Channel information reporting method and apparatus used in node for wireless communication

Abstract

Disclosed in the present application are a channel information reporting method and apparatus used in a node for wireless communication. The method comprises: a first node sending a first information block, wherein the first information block indicates a first time interval; and sending a first channel information report, wherein the generation of the first channel information report occupies at least one first-type resource, the first channel information report occupies the at least one first-type resource starting from a first moment, and the first moment depends on the first time interval. The advantages of the method include: ensuring that a sending end and a receiving end have consistent understanding of the occupancy situation of a first-type resource, being better adapted to various different processing capabilities, being better adapted to various different application scenarios or terminals, and having good flexibility and adaptability.

Description

A method and apparatus for reporting channel information in nodes used in wireless communication. Technical Field

[0001] This application relates to transmission methods and apparatus in wireless communication systems, and more particularly to schemes and apparatus for channel information reporting in wireless communication systems. Background Technology

[0002] In traditional wireless communication, the UE (User Equipment) calculates CSI (channel state information) by measuring downlink reference signals. The CSI includes, but is not limited to, one or more of CRI (Channel state information-reference signal resource indicator), RI (Rank indicator), PMI (Precoding Matrix indicator), or CQI (Channel quality indicator).

[0003] With the adoption of new technologies, the increase in the number of antennas, the diversification of application scenarios, and the increasing demands on system performance, traditional measurement and reporting methods incur significant redundancy overhead. Therefore, in NR R (release) 18, research on AI (Artificial Intelligence) / ML (Machine Learning) technologies was initiated to explore their impact on system performance and design. Compared to traditional processing methods, AI / ML offers advantages such as training-based and deployment-required features. Furthermore, AI / ML is a key candidate technology for future 6G communications. Once AI / ML functionality is introduced, existing channel information-related measurement mechanisms, generation and / or reporting mechanisms, and related configuration signaling may become inadequate to meet the demands of AI / ML. Summary of the Invention

[0004] The applicant discovered through research that generating a channel information report requires certain resources, and the transmitting and receiving ends typically need a consistent understanding of the resource occupancy time. Therefore, determining the resource occupancy time is a key issue that needs to be addressed. To address this issue, this application discloses a solution. It should be noted that although many embodiments of this application are geared towards AI / ML, this application is also applicable to other solutions, such as traditional channel information reporting schemes. Furthermore, adopting a unified solution across different scenarios (including but not limited to AI / ML-based schemes and traditional information reporting schemes) helps reduce hardware complexity and cost. Where there is no conflict, the embodiments and features in the embodiments of the first node of this application can be applied to the second node, and vice versa. Where there is no conflict, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other.

[0005] As an example, the interpretation of terms in this application is based on the definitions in the 3GPP specification protocol TS38 series.

[0006] As an example, the interpretation of the terms in this application is based on the definitions in the 3GPP specification protocol TS28 series.

[0007] This application discloses a method used in a first node of wireless communication, characterized by comprising:

[0008] Send a first information block, the first information block indicating a first time interval; send a first channel information report;

[0009] The generation of the first channel information report occupies at least one first type of resource; the first channel information report occupies the at least one first type of resource starting from a first moment, and the first moment depends on the first time interval.

[0010] As an example, the problem this application aims to solve includes: from what time does channel information reporting begin to occupy the resources used for generating the channel information reporting?

[0011] As an example, the essence of the above method is that the timing at which channel information reporting begins to occupy the first type of resource depends on the indication (i.e., the first time interval) of the sender of the channel information reporting.

[0012] As an example, the advantages of the above method include ensuring that the sending and receiving ends have a consistent understanding of the first type of resource occupancy.

[0013] As an example, the advantages of the above method include: better adaptability to various application scenarios or terminals.

[0014] As an example, the advantages of the above method include: good flexibility and adaptability.

[0015] As an example, the advantages of the above method include: better adaptability to various processing capabilities.

[0016] As an example, the advantages of the above method include: better adaptability to various different terminal capabilities.

[0017] As one example, the first node is a user equipment.

[0018] As an example, the first node is a relay node.

[0019] According to one aspect of this application, the first moment depends on the first time interval only when the first condition is met; the first condition includes at least one of the first channel information reporting being generated based on inference or the first channel information reporting generating a corresponding first identifier.

[0020] As an example, the essence of the above method is that the time when the channel information reporting begins to occupy the first type of resource is related to whether the first condition is met.

[0021] As an example, the advantages of the above method include: better adaptability to various types of channel information reporting, and good flexibility and adaptability.

[0022] As an example, the advantages of the above method include: better adaptability to various processing capabilities, better adaptability to various application scenarios or terminals, and good flexibility and adaptability.

[0023] According to one aspect of this application, it is characterized by comprising:

[0024] Receive the first signaling;

[0025] Wherein, the first signaling triggers the first channel information reporting; the first moment is not earlier than at least the first time interval after the physical layer channel carrying the first signaling.

[0026] As an example, the advantages of the above method include: ensuring that a certain time requirement is met between the moment when channel information reporting begins to occupy the first type of resource and the triggering signaling.

[0027] As an example, the advantages of the above method include: better adaptability to various application scenarios or terminals, and good flexibility and adaptability.

[0028] According to one aspect of this application, the first moment is no earlier than at least the first time interval after the time domain resources occupied by the first information block.

[0029] As an example, the advantages of the above method include: ensuring that there is a certain time requirement between the time when the channel information reporting begins to occupy the first type of resource and the time domain resource occupied by the first information block.

[0030] According to one aspect of this application, the first channel information reporting is based on inference generation, the time at which the AI ​​model used for inference begins to be applied depends on the first time interval, and the first time is not earlier than the time at which the AI ​​model used for inference begins to be applied.

[0031] As an example, in the above method, the AI ​​model used to generate channel information reporting is applied from a certain time, ensuring that the time when channel information reporting begins to occupy the first type of resource is no earlier than the time when the AI ​​model used begins to be applied.

[0032] According to one aspect of this application, the generation of the first channel information report also occupies at least one second type of resource; the first channel information report occupies the at least one second type of resource starting from a second time moment; the second time moment depends on the first time interval; the first time moment is not earlier than the second time moment, or the first time moment is later than the second time moment.

[0033] As an example, the essence of the above method is that the generation of channel information reporting occupies at least one first type of resource and at least one second type of resource. The time when the channel information reporting begins to occupy the second type of resource depends on the instruction of the sender of the channel information reporting (i.e., the first time interval). The time when the channel information reporting begins to occupy the first type of resource is not earlier or later than the time when the channel information reporting begins to occupy the second type of resource.

[0034] As an example, the advantages of the above method include: ensuring that the occupation of the first type of resource by channel information reporting does not occur earlier than the occupation of the second type of resource.

[0035] As an example, the advantages of the above method include ensuring that the transmitting and receiving ends have a consistent understanding of the occupancy status of the second type of resources and / or the first type of resources.

[0036] As an example, the advantages of the above method include: better adaptability to various application scenarios or terminals.

[0037] As an example, the advantages of the above method include: good flexibility and adaptability.

[0038] As an example, the advantages of the above method include: better adaptability to various processing capabilities.

[0039] As an example, the advantages of the above method include: better adaptability to various different terminal capabilities.

[0040] According to one aspect of this application, the N time intervals correspond to N identifiers, where N is a positive integer greater than 1; the generation of the first channel information report corresponds to a first identifier, which is one of the N identifiers, and the first time interval is a time interval corresponding to the first identifier among the N time intervals.

[0041] As an example, the advantages of the above method include: better adaptability to various application scenarios or terminals.

[0042] As an example, the advantages of the above method include: good flexibility and adaptability.

[0043] As an example, the advantages of the above method include: better adaptability to various processing capabilities.

[0044] As an example, the advantages of the above method include: better adaptability to various different terminal capabilities.

[0045] According to one aspect of this application, it is characterized by comprising:

[0046] Receive the second information block;

[0047] The second information block is used to configure the first channel information reporting.

[0048] According to one aspect of this application, the second information block indicates a first resource set, the first resource set including one or more RS resources, the first resource set being used for at least one of channel measurement or interference measurement in the first channel information reporting; the first channel information reporting indicates at least one resource in a second resource set, the second resource set including resources not belonging to the first resource set.

[0049] According to one aspect of this application, it is characterized by: receiving RS in the first resource set.

[0050] According to one aspect of this application, the method includes: receiving RS in a second resource set; wherein the second resource set includes one or more RS resources.

[0051] According to one aspect of this application, it is characterized by: not receiving signals in the second resource set.

[0052] As an example, the advantages of the above method include: reducing the overhead required to obtain channel information.

[0053] As an example, the advantages of the above method include: it can reduce the measurement resources required to obtain channel information.

[0054] According to one aspect of this application, the method includes: performing a first operation, wherein the first channel information reporting depends on the output of the first operation.

[0055] As an example, the first operation is based on training or AI.

[0056] As one example, the first operation includes reasoning.

[0057] As an example, the reasoning includes AI reasoning.

[0058] As an example, the AI ​​(Artificial Intelligence) includes ML (Machine Learning).

[0059] As one example, the AI ​​includes ML (Machine Learning).

[0060] As an example, the first operation requires deployment.

[0061] As an example, the first operation is obtained by loading.

[0062] As one embodiment, it includes: deploying the first operation.

[0063] As an example, the advantages of the above method include that it provides sufficient freedom for the first node, adapting to various different scenarios and terminals, and has adaptability and flexibility.

[0064] As an example, the advantages of the above method include: training for the first operation can be performed outside the first node, reducing the processing power requirements and power consumption of the first node.

[0065] This application discloses a method used in a second node for wireless communication, characterized by comprising:

[0066] Receive a first information block, the first information block indicating a first time interval; receive a first channel information report;

[0067] The generation of the first channel information report occupies at least one first type of resource; the first channel information report occupies the at least one first type of resource starting from a first moment, and the first moment depends on the first time interval.

[0068] In one embodiment, the second node is a base station.

[0069] As one embodiment, the second node includes a base station.

[0070] As one embodiment, the second node includes the core network.

[0071] As one embodiment, the second node includes a base station and a core network.

[0072] In one embodiment, the second node is a user equipment.

[0073] As one example, the second node is a relay node.

[0074] According to one aspect of this application, the first moment depends on the first time interval only when the first condition is met; the first condition includes at least one of the first channel information reporting being generated based on inference or the first channel information reporting generating a corresponding first identifier.

[0075] According to one aspect of this application, it includes:

[0076] Send the first signaling;

[0077] Wherein, the first signaling triggers the first channel information reporting; the first moment is not earlier than at least the first time interval after the physical layer channel carrying the first signaling.

[0078] According to one aspect of this application, the first moment is no earlier than at least the first time interval after the time domain resources occupied by the first information block.

[0079] According to one aspect of this application, the first channel information reporting is based on inference generation, the time at which the AI ​​model used for inference begins to be applied depends on the first time interval, and the first time is not earlier than the time at which the AI ​​model used for inference begins to be applied.

[0080] According to one aspect of this application, the generation of the first channel information report also occupies at least one second type of resource; the first channel information report occupies the at least one second type of resource starting from a second time moment; the second time moment depends on the first time interval; the first time moment is not earlier than the second time moment, or the first time moment is later than the second time moment.

[0081] According to one aspect of this application, the N time intervals correspond to N identifiers, where N is a positive integer greater than 1; the generation of the first channel information report corresponds to a first identifier, which is one of the N identifiers, and the first time interval is a time interval corresponding to the first identifier among the N time intervals.

[0082] According to one aspect of this application, it is characterized by comprising:

[0083] Send the second information block;

[0084] The second information block is used to configure the first channel information reporting.

[0085] According to one aspect of this application, the second information block indicates a first resource set, the first resource set including one or more RS resources, the first resource set being used for at least one of channel measurement or interference measurement in the first channel information reporting; the first channel information reporting indicates at least one resource in a second resource set, the second resource set including resources not belonging to the first resource set.

[0086] According to one aspect of this application, it is characterized by: transmitting RS in the first resource set.

[0087] According to one aspect of this application, the method includes: transmitting RS in the second resource set; wherein the second resource set includes one or more RS resources.

[0088] According to one aspect of this application, it is characterized by: not transmitting a signal in the second resource set.

[0089] According to one aspect of this application, the method includes: performing a second operation; wherein the sender of the first channel information report performs a first operation, the output of the first operation includes a first CSI, the first channel information report carries the first CSI, and the first CSI is used as input to the second operation to generate a second CSI.

[0090] As an example, the first operation is based on training or AI.

[0091] As one example, the first operation includes reasoning.

[0092] As an example, the reasoning includes AI reasoning.

[0093] As an example, the first operation requires deployment.

[0094] As an example, the first operation is obtained by loading.

[0095] According to one aspect of this application, the method is characterized by: deploying the second operation.

[0096] As one example, the second operation is based on training or AI.

[0097] As one example, the second operation includes reasoning.

[0098] As an example, the reasoning includes AI reasoning.

[0099] As an example, the second operation needs to be deployed.

[0100] As an example, the second operation is obtained by loading.

[0101] As an example, the advantages of the above method include that it provides sufficient freedom for the second node, adapting to various different scenarios and terminals, and possessing adaptability and flexibility.

[0102] As an example, the advantages of the above method include: training for the second operation can be performed outside the second node, reducing the processing power requirements and power consumption of the second node.

[0103] This application discloses a first node used for wireless communication, characterized in that it comprises:

[0104] The first processor sends a first information block, the first information block indicating a first time interval; and sends a first channel information report.

[0105] The generation of the first channel information report occupies at least one first type of resource; the first channel information report occupies the at least one first type of resource starting from a first moment, and the first moment depends on the first time interval.

[0106] This application discloses a second node used for wireless communication, characterized in that it comprises:

[0107] The second processor receives a first information block, which indicates a first time interval; and receives a first channel information report.

[0108] The generation of the first channel information report occupies at least one first type of resource; the first channel information report occupies the at least one first type of resource starting from a first moment, and the first moment depends on the first time interval.

[0109] As an example, compared with conventional solutions, this application has the following advantages:

[0110] This ensures that the time requirements for resource usage are met when reporting channel information.

[0111] This ensures that the transmitting and receiving ends have a consistent understanding of the time required for channel information reporting to occupy resources;

[0112] Better adaptable to various application scenarios;

[0113] Better adaptable to various processing capabilities;

[0114] Better adaptable to various different terminals;

[0115] Better adaptable to reporting various types of channel information;

[0116] It has good flexibility;

[0117] It has good adaptability;

[0118] Higher accuracy and real-time performance of channel information;

[0119] Enhanced reliability and robustness;

[0120] Enhanced overall system performance. Attached Figure Description

[0121] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0122] Figure 1 shows a flowchart of the reporting of a first information block and a first channel information according to an embodiment of this application;

[0123] Figure 2 shows a schematic diagram of a network architecture according to an embodiment of this application;

[0124] Figure 3 illustrates a schematic diagram of an embodiment of a wireless protocol architecture for the user plane and control plane according to an embodiment of this application;

[0125] Figure 4 shows a schematic diagram of a first communication device and a second communication device according to an embodiment of this application;

[0126] Figure 5 illustrates the transmission between a first node and a second node according to an embodiment of this application;

[0127] Figures 6A-6C respectively illustrate schematic diagrams of a first condition according to an embodiment of this application;

[0128] Figure 7 illustrates a schematic diagram of a first moment depending on a first time interval according to an embodiment of the present application;

[0129] Figures 8A-8B respectively illustrate schematic diagrams of a first moment depending on a first time interval according to an embodiment of the present application;

[0130] Figure 9 illustrates a schematic diagram of a first moment depending on a first time interval according to another embodiment of this application;

[0131] Figure 10 shows a schematic diagram of N time intervals and N identifiers according to an embodiment of this application;

[0132] Figures 11A-11C respectively illustrate schematic diagrams of generating a corresponding first identifier in the first channel information reporting according to an embodiment of this application;

[0133] Figures 12A-12C respectively illustrate schematic diagrams of a first channel information reporting according to an embodiment of this application;

[0134] Figures 13A-13B respectively illustrate schematic diagrams of the deployment of the first node in a first operation according to an embodiment of this application;

[0135] Figure 14 illustrates a schematic diagram of the deployment of AI / ML functions in a RAN (Radio Access Network) domain according to an embodiment of this application;

[0136] Figure 15 shows a schematic diagram of the AI / ML function deployment of a UE according to an embodiment of this application;

[0137] Figure 16 shows a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of this application;

[0138] Figure 17 illustrates a schematic diagram based on artificial intelligence or machine learning according to an embodiment of this application;

[0139] Figure 18 shows a structural block diagram of a processing apparatus for a first node according to an embodiment of the present application;

[0140] Figure 19 shows a structural block diagram of a processing apparatus for a second node according to an embodiment of the present application. Detailed Implementation

[0141] The technical solutions of this application will be further described in detail below with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other. Considering performance, flexibility, complexity, overhead, and compatibility, those skilled in the art are motivated to flexibly combine the embodiments in different drawings without conflict, such as, but not limited to, the embodiments in Figure 1 and the embodiments in Figures 5-19, the embodiments in Figure 5 and the embodiments in Figures 6A-19, etc.

[0142] Example 1

[0143] Example 1 illustrates a flowchart of the reporting of a first information block and first channel information according to an embodiment of this application, as shown in Figure 1. In Figure 1, each block represents a step. In particular, the order of the steps in the blocks does not represent a specific temporal relationship between the steps.

[0144] In Embodiment 1, the first node sends a first information block in step 101 and sends a first channel information report in step 102; wherein, the first information block indicates a first time interval; the generation of the first channel information report occupies at least one first type of resource; the first channel information report occupies the at least one first type of resource from a first moment, the first moment depending on the first time interval.

[0145] As an example, the first channel information report is generated based on inference.

[0146] As an example, the generation of the first channel information report corresponds to a first identifier.

[0147] As an example, the first time interval corresponds to a first identifier, which is used to identify an AI model, and the first channel information report is generated based on inference using the AI ​​model identified by the first identifier.

[0148] As an example, the first identifier is a non-negative integer.

[0149] As an example, the first identifier is a string.

[0150] As one example, the first identifier is used to identify one or more resources of the first type.

[0151] As one embodiment, the first identifier is used to identify a first resource group, which includes one or more first-class resources.

[0152] As one embodiment, the first identifier is used to identify a first resource group, which includes one or more first-class resources and one or more second-class resources.

[0153] As one embodiment, the first identifier is used to identify the amount of information reported in the first channel information report.

