Method and apparatus used in node for wireless communication

Abstract

Disclosed in the present application are a method and apparatus used in a node for wireless communication. The method comprises: a first node receiving a first message and sending a second message, wherein the first message indicates a plurality of measurement configurations; and the second message indicates a first subset of measurement configurations and a second subset of measurement configurations of the plurality of measurement configurations, measurement configurations in the first subset of measurement configurations being available, and the availability of measurement configurations in the second subset of measurement configurations depending on a further indication. The method optimizes the configuration of AI / ML functions, thereby optimizing resource utilization, and reducing signaling overheads and latency.

Description

A method and apparatus for use in nodes for wireless communication Technical Field

[0001] This application relates to transmission methods and apparatus in wireless communication systems, and more particularly to schemes and apparatus related to CSI (Channel Status Information) in wireless communication systems. Background Technology

[0002] In traditional wireless communication, the UE (User Equipment) reports various auxiliary information obtained through measurements of downlink signals and / or channels, such as channel information, beam management-related auxiliary information, and positioning-related auxiliary information. CSI (Channel Status Information) includes, but is not limited to, one or more of CRI (CSI-RS Resource Indicator), RI (Rank Indicator), PMI (Precoding Matrix Indicator), CQI (Channel Quality Indicator), or L1-RSRP (Layer 1 Reference Signal Received Power). The UE can use this information to select appropriate transmission parameters or report this information itself. Network devices select appropriate transmission parameters for the UE based on its reports, such as the cell to be camped, MCS (Modulation and Coding Scheme), TPMI (Transmitted Precoding Matrix Indicator), and TCI (Transmission Configuration Indication). Furthermore, UE reports can be used to optimize network parameters, such as improving cell coverage and switching base stations on / off based on the UE's location.

[0003] In traditional cellular communication, the antenna port is used to describe reference signal resources; unlike the physical antenna, the antenna port can be considered a virtualization / overlay operation of the physical antenna.

[0004] In NRR (release) 18, research on AI (Artificial Intelligence) / ML (Machine Learning) technologies was initiated to explore their impact on system performance and design. AI / ML technologies may also play a crucial role in future 6G communications. Compared to traditional processing methods, AI / ML is characterized by its training-based and deployment-required nature. According to the 3GPP standard TS38.300, AI / ML models and algorithms exceed the scope of 3GPP (3rd Generation Partnership Project).

[0005] At the 117bis meeting of RAN (Radio Access Network) WG (Working Group) 1, Supported Functionality and Applicable Functionality were approved. Supported Functionality is indicated in UE capability reporting, while Applicable Functionality is determined by the UE based on network-side additional conditions and UE-side additional conditions, and is indicated to the network. Applicable Functionality usually means that the corresponding AI model is available, or the corresponding inference configuration can be activated / executed. Summary of the Invention

[0006] The applicant's research revealed that the existing indication mechanism for applicable functions cannot promptly reflect changes in additional conditions on the UE side, thus failing to execute the optimal inference configuration.

[0007] To address the aforementioned problems, this application discloses a solution. It should be noted that while this application is motivated by the application of AI / ML models, and many embodiments are specifically designed for AI / ML, it is also applicable to other solutions, such as traditional receiving algorithms / solutions. Although the specification of this application involves descriptions of some AI / ML models and algorithms, those skilled in the art will understand that these descriptions are not essential or irreplaceable for solutions related to wireless cellular communication. Furthermore, adopting a unified solution for different scenarios (including but not limited to AI / ML-based solutions and traditional receiving algorithms / solutions) helps reduce signaling overhead / complexity, 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.

[0008] Furthermore, although some embodiments of this application are described based on the 5G protocol, this application is also applicable to communication standards after 5G without conflict.

[0009] When necessary, the interpretation of terms used in this application shall be based on the definitions in the 3GPP specification protocol TS38 series, or the definitions in the 3GPP specification protocol TS28 series.

[0010] This application discloses a method used in a first node for wireless communication, characterized by comprising:

[0011] Receive a first message, which indicates multiple measurement configurations;

[0012] Send a second message;

[0013] The second message indicates a first subset of measurement configurations and a second subset of measurement configurations among the plurality of measurement configurations, wherein the measurement configurations in the first subset of measurement configurations are available, and the availability of the measurement configurations in the second subset of measurement configurations depends on further indication.

[0014] As an example, the problem this application aims to solve includes how to optimize the indication mechanism of applicable functions to better adapt to changes in additional conditions on the UE side; in the above method, this problem is solved by indicating the availability dependency of the second measurement configuration subset in the second message.

[0015] As an example, the advantages of the above method include the ability to flexibly update the indication of available measurement configurations based on changes in additional conditions of the first node itself.

[0016] As an example, the advantages of the above method include facilitating network-side optimization of AI / ML function configurations.

[0017] As an example, the advantages of the above method include optimized resource utilization, thereby improving system performance.

[0018] As an example, the advantages of the above method include reduced signaling overhead and latency.

[0019] As an example, the advantages of the above method include facilitating a consensus between the first node and the sender of the first message regarding the AI / ML model used by the first node.

[0020] As an example, the AI / ML model used by the first node is determined by the hardware device manufacturer, but the first node and the second node may still need to reach some consensus on the AI / ML model used by the first node; in this application, the multiple measurement configurations provide a means to facilitate the first node and the second node to reach some consensus on the AI / ML model used by the first node.

[0021] According to one aspect of this application, it is characterized by comprising:

[0022] Send the first report;

[0023] Wherein, the first reported information is based on inference, and the first measurement configuration subset is used to determine the parameters of the inference for the first reported information.

[0024] As an example, the advantages of the above method include supporting the use of AI / ML technology to optimize CSI reporting, improving reporting accuracy and reducing overhead.

[0025] As an example, the advantages of the above method include improved system performance.

[0026] According to one aspect of this application, it is characterized by comprising:

[0027] Receive third message;

[0028] The third message indicates the parameters of the inference for the first reported information, and the indication of the third message depends on the first measurement configuration subset.

[0029] As an example, the advantages of the above method include supporting the network side to configure AI / ML functions based on the reports from the first node, adapting to various different terminals.

[0030] As an example, the advantages of the above method include facilitating global optimization on the network side and further improving system performance.

[0031] According to one aspect of this application, it is characterized by comprising:

[0032] Send the fourth message;

[0033] The fourth message indicates that at least one measurement configuration in the second measurement configuration subset is available.

[0034] As an example, the above method allows the first node to flexibly update the indication of available measurement configurations based on changes in its own additional conditions, optimize the configuration of AI / ML functions, improve resource utilization, and reduce signaling overhead.

[0035] According to one aspect of this application, it is characterized by comprising:

[0036] Send the second reporting information;

[0037] Wherein, the second reported information is based on inference, and at least one measurement configuration in the second measurement configuration subset is used to determine the parameters of the inference for the second reported information.

[0038] As an example, the advantages of the above method include improved accuracy of CSI reporting and reduced overhead.

[0039] As an example, the advantages of the above method include improved system performance.

[0040] According to one aspect of this application, it is characterized by comprising:

[0041] Receive the fifth message;

[0042] The fifth message indicates the parameters of the inference for the second reported information, and the indication of the fifth message depends on at least one measurement configuration in the second measurement configuration subset.

[0043] As an example, the advantages of the above method include supporting network-side updates to the configuration of AI / ML functions based on the reports from the first node, adapting to various terminals, and facilitating global optimization by the network side.

[0044] According to one aspect of this application, each of the plurality of measurement configurations includes an associated identifier.

[0045] As an example, the advantages of the above method include that the first node and the sender of the first message reach some consensus on the AI / ML model.

[0046] As an example, the benefits of the above method include making AI / ML model training and inference more well-matched, further improving the performance of AI / ML solutions.

[0047] As an example, the advantages of the above method include simplified design and good forward compatibility.

[0048] This application discloses a method used in a second node for wireless communication, characterized by comprising:

[0049] Send a first message indicating multiple measurement configurations;

[0050] Receive the second message;

[0051] The second message indicates a first subset of measurement configurations and a second subset of measurement configurations among the plurality of measurement configurations, wherein the measurement configurations in the first subset of measurement configurations are available, and the availability of the measurement configurations in the second subset of measurement configurations depends on further indication.

[0052] According to one aspect of this application, it is characterized by comprising:

[0053] Receive the first reported information;

[0054] Wherein, the first reported information is based on inference, and the first measurement configuration subset is used to determine the parameters of the inference for the first reported information.

[0055] According to one aspect of this application, it is characterized by comprising:

[0056] Send a third message;

[0057] The third message indicates the parameters of the inference for the first reported information, and the indication of the third message depends on the first measurement configuration subset.

[0058] According to one aspect of this application, it is characterized by comprising:

[0059] Received the fourth message;

[0060] The fourth message indicates that at least one measurement configuration in the second measurement configuration subset is available.

[0061] According to one aspect of this application, it is characterized by comprising:

[0062] Receive the second reported information;

[0063] Wherein, the second reported information is based on inference, and at least one measurement configuration in the second measurement configuration subset is used to determine the parameters of the inference for the second reported information.

[0064] According to one aspect of this application, it is characterized by comprising:

[0065] Send the fifth message;

[0066] The fifth message indicates the parameters of the inference for the second reported information, and the indication of the fifth message depends on at least one measurement configuration in the second measurement configuration subset.

[0067] According to one aspect of this application, each of the plurality of measurement configurations includes an associated identifier.

[0068] This application discloses a first node used for wireless communication, characterized in that it includes:

[0069] A first receiver receives a first message, which indicates multiple measurement configurations.

[0070] The first transmitter sends the second message;

[0071] The second message indicates a first subset of measurement configurations and a second subset of measurement configurations among the plurality of measurement configurations, wherein the measurement configurations in the first subset of measurement configurations are available, and the availability of the measurement configurations in the second subset of measurement configurations depends on further indication.

[0072] This application discloses a second node used for wireless communication, characterized by comprising:

[0073] The second transmitter sends a first message, which indicates multiple measurement configurations;

[0074] The second receiver receives the second message;

[0075] The second message indicates a first subset of measurement configurations and a second subset of measurement configurations among a plurality of measurement configurations, wherein the measurement configurations in the first subset of measurement configurations are available, and the availability of the measurement configurations in the second subset of measurement configurations depends on further indications.

[0076] As an example, compared with conventional solutions, this application has the following advantages:

[0077] The indication of available measurement configurations can be updated flexibly based on changes in additional conditions on the UE side.

[0078] This facilitates network-side optimization of AI / ML function configurations;

[0079] It provides a system architecture for communication systems to fully utilize AI / ML technologies;

[0080] Resource utilization was optimized, signaling overhead and latency were reduced, and overall system performance was improved. Attached Figure Description

[0081] 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:

[0082] Figure 1 illustrates a flowchart of a first message and a second message according to an embodiment of this application;

[0083] Figure 2 shows a schematic diagram of a network architecture according to an embodiment of this application;

[0084] 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;

[0085] Figure 4 shows a schematic diagram of a first communication device and a second communication device according to an embodiment of this application;

[0086] Figure 5 illustrates the transmission between a first node and a second node according to an embodiment of this application;

[0087] Figure 6 illustrates a schematic diagram of a first measurement configuration subset according to an embodiment of the present application being used to determine parameters for inference of a first reported information;

[0088] Figure 7 illustrates a schematic diagram of a third message according to an embodiment of this application;

[0089] Figure 8 illustrates a schematic diagram of a third message indicating a dependency on a first measurement configuration subset according to an embodiment of this application;

[0090] Figure 9 illustrates a schematic diagram of a fourth message according to an embodiment of this application;

[0091] Figure 10 illustrates a schematic diagram of at least one measurement configuration in a subset of second measurement configurations according to an embodiment of the present application being used to determine parameters for inference of second reporting information;

[0092] Figure 11 shows a schematic diagram of the fifth message according to an embodiment of this application;

[0093] Figure 12 illustrates a schematic diagram showing that the indication of a fifth message according to an embodiment of the present application depends on at least one measurement configuration in a subset of second measurement configurations;

[0094] Figure 13 illustrates a schematic diagram of any of a plurality of measurement configurations according to an embodiment of the present application, including an association identifier;

[0095] Figure 14 shows a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of this application;

[0096] Figure 15 illustrates a schematic diagram based on artificial intelligence or machine learning according to an embodiment of this application;

[0097] Figure 16 shows a schematic diagram of AI function deployment according to an embodiment of this application;

[0098] Figure 17 shows a schematic diagram of AI function deployment according to an embodiment of this application;

[0099] Figure 18 shows a schematic diagram of AI function deployment according to an embodiment of this application;

[0100] Figure 19 shows a schematic diagram of AI function deployment according to an embodiment of this application;

[0101] Figure 20 shows a structural block diagram of a processing apparatus for a first node according to an embodiment of the present application;

[0102] Figure 21 shows a structural block diagram of a processing apparatus for a second node according to an embodiment of this application;

[0103] Figure 22 shows a schematic diagram of the timing relationship of the first message, the second message, the third message, the fourth message and the fifth message according to an embodiment of this application;

[0104] Figure 23 shows a schematic diagram of the timing relationship of the first message, the second message, the third message and the fourth message according to an embodiment of this application; Detailed Implementation

[0105] 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 5-23, the embodiments in Figure 5 and the embodiments in Figures 6-23, etc.

[0106] Example 1

[0107] Example 1 illustrates a flowchart of a first message and a second message 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.

[0108] In Embodiment 1, the first node receives a first message in step 101 and sends a second message in step 102. The first message indicates multiple measurement configurations; the second message indicates a first subset and a second subset of the multiple measurement configurations, wherein the measurement configurations in the first subset are available, and the availability of the measurement configurations in the second subset depends on further indications.

[0109] As one example, the first message is carried by higher layer signaling.

