Method and apparatus for wireless communication

Abstract

The present application discloses a method and apparatus for wireless communications. A first node sends a first message, the first message indicating a plurality of measurement configurations, and the plurality of measurement configurations being all available or all unavailable. The method reduces the signaling overhead and delay, reduces the hardware complexity and costs required for AI / ML, and improves the overall system performance.

Description

Methods and apparatus used 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 in wireless communication systems involving AI (Artificial Intelligence) or ML (Machine Learning). 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 NR 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 at least one of network-side additional conditions and UE-side additional conditions, and is indicated to the network. Applicable Functionality generally 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 mechanisms for available functions cannot reflect the relationships between multiple functions or multiple inference-related configurations, thus failing to provide optimal configuration and management for AI / ML.

[0007] To address the aforementioned issues, 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 measurement, reporting, and reception algorithms and schemes. 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 measurement, reporting, and reception 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] Send a first message indicating multiple measurement configurations;

[0012] Among them, all of the multiple measurement configurations may be available or all may be unavailable.

[0013] As an example, the problem this application aims to solve includes how to reflect the relationship between multiple functions or multiple inference-related configurations; in the above method, all of the multiple measurement configurations may be available or all may be unavailable, thus solving this problem.

[0014] 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 target recipient of the first message may still need to reach some consensus on the AI / ML model used by the first node; in this application, all of the multiple measurement configurations are available or all are unavailable, which facilitates the first node and the target recipient of the first message to reach some consensus on the AI / ML model at the first node.

[0015] As an example, the advantages of the above method include reducing signaling overhead and latency by utilizing the correlation between multiple measurement configurations or the inference they target, and facilitating network-side optimization of the management and configuration of AI / ML functions.

[0016] As an example, the advantages of the above method include optimized resource utilization, thereby improving the overall system performance.

[0017] As an example, the benefits of the above method include reducing the hardware complexity and cost required for AI / ML.

[0018] According to one aspect of this application, it is characterized by comprising:

[0019] Receive a second message indicating K0 measurement configurations, where K0 is a positive integer greater than 1;

[0020] The plurality of measurement configurations are a subset of the K0 measurement configurations.

[0021] The advantages of the above methods include facilitating unified management and optimization of AI / ML functions on the network side, further improving system performance.

[0022] According to one aspect of this application, it is characterized by comprising:

[0023] Send the first report;

[0024] Wherein, the first report is based on inference, and the first measurement configuration among the plurality of measurement configurations is used to determine the parameters of the inference for the first report.

[0025] 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.

[0026] As an example, the advantages of the above method include improved system performance.

[0027] According to one aspect of this application, it is characterized by comprising:

[0028] Receive third message;

[0029] The third message indicates the parameters for the inference in the first report.

[0030] 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.

[0031] As an example, the advantages of the above method include facilitating global optimization on the network side and further improving system performance.

[0032] As an example, the advantages of the above method include more flexible signaling design and good backward compatibility.

[0033] According to one aspect of this application, the third message activates the plurality of measurement configurations.

[0034] The benefits of the above methods include reduced signaling overhead and latency.

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

[0036] Received the fourth message;

[0037] The fourth message activates or deactivates one of the plurality of measurement configurations; in response to the receipt of the fourth message, all of the plurality of measurement configurations are either activated or deactivated.

[0038] The advantages of the above method include that it fully utilizes the availability synchronization of the multiple measurement configurations to reduce signaling overhead and latency.

[0039] According to one aspect of this application, it is characterized by comprising:

[0040] Receive a fourth message, which activates or deactivates one of the plurality of measurement configurations;

[0041] Specifically, when the fourth message activates one of the plurality of measurement configurations, only that one measurement configuration is activated by the fourth message; when the fourth message deactivates the one of the plurality of measurement configurations, each of the plurality of measurement configurations is deactivated by the fourth message.

[0042] The advantages of the above method include that it fully considers the different needs of activation and deactivation of measurement configurations and optimizes the management of AL / ML functions.

[0043] The benefits of the above method include optimized AL / ML performance while reducing signaling overhead.

[0044] According to one aspect of this application, two of the plurality of measurement configurations are for different cells.

[0045] The benefits of the above methods include reducing the overhead and latency of AI / ML model training, management, performance monitoring, activation / deactivation, etc.

[0046] The benefits of the above methods include improved utilization of AI / ML models, thereby improving the overall performance of the system.

[0047] The benefits of the above methods include the ability to jointly optimize resource allocation and management for AI / ML functions among different cells, thereby improving resource utilization and overall system efficiency.

[0048] According to one aspect of this application, each of the plurality of measurement configurations includes an associated identifier.

[0049] 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.

[0050] 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.

[0051] As an example, the advantages of the above method include reduced hardware complexity and cost, and good forward compatibility.

[0052] According to one aspect of this application, two of the plurality of measurement configurations include different association identifiers.

[0053] The benefits of the above methods include improved utilization of AI / ML models.

[0054] The benefits of the above methods include reducing the demand for storage and / or computing resources for AI / ML, and reducing hardware complexity and cost.

[0055] The advantages of the above method include its better applicability to various terminals with different capabilities.

[0056] The benefits of the above methods include improving the universality of AI / ML models and optimizing the system gains brought by AI / ML.

[0057] This application discloses a method used in a second node for wireless communication, characterized by comprising:

[0058] Receive a first message, which indicates multiple measurement configurations;

[0059] Among them, all of the multiple measurement configurations may be available or all may be unavailable.

[0060] According to one aspect of this application, it is characterized by comprising:

[0061] Send a second message indicating K0 measurement configurations, where K0 is a positive integer greater than 1;

[0062] The plurality of measurement configurations are a subset of the K0 measurement configurations.

[0063] According to one aspect of this application, it is characterized by comprising:

[0064] Receive the first report;

[0065] Wherein, the first report is based on inference, and the first measurement configuration among the plurality of measurement configurations is used to determine the parameters of the inference for the first report.

[0066] According to one aspect of this application, it is characterized by comprising:

[0067] Send a third message;

[0068] The third message indicates the parameters for the inference in the first report.

[0069] According to one aspect of this application, the third message activates the plurality of measurement configurations.

[0070] According to one aspect of this application, it is characterized by comprising:

[0071] Send the fourth message;

[0072] The fourth message activates or deactivates one of the plurality of measurement configurations; in response to receiving the fourth message, the target recipient of the fourth message considers that the plurality of measurement configurations are either activated or deactivated.

[0073] According to one aspect of this application, it is characterized by comprising:

[0074] A fourth message is sent, which activates or deactivates one of the plurality of measurement configurations;

[0075] Specifically, when the fourth message activates one of the plurality of measurement configurations, only that one measurement configuration is activated by the fourth message; when the fourth message deactivates the one of the plurality of measurement configurations, each of the plurality of measurement configurations is deactivated by the fourth message.

[0076] According to one aspect of this application, two of the plurality of measurement configurations are for different cells.

[0077] According to one aspect of this application, each of the plurality of measurement configurations includes an associated identifier.

[0078] According to one aspect of this application, two of the plurality of measurement configurations include different association identifiers.

[0079] This application discloses a first node used for wireless communication, characterized in that it includes:

[0080] A first processor sends a first message indicating multiple measurement configurations;

[0081] Among them, all of the multiple measurement configurations may be available or all may be unavailable.

[0082] This application discloses a second node used for wireless communication, characterized by comprising:

[0083] The second processor receives a first message, which indicates multiple measurement configurations;

[0084] Among them, all of the multiple measurement configurations may be available or all may be unavailable.

[0085] As an example, compared with conventional solutions, this application has the following advantages:

[0086] Signaling overhead and latency have been reduced, and the management and configuration of AI / ML functions have been optimized.

[0087] Resource utilization was optimized, and overall system performance was improved.

[0088] This reduces the hardware complexity and cost required for AI / ML. Attached Figure Description

[0089] 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:

[0090] Figure 1 shows a flowchart of a first message according to an embodiment of this application;

[0091] Figure 2 shows a schematic diagram of a network architecture according to an embodiment of this application;

[0092] 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;

[0093] Figure 4 shows a schematic diagram of a first communication device and a second communication device according to an embodiment of this application;

[0094] Figure 5 illustrates the transmission between a first node and a second node according to an embodiment of this application;

[0095] Figure 6 shows a schematic diagram of a second message indicating K0 measurement configurations according to an embodiment of this application;

[0096] Figure 7 illustrates a schematic diagram of a first measurement configuration according to an embodiment of the present application used to determine parameters for inference for a first reported inference.

[0097] Figure 8 illustrates a schematic diagram of the parameters of a third message instruction for a first reported inference according to an embodiment of this application;

[0098] Figure 9 illustrates a schematic diagram of a third message activating multiple measurement configurations according to an embodiment of this application;

[0099] Figure 10 shows a schematic diagram of a fourth message according to an embodiment of this application;

[0100] Figure 11 illustrates a schematic diagram of multiple measurement configurations being activated or deactivated according to an embodiment of this application;

[0101] Figure 12 shows a schematic diagram of a fourth message according to an embodiment of this application;

[0102] Figure 13 illustrates a schematic diagram of two measurement configurations for different cells in a plurality of measurement configurations according to an embodiment of the present application;

[0103] Figure 14 illustrates a schematic diagram of any of a plurality of measurement configurations according to an embodiment of the present application, including an associated identifier;

[0104] Figure 15 shows a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of this application;

[0105] Figure 16 illustrates a schematic diagram based on artificial intelligence or machine learning according to an embodiment of this application;

[0106] Figure 17 shows a schematic diagram of AI function deployment according to an embodiment of this application;

[0107] Figure 18 shows a schematic diagram of AI function deployment according to an embodiment of this application;

[0108] Figure 19 shows a schematic diagram of AI function deployment according to an embodiment of this application;

[0109] Figure 20 shows a schematic diagram of AI function deployment according to an embodiment of this application;

[0110] Figure 21 shows a structural block diagram of a processing apparatus for a first node according to an embodiment of the present application;

[0111] Figure 22 shows a structural block diagram of a processing apparatus for a second node according to an embodiment of the present application; Detailed Implementation

[0112] 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 5-22, the embodiments in Figure 5 and the embodiments in Figures 6-22, etc.

[0113] Example 1

[0114] Example 1 illustrates a flowchart of a first message according to an embodiment of this application, as shown in Figure 1. In Figure 1, each block represents a step.

[0115] In Example 1, the first node sends a first message in step 101. This first message indicates multiple measurement configurations, which may all be available or all be unavailable.

[0116] As one example, the first message is carried by higher layer signaling.

[0117] As an example, the first message is carried by RRC (Radio Resource Control) signaling.

[0118] As an example, the first message is carried by one or more RRC IE (Information Element).

[0119] As an example, the first message is carried by an RRC message.

[0120] As an example, the first message is carried by a UE Assistance Information message.

[0121] As an example, the first message is carried by a UE Capability Information message.

[0122] As an example, the first message is carried by the UE capability IE.

[0123] As one example, the first message includes all or part of the information in one or more UE capability IEs.

[0124] As one example, the first message includes the UE capability report of the first node.

[0125] As one example, the UE capabilities include AI or ML-related capabilities.

[0126] As an example, the UE capabilities include capabilities related to AI or ML supported functionality.

[0127] As one example, the first message includes all or part of the information in OtherConfig IE.

[0128] As an example, the first message is used for LCM (life cycle management).

[0129] As an example, the first message is used for the LCM of one or more AI models or ML models.

[0130] As an example, the first message is used for LCM of one or more AI functions or ML functions.

[0131] The advantages of the above embodiments include optimized LCM for AI / ML.

[0132] 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.

[0133] As an example, any one of the plurality of measurement configurations is carried by higher-layer signaling.

[0134] As an example, any one of the plurality of measurement configurations is carried by RRC IE.

[0135] As an example, any of the plurality of measurement configurations includes partial information from at least one RRC IE.

[0136] As an example, any of the plurality of measurement configurations includes CSI-ReportConfig IE.

[0137] As an example, any of the plurality of measurement configurations includes a portion of the information in the CSI-ReportConfig IE.

[0138] As one example, the plurality of measurement configurations each include a plurality of CSI-ReportConfig IEs.

[0139] As one example, the multiple measurement configurations each include portions of information from multiple CSI-ReportConfig IEs.

[0140] As an example, any of the plurality of measurement configurations includes a portion of information from a UE Assistance Information message.

[0141] As an example, any of the plurality of measurement configurations includes all or part of the information in the UE capability IE.

[0142] As an example, any one of the plurality of measurement configurations is for one inference.

[0143] As an example, any one of the plurality of measurement configurations is used to configure an inference.

[0144] As an example, any of the plurality of measurement configurations includes inference-related parameters.