[0154] As an example, the first identifier is different from the reporting configuration identifier of the first channel information report.

[0155] As an example, the first identifier is used to identify the AI ​​model.

[0156] As an example, the first identifier is used to identify the AI ​​model, and the first channel information report is generated based on inference using the AI ​​model identified by the first identifier.

[0157] As an example, the first identifier is used by the first node to identify an AI model.

[0158] As an example, the first identifier is used by the first node to determine the AI ​​model used in the first operation of this application.

[0159] As an example, the first identifier is used to identify the AI ​​entity.

[0160] As an example, the first identifier is used to identify AI functionality.

[0161] As an example, the advantages of the above method include that identifying an AI model / entity / function through the first identifier simplifies the design and unifies the understanding of different AI entities or functions across multiple nodes.

[0162] As one embodiment, the first identifier is used to identify or indicate a set of resources.

[0163] As one embodiment, the first identifier is used to identify or indicate a set of resources, the measurement of which is used to obtain a training dataset.

[0164] As one embodiment, the first identifier is used to identify or indicate a resource set, the resource set identified or indicated by the first identifier including one or more RS resources.

[0165] As an example, the first identifier is used to identify or indicate the training dataset.

[0166] As an example, the benefits of the above method include identifying the inference generated by an AI training or AI training dataset by identifying the AI ​​training or AI training dataset, establishing consensus among different AI functions, and further simplifying the design.

[0167] As an example, the first channel information report includes CSI (channel state information).

[0168] As an example, the CSI includes beam information.

[0169] As an example, the CSI includes compressed CSI.

[0170] As an example, the compressed CSI is based on non-codebook channel information.

[0171] As an example, the compressed CSI is not a reporting quantity defined by 3GPP Rel-18, nor is it a reporting quantity defined by versions prior to 3GPP Rel-18.

[0172] As an example, the channel parameters recovered by the target receiver of the compressed CSI based on the compressed CSI are unknown to the sender of the compressed CSI.

[0173] As an example, the compressed CSI is based on channel information derived from artificial intelligence or machine learning.

[0174] As an example, the compressed CSI is based on channel information from a neural network.

[0175] As an example, the compressed CSI is based on channel information from CNN (Conventional Neural Networks).

[0176] As one example, the first channel information report includes channel information generated based on artificial intelligence or machine learning.

[0177] As one example, the first channel information report includes channel information generated based on a neural network.

[0178] As an example, the first channel information report includes channel information generated based on CNN (Conventional Neural Networks).

[0179] As one embodiment, the first channel information report includes a channel matrix.

[0180] As one embodiment, the first channel information report includes at least one of the channel's feature values ​​or feature vectors.

[0181] As one embodiment, the first channel information report includes confidence information.

[0182] As one embodiment, the first channel information report includes beam information.

[0183] As an example, the first channel information report includes at least one of PMI (Precoding Matrix Indicator), CRI (CSI-RS Resource Indicator), CQI (Channel Quality Indicator), RI (Rank Indicator), Layer Indicator (LI), SS / PBCH Block Resource Indicator (SSBRI), RSRP, SINR (signal-to-noise and interference ratio), capability index, TDCP (Time domain channel properties), or confidence information.

[0184] As an example, the first channel information reporting is based on a non-codebook.

[0185] As one embodiment, the first channel information report includes a resource indication, which is used to indicate beam or RS (reference signal) resources.

[0186] As an example, the first channel information report includes at least one of a resource indication or RSRP (reference signal received power), wherein the resource indication is used to indicate a beam or RS resource.

[0187] As one embodiment, the beam information includes a resource indicator, which is used to indicate a beam or RS resource.

[0188] As an example, the beam information includes at least one of a resource indicator or RSRP (reference signal received power), wherein the resource indicator is used to indicate the beam or RS resource.

[0189] As an example, the resource indication is used to indicate one of beam, CSI-RS (Channel State Information Reference Signal) resources, or synchronization signal resources.

[0190] As an example, the resource indicator is a CRI (CSI-RS Resource Indicator, Channel State Information Reference Signal Resource Indicator) or an SS / PBCH Block Resource Indicator (SSBRI).

[0191] As one embodiment, the synchronization signal resources include at least the resources occupied by the synchronization signal.

[0192] As an example, the synchronization signal resource is an SSB (Synchronization Signal Block).

[0193] As an example, the synchronization signal resource is an SS / PBCH (Synchronization Signal / Physical Broadcast Channel) block resource.

[0194] As one embodiment, the second information block is used to configure the first channel information reporting, the second information block indicates a first resource set, the first resource set includes one or more RS resources, the first resource set is used for at least one of the channel measurement or interference measurement in the first channel information reporting; the first channel information reporting indicates at least one RS resource in the first resource set.

[0195] The second information block indicates a first resource set, the first resource set including at least one RS resource for the measurement used in the first channel information reporting; the first channel information reporting indicates at least one RS resource in the first resource set.

[0196] As one embodiment, the second information block is used to configure the first channel information reporting, the second information block indicates a first resource set, the first resource set includes one or more RS resources, the first resource set is used for at least one of the channel measurement or interference measurement in the first channel information reporting; the first channel information reporting indicates at least one resource in the second resource set, the second resource set includes resources that do not belong to the first resource set.

[0197] As an example, the generation of the first channel information report occupies at least one first-class resource among the generators of the first channel information report.

[0198] As an example, the generation of the first channel information report occupies at least one first-class resource among the senders of the first channel information report.

[0199] As an example, the generation of the first channel information report occupies at least one first-class resource in the first node of this application.

[0200] As an example, the first type of resource is different from air interface resources, which include at least one of time domain resources, frequency domain resources, or code domain resources.

[0201] As an example, the first type of resource is among the generators of the first channel information report.

[0202] As an example, the first type of resource is among the senders who reported the first channel information.

[0203] As an example, the first type of resource is in the first node.

[0204] As one embodiment, the generation of the first channel information report includes: the calculation of the first channel information report.

[0205] As one embodiment, the generation of the first channel information report includes: inference of the first channel information report.

[0206] As one embodiment, the generation of the first channel information report includes: reasoning to obtain the first channel information report.

[0207] As one embodiment, the generation of the first channel information report includes: the sender of the first channel information report performing a first operation, wherein the first channel information report depends on the output of the first operation.

[0208] As an example, the operation mentioned includes reasoning.

[0209] As an example, the first channel information report is calculated or generated through artificial intelligence or machine learning.

[0210] As an example, the at least one first type of resource is used to calculate or generate the first channel information report.

[0211] As an example, the at least one first type of resource is used to infer the first channel information reporting.

[0212] As an example, the at least one first type of resource is used to obtain the first channel information report based on reasoning.

[0213] As an example, the at least one first type of resource occupied by the generation reported by the first channel information belongs to a processing unit.

[0214] As an example, the first type of resource occupied by the generation of the first channel information reported belongs to a processing unit.

[0215] As an example, the first type of resource occupied by the generation of the first channel information reported is a processing unit.

[0216] As an example, the generation of the first channel information report occupies at least one type of resource and at least one type of resource.

[0217] As one embodiment, the generation of the first channel information report occupies at least one first-class resource among the generators of the first channel information report and at least one second-class resource among the generators of the first channel information report.

[0218] As one embodiment, the generation of the first channel information report occupies at least one first type of resource among the senders of the first channel information report and at least one second type of resource among the senders of the first channel information report.

[0219] As an example, the generation of the first channel information report occupies at least one first-class resource and at least one second-class resource in the first node of this application.

[0220] As an example, the generation of the first channel information report also occupies at least one second type of resource; the first channel information report occupies the at least one second type of resource starting from the first symbol after the physical layer channel of the first signaling.

[0221] As an example, the generation of the first channel information report also occupies at least one second type of resource; the first channel information report occupies the at least one second type of resource starting from a second time moment; the second time moment depends on the first time interval; the first time moment is not earlier than the second time moment, or the first time moment is later than the second time moment.

[0222] As an example, both the first type of resource and the second type of resource are different from air interface resources, which include at least one of time domain resources, frequency domain resources, or code domain resources.

[0223] As an example, both the first type of resource and the second type of resource are generated by the generator of the first channel information report.

[0224] As an example, both the first type of resource and the second type of resource are in the sender that reported the first channel information.

[0225] As an example, both the first type of resource and the second type of resource are in the first node.

[0226] As an example, the generation of the first channel information reported occupies at least one first type of resource in a first resource group, the first resource group including one or more first type of resources.

[0227] As an example, the generation of the first channel information reported occupies only at least one first-class resource in the first resource group and the second resource group. The first resource group includes one or more first-class resources, and the second resource group includes one or more first-class resources.

[0228] As one embodiment, the generation of the first channel information reported occupies only at least one first-class resource in the first resource group and the second resource group. The first resource group includes one or more first-class resources and one or more second-class resources, and the second resource group includes one or more first-class resources. Only the first resource group includes second-class resources.

[0229] As one embodiment, the first resource group includes one or more first-class resources and one or more second-class resources.

[0230] As one embodiment, the second resource group includes one or more first-class resources and one or more second-class resources.

[0231] As one embodiment, the first resource group includes one or more first-class resources and one or more second-class resources; the second resource group includes one or more first-class resources.

[0232] As an example, channel information reports generated based on inference occupy the first type of resources in the first resource group, while channel information reports generated based on inference occupy the first type of resources in the second resource group.

[0233] As an example, some or all of the parameters used in generating the first channel information report are stored in at least one second-class resource.

[0234] As an example, some or all of the parameters of the AI ​​model used in generating the first channel information report are stored in at least one second-class resource.

[0235] As an example, at least one of the parameters of the AI ​​model used in generating the first channel information report, some or all of the parameters, some or all of the intermediate inference results, or some or all of the inference outputs is stored in at least one second-class resource.

[0236] As an example, at least one of the inference intermediate results or inference outputs of the first channel information reported during generation is stored in at least one second type of resource.

[0237] As an example, the first type of resources is used for at least one of processing, computation, or reasoning.

[0238] As an example, the first type of resources is used for at least one of the processing, calculation, or inference of channel information.

[0239] As an example, the first type of resources is used for computation.

[0240] As an example, the first type of resources is used for inference.

[0241] As an example, the first type of resources is used for at least addition and multiplication operations.

[0242] As an example, the first type of resources is used for at least convolution operations.

[0243] As one embodiment, the first type of resource is a processing unit.

[0244] As an example, the first type of resource includes computing power resources.

[0245] As an example, the second type of resource is used for storage.

[0246] As one embodiment, the second type of resource includes storage units or storage space.

[0247] As one example, the second type of resource includes memory.

[0248] As one example, the second type of resource is used to store some or all of the parameters required for inference.

[0249] As an example, the second type of resource is used to store at least one of some or all of the inference intermediate results, or some or all of the inference outputs.

[0250] As an example, the second type of resource is used to store some or all of the parameters of the AI ​​model.

[0251] As an example, the second type of resource is used to store at least one of some or all of the parameters of the AI ​​model, some or all of the intermediate inference results, or some or all of the inference outputs.

[0252] As an example, the second type of resource is used to store one or more of the following: convolution kernel size, number of convolutional layers, convolution stride, pooling kernel size, pooling kernel stride, pooling function, activation function, or number of feature maps.

[0253] As an example, the second type of resource is used to store one or more of the following: convolution kernel, pooling kernel, pooling function, activation function, parameters of pooling function, or parameters of activation function.

[0254] As an example, the second type of resources is used to store some or all of the parameters in the target first type of parameter group in Embodiment 16 of this application.

[0255] As an example, the first type of resources is used for at least one of processing, computing, or reasoning, and the second type of resources is used for storage.

[0256] As one embodiment, the first type of resource is a processing unit, and the second type of resource is used for storage.

[0257] As one embodiment, a processing unit includes one or more first-class resources.

[0258] As one embodiment, a processing unit includes one or more first-class resources and one or more second-class resources.

[0259] Regarding the processing unit described in the above embodiments, some typical but non-limiting implementations are described below:

[0260] As one embodiment, the processing unit is used to calculate or generate channel information.

[0261] As one embodiment, the processing unit is used to process channel information.

[0262] As an example, the processing unit is a CSI processing unit.

[0263] As one embodiment, the processing unit is an AI processing unit (APU).

[0264] As one embodiment, the processing unit is a central processing unit (CPU).

[0265] As an example, the processing unit is a GPU (graphics processing unit).

[0266] As one embodiment, the processing unit is a general-purpose processing unit.

[0267] As one embodiment, the processing unit is a general-purpose computing on graphics processing unit (GPGPU).

[0268] As an example, the first information block indicates a first time interval for inference.

[0269] As one embodiment, the first information block indicates a first time interval for a first resource group; the first resource group includes one or more first-class resources.

[0270] As an example, the first information block indicates a first time interval suitable for reporting channel information generated based on inference.

[0271] As one embodiment, the first information block indicates a first time interval for a first identifier.

[0272] As an example, the first information block also indicates the first identifier.

[0273] As an example, the information outside the first information block indicates the first identifier.

[0274] As one embodiment, the first information block is carried by higher-layer signaling.

[0275] As one example, the first information block includes one or more fields in one or more IEs (information elements).

[0276] As an example, the first information block includes a MAC CE.

[0277] As one embodiment, the first information block includes control information.

[0278] As one embodiment, the first information block includes UCI (uplink control information).

[0279] As one embodiment, the first information block is carried by physical layer signaling.

[0280] As one embodiment, the first information block is carried by physical layer uplink signaling.

[0281] As one example, the first information block is transmitted over a physical layer channel.

[0282] As one embodiment, the first information block is transmitted on the physical layer uplink channel.

[0283] As an example, the first information block is transmitted on PUCCH (Physical Uplink Control Channel).

[0284] As an example, the first information block is transmitted on PUSCH (Physical Uplink Shared Channel).

[0285] As an example, the first information block belongs to the capability information of the first node.

[0286] As one embodiment, the first information block includes the capability information of the first node.

[0287] As one embodiment, the first information block includes one or more capability parameters of the first node.

[0288] As one embodiment, the first information block includes one or more fields in a UE (user equipment) capability IE (information element).

[0289] As one embodiment, the first information block includes one or more fields in one or more UE (user equipment) capability IE (information element).

[0290] As one embodiment, the first information block includes one or more parameters in one or more UE (user equipment) capability IEs.

[0291] As an example, after receiving a UE Capability Enquiry from the network, the first node transmits the first node's capability information, and the first information block belongs to the first node's capability information.

[0292] As an example, the capability information of the first node includes UECapabilityInformation.

[0293] As an example, the capability information of the first node includes the radio access capability of the first node.

[0294] As an example, the first information block explicitly indicates a first time interval.

[0295] As an example, the first information block implicitly indicates a first time interval.

[0296] As one embodiment, the first time interval depends on the information indicated by the first information block.

[0297] As one embodiment, the first information block indicates a first parameter, and the first time interval depends on the value of the first parameter.

[0298] As an example, the value of the first parameter is an integer.

[0299] As an example, the value of the first parameter is a real number.

[0300] As an example, the value of the first parameter is a character or a string.

[0301] As an example, the first time interval and the first parameter have a linear relationship.

[0302] As an example, the first time interval and the first parameter have a non-linear relationship.

[0303] As an example, the first time interval and the value of the first parameter are mapped.

[0304] As an example, the calculation formula for the first time interval depends on the first parameter.

[0305] As an example, the calculation formula for the first time interval and the first parameter have a linear relationship.

[0306] As an example, the calculation formula for the first time interval and the first parameter have a non-linear relationship.

[0307] As an example, when the value of the first parameter is a first candidate value, the first time interval is a first candidate time interval; when the value of the first parameter is a second candidate value, the first time interval is a second candidate time interval.

[0308] As an example, the first time interval is a non-negative real number.

[0309] As an example, the first time interval is a positive real number.

[0310] As an example, the first time interval is a non-negative integer.

[0311] As an example, the first time interval is a positive integer.

[0312] As an example, the unit of the first time interval is milliseconds (ms).

[0313] As an example, the unit of the first time interval is a symbol.

[0314] As an example, the first time interval belongs to the capability information of the first node.

[0315] As an example, the symbol is a single-carrier symbol.

[0316] As an example, the symbol is a multi-carrier symbol.

[0317] As an example, the symbols are obtained by passing the output of the transform precoding through OFDM symbol generation.

[0318] As an example, the multicarrier symbol is an OFDM (Orthogonal Frequency Division Multiplexing) symbol.

[0319] As an example, the multi-carrier symbol is an SC-FDMA (Single Carrier-Frequency Division Multiple Access) symbol.

[0320] As an example, the multicarrier symbol is a DFT-S-OFDM (Discrete Fourier Transform Spread OFDM) symbol.

[0321] As an example, the multi-carrier symbol is an FBMC (Filter Bank Multi Carrier) symbol.

[0322] As one embodiment, the multicarrier symbol includes CP (Cyclic Prefix).

[0323] As an example, the generation of the first channel information reported occupies at least one first type of resource from a first moment.

[0324] As an example, the first channel information reporting starts from a first time point and ends at a third time point, occupying at least one first type of resource; the third time point is later than the first time point.

[0325] As an example, the third moment is the last moment of the physical layer channel carrying the first channel information reported.

[0326] As an example, the third moment is the end time of the last symbol of the physical layer channel carrying the first channel information reported.

[0327] Typically, the last symbol refers to the latest symbol.

[0328] Typically, the "last moment" refers to the latest moment or the end moment.

[0329] Example 2

[0330] Example 2 illustrates a schematic diagram of a network architecture according to an embodiment of this application, as shown in Figure 2.