[0110] As an example, the first message is carried by RRC (Radio Resource Control) signaling.

[0111] As an example, the first message is carried by one or more RRC IE (Information Element).

[0112] As one embodiment, the first message includes some or all of the information of each of one or more RRC IEs.

[0113] As one example, the first message includes all or part of the information in the CSI-ReportConfig IE.

[0114] As an example, the first message is carried by an RRC message.

[0115] As one embodiment, the first message includes some or all of the information of each RRC IE in one or more RRC messages.

[0116] As an example, the first message is used for RRC configuration.

[0117] As an example, the first message is used for RRC reconfiguration.

[0118] As an example, the first message is carried by an RRC reconfiguration message.

[0119] As an example, the first message is carried by an RRC recovery message (RRCResume message).

[0120] As one example, the first message includes some or all of the information in the higher-level parameter reconfigurationWithSync.

[0121] As an example, the first message is used for UE capability query (UECapabilityEnquiry).

[0122] As an example, the first message is carried by a UE Capability Enquiry message.

[0123] As an example, the first message is carried by DL-DCCH-Message.

[0124] As one example, the second message is carried by higher layer signaling.

[0125] As an example, the second message is carried by RRC signaling.

[0126] As one example, the second message is carried by one or more RRC IEs.

[0127] As an example, the second message is carried by an RRC message.

[0128] As an example, the second message is carried by a UE Assistance Information message.

[0129] As an example, the second message is carried by a UE Capability Information message.

[0130] As an example, the second message is carried by the UE capability IE.

[0131] As one example, the second message includes all or part of the information in one or more UE capability IEs.

[0132] As one example, the second message includes the capability report of the first node.

[0133] As one example, the second message includes all or part of the information in OtherConfig IE.

[0134] As one example, the UE capabilities include AI or ML-related capabilities.

[0135] As an example, the UE capabilities include capabilities related to AI or ML supported functionality.

[0136] As an example, both the first message and the second message are used for LCM (life cycle management).

[0137] As an example, both the first message and the second message are used in the LCM of the AI ​​model or ML model.

[0138] As an example, both the first message and the second message are used for the LCM of the same one or more AI models or ML models.

[0139] As an example, both the first message and the second message are used for LCM of AI functions or ML functions.

[0140] As an example, both the first message and the second message are used for the same LCM of one or more AI functions or ML functions.

[0141] The advantages of the above embodiments include optimized LCM for AI / ML.

[0142] The advantages of the above embodiments include making it easier for the network side to better configure AI / ML functions and optimize the utilization of computing or storage resources.

[0143] As an example, any of the plurality of measurement configurations includes higher-layer signaling.

[0144] As an example, any of the plurality of measurement configurations includes RRC IE.

[0145] As an example, any of the plurality of measurement configurations includes partial information from at least one RRC IE.

[0146] As an example, any of the plurality of measurement configurations includes CSI-ReportConfig IE.

[0147] As an example, any of the plurality of measurement configurations includes a portion of the information in the CSI-ReportConfig IE.

[0148] As one example, the plurality of measurement configurations each include a plurality of CSI-ReportConfig IEs.

[0149] As one example, the multiple measurement configurations each include portions of information from multiple CSI-ReportConfig IEs.

[0150] As an example, any of the plurality of measurement configurations includes inference-related parameters.

[0151] As an example, the inference refers to AI (Artificial Intelligence) inference.

[0152] As an example, the inference refers to ML (Machine Learning) inference.

[0153] As an example, the inference refers to AI inference or ML inference.

[0154] As an example, the inference-related parameters are used to determine at least one of the inference inputs and outputs.

[0155] As an example, the inference-related parameters are used to determine at least one of the inference input, output, and model.

[0156] As an example, the inference-related parameters are used to determine the RS (Reference Signal) resources required to obtain the channel measurements needed for inference.

[0157] As an example, the inference-related parameters are used to determine the RS resources required for obtaining the channel measurements for training the inference model.

[0158] As an example, the inference-related parameters are used to determine the output of the inference.

[0159] As an example, the inference-related parameters are used to determine the content of the inference output.

[0160] As an example, the content includes one or more of CRI, SSBRI, RSRP, and SINR.

[0161] As an example, the content includes one or more of CQI, PMI, CRI, LI, RI, SSBRI, RSRP, SINR, Capability Index, and TDCP.

[0162] As one example, the content includes at least one of the predicted beam information and the predicted CSI.

[0163] As one example, the content includes compressed CSI (Channel State Information).

[0164] As an example, any of the plurality of measurement configurations includes RS resource-related configuration information.

[0165] As an example, the configuration information related to the RS resources includes the number of RS resources.

[0166] As an example, the configuration information related to the RS resources includes the number of RS resources used to obtain the channel measurements required for inference.

[0167] As an example, the configuration information related to the RS resources includes the number of RS resources in the RS resource set referred to by the inference output.

[0168] As an example, the configuration information related to the RS resources includes the number of RS resources required for obtaining channel measurements for model training in inference.

[0169] As an example, the configuration information related to the RS resource includes at least one of the beam direction and beamwidth of the RS resource.

[0170] As an example, the configuration information related to the RS resources includes the configuration information of the RS resources.

[0171] As an example, the configuration information related to the RS resources includes configuration information for the RS resources used to obtain channel measurements required for inference.

[0172] As an example, the configuration information related to the RS resources includes the configuration information of the RS resources used to obtain channel measurements required for training the inference model.

[0173] As one embodiment, the configuration information related to the RS resources includes the configuration information of the RS resources in the RS resource set referred to by the inference input.

[0174] As an example, the configuration information of an RS resource includes one or more of the following: frequency domain resources, time domain resources, number of ports, CDM type, density, quasi co-location relationship, TCI (Transmission Configuration Indicator) state, time domain behavior, power control parameters, and BWP (Bandwidth Part) index.

[0175] As an example, the time-domain behavior is one of periodic, semi-persistent, or aperiodic.

[0176] As an example, the configuration information of an RS resource includes the RS resource set to which it belongs.

[0177] As an example, an RS resource is a CSI-RS resource, and the RS resource set to which the RS resource belongs is a CSI-RS resource set.

[0178] As an example, the configuration information of an RS resource includes at least one of the following: center frequency, subcarrier spacing, SFN (System frame number) offset, period, position in a burst, SMTC (SS / PBCH block measurement timing configuration), or measurement interval.

[0179] As an example, the configuration information related to the RS resource includes its purpose.

[0180] As one example, the intended use includes channel measurement or interference measurement.

[0181] As one example, the use includes whether it is used to obtain channel measurements.

[0182] As one example, the use includes whether it is used to obtain channel measurements on which the input for inference depends.

[0183] As one example, the use includes whether it belongs to the set of RS resources involved in the output of the inference.

[0184] As one example, the use includes whether it is used to obtain channel measurements on which input for performance monitoring depends.

[0185] As one example, the use includes whether it is used to obtain channel measurements required to generate predicted CSI or predicted beam information.

[0186] As an example, the configuration information related to the RS resources includes the configuration information of a first RS resource set, and the first node obtains channel measurements for generating an inference input based on the RS resources in the first RS resource set.

[0187] As a sub-implementation of the above embodiments, the configuration information of the first RS resource set includes the number of RS resources included in the first RS resource set.

[0188] As a sub-implementation of the above embodiments, the configuration information of the first RS resource set includes at least one of the beam direction and beamwidth of the RS resources in the first RS resource set.

[0189] As a sub-implementation of the above embodiments, the configuration information of the first RS resource set includes the configuration information of the RS resources in the first RS resource set.

[0190] As an example, the configuration information related to the RS resources includes the configuration information of the second RS resource set, and the output of an inference refers to the second RS resource set.

[0191] As a sub-example of the above embodiment, the output of the inference indicates at least one RS resource in the second RS resource set.

[0192] As a sub-implementation of the above embodiments, the configuration information of the second RS resource set includes the number of RS resources included in the second RS resource set.

[0193] As a sub-implementation of the above embodiments, the configuration information of the second RS resource set includes at least one of the beam direction and beamwidth of the RS resources in the second RS resource set.

[0194] As a sub-implementation of the above embodiments, the configuration information of the second RS resource set includes the configuration information of the RS resources in the second RS resource set.

[0195] As a sub-implementation of the above embodiment, the second RS resource set is used to obtain the channel measurements required for training the inference model.

[0196] As an example, the configuration information related to the RS resources includes the configuration information of the first RS resource set and the configuration information of the second RS resource set.

[0197] As an example, the first RS resource set includes a CSI-RS (Channel State Information Reference Signal) resource set.

[0198] As an example, the first RS resource set includes a CSI-SSB (Channel State Information-Synchronization Signal Block) resource set.

[0199] As an example, any RS resource in the first RS resource set is a CSI-RS resource.

[0200] As an example, any RS resource in the first RS resource set is an SS / PBCH (Synchronisation Signal / Physical Broadcast Channel) block resource.

[0201] As an example, any RS resource in the first RS resource set is a CSI-RS resource or an SS / PBCHblock resource.

[0202] As one embodiment, the second RS resource set includes the CSI-RS resource set.

[0203] As one embodiment, the second RS resource set includes the CSI-SSB resource set.

[0204] As an example, any RS resource in the second RS resource set is a CSI-RS resource.

[0205] As an example, any RS resource in the second RS resource set is an SS / PBCH block resource.

[0206] As an example, any RS resource in the second RS resource set is a CSI-RS resource or an SS / PBCH block resource.

[0207] As an example, the output of an inference relates to a set of RS resources, wherein the output of the inference indicates at least one RS resource in the set of RS resources.

[0208] As an example, the output of an inference relates to a set of RS resources, wherein the output of the inference indicates the CRI (CSI-RS Resource Indicator) or SSBRI (SS / PBCH Block Resource Indicator) of at least one RS resource in the set of RS resources.

[0209] As an example, the output of an inference relates to a set of RS resources, wherein any RS resource indicated by the output of the inference is an RS resource in the set of RS resources.

[0210] As an example, the output of an inference relating to an RS resource set includes any CRI or SSBRI indicated by the output of the inference being the CRI or SSBRI of an RS resource in the RS resource set.

[0211] As an example, the output of an inference relates to a set of RS resources, wherein the output of the inference indicates the RSRP (Reference Signal Received Power) or SINR (Signal-to-Interference and Noise Ratio) of at least one RS resource in the set of RS resources.

[0212] As an example, the output of an inference relating to a set of RS resources includes any RSRP or SINR indicated by the output of the inference being the RSRP or SINR of an RS resource in the set of RS resources.

[0213] As an example, any of the plurality of measurement configurations includes reporting relevant configuration information.

[0214] As one example, the reported configuration information includes the reported content.

[0215] As an example, the reported content includes one or more of CRI, SSBRI, RSRP, and SINR.

[0216] As an example, the reported content includes one or more of CQI (Channel Quality Indicator), PMI (Precoding Matrix Indicator), CRI, LI (Layer Indicator), RI (Rank Indicator), SSBRI, RSRP, SINR, CapabilityIndex, and TDCP (Time Domain Channel Properties).

[0217] As an example, the reported content includes at least one of the predicted beam information and the predicted CSI.

[0218] As an example, the reported content includes at least one of the following: spatial domain predicted beam information, spatial domain predicted CSI, temporal domain predicted beam information, and temporal domain predicted CSI.

[0219] As an example, the beam information includes at least one of CRI, SSBRI, RSRP, and SINR.

[0220] As one example, the reported content includes compressed CSI (Channel State Information).

[0221] As one example, the configuration information related to the reporting includes the number of reports.

[0222] As an example, the number of reported quantities includes at least one of the following: the number of reported CRIs or SSBRIs, and the number of reported RSRPs or SINRs.

[0223] As an example, the reported configuration information includes information related to measurement time.

[0224] As an example, the measurement time-related information is used to determine the RS transmission occasion for obtaining the channel measurements required to calculate CSI.

[0225] As an example, the measurement time-related information includes a lower limit on the number of RS transmission opportunities required to obtain the channel measurements needed to calculate the CSI.

[0226] As an example, the reported configuration information includes information related to the prediction time.

[0227] As an example, the prediction time-related information is used to determine the slot interval targeted by CSI.

[0228] As an example, the prediction time-related information is used to determine at least one of the following: the number of time slots targeted by the CSI, the length of the time slots targeted by the CSI, and the gap between the time slots targeted by the CSI and the time slots occupied by the CSI report.

[0229] As an example, the reported configuration information includes the physical layer channel carrying CSI.

[0230] As a sub-example of the above embodiments, the physical layer channel carrying CSI is PUSCH or PUCCH.

[0231] As one example, the reported configuration information includes time-domain behavior.

[0232] As an example, the reported configuration information includes at least one of period and time slot offset.

[0233] As one example, the reported configuration information includes frequency domain resources.

[0234] As an example, any of the plurality of measurement configurations includes model parameters.

[0235] As an example, the model parameters include parameters used to construct a model.

[0236] As an example, the model parameters include some or all of the parameters used to construct a model.

[0237] As an example, the model parameters include an associated ID to which the model is associated.

[0238] As an example, the model refers to an AI model or an ML model.

[0239] As an example, any one of the plurality of measurement configurations is used for a function.

[0240] As an example, any one of the plurality of measurement configurations is used to configure a function.

[0241] As an example, any of the plurality of measurement configurations includes configuration information for a function.

[0242] As an example, the configuration information for a function includes configuration information related to RS resources for obtaining the channel measurements required for the function.

[0243] As an example, the configuration information for a function includes configuration information related to the RS resources required to implement the function.

[0244] As an example, the configuration information of a function includes the configuration information related to the reporting of the function.

[0245] As an example, the configuration information for a function includes RS resource-related configuration information for obtaining the channel measurements required for the inference of the function.

[0246] As an example, the configuration information of a function includes configuration information related to the reporting generated by the inference output of the function.