[0145] As an example, the inference refers to AI (Artificial Intelligence) inference.

[0146] As an example, the inference refers to ML (Machine Learning) inference.

[0147] As an example, the inference refers to AI inference or ML inference.

[0148] As an example, the inference-related parameters are used to determine at least one of the inference inputs and outputs.

[0149] As an example, the inference-related parameters are used to determine at least one of the inference input, output, and model.

[0150] As an example, the inference-related parameters are used to determine the RS (Reference Signal) resources for obtaining the inference dataset.

[0151] As an example, the inference-related parameters are used to determine the RS resources for obtaining the training dataset or performance monitoring dataset of the model for inference.

[0152] As an example, the inference-related parameters are used to determine the content of the inference output.

[0153] As an example, the content includes one or more of CQI (Channel Quality Indicator), PMI (Precoding Matrix Indicator), CRI (CSI-RS Resource Indicator), LI (Layer Indicator), RI (Rank Indicator), SSBRI (SS / PBCH Block Resource Indicator), RSRP (Reference Signal received power), SINR (Signal-to-Interference and Noise Ratio), Capability Index, and TDCP (Time Domain Channel Properties).

[0154] As an example, the content includes one or more of CRI, SSBRI, RSRP, and SINR.

[0155] As one example, the content includes at least one of the following: predicted beam information, predicted CSI (Channel State Information), and compressed CSI.

[0156] As one example, the content includes location information, scheduling information, decoding or demodulation information, recovered data, or one or more of TB (Transport Block).

[0157] As an example, the inference-related parameters are used to determine a set of RS resources to which the inference output refers.

[0158] As an example, the inference-related parameters are used to determine at least one of the information related to the measurement time instance and the information related to the prediction time instance.

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

[0160] As an example, the inference-related parameters are used to determine some or all of the parameters of the model used to construct the inference.

[0161] As one example, the inference-related parameters include an association identifier.

[0162] As an example, at least two of the plurality of measurement configurations target the same model for inference.

[0163] As an example, any two of the plurality of measurement configurations may share the same model for inference.

[0164] As an example, at least two of the multiple measurement configurations target inference models that share the same training dataset.

[0165] As an example, any two of the plurality of measurement configurations target inference models that share the same training dataset.

[0166] As an example, at least two of the plurality of measurement configurations target training datasets for inference models that at least partially overlap.

[0167] As an example, the training datasets of the inference models targeted by any two of the plurality of measurement configurations at least partially overlap.

[0168] As an example, for any given measurement configuration among the plurality of measurement configurations, the training of the inference model for the given measurement configuration depends on the training of the inference model for another measurement configuration among the plurality of measurement configurations, or the training of the inference model for another measurement configuration among the plurality of measurement configurations depends on the training of the inference model for the given measurement configuration.

[0169] As an example, the reasoning for a measurement configuration is the reasoning used to configure the measurement configuration.

[0170] As an example, a measurement configuration includes inference-related parameters for which it is targeted.

[0171] As an example, any of the plurality of measurement configurations includes inputting relevant configuration information.

[0172] As one example, the input-related configuration information includes the number of RS resources.

[0173] As an example, the input-related configuration information includes the number of RS resources used to obtain the inference dataset.

[0174] As an example, the input-related configuration information includes the number of RS resources used to obtain the training dataset or performance monitoring dataset.

[0175] As an example, the input-related configuration information includes at least one of the beam direction and beamwidth of the RS resource.

[0176] As an example, the input-related configuration information includes at least one of the beam direction and beamwidth of the RS resources used to obtain the inference dataset.

[0177] As an example, the input-related configuration information includes at least one of the beam direction and beamwidth of the RS resources used to obtain the training dataset or performance monitoring dataset.

[0178] As an example, the input-related configuration information includes the configuration information of RS resources.

[0179] As an example, the input-related configuration information includes configuration information for obtaining RS resources for the inference dataset.

[0180] As an example, the input-related configuration information includes configuration information for obtaining RS resources for training datasets or performance monitoring datasets.

[0181] 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, BWP (Bandwidth Part) index, and the RS resource set to which it belongs.

[0182] As an example, the time-domain behavior is one of periodic, semi-persistent, or aperiodic.

[0183] 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.

[0184] 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.

[0185] As an example, the RS resources include at least one of CSI-RS (Channel State Information Reference Signal) resources and SS / PBCH (Synchronization Signal / Physical Broadcast Channel) block resources.

[0186] As an example, the input-related configuration information includes information related to the time instance.

[0187] As an example, the information related to the measurement time instance is used to determine the RS transmission occasion required for the inference input.

[0188] As an example, the information related to the measurement time instance is used to determine the number of RS transmission opportunities required for the inference input.

[0189] As an example, the information related to the measurement time instance includes a lower limit on the number of RS transmission opportunities for the channel measurements required to obtain the input for inference.

[0190] As an example, any of the plurality of measurement configurations includes outputting related configuration information.

[0191] As an example, the output-related configuration information includes the number of RS resources in a set of RS resources to which the inference output refers.

[0192] As an example, the output-related configuration information includes at least one of the beam direction and beamwidth of an RS resource in a set of RS resources to which the inference output refers.

[0193] As an example, the output-related configuration information includes the configuration information of RS resources in a set of RS resources to which the inference output refers.

[0194] 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.

[0195] As an example, the output of an inference involves 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.

[0196] As an example, the output of an inference involves 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.

[0197] As an example, the output of an inference involves a set of RS resources, wherein any CRI or SSBRI indicated by the output of the inference is the CRI or SSBRI of one of the RS resources in the set of RS resources.

[0198] As an example, the output of an inference involves a set of RS resources, and 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.

[0199] As an example, the output of an inference involves a set of RS resources, wherein any RSRP or SINR indicated by the output of the inference is the RSRP or SINR of one of the RS resources in the set of RS resources.

[0200] As an example, the output-related configuration information includes the content of the output.

[0201] As an example, the output-related configuration information includes the number of output quantities.

[0202] As an example, the number of output quantities includes at least one of the following: the number of CRI or SSBRI, and the number of RSRP or SINR.

[0203] As an example, the number of output quantities includes at least one of the following: the number of CRI or SSBRI, the number of RSRP or SINR, and the number of CQI.

[0204] As an example, the output-related configuration information includes information related to the predicted time instance.

[0205] As an example, the information related to the predicted time instance is used to determine the slot interval to which the inference output is targeted.

[0206] As an example, the information related to the prediction time instance is used to determine at least one of the following: the number of time slot intervals targeted by the inference output, the length of the time slot intervals targeted by the inference output, and the gap between the time slot intervals targeted by the inference output and the time slots occupied by the reporting of the inference output.

[0207] As an example, the output-related configuration information includes the physical layer channel for reporting the output that carries inference.

[0208] As a sub-implementation of the above embodiments, the physical layer channel is PUSCH or PUCCH.

[0209] As an example, the output-related configuration information includes time-domain behavior.

[0210] As an example, the output-related configuration information includes at least one of the period and the time slot offset.

[0211] As one example, the configuration information related to the output includes frequency domain resources.

[0212] As an example, the configuration information related to the output includes the frequency domain resources to which the output is targeted.

[0213] As an example, any of the plurality of measurement configurations includes model parameters.

[0214] As an example, the model parameters are used to determine the parameters used to construct a model for inference.

[0215] As an example, the model parameters are used to determine some or all of the parameters used to construct a model for inference.

[0216] As one example, the model parameters include an associated ID.

[0217] As an example, the model parameters are used to determine the training dataset.

[0218] As an example, the model refers to an AI model or an ML model.

[0219] As an example, any one of the plurality of measurement configurations is for a single function.

[0220] As an example, any one of the plurality of measurement configurations is used for a function.

[0221] As an example, any one of the plurality of measurement configurations is used to configure a function.

[0222] As an example, any of the plurality of measurement configurations includes function-related parameters.

[0223] As an example, the function-related parameters are used to determine at least one of the three factors—input, output, and model—for a function's inference.

[0224] As an example, the function refers to AI function or ML function.

[0225] As an example, the function refers to the function that relies on reasoning.

[0226] As one example, the functionality includes RRC IE.

[0227] As one example, the functionality includes CSI-ReportConfig IE.

[0228] As one example, the functionality includes BM (Beam Management) Case-1 and BM Case-2.

[0229] As one example, the functionality includes CSI prediction.

[0230] As one example, the functionality includes CSI compression.

[0231] As one example, the functionality includes one or more of data reception, location, scheduling, and semantic-based error correction.

[0232] As an example, the functionality targeted by a measurement configuration depends on the inference functionality targeted by the measurement configuration.

[0233] As an example, any one of the plurality of measurement configurations is for a single model.

[0234] As an example, any one of the plurality of measurement configurations is used to configure a model.

[0235] As an example, any of the plurality of measurement configurations includes model-dependent parameters.

[0236] As an example, the model-related parameters are used to determine at least one of the following: the input of a model's inference, the output of a model's inference, and the parameters of a model.

[0237] As an example, the model refers to an AI model or an ML model.

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

[0239] As an example, the model targeted by a measurement configuration is the inference model targeted by the measurement configuration.

[0240] As an example, any one of the plurality of measurement configurations is for a single report.

[0241] As an example, any one of the plurality of measurement configurations is used to configure a report.

[0242] As an example, any of the plurality of measurement configurations includes reporting relevant parameters.

[0243] As an example, the reporting-related parameters are used to determine at least one of the three factors of an inference: input, output, and model, wherein the reporting depends on the output of the inference.

[0244] As an example, the reporting includes CSI reporting.

[0245] As an example, the reporting targeted by a measurement configuration is the reporting configured by the measurement configuration.

[0246] As an example, the reporting targeted by a measurement configuration depends on the output of the inference targeted by that measurement configuration.

[0247] As an example, a measurement configuration includes the reporting-related parameters it is targeting.

[0248] As an example, a measurement configuration includes inference-related parameters on which the reporting it targets depends.

[0249] As an example, the availability of a measurement configuration means that the inference targeted by the measurement configuration is available.

[0250] As an example, the availability of a measurement configuration means that the first node is ready to use the measurement configuration for inference.

[0251] As an example, the availability of a measurement configuration means that the training of the inference model for which the measurement configuration is targeted has been completed.

[0252] As an example, the availability of a measurement configuration means that the measurement configuration can be activated or executed.

[0253] As an example, the availability of a measurement configuration means that the inference, function, or reporting targeted by the measurement configuration can be activated or performed.

[0254] As an example, the availability of a measurement configuration means that the inference model for which the measurement configuration is available is available.

[0255] As an example, the availability of a measurement configuration means that the inference model targeted by the measurement configuration can be activated.

[0256] As an example, "a measurement configuration is unavailable" means that the inference for which the measurement configuration is targeted is unavailable.

[0257] As an example, "a measurement configuration is unavailable" means that the first node is not ready to use the inference targeted by the measurement configuration.

[0258] As an example, an unavailable measurement configuration means that the training of the inference model for which the measurement configuration is targeted has not yet been completed.

[0259] As an example, "a measurement configuration is unavailable" means that the measurement configuration is not yet ready to be activated or executed.

[0260] As an example, an unavailable measurement configuration means that the inference, function, or reporting targeted by the measurement configuration is not yet ready to be activated or executed.

[0261] As an example, "a measurement configuration is unavailable" means that the inference model for which the measurement configuration is targeted is unavailable.

[0262] As an example, an unavailable measurement configuration means that the inference model for which the measurement configuration is targeted is not yet ready to be activated.

[0263] As one example, the statement that all or all of the multiple measurement configurations are available includes: either all of the multiple measurement configurations are available, or none of them are available.

[0264] As an example, the essence of the above method includes that the first message indicates that the plurality of measurement configurations have the availability of synchronization.

[0265] As an example, the essence of the above method includes that the inference targeted by the plurality of measurement configurations has synchronous availability, for example, but not limited to, the plurality of measurement configurations all being targeted at the inference of the same model, or the training datasets on which the model training of the inference targeted by the plurality of measurement configurations depends are the same or related, or the model training of the inference targeted by the plurality of measurement configurations is performed simultaneously or is interdependent.

[0266] As an example, the advantages of the above method include reduced signaling overhead and latency, easier network-side optimization of AI / ML function management and configuration, optimized resource utilization, and improved overall system performance.

[0267] 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 target recipient of the first message may still need to reach some consensus on the AI / ML model used by the first node. In the above method, the first message provides a means to facilitate the first node and the target recipient of the first message to reach some consensus on the AI / ML model used by the first node by indicating that the multiple measurement configurations have synchronous applicability.

[0268] As one embodiment, the multiple measurement configurations being all available or all unavailable includes the following characteristics: the multiple measurement configurations are either all available or none available.