[0331] Figure 2 illustrates network architecture 200. Network architecture 200 is a 5G NR (New Radio) / LTE (Long-Term Evolution) / LTE-A (Long-Term Evolution Advanced) system, or a 5G+ network architecture, or a 6G network architecture, or a network architecture adopted in future evolutions by 3GPP; network architecture 200 may be referred to as 5GS (5G System) / EPS (Evolved Packet System), or 6GS (6G System); network architecture 200 includes at least one of UE (User Equipment) 201, RAN (Radio Access Network) 202, core network 210, HSS (Home Subscriber Server) / UDM (Unified Data Management) 220, and Internet service 230. The network architecture 200 can interconnect with other access networks, but these entities / interfaces are not shown for simplicity. As shown, the network architecture 200 provides packet-switched services; however, those skilled in the art will readily understand that the various concepts presented throughout this application can be extended to networks providing circuit-switched services or other cellular networks. The RAN includes node 203. The RAN may also include other nodes 204. Node 203 provides user and control plane protocol termination toward UE 201. Node 203 may be connected to other nodes 204 via an Xn interface (e.g., backhaul) / X2 interface. Node 203 may also be referred to as a base station, base transceiver station, radio base station, radio transceiver, transceiver function, basic service set (BSS), extended service set (ESS), TRP (transmitter-receiver node), or some other suitable term. The core network 210 is a 5GC (5G Core Network) / EPC (Evolved Packet Core), or the core network 210 is a 6GC; node 203 provides UE 201 with an access point to the core network 210.Examples of UE201 include cellular phones, smartphones, Session Initiation Protocol (SIP) phones, laptops, personal digital assistants (PDAs), satellite radios, non-terrestrial base station communications, satellite mobile communications, global positioning systems, multimedia devices, video devices, digital audio players (e.g., MP3 players), cameras, game consoles, drones, aircraft, narrowband IoT devices, machine-type communication devices, land vehicles, automobiles, wearable devices, or any other similar functional devices. Those skilled in the art may also refer to UE201 as a mobile station, subscriber station, mobile unit, subscriber unit, radio unit, remote unit, mobile device, radio device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handheld device, user agent, mobile client, client, or any other suitable term. Node 203 is connected to the core network 210 via an S1 / NG interface. The core network 210 includes an MME (Mobility Management Entity) / AMF (Authentication Management Field) / SMF (Session Management Function) 211, other MMEs / AMFs / SMFs 214, an S-GW (Service Gateway) / UPF (User Plane Function) 212, and a P-GW (Packet Data Network Gateway) / UPF 213. The MME / AMF / SMF 211 is the control node that handles signaling between the UE 201 and the core network 210. Generally, the MME / AMF / SMF 211 provides bearer and connection management. All user IP (Internet Protocol) packets are transmitted through the S-GW / UPF 212, which is itself connected to the P-GW / UPF 213. The P-GW provides UE IP address allocation and other functions. The P-GW / UPF 213 is connected to the Internet service 230. Internet services 230 include operator-compliant Internet protocol services, which may specifically include Internet, intranet, IMS (IP Multimedia Subsystem), and packet switching services.

[0332] As an example, the first node includes the UE201.

[0333] As one embodiment, the second node includes the node 203.

[0334] As one embodiment, the second node includes the core network 210.

[0335] As one embodiment, the second node includes the node 203 and the core network 210.

[0336] As an example, the wireless link between the UE201 and the node203 includes a cellular link.

[0337] As an example, the first type of resources in this application are in the UE201.

[0338] As an example, the second type of resource in this application is in the UE201.

[0339] As an example, the first resource group in this application is in the UE201.

[0340] As an example, the second resource group in this application is in the UE201.

[0341] As an example, the first information block is generated in the UE201.

[0342] As an example, the first channel information is generated in the UE201.

[0343] As an example, the sender of the first channel information report includes the UE201.

[0344] As an example, the target receiver for the first channel information reporting includes the node 203.

[0345] As an example, the second information block is generated in node 203.

[0346] As one embodiment, the sender of the second information block includes the node 203.

[0347] As an example, the target recipient of the second information block includes the UE201.

[0348] As an example, the first signaling is generated in node 203.

[0349] As an example, the sender of the first signaling includes the node 203.

[0350] As an example, the target recipient of the first signaling includes the UE201.

[0351] Example 3

[0352] Example 3 illustrates a schematic diagram of an embodiment of a wireless protocol architecture for the user plane and control plane according to an embodiment of this application, as shown in Figure 3.

[0353] Example 3 illustrates a schematic diagram of an embodiment of a wireless protocol architecture for a user plane and control plane according to this application, as shown in Figure 3. Figure 3 is a schematic diagram illustrating an embodiment of a radio protocol architecture for a user plane 350 and a control plane 300. Figure 3 shows the radio protocol architecture for the control plane 300 between a first communication node device (UE, gNB, or RSU in V2X) and a second communication node device (gNB, UE, or RSU in V2X), or between two UEs, using three layers: Layer 1, Layer 2, and Layer 3. Layer 1 (L1 layer) is the lowest layer and implements various PHY (physical layer) signal processing functions. Layer 1 will be referred to herein as PHY 301. Layer 2 (L2 layer) 305 is above PHY 301 and is responsible for the link between the first communication node device and the second communication node device, or between two UEs. Layer L2 305 includes a MAC (Medium Access Control) sublayer 302, an RLC (Radio Link Control) sublayer 303, and a PDCP (Packet Data Convergence Protocol) sublayer 304, which terminate at the second communication node device. The PDCP sublayer 304 provides multiplexing between different radio bearers and logical channels. It also provides security through encrypted data packets and supports cross-cell mobility between the second communication node devices and the first communication node device. The RLC sublayer 303 provides upper-layer packet segmentation and reassembly, retransmission of lost packets, and packet reordering to compensate for out-of-order reception due to HARQ. The MAC sublayer 302 provides multiplexing between logical and transport channels. It is also responsible for allocating various radio resources (e.g., resource blocks) within a cell among the first communication node devices. Furthermore, the MAC sublayer 302 handles HARQ operations. In the control plane 300, the Radio Resource Control (RRC) sublayer 306 of Layer 3 (L3) is responsible for acquiring radio resources (i.e., radio bearers) and configuring the lower layers using RRC signaling between the second and first communication node devices. The user plane 350's radio protocol architecture includes Layer 1 (L1) and Layer 2 (L2). The radio protocol architecture for the first and second communication node devices in the user plane 350 is largely the same as the corresponding layers and sublayers in the control plane 300 for Physical Layer 351, PDCP sublayer 354 in L2 Layer 355, RLC sublayer 353 in L2 Layer 355, and MAC sublayer 352 in L2 Layer 355. However, PDCP sublayer 354 also provides header compression for upper layer data packets to reduce radio transmission overhead.The L2 layer 355 in the user plane 350 also includes an SDAP (Service Data Adaptation Protocol) sublayer 356, which is responsible for mapping between QoS streams and data radio bearers (DRBs) to support service diversity. Although not illustrated, the first communication node device may have several upper layers above the L2 layer 355, including a network layer (e.g., IP layer) terminating at the P-GW on the network side and an application layer terminating at the other end of the connection (e.g., a remote UE, server, etc.).

[0354] As an example, the wireless protocol architecture in Figure 3 is applicable to the first node.

[0355] As an example, the wireless protocol architecture in Figure 3 is applicable to the second node.

[0356] As an example, the higher layer mentioned in this application refers to the layer above the physical layer.

[0357] As an example, the first information block is generated in the RRC sublayer 306.

[0358] As an example, the first information block is generated in the PHY301 or the PHY351.

[0359] As an example, the first information block is generated in the MAC sublayer 302 or the MAC sublayer 352.

[0360] As an example, the second information block is generated in the RRC sublayer 306.

[0361] As an example, the first signaling is generated in the PHY301 or the PHY351.

[0362] As an example, the first signaling is generated in the MAC sublayer 302 or the MAC sublayer 352.

[0363] As an example, the reference signal in the first resource set is generated in the PHY301 or the PHY351.

[0364] As an example, the signals in the second resource set are generated by the PHY301 or the PHY351.

[0365] As an example, the first channel information report is generated in the PHY301 or the PHY351.

[0366] Example 4

[0367] Example 4 illustrates a schematic diagram of a first communication device and a second communication device according to an embodiment of this application, as shown in Figure 4. Figure 4 is a block diagram of a first communication device 410 and a second communication device 450 communicating with each other in an access network.

[0368] The first communication device 410 includes a controller / processor 475, a memory 476, a receiver processor 470, a transmitter processor 416, a multi-antenna receiver processor 472, a multi-antenna transmitter processor 471, a transmitter / receiver 418, and an antenna 420.

[0369] The second communication device 450 includes a controller / processor 459, a memory 460, a data source 467, a transmitting processor 468, a receiving processor 456, a multi-antenna transmitting processor 457, a multi-antenna receiving processor 458, a transmitter / receiver 454, and an antenna 452.

[0370] In the transmission from the first communication device 410 to the second communication device 450, at the first communication device 410, upper-layer data packets from the core network are provided to the controller / processor 475. The controller / processor 475 implements L2 layer functionality. In DL (Downlink), the controller / processor 475 provides header compression, encryption, packet segmentation and reordering, multiplexing between logical and transport channels, and radio resource allocation to the second communication device 450 based on various priority metrics. The controller / processor 475 is also responsible for HARQ operation, retransmission of lost packets, and signaling to the second communication device 450. The transmit processor 416 and the multi-antenna transmit processor 471 implement various signal processing functions for L1 layer (i.e., physical layer). Transmit processor 416 performs encoding and interleaving to facilitate forward error correction (FEC) at the second communication device 450, and constellation mapping based on various modulation schemes (e.g., binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), M-phase shift keying (M-PSK), and M-quadrature amplitude modulation (M-QAM). Multi-antenna transmit processor 471 performs digital spatial precoding on the encoded and modulated symbols, including codebook-based precoding and non-codebook-based precoding, and beamforming processing, generating one or more parallel... The transmit processor 416 then maps each parallel stream to a subcarrier, multiplexes the modulated symbols with a reference signal (e.g., a pilot) in the time and / or frequency domains, and then uses an inverse fast Fourier transform (IFFT) to generate a physical channel carrying the time-domain multicarrier symbol stream. The multi-antenna transmit processor 471 then performs transmit analog precoding / beamforming operations on the time-domain multicarrier symbol stream. Each transmitter 418 converts the baseband multicarrier symbol stream provided by the multi-antenna transmit processor 471 into an RF stream, which is then provided to a different antenna 420.

[0371] In the transmission from the first communication device 410 to the second communication device 450, at the second communication device 450, each receiver 454 receives a signal through its corresponding antenna 452. Each receiver 454 recovers the information modulated onto the radio frequency carrier and converts the radio frequency stream into a baseband multicarrier symbol stream, which is then provided to the receiver processor 456. The receiver processor 456 and the multi-antenna receiver processor 458 implement various signal processing functions of the L1 layer. The multi-antenna receiver processor 458 performs receive analog precoding / beamforming operations on the baseband multicarrier symbol stream from the receiver 454. The receiver processor 456 uses a Fast Fourier Transform (FFT) to convert the baseband multicarrier symbol stream after the receive analog precoding / beamforming operations from the time domain to the frequency domain. In the frequency domain, the physical layer data signal and the reference signal are demultiplexed by the receiver processor 456, where the reference signal is used for channel estimation, and the data signal is recovered in the multi-antenna receiver processor 458 after multi-antenna detection to recover any parallel stream destined for the second communication device 450. Symbols on each parallel stream are demodulated and recovered in the receive processor 456, generating soft decisions. The receive processor 456 then decodes and deinterleaves the soft decisions to recover the upper-layer data and control signals transmitted over the physical channel by the first communication device 410. The upper-layer data and control signals are then provided to the controller / processor 459. The controller / processor 459 implements the functions of Layer 2 (L2). The controller / processor 459 may be associated with a memory 460 storing program code and data. The memory 460 may be referred to as computer-readable media. In the DL (Layered Logic), the controller / processor 459 provides multiplexing, packet reassembly, decryption, header decompression, and control signal processing between the transmission and logical channels to recover upper-layer packets from the core network. The upper-layer packets are then provided to all protocol layers above Layer 2. Various control signals may also be provided to Layer 3 (L3) for L3 processing. The controller / processor 459 is also responsible for error detection using ACK and / or NACK protocols to support HARQ operation.

[0372] In the transmission from the second communication device 450 to the first communication device 410, at the second communication device 450, a data source 467 is used to provide upper-layer data packets to the controller / processor 459. The data source 467 represents all protocol layers above the L2 layer. Similar to the transmission functions at the first communication device 410 described in the DL, the controller / processor 459 implements header compression, encryption, packet segmentation and reordering, and multiplexing between logical and transport channels based on the radio resource allocation of the first communication device 410, implementing L2 layer functions for the user plane and control plane. The controller / processor 459 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the first communication device 410. Transmit processor 468 performs modulation mapping and channel coding processing, while multi-antenna transmit processor 457 performs digital multi-antenna spatial precoding, including codebook-based and non-codebook-based precoding, and beamforming processing. Subsequently, transmit processor 468 modulates the generated parallel stream into a multi-carrier / single-carrier symbol stream. After analog precoding / beamforming operations in multi-antenna transmit processor 457, the stream is provided to different antennas 452 via transmitter 454. Each transmitter 454 first converts the baseband symbol stream provided by multi-antenna transmit processor 457 into a radio frequency symbol stream before providing it to antenna 452.

[0373] In the transmission from the second communication device 450 to the first communication device 410, the function at the first communication device 410 is similar to the receiving function at the second communication device 450 described in the transmission from the first communication device 410 to the second communication device 450. Each receiver 418 receives radio frequency signals through its corresponding antenna 420, converts the received radio frequency signals into baseband signals, and provides the baseband signals to the multi-antenna receiving processor 472 and the receiving processor 470. The receiving processor 470 and the multi-antenna receiving processor 472 jointly implement the L1 layer functions. The controller / processor 475 implements the L2 layer functions. The controller / processor 475 may be associated with a memory 476 that stores program code and data. The memory 476 may be referred to as computer-readable media. The controller / processor 475 provides multiplexing, packet reassembly, decryption, header decompression, and control signal processing between the transmission and logical channels to recover upper-layer data packets from the second communication device 450. The upper-layer data packets from the controller / processor 475 may be provided to the core network. The controller / processor 475 is also responsible for error detection using ACK and / or NACK protocols to support HARQ operation.

[0374] As one embodiment, the second communication device 450 includes: at least one processor and at least one memory, the at least one memory including computer program code; the at least one memory and the computer program code are configured to be used with the at least one processor. The second communication device 450 means at least: transmitting a first information block, the first information block indicating a first time interval; transmitting a first channel information report; wherein the generation of the first channel information report occupies at least one first type of resource; the first channel information report occupies the at least one first type of resource from a first moment, the first moment depending on the first time interval.

[0375] As one embodiment, the second communication device 450 includes: a memory storing a computer-readable instruction program that generates actions when executed by at least one processor, the actions including: sending a first information block indicating a first time interval; sending a first channel information report; wherein the generation of the first channel information report occupies at least one first type of resource; the first channel information report occupies the at least one first type of resource from a first moment, the first moment depending on the first time interval.

[0376] As one embodiment, the first communication device 410 includes: at least one processor and at least one memory, the at least one memory including computer program code; the at least one memory and the computer program code are configured to be used with the at least one processor. The first communication device 410 means at least: receiving a first information block, the first information block indicating a first time interval; receiving a first channel information report; wherein the generation of the first channel information report occupies at least one first type of resource; the first channel information report occupies the at least one first type of resource from a first moment, the first moment depending on the first time interval.

[0377] As one embodiment, the first communication device 410 includes: a memory storing a computer-readable instruction program, the computer-readable instruction program generating actions when executed by at least one processor, the actions including: receiving a first information block, the first information block indicating a first time interval; receiving a first channel information report; wherein the generation of the first channel information report occupies at least one first type of resource; the first channel information report occupies the at least one first type of resource from a first moment, the first moment depending on the first time interval.

[0378] As an example, the first node in this application includes the second communication device 450.

[0379] As an example, the second node in this application includes the first communication device 410.

[0380] As an example, at least one of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, and the data source 467} is used to receive the first signaling in this application; at least one of {the antenna 420, the transmitter 418, the transmitting processor 416, the multi-antenna transmitting processor 471, the controller / processor 475, and the memory 476} is used to transmit the first signaling in this application.

[0381] As an example, at least one of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, and the data source 467} is used to receive the second information block in this application; at least one of {the antenna 420, the transmitter 418, the transmitting processor 416, the multi-antenna transmitting processor 471, the controller / processor 475, and the memory 476} is used to transmit the second information block in this application.

[0382] As an example, at least one of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, and the data source 467} is used to receive the reference signal in the first resource set of this application; at least one of {the antenna 420, the transmitter 418, the transmitting processor 416, the multi-antenna transmitting processor 471, the controller / processor 475, and the memory 476} is used to transmit the reference signal in the first resource set of this application.

[0383] As one embodiment, at least one of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, and the data source 467} is used to receive the reference signal in the second resource set of this application; at least one of {the antenna 420, the transmitter 418, the transmitting processor 416, the multi-antenna transmitting processor 471, the controller / processor 475, and the memory 476} is used to transmit the reference signal in the second resource set of this application.

[0384] As an example, at least one of {the antenna 452, the transmitter 454, the transmitter processor 468, the multi-antenna transmitter processor 457, the controller / processor 459, the memory 460, and the data source 467} is used to transmit the first information block in this application; at least one of {the antenna 420, the receiver 418, the receiver processor 470, the multi-antenna receiver processor 472, the controller / processor 475, and the memory 476} is used to receive the first information block in this application.

[0385] As an example, at least one of the following is used in the generation of the first channel information report in this application: {antenna 452, transmitter / receiver 454, transmitter processor 468, multi-antenna transmitter processor 457, receiver processor 456, multi-antenna receiver processor 458, controller / processor 459, memory 460, data source 467}.

[0386] As an example, at least one of the following is used in the first operation of this application: {the antenna 452, the transmitter / receiver 454, the transmitter processor 468, the multi-antenna transmitter processor 457, the receiver processor 456, the multi-antenna receiver processor 458, the controller / processor 459, the memory 460, and the data source 467}.

[0387] As an example, at least one of {the antenna 452, the transmitter 454, the transmitter processor 468, the multi-antenna transmitter processor 457, the receiver processor 456, the multi-antenna receiver processor 458, the controller / processor 459, the memory 460, and the data source 467} is used in the first operation of this application; at least one of {the antenna 420, the receiver 418, the receiver processor 470, the multi-antenna receiver processor 472, the transmitter processor 416, the multi-antenna transmitter processor 471, the controller / processor 475, and the memory 476} is used in the second operation of this application.

[0388] As an example, at least one of {the antenna 452, the transmitter 454, the transmitter processor 468, the multi-antenna transmitter processor 457, the receiver processor 456, the multi-antenna receiver processor 458, the controller / processor 459, the memory 460, and the data source 467} is used to transmit the first channel information report in this application; at least one of {the antenna 420, the receiver 418, the receiver processor 470, the multi-antenna receiver processor 472, the transmitter processor 416, the multi-antenna transmitter processor 471, the controller / processor 475, and the memory 476} is used to receive the first channel information report in this application.