[0247] As an example, the configuration information of a function includes model parameters of the AI ​​or ML model for the inference of the function.

[0248] As an example, the function refers to AI function or ML function.

[0249] As an example, the function refers to a function implemented using reasoning.

[0250] As one example, the functionality includes RRC IE.

[0251] As one example, the functionality includes CSI-ReportConfig IE.

[0252] As one example, the functionality includes BM (Beam Management) Case-1 and BM Case-2.

[0253] As one example, the functionality includes CSI prediction.

[0254] As one example, the functionality includes CSI compression.

[0255] As an example, any one of the plurality of measurement configurations is used to configure an inference.

[0256] As an example, any one of the plurality of measurement configurations is used to configure the reasoning of a function.

[0257] As an example, any of the plurality of measurement configurations includes inference configuration information.

[0258] As an example, the configuration information for one inference includes configuration information related to RS resources for obtaining the channel measurements required for the one inference.

[0259] As an example, the configuration information of a reasoning includes configuration information related to RS resources in the RS resource set involved in the output of the reasoning.

[0260] As an example, the configuration information for a reasoning includes the reporting-related configuration information generated by the output of the reasoning.

[0261] As an example, the configuration information for an inference includes the model parameters of the AI ​​or ML model for that inference.

[0262] As an example, any one of the plurality of measurement configurations is used to configure an AI or ML model.

[0263] As an example, any of the plurality of measurement configurations includes model parameters of an AI or ML model.

[0264] As an example, the availability of a measurement configuration means that the inference targeted by the measurement configuration is available.

[0265] As an example, the availability of a measurement configuration means that the first node is ready to use the measurement configuration for inference.

[0266] As an example, the availability of a measurement configuration means that the training of the model for the function or reasoning targeted by the measurement configuration has been completed.

[0267] As an example, the availability of a measurement configuration means that the measurement configuration can be activated or executed.

[0268] As an example, the availability of a measurement configuration means that the inference targeted by the measurement configuration can be activated or executed.

[0269] As an example, the availability of a measurement configuration means that the model for which the measurement configuration is available is available.

[0270] As an example, the availability of a measurement configuration means that the model for which the measurement configuration is targeted can be activated.

[0271] As an example, the first node is ready to apply inference to any of the measurement configurations in the first subset of measurement configurations.

[0272] As an example, the training of the model for the function or inference corresponding to any measurement configuration in the first measurement configuration subset has been completed.

[0273] As an example, any measurement configuration in the first subset of measurement configurations can be activated or executed.

[0274] As an example, inference targeted by any measurement configuration in the first subset of measurement configurations can be activated or executed.

[0275] As an example, any measurement configuration in the first subset of measurement configurations is available for a given model.

[0276] As an example, the model targeted by any measurement configuration in the first subset of measurement configurations can be activated.

[0277] As an example, each measurement configuration in the first measurement configuration subset is available.

[0278] As an example, the first subset of measurement configurations is a proper subset of the plurality of measurement configurations.

[0279] As an example, the first subset of measurement configurations includes only one measurement configuration.

[0280] As one embodiment, the first subset of measurement configurations includes multiple measurement configurations.

[0281] As an example, the availability of a measurement configuration includes whether the first node is ready to use the inference targeted by the measurement configuration.

[0282] As an example, the availability of a measurement configuration includes whether the training of the model for the function or inference targeted by the measurement configuration is complete.

[0283] As an example, the availability of a measurement configuration includes whether the measurement configuration can be activated or executed.

[0284] As an example, the availability of a measurement configuration includes whether the inference targeted by the measurement configuration can be activated or executed.

[0285] As an example, the availability of each measurement configuration in the second measurement configuration subset depends on further instructions.

[0286] As an example, the second measurement configuration subset is a proper subset of the plurality of measurement configurations.

[0287] As one embodiment, the second subset of measurement configurations includes only one measurement configuration.

[0288] As one embodiment, the second measurement configuration subset includes multiple measurement configurations.

[0289] As an example, none of the plurality of measurement configurations belongs to both the first measurement configuration subset and the second measurement configuration subset.

[0290] As an example, the sender of the further instruction is the first node.

[0291] As an example, the further instruction is sent later than the second message.

[0292] As an example, the target recipient of the further indication is the same as the target recipient of the second message.

[0293] As an example, any of the plurality of measurement configurations other than the first subset of measurement configurations is unavailable.

[0294] As an example, any of the plurality of measurement configurations other than the first subset of measurement configurations and the second subset of measurement configurations is unavailable.

[0295] As an example, an unavailable measurement configuration means that the first node is not ready to apply the inference targeted by the measurement configuration.

[0296] As an example, an unavailable measurement configuration means that the training of the model for the function or reasoning that the measurement configuration is targeting has not been completed.

[0297] As an example, an unavailable measurement configuration means that the measurement configuration is not activated or executed.

[0298] As an example, an unavailable measurement configuration means that the inference targeted by the measurement configuration is not activated or executed.

[0299] Example 2

[0300] Example 2 illustrates a schematic diagram of a network architecture according to an embodiment of this application, as shown in Figure 2.

[0301] 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.

[0302] As an example, the first node includes the UE201.

[0303] As one embodiment, the second node includes the node 203.

[0304] As an example, the wireless link between the UE201 and the node203 includes a cellular link.

[0305] As an example, the sender of the first message includes the node 203.

[0306] As an example, the recipient of the first message includes the UE201.

[0307] As an example, the sender of the second message includes the UE201.

[0308] As an example, the recipient of the second message includes the node 203.

[0309] As an example, the sender of the first reported information includes the UE201.

[0310] As an example, the recipient of the first reported information includes the node 203.

[0311] As an example, the sender of the third message includes node 203.

[0312] As an example, the recipient of the third message includes the UE201.

[0313] As an example, the sender of the fourth message includes the UE201.

[0314] As an example, the recipient of the fourth message includes node 203.

[0315] As an example, the sender of the second reporting information includes the UE201.

[0316] As one embodiment, the recipient of the second reported information includes the node 203.

[0317] As an example, the sender of the fifth message includes the node 203.

[0318] As an example, the recipient of the fifth message includes the UE201.

[0319] As an example, the UE201 supports AI- or ML-based operations.

[0320] As an example, node 203 supports AI- or ML-based operations.

[0321] Example 3

[0322] 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.

[0323] 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.).

[0324] As an example, the wireless protocol architecture in Figure 3 is applicable to the first node.

[0325] As an example, the wireless protocol architecture in Figure 3 is applicable to the second node.

[0326] As an example, the higher layer mentioned in this application refers to the layer above the physical layer.

[0327] As an example, the first message is generated in the RRC sublayer 306.

[0328] As an example, the second message is generated in the RRC sublayer 306.

[0329] As an example, the first reporting information is generated in the PHY301 or the PHY351.

[0330] As an example, the third message is generated in the RRC sublayer 306.

[0331] As an example, the fourth message is generated in the RRC sublayer 306.

[0332] As an example, the fourth message is generated in the MAC sublayer 302 or the MAC sublayer 352.

[0333] As an example, the second reporting information is generated in the PHY301 or the PHY351.

[0334] As an example, the fifth message is generated in the RRC sublayer 306.

[0335] Example 4

[0336] 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.

[0337] 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.

[0338] 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.

[0339] 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.

[0340] 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.

[0341] 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.

[0342] 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.

[0343] As one embodiment, the second communication device 450 includes: at least one processor and at least one memory, the at least one memory including computer program code; the at least one memory and the computer program code are configured to be used with the at least one processor. The second communication device 450 means at least: receiving the first message; and sending the second message. The first message indicates a plurality of measurement configurations; the second message indicates a first subset of measurement configurations and a second subset of measurement configurations among the plurality of measurement configurations, wherein the measurement configurations in the first subset of measurement configurations are available, and the availability of the measurement configurations in the second subset of measurement configurations depends on further indication.

[0344] As one embodiment, the second communication device 450 includes: a memory storing a computer-readable instruction program that produces actions when executed by at least one processor, the actions including: receiving the first message; and sending the second message.

[0345] 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: sending the first message; and receiving the second message. The first message indicates a plurality of measurement configurations; the second message indicates a first subset of measurement configurations and a second subset of measurement configurations among the plurality of measurement configurations, wherein the measurement configurations in the first subset of measurement configurations are available, and the availability of the measurement configurations in the second subset of measurement configurations depends on further indication.

[0346] As one embodiment, the first communication device 410 includes: a memory storing a computer-readable instruction program that produces actions when executed by at least one processor, the actions including: sending the first message; and receiving the second message.

[0347] As an example, the first node in this application includes the second communication device 450.

[0348] As an example, the second node in this application includes the first communication device 410.

[0349] 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 message; 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 message.

[0350] As an example, at least one of {the antenna 420, the receiver 418, the receiving processor 470, the multi-antenna receiving processor 472, the controller / processor 475, and the memory 476} is used to receive the second message; at least one of {the antenna 452, the transmitter 454, the transmitting processor 468, the multi-antenna transmitting processor 457, the controller / processor 459, the memory 460, and the data source 467} is used to transmit the second message.

[0351] As an example, at least one of {the antenna 420, the receiver 418, the receiving processor 470, the multi-antenna receiving processor 472, the controller / processor 475, and the memory 476} is used to receive the first reporting information; at least one of {the antenna 452, the transmitter 454, the transmitting processor 468, the multi-antenna transmitting processor 457, the controller / processor 459, the memory 460, and the data source 467} is used to transmit the first reporting information.

[0352] 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 third message; 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 third message.

[0353] As an example, at least one of {the antenna 420, the receiver 418, the receiving processor 470, the multi-antenna receiving processor 472, the controller / processor 475, and the memory 476} is used to receive the fourth message; at least one of {the antenna 452, the transmitter 454, the transmitting processor 468, the multi-antenna transmitting processor 457, the controller / processor 459, the memory 460, and the data source 467} is used to transmit the fourth message.

[0354] As an example, at least one of {the antenna 420, the receiver 418, the receiving processor 470, the multi-antenna receiving processor 472, the controller / processor 475, and the memory 476} is used to receive the second reporting information; at least one of {the antenna 452, the transmitter 454, the transmitting processor 468, the multi-antenna transmitting processor 457, the controller / processor 459, the memory 460, and the data source 467} is used to transmit the second reporting information.

[0355] 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 fifth message; 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 fifth message.

[0356] Example 5

[0357] Example 5 illustrates a transmission flowchart according to an embodiment of this application; as shown in Figure 5. In Figure 5, the second node U1 and the first node U2 are communication nodes transmitting via an air interface. In Figure 5, the steps in blocks F51 to F55 are respectively optional.

[0358] For the second node U1, a first message is sent in step S511; a second message is received in step S512; a third message is sent in step S5101; a first reporting information is received in step S5102; a fourth message is received in step S5103; a fifth message is sent in step S5104; and a second reporting information is received in step S5105.

[0359] For the first node U2, in step S521, a first message is received; in step S521, a second message is sent; in step S5201, a third message is received; in step S5202, a first reporting message is sent; in step S5203, a fourth message is sent; in step S5204, a fifth message is received; and in step S5205, a second reporting message is sent.

[0360] In Embodiment 5, the first message indicates multiple measurement configurations, and the second message indicates a first subset and a second subset of the multiple measurement configurations, wherein the measurement configurations in the first subset are available, and the availability of the measurement configurations in the second subset depends on further indications.

[0361] As an example, the first node U2 is the first node in this application.

[0362] As an example, the second node U1 is the second node in this application.

[0363] As one embodiment, the air interface between the second node U1 and the first node U2 includes a wireless interface between the base station equipment and the user equipment.

[0364] As one embodiment, the air interface between the second node U1 and the first node U2 includes a wireless interface between the relay node device and the user equipment.

[0365] As one embodiment, the air interface between the second node U1 and the first node U2 includes the interface between the core network equipment and the user equipment.

[0366] As one embodiment, the air interface between the second node U1 and the first node U2 includes the interface between the OTT server (Over-The-Top server) and the user equipment.

[0367] As one embodiment, the air interface between the second node U1 and the first node U2 includes the interface between the NAS (Network Access Server) device and the user equipment.

[0368] As one embodiment, the air interface between the second node U1 and the first node U2 includes a wireless interface between user equipment and user equipment.

[0369] As one embodiment, the first node U2 includes a terminal.

[0370] As one embodiment, the first node U2 includes a user equipment.

[0371] As one embodiment, the second node U1 includes the serving cell sustaining base station of the first node U2.

[0372] As one embodiment, the second node U1 includes an OTT server (Over-The-Top server).

[0373] As an example, the second node U1 includes OAM (Operation Administration and Maintenance).

[0374] As one embodiment, the second node U1 includes a NAS device.

[0375] As one embodiment, the second node U1 includes core network equipment.

[0376] As an example, in response to the receipt of the first message, the first node U2 sends the second message.

[0377] As one example, the sending of the second message is later than the receiving of the first message.

[0378] As an example, the first message is transmitted on PDSCH (Physical Downlink Shared Channel).

[0379] As an example, the second message is transmitted on PUSCH (Physical Uplink Shared Channel).

[0380] As an example, the step in block F52 of Figure 5 includes the first reported information being based on inference, and the first measurement configuration subset being used to determine the parameters of the inference for the first reported information.

[0381] As an example, the first reporting information is transmitted on the PUSCH.

[0382] As an example, the first reported information is transmitted on PUCCH (Physical Uplink Control Channel).

[0383] As one example, the sending of the first reported information is later than the sending of the second message.

[0384] As an example, the steps in blocks F51 and F52 of Figure 5 are present, and the third message indicates the parameters of the inference for the first reported information, the indication of the third message depending on the first measurement configuration subset.

[0385] As an example, the receipt of the third message is later than the sending of the second message.

[0386] As an example, the sending of the first reported information is later than the receiving of the third message.

