A method and apparatus used in a node for wireless communication and artificial intelligence

Abstract

The application discloses a method and device used in a node for wireless communication and artificial intelligence. A first node receives a first configuration message and a second configuration message, the first configuration message indicating a first association ID, and a first measurement result depending on the second configuration message; a first reporting message is sent, or the sending of the first reporting message is abandoned; the first association ID is associated to a first RS resource set, the first reporting message depends on reasoning, and an input of the reasoning depends on measurement on the first RS resource set; whether the first reporting message is sent depends on the first measurement result; when the first measurement result meets a first condition, the first node abandons to send the first reporting message. The application makes full use of the generalization ability of model reasoning, and realizes AI reasoning with operation correlation in the same entity, which helps to save resources.

Description

Technical Field

[0001] This application relates to signal transmission methods and apparatus in wireless communication systems, and more particularly to measurement reporting methods and apparatus. Background Technology

[0002] Leveraging AI / ML (Artificial Intelligence / Machine Learning) technologies to enhance 5G network performance is a crucial component of achieving deep integration of 5G and AI / ML and building intelligent dimensions for 5G-Advanced (5.5G) networks. The 3GPP (3rd Generation Partnership Project) standards organization initiated research on standards for RAN (Radio Access Networks) intelligence starting with Rel-16 (Release-16), primarily focusing on intelligent use cases, enhanced data collection, and the potential impact on RAN nodes and interfaces. Rel-18 formally established a project for AI / ML-based 5G air interface enhancement, initiating international standardization work on the integration of 5G air interface and AI / ML, mainly focusing on research use cases, lifecycle management (LCM), simulation verification, and data collection.

[0003] Currently, the development of AI / ML has entered the stage of large-scale models. Large-scale communication models can realize autonomous networks and intelligent services, support network operation optimization, and improve network efficiency. The deep integration of communication and AI is an important direction for the future evolution of communication. AI will empower the development and upgrading of 5G, 5.5G to 6G, bringing new management models such as automated management of frequency bands and traffic, real-time analysis of user data and network load, and prediction of network status. Summary of the Invention

[0004] In the RAN1 discussion on LCM, the concept of association ID was introduced to address the data collection mechanism of the UE-side AI / ML model during the update, training, and inference processes. The basic principle is to identify beams, beam lists, or beam sets with similar characteristics using the same association ID in order to optimize the training, inference, and update of the AI ​​model. The key issues that need to be addressed are how to determine the relationship between the association ID and the reference signal resource set, and the relationship between the association ID and the AI ​​model.

[0005] To address the aforementioned issues, this application discloses a solution. It should be noted that while this application is initially intended for AI / ML scenarios, it can also be applied to other non-AI / ML scenarios. Furthermore, adopting a unified design scheme for different scenarios (such as other non-AI / ML scenarios, including but not limited to Vehicle to Everything (V2X), capacity enhancement systems, short-range communication systems, NTN (Non-Terrestrial Network), IoT (Internet of Things), and URLLC (Ultra-Reliable Low-Latency Communication) networks) helps reduce hardware complexity and cost. Where there is no conflict, embodiments and features in any node of this application can be applied to any other node. Where there is no conflict, embodiments and features in any embodiment of this application can be arbitrarily combined with each other.

[0006] In particular, the interpretation of terms, nouns, functions, and variables in this application (unless otherwise specified) can be found in the definitions of the TS38 and TS37 series of 3GPP (3rd Generation Partnership Project) Technical Specifications (TS). Where necessary, reference can be made to TS38.211, TS38.212, TS38.213, TS38.214, TS38.215, TS38.300, TS38.304, TS38.305, TS38.321, TS38.331, TS37.355, and TS38.423 in the 3GPP technical specifications to aid in understanding this application.

[0007] As an example, the interpretation of terms in this application is based on the definitions in the 3GPP specification protocol TS38 series.

[0008] As an example, the interpretation of terms in this application is based on the definitions in the 3GPP specification protocol TS37 series.

[0009] As an example, the interpretation of the terms used in this application is based on the definitions in 3GPP specification protocol Rel-17.

[0010] As an example, the interpretation of the terms used in this application is based on the definitions in 3GPP specification protocol Rel-18.

[0011] This application discloses a method for a first node in wireless communication and artificial intelligence, comprising:

[0012] Receive a first configuration message, which indicates a first association ID and a second association ID;

[0013] Send the first report;

[0014] Wherein, the first association ID and the second association ID are respectively associated with the first RS resource set and the second RS resource set; the first RS resource set and the second RS resource set are both configured to the first entity, the first entity corresponds to a model or the first entity corresponds to a function; the first reporting message depends on the reasoning of the first entity, and the input of the reasoning of the first entity depends on the measurement on at least one of the first RS resource set or the second RS resource set.

[0015] As an example, the problem this application aims to solve includes: the relationship between association IDs and AI entities.

[0016] As an example, the problem this application aims to solve includes: the relationship between the association ID and the RS resource set.

[0017] As an example, the problem this application aims to solve includes: how the first node performs performance monitoring on AI entities.

[0018] As an example, the features of the above method include: in this application, the first entity is associated with two association IDs, the two association IDs are associated with two RS resource sets, and the first node performs inference based on the two RS resource sets and reports the inference results, thereby solving the above problem.

[0019] As an example, the features of the above method include: the first node is a terminal.

[0020] As an example, the features of the above method include: the first entity is simultaneously associated with the first association ID and the second association ID.

[0021] As an example, the features of the above method include: the first reporting message includes reasoning by the first entity regarding at least one of the first RS resource set and the second RS resource set.

[0022] As an example, the advantages of the above method include: this application supports the deep integration of AI and communication, improves the adaptability and intelligence level of the communication system, and thus enhances the performance, efficiency and user experience of the communication system.

[0023] As an example, the advantages of the above method include: timely reporting of inference output by the terminal can help the base station configure transmission parameters based on the inference results, improve the reliability of signal transmission, and reduce the probability of communication interruption.

[0024] As an example, the advantages of the above method include: timely reporting of inference output by the terminal can help the base station optimize resource scheduling based on the inference results, thereby improving system capacity and efficiency.

[0025] As an example, the advantages of the above method include: associating an entity with two IDs simultaneously can fully utilize the generalization ability of model reasoning, and can concentrate AI reasoning with computational relevance into the same entity, which helps to save resources.

[0026] According to one aspect of this application, the above method is characterized in that the first association ID is public and the second association ID is private.

[0027] As an example, the features of the above method include: an AI entity can be associated with both a public association ID and a UE-specific association ID.

[0028] As an example, the features of the above method include: the first node considers the first association ID to be unique in the first node's serving cell or serving cell group.

[0029] As an example, the features of the above method include: the second association ID is exclusive to the first node.

[0030] As an example, the benefits of the above method include: supporting efficient management of public AI inference and allowing unified management of training data within the community.

[0031] As an example, the advantages of the above method include: supporting terminal-specific AI inference and improving the accuracy of inference.

[0032] As an example, the advantages of the above method include: reducing the number of AI models deployed on the terminal, simplifying terminal operation, and improving terminal performance.

[0033] According to one aspect of this application, the above method is characterized in that the first RS resource set is public and the second RS resource set is private.

[0034] As an example, the features of the above method include: the first RS resource set is cell-specific, and the second RS resource set is UE-specific.

[0035] As an example, the features of the above method include: the first RS resource set includes at least one cell synchronization signal, and the second RS resource set includes at least one terminal synchronization signal.

[0036] As an example, the advantages of the above method include: different RS resource types are used for AI inference of different functions of the same AI model, which reduces the complexity of deploying AI models on the terminal while ensuring the accuracy of inference.

[0037] As an example, the advantages of the above method include: simplifying terminal operation and saving terminal power consumption.

[0038] As an example, the benefits of the above method include: reducing the management and training difficulty of AI models and improving the accuracy of model inference.

[0039] According to one aspect of this application, the above method is characterized in that the first reporting message includes a first type of result, which is used simultaneously for layer 1 and layer 3.

[0040] As an example, the features of the above method include: the first type of results are used simultaneously for cell-level and beam-level mobility management.

[0041] As an example, the features of the above method include: the first reporting message carries the inference results of both layer 1 and layer 3.

[0042] As one example, the advantages of the above method include: cell-level and beam-level mobility management involves the allocation and reuse of radio resources, and resource waste or conflicts must be avoided.

[0043] As an example, the advantages of the above method include: reducing the measurement overhead and feedback burden of the terminal.