[0389] Example 5

[0390] Example 5 illustrates a transmission flowchart according to an embodiment of this application; as shown in Figure 5. In Figure 5, the second node N1 and the first node U1 are communication nodes transmitting via an air interface. In Figure 5, the steps in blocks F51 and F52 are optional.

[0391] For the second node N1, the first information block is received in step S511; the second information block is sent in step S512; the first signaling is sent in step S513; and the first channel information report is received in step S514.

[0392] For the first node U1, a first information block is sent in step S521; a second information block is received in step S522; a first signaling is received in step S523; and a first channel information report is sent in step S524.

[0393] In Embodiment 5, the first information block indicates a first time interval; the generation of the first channel information report occupies at least one type of first resource; the first channel information report occupies the at least one type of first resource starting from a first moment, the first moment depending on the first time interval. The second information block is used to configure the first channel information report. The first signaling triggers the first channel information report.

[0394] As an example, the first node U1 is the first node in this application.

[0395] As an example, the second node N1 is the second node in this application.

[0396] As one embodiment, the air interface between the second node N1 and the first node U1 includes a wireless interface between the base station equipment and the user equipment.

[0397] As one embodiment, the air interface between the second node N1 and the first node U1 includes a wireless interface between the relay node device and the user equipment.

[0398] As one embodiment, the air interface between the second node N1 and the first node U1 includes a wireless interface between user equipment and user equipment.

[0399] As one example, the second node N1 is the serving cell sustaining base station of the first node U1.

[0400] As one embodiment, the sending of the second information block is later than the receiving of the first information block, and the receiving of the second information block is later than the sending of the first information block.

[0401] As one example, the transmission of the second information block is later than the transmission of the first information block.

[0402] As one embodiment, the sending of the first information block is later than the receiving of the second information block, and the receiving of the first information block is later than the sending of the second information block.

[0403] As an example, the transmission of the first information block is later than the transmission of the second information block.

[0404] As one embodiment, the second information block is carried by higher layer signaling.

[0405] As an example, the second information block is carried by RRC (Radio Resource Control) signaling.

[0406] As one embodiment, the second information block includes some or all of the fields in one or more RRC IEs (Information Elements).

[0407] As one embodiment, the second information block includes some or all of the fields in an IE CSI-ReportConfig.

[0408] As one embodiment, the second information block includes some or all of the domains in IE ServingCellConfig.

[0409] As one embodiment, the second information block includes some or all of the domains in IE CSI-MeasConfig.

[0410] As one example, the second information block includes some or all of the domains in IE ServingCellConfigCommon.

[0411] As one embodiment, the second information block includes some or all of the domains in IE ServingCellConfig.

[0412] As one embodiment, the second information block indicates at least one of a first resource set, a reporting type, or a reporting quantity; the first resource set includes one or more RS resources, and the first resource set is used for at least one of channel measurement or interference measurement in the first channel information reporting.

[0413] As one embodiment, the second information block indicates at least one of a first resource set, a second resource set, a reporting type, or a reporting quantity; the first resource set includes one or more RS resources, and the first resource set is used for at least one of channel measurement or interference measurement in the first channel information reporting; the second resource set includes one or more resources.

[0414] As a sub-implementation of the above embodiments, the second resource set includes one or more RS resources.

[0415] As a sub-implementation of the above embodiments, the resources in the second resource set include at least one of antenna port, TCI status, QCL information, time-frequency resources, time-frequency code resources, beam, RS resources, vector, or matrix.

[0416] As an example, the reporting type indicates at least one of periodic reporting, semi-persistent reporting, non-periodic reporting, or event-triggered reporting.

[0417] As an example, the reporting type indicates at least one of periodic reporting, semi-persistent reporting, or non-periodic reporting.

[0418] As an example, the first channel information report is one of periodic reporting, semi-persistent reporting, non-periodic reporting, or event-triggered reporting.

[0419] As an example, the first channel information report is one of semi-persistent reporting, non-periodic reporting, or event-triggered reporting.

[0420] As an example, the first channel information report is aperiodic.

[0421] As an example, the first channel information report is a semi-persistent report.

[0422] As an example, the first channel information report is an initial semi-persistent report.

[0423] As an example, the first channel information report is an initial report of a semi-persistent report.

[0424] As an example, the first channel information report is a semi-persistent report activated by MAC CE.

[0425] As an example, the first channel information report is a semi-persistent report activated by DCI (Downlink Control Information).

[0426] As an example, the first channel information report is the initial report in a semi-persistent report activated by DCI.

[0427] As an example, the first channel information report is the initial semi-persistent report triggered by DCI.

[0428] As an example, the first channel information report is an aperiodic report triggered by DCI.

[0429] As an example, the first channel information report is an event-triggered report.

[0430] As an example, the first signaling includes MAC CE.

[0431] As an example, the first signaling is physical layer signaling.

[0432] As one embodiment, the first signaling includes control information.

[0433] As an example, the first signaling is DCI.

[0434] As an example, the first signaling is signaling transmitted over a physical layer channel.

[0435] As one embodiment, the first signaling is control signaling transmitted over the physical layer channel.

[0436] As one embodiment, the first signaling is transmitted on the physical layer control channel.

[0437] As an example, the first signaling is DCI transmitted on PDCCH (Physical Downlink Control Channel).

[0438] As an example, the first signaling includes MAC CE.

[0439] As one embodiment, the first signaling includes a first field, which includes at least one bit; the first field in the first signaling triggers the first channel information reporting.

[0440] As an example, the first field is the CSI request field.

[0441] As one embodiment, the first signaling triggering the first channel information reporting includes: the first signaling includes MAC CE, and the first channel information reporting is a semi-persistent reporting activated by the first signaling.

[0442] As one embodiment, the first signaling triggering the first channel information reporting includes: the first signaling includes physical layer signaling, and the first channel information reporting is a semi-persistent reporting activated by the first signaling.

[0443] As one embodiment, the first signaling triggering the first channel information reporting includes: the first signaling includes physical layer signaling, and the first channel information reporting is the initial reporting in a semi-persistent reporting activated by the first signaling.

[0444] As one embodiment, the first signaling triggering the first channel information reporting includes: the first signaling includes physical layer signaling, and the first channel information reporting is a non-periodic reporting triggered by the first signaling.

[0445] As one embodiment, the first signaling triggering the first channel information reporting includes: the first signaling includes DCI, and the first channel information reporting is a semi-persistent reporting activated by the first signaling.

[0446] As one embodiment, the first signaling triggering the first channel information report includes: the first signaling includes DCI, and the first channel information report is the initial report in a semi-persistent report activated by the first signaling.

[0447] As one embodiment, the first signaling triggering the first channel information reporting includes: the first signaling includes DCI, and the first channel information reporting is a non-periodic reporting triggered by the first signaling.

[0448] As one embodiment, the first signaling triggering the first channel information reporting includes: the first signaling includes physical layer signaling, and the first channel information reporting is an aperiodic reporting triggered by the first signaling or an initial semi-persistent reporting.

[0449] As one embodiment, the first signaling triggering the first channel information reporting includes: the first signaling includes DCI, and the first channel information reporting is an aperiodic reporting triggered by the first signaling or an initial semi-persistent reporting.

[0450] As an example, the first node performs a first operation, and the first channel information reporting depends on the output of the first operation.

[0451] As an example, the first node deploys the first operation.

[0452] As one embodiment, the deployment of the first operation includes: obtaining the first operation.

[0453] As an example, the deployment first operation includes: loading the first operation.

[0454] As one embodiment, the deployment of the first operation includes: submitting a request to load the first operation.

[0455] As an example, the first operation is used for CSI prediction, beam prediction, or CSI compression.

[0456] As an example, the first operation is used for beam prediction or CSI prediction.

[0457] As an example, the first node performs a first operation, and the second node performs a second operation.

[0458] As one embodiment, the second node performs a second operation; wherein the first node performs a first operation, the output of the first operation includes a first CSI, the first channel information report carries the first CSI, and the first CSI is used as input to the second operation to generate a second CSI.

[0459] As an example, the second node deploys the second operation.

[0460] As one embodiment, the deployment of the second operation includes: obtaining the second operation.

[0461] As an example, the deployment of the second operation includes: loading the second operation.

[0462] As one embodiment, the deployment of the second operation includes: submitting a request to load the second operation.

[0463] As an example, the first operation is used for CSI compression, and the second operation is used for CSI recovery.

[0464] As an example, the output of the first operation includes a first CSI, the first channel information report carries the first CSI, and the first CSI is used by the second node as input to the second operation to generate a second CSI.

[0465] Examples 6A-6C

[0466] Examples 6A-6C illustrate schematic diagrams of the first condition according to an embodiment of this application, as shown in Figures 6A-6C respectively.

[0467] In embodiment 6A, the first time moment depends on the first time interval only when the first condition is met; the first condition includes at least one of the first channel information report being generated based on inference or the first channel information report generating a corresponding first identifier.

[0468] As an example, the first condition includes the first channel information report being generated based on inference.

[0469] As one embodiment, the first condition includes the generation of the first identifier corresponding to the first channel information reported.

[0470] As one embodiment, the first condition includes: the first channel information report is generated based on inference, and the generation of the first channel information report corresponds to a first identifier.

[0471] As an example, the first condition includes multiple sub-conditions; the first condition is satisfied only when all of the multiple sub-conditions are satisfied; one of the multiple sub-conditions includes at least one of the following: the first channel information report is generated based on inference or the first channel information report generates a corresponding first identifier.

[0472] As an example, the first condition includes multiple sub-conditions; the first condition is satisfied when one of the multiple sub-conditions is satisfied; one of the multiple sub-conditions includes at least one of the following: the first channel information report is generated based on inference or the first channel information report generates a corresponding first identifier.

[0473] As an example, the first time interval is only applicable to the reporting of channel information generated based on inference.

[0474] As an example, the start time of the occupation of the first type of resource is reported based solely on the channel information generated by inference, which depends on the first time interval.

[0475] As an example, for a channel information report that is not generated based on inference and is triggered by physical layer signaling, it occupies the first type of resources starting from the first symbol after the physical layer channel occupied by the physical layer signaling; the channel information report triggered by physical layer signaling is either an aperiodic report or an initial semi-persistent report.

[0476] As an example, for a channel information report that is not generated based on inference and is either periodic or not initial semi-persistent, it occupies a first type of resource starting from the first symbol of the earliest RS time in a first time set; the first time set includes at least the latest RS time of each RS resource in a third resource set that is no later than the CSI reference resource of the channel information report; the third resource set includes at least one RS resource, and the third resource set is used for at least one of the channel measurement or interference measurement of the channel information report.

[0477] Typically, the first symbol refers to the earliest symbol.

[0478] In the above method, the moment when a channel information report generated based on inference begins to occupy the first type of resource depends on the first time interval, and the first time interval is not applicable to channel information reports that are not generated based on inference.

[0479] In the above method, for a channel information report that is not generated based on inference, the start time of its occupation of the first type of resource is independent of the first time interval.

[0480] As an example, the advantages of the above method include: better adaptability to various types of channel information reporting, and good flexibility and adaptability.

[0481] As an example, the advantages of the above method include: better adaptability to various processing capabilities, better adaptability to various application scenarios or terminals, and good flexibility and adaptability.

[0482] In Example 6B, the first time depends on the first time interval only when the first condition is met; the first condition includes that the first channel information report is triggered by physical layer signaling; wherein, the first channel information report being triggered by physical layer signaling includes: the first channel information report being aperiodic or initial semi-persistent report.

[0483] As an example, the first channel information report is triggered by physical layer signaling, where the physical layer signaling is signaling transmitted on the physical layer channel.

[0484] As an example, the first channel information report is triggered by physical layer signaling, where the physical layer signaling is control signaling transmitted on the physical layer channel.

[0485] As an example, the physical layer signaling in the first channel information reporting triggered by physical layer signaling is DCI.

[0486] As an example, the first channel information report is triggered by physical layer signaling, where the physical layer signaling is DCI transmitted on the PDCCH.

[0487] As an example, the first channel information report is triggered by physical layer signaling, where the physical layer signaling is DCI transmitted on the PDCCH.

[0488] As an example, the first time depends on the first time interval only when the first condition is met; the first condition includes at least one of the first channel information report being generated based on inference or the first channel information report being generated corresponding to the first identifier; the first condition also includes the first channel information report being triggered by physical layer signaling; wherein, the first channel information report being triggered by physical layer signaling includes: the first channel information report being aperiodic or initial semi-persistent report.

[0489] As an example, the first condition includes multiple sub-conditions; the first condition is satisfied only when all of the multiple sub-conditions are satisfied; one of the multiple sub-conditions includes that the first channel information reporting is triggered by physical layer signaling.

[0490] As an example, the first condition includes multiple sub-conditions; the first condition is satisfied only when all of the multiple sub-conditions are satisfied; one of the multiple sub-conditions includes that the first channel information report is triggered by physical layer signaling; another of the multiple sub-conditions includes that the first channel information report is generated based on inference or that the first channel information report generates a corresponding first identifier, at least one of these.

[0491] As an example, the first condition includes multiple sub-conditions; the first condition is satisfied when one of the multiple sub-conditions is satisfied; one of the multiple sub-conditions includes that the first channel information reporting is triggered by physical layer signaling.

[0492] As an example, the first condition includes multiple sub-conditions; the first condition is satisfied when one of the multiple sub-conditions is satisfied; the first sub-condition is one of the multiple sub-conditions, and the first sub-condition includes that the first channel information reporting is triggered by physical layer signaling, and the first sub-condition also includes at least one of the following: the first channel information reporting is generated based on inference or the first channel information reporting generates a corresponding first identifier.

[0493] In embodiment 6C, the first time depends on the first time interval only when the first condition is met; the first resource group includes one or more first-class resources, and the second resource group includes one or more first-class resources; the first condition includes the generation of the first channel information report occupying at least one first-class resource in the first resource group.

[0494] As one embodiment, the first condition includes that the generation of the first channel information report occupies at least one of the first type of resources in the first resource group; the first condition also includes that the first channel information report is generated based on inference or that the generation of the first channel information report corresponds to at least one of the first identifiers.

[0495] As an example, the first condition includes that the generation of the first channel information report occupies at least one first type of resource in the first resource group; the first condition also includes that the first channel information report is triggered by physical layer signaling; wherein, the first channel information report being triggered by physical layer signaling includes: the first channel information report is aperiodic reporting or initial semi-persistent reporting.

[0496] As an example, the first condition includes multiple sub-conditions; the first condition is satisfied only when all of the multiple sub-conditions are satisfied; one of the multiple sub-conditions includes the generation of the first channel information reported occupying at least one first type of resource in the first resource group.

[0497] As an example, the first condition includes multiple sub-conditions; the first condition is satisfied when one of the multiple sub-conditions is satisfied; one of the multiple sub-conditions includes the generation of the first channel information reported occupying at least one first type of resource in the first resource group.

[0498] As one embodiment, the first resource group includes one or more first-class resources and one or more second-class resources.

[0499] As one embodiment, the second resource group includes one or more first-class resources and one or more second-class resources.

[0500] As one embodiment, the first resource group includes one or more first-class resources and one or more second-class resources; the second resource group includes one or more first-class resources.

[0501] As an example, channel information reports generated based on inference occupy the first type of resources in the first resource group, while channel information reports generated based on inference occupy the first type of resources in the second resource group.

[0502] Example 7

[0503] Example 7 illustrates a schematic diagram of a first moment depending on a first time interval according to an embodiment of the present application; as shown in Figure 7.

[0504] In Embodiment 7, the first signaling triggers the first channel information reporting; the first moment is no earlier than at least the first time interval after the physical layer channel carrying the first signaling.

[0505] As an example, the first channel information report is either an aperiodic report triggered by the first signaling or an initial semi-persistent report.

[0506] As an example, the first moment is no earlier than at least a second time interval after the physical layer channel carrying the first signaling, and the second time interval is greater than the first time interval.

[0507] As an example, the first moment is no earlier than the start time of the first symbol of at least a second time interval following the physical layer channel carrying the first signaling, and the second time interval is greater than the first time interval.

[0508] As an example, the first moment is the start time of the first symbol of at least the second time interval after the physical layer channel carrying the first signaling, where the second time interval is greater than the first time interval.

[0509] As one example, the second time interval and the first time interval are linearly related.

[0510] As one example, the second time interval and the first time interval have a non-linear relationship.

[0511] As an example, the first moment is no earlier than the start time of the first symbol of at least the first time interval after the physical layer channel carrying the first signaling.

[0512] As an example, the first moment is the start time of the first symbol after at least the first time interval following the physical layer channel carrying the first signaling.

[0513] Typically, the first symbol refers to the earliest symbol.

[0514] Examples 8A-8B

[0515] Examples 8A-8B illustrate schematic diagrams of a first moment depending on a first time interval according to another embodiment of this application, as shown in Figures 8A-8B respectively.

[0516] In Embodiment 8A, the first moment is no earlier than at least the first time interval after the time domain resources occupied by the first information block.

[0517] As an example, the first moment is no earlier than at least a third time interval after the time domain resources occupied by the first information block, and the third time interval is greater than the first time interval.

[0518] As an example, the third time interval and the first time interval are linearly related.

[0519] As one example, the third time interval and the first time interval have a non-linear relationship.

[0520] As an example, the first moment is no earlier than the start time of the first symbol of the first time interval after the time domain resources occupied by the first information block.

[0521] As an example, the first moment is the start time of the first symbol at least after the first time interval following the time domain resources occupied by the first information block.

[0522] As an example, the first moment is no earlier than the start time of the first time slot of the first time interval after the time domain resources occupied by the first information block.

[0523] As an example, the first moment is the start time of the first time slot at least after the first time interval following the time domain resources occupied by the first information block.

[0524] As an example, the first moment is no earlier than the start time of the first subframe of the first time interval after the time domain resources occupied by the first information block.

[0525] As an example, the first moment is the start time of the first subframe at least after the first time interval following the temporal resources occupied by the first information block.

[0526] As an example, "after" the time domain resources occupied by the first information block means after the time slot in which the first information block is located.

[0527] As an example, the time-domain resources occupied by the first information block are after the subframe in which the first information block is located.

[0528] As an example, the time-domain resources occupied by the first information block are after: the physical layer channel carrying the first information block.

[0529] As an example, the phrase "after the time-domain resources occupied by the first information block" refers to the period after the last symbol of the physical layer channel carrying the first information block.

[0530] As an example, "after the time domain resources occupied by the first information block" means after the termination time of the physical layer channel carrying the first information block.