[0387] As an example, the third message is transmitted on the PDSCH.

[0388] As an example, the step in block F53 of Figure 5 is present, wherein the fourth message indicates that at least one measurement configuration in the second measurement configuration subset is available.

[0389] As an example, the fourth message is transmitted on the PUSCH.

[0390] As an example, the receipt of the third message occurs earlier than the transmission of the fourth message.

[0391] As an example, the receipt of the third message is later than the sending of the fourth message.

[0392] As an example, the fourth message is sent later than the second message.

[0393] As an example, the fourth message is sent later than the first reporting message.

[0394] As an example, the fourth message is sent earlier than the first reporting message.

[0395] As an example, the steps in blocks F55 and F53 of Figure 5 are present, the second reported information is based on inference, and the at least one measurement configuration in the second measurement configuration subset is used to determine the parameters of the inference for the second reported information.

[0396] As an example, the second reporting information is transmitted on the PUSCH.

[0397] As an example, the second reporting information is transmitted on the PUCCH (Physical Uplink Control Channel).

[0398] As one example, the sending of the second reporting information is later than the sending of the fourth message.

[0399] As one example, the sending of the second reporting information is later than the receiving of the third message.

[0400] As an example, the steps in blocks F53, F54 and F55 of Figure 5 are present, and the fifth message indicates the parameters of the inference for the second reported information, the indication of the fifth message depending on the at least one measurement configuration in the second measurement configuration subset.

[0401] As an example, the fifth message is transmitted on the PDSCH.

[0402] As an example, the reception of the fifth message is later than the transmission of the fourth message.

[0403] As one example, the sending of the second reporting information is later than the receiving of the fifth message.

[0404] As an example, the step in block F54 of Figure 5 is not present.

[0405] As a sub-example of the above embodiment, the receipt of the third message is later than the sending of the fourth message.

[0406] As a sub-implementation of the above embodiments, the third message indicates the parameters of the inference for the second reported information, and the indication of the third message depends on at least one measurement configuration in the second measurement configuration subset.

[0407] As an example, any of the plurality of measurement configurations includes an associated identifier.

[0408] Example 6

[0409] Example 6 illustrates a schematic diagram of a first measurement configuration subset according to an embodiment of the present application being used to determine parameters for inference of a first reported information; as shown in Figure 6.

[0410] As an example, the inference refers to AI (Artificial Intelligence) inference.

[0411] As an example, the inference refers to ML (Machine Learning) inference.

[0412] As an example, the inference refers to AI inference or ML inference.

[0413] As an example, the first reported information is based on the reasoning given regarding the first reported information.

[0414] As one example, the first reported information includes CSI (Channel State Information).

[0415] As an example, the first reported information includes one or more of CQI, PMI, CRI, LI, RI, SSBRI, RSRP, SINR, capability index, and TDCP.

[0416] As one example, the first reported information includes compressed CSI.

[0417] As one example, the first reported information includes the predicted CSI.

[0418] As one embodiment, the first reported information includes predicted beam information.

[0419] As an example, the beam information includes one or more of CRI, SSBRI, RSRP, and SINR.

[0420] As an example, the prediction includes at least one of temporal prediction and spatial prediction.

[0421] As one example, the first reported information includes a channel matrix.

[0422] As one example, the first reported information includes a precoding matrix.

[0423] As an example, the first reported information depends on the output of the inference given the first reported information.

[0424] As an example, the output of the inference for the first reported information is used to generate the first reported information.

[0425] As an example, all or part of the inference output for the first reported information is used to generate the first reported information.

[0426] As an example, all or part of the output of the reasoning for the first reported information is post-processed and used to generate the first reported information.

[0427] As one embodiment, the first reported information includes all or part of the output of the reasoning given for the first reported information.

[0428] As one embodiment, the first reported information includes all or part of the post-processed output of the reasoning given for the first reported information.

[0429] As an example, the post-processing includes one or more of quantization, shortening, puncture, padding, matrix factorization, domain transformation, and DFT (Discrete Fourier Transform).

[0430] As an example, the domain transformation 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, frequency domain to time domain transformation, delay domain to frequency domain transformation, frequency domain to delay domain transformation, Doppler domain to time domain transformation, and time domain to Doppler domain transformation.

[0431] As an example, the first measurement configuration subset is used for some parameters of the inference for the first reported information.

[0432] As an example, the first measurement configuration subset indicates the parameters for the inference of the first reported information.

[0433] As an example, the first measurement configuration subset indicates some parameters of the inference for the first reported information.

[0434] As an example, one measurement configuration in the first measurement configuration subset is used to determine the parameters for the inference of the first reported information.

[0435] As an example, one measurement configuration in the first measurement configuration subset indicates a portion of the parameters for the inference of the first reported information.

[0436] As an example, the parameters for the inference of the first reported information include at least one of the following: input-related parameters, output-related parameters, and model parameters.

[0437] As one example, the input-related parameters include configuration information related to RS resources for obtaining the channel measurements on which the input depends.

[0438] As one example, the input-related parameters include the number of RS resources used to obtain the channel measurements on which the input depends.

[0439] As an example, the input-related parameters include at least one of the beam direction and beamwidth of the RS resources on which the input depends for obtaining the channel measurement.

[0440] As one example, the input-related parameters include configuration information for obtaining the RS resources on which the input depends for channel measurements.

[0441] As an example, the output-related parameters include the number of RS resources in the RS resource set involved in CSI.

[0442] As an example, the output-related parameters include at least one of the beam direction and beamwidth of the RS resources in the RS resource set involved in CSI.

[0443] As an example, the output-related parameters include the reported content.

[0444] As an example, the output-related parameters include the number of reported quantities.

[0445] As an example, the output-related parameters are used to determine the CSI reference resource.

[0446] As an example, the definition of the CSI reference resource is based on 3GPP TS38.214.

[0447] As an example, the output-related parameters include measurement time-related information.

[0448] As an example, the output-related parameters include prediction time-related information.

[0449] As an example, the output-related parameters are used to determine at least one of the following: the number of time slots targeted, the length of the time slots targeted, and the gap between the time slots targeted and the time slots occupied by the CSI.

[0450] As an example, the output-related parameters include the physical layer channel carrying the CSI.

[0451] As an example, the parameters related to the output include time-domain behavior.

[0452] As an example, the output-related parameters include at least one of the period and the time slot offset.

[0453] As an example, the output-related parameters include frequency domain resources.

[0454] As an example, the model parameters include parameters used to construct the AI ​​or ML model.

[0455] As an example, the model parameters include the association identifiers to which the AI ​​or ML model is associated.

[0456] As an example, the model parameters include the training dataset.

[0457] As an example, the model parameters are used to determine the training dataset.

[0458] As an example, the first node obtains channel measurements based on a first RS resource set for generating the inference input for the first reported information. The parameters related to the input for the inference for the first reported information include at least one of the following: the number of RS resources in the first RS resource set, beam direction, and beamwidth.

[0459] As an example, the output of the inference for the first reported information refers to a second RS resource set, and the parameters related to the output of the inference for the first reported information include at least one of the following: the number of RS resources in the second RS resource set, the beam direction, and the beamwidth.

[0460] As an example, the first measurement configuration subset is used to determine at least one of the input-related parameters, the output-related parameters, and the model parameters for the inference of the first reported information.

[0461] As a sub-implementation of the above embodiments, the first measurement configuration subset indicates that relevant parameters are input.

[0462] As a sub-implementation of the above embodiments, the first measurement configuration subset indicates that the relevant parameters are output.

[0463] As a sub-implementation of the above embodiments, the first measurement configuration subset indicates some model parameters.

[0464] As an example, the reasoning for the first reported information is performed based on the first measurement configuration subset.

[0465] As an example, the first node performs the inference for the first reported information based on the parameters of the inference for the first reported information indicated by the first measurement configuration subset.

[0466] Example 7

[0467] Example 7 illustrates a schematic diagram of a third message according to an embodiment of this application; as shown in Figure 7. In Example 7, the third message indicates the parameters of the inference for the first reported information.

[0468] As an example, the third message is carried by higher layer signaling.

[0469] As an example, the third message is carried by RRC signaling.

[0470] As an example, the third message is carried by one or more RRC IEs.

[0471] As one example, the third message includes some or all of the information in each of one or more RRC IEs.

[0472] As one example, the third message includes all or part of the information in the CSI-ReportConfig IE.

[0473] As an example, the third message is CSI-ReportConfig IE.

[0474] As an example, the third message is carried by an RRC message.

[0475] As one example, the third message includes some or all of the information from one or more RRC messages.

[0476] As an example, the third message is carried by MAC CE (Medium Access Control layer Control Element).

[0477] As one embodiment, the first message includes an RRC reconfiguration message, and the third message includes some or all of the information in one or more RRC IEs.

[0478] As one embodiment, the first message includes an RRC reconfiguration message, and the third message includes all or part of the information in the CSI-ReportConfig IE.

[0479] As an example, the reception of the third message is later than the sending of the second message.

[0480] As an example, the first measurement configuration subset indicates a portion of the parameters for the inference of the first reported information, and the third message indicates another portion of the parameters for the inference of the first reported information.

[0481] As an example, the first measurement configuration subset indicates inputting relevant parameters for a portion of the inference of the first reported information, and the third message indicates inputting relevant parameters for another portion of the inference of the first reported information.

[0482] As a sub-implementation of the above embodiment, the input-related parameters include at least one of the following: the number of RS resources on which the channel measurement on which the first reporting information depends, the beam direction, and the beamwidth.

[0483] As a sub-implementation of the above embodiments, the other part of the input-related parameters includes one or more of the following: frequency domain resources, time domain resources, number of ports, CDM type, density, quasi-co-location relationship, TCI status, time domain behavior, and power control parameters for obtaining the RS resources on which the first reporting information depends.

[0484] As an example, the first measurement configuration subset indicates the output of relevant parameters for a portion of the inference of the first reported information, and the third message indicates the output of relevant parameters for another portion of the inference of the first reported information.

[0485] As a sub-implementation of the above embodiment, the parameters related to the output include at least one of the following: the number of RS resources in the RS resource set involved in the first reported information, the beam direction, and the beamwidth.

[0486] As a sub-implementation of the above embodiments, the parameters related to the output include at least one of the reporting content of the first reporting information and the number of reporting quantities.

[0487] As a sub-implementation of the above embodiment, the parameters related to the output include at least one of the measurement time-related information and the prediction time-related information of the first reported information.

[0488] As a sub-implementation of the above embodiment, the other part of the output-related parameters includes at least one of the time-domain behavior of the first reported information and the physical layer channel carrying the first reported information.

[0489] As a sub-implementation of the above embodiment, the other part of the output related parameters includes at least one of the following three: the period of the first reported information, the time slot offset, and the frequency domain resources.

[0490] As one embodiment, the first measurement configuration subset indicates at least one of the number of RS resources included in the first RS resource set and the number of RS resources included in the second RS resource set; the first node obtains channel measurements for calculating the first reporting information based on the first RS resource set, wherein the first reporting information relates to the second RS resource set.

[0491] As an example, a reporting message relating to a set of RS resources includes the reporting message indicating at least one RS resource in the set of RS resources.

[0492] As an example, a report relating to an RS resource set includes the report indicating the CRI or SSBRI of at least one RS resource in the RS resource set.

[0493] As an example, a reporting message relating to an RS resource set includes the reporting message indicating the RSRP or SINR of at least one RS resource in the RS resource set.

[0494] As an example, a report relating to an RS resource set includes any RS resource indicated by the report being an RS resource in the RS resource set.

[0495] As an example, a report relating to an RS resource set includes any CRI or SSBRI indicated by the report being the CRI or SSBRI of an RS resource in the RS resource set.

[0496] As an example, a report relating to an RS resource set includes any RSRP or SINR indicated by the report being the RSRP or SINR of an RS resource in the RS resource set.

[0497] As one embodiment, the first measurement configuration subset indicates the reporting content of the first reported information.

[0498] As an example, the first measurement configuration subset indicates the number of reported amounts of the first reported information.

[0499] As an example, the first measurement configuration subset indicates at least one of the measurement time-related information of the first reported information and the prediction time-related information of the first reported information.

[0500] As an example, the measurement time-related information of a reported message is used to determine the RS transmission timing for obtaining the channel measurements required to calculate the reported message.

[0501] As an example, the measurement time-related information of a reported message includes a lower limit on the number of RS transmission opportunities required to calculate the channel measurements needed for the reported message.

[0502] As an example, the prediction time-related information in a reported message includes at least one of the number and length of the time slot interval to which the reported message is targeted.

[0503] As an example, the prediction time-related information of a reported message includes the time slot interval to which the reported message is targeted and the gap between the time slot occupied by the reported message.

[0504] As one embodiment, the third information indicates at least one of the configuration information of RS resources in the first RS resource set and the configuration information of RS resources in the second RS resource set; the first node obtains channel measurements for calculating the first reporting information based on the first RS resource set, and the first reporting information relates to the second RS resource set.

[0505] As an example, the third information indicates one or more of the following: frequency domain resources, time domain resources, number of ports, CDM type, density, quasi-co-location relationship, TCI status, time domain behavior, power control parameters, and BWP index of the RS resources in the first RS resource set.

[0506] As an example, the third information indicates one or more of the following: frequency domain resources, time domain resources, number of ports, CDM type, density, quasi-co-location relationship, TCI status, time domain behavior, power control parameters, and BWP index of RS resources in the second RS resource set.

[0507] As an example, the third information indicates at least one of the temporal behavior of the first reported information and the physical layer channel carrying the first reported information.

[0508] As an example, the first measurement configuration subset indicates the number of RS resources included in the first RS resource set, and the third message indicates one or more of the following: frequency domain resources, time domain resources, number of ports, CDM type, density, quasi-co-location relationship, TCI status, time domain behavior, and power control parameters of the RS resources in the first RS resource set.