[0044] As an example, the benefits of the above method include: enabling multimodal learning and enhancing the generalization ability of AI models.

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

[0046] Measurements are performed in the third RS resource set and the fourth RS resource set to obtain a first measurement result and a second measurement result; when at least one of the first measurement result or the second measurement result satisfies a first condition, first information is sent.

[0047] The first association ID and the second association ID are respectively associated with the third RS resource set and the fourth RS resource set, and the first information indicates that the first entity is not applicable.

[0048] As an example, the problem this application aims to solve includes: how to improve the reliability of AI / ML model inference.

[0049] As an example, the problem to be solved by this application includes: a response mechanism after the performance of the UE-side model degrades.

[0050] As an example, the problems to be solved by this application include: how to enhance the measurement reporting mechanism to promote the deep integration of AI / ML and communication.

[0051] As an example, the features of the above method include: in this application, the terminal monitors the performance of the AI ​​model on the terminal side, standardizes the conditions for the AI ​​model performance to decline, and sends the first information when the terminal detects that the AI ​​model performance has declined to an unacceptable level, thereby solving the above-mentioned problem.

[0052] As an example, the features of the above method include: the first measurement result and the second measurement result are KPIs respectively.

[0053] As an example, the advantages of the above method include: the first node does not need to periodically report the first information, which can reduce air interface resource overhead and save power consumption.

[0054] As an example, the benefits of the above method include: it helps to improve the reliability of model inference.

[0055] As an example, the benefits of the above method include: helping base stations monitor the inference performance of AI / ML models in a timely manner and quickly identifying whether the inference performance of AI / ML models has declined.

[0056] According to one aspect of this application, the above method is characterized in that satisfying the first condition means at least one of the following:

[0057] - At least one of the first measurement result or the second measurement result is lower than the first threshold in the first time window;

[0058] - The number of times that at least one of the first measurement result or the second measurement result is below the first threshold within the first time window is greater than a first integer.

[0059] As an example, the characteristics of the above method include: satisfying the first condition indicates that the performance of the AI ​​model or AI entity has degraded to an unacceptable level.

[0060] As an example, the characteristics of the above method include: the AI ​​model, AI entity, or AI function that satisfies the first condition becomes inapplicable.

[0061] As an example, the advantages of the above method include: timely monitoring of the inference performance of AI / ML models and rapid identification of whether the inference performance of AI / ML models has declined.

[0062] As an example, the benefits of the above method include: standardizing the first condition helps improve the quality of business decisions.

[0063] As an example, the benefits of the above method include: improving the reliability and robustness of model inference.

[0064] According to one aspect of this application, the above method is characterized in that the first type of result being used for Layer 1 means that the first type of result includes CSI of Layer 1; the first type of result being used for Layer 3 means that the first type of result is used for at least one of CSI determination, cell selection or cell reselection, handover or location of Layer 3.

[0065] As an example, the characteristics of the above method include: the first type of result is used for cell-level mobility management.

[0066] As an example, the characteristics of the above method include: the first type of result is used for beam-level mobility management.

[0067] As an example, the advantages of the above method include: improving the efficiency of cell handover and reducing the probability of handover failure.

[0068] As an example, the benefits of the above method include: optimizing inter-cell load balancing and improving network capacity.

[0069] As an example, the benefits of the above method include: AI models can capture complex channel characteristics that are difficult to represent by traditional methods, improving the accuracy of CSI reports and measurement reports.

[0070] As an example, the advantages of the above method include: improving the real-time performance of feedback.

[0071] As an example, the advantages of the above method include: enhancing the intelligence level of the network.

[0072] According to one aspect of this application, the above method is characterized in that the first node is a user equipment.

[0073] According to one aspect of this application, the above method is characterized in that the first node is a terminal.

[0074] This application discloses a method for a second node in wireless communication and artificial intelligence, comprising:

[0075] Send a first configuration message, which indicates a first association ID and a second association ID;

[0076] Receive the first reported information;

[0077] Wherein, the first association ID and the second association ID are respectively associated with the first RS resource set and the second RS resource set; the first RS resource set and the second RS resource set are both configured to the first entity, the first entity corresponds to a model or the first entity corresponds to a function; the first reporting message depends on the reasoning of the first entity, and the input of the reasoning of the first entity depends on the measurement on at least one of the first RS resource set or the second RS resource set.

[0078] As an example, the features of the above method include: the second node includes a base station and a core network.

[0079] As an example, the features of the above method include: the second node includes a core network.

[0080] As an example, the features of the above method include: the second node includes an entity for deploying AI / ML models.

[0081] As an example, the features of the above method include: the second node includes a node for deploying AI / ML models.

[0082] As an example, the features of the above method include: the second node includes a base station.

[0083] As an example, the features of the above method include: the second node is a base station.

[0084] As an example, the features of the above method include: the second node is an eNB.

[0085] As an example, the features of the above method include: the second node is a gNB.

[0086] As an example, the features of the above method include: the second node is a network device, which includes at least one of a core network device and an access network device.

[0087] As an example, the features of the above method include: the second node is a device that provides wireless communication function services, can communicate with terminal devices, and is usually located on the network side.

[0088] As an example, the features of the above method include: the base station in this application includes a core network.

[0089] As an example, the features of the above method include: the base station in this application includes core network equipment.

[0090] As an example, the features of the above method include: the base station in this application includes an entity for deploying AI / ML models.

[0091] As an example, the features of the above method include: the base station in this application includes nodes for deploying AI / ML models.

[0092] According to one aspect of this application, the above method is characterized in that the first association ID is public and the second association ID is private.

[0093] According to one aspect of this application, the above method is characterized in that the first RS resource set is public and the second RS resource set is private.

[0094] According to one aspect of this application, the above method is characterized in that the first reporting message includes a first type of result, which is used simultaneously for layer 1 and layer 3.

[0095] According to one aspect of this application, the above method is characterized by comprising:

[0096] Monitoring first information;

[0097] In this process, the sender of the first information performs measurements in the third RS resource set and the fourth RS resource set respectively and obtains a first measurement result and a second measurement result; when at least one of the first measurement result or the second measurement result satisfies a first condition, the sender of the first information sends the first information; the first association ID and the second association ID are respectively associated with the third RS resource set and the fourth RS resource set, and the first information indicates that the first entity is not applicable.

[0098] According to one aspect of this application, the above method is characterized in that satisfying the first condition means at least one of the following:

[0099] - At least one of the first measurement result or the second measurement result is lower than the first threshold in the first time window;

[0100] - The number of times that at least one of the first measurement result or the second measurement result is below the first threshold within the first time window is greater than a first integer.

[0101] According to one aspect of this application, the above method is characterized in that the first type of result being used for Layer 1 means that the first type of result includes CSI of Layer 1; the first type of result being used for Layer 3 means that the first type of result is used for at least one of CSI determination, cell selection or cell reselection, handover or location of Layer 3.

[0102] According to one aspect of this application, the method described above is characterized in that the second node is a base station.

[0103] This application discloses a device for a first node in wireless communication and artificial intelligence, comprising:

[0104] A first receiver receives a first configuration message, the first configuration message indicating a first association ID and a second association ID;

[0105] The first processor sends the first reported information;

[0106] Wherein, the first association ID and the second association ID are respectively associated with the first RS resource set and the second RS resource set; the first RS resource set and the second RS resource set are both configured to the first entity, the first entity corresponds to a model or the first entity corresponds to a function; the first reporting message depends on the reasoning of the first entity, and the input of the reasoning of the first entity depends on the measurement on at least one of the first RS resource set or the second RS resource set.

[0107] This application discloses a device for a second node in wireless communication and artificial intelligence, comprising:

[0108] A first transmitter sends a first configuration message, the first configuration message indicating a first association ID and a second association ID;

[0109] The second receiver receives the first reported information;

[0110] Wherein, the first association ID and the second association ID are respectively associated with the first RS resource set and the second RS resource set; the first RS resource set and the second RS resource set are both configured to the first entity, the first entity corresponds to a model or the first entity corresponds to a function; the first reporting message depends on the reasoning of the first entity, and the input of the reasoning of the first entity depends on the measurement on at least one of the first RS resource set or the second RS resource set.

[0111] As an example, compared with conventional solutions, this application has the following advantages, but is not limited to:

[0112] This application supports the deep integration of AI and communication to improve the adaptability and intelligence of communication systems, thereby enhancing the performance, efficiency, and user experience of communication systems.

[0113] Associating an entity with two IDs simultaneously can fully leverage the generalization ability of model reasoning, allowing AI reasoning with computational relevance to be concentrated on the same entity, which helps save resources.