[0269] As one embodiment, the statement that all or all of the plurality of measurement configurations are available includes: either each of the plurality of measurement configurations is available, or each of the plurality of measurement configurations is unavailable.

[0270] As one embodiment, the availability or unavailability of the plurality of measurement configurations includes: when one of the plurality of measurement configurations is available, the other measurement configurations in the plurality of measurement configurations are also available at the same time; when one of the plurality of measurement configurations is unavailable, the other measurement configurations in the plurality of measurement configurations are also unavailable at the same time.

[0271] As one example, the statement that all of the multiple measurement configurations are available or all of them are unavailable includes: the multiple measurement configurations do not simultaneously include both available and unavailable measurement configurations.

[0272] As one example, the condition that all or all of the multiple measurement configurations are available includes: there will not be a situation where some of the multiple measurement configurations are available while others are unavailable.

[0273] As one embodiment, the availability or unavailability of all the multiple measurement configurations includes: the first node does not expect some of the multiple measurement configurations to be available while others are unavailable.

[0274] As one embodiment, the statement that all or all of the multiple measurement configurations are available includes: the first node will not indicate that one part of the multiple measurement configurations is available while another part of the multiple measurement configurations is unavailable.

[0275] As one embodiment, the option that all or all of the multiple measurement configurations are available includes: if some of the multiple measurement configurations are available while others are unavailable, the first node or the target recipient of the first message considers an error to have occurred.

[0276] As one example, the availability or unavailability of all the multiple measurement configurations includes: the multiple measurement configurations having synchronized availability.

[0277] As an example, when any one of the plurality of measurement configurations is activated, the other measurement configurations among the plurality of measurement configurations are also activated.

[0278] As an example, among the plurality of measurement configurations, there is simultaneously an active measurement configuration and a deactivated or inactive measurement configuration.

[0279] As an example, activating or deactivating a measurement configuration includes activating or deactivating the inference, function, model, or reporting targeted by the measurement configuration.

[0280] As an example, the first message indicates each of the plurality of measurement configurations.

[0281] As an example, the first message indicates the identifier of each of the plurality of measurement configurations.

[0282] As an example, the first message indicates the plurality of measurement configurations by indicating an identifier for each of the plurality of measurement configurations.

[0283] As an example, any one of the plurality of measurement configurations is identified by its identifier.

[0284] As an example, the identifier of any of the plurality of measurement configurations is a non-negative integer.

[0285] As an example, the identifier of any of the plurality of measurement configurations is CSI-ReportConfigId.

[0286] As an example, one of the plurality of measurement configurations has an identifier of CSI-ReportConfigId.

[0287] As an example, one of the plurality of measurement configurations has an identifier that is different from CSI-ReportConfigId.

[0288] As an example, the identifier of any of the plurality of measurement configurations is different from CSI-ReportConfigId.

[0289] As an example, the first message indicates the plurality of measurement configurations from the K0 measurement configurations.

[0290] The advantages of the above methods include facilitating unified management and optimization on the network side, and further improving network performance.

[0291] As one example, the first message includes the plurality of measurement configurations.

[0292] The advantages of the above method include greater flexibility and applicability to different terminals.

[0293] As an example, the first message indicates that all of the plurality of measurement configurations are either available or all are unavailable.

[0294] As an example, the first message indicates that the plurality of measurement configurations are either all available or all unavailable.

[0295] As an example, the first message indicates that the plurality of measurement configurations have the following characteristics: either all of them are available, or none of them are available.

[0296] As an example, the first message indicates that the plurality of measurement configurations have the availability for synchronization.

[0297] As an example, the first message indicates that each of the plurality of measurement configurations is available.

[0298] In a preferred embodiment, the first message indicates that the plurality of measurement configurations have synchronous availability and that each of the plurality of measurement configurations is available.

[0299] As an example, the first message indicates K1 measurement configurations, where K1 is a positive integer greater than 1, and the plurality of measurement configurations are a subset of the K1 measurement configurations.

[0300] As an example, any one of the plurality of measurement configurations is one of the K1 measurement configurations.

[0301] As an example, the plurality of measurement configurations are the K1 measurement configurations.

[0302] As an example, at least one of the K1 measurement configurations does not belong to the plurality of measurement configurations.

[0303] As an example, the first message includes the K1 measurement configurations.

[0304] As an example, the K1 measurement configurations are a subset of the K0 measurement configurations, and the first message indicates the K1 measurement configurations from the K0 measurement configurations.

[0305] As an example, at least one of the K1 measurement configurations does not belong to the plurality of measurement configurations, and any measurement configuration other than the plurality of measurement configurations in the K1 measurement configurations does not have synchronous availability with the plurality of measurement configurations.

[0306] As an example, the first message indicates the plurality of measurement configurations from the K1 measurement configurations.

[0307] As an example, the first message explicitly indicates the plurality of measurement configurations from the K1 measurement configurations.

[0308] As an example, the first message indicates the same first-class index for each of the plurality of measurement configurations.

[0309] As an example, the first message indicates the plurality of measurement configurations by specifying the same first-class index for each of the plurality of measurement configurations.

[0310] As an example, the first message indicates a first-class index for each of at least some of the K1 measurement configurations, and each of the plurality of measurement configurations is indicated by the same first-class index.

[0311] As an example, the first type of index is a non-negative integer.

[0312] As an example, the first type of index is a string.

[0313] As an example, the first type of index indicates which measurement configurations have synchronization availability.

[0314] As an example, the synchronous availability of multiple measurement configurations means that either all of the multiple measurement configurations are available or none of them are available.

[0315] As an example, the synchronous availability of multiple measurement configurations means that there will not be a situation where one of the multiple measurement configurations is available while another is unavailable.

[0316] As an example, the first message implicitly indicates the plurality of measurement configurations from the K1 measurement configurations.

[0317] As an example, the first message sequentially indicates the K1 measurement configurations, wherein the plurality of measurement configurations are located in the default position among the K1 measurement configurations.

[0318] As an example, the first message sequentially indicates the K1 measurement configurations, the number of measurement configurations in the plurality of measurement configurations is equal to K, and the plurality of measurement configurations are the K measurement configurations that are first or last in the K1 measurement configurations.

[0319] As a sub-implementation of the above embodiments, the first message indicates the K.

[0320] As a sub-implementation of the above embodiment, the second message indicates the K.

[0321] Example 2

[0322] Example 2 illustrates a schematic diagram of a network architecture according to an embodiment of this application, as shown in Figure 2.

[0323] 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.

[0324] As an example, the first node includes the UE201.

[0325] As one embodiment, the second node includes the node 203.

[0326] As an example, the wireless link between the UE201 and the node203 includes a cellular link.

[0327] As an example, the sender of the first message includes the UE201.

[0328] As an example, the recipient of the first message includes the node 203.

[0329] As an example, the sender of the second message includes the node 203.

[0330] As an example, the recipient of the second message includes the UE201.

[0331] As an example, the sender of the first report includes the UE201.

[0332] As an example, the recipient of the first report includes the node 203.

[0333] As an example, the sender of the third message includes node 203.

[0334] As an example, the recipient of the third message includes the UE201.

[0335] As an example, the sender of the fourth message includes the node 203.

[0336] As an example, the recipient of the fourth message includes the UE201.

[0337] As an example, the UE201 supports AI- or ML-based operations.

[0338] As an example, node 203 supports AI- or ML-based operations.

[0339] Example 3

[0340] 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.

[0341] 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.).

[0342] As an example, the wireless protocol architecture in Figure 3 is applicable to the first node.

[0343] As an example, the wireless protocol architecture in Figure 3 is applicable to the second node.

[0344] As an example, the higher layer mentioned in this application refers to the layer above the physical layer.

[0345] As an example, the first message is generated in the RRC sublayer 306.

[0346] As an example, the second message is generated in the RRC sublayer 306.

[0347] As an example, the first report is generated in the PHY301 or the PHY351.

[0348] As an example, the third message is generated in the RRC sublayer 306.

[0349] As an example, the fourth message is generated in the RRC sublayer 306.

[0350] As an example, the fourth message is generated in the MAC sublayer 302 or the MAC sublayer 352.

[0351] As an example, the fourth message is generated in the PHY301 or the PHY351.

[0352] Example 4

[0353] 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.

[0354] 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.

[0355] 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.

[0356] 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.

[0357] 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.

[0358] 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.

[0359] 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.

[0360] 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 is at least capable of: transmitting the first message. The first message indicates a plurality of measurement configurations, all of which may be available or all of which may be unavailable.

[0361] As one embodiment, the second communication device 450 includes: a memory storing a computer-readable instruction program that produces an action when executed by at least one processor, the action including: sending the first message.

[0362] As one embodiment, the first communication device 410 includes: at least one processor and at least one memory, the at least one memory including computer program code; the at least one memory and the computer program code are configured to be used with the at least one processor. The first communication device 410 means at least: receiving the first message. The first message indicates a plurality of measurement configurations, all of which may be available or all of which may be unavailable.

[0363] As one embodiment, the first communication device 410 includes: a memory storing a computer-readable instruction program that produces an action when executed by at least one processor, the action including: receiving the first message.

[0364] As an example, the first node in this application includes the second communication device 450.

[0365] As an example, the second node in this application includes the first communication device 410.

[0366] 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 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 first message.

[0367] As an example, at least one of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, and the data source 467} is used to receive the second 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 second message.

[0368] 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 report; 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 report.

[0369] 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.

[0370] 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 fourth 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 fourth message.

[0371] Example 5

[0372] Example 5 illustrates a transmission flowchart according to one 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 F54 are respectively optional.

[0373] For the second node U1, a second message is sent in step S5101; a first message is received in step S511; a third message is sent in step S5102; a first report is received in step S5103; and a fourth message is sent in step S5104.

[0374] For the first node U2, a second message is received in step S5201; a first message is sent in step S521; a third message is received in step S5202; a first report is sent in step S5203; and a fourth message is received in step S5204.

[0375] In Example 5, the first message indicates multiple measurement configurations, all of which may be available or all of which may be unavailable.

[0376] As an example, the first node U2 is the first node in this application.

[0377] As an example, the second node U1 is the second node in this application.

[0378] 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.

[0379] 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.

[0380] 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.

[0381] 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.

[0382] 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.

[0383] 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.

[0384] As one embodiment, the first node U2 includes a terminal.

[0385] As one embodiment, the first node U2 includes a user equipment.

[0386] As one embodiment, the second node U1 includes the serving cell sustaining base station of the first node U2.

[0387] As one embodiment, the second node U1 includes an OTT server (Over-The-Top server).

[0388] As an example, the second node U1 includes OAM (Operation Administration and Maintenance).

[0389] As one embodiment, the second node U1 includes a NAS device.

[0390] As one embodiment, the second node U1 includes core network equipment.

[0391] As an example, the first message is transmitted on PDSCH (Physical Downlink Shared Channel).

[0392] As an example, the second message is transmitted on the PDSCH.

[0393] As one example, the receipt of the second message is earlier than the sending of the first message.

[0394] As an example, the step in block F51 of Figure 5 exists, where the second message indicates K0 measurement configurations, where K0 is a positive integer greater than 1, and the plurality of measurement configurations are a subset of the K0 measurement configurations.

[0395] As an example, the step in block F53 of Figure 5 includes the first report being based on inference, wherein a first measurement configuration of the plurality of measurement configurations is used to determine parameters for the inference of the first report.

[0396] As an example, the first report is transmitted on the PUSCH.

[0397] As an example, the first report is transmitted on PUCCH (Physical Uplink Control Channel).

[0398] As an example, the first report is sent later than the first message is sent.

[0399] As an example, the steps in blocks F52 and F53 of Figure 5 are present, and the third message indicates the parameters for the inference of the first report.

[0400] As an example, the third message activates the plurality of measurement configurations.

[0401] As an example, the third message activates the first measurement configuration, and with the activation of the first measurement configuration, each of the other configurations in the plurality of measurement configurations is also activated.

[0402] As an example, the third message activates the first measurement configuration, and in response to the activation of the first measurement configuration, each of the other multiple measurement configurations is also activated.

[0403] As an example, the third message is transmitted on the PDSCH.

[0404] As an example, the receipt of the third message is later than the sending of the first message.

[0405] As an example, the receipt of the third message is earlier than the sending of the first report.

[0406] As an example, the step in block F54 of Figure 5 includes the fourth message activating or deactivating one of the plurality of measurement configurations; in response to the receipt of the fourth message, all of the plurality of measurement configurations are either activated or deactivated.

[0407] As an example, with the activation of one of the plurality of measurement configurations, each of the other plurality of measurement configurations is also activated.

[0408] As an example, in response to the activation of one of the plurality of measurement configurations, each of the other plurality of measurement configurations is also activated.