[0509] As an example, the first measurement configuration subset indicates the number of RS resources included in the second RS resource set, and the third message indicates one or more of the following: frequency domain resources, time domain resources, number of ports, CDM type, density, quasi-co-location relationship, TCI status, time domain behavior, and power control parameters of the RS resources in the second RS resource set.

[0510] As an example, the first measurement configuration subset indicates at least one of the following: the content of the first reported information, the number of reported quantities, measurement time-related information, and prediction time-related information; the third message indicates at least one of the following: the time-domain behavior of the first reported information and the physical layer channel carrying the first reported information.

[0511] Example 8

[0512] Example 8 illustrates a schematic diagram of an instruction of a third message according to an embodiment of the present application that depends on a subset of the first measurement configuration; as shown in Figure 8.

[0513] As an example, the third message indicates the parameters of each inference in the first inference set.

[0514] As a sub-implementation of the above embodiments, the parameters of each inference in the first inference set indicated by the third message depend on the first measurement configuration subset.

[0515] As a sub-implementation of the above embodiments, the first inference set depends on the first measurement configuration subset.

[0516] As an example, the reasoning regarding the first reported information is one of the reasons in the first set of reasoning.

[0517] As one embodiment, the number of inferences included in the first inference set depends on the first measurement configuration subset.

[0518] As one embodiment, the first inference set includes which inferences, depending on the first measurement configuration subset.

[0519] As an example, any reasoning in the first reasoning set is the reasoning targeted by a measurement configuration in the first measurement configuration subset.

[0520] As an example, for any inference in the first inference set, a measurement configuration from the first measurement configuration subset is used to determine the parameters of that inference.

[0521] As an example, the parameters of at least a portion of the inference in the first inference set indicated by the third message depend on the first measurement configuration subset.

[0522] As an example, the parameters of at least a portion of the inference in the first inference set indicated by the third message depend on the number of measurement configurations included in the first measurement configuration subset.

[0523] As an example, the indication of the third message depends on the second measurement configuration subset.

[0524] The benefits of the above approach include optimizing AI / ML feature-related configurations by taking into account potential updates to available features when configuring AI or ML-based features.

[0525] The advantages of the above method include optimizing resource utilization on the UE side.

[0526] The benefits of the above methods include reduced signaling overhead and latency.

[0527] The essence of the above method is that when the network side determines the third message, it takes into account that at least some of the measurement configurations in the second measurement configuration subset will become available from unavailable, and reserves resources for these measurement configurations. This method further optimizes the configuration related to AI / ML functions, improves resource utilization, and reduces signaling overhead and latency.

[0528] As an example, the indication of the third message depends on the first measurement configuration subset and the second measurement configuration subset.

[0529] As an example, the first inference set depends on the second measurement configuration subset.

[0530] As one example, the number of inferences included in the first inference set depends on the second measurement configuration subset.

[0531] As one embodiment, the number of inferences included in the first inference set depends on the first measurement configuration subset and the second measurement configuration subset.

[0532] As one embodiment, the first inference set includes which inferences, depending on the second measurement configuration subset.

[0533] As one embodiment, the first inference set includes which inferences depend on the first measurement configuration subset and the second measurement configuration subset.

[0534] As an example, the parameters of at least a portion of the inference in the first inference set indicated by the third message depend on the second measurement configuration subset.

[0535] As an example, the indication of the third message is based on the assumption that some or all of the measurement configurations in the second measurement configuration subset are indicated to be available or activated.

[0536] As an example, the sender of the third message determines the indication of the third message based on the first measurement configuration subset.

[0537] Generally, how the indication of the third message depends on the first measurement configuration subset is determined by the hardware device vendor. Some non-limiting implementations are described below:

[0538] As an example, the sender of the third message determines the first inference set based on the first measurement configuration subset.

[0539] As an example, the sender of the third message determines the first inference set based on the measurement configurations included in the first measurement configuration subset.

[0540] In a preferred embodiment, any reasoning in the first reasoning set is the reasoning targeted by a measurement configuration in the first measurement configuration subset.

[0541] As an example, the sender of the third message randomly selects the first inference set from the candidate inference subset, the candidate inference subset consisting of the inference targeted by each measurement configuration in the first measurement configuration subset.

[0542] As an example, the sender of the third message selects the first inference set from the candidate inference subset in descending order of priority, the candidate inference subset consisting of the inference targeted by each measurement configuration in the first measurement configuration subset.

[0543] As a sub-implementation of the above embodiments, the second message indicates the priority of inference for each measurement configuration in the first measurement configuration subset.

[0544] As a sub-implementation of the above embodiments, the first message indicates the priority of inference for each measurement configuration in the first measurement configuration subset.

[0545] As a sub-implementation of the above embodiments, the priority depends on the order in which the model training is completed.

[0546] As a sub-implementation of the above embodiments, the priority depends on the amount of resources required.

[0547] As a sub-example of the above embodiments, the priority depends on the content of the output.

[0548] As a sub-example of the above embodiments, the priority depends on the output load size.

[0549] As a sub-example of the above embodiments, the priority depends on the number of RS resources used to obtain channel measurements.

[0550] As an example, the sender of the third message determines the first inference set based on the number of measurement configurations included in the first measurement configuration subset.

[0551] As an example, the sender of the third message determines the first inference set based on the inference targeted by the measurement configurations included in the first measurement configuration subset.

[0552] As an example, the sender of the third message determines the first inference set based on the resources required for inference targeted by the measurement configurations in the first measurement configuration subset.

[0553] As an example, the sender of the third message determines that the first inference set is such that the total number of resources required by the first inference set does not exceed an upper limit.

[0554] As an example, the sender of the third message determines the first inference set such that the first inference set includes as many measurement configurations as possible from the first measurement configuration subset for inference, and such that the total number of resources required by the first inference set does not exceed an upper limit.

[0555] As an example, the sender of the third message determines the parameters of the inference for the first reported information indicated by the third message based on the first measurement configuration subset.

[0556] As an example, the amount of resources required for the inference of the first reported information depends on the parameters of the inference of the first reported information; the sender of the third message determines the parameters of the inference of the first reported information such that the total number of resources required for the first inference set does not exceed an upper limit.

[0557] As an example, the sender of the third message determines the parameters of each inference in the first inference set indicated by the third message such that the total number of resources required by the first inference set does not exceed an upper limit.

[0558] As a sub-implementation of the above embodiments, the amount of resources required for each inference in the first inference set depends on its parameters.

[0559] As an example, the resources required for inference include computing resources.

[0560] As an example, the resources required for inference include storage resources.

[0561] As an example, the resources required for inference include computing resources and storage resources.

[0562] As an example, the resources required for inference include a CPU (CSI Processing Unit).

[0563] As an example, the sender of the third message determines the first inference set based on the first measurement configuration subset and the second measurement configuration subset.

[0564] As an example, the sender of the third message determines the third message based on the assumption that some or all of the measurement configurations in the second measurement configuration subset are indicated to be available.

[0565] As an example, the sender of the third message determines the first inference set such that the total number of resources required by the first inference set is less than an upper limit, and the difference between the total number of resources and the upper limit is not less than the number of resources required for inference by one or more measurement configurations in the second measurement configuration subset.

[0566] As an example, the sender of the third message determines the parameters of the inference for the first reported information such that the total number of resources required by the first inference set is less than an upper limit, and the difference between the total number of resources and the upper limit is not less than the number of resources required for the inference for one or more measurement configurations in the second measurement configuration subset.

[0567] As an example, the sender of the third message determines the third message based on the assumption that some or all of the measurement configurations in the second measurement configuration subset are indicated to be available.

[0568] As a sub-implementation of the above embodiments, the sender of the third message determines the first inference set such that the sum of the number of resources required by the first inference set and the number of resources required for inference by the measurement configurations indicated as available in the second measurement configuration subset is not greater than an upper limit.

[0569] As an example, the sender of the third message determines the parameters of the inference for the first reported information indicated by the third message based on the assumption that some or all of the measurement configurations in the second measurement configuration subset are indicated to be available.

[0570] Example 9

[0571] Example 9 illustrates a schematic diagram of a fourth message according to one embodiment of this application; as shown in Figure 9. In Example 9, the fourth message indicates that at least one measurement configuration in the second measurement configuration subset is available.

[0572] As an example, the fourth message is carried by higher layer signaling.

[0573] As an example, the fourth message is carried by RRC signaling.

[0574] As an example, the fourth message is carried by a UE Assistance Information message.

[0575] As an example, the fourth message is carried by a UE Capability Information message.

[0576] As an example, the fourth message is carried by the UE capability IE.

[0577] As an example, the fourth message is carried by the MAC CE.

[0578] As an example, the reception of the third message precedes the sending of the fourth message.

[0579] As an example, the reception of the third message is later than the sending of the fourth message.

[0580] As an example, the fourth message indicates that the first node is ready to apply inference to any of the at least one measurement configuration in the second measurement configuration subset.

[0581] As an example, the fourth message indicates that training of the model corresponding to any one of the at least one measurement configuration in the second measurement configuration subset has been completed.

[0582] As an example, the fourth message indicates that any one of the at least one measurement configuration in the second measurement configuration subset can be activated or executed.

[0583] As an example, the fourth message indicates that inference for any of the at least one measurement configuration in the second measurement configuration subset can be activated or executed.

[0584] As an example, the fourth message indicates that the model targeted by any of the at least one measurement configuration in the second measurement configuration subset is available.

[0585] As an example, the indication of the third message depends on at least one measurement configuration in the second measurement configuration subset.

[0586] As an example, the third message indicates the parameters of each inference in the first inference set, wherein the inference for the first reported information is a inference in the first inference set; any inference in the first inference set is a inference for a measurement configuration in the first measurement configuration subset or a inference for a measurement configuration in at least one measurement configuration in the second measurement configuration subset.

[0587] As an example, the parameters of the inference indicated by the third message for the first reported information depend on at least one measurement configuration in the second measurement configuration subset.

[0588] As an example, the sender of the third message determines the parameters of the inference for the first reported information such that the sum of the number of resources required by the first inference set and the number of resources required by all or part of the measurement configurations in the second measurement configuration subset for the inference is not greater than an upper limit.

[0589] As a sub-implementation of the above embodiments, the amount of resources required for the inference of the first reported information depends on the parameters of the inference of the first reported information indicated by the third message.

[0590] Example 10

[0591] Example 10 illustrates a schematic diagram of at least one measurement configuration in a second measurement configuration subset according to an embodiment of the present application being used to determine parameters for inference of second reporting information; as shown in Figure 10.

[0592] As one embodiment, the second reported information is based on the inference given the second reported information.

[0593] As one example, the second reported information includes CSI.

[0594] As an example, the second reported information includes one or more of CQI, PMI, CRI, LI, RI, SSBRI, RSRP, SINR, capability index, and TDCP.

[0595] As one example, the second reported information includes compressed CSI.

[0596] As one example, the second reported information includes the predicted CSI.

[0597] As one embodiment, the second reported information includes predicted beam information.

[0598] As one example, the second reported information includes a channel matrix.

[0599] As one example, the second reported information includes a precoding matrix.

[0600] As one example, the second reported information depends on the output of the inference given the second reported information.

[0601] As an example, the output of the inference for the second reported information is used to generate the second reported information.

[0602] As an example, all or part of the output of the reasoning for the second reported information is used to generate the second reported information.

[0603] As an example, all or part of the output of the reasoning for the second reported information is post-processed and used to generate the second reported information.

[0604] As one embodiment, the second reported information includes all or part of the output of the reasoning given for the second reported information.

[0605] As one embodiment, the second reported information includes all or part of the post-processed output of the reasoning for the second reported information.

[0606] As an example, at least one measurement configuration in the second measurement configuration subset is used to determine some parameters of the inference for the second reported information.

[0607] As an example, the at least one measurement configuration in the second measurement configuration subset indicates the parameters for the inference of the second reported information.

[0608] As an example, the at least one measurement configuration in the second measurement configuration subset indicates some parameters of the inference for the second reported information.

[0609] As an example, the parameters for the inference of the second reported information include at least one of the following: input-related parameters, output-related parameters, and model parameters.

[0610] As an example, the first node obtains channel measurements based on a third RS resource set for generating the inference input for the second reported information. The parameters related to the input for the inference input for the second reported information include at least one of the number of RS resources in the third RS resource set, beam direction, and beamwidth.

[0611] As an example, the third RS resource set includes the CSI-RS resource set.

[0612] As an example, any RS resource in the third RS resource set is a CSI-RS resource or an SS / PBCHblock resource.

[0613] As an example, the output of the inference for the second reported information refers to a fourth RS resource set, and the parameters related to the output of the inference for the second reported information include at least one of the number of RS resources in the fourth RS resource set, beam direction, and beamwidth.

[0614] As an example, the output of the inference for the second reported information indicates at least one RS resource in the fourth RS resource set.

[0615] As an example, at least one measurement configuration in the second measurement configuration subset is used to determine at least one of the input-related parameters, the output-related parameters, and the model parameters for the inference of the second reported information.

[0616] As a sub-implementation of the above embodiments, the at least one measurement configuration indication portion of the second measurement configuration subset inputs relevant parameters.

[0617] As a sub-implementation of the above embodiments, at least one measurement configuration indication portion in the second measurement configuration subset outputs relevant parameters.

[0618] As a sub-implementation of the above embodiments, the at least one measurement configuration in the second measurement configuration subset indicates part of the model parameters.

[0619] As an example, the reasoning for the second reported information is performed based on at least one measurement configuration in the second measurement configuration subset.

[0620] As an example, the first node performs the inference for the second reported information based on the parameters of the inference for the second reported information indicated by at least one measurement configuration in the second measurement configuration subset.