[0114] It helps base stations monitor the inference performance of AI / ML models in a timely manner and quickly identify whether the inference performance of AI / ML models has declined;

[0115] Reduce the number of AI models deployed on the terminal, simplify terminal operation, and improve terminal performance. Attached Figure Description

[0116] 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:

[0117] Figure 1 A flowchart of the first node transmission according to an embodiment of this application is shown;

[0118] Figure 2 A schematic diagram of a network architecture according to an embodiment of this application is shown;

[0119] Figure 3 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 is shown;

[0120] Figure 4 A schematic diagram of a first communication device and a second communication device according to an embodiment of this application is shown;

[0121] Figure 5 A first flowchart illustrating the transmission between a first node and a second node according to an embodiment of this application is shown;

[0122] Figure 6 A second flowchart illustrating the transmission between a first node and a second node according to an embodiment of this application is shown;

[0123] Figure 7 A first schematic diagram of a first condition according to an embodiment of this application is shown;

[0124] Figure 8 A second schematic diagram illustrating the first condition according to an embodiment of this application is shown;

[0125] Figure 9 A schematic diagram of a first associated ID and a second associated ID according to an embodiment of this application is shown;

[0126] Figure 10 A schematic diagram of a first RS resource set and a second RS resource set according to an embodiment of this application is shown;

[0127] Figure 11 A schematic diagram of a first type of result according to an embodiment of this application is shown;

[0128] Figure 12 A schematic diagram of an AI entity according to an embodiment of this application is shown;

[0129] Figure 13 A schematic diagram illustrating the deployment of RAN domain AI / ML functionality according to an embodiment of this application is shown;

[0130] Figure 14 A schematic diagram illustrating the deployment of AI / ML functions in a UE according to an embodiment of this application is shown;

[0131] Figure 15 A schematic diagram of an artificial intelligence or machine learning-based processing system according to an embodiment of this application is shown;

[0132] Figure 16 A schematic diagram illustrating artificial intelligence or machine learning according to an embodiment of this application is shown;

[0133] Figure 17 A structural block diagram of a processing apparatus for a first node according to an embodiment of this application is shown;

[0134] Figure 18 A structural block diagram of a processing apparatus for a second node according to an embodiment of this application is shown. Detailed Implementation

[0135] 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, including but not limited to the accompanying drawings. Figure 1 Examples and appendices Figure 5 - Appendix Figure 18 The embodiments in the appendix Figure 5 Examples and appendices Figure 6 - Appendix Figure 18 Examples, etc.

[0136] Example 1

[0137] Example 1 illustrates a flowchart of a first node transmission according to an embodiment of this application, as shown in the attached diagram. Figure 1 As shown. In the appendix Figure 1 In this diagram, each box represents a step. Specifically, the order of the steps within the boxes does not indicate a specific temporal sequence between them.

[0138] The first node receives a first configuration message in step 101, which indicates a first association ID and a second association ID; and sends a first reporting message in step 102.

[0139] In Example 1, the first association ID and the second association ID are respectively associated with a first RS resource set and a second RS resource set; both the first RS resource set and the second RS resource set are configured to a first entity, the first entity corresponding to a model or a function; the first reporting message depends on the reasoning of the first entity, and the input of the reasoning of the first entity depends on the measurement on at least one of the first RS resource set or the second RS resource set.

[0140] As one example, the first node is a user equipment (UE).

[0141] As one example, the first node is a terminal.

[0142] As an example, the first node is the first node in this application.

[0143] As an example, the ID refers to: IDentify, proof.

[0144] As an example, the ID refers to: IDentification, identity verification.

[0145] As an example, the ID refers to: IDentity, identity, or identifier.

[0146] As an example, the ID refers to: Identifier, identifier.

[0147] As an example, the ID refers to: InDex, index.

[0148] As an example, the ID refers to: InDicator, indicator.

[0149] As an example, RS stands for Reference Signal.

[0150] As an example, the first node receives the first configuration message.

[0151] As one example, the first configuration message is transmitted via higher layer signaling.

[0152] As an example, the first configuration message is transmitted via RRC (Radio Resource Control) signaling.

[0153] As an example, the first configuration message includes one or more RRC IEs (Information Elements).

[0154] As an example, the first configuration message includes one or more fields in an RRC IE.

[0155] As one example, the first configuration message includes one or more domains for each of the plurality of RRC IEs.

[0156] As an example, the first configuration message indicates higher-level parameters.

[0157] As one example, the first configuration message includes RRCReconfiguration IE.

[0158] As one example, the first configuration message includes one or more domains in the RRCReconfiguration IE.

[0159] As an example, the first configuration message includes ServingCellConfig IE.

[0160] As one example, the first configuration message includes one or more domains in the ServingCellConfig IE.

[0161] As an example, the first configuration message includes CSI-MeasConfig IE.

[0162] As one example, the first configuration message includes one or more domains in the CSI-MeasConfig IE.

[0163] As one example, the first configuration message includes CSI-ReportConfig IE.

[0164] As one example, the first configuration message includes one or more domains in the CSI-ReportConfig IE.

[0165] As one example, the first configuration message includes a CSI-AperiodicTriggerStateList IE.

[0166] As one example, the first configuration message includes one or more domains in the CSI-AperiodicTriggerStateList IE.

[0167] As an example, the first configuration message includes a CSI-AperiodicTriggerState IE.

[0168] As one example, the first configuration message includes one or more domains in the CSI-AperiodicTriggerState IE.

[0169] As an example, the first configuration message includes CSI-SemiPersistentOnPUSCH-TriggerStateListIE.

[0170] As one example, the first configuration message includes one or more domains in CSI-SemiPersistentOnPUSCH-TriggerStateListIE.

[0171] As an example, the first configuration message includes CSI-SemiPersistentOnPUSCH-TriggerState IE.

[0172] As an example, the first configuration message includes one or more domains in the CSI-SemiPersistentOnPUSCH-TriggerState IE.

[0173] As one example, the first configuration message includes a CSI-ReportSubConfigTriggerList IE.

[0174] As one example, the first configuration message includes one or more domains in the CSI-ReportSubConfigTriggerList IE.

[0175] As one example, the first configuration message includes a CSI-ReportSubConfig IE.

[0176] As one example, the first configuration message includes one or more domains in a CSI-ReportSubConfig IE.

[0177] As one example, the first configuration message includes CSI-ResourceConfig IE.

[0178] As one example, the first configuration message includes one or more domains in the CSI-ResourceConfig IE.

[0179] As one example, the first configuration message includes a CSI-IM-Resource IE.

[0180] As one example, the first configuration message includes one or more domains in the CSI-IM-Resource IE.

[0181] As one example, the first configuration message includes CSI-IM-ResourceSetIE.

[0182] As one example, the first configuration message includes one or more domains in CSI-IM-ResourceSetIE.

[0183] As an example, the first configuration message includes NZP-CSI-RS-ResourceSet IE.

[0184] As one example, the first configuration message includes one or more domains in the NZP-CSI-RS-ResourceSet IE.

[0185] As an example, the first configuration message includes NZP-CSI-RS-Resource IE.

[0186] As one example, the first configuration message includes one or more domains in the NZP-CSI-RS-Resource IE.

[0187] As one example, the first configuration message includes MeasObjectNR IE.

[0188] As one example, the first configuration message includes one or more domains in the MeasObjectNR IE.

[0189] As an example, the first configuration message includes CSI-RS-ResourceConfigMobility IE.

[0190] As one example, the first configuration message includes one or more domains in the CSI-RS-ResourceConfigMobility IE.

[0191] As an example, the name of the RRC signaling used to transmit the first configuration message includes CSI.

[0192] As an example, the name of the RRC signaling used to transmit the first configuration message includes CSI-RS.

[0193] As an example, the name of the RRC signaling used to transmit the first configuration message includes Report.

[0194] As an example, the name of the RRC signaling used to transmit the first configuration message includes Config.

[0195] As an example, the name of the RRC signaling used to transmit the first configuration message includes Resource.

[0196] As an example, the name of the RRC signaling used to transmit the first configuration message includes CSI-ReportConfig.

[0197] As an example, the name of the RRC signaling used to transmit the first configuration message includes Associated.

[0198] As an example, the name of the RRC signaling used to transmit the first configuration message includes AI.

[0199] As an example, the name of the RRC signaling used to transmit the first configuration message includes ML.

[0200] As an example, the name of the RRC signaling used to transmit the first configuration message includes Model.