[0409] As an example, the steps in block F54 of Figure 5 include: the fourth message activates or deactivates one of the plurality of measurement configurations; when the fourth message activates the one of the plurality of measurement configurations, only the one of the plurality of measurement configurations is activated by the fourth message; when the fourth message deactivates the one of the plurality of measurement configurations, each of the plurality of measurement configurations is deactivated by the fourth message.

[0410] As a sub-example of the above embodiment, whether the other measurement configurations among the plurality of measurement configurations besides the one mentioned above are activated or deactivated by the fourth message depends on whether the one mentioned above is activated or deactivated by the fourth message.

[0411] As an example, the fourth message is transmitted on the PDSCH.

[0412] As an example, the fourth message is transmitted on the PDCCH (Physical Downlink Control Channel).

[0413] As an example, the steps in blocks F52 and F54 of Figure 5 are present, the third message indicates the parameters for the inference of the first reported, and the fourth message deactivates one of the plurality of measurement configurations.

[0414] As an example, the fourth message is received later than the third message.

[0415] As an example, the fourth message is received earlier than the third message.

[0416] As one example, two of the multiple measurement configurations are for different cells.

[0417] As an example, any of the plurality of measurement configurations includes an associated identifier.

[0418] As an example, two of the multiple measurement configurations include different association identifiers.

[0419] As an example, any two of the plurality of measurement configurations include different association identifiers.

[0420] As an example, two of the plurality of measurement configurations may have different association identifiers.

[0421] As an example, two of the plurality of measurement configurations may include the same association identifier.

[0422] As a preferred embodiment, the two measurement configurations including different association identifiers means that the inference dependencies of the two measurement configurations are on different RS resources.

[0423] As a preferred embodiment, the two measurement configurations including different association identifiers means that different RS resources are used to generate the inference datasets for the inference targeted by the two measurement configurations.

[0424] The above methods improve the utilization of AI / ML models and can better adapt to different terminals, different channel environments and different application scenarios.

[0425] In practical systems, the training of an AI / ML model can be based on measurement results obtained under different scenarios and channel environments. This results in a model with better universality, reducing training costs and the costs of using the AI / ML model, such as storage and computing resources. In the above method, the multiple measurement configurations target the same model, which can operate in different environments or scenarios with channel measurements obtained on different RS resources as input. This method improves the utilization rate of AI / ML models, reduces the cost of using AI / ML, and optimizes the system gains brought by AI / ML.

[0426] Example 6

[0427] Example 6 illustrates a schematic diagram of a second message instruction K0 measurement configuration according to an embodiment of this application; as shown in Figure 6.

[0428] As one example, the second message is carried by higher layer signaling.

[0429] As an example, the second message is carried by RRC signaling.

[0430] As an example, the second message is carried by one or more RRC IE (Information Element).

[0431] As one embodiment, the second message includes some or all of the information of each of one or more RRC IEs.

[0432] As one example, the second message includes all or part of the information in the CSI-ReportConfig IE.

[0433] As an example, the second message is carried by an RRC message.

[0434] As one embodiment, the second message includes some or all of the information from each of one or more RRC messages.

[0435] As an example, the second message is used for RRC configuration.

[0436] As an example, the second message is used for RRC reconfiguration.

[0437] As an example, the second message is carried by an RRC reconfiguration message.

[0438] As an example, the second message is carried by an RRC recovery message (RRCResume message).

[0439] As one example, the second message includes some or all of the information in the higher-level parameter reconfigurationWithSync.

[0440] As an example, the second message is used for UE capability query (UECapabilityEnquiry).

[0441] As an example, the second message is carried by a UE Capability Enquiry message.

[0442] As an example, the second message is carried by DL-DCCH-Message.

[0443] As one example, the second message includes the K0 measurement configurations.

[0444] As an example, both the first message and the second message are used for LCM (life cycle management).

[0445] As an example, both the first message and the second message are used in the LCM of the AI ​​model or ML model.

[0446] As an example, the first message and the second message are used for the LCM of the same one or more AI models or ML models.

[0447] As an example, both the first message and the second message are used for LCM based on AI functionality.

[0448] As an example, the first message and the second message are used for the same LCM of one or more AI-based functions.

[0449] The advantages of the above embodiments include optimized LCM for AI / ML.

[0450] 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.

[0451] As an example, any of the K0 measurement configurations includes higher-layer signaling.

[0452] As an example, any of the K0 measurement configurations includes partial information from at least one RRC IE.

[0453] As an example, any of the K0 measurement configurations includes CSI-ReportConfig IE.

[0454] As an example, any of the K0 measurement configurations includes a portion of the information in the UE assistance information message.

[0455] As an example, any of the K0 measurement configurations includes all or part of the information in the UE capability IE.

[0456] As an example, any one of the K0 measurement configurations is for one inference.

[0457] As an example, any one of the K0 measurement configurations is used to configure an inference.

[0458] As an example, any of the K0 measurement configurations includes inference-related parameters.

[0459] As an example, any one of the plurality of measurement configurations is one of the K0 measurement configurations.

[0460] As an example, the plurality of measurement configurations includes a number equal to K0, and the plurality of measurement configurations are K0 measurement configurations.

[0461] As an example, at least one of the K0 measurement configurations does not belong to the plurality of measurement configurations.

[0462] As an example, the first message indicates the plurality of measurement configurations from the K0 measurement configurations.

[0463] The advantages of the above methods include facilitating unified management and optimization on the network side, and further improving network performance.

[0464] As an example, the first message indicates which of the K0 measurement configurations belong to the plurality of measurement configurations.

[0465] As an example, the first message indicates K0 status values, and the K0 status values ​​correspond one-to-one with the K0 measurement configurations. For any measurement configuration among the K0 measurement configurations, if the status value corresponding to any measurement configuration belongs to the first set of status values, the any measurement configuration belongs to the plurality of measurement configurations.

[0466] As a sub-implementation of the above embodiment, any one of the K0 state values ​​is a non-negative integer.

[0467] As a sub-implementation of the above embodiment, any one of the K0 state values ​​is 0 or 1, and the first state value set includes only one state value, which is 0 or 1.

[0468] As a sub-implementation of the above embodiment, any one of the K0 state values ​​has more than two candidates, and the first set of state values ​​includes one or more state values.

[0469] As an example, the first message indicates a first-class index for each of at least a portion of the K0 measurement configurations, and the plurality of measurement configurations are indicated with the same first-class index.

[0470] As a sub-example of the above embodiment, the plurality of measurement configurations consist of measurement configurations among the K0 measurement configurations whose first type index is equal to a given index value.

[0471] As an example, the first message indicates that the plurality of measurement configurations of the K0 measurement configurations have the following characteristics: either all of them are available, or none of them are available.

[0472] As an example, the first message indicates that the plurality of measurement configurations of the K0 measurement configurations have the availability of synchronization.

[0473] Example 7

[0474] Example 7 illustrates a schematic diagram of a first measurement configuration according to an embodiment of the present application used to determine parameters for inference for a first report; as shown in Figure 7.

[0475] As an example, the inference refers to AI (Artificial Intelligence) inference.

[0476] As an example, the inference refers to ML (Machine Learning) inference.

[0477] As an example, the inference refers to AI inference or ML inference.

[0478] As an example, the first report is a report for the first measurement configuration.

[0479] As an example, the first measurement configuration is used to configure the first report.

[0480] As an example, the first report is based on the reasoning given for the first report.

[0481] As an example, the first report includes CSI (Channel State Information).

[0482] As an example, the first report includes one or more of CQI, PMI, CRI, LI, RI, SSBRI, RSRP, SINR, capability index, and TDCP.

[0483] As an example, the first report includes compressed CSI.

[0484] As an example, the first report includes the predicted CSI.

[0485] As one embodiment, the first report includes predicted beam information.

[0486] As an example, the beam information includes one or more of CRI, SSBRI, RSRP, and SINR.

[0487] As an example, the prediction includes at least one of temporal prediction and spatial prediction.

[0488] As one example, the first report includes a channel matrix.

[0489] As one example, the first report includes a precoding matrix.

[0490] As an example, the first report depends on the output of the inference for the first report.

[0491] As an example, the output of the inference for the first report is used to generate the first report.

[0492] As an example, all or part of the output of the inference for the first report is used to generate the first report.

[0493] As an example, all or part of the output of the inference for the first report is post-processed and used to generate the first report.

[0494] As one embodiment, the first report includes all or part of the output of the reasoning for the first report.

[0495] As an example, the first report includes all or part of the post-processed output of the reasoning for the first report.

[0496] As an example, the post-processing includes one or more of quantization, shortening, puncture, padding, matrix factorization, domain transformation, and DFT (Discrete Fourier Transform).

[0497] 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.

[0498] As an example, the first measurement configuration is used to determine at least some parameters of the inference for the first report.

[0499] As an example, the first measurement configuration indicates at least some parameters for the inference of the first reported data.

[0500] As an example, the parameters for the inference in the first report include at least one of the following: input-related parameters, output-related parameters, and model parameters.

[0501] As one example, the input-related parameters include the number of RS resources used to calculate the first reported channel measurement.

[0502] As an example, the input-related parameters include at least one of the beam direction and beamwidth used to obtain the RS resources for calculating the first reported channel measurement.

[0503] As one embodiment, the input-related parameters include configuration information for obtaining RS resources used to calculate the first reported channel measurement.

[0504] As an example, the input-related parameters include information related to the measurement time instance.

[0505] As an example, the information related to the measurement time instance is used to determine the number of RS transmission opportunities used to calculate the first reported channel measurement.

[0506] As an example, the information related to the measurement time instance includes a lower limit for obtaining the number of RS transmission opportunities used to calculate the first reported channel measurement.

[0507] As an example, the output-related parameters include the number of RS resources in the group of RS resources involved in the first report.

[0508] As an example, the output-related parameters include at least one of the beam direction and beamwidth of the RS resources in the set of RS resources involved in the first report.

[0509] As an example, the output-related parameters include the content of the first report.

[0510] As an example, the output-related parameters include the number of the first reported quantities.

[0511] As an example, the output-related parameters are used to determine the first reported CSI reference resource.

[0512] As an example, the definition of the CSI reference resource is based on 3GPP TS38.214.

[0513] As an example, the output-related parameters include information related to the prediction time instance.

[0514] As an example, the information related to the predicted time instance is used to determine the time slot interval to which the first report is targeted.

[0515] As an example, the output-related parameters include the physical layer channel carrying the first report.

[0516] As an example, the parameters related to the output include time-domain behavior.

[0517] As an example, the output-related parameters include at least one of the period and the time slot offset.

[0518] As an example, the output-related parameters include frequency domain resources.

[0519] As an example, the output-related parameters include the frequency domain resources targeted by the first report.

[0520] As an example, the model parameters include parameters used to construct the model for the inference of the first report.

[0521] As an example, the model parameters are used to determine the training dataset for the model of the inference for the first reported inference.

[0522] As an example, the reasoning for the first report is performed based on the first measurement configuration.

[0523] As an example, the first node performs the inference for the first report based on the parameters indicated by the first measurement configuration for the inference for the first report.

[0524] As an example, the model for the inference of the first report is unknown to the target recipient of the first message.

[0525] As an example, the first node and the target recipient of the first message have certain consensus on the inference model for the first report, such as, but not limited to, the inference model for the first report and the inference models for the other measurement configurations besides the first measurement configuration among the plurality of measurement configurations have synchronous availability, the association identifier associated with the inference model for the first report, and RS resources for generating inference datasets, training datasets or performance monitoring datasets for the inference model for the first report.

[0526] Example 8

[0527] Example 8 illustrates a schematic diagram of a third message instruction for parameters of a first reported inference according to an embodiment of this application; as shown in Figure 8.

[0528] As an example, the third message is carried by higher layer signaling.

[0529] As an example, the third message is carried by RRC signaling.

[0530] As an example, the third message is carried by one or more RRC IEs.

[0531] As one example, the third message includes some or all of the information in each of one or more RRC IEs.

[0532] As one example, the third message includes all or part of the information in the CSI-ReportConfig IE.

[0533] As an example, the third message is CSI-ReportConfig IE.

[0534] As an example, the third message is carried by an RRC message.

[0535] As one example, the third message includes some or all of the information from one or more RRC messages.

[0536] As an example, the third message is carried by MAC CE (Medium Access Control layer Control Element).

[0537] As one embodiment, the second message includes an RRC reconfiguration message, and the third message includes some or all of the information in one or more RRC IEs.

[0538] As one embodiment, the second message includes an RRC reconfiguration message, and the third message includes all or part of the information in the CSI-ReportConfig IE.

[0539] As an example, the first measurement configuration is used to determine a portion of the parameters for the inference of the first report, and the third message indicates another portion of the parameters for the inference of the first report.