[0531] As an example, "after the time-domain resources occupied by the first information block" means after the last symbol occupied by the first information block.

[0532] As an example, "after" the time domain resources occupied by the first information block refers to "after" the termination time of the first information block's occupation.

[0533] In Example 8B, the first channel information report is generated based on inference, and the time at which the AI ​​model used for inference begins to be applied depends on the first time interval, and the first time is not earlier than the time at which the AI ​​model used for inference begins to be applied.

[0534] As an example, the first channel information report is generated based on inference, and the time when the AI ​​model used for inference begins to be applied depends on the first time interval, and the first time is later than the time when the AI ​​model used for inference begins to be applied.

[0535] As an example, a first identifier is used to identify an AI model, and the first channel information report is generated based on inference using the AI ​​model identified by the first identifier; the time at which the AI ​​model used for inference begins to be applied is the time at which the AI ​​model identified by the first identifier begins to be applied.

[0536] As an example, the timing at which the AI ​​model used for inference begins to be applied depends on the first time interval, including: the timing at which the AI ​​model used for inference begins to be applied is no earlier than at least the first time interval after the time domain resources occupied by the first information block.

[0537] As an example, the timing at which the AI ​​model used for inference begins to be applied depends on the first time interval, including: the first signaling triggers the first channel information reporting; the timing at which the AI ​​model used for inference begins to be applied is no earlier than at least the first time interval after the physical layer channel carrying the first signaling.

[0538] As one embodiment, the timing at which the AI ​​model used for inference begins to be applied depends on the first time interval, including: the second information block being used to configure the first channel information reporting; and the timing at which the AI ​​model used for inference begins to be applied is no earlier than at least the first time interval after the physical layer channel carrying the second information block.

[0539] As one embodiment, the time at which the AI ​​model used for inference begins to be applied depends on the first time interval, including: the time at which the AI ​​model used for inference begins to be applied is no earlier than at least the first time interval after the reference time.

[0540] As a sub-implementation of the above embodiments, the reference time belongs to a physical layer channel or a physical layer signaling.

[0541] As a sub-implementation of the above embodiments, the reference time is indicated by the first node.

[0542] As a sub-example of the above embodiments, the reference time is the termination time of a physical layer uplink channel.

[0543] As a sub-implementation of the above embodiments, the reference time is indicated by the second node.

[0544] As a sub-example of the above embodiments, the reference time is the termination time of a physical layer downlink channel.

[0545] As a sub-example of the above embodiments, the reference time is the time when the AI ​​model used for inference begins to be loaded.

[0546] As a sub-example of the above embodiments, the reference time is the time when the AI ​​model used for inference is loaded.

[0547] As a sub-implementation of the above embodiments, the reference time belongs to the time-domain resources occupied by a signaling that indicates the AI ​​model used for loading the inference.

[0548] As a sub-implementation of the above embodiments, the reference time is the termination time of a physical layer channel carrying a signaling indicating the AI ​​model used for the inference.

[0549] As an example, an AI model is applied after it has been loaded.

[0550] As an example, the N identifiers are used to identify N AI models, and the time at which the N AI models are applied depends on the N time intervals; the first identifier is any one of the N identifiers, and the time at which the AI ​​model identified by the first identifier is applied depends on the first time interval, which is one of the N time intervals; the first time is not earlier than the time at which the AI ​​model identified by the first identifier is applied.

[0551] In the above method, the AI ​​model used to generate channel information reporting is applied from a certain time, ensuring that the time when channel information reporting begins to occupy the first type of resources is neither earlier nor later than the time when the AI ​​model used begins to be applied.

[0552] Example 9

[0553] Example 9 illustrates a schematic diagram of a first moment depending on a first time interval according to another embodiment of this application; as shown in Figure 9.

[0554] In Embodiment 9, the generation of the first channel information report also occupies at least one second type of resource; the first channel information report occupies the at least one second type of resource starting from a second time moment; the second time moment depends on the first time interval; the first time moment is not earlier than the second time moment, or the first time moment is later than the second time moment.

[0555] As an example, the second moment is no earlier than at least the first time interval after the time domain resources occupied by the first information block.

[0556] As one embodiment, the second moment is no earlier than at least the first time interval after the physical layer channel carrying the first signaling.

[0557] As an example, the first channel information report is generated based on inference, and the time when the AI ​​model used for inference begins to be applied depends on the first time interval, and the second time is not earlier than the time when the AI ​​model used for inference begins to be applied.

[0558] As an example, the first moment is not earlier than the second moment.

[0559] As an example, the first moment is later than the second moment.

[0560] As one embodiment, the first channel information is reported from the second time point to the fourth time point, occupying at least one second type of resource; the fourth time point is later than the second time point.

[0561] As an example, the fourth moment is indicated by the second node.

[0562] As an example, the fourth moment is determined by the first node itself or is implementation-related.

[0563] As an example, the fourth moment is the last moment of the physical layer channel carrying the first channel information reported.

[0564] As an example, the fourth moment is later than the last moment of the physical layer channel carrying the first channel information reported.

[0565] As an example, the fourth moment is the end time of the last symbol of the physical layer channel carrying the first channel information reported.

[0566] As an example, the fourth time point is later than the end time of the last symbol of the physical layer channel carrying the first channel information reported.

[0567] Example 10

[0568] Example 10 illustrates a schematic diagram of N time intervals and N identifiers according to an embodiment of this application; as shown in Figure 10, identifiers #1, ..., identifiers #n, ..., identifiers #N are the N identifiers; time intervals #1, ..., time intervals #n, ..., time intervals #N are the N time intervals.

[0569] In Example 10, N time intervals correspond to N identifiers, where N is a positive integer greater than 1; the generation of the first channel information report corresponds to a first identifier, which is one of the N identifiers, and the first time interval is a time interval corresponding to the first identifier among the N time intervals.

[0570] As an example, the N time intervals correspond to N identifiers, where N is a positive integer greater than 1; the generation of the first channel information report corresponds to a first identifier, which is any one of the N identifiers, and the first time interval is a time interval corresponding to the first identifier among the N time intervals.

[0571] As an example, any one of the N identifiers is a non-negative integer.

[0572] As an example, any one of the N identifiers includes one or more characters.

[0573] As an example, any one of the N identifiers is used to identify one or more Class 1 resources.

[0574] As an example, any one of the N identifiers is used to identify a first resource group, which includes one or more first-class resources.

[0575] As an example, any one of the N identifiers is used to identify a resource group, which includes one or more first-class resources.

[0576] As an example, any one of the N identifiers is used to identify a resource group, which includes one or more first-class resources and one or more second-class resources.

[0577] As an example, any one of the N identifiers is different from the reporting configuration identifier of the first channel information report.

[0578] As an example, any one of the N identifiers is used to identify the AI ​​model.

[0579] As an example, any one of the N identifiers is used to identify the inference used to generate channel information reports.

[0580] As an example, any one of the N identifiers is used to identify the AI ​​entity.

[0581] As an example, any one of the N identifiers is used to identify the AI ​​function.

[0582] As an example, the advantages of the above method include that the N AI models / entities / functions are identified by the N identifiers respectively, which simplifies the design and unifies the understanding of different AI entities or functions across multiple nodes.

[0583] As an example, any one of the N identifiers is used to identify or indicate a set of resources.

[0584] As an example, any one of the N identifiers is used to identify or indicate a resource set, and the measurement of the resource set is used to obtain a training dataset.

[0585] As an example, any one of the N identifiers is used to identify or indicate a resource set, and the resource set identified or indicated by any one of the N identifiers includes one or more RS resources.

[0586] As an example, any one of the N identifiers is used to identify or indicate the training dataset.

[0587] As an example, the benefits of the above method include identifying the inference generated by an AI training or AI training dataset by identifying the AI ​​training or AI training dataset, establishing consensus among different AI functions, and further simplifying the design.

[0588] As an example, the first information block indicates the N time intervals.

[0589] As an example, the first information block indicates the N time intervals and the N identifiers.

[0590] As an example, the first information block indicates only the first time interval among the N time intervals.

[0591] As one embodiment, the first information block includes N information sub-blocks, and the N information sub-blocks respectively indicate the N time intervals.

[0592] As one embodiment, the first information block includes N information sub-blocks, each of which indicates one of the N time intervals and also indicates one of the N identifiers.

[0593] As an example, the N identifiers are used to identify N AI models, and the N time intervals are the loading times required for each of the N AI models.

[0594] As an example, the N identifiers are used to identify N AI models, and the N time intervals are each not less than the loading time required by the N AI models.

[0595] As an example, the N identifiers are used to identify N AI models, and the time at which the N AI models are applied depends on the N time intervals.

[0596] As an example, the start time of the channel information reporting corresponding to each of the N identifiers for the occupation of the first type of resource depends on the N time intervals.

[0597] As an example, the given identifier is any one of the N identifiers, and the given time interval is a time interval corresponding to the given identifier among the N time intervals; the start time of a channel information report corresponding to the given identifier for occupying the first type of resource depends on the given time interval.

[0598] Without loss of generality, in the above method, the specific implementation in which the start time of a channel information report corresponding to the given identifier occupancy of the first type of resource depends on the given time interval is similar to the specific implementation in which the first time depends on the first time interval.

[0599] Examples 11A-11C

[0600] Examples 11A-11C illustrate schematic diagrams of generating a corresponding first identifier for the first channel information reporting according to an embodiment of this application, as shown in Figures 11A-11C.

[0601] In embodiment 11A, the generation of the first channel information report corresponding to the first identifier includes: a second information block is used to configure the first channel information report, and the second information block indicates the first identifier.

[0602] In the above method, the generation of the first channel information report corresponds to the first identifier indicated by the second information block.

[0603] As an example, the advantages of the above method include: simplified design, and the ability to flexibly configure the corresponding first identifier for the first channel information reporting.

[0604] In embodiment 11B, the generation of the first channel information report corresponding to the first identifier includes: the first node or the generator of the first channel information report performing a first operation, the first channel information report depending on the output of the first operation, and the first operation corresponding to the first identifier.

[0605] As an example, the generation of the first channel information report corresponding to the first identifier includes: the first node or the generator of the first channel information report performing a first operation, the first operation including inference, the first channel information report depending on the output of the first operation, and the first operation corresponding to the first identifier.

[0606] As an example, the first operation is based on training or AI.

[0607] As an example, the first operation includes inference.

[0608] As one example, the first operation includes an AI entity.

[0609] As an example, the first operation includes an AI entity for inference.

[0610] As an example, the first operation includes a portion of an AI entity.

[0611] As an example, the first operation includes a portion of an AI entity used for inference.

[0612] As one embodiment, the first operation includes inference for obtaining the first channel information report.

[0613] As an example, the reasoning includes AI (Artificial Intelligence) inference.

[0614] As an example, the first operation includes AI inference for obtaining CSI.

[0615] As one example, the first operation includes AI inference for obtaining channel information.

[0616] As one example, the first operation includes AI inference for obtaining information other than channel information.

[0617] As an example, the first operation is used for an AI function.

[0618] As an example, the first operation is performed by the physical layer of the first node.

[0619] As an example, the first operation is performed at a higher level than the first node.

[0620] As an example, the model for the first operation is obtained through training.

[0621] As an example, the training for the first operation is performed by the first node.

[0622] As an example, the training for the first operation is performed by the target receiver that reports the first channel information.

[0623] As an example, the training for the first operation is performed by the core network.

[0624] As an example, the training of the first operation is performed by an AI training producer.

[0625] As an example, the training of the first operation is performed by the MDA (Management Data Analytics Function).

[0626] As an example, the training of the first operation is performed by the MDA function located at the first node.

[0627] As an example, the training of the first operation is performed by the MDA function of the target receiver where the first channel information is reported.

[0628] As an example, the training of the first operation is performed by NWDAF (Network Data Analytics Function).

[0629] As an example, the training of the first operation is performed by the MDAS (Management Data Analytics Service) producer.

[0630] As an example, the training of the first operation is performed by the MnS (Management Service) producer.

[0631] As an example, the first operation requires deployment.

[0632] As an example, the first operation is obtained by loading.

[0633] As an example, the first operation is obtained from the serving cell of the first node.

[0634] As an example, the first operation is obtained from the sustaining base station of the serving cell of the first node.

[0635] As an example, the first node deploys the first operation.

[0636] As an example, the first operation does not require deployment.

[0637] As an example, the first operation is obtained from the core network.

[0638] As an example, the first operation is based on artificial intelligence or machine learning.

[0639] As an example, the first operation is based on a neural network.

[0640] As an example, the first operation is based on CNN (Conventional Neural Networks).

[0641] As one example, the first operation includes preprocessing.

[0642] As one example, the first operation includes post-processing.

[0643] As one example, the post-processing includes DFT.

[0644] As one example, the post-processing includes quantization.

[0645] As an example, the post-processing includes one or more of the following: angular domain to spatial domain transformation, spatial domain to angular domain transformation, time domain to frequency domain transformation, and frequency domain to time domain transformation.

[0646] As one example, the post-processing includes truncation and / or padding.

[0647] As an example, the first operation includes one or more of convolution, pooling, cascading, and activation.

[0648] As one embodiment, the first operation includes a fully connected layer.

[0649] As an example, the first operation includes a pooling layer.

[0650] As one embodiment, the first operation includes at least one convolutional layer.

[0651] As an example, the first operation includes at least one encoding layer.

[0652] As an example, an encoding layer includes at least one convolutional layer and one pooling layer.

[0653] As an example, in a convolutional layer, at least one convolutional kernel is used to convolve the input to generate a corresponding feature map, and at least one feature map output by the convolutional layer is reshaped into a vector and input to a fully connected layer; the fully connected layer transforms the vector into an output.

[0654] As an example, some or all of the following parameters in the first operation—convolution kernel size, number of convolutional layers, convolution stride, pooling kernel size, pooling kernel stride, pooling function, activation function, and number of feature maps—are obtained through training.

[0655] As an example, some or all of the convolution kernel, pooling kernel, pooling function, activation function, parameters of the pooling function, and parameters of the activation function in the first operation are obtained through training.

[0656] As an example, the output of the first operation includes channel information.

[0657] As an example, the output of the first operation includes information other than channel information.

[0658] As an example, the output of the first operation includes a channel matrix.

[0659] As an example, the output of the first operation includes CSI.

[0660] As an example, the output of the first operation includes compressed CSI.

[0661] As an example, the output of the first operation includes non-codebook-based CSI.

[0662] As an example, the output of the first operation includes a channel impulse response.

[0663] As an example, the output of the first operation includes small-scale characteristics.

[0664] As an example, the output of the first operation is used to determine one or more precoding matrices.

[0665] As an example, the first operation includes CSI compression based on artificial intelligence or machine learning.

[0666] As an example, the first operation includes an encoder for CSI compression based on artificial intelligence or machine learning.

[0667] As an example, the first operation includes CSI prediction or CSI estimation based on artificial intelligence or machine learning.

[0668] As one example, the first operation includes beam management based on artificial intelligence or machine learning.

[0669] As one embodiment, the beam management includes at least one of beam prediction, beam switching, beam failure prediction, or beam failure recovery.

[0670] As an example, the input to the first operation includes measurements obtained based on at least one RS resource.

[0671] As an example, the input to the first operation includes channel measurements obtained based on CSI-RS resources or SS / PBCHblock resources.

[0672] As an example, the input to the first operation includes interference measurements obtained based on CSI-RS resources or CSI-IM resources.

[0673] As an example, the input to the first operation includes the reception quality of at least one physical channel or physical signal.

[0674] As an example, the input to the first operation includes a matrix or vector obtained by preprocessing the channel matrix obtained from measurements based on at least one RS resource.

[0675] As one example, the AI ​​function includes AI inference functionality.

[0676] As one example, the AI ​​functionality includes AI training functionality.

[0677] As one example, the AI ​​functionality includes AI management functionality.

[0678] As one example, the AI ​​function includes AI performance monitoring.

[0679] As one example, the AI ​​includes ML (Machine Learning).

[0680] As one example, the AI ​​includes AI and ML.

[0681] As one example, the AI ​​includes AI or ML.

[0682] As an example, the preprocessing includes one or more of the following: quantization, DFT (Discrete Fourier Transform), matrix decomposition, matrix transformation or projection, spatial-to-angular-domain transformation, angular-to-spatial-domain transformation, frequency-to-time-domain transformation, time-to-frequency-domain transformation, truncation, padding, mapping, or labeling.

[0683] As an example, the preprocessing includes DFT (Discrete Fourier Transform).

[0684] As one example, the preprocessing includes one or more of matrix decomposition, matrix transformation, or projection.

[0685] As an example, the preprocessing includes one or more of the following: quantization, spatial-to-angular-domain transformation, angular-to-spatial-domain transformation, frequency-to-time-domain transformation, or time-to-frequency-domain transformation.

[0686] As one example, the preprocessing includes truncation and / or padding.

[0687] As one example, the preprocessing includes mapping.

[0688] As one example, the preprocessing includes mapping to vectors.

[0689] As one example, the preprocessing includes labeling.

[0690] As an example, the label refers to a mark made with a label.

[0691] As one example, the post-processing includes DFT.

[0692] As one example, the post-processing includes quantization.

[0693] As an example, the post-processing includes one or more of the following: angular domain to spatial domain transformation, spatial domain to angular domain transformation, time domain to frequency domain transformation, and frequency domain to time domain transformation.

[0694] As one example, the post-processing includes truncation and / or padding.

[0695] As an example, an encoding layer includes at least one convolutional layer and one pooling layer.

[0696] As an example, in a convolutional layer, at least one convolutional kernel is used to convolve the input to generate a corresponding feature map, and at least one feature map output by the convolutional layer is reshaped into a vector and input to a fully connected layer; the fully connected layer transforms the vector into an output.

[0697] As one embodiment, the first operation corresponding to the first identifier includes: the first operation being identified by the first identifier.

[0698] As an example, the first operation corresponding to the first identifier includes: the AI ​​model used in the first operation is identified by the first identifier.

[0699] As one embodiment, the first operation corresponding to the first identifier includes: the AI ​​entity included in the first operation is identified by the first identifier.

[0700] As one embodiment, the first operation corresponding to the first identifier includes: the AI ​​function to which the first operation is used is identified by the first identifier.

[0701] As an example, the advantages of the above method include that identifying an AI entity or function through the first identifier simplifies the design and unifies the understanding of different AI entities or functions across multiple nodes.

[0702] As one embodiment, the first operation corresponding to the first identifier includes: the AI ​​entity performing the first operation is identified by the first identifier.