[0201] As an example, the name of the RRC signaling used to transmit the first configuration message includes Functionality.

[0202] As an example, the name of the RRC signaling used to transmit the first configuration message includes Inference.

[0203] As an example, the name of the RRC signaling used to transmit the first configuration message includes Infer.

[0204] As an example, the name of the RRC signaling used to transmit the first configuration message includes Monitoring.

[0205] As an example, the first configuration message indicates the first association ID and the second association ID.

[0206] As an example, the first configuration message explicitly indicates the first association ID and the second association ID.

[0207] As an example, the first configuration message implicitly indicates the first association ID and the second association ID.

[0208] As an example, the first configuration message directly indicates the first association ID and the second association ID.

[0209] As an example, the first configuration message indirectly indicates the first association ID and the second association ID.

[0210] As an example, the two fields included in the first configuration message indicate the first association ID and the second association ID, respectively.

[0211] As an example, at least one field included in the first configuration message indicates the first association ID and the second association ID.

[0212] As an example, the first configuration message directly configures the first association ID and the second association ID.

[0213] As an example, the first configuration message indirectly indicates the first association ID and the second association ID by instructing other configuration messages.

[0214] As an example, the first configuration message directly configures the first associated ID, and indirectly indicates the second associated ID through other configuration messages.

[0215] As an example, the first configuration message directly configures the second association ID, and indirectly indicates the first association ID through other configuration messages.

[0216] As an example, the first associated ID is a non-negative integer.

[0217] As an example, the first associated ID is a positive integer.

[0218] As an example, the first associated ID is a string.

[0219] As an example, the first associated ID is an associated ID.

[0220] As an example, the first associated ID is an Associated ID.

[0221] As an example, the first associated ID is an Associated-Id.

[0222] As an example, the first associated ID identifies a Functionality.

[0223] As an example, the first associated ID is associated with an AI (Artificial Intelligence) model.

[0224] As one example, the first association ID is associated with multiple AI models, which are trained using the same training dataset.

[0225] As an example, the first association ID is associated with a training dataset.

[0226] As an example, the first association ID is associated with at least one RS resource set.

[0227] As an example, the first association ID is associated with at least one SSB-Index.

[0228] As an example, the first associated ID is associated with at least one CSI-SSB-ResourceSetId.

[0229] As an example, the second associated ID is a non-negative integer.

[0230] As an example, the second associated ID is a positive integer.

[0231] As an example, the second associated ID is a string.

[0232] As an example, the second association ID is an association ID.

[0233] As an example, the second associated ID is an Associated ID.

[0234] As an example, the second association ID is an Associated-Id.

[0235] As an example, the second association ID identifies a Functionality.

[0236] As an example, the second association ID is associated with an AI model.

[0237] As one example, the second association ID is associated with multiple AI models that are trained using the same training dataset.

[0238] As an example, the second association ID is associated with a training dataset.

[0239] As an example, the second association ID is associated with at least one RS resource set.

[0240] As an example, the second association ID is associated with multiple CSI-ResourceConfigIds.

[0241] As an example, the second association ID is associated with two CSI-ResourceConfigIds, and the two CSI-ResourceConfigIds are associated with the same CSI-ReportConfigId.

[0242] As an example, the first association ID and the second association ID are two different non-negative integers.

[0243] As an example, the first association ID and the second association ID are two different positive integers.

[0244] As an example, the first association ID and the second association ID are two different strings.

[0245] As an example, the first association ID and the second association ID are two different association IDs.

[0246] As an example, the first associated ID and the second associated ID are two different Associated IDs.

[0247] As an example, the first association ID and the second association ID are two different Associated-Ids.

[0248] As an example, the first association ID and the second association ID identify two different functions.

[0249] As an example, the first association ID and the second association ID are associated with the same AI model.

[0250] As an example, the first association ID and the second association ID are associated with two AI models.

[0251] As an example, the first association ID and the second association ID are associated with two different training datasets, respectively.

[0252] As an example, the first association ID and the second association ID are associated with different RS resource sets.

[0253] As an example, the Functionality described in this application refers to a feature or feature group (FG) that supports AI / ML (Artificial Intelligence / Machine Learning) through configuration, wherein the configuration is supported according to conditions indicated by the UE capability.

[0254] As an example, the associated ID described in this application is used to indicate the generalization ability of the model.

[0255] As an example, the associated ID described in this application is used to ensure the consistency of network-side (NW-side) additional conditions during model training and model inference.

[0256] As an example, multiple beams, multiple beam sets, or multiple beam lists that are associated with the same association ID described in this application have similar properties.

[0257] As an example, the same first association ID is used to represent similar characteristics of beams or beam sets or beam lists.

[0258] As an example, the first association ID indicates whether at least two beams, two beam sets, or two beam lists have similar characteristics.

[0259] As a sub-implementation of this embodiment, one of the at least two beams includes at least the RS resources in the first RS resource set of this application.

[0260] As a sub-implementation of this embodiment, one of the at least two beam sets includes at least the first RS resource set in this application.

[0261] As a sub-implementation of this embodiment, one of the at least two beam lists includes at least the first RS resource set in this application.

[0262] As an example, the second association ID indicates whether at least two beams, two beam sets, or two beam lists have similar characteristics.

[0263] As a sub-implementation of this embodiment, one of the at least two beams includes at least the RS resources in the second RS resource set of this application.

[0264] As a sub-implementation of this embodiment, one of the at least two beam sets includes at least the second RS resource set in this application.

[0265] As a sub-implementation of this embodiment, one of the at least two beam lists includes at least the second RS resource set in this application.

[0266] As an example, the beam described in this application includes a downlink (DL) beam.

[0267] As an example, the beam described in this application includes an uplink (UL) beam.

[0268] As an example, the beam described in this application includes: RS resources.

[0269] As an example, the features described in this application include: spatial relation.

[0270] As an example, the features described in this application include: spatial domain filtering.

[0271] As an example, the features described in this application include: spatial filtering.

[0272] As one embodiment, the features described in this application include: spatial receiving features.

[0273] As one embodiment, the features described in this application include: spatial Rxparameter.

[0274] As an example, the features described in this application include: spatial transmission features.

[0275] As an example, the feature described in this application includes: spatialRxparameter.

[0276] As an example, the features described in this application include: channel reciprocity.

[0277] As one embodiment, the features described in this application include: beam shape.

[0278] As an example, the feature described in this application includes: beamwidth.

[0279] As an example, the features described in this application include: side lobe levels.

[0280] As an example, the features described in this application include: beam angle.

[0281] As an example, the features described in this application include: codeword.

[0282] As one embodiment, the features described in this application include: beam indexing.

[0283] As one embodiment, the features described in this application include: beam set index.

[0284] As one embodiment, the features described in this application include: beam list index.

[0285] As one example, the features described in this application include: RS resource index.

[0286] As an example, the features described in this application include: RS resource collection index.

[0287] As an example, the features described in this application include: an RS resource list index.

[0288] As an example, the features described in this application include: TCI (Transmission Configuration Indicator) state.

[0289] As an example, the features described in this application include: qcl-info.

[0290] As an example, the features described in this application include: QCL relationships.

[0291] As an example, the features described in this application include: QCL type.

[0292] As an example, the features described in this application include at least one of QCL typeA, QCL typeB, QCL typeC, and QCL typeD.

[0293] As an example, the features described in this application include QCL type E.

[0294] As an example, the features described in this application include QCL types other than QCL type A, QCL type B, QCL type C, and QCL type D.

[0295] As an example, QCL in this application refers to Quasi Co-Location.

[0296] As an example, QCL in this application refers to Quasi Co-Located.

[0297] As an example, the QCL types described in this application include typeA, typeB, typeC, and typeD.

[0298] As an example, the QCL types described in this application include QCL types other than typeA, typeB, typeC, and typeD.

[0299] As an example, the QCL type described in this application includes typeE.

[0300] As an example, the QCL parameters of type A in this application include Doppler shift, Doppler spread, average delay, and delay spread; the QCL parameters of type B include Doppler shift and Doppler spread; the QCL parameters of type C include Doppler shift and average delay; and the QCL parameters of type D include spatial Rx parameters.

[0301] As an example, the specific definitions of type A, type B, type C and type D in this application can be found in section 5.1.5 of 3GPP TS (Technical Specification) 38.214.

[0302] As an example, the QCL parameters of type E described in this application include at least the spatial transmission parameter (Tx parameter).

[0303] As one example, the first node sends the first reporting information.