[0540] As an example, the first measurement configuration indicates a portion of the parameters for the first reported inference, and the third message indicates another portion of the parameters for the first reported inference.

[0541] As an example, the first measurement configuration indicates that relevant parameters are input for a portion of the inference reported in the first report, and the third message indicates that relevant parameters are input for another portion of the inference reported in the first report.

[0542] As a sub-implementation of the above embodiment, the input-related parameters include at least one of the following three: the number of RS resources used to calculate the first reported channel measurement, beam direction, and beamwidth.

[0543] As a sub-implementation of the above embodiment, the input-related parameters include information related to the first reported measurement time instance.

[0544] As a sub-implementation of the above embodiments, the other part of the input-related parameters includes one or more of the following for obtaining the RS resources used to calculate the first reported channel measurement: frequency domain resources, time domain resources, number of ports, CDM type, density, quasi-co-location relationship, TCI status, time domain behavior, power control parameters, BWP index, and the RS resource set to which it belongs.

[0545] As an example, the first measurement configuration indicates the output of relevant parameters for a portion of the inference reported in the first report, and the third message indicates the output of relevant parameters for another portion of the inference reported in the first report.

[0546] 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 group of RS resources involved in the first report, the beam direction, and the beamwidth.

[0547] As a sub-implementation of the above embodiments, the parameters related to the output include at least one of the first reported content and the number of reported quantities.

[0548] As a sub-implementation of the above embodiments, the output-related parameters include information related to the first reported prediction time instance.

[0549] As a sub-implementation of the above embodiments, the other part of the output 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, power control parameters, BWP index, and the RS resource set to which the first report is involved.

[0550] As a sub-implementation of the above embodiments, the other part of the output-related parameters includes at least one of the time-domain behavior of the first report, the physical layer channel carrying the first report, the period of the first report, and the time slot offset of the first report.

[0551] As a sub-implementation of the above embodiments, the frequency domain resources targeted by the first report.

[0552] As an example, a report involving a set of RS resources includes, wherein the report indicates at least one RS resource in the set of RS resources.

[0553] As an example, a report involving a set of RS resources includes the report indicating the CRI or SSBRI of at least one RS resource in the set of RS resources.

[0554] As an example, a report involving a set of RS resources includes the report indicating the RSRP or SINR of at least one RS resource in the set of RS resources.

[0555] As an example, a report involving a set of RS resources includes any RS resource indicated by the report being one of the RS resources in the set of RS resources.

[0556] As an example, a report involving a set of RS resources includes any CRI or SSBRI indicated by the report being the CRI or SSBRI of one of the RS resources in the set of RS resources.

[0557] As an example, a report involving a set of RS resources includes any RSRP or SINR indicated by the report being the RSRP or SINR of one of the RS resources in the set of RS resources.

[0558] As an example, the first measurement configuration indicates at least one of the following three factors: the number of RS resources used to calculate the first reported channel measurement, the beam direction, and the beamwidth.

[0559] As an example, the first measurement configuration indicates at least one of the following three factors: the number of RS resources in the set of RS resources involved in the first report, the beam direction, and the beamwidth.

[0560] As an example, the first measurement configuration indicates the reporting content of the first report.

[0561] As an example, the first measurement configuration indicates at least one of the information related to the first reported measurement time instance and the information related to the first reported prediction time instance.

[0562] As an example, the third information indicates configuration information for obtaining RS resources used to calculate the first reported channel measurement.

[0563] As an example, the third information indicates the configuration information of a set of RS resources involved in the first report.

[0564] As an example, the third information indicates at least one of the time-domain behavior of the first report and the physical layer channel carrying the first report.

[0565] Example 9

[0566] Example 9 illustrates a schematic diagram of a third message activating multiple measurement configurations according to an embodiment of this application; as shown in Figure 9.

[0567] As an example, in response to the receipt of the third message, the plurality of measurement configurations are activated.

[0568] As an example, in response to the receipt of the third message, each of the plurality of measurement configurations other than the first measurement configuration is also activated.

[0569] As one embodiment, the third message is used to configure the reporting targeted by the first measurement configuration; in response to the receipt of the third message, the plurality of measurement configurations are activated.

[0570] As one embodiment, the third message is used to configure the first report; in response to the receipt of the third message, the plurality of measurement configurations are activated.

[0571] As one embodiment, the third message is the reported configuration IE targeted by the first measurement configuration; as a response to the receipt of the third message, the plurality of measurement configurations are activated.

[0572] As one example, the third message is the configuration IE reported by the first report; as a response to the receipt of the third message, the plurality of measurement configurations are activated.

[0573] The advantages of the above method include that by activating the reporting of other measurement configurations among the plurality of measurement configurations through the reporting configuration IE targeted by one of the measurement configurations, the availability synchronization feature of the plurality of measurement configurations is fully utilized to reduce signaling overhead and latency, thereby improving system efficiency.

[0574] As an example, a reported configuration IE includes some or all of the information in the CSI-ReportConfig IE.

[0575] As an example, one reporting configuration IE is the CSI-ReportConfig IE.

[0576] As an example, a reported configuration IE indication is used to obtain RS resources for calculating the reported channel measurement.

[0577] As an example, a reporting configuration IE indicates the reporting volume of the reported data.

[0578] As an example, a reported configuration IE indicates the frequency domain resource to which the report is targeted.

[0579] As one embodiment, the third message activates the first measurement configuration, and in response to the receipt of the third message, each of the plurality of measurement configurations other than the first measurement configuration is also activated.

[0580] The advantages of the above method include that by activating one of the multiple measurement configurations, the other measurement configurations in the multiple measurement configurations are activated, which makes full use of the availability synchronization feature of the multiple measurement configurations to reduce signaling overhead and latency, thereby improving system efficiency.

[0581] As an example, the third message indicates the parameters of the inference targeted by only the first measurement configuration among the plurality of measurement configurations.

[0582] As an example, among the plurality of measurement configurations, only the parameters of the inference targeted by the first measurement configuration depend on the third message.

[0583] As an example, the third message does not indicate the parameters of the inference for any of the plurality of measurement configurations other than the first measurement configuration.

[0584] As an example, the inference parameters for any of the plurality of measurement configurations other than the first measurement configuration do not depend on the third message.

[0585] As an example, activating a measurement configuration includes activating the reporting targeted by the measurement configuration.

[0586] Example 10

[0587] Example 10 illustrates a schematic diagram of a fourth message according to an embodiment of this application; as shown in Figure 10. In Example 10, the fourth message activates or deactivates one of the plurality of measurement configurations; in response to receiving the fourth message, all of the plurality of measurement configurations are either activated or deactivated.

[0588] As an example, the fourth message is carried by higher layer signaling.

[0589] As an example, the fourth message is carried by RRC signaling.

[0590] As an example, the fourth message is carried by one or more RRC IEs.

[0591] As an example, the fourth message is carried by the MAC CE.

[0592] As an example, the fourth message is carried by DCI (Downlink Control Information).

[0593] As an example, the fourth message indicates one of the plurality of measurement configurations.

[0594] As an example, the fourth message indicates only one of the plurality of measurement configurations.

[0595] As an example, the fourth message indicates the inference, model, function, or reporting target of only one of the plurality of measurement configurations.

[0596] As an example, the fourth message activates one of the plurality of measurement configurations, and in response to the receipt of the fourth message, all of the plurality of measurement configurations are activated.

[0597] The advantages of the above method include that by activating one of the multiple measurement configurations, the other measurement configurations in the multiple measurement configurations are activated, which makes full use of the availability synchronization feature of the multiple measurement configurations to reduce signaling overhead and latency, thereby improving system efficiency.

[0598] As an example, the fourth message activates each of the plurality of measurement configurations by activating one of the measurement configurations.

[0599] As an example, the fourth message activates one of the plurality of measurement configurations, and in response to the receipt of the fourth message, each of the plurality of measurement configurations that was in a deactivated state is activated.

[0600] In a preferred embodiment, the fourth message deactivates one of the plurality of measurement configurations, and in response to the receipt of the fourth message, all of the plurality of measurement configurations are deactivated.

[0601] The advantages of the above method include that by deactivating one of the multiple measurement configurations to activate the other measurement configurations, the availability synchronization feature of the multiple measurement configurations is fully utilized to reduce signaling overhead and latency, thereby improving system efficiency.

[0602] As an example, the fourth message deactivates each of the plurality of measurement configurations by deactivating one of the measurement configurations.

[0603] As an example, the fourth message deactivates one of the plurality of measurement configurations, and in response to the receipt of the fourth message, each of the plurality of measurement configurations that is in an active state is deactivated.

[0604] Example 11

[0605] Example 11 illustrates a schematic diagram of the activation or deactivation of multiple measurement configurations according to an embodiment of this application, as shown in Figure 11. In Example 11(a), if one of the multiple measurement configurations is activated, all of the multiple measurement configurations are activated; in Example 11(b), if one of the multiple measurement configurations is deactivated, all of the multiple measurement configurations are deactivated.

[0606] As an example, all of the plurality of measurement configurations are activated in response to the activation of one of the plurality of measurement configurations.

[0607] As an example, in response to one of the plurality of measurement configurations being deactivated, all of the plurality of measurement configurations are deactivated.

[0608] Example 12

[0609] Example 12 illustrates a schematic diagram of a fourth message according to an embodiment of this application; as shown in Figure 12. In Example 12, the fourth message activates or deactivates one of the plurality of measurement configurations; when the fourth message activates the one of the plurality of measurement configurations, only the one of the plurality of measurement configurations is activated by the fourth message; when the fourth message deactivates the one of the plurality of measurement configurations, each of the plurality of measurement configurations is deactivated by the fourth message.

[0610] As one example, the fourth message activates or deactivates one of the plurality of measurement configurations; whether the other measurement configurations among the plurality of measurement configurations, other than the one measurement configuration, are activated or deactivated by the fourth message depends on whether the fourth message activates or deactivates the one measurement configuration.

[0611] As an example, when the fourth message deactivates one of the plurality of measurement configurations, each of the plurality of measurement configurations that is in an active state is deactivated by the fourth message.

[0612] As an example, the fourth message indicates only one of the plurality of measurement configurations.

[0613] As an example, the fourth message indicates the inference, function, model, or reporting target of only one of the plurality of measurement configurations.

[0614] Example 13

[0615] Example 13 illustrates a schematic diagram of two measurement configurations for different cells in multiple measurement configurations according to one embodiment of the present application; as shown in Figure 13.

[0616] As an example, any two of the plurality of measurement configurations are for different cells.

[0617] As an example, two of the multiple measurement configurations are for the same cell.

[0618] As an example, one of the multiple measurement configurations targets the serving cell of the first node.

[0619] As an example, any of the plurality of measurement configurations targets the serving cell of the first node.

[0620] The advantages of the above method include reduced implementation complexity.

[0621] As an example, among the multiple measurement configurations, there are two measurement configurations that are respectively for the SpCell (Special Cell) and SCell (Secondary Cell) of the first node.

[0622] As an example, among the multiple measurement configurations, there are two measurement configurations that are respectively for two serving cells in the same CG (Cell Group) of the first node, wherein the same CG is either MCG (Master Cell Group) or SCG (Secondary Cell Group).

[0623] As an example, among the multiple measurement configurations, there are two measurement configurations that are respectively for the PCell (Primary Cell) in the MCG and the PSCell (Primary SCG Cell) in the SCG of the first node.

[0624] As an example, among the multiple measurement configurations, two measurement configurations are respectively for a serving cell in the MCG and a serving cell in the SCG of the first node.

[0625] As an example, one of the multiple measurement configurations may target a cell that is not the serving cell of the first node.

[0626] The problem to be solved by the above method includes how to reduce the negative impact of the serving cell change of the first node on AI / ML functions; in the above method, the availability of the measurement configuration indicated by the first node for serving cell and non-serving cell is synchronized, which solves this problem.

[0627] The benefits of the above method include reduced latency and signaling overhead in the use of AI / ML functions, especially when the first node updates the serving cell.

[0628] The advantages of the above method include that the AI / ML function can still be used without interruption when the serving cell of the first node changes.

[0629] As an example, one of the multiple measurement configurations targets an additional cell of the first node.

[0630] As an example, one of the plurality of measurement configurations targets a cell that is waiting to be designated as a serving cell.

[0631] As an example, one of the multiple measurement configurations targets a cell that is an LTM (L1 / L2 Triggered Mobility) candidate cell.

[0632] As an example, among the plurality of measurement configurations, one measurement configuration targets the serving cell of the first node, and another measurement configuration targets a cell that is not the serving cell of the first node.

[0633] As an example, one of the plurality of measurement configurations targets a cell that is the PCell, SpCell, or SCell of the first node, while another of the plurality of measurement configurations targets a cell that is not the PCell, SpCell, or SCell of the first node.

