive integer; the first condition comprises a first value being less than a first threshold; the first value depends on a transmission and reception condition of the N1 CSI.

2. The method of claim 1, wherein, any CSI in the plurality of CSI depends on an output of an inference.

3. The method according to claim 1 or 2, characterized in that, a first CSI and the target CSI respectively depend on an output of a first inference and an output of a second inference, the first CSI being one of the N1 CSI; the output of the first inference comprises a first output and a second output, the first CSI depending on the first output, the input of the second inference comprising the second output.

4. The method according to any one of claims 1 to 3, characterized in that, the transmission and reception condition of the N1 CSI comprises a number of CSI in the N1 CSI that is abandoned to be transmitted by the first node.

5. The method according to any one of claims 1 to 4, characterized in that, The method comprises: when a second condition is met, abandoning to transmit a second CSI in a second time domain resource; wherein the second CSI is one of the N1 CSI; the second condition comprises that the second time domain resource and a first signal overlap in time domain, or the second condition comprises that the second time domain resource comprises at least one DL (downlink) symbol.

6. The method according to any one of claims 1 to 5, characterized in that, the transmission and reception condition of the N1 CSI comprises a number of CSI in the N1 CSI that is not successfully received by a transmitter of the first information block, or the transmission and reception condition of the N1 CSI comprises a number of bits in the N1 CSI that is not successfully received by the transmitter of the first information block.

7. The method of claim 6, wherein, The method comprises: receiving a second information block; wherein the second information block indicates that a second CSI is not successfully received by the transmitter of the first information block, the second CSI being one of the N1 CSI.

8. The method according to any one of claims 1 to 6, characterized in that, the reporting of the plurality of CSI is configured by a same CSI reporting configuration.

9. The method according to any one of claims 1 to 8, characterized in that, The method comprises: transmitting a third information block; wherein the third information block indicates the first threshold.

10. A terminal, characterized by comprising: The terminal comprises one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is configured to store computer program codes, the computer program codes comprising computer instructions, and the one or more processors are configured to invoke the computer instructions to enable the terminal to perform the method according to 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 block; the first information block indicates reporting of a plurality of CSI; only when a first condition is met, receiving a target CSI in a first time domain resource; wherein the plurality of CSI comprises N1 CSI and the target CSI, N1 being a positive integer; the first condition comprises a first value being less than a first threshold; the first value depends on a transmission and reception condition of the N1 CSI.

12. The method of claim 11, wherein, any CSI in the plurality of CSI depends on an output of an inference.

13. The method according to claim 11 or 12, characterized in that, The first CSI and the target CSI depend on an output of a first inference and an output of a second inference respectively, the first CSI being one of the N1 CSIs; the output of the first inference comprising a first output and a second output, the first CSI depending on the first output, the input of the second inference comprising the second output.

14. The method of any one of claims 11-13, wherein, The transceiving condition of the N1 CSIs comprises a number of CSIs of the N1 CSIs that are dropped by the first node for transmission.

15. The method according to any one of claims 11 to 14, characterized in that, The receiver of the first information block drops transmission of a second CSI in a second time domain resource when a second condition is met; wherein the second CSI is one of the N1 CSIs; the second condition comprises that the second time domain resource and a first signal overlap in time domain, or the second condition comprises that the second time domain resource comprises at least one DL (downlink) symbol.

16. The method according to any one of claims 11 to 15, characterized in that, The transceiving condition of the N1 CSIs comprises a number of CSIs of the N1 CSIs that are not successfully received by the transmitter of the first information block, or the transceiving condition of the N1 CSIs comprises a number of bits of the N1 CSIs that are not successfully received by the transmitter of the first information block.

17. The method of claim 16, wherein, The method comprises: transmitting a second information block; wherein the second information block indicates that a second CSI is not successfully received, the second CSI being one of the N1 CSIs.

18. The method of any one of claims 11-16, wherein, The reporting of the plurality of CSIs is a plurality of CSI reporting configured by a same CSI reporting configuration.

19. The method of any one of claims 11-18, wherein, The method comprises: receiving a third information block; wherein the third information block indicates the first threshold.

20. A base station, comprising: The base station comprises one or more processors and a memory; The memory is coupled to the one or more processors, and the memory is configured to store computer program codes, the computer program codes comprising 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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