[0621] Example 11

[0622] Example 11 illustrates a schematic diagram of a fifth message according to one embodiment of this application; as shown in Figure 11. In Example 11, the fifth message indicates the parameters for the inference of the second reported information.

[0623] As an example, the fifth message is carried by higher-level signaling.

[0624] As an example, the fifth message is carried by RRC signaling.

[0625] As an example, the fifth message is carried by one or more RRC IEs.

[0626] As one example, the fifth message includes some or all of the information in each of one or more RRC IEs.

[0627] As one example, the fifth message includes all or part of the information in the CSI-ReportConfig IE.

[0628] As an example, the fifth message is CSI-ReportConfig IE.

[0629] As an example, the fifth message is carried by an RRC message.

[0630] As an example, the fifth message is carried by the MAC CE.

[0631] As one embodiment, the first message includes an RRC reconfiguration message, and the fifth message includes some or all of the information in one or more RRC IEs.

[0632] As one embodiment, the first message includes an RRC reconfiguration message, and the fifth message includes all or part of the information in the CSI-ReportConfig IE.

[0633] As an example, the reception of the fifth message is later than the sending of the fourth message.

[0634] As an example, the at least one measurement configuration in the second measurement configuration subset indicates a portion of the parameters for the inference of the second reported information, and the fifth message indicates another portion of the parameters for the inference of the second reported information.

[0635] As an example, the at least one measurement configuration in the second measurement configuration subset indicates inputting relevant parameters for a portion of the inference of the second reported information, and the fifth message indicates inputting relevant parameters for another portion of the inference of the second reported information.

[0636] As a sub-implementation of the above embodiment, the input-related parameters include at least one of the following: the number of RS resources on which the channel measurement on which the second reporting information depends, the beam direction, and the beamwidth.

[0637] As a sub-implementation of the above embodiments, the other part of the input-related parameters includes one or more of the following: frequency domain resources, time domain resources, number of ports, CDM type, density, quasi-co-location relationship, TCI status, time domain behavior, and power control parameters for obtaining the RS resources on which the second reporting information depends.

[0638] As an example, the at least one measurement configuration in the second measurement configuration subset indicates the output of relevant parameters for a portion of the inference of the second reported information, and the fifth message indicates the output of relevant parameters for another portion of the inference of the second reported information.

[0639] As a sub-implementation of the above embodiment, the parameters related to the output include at least one of the following: the number of RS resources in the RS resource set involved in the second reported information, the beam direction, and the beamwidth.

[0640] As a sub-implementation of the above embodiment, the parameters related to the output include at least one of the reporting content of the second reporting information and the number of reporting quantities.

[0641] As a sub-implementation of the above embodiment, the parameters related to the output include at least one of the measurement time-related information and the prediction time-related information of the second reported information.

[0642] As a sub-implementation of the above embodiment, the other part of the output-related parameters includes at least one of the time-domain behavior of the second reported information and the physical layer channel carrying the second reported information.

[0643] As a sub-implementation of the above embodiment, the other part of the output related parameters includes at least one of the following: the period of the second reported information, the time slot offset, and the frequency domain resources.

[0644] As one embodiment, the at least one measurement configuration in the second measurement configuration subset indicates at least one of the number of RS resources included in the third RS resource set and the number of RS resources included in the fourth RS resource set; the first node obtains channel measurements for calculating the second reporting information based on the third RS resource set, the second reporting information relating to the fourth RS resource set.

[0645] As an example, the at least one measurement configuration in the second measurement configuration subset indicates the reporting content of the second reporting information.

[0646] As an example, the at least one measurement configuration in the second measurement configuration subset indicates the number of reports of the second reporting information.

[0647] As an example, the at least one measurement configuration in the second measurement configuration subset indicates at least one of the measurement time-related information and the prediction time-related information of the second reported information.

[0648] As an example, the fifth information indicates at least one of the configuration information of RS resources in the third RS resource set and the configuration information of RS resources in the fourth RS resource set; the first node obtains channel measurements for calculating the second reporting information based on the third RS resource set, the second reporting information referring to the fourth RS resource set.

[0649] As an example, the fifth information indicates one or more of the following: frequency domain resources, time domain resources, number of ports, CDM type, density, quasi-co-location relationship, TCI status, time domain behavior, power control parameters, and BWP index of the RS resources in the third RS resource set.

[0650] As an example, the fifth information indicates one or more of the following: frequency domain resources, time domain resources, number of ports, CDM type, density, quasi-co-location relationship, TCI status, time domain behavior, power control parameters, and BWP index of the RS resources in the fourth RS resource set.

[0651] As an example, the fifth information indicates at least one of the temporal behavior of the second reported information and the physical layer channel carrying the second reported information.

[0652] As an example, the at least one measurement configuration in the second measurement configuration subset indicates the number of RS resources included in the third RS resource set, and the fifth message indicates one or more of the following: frequency domain resources, time domain resources, number of ports, CDM type, density, quasi-co-location relationship, TCI status, time domain behavior, power control parameters, and BWP index of the RS resources in the third RS resource set.

[0653] As an example, the at least one measurement configuration in the second measurement configuration subset indicates the number of RS resources included in the fourth RS resource set, and the fifth message indicates one or more of the following: frequency domain resources, time domain resources, number of ports, CDM type, density, quasi-co-location relationship, TCI status, time domain behavior, power control parameters, and BWP index of the RS resources in the fourth RS resource set.

[0654] As an example, the at least one measurement configuration in the second measurement configuration subset indicates at least one of the following: the content of the second reporting information, the quantity of the reporting amount, measurement time-related information, and prediction time-related information; the fifth message indicates at least one of the time-domain behavior of the second reporting information and the physical layer channel carrying the second reporting information.

[0655] Example 12

[0656] Example 12 illustrates a schematic diagram of a fifth message according to an embodiment of the present application, where the indication depends on at least one measurement configuration in a second subset of measurement configurations; as shown in Figure 12.

[0657] As an example, the fifth message indicates the parameters of each inference in the second inference set.

[0658] As a sub-implementation of the above embodiments, the parameters of each inference in the second inference set indicated by the fifth message depend on at least one measurement configuration in the second measurement configuration subset.

[0659] As a sub-implementation of the above embodiments, the second inference set depends on at least one measurement configuration in the second measurement configuration subset.

[0660] As an example, the reasoning for the second reported information is one of the reasons in the second set of reasoning.

[0661] As an example, the number of inferences included in the second inference set depends on the at least one measurement configuration in the second measurement configuration subset.

[0662] As one embodiment, the second inference set includes which inferences depend on at least one measurement configuration in the second measurement configuration subset.

[0663] As an example, any reasoning in the second reasoning set is the reasoning targeted by one of the at least one measurement configurations in the second measurement configuration subset.

[0664] As an example, for any inference in the second inference set, at least one measurement configuration in the second measurement configuration subset is used to determine the parameters of any inference.

[0665] As an example, the parameters of at least a portion of the inference in the second inference set indicated by the fifth message depend on the at least one measurement configuration in the second measurement configuration subset.

[0666] As an example, the sender of the fifth message determines the indication of the fifth message based on at least one measurement configuration in the second measurement configuration subset.

[0667] Generally, how the indication of the fifth message depends on the at least one measurement configuration in the second measurement configuration subset is determined by the hardware device vendor. Some non-limiting implementations are described below:

[0668] As an example, the sender of the fifth message determines the second inference set based on at least one measurement configuration in the second measurement configuration subset.

[0669] In a preferred embodiment, any reasoning in the second reasoning set is the reasoning targeted by one of the at least one measurement configurations in the second measurement configuration subset.

[0670] As an example, the sender of the fifth message randomly selects the second inference set from the candidate inference subset, the candidate inference subset consisting of the inference targeted by each of the at least one measurement configuration in the second measurement configuration subset.

[0671] As an example, the sender of the fifth message selects the second inference set from the candidate inference subset in descending order of priority, the candidate inference subset consisting of the inference targeted by each of the at least one measurement configuration in the second measurement configuration subset.

[0672] As a sub-implementation of the above embodiment, the fourth message indicates the priority of each measurement configuration in the at least one measurement configuration in the second measurement configuration subset.

[0673] As a sub-example of the above embodiment, the first message indicates the priority of each measurement configuration in the at least one measurement configuration in the second measurement configuration subset.

[0674] As a sub-implementation of the above embodiments, the priority depends on the order in which the model training is completed.

[0675] As a sub-implementation of the above embodiments, the priority depends on the amount of resources required.

[0676] As a sub-example of the above embodiments, the priority depends on the content of the output.

[0677] As a sub-example of the above embodiments, the priority depends on the output load size.

[0678] As a sub-example of the above embodiments, the priority depends on the number of RS resources used to obtain channel measurements.

[0679] As an example, the sender of the fifth message determines the second inference set based on the number of measurement configurations included in the at least one measurement configuration in the second measurement configuration subset.

[0680] As an example, the sender of the fifth message determines the second inference set based on the inference targeted by at least one measurement configuration in the second measurement configuration subset.

[0681] As an example, the sender of the fifth message determines the second inference set based on the resources required for inference targeted by at least one measurement configuration in the second measurement configuration subset.

[0682] As an example, the sender of the fifth message determines the second inference set such that the total number of resources required by the second inference set and the first inference set does not exceed an upper limit.

[0683] As an example, the sender of the fifth message determines the parameters of the inference for the second reported information indicated by the fifth message based on at least one measurement configuration in the second measurement configuration subset.

[0684] As a sub-implementation of the above embodiments, the amount of resources required for the inference of the second reported information depends on the parameters of the inference of the second reported information; the sender of the fifth message determines the parameters of the inference of the second reported information such that the total number of resources required by the second inference set and the first inference set does not exceed an upper limit.

[0685] As an example, the sender of the fifth message determines the parameters of each inference in the second inference set indicated by the fifth message such that the total number of resources required by the second inference set and the first inference set does not exceed an upper limit.

[0686] As a sub-implementation of the above embodiments, the amount of resources required for each inference in the second inference set depends on its parameters.

[0687] Example 13

[0688] Example 13 illustrates a schematic diagram of any of a plurality of measurement configurations according to an embodiment of the present application, including an associated identifier; as shown in Figure 13.

[0689] As an example, the associated ID is a non-negative integer.

[0690] As an example, the associated ID is a string.

[0691] As an example, any two of the plurality of measurement configurations include different association identifiers.

[0692] As an example, two of the plurality of measurement configurations may have different association identifiers.

[0693] As an example, two of the plurality of measurement configurations may include the same association identifier.

[0694] As an example, the associated ID indicates an association between two or more RS resources, or an association between two or more RS sets.

[0695] As a sub-implementation of the above embodiments, the association includes having the same or similar characteristics.

[0696] As a sub-implementation of the above embodiments, the association includes quasi-co-located.

[0697] As a sub-implementation of the above embodiments, the association includes quasi-co-addressing and the corresponding quasi-co-addressing type includes TypeD.

[0698] As a sub-example of the above embodiments, the association includes training datasets used to generate the same model.

[0699] As a sub-example of the above embodiments, the association includes inference datasets used to generate the same model.

[0700] As a sub-example of the above embodiments, the association includes training datasets and inference datasets used to generate the same model.

[0701] As an example, the features include one or more of delay spread, Doppler spread, Doppler shift, average delay, or spatial reception parameters.

[0702] As one embodiment, the feature includes a downlink transmit beam or a set of downlink transmit beams.

[0703] As an example, if two RS resources are associated with the same association identifier, the two RS resources are used to generate the same training dataset or inference dataset for the same model.

[0704] As an example, if two RS resources are associated with the same association identifier, the two RS resources are used to generate the training dataset and inference dataset of the same model, respectively.

[0705] As an example, if two RS resources are associated with the same association identifier, the two RS resources are quasi-co-located.

[0706] As an example, if two RS resources are associated with the same association identifier, the two RS resources have the same or similar characteristics.

[0707] As an example, if two RS resource sets are associated with the same association identifier, the two RS resource sets are used to generate the same training dataset or inference dataset for the same model.

[0708] As an example, if two RS resource sets are associated with the same association identifier, the two RS resource sets are used to generate the training dataset and inference dataset of the same model, respectively.

[0709] As an example, if two RS resource sets are associated with the same association identifier, one of the two RS resource sets is used to generate a training dataset or inference dataset for a model, and the inference output of the model involves the other RS ​​resource set.

[0710] As an example, if two RS resource sets are associated with the same association identifier, any RS resource in one of the two RS resource sets and any RS resource in the other of the two RS resource sets are quasi-co-located.

[0711] As an example, if two RS resource sets are associated with the same association identifier, any RS resource in one of the two RS resource sets and any RS resource in the other of the two RS resource sets have the same or similar characteristics.

[0712] As an example, the association identifier indicates the association between a dataset and a model.

[0713] As a sub-example of the above embodiments, the association includes that the dataset belongs to the training dataset of the model.

[0714] As a sub-example of the above embodiments, the association includes the fact that the dataset belongs to the inference dataset of the model.

[0715] As an example, the association identifier indicates the association between an RS resource or a collection of RS resources and a model.

[0716] As a sub-example of the above embodiments, the association includes the use of the RS resource or RS resource set to generate the training dataset of the model.

[0717] As a sub-example of the above embodiments, the association includes the use of the RS resource or RS resource set to generate the inference dataset of the model.

[0718] As a sub-example of the above embodiments, the association includes the fact that the output of the inference of the model relates to the RS resource or RS resource set.

[0719] As an example, if an RS resource or RS resource set is associated with a model to the same association identifier, the RS resource or RS resource set is used to generate a training dataset or inference dataset for the model.

[0720] As an example, if an RS resource set and a model are associated with the same association identifier, the inference output of the model involves the RS resource set.

[0721] As an example, the association identifier indicates the association between a model and a function.

[0722] As a sub-implementation of the above embodiments, the association includes the fact that the one function depends on the one model.