[0634] As an example, among the multiple measurement configurations, two measurement configurations target the serving cell and the supplementary cell of the first node, respectively.

[0635] As an example, among the multiple measurement configurations, two measurement configurations target the serving cell of the first node and the cell waiting to be indicated as the serving cell.

[0636] As an example, among the multiple measurement configurations, two measurement configurations target the serving cell of the first node and the LTM candidate cell, respectively.

[0637] As an example, the serving cell of the first node includes at least PCell, SpCell (Special Cell), and SCell.

[0638] As an example, the additional cell is the cell identified by the additional PCI.

[0639] As an example, the additional cell is the cell indicated by AdditionalPCIIndex-r17.

[0640] As an example, the additional cell is a cell whose name includes the higher-level parameter indicated by additionalPCI-ToAddModList.

[0641] As an example, the cell waiting to be designated as the serving cell refers to a cell waiting to be designated as the serving cell by DCI or MAC CE.

[0642] As an example, the cell waiting to be designated as the serving cell refers to a cell waiting to be designated as the serving cell by the MAC CE.

[0643] As an example, the cell waiting to be indicated as the serving cell refers to a cell waiting to be indicated as the serving cell by the MAC CE used for cell handover.

[0644] As an example, the cell waiting to be designated as the serving cell refers to a cell waiting to be designated as the serving cell by a cell switch command.

[0645] As an example, the LTM candidate cell refers to the candidate cell configured for LTM.

[0646] As an example, the LTM candidate cell refers to the candidate cell configured for LTM cell handover.

[0647] As an example, the LTM candidate cell refers to the candidate cell of the cell indicated by the LTM Cell Switch Command MAC CE.

[0648] As an example, the LTM candidate cell refers to a cell whose name includes the RRC IE indication of LTM-Candidate.

[0649] As an example, a measurement configuration for a cell includes a measurement configuration configured for that cell.

[0650] As an example, a measurement configuration for a cell includes the configuration information of the cell, which includes the measurement configuration.

[0651] As an example, the configuration information of a cell is used to configure the cell.

[0652] As an example, the configuration information of a cell is used to configure the cell to the first node.

[0653] As an example, the configuration information of a cell includes the cell's SpCellConfig or SCellConfig.

[0654] As an example, the configuration information of a cell includes the ServingCellConfig IE of that cell.

[0655] As one example, among the multiple measurement configurations, two measurement configurations are configured for different cells.

[0656] As an example, two of the multiple measurement configurations are included in the configuration information of different cells.

[0657] As one example, the multiple measurement configurations are configured for different cells.

[0658] As one example, the multiple measurement configurations are included in the configuration information of different cells.

[0659] As an example, a measurement configuration for a cell includes an RS resource indicated by the measurement configuration in the cell.

[0660] As an example, a measurement configuration for a cell includes an RS resource indicated by the measurement configuration being configured for the cell.

[0661] As an example, a measurement configuration for a cell includes configuration information of the CSI-RS resources indicated by the measurement configuration, which is included in the configuration information of the cell.

[0662] As an example, the configuration information of a CSI-RS resource includes at least one of NZP-CSI-RS-Resource IE, NZP-CSI-RS-ResourceSet, and CSI-ResourceConfig.

[0663] As an example, a measurement configuration for a cell includes the cell's PCI (Physical Cell Identifier) ​​being used to generate an SS (synchronization signal) sequence of SS / PBCH block resources indicated by the measurement configuration.

[0664] As an example, a measurement configuration for a cell includes the physical-layer cell identity of the cell being used to generate an SS sequence of SS / PBCH block resources indicated by the measurement configuration.

[0665] As an example, a measurement configuration for a cell includes obtaining the physical layer cell identity of the cell from the SS sequence of SS / PBCH block resources indicated by the measurement configuration without question.

[0666] As an example, a measurement configuration for a cell includes a CSI-RS resource and an SS / PBCH block resource quasi-co-located indicated by the measurement configuration, wherein the physical layer cell identity or PCI of the cell is used to generate the SS sequence of the SS / PBCH block resource.

[0667] As a sub-example of the above embodiments, the quasi-co-address type between the CSI-RS resource and the SS / PBCH block resource indicated by the measurement configuration includes TypeD.

[0668] As an example, a measurement configuration for a cell includes a CSI-RS resource indicated by the measurement configuration and another CSI-RS resource quasi-co-located, the other CSI-RS resource and an SS / PBCH block resource quasi-co-located, and the physical layer cell identity or PCI of the cell is used to generate the SS sequence of the SS / PBCH block resource.

[0669] As a sub-implementation of the above embodiments, the quasi-co-address type between the CSI-RS resource and the other CSI-RS resource indicated by the one measurement configuration includes Type D, and the quasi-co-address type between the other CSI-RS resource and the one SS / PBCH block resource includes Type D.

[0670] As an example, the first given measurement configuration and the second given measurement configuration are two measurement configurations among the plurality of measurement configurations. The physical layer cell identities of the two different cells are used to generate the SS sequence of the SS / PBCH block resources indicated by the first given measurement configuration and the SS sequence of the SS / PBCH block resources indicated by the second given measurement configuration, respectively.

[0671] As an example, the first given measurement configuration and the second given measurement configuration are two measurement configurations among the plurality of measurement configurations. The first given measurement configuration indicates that the RS resource and the first SS / PBCH block resource are quasi-co-located, and the second given measurement configuration indicates that the RS resource and the second SS / PBCH block resource are quasi-co-located. The physical layer cell identities of the two different cells are used to generate the SS sequence of the first SS / PBCH block resource and the SS sequence of the second SS / PBCH block resource, respectively.

[0672] As a sub-implementation of the above embodiments, the quasi-co-address type between the RS resource and the first SS / PBCH block resource indicated by the first given measurement configuration includes Type D, and the quasi-co-address type between the RS resource and the second SS / PBCH block resource indicated by the second given measurement configuration also includes Type D.

[0673] As an example, the first given measurement configuration and the second given measurement configuration are two of the plurality of measurement configurations. The first given measurement configuration indicates the first SS / PBCH block resource, and the second given measurement configuration indicates the RS resource and the second SS / PBCH block resource quasi-co-located. The physical layer cell identities of the two different cells are used to generate the SS sequence of the first SS / PBCH block resource and the SS sequence of the second SS / PBCH block resource, respectively.

[0674] As a sub-implementation of the above embodiments, the quasi-co-address type between the RS resource indicated by the second given measurement configuration and the second SS / PBCH block resource includes TypeD.

[0675] As an example, the first given measurement configuration and the second given measurement configuration are any two of the plurality of measurement configurations.

[0676] As an example, a measurement configuration for a cell includes inference or reporting configured for the cell.

[0677] As an example, a measurement configuration for a cell includes configuration information for the cell, which includes inference or reporting configuration information for the measurement configuration.

[0678] As an example, among the multiple measurement configurations, two measurement configurations are configured to target inference or reporting to different cells.

[0679] As an example, among the multiple measurement configurations, the inference or reporting configuration information for two measurement configurations is included in the configuration information of different cells.

[0680] As an example, any two of the plurality of measurement configurations are configured to target different cells for inference or reporting.

[0681] As an example, the inference or reporting configuration information for any two of the plurality of measurement configurations is included in the configuration information of different cells.

[0682] As an example, a reasoning or reporting configuration information includes some or all of the information in the CSI-ReportConfig IE.

[0683] As an example, a configuration message for inference or reporting is CSI-ReportConfig IE.

[0684] As an example, the configuration information for an inference indicates the RS resources used to obtain the inference dataset for the inference.

[0685] As an example, the configuration information of an inference indicates at least one of the output content of the inference and the frequency domain resource to which it is targeted.

[0686] As an example, a reported configuration information indicates the RS resources used to calculate the reported channel measurement.

[0687] As an example, a reported configuration information indicates at least one of the reported amount and the targeted frequency domain resource.

[0688] As an example, a measurement configuration for a cell includes the inference output of the measurement configuration or the reporting of the measurement configuration being transmitted on the cell.

[0689] As an example, a measurement configuration for a cell includes the inference output of the measurement configuration or the report of the measurement configuration being transmitted on the physical layer channel of the cell.

[0690] As an example, in the plurality of measurement configurations, the inference outputs of two measurement configurations are transmitted on different cells.

[0691] As one example, among the multiple measurement configurations, two measurement configurations target reports transmitted on different cells.

[0692] As an example, the inference outputs of any two of the plurality of measurement configurations are transmitted on different cells.

[0693] As an example, the reports for any two of the plurality of measurement configurations are transmitted on different cells.

[0694] As an example, a measurement configuration for a cell includes a measurement configuration for which inference or reporting is triggered by a DCI received on the cell.

[0695] As an example, in one of the plurality of measurement configurations, inference for two measurement configurations is triggered by DCI received on different cells.

[0696] As an example, among the multiple measurement configurations, there are two measurement configurations that target DCI triggers received on different cells.

[0697] As an example, the inference for any two of the plurality of measurement configurations is triggered by DCI received on different cells.

[0698] As an example, the reporting of any two of the plurality of measurement configurations is triggered by DCI received on different cells.

[0699] Example 14

[0700] Example 14 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 14.

[0701] As an example, the associated ID is a non-negative integer.

[0702] As an example, the associated ID is a string.

[0703] As an example, the associated ID indicates an association between two or more RS resources, or between two or more groups of RS resources.

[0704] As a sub-implementation of the above embodiments, the association includes having the same or similar characteristics.

[0705] As a sub-implementation of the above embodiments, the association includes quasi-co-located.

[0706] As a sub-implementation of the above embodiments, the association includes quasi-co-addressing and the corresponding quasi-co-addressing type includes TypeD.

[0707] As a sub-example of the above embodiments, the association includes training datasets used to generate the same model.

[0708] As a sub-example of the above embodiments, the association includes inference datasets used to generate the same model.

[0709] As a sub-example of the above embodiments, the association includes training datasets or inference datasets used to generate the same model.

[0710] As a sub-example of the above embodiments, the association includes performance monitoring datasets or inference datasets used to generate the same model.

[0711] As an example, the features include one or more of delay spread, Doppler spread, Doppler shift, average delay, or spatial reception parameters.

[0712] As one embodiment, the feature includes a downlink transmit beam or a set of downlink transmit beams.

[0713] As an example, if two or more RS resources are associated with the same association identifier, the two or more RS resources are used to generate the same training dataset, inference dataset, or performance monitoring dataset for the same model.

[0714] As an example, if two or more RS resources are associated with the same association identifier, the two or more RS resources are used to generate the training dataset and inference dataset of the same model, respectively.

[0715] As an example, if two or more RS resources are associated with the same association identifier, the two or more RS resources are used to generate the same model's performance monitoring dataset and inference dataset, respectively.

[0716] As an example, if two RS resources are associated with the same association identifier, the two RS resources are quasi-co-located.

[0717] As an example, if two or more RS resources are associated with the same association identifier, the two or more RS resources have the same or similar characteristics.

[0718] As an example, if two sets of RS resources are associated with the same association identifier, one set of RS resources is used to generate a training dataset or inference dataset for a model, and the inference output of the model involves the other set of RS resources.

[0719] As an example, if two sets of RS resources are associated with the same association identifier, any RS resource in one set of RS resources and one RS resource in the other set of RS resources are quasi-co-located.

[0720] As an example, if two sets of RS resources are associated with the same association identifier, any RS resource in one set of RS resources and any RS resource in the other set of RS resources have the same or similar characteristics.

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

[0722] As a sub-example of the above embodiments, the association includes that the dataset belongs to the training dataset of the model.

[0723] As a sub-example of the above embodiments, the association includes that the dataset belongs to the inference dataset of the model.

[0724] As a sub-example of the above embodiments, the association includes that the dataset belongs to the performance monitoring dataset of the model.

[0725] As an example, the association identifier indicates the association between one or a group of RS resources and a model.

[0726] As a sub-example of the above embodiments, the association includes that the one or a group of RS resources are used to generate the inference dataset, training dataset or performance monitoring dataset of the model.

[0727] 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 one or a set of RS resources.

[0728] As an example, if one or a group of RS resources are associated with the same association identifier as a model, the one or a group of RS resources are used to generate at least one of the training dataset, inference dataset, and performance monitoring dataset of the model.

[0729] As an example, if a set of RS resources and a model are associated with the same association identifier, the inference output of the model involves the set of RS resources.

[0730] As an example, the association identifier indicates the association between a model and a function.

[0731] As a sub-implementation of the above embodiments, the association includes the fact that the one function depends on the one model.

[0732] As a sub-implementation of the above embodiments, the association includes the fact that the model is used for the function.

[0733] As a sub-implementation of the above embodiments, the association includes the inference of the model being used for the function.