[0703] As one embodiment, the first operation corresponding to the first identifier includes: the first identifier is used by the first node to determine the AI ​​model adopted by the first operation.

[0704] As an example, the advantages of the above method include that identifying an AI model / entity / function through the first identifier simplifies the design and unifies the understanding of different AI entities / functions across multiple nodes.

[0705] As one embodiment, the first operation corresponding to the first identifier includes: the first identifier is used to identify or indicate a set of RS resources, and the measurement of the set of RS resources is used to obtain a training dataset for the first operation.

[0706] As one embodiment, the first operation corresponding to the first identifier includes: obtaining the training for the first operation identified by the first identifier.

[0707] As one embodiment, the first operation corresponding to the first identifier includes: the dataset used for training the first operation is identified by the first identifier.

[0708] As an example, the benefits of the above method include identifying the inference generated by an AI training or AI training dataset by identifying the AI ​​training or AI training dataset, establishing consensus among different AI functions, and further simplifying the design.

[0709] As one embodiment, the first operation corresponding to the first identifier includes: the first operation performs spatial beam prediction for a second resource set based on measurements of a first resource set, the second resource set depending on the first identifier.

[0710] As an example, the advantages of the above method include reduced RS overhead and reduced feedback latency.

[0711] As one embodiment, the first operation corresponding to the first identifier includes: the first operation performs channel information prediction for a second resource set based on the measurement of a first resource set, the second resource set depending on the first identifier.

[0712] As one embodiment, the first operation corresponding to the first identifier includes: the first operation performs temporal beam prediction for a second resource set based on historical measurements of a first resource set, the second resource set depending on the first identifier.

[0713] As an example, the advantages of the above method include reducing beam feedback delay and improving the real-time performance of beam acquisition.

[0714] As one embodiment, the first operation corresponding to the first identifier includes: the first operation performs temporal channel information prediction for a second resource set based on historical measurements of a first resource set, the second resource set depending on the first identifier.

[0715] As an example, the advantages of the above method include reducing channel information feedback delay and improving the real-time performance of channel information acquisition.

[0716] As an example, the output of the first operation is used to generate the first channel information report.

[0717] As an example, the first channel information report includes the output of the first operation.

[0718] As one embodiment, the first channel information report includes the post-processed output of the first operation.

[0719] As one embodiment, the first channel information report includes the truncated and / or quantized output of the first operation.

[0720] As an example, the output of the first operation, after post-processing, is used to generate the first channel information report.

[0721] As an example, the output of the first operation, after being truncated and / or quantized, is used to generate the first channel information report.

[0722] As an example, some or all of the output of the first operation, after post-processing, is used to generate the first channel information report.

[0723] As an example, some or all of the output of the first operation, after being truncated and / or quantized, is used to generate the first channel information report.

[0724] As an example, the output of the first operation includes a first CSI, which is used to generate the first channel information report.

[0725] As an example, the advantages of the above method include improved CSI reporting performance by leveraging the advantages of the first operation, including more accurate reporting and / or lower overhead.

[0726] As an example, the first channel information report includes the first CSI.

[0727] As an example, the first CSI is post-processed and used to generate the first channel information report.

[0728] As one embodiment, the first channel information report includes the first CSI after post-processing.

[0729] As an example, the first channel information report carries the first CSI after post-processing.

[0730] As an example, the first CSI is truncated and / or quantized and used to generate the first channel information report.

[0731] As one embodiment, the first channel information reported includes the first CSI after truncation and / or quantization.

[0732] As an example, the first channel information report carries the first CSI after truncation and / or quantization.

[0733] As an example, the first CSI includes one or more of PMI, CRI, CQI, RI, LI, SSBRI, RSRP, SINR, Capability Index, and TDCP.

[0734] As an example, the first CSI includes one or more of PMI, CRI, CQI, RI, LI, SSBRI, RSRP, SINR, capability index, TDCP, predicted channel information, predicted beam information, or confidence information.

[0735] As one embodiment, the first CSI includes a channel matrix.

[0736] As one example, the first CSI includes a feature vector.

[0737] As an example, the first CSI includes a feature vector and feature values.

[0738] As an example, the first CSI includes precoded information.

[0739] As one embodiment, the first CSI includes pre-encoded information based on a non-codebook.

[0740] As an example, the first CSI is used to determine at least one precoding matrix.

[0741] As an example, the first CSI indicates at least one precoding matrix.

[0742] As an example, the precoding matrix is ​​in the spatial-frequency domain.

[0743] As an example, the precoding matrix is ​​an angular-delay domain projection.

[0744] As one embodiment, the first CSI includes information on the relative phase, amplitude, and / or coefficients between multiple antenna ports.

[0745] As an example, the first CSI includes a compressed CSI.

[0746] As an example, the first CSI includes predicted / estimated CSI.

[0747] As one example, how to generate the first channel information report based on the first operation is determined by the manufacturer of the first node, or is implementation-related. A typical but non-limiting implementation is described below:

[0748] The first node first measures the RS resources used for channel measurement to obtain the channel parameter matrix H. r×t Where r and t are the number of receiving antennas and the number of antenna ports, respectively; at least the channel parameter matrix H r×t Alternatively, its feature vector is input into an AI model, and the output of the AI ​​model is used to obtain the first channel information report.

[0749] If the first channel information reporting requires the first node to estimate interference (including noise), the first node can measure the RS resources used for interference measurement to obtain the measured interference.

[0750] In one implementation, measurement interference is also input into the AI ​​model.

[0751] In another implementation, the measurement interference is not input into the AI ​​model; the output of the AI ​​model and the measurement interference are used together to generate the first channel information report.

[0752] Without loss of generality, the AI ​​model or the parameters of the AI ​​model used to generate the first channel information report are determined by the manufacturer of the first node.

[0753] In embodiment 11C, the generation of the first channel information report corresponding to the first identifier includes: the generation of the first channel information report uses an AI model identified by the first identifier, or the first channel information report is generated in an AI entity identified by the first identifier, or the first channel information report is used for an AI function identified by the first identifier.

[0754] As an example, the generation of the first channel information report corresponding to the first identifier includes: the first identifier is used to identify an AI model, and the first channel information report is generated based on inference using the AI ​​model identified by the first identifier.

[0755] As an example, the generation of the first channel information reported uses an AI model identified by the first identifier.

[0756] As an example, the first channel information report is generated in the AI ​​entity identified by the first identifier.

[0757] As an example, the first channel information report is used for the AI ​​function identified by the first identifier.

[0758] As an example, the advantages of the above method include: better adaptability to various application scenarios or terminals, and good flexibility and adaptability.

[0759] Examples 12A-12C

[0760] Examples 12A-12C illustrate schematic diagrams of a first channel information reporting according to an embodiment of this application, as shown in Figures 12A-12C respectively.

[0761] In Example 12A, the first channel information report is generated based on inference.

[0762] As an example, the reasoning includes AI reasoning.

[0763] As one embodiment, the first channel information report is generated based on inference, including: the generation of the first channel information report is based on training.

[0764] As one embodiment, the first channel information report is generated based on inference, including the generation of the first channel information report using an AI model.

[0765] As one embodiment, the first channel information report is generated based on inference, including: the generation of the first channel information report uses information generated based on artificial intelligence or machine learning.

[0766] As one embodiment, the first channel information report is generated based on inference, including: the generation of the first channel information report uses information generated based on a neural network.

[0767] As one embodiment, the first channel information report is generated based on inference, including: the generation of the first channel information report uses information generated based on CNN (Conventional Neural Networks).

[0768] As one embodiment, the first channel information report is generated based on inference, including: the first channel information report includes information generated based on artificial intelligence or machine learning.

[0769] As one embodiment, the first channel information report is based on inference and includes: the first channel information report includes information generated based on a neural network.

[0770] As one embodiment, the first channel information report is generated based on inference and includes: the first channel information report includes information generated based on CNN (Conventional Neural Networks).

[0771] As one embodiment, the first channel information report is generated based on inference and includes: a second information block used to configure the first channel information report, the second information block indicating a first identifier.

[0772] As one embodiment, the first channel information reporting is generated based on inference and includes: a second information block used to configure the first channel information reporting, the second information block indicating a first resource set, the first resource set including one or more RS resources, the first resource set being used for at least one of channel measurements or interference measurements in the first channel information reporting; the generation of the first channel information reporting includes the first node or the generator of the first channel information reporting performing a first operation, the first operation including inference, the input of the first operation depending on measurements based on the first resource set, and the first channel information reporting depending on the output of the first operation.

[0773] As one embodiment, the first channel information report is generated based on inference and includes: a second information block used to configure the first channel information report, the second information block indicating a first identifier, the generation of the first channel information report including performing a first operation, the first operation including inference, the first operation corresponding to the first identifier indicated by the second information block.

[0774] As one embodiment, the first channel information report is generated based on inference and includes: the generation of the first channel information report corresponding to the first identifier.

[0775] As an example, the advantages of the above method include: better adaptability to various application scenarios or terminals, and good flexibility and adaptability.

[0776] As an example, the advantages of the above method include improved accuracy and real-time performance of channel information reporting.

[0777] In embodiment 12B, a second information block is used to configure the first channel information reporting. The second information block indicates a first resource set, which includes one or more RS resources. The first resource set is used for at least one of the channel measurements or interference measurements in the first channel information reporting. The first channel information reporting indicates at least one resource in the second resource set. In Figure 12B, the first resource set includes RS resources #1, ..., RS resources #J1; the second resource set includes resources #1, ..., resources #J2; where J1 is a positive integer and J2 is a positive integer.

[0778] As one embodiment, the first channel information report includes a resource indication, which is used to indicate at least one resource in the second resource set.

[0779] As an example, the first channel information report includes at least one of resource indication or RSRP (reference signal received power); the resource indication is used to indicate at least one resource in the second resource set.

[0780] As one embodiment, the first node or the sender of the first channel information reporting performs a first operation; wherein the first resource set is used for at least one of the channel measurement or interference measurement of the first channel information reporting, the input of the first operation depends on the at least one of the channel measurement or interference measurement of the first resource set, and the first channel information reporting depends on the output of the first operation.

[0781] As an example, the first operation performs spatial beam prediction for a second resource set based on measurements of the first resource set.

[0782] As an example, the first operation performs spatial beam prediction for a second resource set based on measurements of a first resource set, the second resource set including resources that do not belong to the first resource set.

[0783] As an example, the advantages of the above method include reduced RS overhead and reduced feedback latency.

[0784] As an example, the first operation performs channel information prediction for a second resource set based on measurements of the first resource set.

[0785] As an example, the channel information in this application includes beam information.

[0786] As an example, the first operation performs temporal beam prediction for the second resource set based on historical measurements of the first resource set.

[0787] As an example, the advantages of the above method include reducing beam feedback delay and improving the real-time performance of beam acquisition.

[0788] As an example, the first operation performs temporal channel information prediction for the second resource set based on historical measurements of the first resource set.

[0789] As an example, the advantages of the above method include reducing channel information feedback delay and improving the real-time performance of channel information acquisition.

[0790] As an example, the input to the first operation also includes the second resource set.

[0791] As an example, the input to the first operation may also include some or all of the resources in the second resource set.

[0792] As one embodiment, the measurement based on the first resource set includes uncompressed channel information, and the output of the first operation includes compressed channel information.

[0793] As an example, the advantages of the above method include its applicability to channel compression and the saving of feedback overhead.

[0794] As one embodiment, the measurement based on the first resource set includes measured channel information, and the output of the first operation includes predicted channel information.

[0795] As one embodiment, the measurement based on the first resource set includes channel information obtained from the measurement, and the output of the first operation includes spatial beam prediction.

[0796] As one embodiment, the measurement based on the first resource set includes channel information obtained from the measurement, and the output of the first operation includes spatial beam prediction for the second resource set.

[0797] As one embodiment, the resources in the second resource set include at least one of antenna ports, time-frequency resources, time-frequency code resources, beams, RS resources, vectors, or matrices.

[0798] As an example, the advantages of the above method include reduced RS overhead and reduced feedback latency.

[0799] As an example, the channel information in this application includes beam information.

[0800] As one embodiment, the measurement based on the first resource set includes current channel information, and the output of the first operation includes predicted channel information.

[0801] As an example, the measurement based on the first resource set includes historical channel information, and the output of the first operation includes predicted channel information.

[0802] As one embodiment, the measurement based on the first resource set includes historical channel information, and the output of the first operation includes temporal beam prediction.

[0803] As one embodiment, the measurement based on the first resource set includes historical channel information, and the output of the first operation includes temporal beam prediction for the second resource set.

[0804] As an example, the advantages of the above method include reducing channel information feedback delay and improving the real-time performance of channel information acquisition.

[0805] As one embodiment, the measurement based on the first resource set includes current channel information, and the output of the first operation includes channel information after a period of time.

[0806] As one example, the measurement based on the first resource set includes current channel information, and the output of the first operation includes future channel information.

[0807] As one embodiment, the measurement based on the first resource set includes historical channel information, and the output of the first operation includes future channel information.

[0808] As an example, the advantages of the above method include improved CSI accuracy and real-time performance, and reduced RS overhead.

[0809] As one embodiment, the measurement based on the first resource set includes incomplete channel information, while the output of the first operation includes complete channel information.

[0810] As an example, the advantages of the above method include reduced RS overhead and improved accuracy and completeness of CSI.

[0811] As an example, the measurement based on the first resource set includes channel information of P1 antenna ports, and the output of the first operation includes channel information of P2 antenna ports, where P1 and P2 are positive integers greater than 1, and P1 is less than P2.

[0812] As a sub-implementation of the above embodiment, the P1 antenna ports are a proper subset of the P2 antenna ports.

[0813] As a sub-implementation of the above embodiment, the P2 antenna ports belong to the second resource set.

[0814] As an example, the measurement based on the first resource set includes channel information of the first frequency domain resources, and the output of the first operation includes channel information of the second frequency domain resources, which include frequency domain resources that do not belong to the first frequency domain resources.

[0815] As a sub-implementation of the above embodiments, the first frequency domain resource is a proper subset of the second frequency domain resource.

[0816] As one example, how the first channel information report is generated is determined by the manufacturer of the first node, or is implementation-related. Some typical but non-limiting implementations are described below:

[0817] In one implementation, the first channel information reporting includes L1-RSRP or L1-SINR; the first node obtains L1-RSRP or L1-SINR based on measurements of at least one RS resource in the first resource set. Generally, the filtering algorithm for L1-RSRP or L1-SINR is determined by the manufacturer of the first node, or is implementation-dependent, and can be implemented by an algorithm or by hardware.

[0818] In another implementation, the first node performs channel measurements on at least one RS resource in the first resource set to obtain a channel parameter matrix H. r×P For the channel parameter matrix H r×P Power adjustment is performed, and the adjusted channel parameter matrix is ​​as follows: Where Q is the ratio of the assumed PDSCH (Physical Downlink Shared Channel) EPRE (Energy Per Resource Element) to the NZP CSI-RS EPRE. When using the precoding matrix W... P×l Under these conditions, the precoded channel parameter matrix is: Where l is the rank or the number of layers. In one case, l is a positive integer not greater than P; in another case, the precoding matrix is ​​an identity matrix, in which case P = l. H is calculated using criteria such as SINR (Signal Interference Noise Ratio), EESM (Exponential Effective SINR Mapping), or RBIR (Received Block Mean Mutual Information Ratio). r×P ·W P×l The equivalent channel capacity is calculated, and then the first channel information is reported using methods such as table lookup based on the equivalent channel capacity. Generally, the calculation of the equivalent channel capacity requires the first node to estimate interference (including noise). The first resource set includes RS resources for channel measurement and RS resources for interference measurement. The first node can obtain the interference by measuring at least one RS resource in the first resource set in this application. Generally, the mapping from equivalent channel capacity to CSI depends on receiver performance or hardware-related factors such as modulation scheme.

[0819] In another implementation, the first node first measures the RS resources in the first resource set to obtain the channel parameter matrix H. r×t Where r and t are the number of receiving antennas and the number of antenna ports, respectively; at least the channel parameter matrix H r×t Alternatively, its feature vector is input into the first operation in this application, and the output of the first operation is used to obtain the first channel information report.

[0820] Without loss of generality, the parameters or AI model used in the first operation are determined by the manufacturer of the first node.

[0821] As one embodiment, the first resource set includes one or more RS (Reference Signal) resource sets, and an RS resource set includes one or more RS resources.

[0822] As one embodiment, the first resource set includes at least one of at least a CSI-RS resource set, at least one CSI-SSB (Channel State Information-Synchronization Signal Block) resource set, or at least one CSI-IM (Channel State Information-Interference Measurement) resource set.

[0823] As one embodiment, the first resource set includes at least one RS resource set for channel measurement, and an RS resource set for channel measurement includes one or more RS resources.

[0824] As one embodiment, the first resource set includes at least one RS resource set for channel measurement and at least one RS resource set for interference measurement; an RS resource set for channel measurement includes one or more RS resources, and an RS resource set for interference measurement includes one or more RS resources.

[0825] As one embodiment, the first resource set includes at least one RS resource set for interference measurement; an RS resource set for interference measurement includes one or more RS resources.

[0826] As an example, a set of RS resources for channel measurement includes one or more RS resources, wherein any RS resource in the set of RS resources for channel measurement is a CSI-RS resource or a synchronization signal resource.

[0827] As an example, a set of RS resources for interference measurement includes one or more RS resources.

[0828] As an example, an RS resource set for interference measurement includes one or more RS resources, wherein any RS resource in the RS resource set for interference measurement is a CSI-IM resource or an NZP (non-zero power) CSI-RS resource for interference measurement.

[0829] As an example, the first resource set includes at least one of CSI-RS (Channel State Information Reference Signal) resources or synchronization signal resources.

[0830] As one embodiment, the synchronization signal resources include at least the resources occupied by the synchronization signal.

[0831] As an example, the synchronization signal resource is an SSB (Synchronization Signal Block).

[0832] As an example, the synchronization signal resource is an SS / PBCH (synchronization signal / physical broadcast channel) block resource.

[0833] As one embodiment, the second information block is used to configure the first channel information reporting, the second information block indicating at least one resource configuration, the at least one resource configuration indicating a first resource set.

[0834] As an example, a resource configuration is used to configure CSI resources.

[0835] As an example, a resource configuration is an IE CSI-ResourceConfig.

[0836] As an example, a resource configuration includes an RRC IE.

[0837] As an example, a resource configuration includes IE CSI-ResourceConfig.