[0723] As a sub-implementation of the above embodiments, the association includes the fact that the model is used for the function.

[0724] As a sub-implementation of the above embodiments, the association includes the inference of the model being used for the function.

[0725] As a sub-implementation of the above embodiments, the association includes the use of a model to generate the CSI corresponding to the function.

[0726] As an example, if a model and a function are associated with the same association identifier, the model is used for the function.

[0727] As an example, if a model and a function are associated with the same association identifier, the model is used to generate the CSI corresponding to the function.

[0728] As an example, the association identifier indicates the association between an RS resource or a collection of RS resources and a dataset.

[0729] As a sub-example of the above embodiments, the association includes the use of the RS resource or RS resource set to generate the dataset.

[0730] As a sub-example of the above embodiments, the association includes that measurements on the one RS resource or set of RS resources are used to generate the one dataset.

[0731] As a sub-example of the above embodiments, the association includes the use of channel measurements on the one RS resource or set of RS resources to generate the dataset.

[0732] As an example, if an RS resource or RS resource set and a dataset are associated with the same association identifier, measurements on the RS resource or RS resource set are used to generate the dataset.

[0733] As an example, the association identifier indicates the association between an RS resource or a set of RS resources and a reporting message.

[0734] As a sub-implementation of the above embodiments, the association includes the use of the RS resource or RS resource set to obtain the measurement that generates the reporting information.

[0735] As a sub-implementation of the above embodiments, the association includes the use of the RS resource or RS resource set to obtain channel measurements that generate the reporting information.

[0736] As a sub-implementation of the above embodiments, the association includes the fact that the reported information relates to the RS resource or RS resource set.

[0737] As an example, if an RS resource or RS resource set is associated with the same association identifier as a reporting message, a measurement on the RS resource or RS resource set is used to generate the reporting message.

[0738] As an example, if an RS resource set and a reporting message are associated with the same association identifier, the reporting message relates to the RS resource set.

[0739] As an example, the association identifier indicates the association between a model and a reported message.

[0740] As a sub-example of the above embodiments, the association includes the fact that the reported information depends on the output of the model.

[0741] As a sub-implementation of the above embodiments, the association includes the output of the model being used to generate the reported information.

[0742] As a sub-implementation of the above embodiments, the association includes that the reported information includes all or part of the output of the model, or includes all or part of the post-processed output of the model.

[0743] As an example, if a model and a reported message are associated with the same association identifier, the reported message depends on the output of the model.

[0744] As an example, if a model and a reported message are associated with the same association identifier, the output of the model is used to generate the reported message.

[0745] As an example, the inference model targeted by any of the plurality of measurement configurations is associated with the association identifier included in any of the measurement configurations.

[0746] As an example, the inference model for the first reported information is associated with a first identifier, which is the associated identifier included in a measurement configuration within the first measurement configuration subset.

[0747] As an example, the inference model for the second reported information is associated with a second identifier, which is the associated identifier included in one of the at least one measurement configuration in the second measurement configuration subset.

[0748] As an example, a model being associated with an association identifier includes the model being identified by the association identifier.

[0749] As an example, a model being associated with an association identifier includes the inference of the model being identified by the association identifier.

[0750] As an example, associating a model with an association identifier includes the inference dataset of the model being identified by the association identifier.

[0751] As an example, a model being associated with an association identifier includes an AI function or AI entity that performs inference on the model being identified by the association identifier.

[0752] As an example, associating a model with an association identifier includes the training of the model being identified by the association identifier.

[0753] As an example, associating a model with an association identifier includes the training dataset of the model being identified by the association identifier.

[0754] As an example, a model being associated with an association identifier includes an AI function or AI entity that performs the training of the model being identified by the association identifier.

[0755] As an example, a model is associated with an association identifier, and the performance monitoring of the model is identified by the association identifier.

[0756] As an example, a model is associated with an association identifier, which includes the performance monitoring dataset of the model being identified by the association identifier.

[0757] As an example, a model being associated with an association identifier includes the function that the model targets being identified by the association identifier.

[0758] As an example, associating a model with an association identifier includes the use of RS resources or sets of RS resources associated with the association identifier to obtain channel measurements of the inference dataset or training dataset that generates the model.

[0759] As an example, a model associated with an association identifier includes the output of the model relating to an RS resource or a set of RS resources associated with the association identifier.

[0760] Example 14

[0761] Example 14 illustrates a schematic diagram of an artificial intelligence or machine learning-based processing system according to an embodiment of this application, as shown in Figure 14. In Example 14, the first processor sends a first dataset to the second processor and a second dataset to the third processor; the second processor generates a target first-class parameter set based on the first dataset, and sends the generated target first-class parameter set to the third processor; the third processor processes the second dataset using the target first-class parameter set to obtain a first-class output, and sends the first-class output to the fourth processor. In Figure 14, the first-class feedback and the second-class feedback are optional; the second processor includes ML training functionality; the third processor includes ML inference functionality.

[0762] As one embodiment, the fourth processor includes ML testing functionality.

[0763] As one example, the fourth processor includes performance monitoring / evaluation of the ML model.

[0764] As one embodiment, the fourth processor includes the inverse operation of the third processor.

[0765] As an example, the third processor sends a first type of feedback to the second processor. The first type of feedback is used to trigger the recalculation or update of the target first type of parameter set, that is, to trigger ML initial training or ML retraining.

[0766] As one embodiment, the fourth processor sends a second type of feedback to the first 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.

[0767] As one embodiment, the first processor generates the first dataset and the second dataset based on the measurement of the reference signal.

[0768] As one embodiment, the third processor is located at the first node.

[0769] As one embodiment, the fourth processor is located at either the first node or the second node.

[0770] As an example, the second dataset includes measurements for RS.

[0771] As an example, the second dataset includes the reception of PDSCH.

[0772] As an example, the first dataset includes training data.

[0773] As an example, the first dataset is a training dataset.

[0774] As one embodiment, the second processor is used to train an ML model, and the trained model is described by the target first class of parameter sets.

[0775] As one embodiment, the second processor is located at the first node.

[0776] The above embodiments avoid passing the first dataset to the second node.

[0777] As one embodiment, the second processor is located in the core network.

[0778] The above embodiments support network-wide joint training, further optimizing system performance.

[0779] As an example, the second dataset includes inference data.

[0780] As an example, the second dataset is an inference dataset.

[0781] As an example, the input to an inference belongs to an inference dataset.

[0782] As an example, the third 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.

[0783] As an example, the third processor compares the real data with the first type of output, and the resulting error is used to generate the first type of feedback.

[0784] As an example, the third processor generates the first type of feedback through performance monitoring.

[0785] 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 second processing opportunity will recalculate the target first type of parameter set.

[0786] As an example, the fourth processor compares the real data with the first type of output, and the resulting error is used to generate the second type of feedback.

[0787] As an example, the fourth processor generates the second type of feedback through performance monitoring.

[0788] As an example, the second 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 first processor sends the first dataset to trigger or assist the second processor in recalculating the target first type of parameter set.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] As one example, the ML includes AI.

[0793] As an example, the ML includes ML and AI.

[0794] Example 15

[0795] Example 15 illustrates a schematic diagram based on artificial intelligence or machine learning according to an embodiment of this application, as shown in Figure 15. Figure 15 includes a first operation, a second operation, a third operation, a fourth operation, and a fifth operation. In Example 15, the first and second operations belong to a first stage, the third operation belongs to a second stage, the fourth operation belongs to a third stage, and the fifth operation belongs to a fourth stage. In Figure 15, lines with arrows indicate the sequence of processes.

[0796] As an example, the first operation includes ML training, the second operation includes ML testing, the third operation includes ML emulation, the fourth operation includes ML entity loading, and the fifth operation includes AI inference.

[0797] 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.

[0798] As an example, the first stage includes ML model training.

[0799] As an example, the first stage includes ML model training and ML testing.

[0800] As an example, the ML model training includes initial training and re-training of one or a group of ML models.

[0801] As an example, the training of the ML model depends on training data.

[0802] As an example, the ML model training includes ML entity validation.

[0803] As an example, the ML entity verification is used to evaluate the performance of the ML entity.

[0804] As an example, the ML entity verification depends on verification data.

[0805] As an example, if the results of ML entity verification do not meet expectations, the ML model will be retrained.

[0806] As an example, the ML testing includes testing the validated ML entities to estimate the performance of the trained ML model.

[0807] As an example, if the ML test results meet expectations, the ML entity proceeds to the next stage; otherwise, the ML model will be retrained.

[0808] As an example, the ML test relies on test data.

[0809] As one embodiment, the second stage includes ML simulation, which performs inference of ML entities in a simulation environment.

[0810] As an example, the ML simulation estimates the performance of ML entity reasoning in a simulation environment before using ML entities.

[0811] As one embodiment, the second stage is optional.

[0812] As an example, the third stage includes ML entity loading, which is to obtain trained ML entities to obtain the desired AI inference capabilities.

[0813] As an example, the third stage is optional.

[0814] As an example, the third stage is no longer needed when the training and inference functions are co-located.

[0815] As an example, the fourth stage includes AI inference.

[0816] As an example, the AI ​​inference relies on inference data.

[0817] As an example, the input to an AI inference belongs to the inference dataset of the AI ​​inference model.

[0818] As one example, the ML includes AI.

[0819] As one example, the AI ​​includes ML.

[0820] Example 16

[0821] Example 16 illustrates a schematic diagram of AI function deployment according to one embodiment of this application; as shown in Figure 16.

[0822] In Example 16, the AI ​​training function of the RAN (RadioAccess Network) domain is located in the 3GPP RAN domain-specific management function, while the AI ​​inference function is located in the UE.

[0823] In Example 16, RAN domain-specific management functions provide AI training function management capabilities and AI inference function management capabilities.

[0824] Example 17

[0825] Example 17 illustrates a schematic diagram of AI function deployment according to one embodiment of this application; as shown in Figure 17.

[0826] In Example 17, the AI ​​training function is a management function specific to the RAN domain, while the AI ​​inference function is located locally on the UE.

[0827] In Example 17, the management capability of the AI ​​training function is provided by the RAN domain-specific management function, while the management capability of the AI ​​inference function is provided locally by the UE.

[0828] In Figure 17, MnF refers to Management Function.

[0829] Example 18

[0830] Example 18 illustrates a schematic diagram of AI function deployment according to one embodiment of this application; as shown in Figure 18.

[0831] In Example 18, both the AI ​​training function and the AI ​​inference function are located in the UE, wherein the UE provides the ability to train and infer.

[0832] In Example 18, RAN domain-specific management functions provide management capabilities for both AI training and AI inference functions.

[0833] Example 19

[0834] Example 19 illustrates a schematic diagram of AI function deployment according to one embodiment of this application; as shown in Figure 19.

[0835] In Example 19, both the AI ​​training function and the AI ​​inference function are located in the UE.

[0836] In Example 19, the management capabilities of both the AI ​​training function and the AI ​​inference function are provided locally by the UE.

[0837] In Figure 19, MnF refers to Management Function.

[0838] Example 20

[0839] Example 20 illustrates a structural block diagram of a processing apparatus for a first node according to an embodiment of this application, as shown in Figure 20. In Figure 20, the processing apparatus 2000 in the first node includes a first receiver 2001 and a first transmitter 2002.

[0840] In embodiment 20, the first receiver 2001 receives the first message, and the first transmitter 2002 sends the second message.

[0841] In embodiment 20, the first message indicates a plurality of measurement configurations, and the second message indicates a first subset of measurement configurations and a second subset of measurement configurations among the plurality of measurement configurations, wherein the measurement configurations in the first subset of measurement configurations are available, and the availability of the measurement configurations in the second subset of measurement configurations depends on further indications.

[0842] As an example, any of the plurality of measurement configurations includes inference-related parameters.

[0843] As a sub-example of the above embodiments, the inference-related parameters include RS resource-related configuration information.

[0844] As a reference embodiment of the above sub-example, the configuration information related to the RS resources includes the number of RS resources.

[0845] As a reference embodiment of the above sub-example, the configuration information related to the RS resource includes at least one of the beam direction and beamwidth of the RS resource.

[0846] As a reference embodiment of the above sub-example, the configuration information related to the RS resources includes their uses.

[0847] As a sub-implementation of the above embodiments, the inference-related parameters include reporting-related configuration information.

[0848] As a reference embodiment of the above sub-example, the reported configuration information includes the reported content.

[0849] As a reference embodiment of the above sub-example, the configuration information related to the reporting includes the number of reports.

[0850] As a sub-implementation of the above embodiments, the inference-related parameters include model parameters.

[0851] As a reference embodiment of the above sub-example, the model parameters include an associated ID.

[0852] As one embodiment, the first transmitter 2002 sends a first reporting information; wherein the first reporting information is based on inference, and the first measurement configuration subset is used to determine the parameters of the inference for the first reporting information.

[0853] As an example, the first receiver 2001 receives a third message; wherein the third message indicates the parameters of the inference for the first reported information, and the indication of the third message depends on the first measurement configuration subset.

[0854] As a sub-implementation of the above embodiments, the first measurement configuration subset indicates a portion of the parameters for the inference of the first reported information, and the third message indicates another portion of the parameters for the inference of the first reported information.

[0855] As an example, the first transmitter 2002 sends a fourth message, wherein the fourth message indicates that at least one measurement configuration in the second measurement configuration subset is available.

[0856] As one embodiment, the first transmitter 2002 transmits second reporting information; wherein the second reporting information is based on inference, and at least one measurement configuration in the second measurement configuration subset is used to determine parameters for the inference of the second reporting information.

[0857] As an example, the first receiver 2001 receives a fifth message; wherein the fifth message indicates the parameters of the inference for the second reported information, and the indication of the fifth message depends on at least one measurement configuration in the second measurement configuration subset.