[0734] As a sub-implementation of the above embodiments, the association includes the use of a model to generate a report corresponding to the function.

[0735] As an example, if a model and a function are associated with the same association identifier, the model is used for the function.

[0736] As an example, if a model and a function are associated with the same association identifier, the model is used to generate the report corresponding to the function.

[0737] As an example, the association identifier indicates the association between one or a group of RS resources and a dataset.

[0738] As a sub-example of the above embodiments, the association includes the use of one or a group of RS resources to generate the dataset.

[0739] As a sub-example of the above embodiments, the association includes that measurements on the one or a set of RS resources are used to generate the dataset.

[0740] As a sub-example of the above embodiments, the association includes that channel measurements on the one or a set of RS resources are used to generate the dataset.

[0741] As an example, if one or a group of RS resources and a dataset are associated with the same association identifier, measurements on the one or a group of RS resources are used to generate the dataset.

[0742] As an example, the association identifier indicates the association between one or a group of RS resources and a report.

[0743] As a sub-example of the above embodiments, the association includes that the one or a group of RS resources are used to obtain the measurement that generates the one report.

[0744] As a sub-example of the above embodiments, the association includes that the one or a group of RS resources are used to obtain the channel measurement that generates the one report.

[0745] As a sub-implementation of the above embodiments, the association includes the fact that the one report relates to the one or a group of RS resources.

[0746] As an example, if one or a group of RS resources and a report are associated with the same association identifier, the measurement on the one or a group of RS resources is used to generate the report.

[0747] As an example, if a set of RS resources and a report are associated with the same association identifier, the report relates to the set of RS resources.

[0748] As an example, the association identifier indicates the association between a model and a report.

[0749] As a sub-implementation of the above embodiments, the association includes the fact that the reporting depends on the output of the model.

[0750] As a sub-implementation of the above embodiments, the association includes that the output of the model is used to generate the report.

[0751] As a sub-implementation of the above embodiments, the association includes that the one report includes all or part of the output of the one model, or includes all or part of the post-processed output of the one model.

[0752] As an example, if a model and a report are associated with the same association identifier, the report depends on the output of the model.

[0753] As an example, if a model and a report are associated with the same association identifier, the output of the model is used to generate the report.

[0754] 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.

[0755] As an example, one of the plurality of measurement configurations includes only one of the associated identifiers.

[0756] As an example, one of the plurality of measurement configurations includes a plurality of the associated identifiers.

[0757] As an example, the model for the inference reported first is associated with a first association identifier, which is the association identifier included in the first measurement configuration.

[0758] As one example, the second message indicates the first associated identifier.

[0759] As one example, the first message indicates the first associated identifier.

[0760] As an example, the first measurement configuration includes a plurality of associated identifiers, and the first associated identifier is one of the plurality of associated identifiers.

[0761] As a sub-implementation of the above embodiments, the first message indicates the first association identifier from the plurality of association identifiers.

[0762] As a sub-implementation of the above embodiments, one or a group of RS resources associated with the first association identifier are used to obtain channel measurements for generating an inference dataset, performance monitoring dataset, or training dataset for the inference model reported for the first time.

[0763] As a sub-example of the above embodiments, the output of the inference for the first report involves a set of RS resources associated with the first association identifier.

[0764] As an example, a model being associated with an association identifier includes the model being identified by the association identifier.

[0765] As an example, a model being associated with an association identifier includes the inference of the model being identified by the association identifier.

[0766] As an example, associating a model with an association identifier includes the inference dataset of the model being identified by the association identifier.

[0767] 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.

[0768] As an example, associating a model with an association identifier includes the training of the model being identified by the association identifier.

[0769] As an example, associating a model with an association identifier includes the training dataset of the model being identified by the association identifier.

[0770] 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.

[0771] As an example, a model is associated with an association identifier, and the performance monitoring of the model is identified by the association identifier.

[0772] 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.

[0773] As an example, a model being associated with an association identifier includes the function that the model targets being identified by the association identifier.

[0774] As a preferred embodiment, associating a model with an association identifier includes using one or more RS resources associated with the association identifier to obtain channel measurements of the inference dataset, performance monitoring dataset, or training dataset that generated the model.

[0775] As an example, a model associated with an association identifier includes the output of the model relating to a set of RS resources associated with the association identifier.

[0776] Example 15

[0777] Example 15 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 15. In Example 15, the third processor sends a first dataset to the fourth processor and a second dataset to the fifth processor; the fourth processor generates a target first-class parameter set based on the first dataset, and sends the generated target first-class parameter set to the fifth processor; the fifth 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 sixth processor. In Figure 15, the first-class feedback and the second-class feedback are optional; the fourth processor includes ML training functionality; the fifth processor includes ML inference functionality.

[0778] As one embodiment, the sixth processor includes ML testing functionality.

[0779] As one example, the sixth processor includes performance monitoring / evaluation of the ML model.

[0780] As one embodiment, the sixth processor includes the inverse operation of the fifth processor.

[0781] As an example, the fifth processor sends a first type of feedback to the fourth 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.

[0782] As one embodiment, the sixth processor sends a second type of feedback to the third processor, the second type of feedback being used to generate the first dataset or the second dataset, or the second type of feedback being used to trigger the sending of the first dataset or the second dataset.

[0783] As one embodiment, the third processor generates the first dataset and the second dataset based on the measurement of the reference signal.

[0784] As one embodiment, the fifth processor is located at the first node.

[0785] As one embodiment, the sixth processor is located at either the first node or the second node.

[0786] As an example, the second dataset includes measurements for RS.

[0787] As an example, the second dataset includes the reception of PDSCH.

[0788] As an example, the first dataset includes training data.

[0789] As an example, the first dataset is a training dataset.

[0790] As an example, the fourth processor is used to train an ML model, and the trained model is described by the target first class of parameter sets.

[0791] As one embodiment, the fourth processor is located at the first node.

[0792] The above embodiments avoid passing the first dataset to the second node.

[0793] As one embodiment, the fourth processor is located in the core network.

[0794] The above embodiments support network-wide joint training, further optimizing system performance.

[0795] As an example, the second dataset includes inference data.

[0796] As an example, the second dataset is an inference dataset.

[0797] As an example, the input to an inference belongs to an inference dataset.

[0798] As an example, the fifth processor constructs a model based on the target first type of parameter group, and then inputs the second dataset into the constructed model to obtain the first type of output.

[0799] As an example, the fifth processor compares the real data with the first type of output, and the resulting error is used to generate the first type of feedback.

[0800] As an example, the fifth processor generates the first type of feedback through performance monitoring.

[0801] As an example, the first type of feedback is used to reflect the performance of the trained model; when the performance of the trained model fails to meet the requirements, the fourth processing opportunity recalculates the target first type of parameter set.

[0802] As an example, the sixth processor compares the real data with the first type of output, and the resulting error is used to generate the second type of feedback.

[0803] As an example, the sixth processor generates the second type of feedback through performance monitoring.

[0804] 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 third processor sends the first dataset to trigger or assist the fourth processor in recalculating the target first type of parameter set.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] As one example, the ML includes AI.

[0809] As an example, the ML includes ML and AI.

[0810] Example 16

[0811] Example 16 illustrates a schematic diagram based on artificial intelligence or machine learning according to an embodiment of this application; as shown in Figure 16. Figure 16 includes a first operation, a second operation, a third operation, a fourth operation, and a fifth operation. In Example 16, 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 16, lines with arrows indicate the sequence of processes.

[0812] 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.

[0813] 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.

[0814] As an example, the first stage includes ML model training.

[0815] As an example, the first stage includes ML model training and ML testing.

[0816] As an example, the ML model training includes initial training and re-training of one or a group of ML models.

[0817] As an example, the training of the ML model depends on training data.

[0818] As an example, the ML model training includes ML entity validation.

[0819] As an example, the ML entity verification is used to evaluate the performance of the ML entity.

[0820] As an example, the ML entity verification depends on verification data.

[0821] As an example, if the results of ML entity verification do not meet expectations, the ML model will be retrained.

[0822] As an example, the ML testing includes testing the validated ML entities to estimate the performance of the trained ML model.

[0823] 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.

[0824] As an example, the ML test relies on test data.

[0825] As one embodiment, the second stage includes ML simulation, which performs inference of ML entities in a simulation environment.

[0826] As an example, the ML simulation estimates the performance of ML entity reasoning in a simulation environment before using ML entities.

[0827] As one embodiment, the second stage is optional.

[0828] As an example, the third stage includes ML entity loading, which is to obtain trained ML entities to obtain the desired AI inference capabilities.

[0829] As an example, the third stage is optional.

[0830] As an example, the third stage is no longer needed when the training and inference functions are co-located.

[0831] As an example, the fourth stage includes AI inference.

[0832] As an example, the AI ​​inference relies on inference data.

[0833] As an example, the input to an AI inference belongs to the inference dataset of the AI ​​inference model.

[0834] As one example, the ML includes AI.

[0835] As one example, the AI ​​includes ML.

[0836] Example 17

[0837] Example 17 illustrates a schematic diagram of AI function deployment according to one embodiment of this application; as shown in Figure 17.

[0838] In Example 17, the AI ​​training function of the RAN (Radio Access Network) domain is located in the 3GPP RAN domain-specific management function, while the AI ​​inference function is located in the UE.

[0839] In Example 17, RAN domain-specific management functions provide AI training function management capabilities and AI inference function management capabilities.

[0840] Example 18

[0841] Example 18 illustrates a schematic diagram of AI function deployment according to one embodiment of this application; as shown in Figure 18.

[0842] In Example 18, the AI ​​training function is a management function specific to the RAN domain, while the AI ​​inference function is located locally on the UE.

[0843] In Example 18, 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.

[0844] In Figure 18, MnF refers to Management Function.

[0845] Example 19

[0846] Example 19 illustrates a schematic diagram of AI function deployment according to one embodiment of this application; as shown in Figure 19.

[0847] In Example 19, 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.

[0848] In Example 19, RAN domain-specific management functions provide management capabilities for both AI training and AI inference functions.

[0849] Example 20

[0850] Example 20 illustrates a schematic diagram of AI function deployment according to one embodiment of this application; as shown in Figure 20.

[0851] In Example 20, both the AI ​​training function and the AI ​​inference function are located in the UE.

[0852] In Example 20, the management capabilities of both the AI ​​training function and the AI ​​inference function are provided locally by the UE.

[0853] In Figure 20, MnF refers to Management Function.

[0854] Example 21

[0855] Example 21 illustrates a structural block diagram of a processing apparatus for a first node according to an embodiment of the present application; as shown in Figure 21. In Figure 21, the processing apparatus 2100 in the first node includes a first processor 2101.

[0856] In embodiment 21, the first processor 2101 sends a first message.

[0857] In Example 21, the first message indicates multiple measurement configurations, all of which may be available or all of which may be unavailable.

[0858] As an example, any one of the plurality of measurement configurations is used to configure an inference.

[0859] As an example, any one of the plurality of measurement configurations is used to determine an inference-related parameter.

[0860] As a sub-example of the above embodiments, the inference-related parameters are used to determine at least one of the inference input, output, and model.

[0861] As an example, the multiple measurement configurations may all be available or none of them may be available.

[0862] As an example, when one of the plurality of measurement configurations is available, the other measurement configurations in the plurality of measurement configurations are also available; when one of the plurality of measurement configurations is unavailable, the other measurement configurations in the plurality of measurement configurations are also unavailable.

[0863] As an example, the plurality of measurement configurations will not simultaneously include both available and unavailable measurement configurations.

[0864] As an example, the first processor 2101 receives a second message indicating K0 measurement configurations, where K0 is a positive integer greater than 1; wherein the plurality of measurement configurations is a subset of the K0 measurement configurations.

[0865] As one embodiment, the first processor 2101 sends a first report; wherein the first report is based on inference, and a first measurement configuration among the plurality of measurement configurations is used to determine parameters for the inference of the first report.

[0866] As an example, the first processor 2101 receives a third message; wherein the third message indicates the parameters for the inference of the first reported inference.

[0867] As an example, the third message activates the plurality of measurement configurations.

[0868] As one embodiment, the first processor 2101 receives a fourth message; wherein the fourth message activates or deactivates one of the plurality of measurement configurations; and as a response to the receipt of the fourth message, all of the plurality of measurement configurations are activated or deactivated.

[0869] As an example, with the activation of one of the plurality of measurement configurations, each of the other plurality of measurement configurations is also activated.

[0870] As an example, with the deactivation of one of the plurality of measurement configurations, each of the other configurations in the plurality of measurement configurations is also deactivated.