[0304] As one example, the first reporting message includes a baseband signal.

[0305] As one example, the first reporting message includes a radio frequency signal.

[0306] As one example, the first reporting message includes a wireless signal.

[0307] As an example, the first reported message includes UCI (Uplink Control Information).

[0308] As an example, the first reporting message includes Layer 1 (L1) information.

[0309] As one example, the first reporting message includes physical layer information.

[0310] As an example, the first reported information includes a beam indication, which includes at least one of SSBRI (SS / PBCH Block Resource indicator, Synchronization / Physical Broadcast Channel Block Resource indicator) and CRI (CSI-RS resource indicator, Channel State Information Reference Signal Resource indicator).

[0311] As one example, the first reporting message includes channel state information.

[0312] As a sub-implementation of this embodiment, the channel state information includes LI (layer indicator).

[0313] As a sub-example of this embodiment, the channel state information includes RI (rank indicator).

[0314] As a sub-example of this embodiment, the channel state information includes CQI (Channel Quality Indicator).

[0315] As a sub-implementation of this embodiment, the channel state information includes PMI (Precoding Matrix Indicator).

[0316] As a sub-example of this embodiment, the channel state information includes CRI.

[0317] As a sub-implementation of this embodiment, the channel state information includes SSBRI.

[0318] As a sub-example of this embodiment, the channel state information includes L1-RSRP (Layer 1 Reference Signal Received Power).

[0319] As an example, the first reporting message includes Layer 1 (L3) information.

[0320] As one example, the first reporting message includes a prediction report.

[0321] As an example, the first reporting message includes a measurement report.

[0322] As one example, the first reporting message includes a cell indication.

[0323] As an example, the first reporting message does not include the results of the performance monitoring of the inference.

[0324] As an example, the first reporting message does not carry performance monitoring for the inference.

[0325] As an example, the first association ID and the second association ID are respectively associated with the first RS resource set and the second RS resource set.

[0326] As one embodiment, the first RS resource set includes antenna ports(s).

[0327] As one embodiment, the first RS resource set includes a reference signal port.

[0328] As an example, the first RS resource set includes RS.

[0329] As an example, the first RS resource set includes at least one RS resource.

[0330] As one embodiment, the first RS resource set includes downlink RS resources.

[0331] As an example, the first RS resource set includes broadcast RS resources.

[0332] As an example, the first RS resource set includes multicast RS resources.

[0333] As one example, the first RS resource set includes cell-specific RS resources.

[0334] As an example, the first RS resource set includes RS resources specific to the cell group.

[0335] As one embodiment, the first RS resource set includes RS resources specific to the cell set.

[0336] As an example, the first RS resource set includes positioning RS resources.

[0337] As an example, the first RS resource set includes PRS (Positioning Reference Signal) resources.

[0338] As an example, the first RS resource set includes sensing RS resources.

[0339] As an example, the first RS resource set includes ISAC (Integrated Sensing and Communication) sensing signal resources.

[0340] As an example, the first RS resource set includes ISAC-RS resources.

[0341] As an example, the first RS resource set includes IRS (ISAC Reference Signal) resources.

[0342] As one embodiment, the first RS resource set includes RS resources for mobility management.

[0343] As one embodiment, the first RS resource set includes RS resources for cell-level mobility management.

[0344] As one embodiment, the first RS resource set includes RS resources used for cell synchronization.

[0345] As one embodiment, the first RS resource set includes synchronization signals in at least 5G systems and systems after 5G systems.

[0346] As one embodiment, the first RS resource set includes at least a synchronization signal in a 6G system.

[0347] As one embodiment, the first RS resource set includes at least a synchronization signal (SS).

[0348] As one embodiment, the first RS resource set includes at least a Primary Synchronization Signal (PSS).

[0349] As one embodiment, the first RS resource set includes at least a Secondary Synchronization Signal (SSS).

[0350] As an example, the first RS resource set includes at least PBCH (Physical Broadcast Channel).

[0351] Typically, the PBCH, PSS, and SSS are received in consecutive symbols and form an SS / PBCH block.

[0352] As an example, the first RS resource set includes SSB.

[0353] As an example, the first RS resource set includes SSB resources.

[0354] As an example, the first RS resource set includes at least one SSB.

[0355] As an example, the first RS resource set includes an SSB.

[0356] As one embodiment, the first RS resource set includes multiple SSBs.

[0357] As an example, the first RS resource set includes at least one SSB in an SSB burst set.

[0358] As an example, SSB in this application refers to Synchronization Signal Block.

[0359] As an example, the SSB mentioned in this application refers to the SS / PBCH block.

[0360] As an example, the cell described in this application is a camping cell.

[0361] As an example, the cell described in this application is a serving cell.

[0362] As an example, the cell described in this application is an additional cell.

[0363] As an example, the cell described in this application is a PCell (Primary Cell).

[0364] As an example, the cell described in this application is a SCell (Secondary Cell).

[0365] As an example, the cell described in this application is a SpCell (Special Cell).

[0366] As an example, the cell described in this application is an MCG (Master Cell Group) cell.

[0367] As an example, the cell described in this application is an SCG (Secondary Cell Group) cell.

[0368] Typically, the first association ID is associated with the first RS resource set.

[0369] As an example, the meaning of the first association ID being associated with the first RS resource set includes: the first association ID is configured to be associated with the first RS resource set.

[0370] As an example, the meaning of the first association ID being associated with the first RS resource set includes: the RRC signaling that configures the first association ID is also used to configure the first RS resource set.

[0371] As an example, the meaning of the first association ID being associated with the first RS resource set includes: the RRC signaling that configures the first association ID is also used to indicate the first RS resource set.

[0372] As an example, the meaning of the first association ID being associated with the first RS resource set includes: the RRC signaling that configures the first association ID is also used to indicate the ID corresponding to the first RS resource set.

[0373] As an example, the meaning of the first association ID being associated with the first RS resource set includes: the first association ID being used to characterize the spatial characteristics of the first RS resource set.

[0374] As an example, the meaning of the first association ID being associated with the first RS resource set includes: the first association ID being used to indicate the spatial characteristics of the first RS resource set.

[0375] As an example, the meaning of the first association ID being associated with the first RS resource set includes: the AI / ML model corresponding to the first association ID depends on the first RS resource set.

[0376] As an example, the meaning of the first association ID being associated with the first RS resource set includes: the first RS resource set is used for training the AI / ML model corresponding to the first association ID.

[0377] As an example, the meaning of the first association ID being associated with the first RS resource set includes: the first RS resource set is used for inference of the AI / ML model corresponding to the first association ID.

[0378] As an example, the meaning of the first association ID being associated with the first RS resource set includes: the first RS resource set is used for performance monitoring of the AI / ML model corresponding to the first association ID.

[0379] As an example, the meaning of the first association ID being associated with the first RS resource set includes: the entity corresponding to the first association ID depends on the first RS resource set.

[0380] As an example, the meaning of the first association ID being associated with the first RS resource set includes: the first RS resource set is used for training the entity corresponding to the first association ID.

[0381] As an example, the meaning of the first association ID being associated with the first RS resource set includes: the first RS resource set is used for reasoning of the entity corresponding to the first association ID.

[0382] As an example, the meaning of the first association ID being associated with the first RS resource set includes: the first RS resource set is used for performance monitoring of the entity corresponding to the first association ID.

[0383] As one embodiment, the second RS resource set includes an antenna port.

[0384] As one embodiment, the second RS resource set includes a reference signal port.

[0385] As one embodiment, the second RS resource set includes RS.

[0386] As one embodiment, the second RS resource set includes at least one RS resource.

[0387] As one embodiment, the second RS resource set includes downlink RS resources.

[0388] As one embodiment, the second RS resource set includes RS resources used for measurement.

[0389] As one embodiment, the second RS resource set includes CSI-RS (Channel State Information-Reference Signal) resources.

[0390] As an example, the second RS resource set includes at least one CSI-RS resource.

[0391] As an example, the second RS resource set includes NZP (Non-Zero Power) CSI-RS resources.

[0392] As an example, the second RS resource set corresponds to an NZP-CSI-RS-ResourceSetId.

[0393] As an example, the second RS resource set corresponds to a CSI-ResourceConfigId.

[0394] Typically, the second association ID is associated with the second RS resource set.

[0395] As an example, the meaning of the second association ID being associated with the second RS resource set includes: the second association ID is configured to be associated with the second RS resource set.

[0396] As an example, the meaning of the second association ID being associated with the second RS resource set includes: the RRC signaling that configures the second association ID is also used to configure the second RS resource set.