[0838] As one embodiment, the second information block is used to configure the first channel information reporting, and the second information block indicates the identifier of the first resource set.

[0839] As one embodiment, the second resource set is the first resource set.

[0840] As one embodiment, the second resource set includes resources that do not belong to the first resource set.

[0841] As one embodiment, the second information block is used to configure the first channel information reporting, the second information block indicates a first resource set, the first resource set includes one or more RS resources, the first resource set is used for at least one of the channel measurement or interference measurement in the first channel information reporting; the first channel information reporting indicates at least one resource in the second resource set and RSRP, the second resource set includes resources that do not belong to the first resource set.

[0842] As an example, the first node is not required to measure some or all of the resources in the second resource set.

[0843] As one embodiment, the first resource set includes one or more RS resources, the first resource set is used for at least one of the channel measurement or interference measurement reported by the first channel information, and the second resource set is used for prediction.

[0844] As an example, only the first resource set is used for measurement, either the first resource set or the second resource set.

[0845] As one embodiment, using only the first resource set in the first resource set and the second resource set for measurement includes: using only the first resource set in the first resource set and the second resource set for at least one of channel measurement or interference measurement by the first node.

[0846] As one embodiment, the use of only the first resource set in the first resource set and the second resource set for measurement includes: the first resource set being used by the first node for at least one of channel measurement or interference measurement, and the first node not being required to measure some or all of the resources in the second resource set.

[0847] As one embodiment, the first node not being required to measure the second resource set includes: the first node not measuring some or all of the resources in the second resource set.

[0848] As one embodiment, the first node not being required to measure the second resource set includes: whether the first node measures some or all of the resources in the second resource set is implementation-related or determined by the first node itself.

[0849] As one embodiment, the second resource set includes the first resource set and resources outside the first resource set.

[0850] As one embodiment, the first resource set includes one or more RS resources, the second resource set includes one or more RS resources, and the second resource set includes the first resource set and RS resources outside the first resource set.

[0851] As an example, the number of resources included in the first resource set is less than the number of resources included in the second resource set.

[0852] As an example, the number of RS resources included in the first resource set is less than the number of RS resources included in the second resource set.

[0853] As one embodiment, the second resource set includes resources that do not belong to the first resource set.

[0854] As one embodiment, the second resource set includes antenna ports that do not belong to the first resource set.

[0855] As one embodiment, the second resource set includes resources that do not belong to the first resource set, and the resources in the second resource set include at least one of antenna ports, TCI status, QCL information, time and frequency resources, time and frequency code resources, beams, RS resources, vectors, or matrices.

[0856] As one example, the second resource set includes at least one training dataset.

[0857] As an example, the second resource set is used to train an AI model.

[0858] As an example, the second resource set is used to train the first operation in this application.

[0859] As one embodiment, the second resource set includes one or more RS (Reference Signal) resource sets, and an RS resource set includes one or more RS resources.

[0860] As one embodiment, the second information block is used to configure the first channel information reporting, the second information block indicates a first identifier, and the second resource set depends on the first identifier in the second information block.

[0861] As one embodiment, the second resource set depends on the first identifier, which is used to identify the second resource set.

[0862] As one embodiment, the second resource set depends on the first identifier, which is used to identify a reference resource set, the reference resource set including the second resource set.

[0863] As one embodiment, the second resource set depends on the first identifier, which includes: the first identifier being used to identify a reference resource set, the reference resource set including the second resource set, and the second information block being used to indicate the second resource set from the reference resource set.

[0864] As one embodiment, the second information block is used to configure the first channel information reporting, and the information outside the second information block indicates the second resource set.

[0865] As one embodiment, the information indicating the second resource set, in addition to the second information block, includes higher-level parameters.

[0866] As one embodiment, the information indicating the second resource set, in addition to the second information block, includes RRC parameters.

[0867] As one embodiment, the information indicating the second resource set outside the second information block includes part or all of an RRC IE field.

[0868] As an example, the information indicating the second information block of the second resource set, other than the MAC CE, includes MAC CE.

[0869] As one embodiment, the information indicating the second information block of the second resource set, other than the second information block, includes DCI (downlink control information).

[0870] In embodiment 12C, the output of the first operation includes a first CSI, the first channel information report carries the first CSI, and the first CSI is used as input to the second operation by the target receiver of the first channel information report to generate a second CSI.

[0871] As an example, the advantages of the above method include improved CSI reporting performance by leveraging the advantages of the first operation, including more accurate reporting and / or lower overhead.

[0872] As an example, the first CSI is used to generate the first channel information report.

[0873] As an example, the first channel information report includes the first CSI.

[0874] As an example, the first CSI includes a compressed CSI.

[0875] As one example, the first CSI includes compressed predicted channel information.

[0876] As an example, the first CSI is post-processed and used to generate the first channel information report.

[0877] As one embodiment, the first channel information report includes the first CSI after post-processing.

[0878] As an example, the first channel information report carries the first CSI after post-processing.

[0879] As an example, the first CSI is truncated and / or quantized and used to generate the first channel information report.

[0880] As one embodiment, the first channel information reported includes the first CSI after truncation and / or quantization.

[0881] As an example, the first channel information report carries the first CSI after truncation and / or quantization.

[0882] As one embodiment, the first CSI includes a channel matrix.

[0883] As one example, the first CSI includes a feature vector.

[0884] As an example, the first CSI includes a feature vector and feature values.

[0885] As an example, the first CSI includes precoded information.

[0886] As one embodiment, the first CSI includes pre-encoded information based on a non-codebook.

[0887] As an example, the first CSI is used to determine at least one precoding matrix.

[0888] As an example, the first CSI indicates at least one precoding matrix.

[0889] As an example, the precoding matrix is ​​in the spatial-frequency domain.

[0890] As an example, the precoding matrix is ​​an angular-delay domain projection.

[0891] As one embodiment, the first CSI includes information on the relative phase, amplitude, and / or coefficients between multiple antenna ports.

[0892] As an example, the first CSI includes a compressed CSI.

[0893] As an example, the first CSI includes predicted / estimated CSI.

[0894] As an example, the first operation is used for CSI compression, and the second operation is used for CSI recovery.

[0895] As one embodiment, the second CSI includes the recovery of at least a portion of the input of the first operation.

[0896] As one embodiment, the second CSI includes a channel matrix.

[0897] As one embodiment, the second CSI includes a feature vector and / or feature values.

[0898] As one embodiment, the second CSI includes a precoding matrix.

[0899] As one embodiment, the second CSI includes one or more of the following: channel matrix, eigenvector, eigenvalue, or precoding matrix.

[0900] As one example, the target receiver reported by the first channel information is the sender of the second information block.

[0901] As an example, the second operation is the inverse operation of the first operation.

[0902] As an example, the second operation is based on training.

[0903] As one example, the training for obtaining the second operation is performed by the target receiver reported by the first channel information.

[0904] As one example, the training for obtaining the second operation is performed by the MDA function.

[0905] As an example, the training for obtaining the second operation is performed by the MDAS producer.

[0906] As an example, the training for obtaining the second operation is performed by NWDAF.

[0907] As an example, the training for obtaining the second operation is performed by the core network.

[0908] As an example, the training for obtaining the second operation is performed by an AI (Artificial Intelligence) training producer.

[0909] As an example, the first operation and the second operation are obtained through different training.

[0910] As an example, the first operation and the second operation are obtained through independent training.

[0911] As an example, the advantages of the above method include: saving air interface overhead, having better flexibility, being adaptable to different terminals, and having better forward compatibility.

[0912] As an example, the first operation and the second operation are obtained through joint training.

[0913] As an example, the advantages of the above method include: optimized performance.

[0914] As an example, the training of the second operation depends on the first operation.

[0915] As an example, the producer of the second operation trains the second operation based on the output of the first operation.

[0916] As one example, the second operation includes inference.

[0917] As one example, the second operation includes AI inference.

[0918] As one example, the second operation includes AI inference for CSI.

[0919] As an example, the second operation is AI inference for CSI recovery.

[0920] As an example, the second operation is AI inference for CSI decompression.

[0921] As an example, the second operation is performed by the AI ​​entity deployed on the second node in this application.

[0922] As an example, the second operation is used for the AI ​​function of the second node in this application.

[0923] As an example, the second operation requires deployment.

[0924] As an example, the second operation is obtained by loading.

[0925] As an example, the second operation is obtained from the core network.

[0926] As an example, the second operation is obtained from the producer.

[0927] As an example, the second operation is obtained from the producer of the second operation.

[0928] As an example, the second operation is obtained from loading from the AL entity producer.

[0929] As an example, the second operation is obtained from the AL function producer.

[0930] As an example, the second operation is obtained from loading from the MnS producer.

[0931] As an example, the second operation is based on artificial intelligence or machine learning.

[0932] As an example, the second operation is based on a neural network.

[0933] As one example, the second operation includes a decoder for CSI compression based on a neural network.

[0934] As one example, the second operation includes a CNN-based CSI compression encoder.

[0935] As an example, the second operation is performed by the physical layer of the second node.

[0936] As one example, the second operation is performed at a higher level of the second node.

[0937] Examples 13A-13B

[0938] Examples 13A-13B illustrate schematic diagrams of the deployment of the first operation of the first node according to an embodiment of this application, as shown in Figures 13A-13B respectively.

[0939] In Example 13A, the first node requests the first producer to load the first operation and obtains the first operation from the first producer.

[0940] As one embodiment, the deployment includes obtaining the first operation.

[0941] As one example, the deployment includes obtaining an AI entity.

[0942] As one example, the deployment includes obtaining an AI entity that performs the first operation.

[0943] As one example, the deployment includes obtaining an AI entity that includes AI functions to perform the first operation.

[0944] As one example, the deployment includes loading the first operation.

[0945] As one example, the deployment includes submitting a request to load the first operation.

[0946] As an example, the first operation is obtained from the serving cell of the first node.

[0947] As an example, the first operation is obtained from the sustaining base station of the serving cell of the first node.

[0948] As an example, the first operation is obtained from the core network.

[0949] As an example, the first operation is obtained from loading from the first producer.

[0950] As an example, the deployment is accomplished by an AI function.

[0951] As an example, the deployment is accomplished by AI functionality deployed on the first node.

[0952] As an example, the deployment is accomplished by an AI deployment function.

[0953] As an example, the deployment is accomplished by the AI ​​deployment function deployed on the first node.

[0954] As an example, the deployment is accomplished by AI inference functionality.

[0955] As an example, the deployment is accomplished by an AI inference function deployed on the first node.

[0956] As an example, the deployment is performed by an AI entity.

[0957] As an example, the deployment is performed by an AI entity deployed on the first node.

[0958] As an example, the deployment is performed by an AI entity with a deployment function.

[0959] As an example, the deployment is performed by an AI entity with deployment capabilities deployed on the first node.

[0960] As an example, the deployment is performed by an AI entity with an inference function.

[0961] As an example, the deployment is performed by an AI entity with reasoning capabilities deployed on the first node.

[0962] As one embodiment, the deployment includes obtaining the first operation from a first producer.

[0963] As one embodiment, the deployment includes requesting a first producer to load the first operation.

[0964] As one embodiment, the deployment includes loading the first operation from the first producer.

[0965] As an example, the first producer generates and provides the AL entity.

[0966] As an example, the first producer generates and provides AL functionality.

[0967] As an example, the first producer is the producer of the first operation.

[0968] As an example, the first producer includes an AL entity producer.

[0969] As one example, the first producer includes an AL function producer.

[0970] As one example, the first producer includes an AL deployment producer.

[0971] As one example, the first producer includes an AL loading producer.

[0972] As one example, the first producer includes an AL-trained producer.

[0973] As an example, the first producer includes an AL inference producer.

[0974] As an example, the first producer includes the producer of the AL entity deployment.

[0975] As one example, the first producer includes the producer that loads the AL entity.

[0976] As an example, the first producer includes an MnS (Management Service) producer.

[0977] In one embodiment, the sender of the second information block is the first producer.

[0978] In one embodiment, the sender of the second information block is different from the first producer.

[0979] As an example, the training for obtaining the first operation is performed by the first producer.

[0980] As an example, the executor used to obtain the training for the first operation is different from the first producer.

[0981] As one example, the AI ​​includes ML (Machine Learning).

[0982] In Example 13B, the first node requests the second producer to load the first operation and obtains the first operation from the first producer.

[0983] As one embodiment, the deployment includes obtaining the first operation.

[0984] As one example, the deployment includes obtaining an AI entity or AI function to perform the first operation.

[0985] As one example, the deployment includes loading the first operation.

[0986] As one example, the deployment includes submitting a request to load the first operation.

[0987] As an example, the deployment is accomplished by AI functionality deployed on the first node.

[0988] As an example, the deployment is accomplished by the AI ​​deployment function deployed on the first node.

[0989] As an example, the deployment is performed by an AI entity with a deployment function.

[0990] As one example, the second producer generates and provides AI entities or AI functions.

[0991] As one example, the second producer includes an MnS (Management Service) producer.

[0992] As an example, the second producer includes the producer of the AI ​​model training.

[0993] In one embodiment, the second producer is the target receiver of the first channel information report.

[0994] In one embodiment, the second producer is different from the target receiver that reported the first channel information.

[0995] As one example, the second producer is the serving cell of the first node.

[0996] As one example, the second producer is the maintenance base station of the serving cell of the first node.

[0997] As one example, the second producer is the core network.

[0998] As an example, the first operation is obtained from the serving cell of the first node.

[0999] As an example, the first operation is obtained from the sustaining base station of the serving cell of the first node.

[1000] As an example, the first operation is obtained from the core network.

[1001] As an example, the training for obtaining the first operation is performed by the second producer.

[1002] As an example, the second producer is different from the first producer.

[1003] As an example, the first producer generates and provides the AL entity.

[1004] As an example, the first producer generates and provides AL functionality.

[1005] As an example, the first producer is the producer of the first operation.

[1006] As an example, the first producer includes an AL entity producer.

[1007] As one example, the first producer includes an AL function producer.

[1008] As one example, the first producer includes an AL deployment producer.

[1009] As one example, the first producer includes an AL loading producer.

[1010] As one example, the first producer includes an AL-trained producer.

[1011] As an example, the first producer includes an AL inference producer.

[1012] As an example, the first producer includes the producer of the AL entity deployment.

[1013] As one example, the first producer includes the producer that loads the AL entity.

[1014] As an example, the first producer includes an MnS (Management Service) producer.

[1015] Example 14

[1016] Example 14 illustrates a schematic diagram of RAN (Radio Access Network) domain AI / ML function deployment according to one embodiment of this application; as shown in Figure 14. The gNB in ​​Example 14 can be replaced with, for example, an eNB, or a network device such as a 6G base station.

[1017] AI / ML related functions include ML training (also known as AI training, or AI / ML training), ML testing, and ML inference (also known as AI inference, or AI / ML inference), etc. ML training, ML testing, and ML inference functions can be deployed independently or co-located. Deployment of AI / ML related functions can be implemented through software, such as downloading and / or running executable files; or it can be implemented through a combination of software and hardware, such as accelerating specific computing units through hardware to improve computing speed or save power.

[1018] ML training functionality can be deployed in a cross-domain management system or a domain-specific management system; the domain-specific management system is used to manage the RAN domain or the CN (Core Network) domain. For example, ML training functionality for MDA (Management Data Analytics) can be deployed on MDAF (MDA Function); ML training for network data analytics can be deployed on NWDAF (Network Data Analytics Function), meaning the ML training functionality is an MTLF (Model Training Logical Function).

[1019] The ML inference function can also be deployed in a cross-domain management system or a domain-specific management system; for example, the ML inference function is MDAF, or the ML inference function is AnLF (Analytics logical function) located in NWDAF.

[1020] Similarly, ML testing capabilities can also be deployed in cross-domain management systems or domain-specific management systems.

[1021] In Example 14, the RAN domain ML training function 1402 is located in the RAN domain management function 1403; while the ML inference function is located in the base station, that is, the AI / ML inference function 1404 is located in gNB 1405, the AI / ML inference function 1406 is located in gNB 1407, and so on.

[1022] In Figure 14, the management of ML inference functions of multiple base stations is completed by RAN domain management function 1403, that is, data interaction with RAN domain MnS (Management Service) consumer / cross-domain management 1401 (as shown by the dashed arrow in Figure 14).

[1023] Optionally, the management of ML inference function can also be completed by the base station itself, that is, each base station can independently interact with the RAN domain MnS consumer / cross-domain management 1401.

[1024] It should be noted that Example 14 is merely a non-limiting implementation; optionally, the ML training function of the RAN domain may also be deployed at the base station; or optionally, some base stations may deploy both the ML inference function and the ML training function of the RAN domain, while some base stations may only deploy the ML inference function.

[1025] As an example, one of the gNBs (or base stations) in Example 14 is the second node of this application.

[1026] As an example, the second processor in this application includes an AL / ML inference function, namely 1404 or 1406, as shown in Figure 14.

[1027] Example 15

[1028] Example 15 illustrates a schematic diagram of the deployment of AI / ML functionality in a UE according to one embodiment of this application; as shown in Figure 15. The RAN domain ML training function 1505 in Figure 15 is optional.

[1029] UE function 1504 is deployed in the first node of this application, and the UE function 1504 includes AI / ML inference function 1506; the AI / ML inference function 1506 uses an ML model (also called an AI model) for inference; an ML model is typically trained before being used for AI / ML inference.

[1030] As an example, the first channel information report in this application is obtained through inference by the AI / ML inference function 1506.

[1031] As an example, the first processor in this application includes an AL / ML inference function 1506 in Figure 15.

[1032] As an example, the UE function 1504 includes a RAN domain ML training function 1505, which runs training data through an ML model to obtain a relevant loss and adjusts the parameters of the ML model based on the calculated loss; the ML training includes at least one of ML initial training, ML re-training, and reinforcement learning.

[1033] The above embodiments can reduce the complexity of the base station, or save air interface resources caused by reporting training data; however, the above embodiments place high demands on the processing capabilities of the UE side.

[1034] Optionally, the UE function 1504 also includes a CN domain ML training function (not shown in Figure 15).

[1035] Optionally, the UE function 1504 also includes an AI / ML deployment function—not shown in Figure 15—for loading ML models and data.

[1036] As an example, the first node indicates whether it supports ML training function (RAN domain or CN domain) through capability reporting. The capability reporting is RRC signaling or NAS (Non-Access Stratum) signaling.

[1037] As an example, the ML model and the associated metadata are loaded by the first node from a network device or a remote server.