[0871] As one embodiment, the first processor 2101 receives a fourth message, which activates or deactivates one of the plurality of measurement configurations; wherein, when the fourth message activates the one of the plurality of measurement configurations, only the one of the plurality of measurement configurations is activated by the fourth message; when the fourth message deactivates the one of the plurality of measurement configurations, each of the plurality of measurement configurations is deactivated by the fourth message.

[0872] As one example, two of the multiple measurement configurations are for different cells.

[0873] As an example, any of the plurality of measurement configurations targets the serving cell of the first node.

[0874] As an example, one of the multiple measurement configurations may target a cell that is not the serving cell of the first node.

[0875] As an example, any of the plurality of measurement configurations includes an associated identifier.

[0876] As an example, two of the multiple measurement configurations include different association identifiers.

[0877] As a preferred embodiment, the two measurement configurations including different association identifiers mean that measurements on different RS resource groups are used to generate the inference dataset for the inference targeted by the two measurement configurations.

[0878] As one embodiment, the first node includes a terminal.

[0879] As one embodiment, the first node includes a user equipment.

[0880] As one embodiment, the first node includes a relay node device.

[0881] As an example, the first processor 2101 includes at least one of the following in embodiment 4: {antenna 452, receiver / transmitter 454, receiving processor 456, transmitting processor 468, multi-antenna receiving processor 458, multi-antenna transmitting processor 457, controller / processor 459, memory 460, data source 467}.

[0882] Example 22

[0883] Example 22 illustrates a structural block diagram of a processing apparatus in a second node according to an embodiment of the present application; as shown in Figure 22. In Figure 22, the processing apparatus 2200 in the second node includes a second processor 2201.

[0884] In embodiment 22, the second processor 2201 receives the first message.

[0885] In Example 22, the first message indicates multiple measurement configurations, all of which may be available or all of which may be unavailable.

[0886] As an example, any one of the plurality of measurement configurations is used to configure an inference.

[0887] As an example, any one of the plurality of measurement configurations is used to determine an inference-related parameter.

[0888] As a sub-example of the above embodiments, the inference-related parameters are used to determine at least one of the inference input, output, and model.

[0889] As an example, the multiple measurement configurations may all be available or none of them may be available.

[0890] As an example, when one of the plurality of measurement configurations is available, the other measurement configurations in the plurality of measurement configurations are also available; when one of the plurality of measurement configurations is unavailable, the other measurement configurations in the plurality of measurement configurations are also unavailable.

[0891] As an example, the plurality of measurement configurations will not simultaneously include both available and unavailable measurement configurations.

[0892] As one embodiment, the second processor 2201 sends a second message indicating K0 measurement configurations, where K0 is a positive integer greater than 1; wherein the plurality of measurement configurations is a subset of the K0 measurement configurations.

[0893] As one embodiment, the second processor 2201 receives a first report; wherein the first report is based on inference, and a first measurement configuration of the plurality of measurement configurations is used to determine parameters for the inference of the first report.

[0894] As one embodiment, the second processor 2201 sends a third message; wherein the third message indicates the parameters for the inference of the first reported inference.

[0895] As an example, the third message activates the plurality of measurement configurations.

[0896] As one embodiment, the second processor 2201 sends a fourth message; wherein the fourth message activates or deactivates one of the plurality of measurement configurations; in response to receiving the fourth message, the target recipient of the fourth message considers that the plurality of measurement configurations are either activated or deactivated.

[0897] As an example, with the activation of one of the plurality of measurement configurations, each of the other plurality of measurement configurations is also activated.

[0898] As an example, with the deactivation of one of the plurality of measurement configurations, each of the other configurations in the plurality of measurement configurations is also deactivated.

[0899] As one embodiment, the second processor 2201 sends a fourth message, which activates or deactivates one of the plurality of measurement configurations; wherein, when the fourth message activates the one of the plurality of measurement configurations, only the one of the plurality of measurement configurations is activated by the fourth message; when the fourth message deactivates the one of the plurality of measurement configurations, each of the plurality of measurement configurations is deactivated by the fourth message.

[0900] As one example, two of the multiple measurement configurations are for different cells.

[0901] As an example, any of the plurality of measurement configurations targets the serving cell of the first node.

[0902] As an example, one of the multiple measurement configurations may target a cell that is not the serving cell of the first node.

[0903] As an example, any of the plurality of measurement configurations includes an associated identifier.

[0904] As an example, two of the multiple measurement configurations include different association identifiers.

[0905] As a preferred embodiment, the two measurement configurations including different association identifiers mean that measurements on different RS resource groups are used to generate the inference dataset for the inference targeted by the two measurement configurations.

[0906] As one embodiment, the second node includes a base station.

[0907] As one embodiment, the second node includes a base station device.

[0908] As one embodiment, the second node includes a relay node device.

[0909] As one embodiment, the second node includes the sustaining base station of the serving cell of the first node.

[0910] As one embodiment, the second node includes an OTT (Over-The-Top) server.

[0911] As an example, the second node provides OAM (Operation Administration and Maintenance).

[0912] As one embodiment, the second node includes a NAS (Network Access Server).

[0913] As one embodiment, the second node includes a NAS device.

[0914] As one example, the second node provides network access services.

[0915] As one embodiment, the second node includes core network equipment.

[0916] As one embodiment, the second node includes base station equipment and core network equipment.

[0917] As one embodiment, the second node includes a base station device and a NAS device.

[0918] As one embodiment, the second processor 2201 includes at least one of the following in embodiment 4: {antenna 420, transmitter / receiver 418, transmitter processor 416, receiver processor 470, multi-antenna transmitter processor 471, multi-antenna receiver processor 472, controller / processor 475, memory 476}.

[0919] 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.

[0920] 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 processor sends a first message indicating multiple measurement configurations; The multiple measurement configurations may be available or unavailable as a whole.

2. The first node according to claim 1, characterized in that, The first processor receives a second message indicating K0 measurement configurations, where K0 is a positive integer greater than 1; wherein the plurality of measurement configurations are a subset of the K0 measurement configurations.

3. The first node according to claim 1 or 2, characterized in that, The first processor sends a first report; wherein the first report is based on inference, and a first measurement configuration of the plurality of measurement configurations is used to determine parameters for the inference of the first report.

4. The first node according to claim 3, characterized in that, The first processor receives a third message; wherein the third message indicates the parameters for the inference of the first report.

5. The first node according to claim 4, characterized in that, The third message activates the multiple measurement configurations.

6. The first node according to any one of claims 1 to 5, characterized in that, The first processor receives a fourth message; wherein the fourth message activates or deactivates one of the plurality of measurement configurations; in response to the receipt of the fourth message, all of the plurality of measurement configurations are either activated or deactivated.

7. The first node according to any one of claims 1 to 5, characterized in that, The first processor receives a fourth message, which activates or deactivates one of the plurality of measurement configurations; wherein, when the fourth message activates the one of the plurality of measurement configurations, only the one of the plurality of measurement configurations is activated by the fourth message; when the fourth message deactivates the one of the plurality of measurement configurations, each of the plurality of measurement configurations is deactivated by the fourth message.

8. The first node according to any one of claims 1 to 7, characterized in that, Two of the multiple measurement configurations are for different cells.

9. The first node according to any one of claims 1 to 8, characterized in that, Each of the plurality of measurement configurations includes an associated identifier.

10. The first node according to claim 9, characterized in that, Among the multiple measurement configurations, two measurement configurations have different associated identifiers.

11. A second node used for wireless communication, characterized in that, include: The second processor receives a first message, which indicates multiple measurement configurations; The multiple measurement configurations may be available or unavailable as a whole.

12. The second node according to claim 11, characterized in that, The second processor sends a second message indicating K0 measurement configurations, where K0 is a positive integer greater than 1; wherein the plurality of measurement configurations are a subset of the K0 measurement configurations.

13. The second node according to claim 11 or 12, characterized in that, The second processor receives a first report; wherein the first report is based on inference, and a first measurement configuration of the plurality of measurement configurations is used to determine parameters for the inference of the first report.

14. The second node according to claim 13, characterized in that, The second processor sends a third message; wherein the third message indicates the parameters for the inference of the first report.

15. The second node according to claim 14, characterized in that, The third message activates the multiple measurement configurations.

16. The second node according to any one of claims 11 to 15, characterized in that, The second processor sends a fourth message; wherein the fourth message activates or deactivates one of the plurality of measurement configurations; in response to receiving the fourth message, the target recipient of the fourth message considers that the plurality of measurement configurations are either activated or deactivated.

17. The second node according to any one of claims 11 to 15, characterized in that, The second processor sends a fourth message, which activates or deactivates one of the plurality of measurement configurations; wherein, when the fourth message activates the one of the plurality of measurement configurations, only the one of the plurality of measurement configurations is activated by the fourth message; when the fourth message deactivates the one of the plurality of measurement configurations, each of the plurality of measurement configurations is deactivated by the fourth message.

18. The second node according to any one of claims 11 to 17, characterized in that, Two of the multiple measurement configurations are for different cells.

19. The second node according to any one of claims 11 to 18, characterized in that, Each of the plurality of measurement configurations includes an associated identifier.

20. The second node according to claim 19, characterized in that, Among the multiple measurement configurations, two measurement configurations have different associated identifiers.

21. A method used in a first node of wireless communication, characterized in that, include: Send a first message indicating multiple measurement configurations; The multiple measurement configurations may be available or unavailable as a whole.

22. The method in the first node according to claim 21, characterized in that, include: Receive a second message indicating K0 measurement configurations, where K0 is a positive integer greater than 1; The plurality of measurement configurations are a subset of the K0 measurement configurations.

23. The method in the first node according to claim 21 or 22, characterized in that, include: Send the first report; Wherein, the first report is based on inference, and the first measurement configuration among the plurality of measurement configurations is used to determine the parameters of the inference for the first report.

24. The method in the first node according to claim 23, characterized in that, include: Receive third message; The third message indicates the parameters for the inference in the first report.

25. The method in the first node according to claim 24, characterized in that, The third message activates the multiple measurement configurations.

26. The method in the first node according to any one of claims 21 to 25, characterized in that, include: Received the fourth message; The fourth message activates or deactivates one of the plurality of measurement configurations; In response to the receipt of the fourth message, the plurality of measurement configurations are either activated or deactivated.

27. The method in the first node according to any one of claims 21 to 25, characterized in that, include: Receive a fourth message, which activates or deactivates one of the plurality of measurement configurations; Specifically, when the fourth message activates one of the plurality of measurement configurations, only one of the plurality of measurement configurations is activated by the fourth message; When the fourth message deactivates one of the plurality of measurement configurations, each of the plurality of measurement configurations is deactivated by the fourth message.

28. The method in the first node according to any one of claims 21 to 27, characterized in that, Two of the multiple measurement configurations are for different cells.

29. The method in the first node according to any one of claims 21 to 28, characterized in that, Each of the plurality of measurement configurations includes an associated identifier.

30. The method in the first node according to claim 29, characterized in that, Among the multiple measurement configurations, two measurement configurations have different associated identifiers.

31. A method used in a second node of wireless communication, characterized in that, include: Receive a first message, which indicates multiple measurement configurations; The multiple measurement configurations may be available or unavailable as a whole.

32. The method in the second node according to claim 31, characterized in that, include: Send a second message indicating K0 measurement configurations, where K0 is a positive integer greater than 1; The plurality of measurement configurations are a subset of the K0 measurement configurations.

33. The method in the second node according to claim 31 or 32, characterized in that, include: Receive the first report; Wherein, the first report is based on inference, and the first measurement configuration among the plurality of measurement configurations is used to determine the parameters of the inference for the first report.

34. The method in the second node according to claim 33, characterized in that, include: Send a third message; The third message indicates the parameters for the inference in the first report.

35. The method in the second node according to claim 34, characterized in that, The third message activates the multiple measurement configurations.

36. The method in the second node according to any one of claims 31 to 35, characterized in that, include: Send the fourth message; The fourth message activates or deactivates one of the plurality of measurement configurations; In response to receiving the fourth message, the target recipient of the fourth message considers that the plurality of measurement configurations are either activated or deactivated.

37. The method in the second node according to any one of claims 31 to 35, characterized in that, include: A fourth message is sent, which activates or deactivates one of the plurality of measurement configurations; Specifically, when the fourth message activates one of the plurality of measurement configurations, only one of the plurality of measurement configurations is activated by the fourth message; When the fourth message deactivates one of the plurality of measurement configurations, each of the plurality of measurement configurations is deactivated by the fourth message.

38. The method in the second node according to any one of claims 31 to 37, characterized in that, Two of the multiple measurement configurations are for different cells.

39. The method in the second node according to any one of claims 31 to 38, characterized in that, Each of the plurality of measurement configurations includes an associated identifier.

40. The method in the second node according to claim 39, characterized in that, Among the multiple measurement configurations, two measurement configurations have different associated identifiers.

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