[0397] As one embodiment, the meaning of the second association ID being associated with the second RS resource set includes: the RRC signaling configuring the second association ID is also used to indicate the second RS resource set.

[0398] As one embodiment, the meaning of the second association ID being associated with the second RS resource set includes: the RRC signaling that configures the second association ID is also used to indicate the ID corresponding to the second RS resource set.

[0399] As an example, the meaning of the second association ID being associated with the second RS resource set includes: the second association ID is used to characterize the spatial characteristics of the second RS resource set.

[0400] As an example, the meaning of the second association ID being associated with the second RS resource set includes: the second association ID being used to indicate the spatial characteristics of the second RS resource set.

[0401] As an example, the meaning of the second association ID being associated with the second RS resource set includes: the AI / ML model corresponding to the second association ID depends on the second RS resource set.

[0402] As an example, the meaning of the second association ID being associated with the second RS resource set includes: the second RS resource set is used for training the AI / ML model corresponding to the second association ID.

[0403] As an example, the meaning of the second association ID being associated with the second RS resource set includes: the second RS resource set is used for inference of the AI / ML model corresponding to the second association ID.

[0404] As an example, the meaning of the second association ID being associated with the second RS resource set includes: the second RS resource set is used for performance monitoring of the AI / ML model corresponding to the second association ID.

[0405] As an example, the meaning of the second association ID being associated with the second RS resource set includes: the entity corresponding to the second association ID depends on the second RS resource set.

[0406] As an example, the meaning of the second association ID being associated with the second RS resource set includes: the second RS resource set is used for training the entity corresponding to the second association ID.

[0407] As an example, the meaning of the second association ID being associated with the second RS resource set includes: the second RS resource set is used for reasoning of the entity corresponding to the second association ID.

[0408] As an example, the meaning of the second association ID being associated with the second RS resource set includes: the second RS resource set is used for performance monitoring of the entity corresponding to the second association ID.

[0409] As one embodiment, the first RS resource set and the second RS resource set each include different antenna ports.

[0410] As one embodiment, the first RS resource set and the second RS resource set each include different reference signal ports.

[0411] As one embodiment, the first RS resource set and the second RS resource set each include different RSs.

[0412] As one embodiment, the first RS resource set and the second RS resource set each include different RS resources.

[0413] As one embodiment, the first RS resource set and the second RS resource set include orthogonal RS resources.

[0414] As one embodiment, the first RS resource set and the second RS resource set include orthogonal RS.

[0415] As an example, both the first RS resource set and the second RS resource set are configured to the first entity.

[0416] As an example, configuring both the first RS resource set and the second RS resource set to the first entity means configuring the first RS resource set and the second RS resource set with higher-level parameters of the first entity.

[0417] As a sub-implementation of this embodiment, the higher-level parameters include the first configuration message.

[0418] As an example, configuring both the first RS resource set and the second RS resource set to the first entity means that configuring the first RS resource set and configuring the second RS resource set respectively include higher-level parameters indicating the first entity.

[0419] As an example, the fact that both the first RS resource set and the second RS resource set are configured for the first entity means that the first RS resource set and the second RS resource set are configured to generate the training dataset for the first entity.

[0420] As an example, the fact that both the first RS resource set and the second RS resource set are configured to the first entity means that the first RS resource set and the second RS resource set are configured to generate a prediction dataset for the first entity.

[0421] As an example, the fact that both the first RS resource set and the second RS resource set are configured to the first entity means that the first RS resource set and the second RS resource set are configured to generate the inference results of the first entity.

[0422] As an example, the meaning of configuring both the first RS resource set and the second RS resource set to the first entity includes: the first RS resource set and the second RS resource set are configured as the first entity.

[0423] As an example, the fact that both the first RS resource set and the second RS resource set are configured for the first entity means that the first RS resource set and the second RS resource set are respectively configured for the inference of the first entity.

[0424] As an example, the meaning of configuring both the first RS resource set and the second RS resource set to the first entity includes: the first RS resource set and the second RS resource set are respectively configured for training the first entity.

[0425] As an example, the fact that both the first RS resource set and the second RS resource set are configured for the first entity means that the first RS resource set and the second RS resource set are respectively configured for the training and inference of the first entity.

[0426] As an example, the meaning of configuring both the first RS resource set and the second RS resource set to the first entity includes: the first RS resource set and the second RS resource set are respectively configured for performance monitoring of the first entity.

[0427] As an example, the fact that both the first RS resource set and the second RS resource set are configured for the first entity means that the first RS resource set and the second RS resource set are respectively configured for the inference and performance monitoring of the first entity.

[0428] As an example, the first entity may correspond to a model or a function.

[0429] As an example, the first entity corresponds to a Functionality.

[0430] As an example, the first entity corresponds to a Functionality ID.

[0431] As an example, the first entity corresponds to an Entity.

[0432] As an example, the first entity is an AI entity.

[0433] As an example, the first entity is the AI ​​entity in Embodiment 12 of this application.

[0434] As an example, the first entity is the AI ​​entity in Embodiment 12 of this application.

[0435] As an example, the first entity is part of the AI ​​entity in embodiment 12 of this application.

[0436] As an example, the first entity includes the AI ​​entity in embodiment 12 of this application.

[0437] As an example, the first entity corresponds to an AI / ML model.

[0438] As an example, the first entity corresponds to an AI / ML model ID.

[0439] As an example, the first entity corresponds to an AI / ML entity.

[0440] As an example, the first entity corresponds to multiple AI / ML models under a Functionality.

[0441] As an example, the first entity corresponds to multiple functions of an AI / ML model being applied.

[0442] As an example, this application does not limit whether the AI / ML model and Functionality are in a one-to-one correspondence.

[0443] As one example, the first reporting message relies on the reasoning of the first entity.

[0444] As an example, the reasoning mentioned in this application refers to infer.

[0445] As an example, the reasoning mentioned in this application refers to prediction.

[0446] As an example, the inference described in this application includes AI / ML inference.

[0447] As an example, the reasoning described in this application is based on training or AI.

[0448] As an example, the reasoning described in this application includes an AI entity used for reasoning.

[0449] As an example, the reasoning described in this application includes at least a portion of an AI entity.

[0450] As an example, the reasoning described in this application includes a portion of an AI entity used for reasoning.

[0451] As an example, the reasoning model described in this application is obtained through training.

[0452] As an example, the meaning of "the first reporting message depends on the reasoning of the first entity" includes: the first reporting message is generated based on the reasoning of the first entity.

[0453] As an example, the meaning of "the first reporting message depends on the reasoning of the first entity" includes: the first reporting message includes the result obtained by the reasoning of the first entity.

[0454] As an example, the meaning of "the first reporting message depends on the reasoning of the first entity" includes: the first reporting message is a message used by the first entity for reasoning.

[0455] As an example, the meaning of "the first reporting message depends on the reasoning of the first entity" includes: the first reporting message depends on the result generated by the reasoning of the first entity.

[0456] As an example, the meaning of "the first reporting message depends on the reasoning of the first entity" includes: the first reporting message is generated based on the prediction of the first entity.

[0457] As an example, the meaning of "the first reporting message depends on the reasoning of the first entity" includes: the first reporting message includes the result predicted by the first entity.

[0458] As an example, the meaning of "the first reporting message depends on the reasoning of the first entity" includes: the first reporting message is a message used by the first entity for prediction.

[0459] As an example, the meaning of the first reporting message relying on the reasoning of the first entity includes: the first reporting message relying on the result predicted by the first entity.

[0460] As an example, the meaning of "the first reporting message depends on the reasoning of the first entity" includes: the first reporting message is generated based on the AI / ML model corresponding to the first entity.

[0461] As an example, the input to the reasoning of the first entity depends on measurements on at least one of the first RS resource set or the second RS resource set.

[0462] As an example, the input to the reasoning of the first entity depends on measurements on the first RS resource set.

[0463] As an example, the statement that the input to the reasoning of the first entity depends on the measurement on the first RS resource set means that the training dataset for the reasoning of the first entity comes from the measurement on the first RS resource set.

[0464] As an example, the meaning of "the input of the reasoning of the first entity depends on the measurement on the first RS resource set" includes: the reasoning dataset used for the reasoning of the first entity comes from the measurement on the first RS resource set.

[0465] As an example, the statement that the input of the inference of the first entity depends on the measurement on the first RS resource set means that the measurement of the first node on the first RS resource set is used to train the model of the inference of the first entity.