[0858] As a sub-implementation of the above embodiments, the at least one measurement configuration in the second measurement configuration subset indicates a portion of the parameters for the inference of the second reported information, and the fifth message indicates another portion of the parameters for the inference of the second reported information.

[0859] As an example, any of the plurality of measurement configurations includes an associated identifier.

[0860] As one embodiment, the first node includes a terminal.

[0861] As one embodiment, the first node includes a user equipment.

[0862] As one embodiment, the first node includes a relay node device.

[0863] As an example, the first receiver 2001 includes at least one of the following in embodiment 4: {antenna 452, receiver 454, receiver processor 456, multi-antenna receiver processor 458, controller / processor 459, memory 460, data source 467}.

[0864] As one embodiment, the first transmitter 2002 includes at least one of the following in embodiment 4: {antenna 452, transmitter 454, transmission processor 468, multi-antenna transmission processor 457, controller / processor 459, memory 460, data source 467}.

[0865] Example 21

[0866] Example 21 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 21. In Figure 21, the processing apparatus 2100 in the second node includes a second transmitter 2101 and a second receiver 2102.

[0867] In embodiment 21, the second transmitter 2101 receives and transmits the first message; the second receiver 2102 receives the second message.

[0868] In embodiment 21, the first message indicates multiple measurement configurations, and the second message indicates a first subset of measurement configurations and a second subset of measurement configurations among the multiple measurement configurations, wherein the measurement configurations in the first subset of measurement configurations are available, and the availability of the measurement configurations in the second subset of measurement configurations depends on further indications.

[0869] As an example, any of the plurality of measurement configurations includes inference-related parameters.

[0870] As a sub-example of the above embodiments, the inference-related parameters include RS resource-related configuration information.

[0871] As a reference embodiment of the above sub-example, the configuration information related to the RS resources includes the number of RS resources.

[0872] As a reference embodiment of the above sub-example, the configuration information related to the RS resource includes at least one of the beam direction and beamwidth of the RS resource.

[0873] As a reference embodiment of the above sub-example, the configuration information related to the RS resources includes their uses.

[0874] As a sub-implementation of the above embodiments, the inference-related parameters include reporting-related configuration information.

[0875] As a reference embodiment of the above sub-example, the reported configuration information includes the reported content.

[0876] As a reference embodiment of the above sub-example, the configuration information related to the reporting includes the number of reports.

[0877] As a sub-implementation of the above embodiments, the inference-related parameters include model parameters.

[0878] As a reference embodiment of the above sub-example, the model parameters include an associated ID.

[0879] As one embodiment, the second receiver 2102 receives first reported information; wherein the first reported information is based on inference, and the first measurement configuration subset is used to determine the parameters of the inference for the first reported information.

[0880] As an example, the second transmitter 2101 sends a third message; wherein the third message indicates the parameters of the inference for the first reported information, and the indication of the third message depends on the first measurement configuration subset.

[0881] As a sub-implementation of the above embodiments, the first measurement configuration subset indicates a portion of the parameters for the inference of the first reported information, and the third message indicates another portion of the parameters for the inference of the first reported information.

[0882] As one embodiment, the second receiver 2102 receives a fourth message; wherein the fourth message indicates that at least one measurement configuration in the second measurement configuration subset is available.

[0883] As one embodiment, the second receiver 2102 receives second reported information; wherein the second reported information is based on inference, and at least one measurement configuration in the second measurement configuration subset is used to determine parameters of the inference for the second reported information.

[0884] As an example, the second transmitter 2101 sends a fifth message; wherein the fifth message indicates the parameters of the inference for the second reported information, and the indication of the fifth message depends on at least one measurement configuration in the second measurement configuration subset.

[0885] As a sub-implementation of the above embodiments, the at least one measurement configuration in the second measurement configuration subset indicates a portion of the parameters for the inference of the second reported information, and the fifth message indicates another portion of the parameters for the inference of the second reported information.

[0886] As an example, any of the plurality of measurement configurations includes an associated identifier.

[0887] As one embodiment, the second node includes a base station.

[0888] As one embodiment, the second node includes a base station device.

[0889] As one embodiment, the second node includes a relay node device.

[0890] As one embodiment, the second node includes the sustaining base station of the serving cell of the first node.

[0891] As one embodiment, the second node includes an OTT (Over-The-Top) server.

[0892] As an example, the second node provides OAM (Operation Administration and Maintenance).

[0893] As one embodiment, the second node includes a NAS (Network Access Server).

[0894] As one embodiment, the second node includes a NAS device.

[0895] As one example, the second node provides network access services.

[0896] As one embodiment, the second node includes core network equipment.

[0897] As one embodiment, the second node includes base station equipment and core network equipment.

[0898] As one embodiment, the second node includes a base station device and a NAS device.

[0899] As one embodiment, the second node includes an MDA function producer.

[0900] As one embodiment, the second node includes an NWDAF producer.

[0901] As one example, the second node includes an MDAS producer.

[0902] As one embodiment, the second node includes an MnS producer.

[0903] As one embodiment, the second transmitter 2101 includes at least one of the following in embodiment 4: {antenna 420, transmitter 418, transmission processor 416, multi-antenna transmission processor 471, controller / processor 475, memory 476}.

[0904] As one embodiment, the second receiver 2102 includes at least one of the following in embodiment 4: {antenna 420, receiver 418, receiver processor 470, multi-antenna receiver processor 472, controller / processor 475, memory 476}.

[0905] Example 22

[0906] Example 22 illustrates a schematic diagram of the timing relationship of the first message, the second message, the third message, the fourth message, and the fifth message according to an embodiment of this application; as shown in Figure 22.

[0907] In embodiment 22, the receipt of the first message is earlier than the sending of the second message, the sending of the second message is earlier than the receipt of the third message, the receipt of the third message is earlier than the sending of the fourth message, and the sending of the fourth message is earlier than the receipt of the fifth message.

[0908] In embodiment 22(a), the transmission of the first reporting information is later than the reception of the third message but earlier than the transmission of the fourth message.

[0909] In embodiment 22(b), the transmission of the first reporting information is later than the transmission of the fourth message but earlier than the reception of the fifth message.

[0910] In embodiment 22(c), the transmission of the first reporting information is later than the reception of the fifth message.

[0911] As a sub-implementation of the above embodiments, the transmission of the first reporting information is earlier than the transmission of the second reporting information.

[0912] As a sub-implementation of the above embodiments, the transmission of the first reporting information is later than the transmission of the second reporting information.

[0913] As a sub-implementation of the above embodiments, the first reporting information and the second reporting information are transmitted on the same physical layer channel.

[0914] As a sub-implementation of the above embodiments, the first reporting information and the second reporting information are transmitted on different physical layer channels.

[0915] Example 23

[0916] Example 23 illustrates a schematic diagram of the timing relationship of a first message, a second message, a third message, and a fourth message according to an embodiment of this application; as shown in Figure 23.

[0917] In embodiment 23, the receipt of the first message is earlier than the sending of the second message, the sending of the second message is earlier than the sending of the fourth message, and the sending of the fourth message is earlier than the receipt of the third message.

[0918] As one example, the transmission of the first reporting information is earlier than the transmission of the second reporting information.

[0919] As one example, the transmission of the first reporting information is later than the transmission of the second reporting information.

[0920] As an example, the first reporting information and the second reporting information are transmitted on the same physical layer channel.

[0921] As one embodiment, the first reporting information and the second reporting information are transmitted on different physical layer channels.

[0922] As an example, the third message indicates the parameters of the inference for the second reported information, and the indication of the third message depends on at least one measurement configuration in the second measurement configuration subset.

[0923] 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.

[0924] Those skilled in the art will understand that the present invention can be practiced in other specified forms without departing from its core or essential characteristics. Therefore, the embodiments disclosed herein should in any way be considered descriptive rather than restrictive. The scope of the invention is defined by the appended claims rather than the foregoing description, and all modifications within their equivalent meaning and scope are considered to be included therein.

Claims

1. A first node used for wireless communication, characterized in that, include: A first receiver receives a first message, which indicates multiple measurement configurations. The first transmitter sends the second message; The second message indicates a first subset of measurement configurations and a second subset of measurement configurations among the plurality of measurement configurations, wherein the measurement configurations in the first subset of measurement configurations are available, and the availability of the measurement configurations in the second subset of measurement configurations depends on further indication.

2. The first node according to claim 1, characterized in that, The first transmitter sends a first reporting information; wherein the first reporting information is based on inference, and the first measurement configuration subset is used to determine the parameters of the inference for the first reporting information.

3. The first node according to claim 2, characterized in that, The first receiver receives a third message; wherein the third message indicates the parameters of the inference for the first reported information, and the indication of the third message depends on the first measurement configuration subset.

4. The first node according to any one of claims 1 to 3, characterized in that, The first transmitter sends a fourth message, wherein the fourth message indicates that at least one measurement configuration in the second measurement configuration subset is available.

5. The first node according to claim 4, characterized in that, The first transmitter sends a second reporting information; wherein the second reporting information is based on inference, and at least one measurement configuration in the second measurement configuration subset is used to determine parameters for the inference of the second reporting information.

6. The first node according to claim 5, characterized in that, The first receiver receives a fifth message; wherein the fifth message indicates the parameters of the inference for the second reported information, and the indication of the fifth message depends on at least one measurement configuration in the second measurement configuration subset.

7. The first node according to any one of claims 1 to 6, characterized in that, Each of the plurality of measurement configurations includes an associated identifier.

8. A second node used for wireless communication, characterized in that, include: The second transmitter sends a first message, which indicates multiple measurement configurations; The second receiver receives the second message; The second message indicates a first subset of measurement configurations and a second subset of measurement configurations among a plurality of measurement configurations, wherein the measurement configurations in the first subset of measurement configurations are available, and the availability of the measurement configurations in the second subset of measurement configurations depends on further indications.

9. The second node according to claim 8, characterized in that, The second receiver receives first reported information; wherein the first reported information is based on inference, and the first measurement configuration subset is used to determine the parameters of the inference for the first reported information.

10. The second node according to claim 9, characterized in that, The second transmitter sends a third message; wherein the third message indicates the parameters of the inference for the first reported information, and the indication of the third message depends on the first measurement configuration subset.

11. The second node according to any one of claims 8 to 10, characterized in that, The second receiver receives a fourth message; wherein the fourth message indicates that at least one measurement configuration in the second measurement configuration subset is available.

12. The second node according to claim 11, characterized in that, The second receiver receives second reported information; wherein the second reported information is based on inference, and at least one measurement configuration in the second measurement configuration subset is used to determine parameters for the inference of the second reported information.

13. The second node according to claim 12, characterized in that, The second transmitter sends a fifth message; wherein the fifth message indicates the parameters of the inference for the second reported information, and the indication of the fifth message depends on at least one measurement configuration in the second measurement configuration subset.

14. The second node according to any one of claims 8 to 13, characterized in that, Each of the plurality of measurement configurations includes an associated identifier.

15. A method used in a first node of wireless communication, characterized in that, include: Receive a first message, which indicates multiple measurement configurations; Send a second message; The second message indicates a first subset of measurement configurations and a second subset of measurement configurations among the plurality of measurement configurations, wherein the measurement configurations in the first subset of measurement configurations are available, and the availability of the measurement configurations in the second subset of measurement configurations depends on further indication.

16. The method in the first node according to claim 15, characterized in that, include: Send the first report; Wherein, the first reported information is based on inference, and the first measurement configuration subset is used to determine the parameters of the inference for the first reported information.

17. The method in the first node according to claim 16, characterized in that, include: Receive third message; The third message indicates the parameters of the inference for the first reported information, and the indication of the third message depends on the first measurement configuration subset.

18. The method in the first node according to any one of claims 15 to 17, characterized in that, include: Send the fourth message; The fourth message indicates that at least one measurement configuration in the second measurement configuration subset is available.

19. The method in the first node according to claim 18, characterized in that, include: Send the second reporting information; Wherein, the second reported information is based on inference, and at least one measurement configuration in the second measurement configuration subset is used to determine the parameters of the inference for the second reported information.

20. The method in the first node according to claim 19, characterized in that, include: Receive the fifth message; The fifth message indicates the parameters of the inference for the second reported information, and the indication of the fifth message depends on at least one measurement configuration in the second measurement configuration subset.

21. The method in the first node according to any one of claims 15 to 20, characterized in that, Each of the plurality of measurement configurations includes an associated identifier.

22. A method used in a second node of wireless communication, characterized in that, include: Send a first message indicating multiple measurement configurations; Receive the second message; The second message indicates a first subset of measurement configurations and a second subset of measurement configurations among the plurality of measurement configurations, wherein the measurement configurations in the first subset of measurement configurations are available, and the availability of the measurement configurations in the second subset of measurement configurations depends on further indication.

23. The method in the second node according to claim 22, characterized in that, include: Receive the first reported information; Wherein, the first reported information is based on inference, and the first measurement configuration subset is used to determine the parameters of the inference for the first reported information.

24. The method in the second node according to claim 22, characterized in that, include: Send a third message; The third message indicates the parameters of the inference for the first reported information, and the indication of the third message depends on the first measurement configuration subset.

25. The method in the second node according to any one of claims 22 to 24, characterized in that, include: Received the fourth message; The fourth message indicates that at least one measurement configuration in the second measurement configuration subset is available.

26. The method in the second node according to claim 25, characterized in that, include: Receive the second reported information; Wherein, the second reported information is based on inference, and at least one measurement configuration in the second measurement configuration subset is used to determine the parameters of the inference for the second reported information.

27. The method in the second node according to claim 26, characterized in that, include: Send the fifth message; The fifth message indicates the parameters of the inference for the second reported information, and the indication of the fifth message depends on at least one measurement configuration in the second measurement configuration subset.

28. The method in the second node according to any one of claims 22 to 27, characterized in that, Each of the plurality of measurement configurations includes an associated identifier.

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