[1038] Optionally, the UE function 1504 is an MnS (Management Service) producer that provides data to the CN domain MnF (Management Function) 1501, and / or the RAN domain MnF 1502, and / or the cross-domain management system 1503 for management or analysis (as shown by double arrow 1507).

[1039] Optionally, the UE function 1504 is an MnS consumer that loads data from the CN domain MnF (Management Function) 1501, and / or the RAN domain MnF 1502, and / or the cross-domain management system 1503 for AI / ML-related management, such as managing data requests, ML model activation, and / or ML training (as shown by double arrow 1507).

[1040] As an example, the ML model is based on a neural network.

[1041] As an example, the ML model is based on CNN (Conventional Neural Networks).

[1042] As an example, the ML model is based on the Transformer architecture.

[1043] Example 16

[1044] Example 16 illustrates a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of this application; as shown in Figure 16. Figure 16(a) includes a third processor, a fourth processor, and a fifth processor, and Figure 16(b) includes a third processor, a fourth processor, a fifth processor, and a sixth processor.

[1045] In Example 16(a), the third processor sends a first dataset to the fourth processor and a second dataset to the fifth processor; the fourth processor generates a target first-type parameter set based on the first dataset, and sends the generated target first-type parameter set to the fifth processor; the fifth processor processes the second dataset using the target first-type parameter set to obtain a first-type output. In Figure 16(a), the first-type feedback is optional.

[1046] In Example 16(b), the third processor sends a first dataset to the fourth processor and a second dataset to the fifth processor; the fourth processor generates a target first-type parameter set based on the first dataset, and sends the generated target first-type parameter set to the fifth processor; the fifth processor processes the second dataset using the target first-type parameter set to obtain a first-type output, and sends the first-type output to the sixth processor. In Figure 16(b), the first-type feedback and the second-type feedback are optional.

[1047] As an example, in Figure 16(a), the fifth processor sends the first type of output to the second node in this application.

[1048] As an example, Figure 16(a) employs a single-side AI model, in which the fifth processor performs the first operation of this application.

[1049] As an example, Figure 16(b) employs a two-sided AI model, in which the fifth processor performs the first operation of this application, and the sixth processor performs the second operation of this application.

[1050] As an example, the AI ​​includes ML (Machine Learning) inference.

[1051] As an example, the fifth processor performs the first operation in this application.

[1052] As one embodiment, the sixth processor includes the second operation described in this application.

[1053] As an example, the fifth processor sends a first type of feedback to the fourth processor, and the first type of feedback is used to trigger a recalculation or update of the target first type of parameter group.

[1054] As one embodiment, the sixth processor sends a second type of feedback to the third processor, the second type of feedback being used to generate the first dataset or the second dataset, or the second type of feedback being used to trigger the sending of the first dataset or the second dataset.

[1055] As one embodiment, the third processor generates the first dataset and the second dataset based on measurements of a first type of wireless signal, the first type of wireless signal including downlink RS.

[1056] As one embodiment, the fifth processor belongs to the first node, and the sixth processor belongs to the second node.

[1057] As an example, the first channel information report belongs to the first type of output.

[1058] As an example, the second dataset includes the input of the first operation.

[1059] As an example, for the first operation in this application, the second dataset includes information obtained based on the second information block.

[1060] As an example, the first dataset includes training data.

[1061] As an example, the fourth processor belongs to the producer of the first operation.

[1062] As one embodiment, the fourth processor includes an AI training producer.

[1063] As one embodiment, the fourth processor includes an AI training function.

[1064] As an example, the fourth processor is used for model training, and the trained model is described by the target first class of parameter sets.

[1065] As one embodiment, the fourth processor belongs to the first node.

[1066] The above embodiments avoid passing the first dataset to the second node.

[1067] As one example, the fourth processor belongs to the second node.

[1068] The above embodiments support joint training and optimize system performance.

[1069] As an example, the fourth processor belongs to the core network.

[1070] The above embodiments support network-wide joint training, further optimizing system performance.

[1071] As an example, the second dataset includes inference data.

[1072] As one embodiment, the fifth processor includes an AI inference producer.

[1073] As one embodiment, the fifth processor includes an AI inference function.

[1074] As an example, the fifth processor belongs to the first node.

[1075] As an example, the fifth processor constructs a model based on the target first type of parameter group, and then inputs the second dataset into the constructed model to obtain the first type of output.

[1076] As an example, the first operation is described by the target first type of parameter group.

[1077] As an example, the target first type of parameter group is used to construct the first operation.

[1078] As one embodiment, the fifth processor includes the second operation.

[1079] As an example, the fifth processor generates a recovery dataset based on the first type of output, and the error between the recovery dataset and the second dataset is used to generate the first type of feedback.

[1080] As a sub-example of the above embodiment, the generation of the recovery dataset adopts a similar operation to the second one.

[1081] As an example, the first type of feedback is used to reflect the performance of the trained model; when the performance of the trained model fails to meet the requirements, the fourth processing opportunity recalculates the target first type of parameter set.

[1082] As an example, when the error is too large or the update has not been performed for too long, the performance of the trained model is considered to be unsatisfactory.

[1083] As an example, the target first type of parameter group includes one or more of the following: convolution kernel size, number of convolution layers, convolution stride, pooling kernel size, pooling kernel stride, pooling function, activation function, or number of feature maps.

[1084] As an example, the target first type of parameter group includes one or more of the following: convolution kernel, pooling kernel, pooling function, activation function, parameters of pooling function, or parameters of activation function.

[1085] Example 17

[1086] Example 17 illustrates a schematic diagram based on artificial intelligence or machine learning according to an embodiment of this application; as shown in Figure 17. Figure 17 includes a third operation, a fourth operation, a fifth operation, a sixth operation, and a seventh operation. In Example 17, the third and fourth operations belong to a first stage, the fifth operation belongs to a second stage, the sixth operation belongs to a third stage, and the seventh operation belongs to a fourth stage. In Figure 17, the lines with arrows indicate the sequence of processes.

[1087] As an example, the third operation includes AI training, the fourth operation includes AI testing, the fifth operation includes AI emulation, the sixth operation includes AI entity loading, and the seventh operation includes AI inference.

[1088] As one embodiment, the first stage includes a training phase, the second stage includes an emulation phase, the third stage includes a deployment phase, and the fourth stage includes an emulation phase.

[1089] As an example, the first stage includes AI model training.

[1090] As an example, the first stage includes AI model training and AI testing.

[1091] As an example, the AI ​​includes ML (Machine Learning) inference.

[1092] As an example, the AI ​​model training includes initial training and re-training of one or a group of AI entities.

[1093] As an example, the training of the AI ​​model depends on training data.

[1094] As an example, the AI ​​model training includes AI entity validation.

[1095] As an example, the AI ​​entity verification is used to evaluate the performance of the AI ​​entity.

[1096] As an example, the AI ​​entity verification relies on verification data.

[1097] As an example, if the AI ​​entity verification results do not meet expectations, the AI ​​model will be retrained.

[1098] As an example, the AI ​​testing includes testing the validated AI entity to estimate the performance of the trained AI model.

[1099] As an example, if the AI ​​test results meet expectations, the AI ​​entity proceeds to the next stage; otherwise, the AI ​​model will be retrained.

[1100] As an example, the AI ​​test relies on test data.

[1101] As one embodiment, the second stage includes AI simulation, which performs inference of AI entities in a simulation environment.

[1102] As an example, the AI ​​simulation estimates the performance of AI entity reasoning in a simulation environment before using the AI ​​entity.

[1103] As one embodiment, the second stage is optional.

[1104] As an example, the third stage includes AI entity loading, which is to obtain trained AI entities to obtain the desired AI inference function.

[1105] As an example, the third stage is optional.

[1106] As an example, the third stage is no longer needed when the training and inference functions are co-located.

[1107] As an example, the fourth stage includes AI inference.

[1108] As an example, the seventh operation includes the first operation.

[1109] As an example, the seventh operation includes the second operation.

[1110] Example 18

[1111] Example 18 illustrates a structural block diagram of a processing apparatus for a first node according to an embodiment of this application; as shown in Figure 18. In Figure 18, the processing apparatus 1800 in the first node includes a first processor 1801.

[1112] As one example, the first node is a user equipment.

[1113] As an example, the first node is a relay node device.

[1114] As an example, the first processor 1801 includes at least one of the following in embodiment 4: {antenna 452, receiver / transmitter 454, receiver processor 456, transmitter processor 468, multi-antenna receiver processor 458, multi-antenna transmitter processor 457, controller / processor 459, memory 460, data source 467}.

[1115] The first processor 1801 sends the first information block and sends the first channel information report.

[1116] In Embodiment 18, the first information block indicates a first time interval; the generation of the first channel information report occupies at least one first type of resource; the first channel information report occupies the at least one first type of resource from a first moment, the first moment depending on the first time interval.

[1117] As an example, the first type of resource is located in the first processor 1801.

[1118] As an example, the first moment depends on the first time interval only when the first condition is met; the first condition includes at least one of the first channel information report being generated based on inference or the first channel information report generating a corresponding first identifier.

[1119] As one embodiment, the first processor 1801 receives the first signaling;

[1120] Wherein, the first signaling triggers the first channel information reporting; the first moment is not earlier than at least the first time interval after the physical layer channel carrying the first signaling.

[1121] As an example, the first moment is no earlier than at least the first time interval after the time domain resources occupied by the first information block.

[1122] As an example, the first channel information report is generated based on inference, and the time when the AI ​​model used for inference begins to be applied depends on the first time interval, and the first time is not earlier than the time when the AI ​​model used for inference begins to be applied.

[1123] As an example, the generation of the first channel information report also occupies at least one second type of resource; the first channel information report occupies the at least one second type of resource starting from a second time moment; the second time moment depends on the first time interval; the first time moment is not earlier than the second time moment, or the first time moment is later than the second time moment.

[1124] As an example, the N time intervals correspond to N identifiers, where N is a positive integer greater than 1; the generation of the first channel information report corresponds to the first identifier, which is one of the N identifiers, and the first time interval is a time interval corresponding to the first identifier among the N time intervals.

[1125] As one embodiment, the first processor 1801 receives the second information block;

[1126] The second information block is used to configure the first channel information reporting.

[1127] As one embodiment, the second information block indicates a first resource set, the first resource set including one or more RS resources, the first resource set being used for at least one of the channel measurement or interference measurement in the first channel information report; the first channel information report indicates at least one resource in a second resource set, the second resource set including resources not belonging to the first resource set.

[1128] As an example, the first processor 1801 receives RS in the first resource set.

[1129] As one embodiment, the first processor 1801 receives RS in the second resource set; wherein the second resource set includes one or more RS resources.

[1130] As an example, the first processor 1801 does not receive signals in the second resource set.

[1131] As an example, the first processor 1801 performs a first operation, and the first channel information reporting depends on the output of the first operation.

[1132] As an example, the first operation is based on training or AI.

[1133] As an example, the first operation requires deployment.

[1134] As an example, the first operation is obtained by loading.

[1135] As an example, the first processor 1801 deploys the first operation.

[1136] Example 19

[1137] Example 19 illustrates a structural block diagram of a processing apparatus for a second node according to an embodiment of the present application; as shown in Figure 19. In Figure 19, the processing apparatus 1900 in the second node includes a second processor 1901.

[1138] In one embodiment, the second node is a base station device.

[1139] In one embodiment, the second node is a user equipment.

[1140] As one embodiment, the second node is a relay node device.

[1141] As one embodiment, the second processor 1901 includes at least one of the following in embodiment 4: {antenna 420, receiver / transmitter 418, receiver processor 470, transmitter processor 416, multi-antenna receiver processor 472, multi-antenna transmitter processor 471, controller / processor 475, memory 476}.

[1142] The second processor 1901 receives the first information block and receives the first channel information report.

[1143] In embodiment 19, the first information block indicates a first time interval; the generation of the first channel information report occupies at least one first type of resource; the first channel information report occupies the at least one first type of resource from a first moment, the first moment depending on the first time interval.

[1144] As an example, the first moment depends on the first time interval only when the first condition is met; the first condition includes at least one of the first channel information report being generated based on inference or the first channel information report generating a corresponding first identifier.

[1145] As one embodiment, the second processor 1901 sends a first signaling;

[1146] Wherein, the first signaling triggers the first channel information reporting; the first moment is not earlier than at least the first time interval after the physical layer channel carrying the first signaling.

[1147] As an example, the first moment is no earlier than at least the first time interval after the time domain resources occupied by the first information block.

[1148] As an example, the first channel information report is generated based on inference, and the time when the AI ​​model used for inference begins to be applied depends on the first time interval, and the first time is not earlier than the time when the AI ​​model used for inference begins to be applied.

[1149] As an example, the generation of the first channel information report also occupies at least one second type of resource; the first channel information report occupies the at least one second type of resource starting from a second time moment; the second time moment depends on the first time interval; the first time moment is not earlier than the second time moment, or the first time moment is later than the second time moment.

[1150] As an example, the N time intervals correspond to N identifiers, where N is a positive integer greater than 1; the generation of the first channel information report corresponds to the first identifier, which is one of the N identifiers, and the first time interval is a time interval corresponding to the first identifier among the N time intervals.

[1151] As one embodiment, the second processor 1901 sends a second information block;

[1152] The second information block is used to configure the first channel information reporting.

[1153] As one embodiment, the second information block indicates a first resource set, the first resource set including one or more RS resources, the first resource set being used for at least one of the channel measurement or interference measurement in the first channel information report; the first channel information report indicates at least one resource in a second resource set, the second resource set including resources not belonging to the first resource set.

[1154] As one embodiment, the second processor 1901 sends RS in the first resource set.

[1155] As one embodiment, the second processor 1901 transmits RS in the second resource set; wherein the second resource set includes one or more RS resources.

[1156] As an example, the second processor 1901 does not send signals in the second resource set.

[1157] As one embodiment, the second processor 1901 performs a second operation; wherein the sender of the first channel information report performs a first operation, the output of the first operation includes a first CSI, the first channel information report carries the first CSI, and the first CSI is used as input to the second operation to generate a second CSI.

[1158] As an example, the first operation is based on training or AI.

[1159] As an example, the first operation requires deployment.

[1160] As an example, the first operation is obtained by loading.

[1161] As an example, the second processor 1901 deploys the second operation.

[1162] As one example, the second operation is based on training or AI.

[1163] As an example, the second operation needs to be deployed.

[1164] As an example, the second operation is obtained by loading.

[1165] Those skilled in the art will understand that all or part of the steps in the above methods can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium, such as a read-only memory, hard disk, or optical disk. Optionally, all or part of the steps in the above embodiments can also be implemented using one or more integrated circuits. Accordingly, each module unit in the above embodiments can be implemented in hardware or in the form of software functional modules. This application is not limited to any specific combination of software and hardware. The user equipment, terminal, and UE in this application include, but are not limited to, drones, communication modules on drones, remote-controlled aircraft, aircraft, small aircraft, mobile phones, tablets, laptops, vehicle-mounted communication equipment, vehicles, RSUs, wireless sensors, internet access cards, IoT terminals, RFID terminals, NB-IoT terminals, MTC (Machine Type Communication) terminals, eMTC (enhanced MTC) terminals, data cards, internet access cards, vehicle-mounted communication equipment, low-cost mobile phones, low-cost tablets, and other wireless communication devices. The base stations or system equipment in this application include, but are not limited to, macrocell base stations, microcell base stations, small cell base stations, home base stations, relay base stations, eNBs, gNBs, TRPs (Transmitter Receiver Points), GNSS, relay satellites, satellite base stations, airborne base stations, RSUs (Road Side Units), drones, and testing equipment, such as transceivers or signaling testers that simulate some functions of a base station, and other wireless communication equipment.

[1166] Those skilled in the art will understand that the present invention can be practiced in other specified forms without departing from its core or essential characteristics. Therefore, the embodiments disclosed herein should in any way be considered descriptive rather than restrictive. The scope of the invention is defined by the appended claims rather than the foregoing description, and all modifications within their equivalent meaning and scope are considered to be included therein.

Claims

A first node used for wireless communication, characterized in that, include: A first processor sends a first information block, the first information block indicating a first time interval; Send the first channel information report; The generation of the first channel information report occupies at least one first type of resource; the first channel information report occupies the at least one first type of resource starting from a first moment, and the first moment depends on the first time interval. The first node according to claim 1 is characterized in that, The first moment depends on the first time interval only if the first condition is met; the first condition includes at least one of the following: the first channel information report is generated based on inference or the first channel information report generates a corresponding first identifier. The first node according to claim 1 or 2 is characterized in that, include: The first processor receives the first signaling; The first signaling triggers the first channel information reporting; The first moment is no earlier than at least the first time interval after the physical layer channel carrying the first signaling. The first node according to claim 1 or 2 is characterized in that, The first moment is no earlier than at least the first time interval after the time domain resources occupied by the first information block. The first node according to any one of claims 1 to 4 is characterized in that, The first channel information report is generated based on inference, and the time at which the AI ​​model used for inference begins to be applied depends on the first time interval, and the first time is not earlier than the time at which the AI ​​model used for inference begins to be applied. The first node according to any one of claims 1 to 5 is characterized in that, The generation of the first channel information report also occupies at least one second type of resource; the first channel information report occupies the at least one second type of resource starting from a second time point; the second time point depends on the first time interval; the first time point is not earlier than the second time point, or the first time point is later than the second time point. The first node according to any one of claims 1 to 6 is characterized in that, The N time intervals correspond to N identifiers, where N is a positive integer greater than 1; the generation of the first channel information report corresponds to the first identifier, which is one of the N identifiers, and the first time interval is one of the N time intervals corresponding to the first identifier. A second node used for wireless communication, characterized in that, include: The second processor receives a first information block, which indicates a first time interval. Receive the first channel information report; The generation of the first channel information report occupies at least one first type of resource; the first channel information report occupies the at least one first type of resource starting from a first moment, and the first moment depends on the first time interval. A method used in a first node of wireless communication, characterized in that, include: Send a first information block, the first information block indicating a first time interval; Send the first channel information report; The generation of the first channel information report occupies at least one first type of resource; the first channel information report occupies the at least one first type of resource starting from a first moment, and the first moment depends on the first time interval. A method used in a second node for wireless communication, characterized in that, include: Receive a first information block, the first information block indicating a first time interval; Receive the first channel information report; The generation of the first channel information report occupies at least one first type of resource; the first channel information report occupies the at least one first type of resource starting from a first moment, and the first moment depends on the first time interval.

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