[0466] As an example, the meaning of the first entity's inference input depending on the measurement on the first RS resource set includes: the first entity's inference input and the RS resources in the first RS resource set are QCLs.

[0467] As an example, the meaning of the first entity's reasoning input depending on the measurement on the first RS resource set includes: the first entity's reasoning input is the result obtained by the first node receiving RS resources of a given RS resource set, wherein the RS resources in the given RS resource set and the RS resources in the first RS resource set are QCL.

[0468] As an example, the meaning of the first entity's reasoning input depending on the measurement on the first RS resource set includes: the first entity's reasoning input is the historical results of the first node receiving RS resources in the first RS resource set.

[0469] As an example, the meaning of "the input of the reasoning of the first entity depends on the measurement on the first RS resource set" includes: the type of the input of the reasoning of the first entity depends on the output of the measurement of the first node on the first RS resource set.

[0470] As a sub-example of this embodiment, the candidates for the output result of the measurement of the first node on the first RS resource set include one or more of RSRP (Reference Signal Received Power), RSRQ (Reference Signal Received Quality), RSSI (Received Signal Strength Indicator), and SNR (Signal To Noise Ratio).

[0471] As a sub-example of this embodiment, the candidates for the output results of the measurement of the first node on the first RS resource set include one or more of L1-RSRP, CRI, RI, PMI, CQI, LI, SSB-Index, SSBRI, SINR, L1-SINR, Capability Index, and Capability Set Index.

[0472] As an example, the input to the reasoning of the first entity depends on measurements on the second RS resource set.

[0473] As an example, the statement that the input to the reasoning of the first entity depends on the measurement on the second RS resource set means that the training dataset for the reasoning of the first entity comes from the measurement on the second RS resource set.

[0474] As an example, the meaning of "the input of the reasoning of the first entity depends on the measurement on the second RS resource set" includes: the reasoning dataset used for the reasoning of the first entity comes from the measurement on the second RS resource set.

[0475] As an example, the statement that the input of the inference of the first entity depends on the measurement on the second RS resource set means that the measurement of the first node on the second RS resource set is used to train the model of the inference of the first entity.

[0476] As an example, the meaning of the first entity's inference input depending on the measurement on the second RS resource set includes: the first entity's inference input and the RS resources in the second RS resource set are QCLs.

[0477] As an example, the meaning of the first entity's reasoning input depending on the measurement on the second RS resource set includes: the first entity's reasoning input is the result obtained by the first node receiving RS resources of a given RS resource set, wherein the RS resources in the given RS resource set and the RS resources in the second RS resource set are QCL.

[0478] As an example, the meaning of the first entity's reasoning input depending on the measurement on the second RS resource set includes: the first entity's reasoning input is the historical results of the first node receiving RS resources in the second RS resource set.

[0479] As an example, the meaning of "the input of the reasoning of the first entity depends on the measurement on the second RS resource set" includes: the type of the input of the reasoning of the first entity depends on the output of the measurement of the first node on the second RS resource set.

[0480] As a sub-example of this embodiment, the candidates for the output result of the measurement of the first node on the second RS resource set include one or more of RSRP, RSRQ, RSSI and SNR.

[0481] As a sub-example of this embodiment, the candidates for the output results of the measurement of the first node on the second RS resource set include one or more of L1-RSRP, CRI, RI, PMI, CQI, LI, SSB-Index, SSBRI, SINR, L1-SINR, Capability Index, and Capability Set Index.

[0482] As an example, the input to the reasoning of the first entity depends on measurements on the first RS resource set and the second RS resource set.

[0483] As an example, the meaning of the first entity's inference input depending on measurements on the first RS resource set and the second RS resource set includes: the training dataset for the first entity's inference comes from the measurements on the first RS resource set and the second RS resource set.

[0484] As an example, the meaning of the first entity's inference input depending on measurements on the first RS resource set and the second RS resource set includes: the inference dataset used for the first entity's inference comes from the measurements on the first RS resource set and the second RS resource set.

[0485] As an example, the statement that the input of the reasoning of the first entity depends on measurements on the first RS resource set and the second RS resource set means that the measurements of the first node on the first RS resource set and the second RS resource set are used together to train the model of the reasoning of the first entity.

[0486] As an example, the meaning of the first entity's inference input depending on measurements on the first RS resource set and the second RS resource set includes: the first entity's inference input and the RS resources in the first RS resource set and the second RS resource set are both QCL.

[0487] As an example, the meaning of the first entity's reasoning input depending on measurements on the first RS resource set and the second RS resource set includes: the first entity's reasoning input is the result obtained by the first node receiving RS resources of a given RS resource set, wherein the RS resources in the given RS resource set are QCLs as well as the RS resources in the first RS resource set and the second RS resource set.

[0488] As an example, the meaning of the first entity's reasoning input depending on measurements on the first RS resource set and the second RS resource set includes: the first entity's reasoning input is the historical results of the first node receiving RS resources in the first RS resource set and RS resources in the second RS resource set.

[0489] As an example, the meaning of the first entity's inference input depending on the measurements on the first RS resource set and the second RS resource set includes: the type of the first entity's inference input depends on the output of the first node's measurements on the first RS resource set and the second RS resource set.

[0490] As a sub-example of this embodiment, the candidates for the output results of the measurements taken by the first node on the first RS resource set and the second RS resource set include one or more of RSRP, RSRQ, RSSI and SNR.

[0491] As a sub-example of this embodiment, the candidates for the output results of the measurements taken by the first node on the first RS resource set and the second RS resource set include one or more of L1-RSRP, CRI, RI, PMI, CQI, LI, SSB-Index, SSBRI, SINR, L1-SINR, Capability Index, and Capability Set Index.

[0492] Example 2

[0493] Example 2 illustrates a schematic diagram of a network architecture according to an embodiment of this application, as shown in the attached diagram. Figure 2 As shown.

[0494] Appendix Figure 2Network architecture 200 is described. Network architecture 200 refers to the network architectures of LTE (Long-Term Evolution), LTE-A (Long-Term Evolution Advanced), 5G systems, 5G-Advanced, and future 6G systems. The network architectures of LTE, LTE-A, 5G systems, 5G-Advanced, and future 6G systems are referred to as EPS (Evolved Packet System). The 5G NR or LTE network architecture may be referred to as 5GS (5G System) / EPS or some other suitable terminology; the 6G network architecture may be referred to as 6GS (6G System) / EPS or some other suitable terminology.

[0495] The network architecture 200 may include one or more UEs 201, a RAN (Radio Access Network) 202, a core network 210, an HSS (Home Subscriber Server) / UDM (Unified Data Management) 220, and an Internet service 230. The network architecture 200 may interconnect with other access networks, but these entities / interfaces are not shown for simplicity.

[0496] As attached Figure 2As shown, the network architecture 200 provides packet switching 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 202 includes Node B 203 and other nodes 204. Node B 203 provides user and control plane protocol termination toward the UE 201. Node B 203 may be connected to other nodes 204 via an Xn interface (e.g., backhaul). Node B 203 may also be referred to as eNB (evolved Node B), gNB, base station, base transceiver station, wireless base station, wireless transceiver, transceiver function, Basic Service Set (BSS), Extended Service Set (ESS), TRP (Transmitter Receiver Point), or some other suitable term. Node B 203 provides UE 201 with an access point to the core network 210; the core network 210 is a 5GC (5G Core network) / EPC (Evolved Packet Core), or the core network 210 is a 6GC (6G Core network). Examples of the UE 201 include cellular phones, smartphones, Session Initiation Protocol (SIP) phones, laptops, personal digital assistants (PDAs), satellite radios, GPS devices, multimedia devices, video devices, digital audio players (e.g., MP3 players), cameras, game consoles, drones, aircraft, narrowband physical network 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 the UE 201 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, radio terminal, remote terminal, handheld device, user agent, mobile client, client, or any other suitable term. The Node B 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. The Internet service 230 includes carrier-compliant Internet protocol services, specifically including the Internet, intranet, IMS (IP Multimedia Subsystem), and packet-switched streaming services.

[0497] As an example, the first node in this application includes the UE 201.

[0498] As an example, the second node in this application includes node B 203.

[0499] As an example, node B 203 is a macrocell base station.

[0500] As an example, node B 203 is a microcell base station.

[0501] As an example, node B 203 is a pico cell base station.

[0502] As an example, node B 203 is a femtocell.

[0503] As an example, node B 203 is a base station device that supports large latency differences.

[0504] As an example, node B 203 is a flight platform device.

[0505] As an example, node B 203 is a satellite device.

[0506] As one embodiment, the node B 203 is a test device (e.g., a transceiver device simulating part of the base station's functions, a signaling tester).

[0507] As an example, the UE 201 includes a mobile phone.

[0508] As an example, the UE 201 is a vehicle including a car.

[0509] As an example, the wireless link from the UE 201 to the node B 203 is an uplink, which is used to perform uplink transmissions.

[0510] As an example, the radio link from the node B 203 to the UE 201 is a downlink, which is used to perform downlink transmissions.

[0511] As an example, the wireless link between the node B 203 and the UE 201 includes a cellular link.

[0512] As an example, the node B 203 and the UE 201 are connected via the Uu air interface.

[0513] As an example, the sender of the first configuration message in this application includes the node B 203.

[0514] As an example, the recipient of the first configuration message in this application includes the UE 201.

[0515] As an example, the sender of the first reporting message in this application includes the UE 201.

[0516] As an example, the recipient of the first reporting message in this application includes the node B 203.

[0517] As an example, the sender of the first information in this application includes the UE 201.

[0518] As an example, the recipient of the first information in this application includes the node B 203.

[0519] As an example, the node B 203 supports the deployment of network-side (NW-side) AI / ML models.

[0520] As an example, the UE 201 supports the deployment of UE-side AI / ML models.

[0521] As an example, the UE 201 supports a 5G system.

[0522] As an example, the node B 203 supports a 5G system.

[0523] As an example, the UE 201 supports at least a 6G system.

[0524] As an example, the node B 203 supports at least a 6G system.

[0525] Example 3

[0526] 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 the attached diagram. Figure 3 As shown.

[0527] Figure 3 This is a schematic diagram illustrating an embodiment of a wireless protocol architecture for the user plane 350 and the control plane 300. Figure 3The wireless protocol architecture for the control plane 300 between the first communication node device (UE or RSU in V2X, onboard equipment or onboard communication module) and the second node device (gNB, UE or RSU in V2X, onboard equipment or onboard communication module), or between two UEs, is illustrated using three layers: Layer 1 (L1), Layer 2 (L2), and Layer 3 (L3). L1 is the lowest layer and implements various PHY (Physical layer) signal processing functions. L1 will be referred to as PHY 301 in this document. L2305 sits above PHY 301 and is responsible for the link between the first and second node devices, or between two UEs, via PHY 301. L2305 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 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 (Hybrid Automatic Repeat Quest). 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. The MAC sublayer 302 is also responsible for HARQ operations. The RRC (Radio Resource Control) sublayer 306 in L3 of the control plane 300 is responsible for obtaining radio resources (i.e., radio bearers) and using RRC signaling between the second communication node device and the first communication node device to configure the lower layer.The wireless protocol architecture of user plane 350 includes Layer 1 (L1) and Layer 2 (L2). The wireless protocol architecture for the first and second communication node devices in user plane 350 is largely the same as the corresponding layers and sublayers in control plane 300 for Physical Layer 351, PDCP sublayer 354 in L2355, RLC sublayer 353 in L2355, and MAC sublayer 352 in L2355. However, PDCP sublayer 354 also provides header compression for upper-layer packets to reduce wireless transmission overhead. L2355 in user plane 350 also includes SDAP (Service Data Adaptation Protocol) sublayer 356. SDAP sublayer 356 is responsible for mapping between QoS (Quality of Service) streams and Data Radio Bearer (DRB) to support service diversity. Although not illustrated, the first communication node device may have several upper layers above L2355, including a network layer (e.g., IP (Internet Protocol) layer) terminating at the P-GW on the network side and an application layer terminating at the other end of the connection (e.g., remote UE, server, etc.).

[0528] As an example, Appendix Figure 3 The wireless protocol architecture described herein is applicable to the first node in this application.

[0529] As an example, Appendix Figure 3 The wireless protocol architecture described herein is applicable to the second node in this application.

[0530] As an example, in this application, the first configuration message is generated in the RRC 306.

[0531] As an example, in this application, the first reporting message is generated in the RRC 306.

[0532] As an example, in this application, the first reporting message is generated in the MAC sublayer 302 or the MAC sublayer 352.

[0533] As an example, in this application, the first reporting message is generated in the PHY 301 or the PHY 351.

[0534] As an example, in this application, the first information is generated in the RRC 306.

[0535] As an example, in this application, the first information is generated in the MAC sublayer 302 or the MAC sublayer 352.

[0536] As an example, in this application, the first information is generated in the PHY 301 or the PHY 351.

[0537] As an example, the higher layer mentioned in this application refers to the layer above the physical layer.

[0538] As an example, the higher layer described in this application includes the RRC layer.

[0539] As an example, the higher-layer signaling described in this application includes RRC IE.

[0540] As an example, the higher-level signaling described in this application includes RRC messages.

[0541] As an example, the higher layer described in this application includes the MAC layer.

[0542] As an example, the higher-layer signaling described in this application includes MAC CE.

[0543] Example 4

[0544] 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 the attached diagram. Figure 4 As shown. (Attached) Figure 4 This is a block diagram of a first communication device 410 and a second communication device 450 communicating with each other in an access network.

[0545] 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.

[0546] 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.

[0547] 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 functionality. In the DL, 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 (i.e., the physical layer). Transmit processor 416 performs encoding and interleaving to facilitate forward error correction (FEC) at the second communication device 450, and mapping of signal clusters based on various modulation schemes (e.g., Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying (QPSK), 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 streams. 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.

[0548] 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 L1 signal processing functions. 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 by the first communication device 410 over the physical channel. The upper-layer data and control signals are then provided to the controller / processor 459. The controller / processor 459 implements L2 functionality. 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, 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 L2. Various control signals may also be provided to L3 for L3 processing. The controller / processor 459 is also responsible for error detection using ACK and / or NACK protocols to support HARQ operation.

[0549] 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 L2. 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 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.

[0550] 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 function. The controller / processor 475 implements the L2 function. The controller / processor 475 may be associated with a memory 476 storing 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.

[0551] 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 receives at least the first configuration message in this application, the first configuration message indicating a first association ID and a second association ID; sends the first reporting information in this application; the first association ID and the second association ID are respectively associated with a first RS resource set and a second RS resource set; both the first RS resource set and the second RS resource set are configured to a first entity, the first entity corresponding to a model or a function; the first reporting message depends on the reasoning of the first entity, the input of the reasoning of the first entity depending on measurements on at least one of the first RS resource set or the second RS resource set.

[0552] As one embodiment, the second communication device 450 includes: a memory storing a computer-readable instruction program that produces actions when executed by at least one processor, the actions including: receiving the first configuration message in this application; and sending the first reporting message in this application.

[0553] 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 at least sends the first configuration message in this application, the first configuration message indicating a first association ID and a second association ID; receives the first reporting information in this application; the first association ID and the second association ID are respectively associated with a first RS resource set and a second RS resource set; both the first RS resource set and the second RS resource set are configured to a first entity, the first entity corresponding to a model or a function; the first reporting message depends on the reasoning of the first entity, the input of the reasoning of the first entity depending on measurements on at least one of the first RS resource set or the second RS resource set.

[0554] As one embodiment, the first communication device 410 includes: a memory storing a computer-readable instruction program, which generates actions when executed by at least one processor, the actions including: sending the first configuration message in this application; and receiving the first reporting message in this application.

[0555] As an example, the first node in this application includes the second communication device 450.

[0556] As an example, the second node in this application includes the first communication device 410.

[0557] As an example, at least one of {the antenna 420, the transmitter 418, the transmitter processor 416, the multi-antenna transmitter processor 471, the controller / processor 475, and the memory 476} is used to transmit the first configuration message in this application; at least one of {the antenna 452, the receiver 454, the receiver processor 456, the multi-antenna receiver processor 458, the controller / processor 459, the memory 460, and the data source 467} is used to receive the first configuration message in this application.

[0558] As an example, at least one of {the antenna 452, the transmitter / receiver 454, the transmission processor 468, the multi-antenna transmission processor 457, the controller / processor 459, the memory 460, and the data source 467} is used to send the first reporting message in this application; at least one of {the antenna 420, the receiver 418, the receiving processor 470, the multi-antenna receiving processor 472, the controller / processor 475, and the memory 476} is used to receive the first reporting message in this application.

[0559] As an example, at least one of {the antenna 452, the transmitter / receiver 454, the transmitter processor 468, the multi-antenna transmitter processor 457, the controller / processor 459, the memory 460, and the data source 467} is used to perform measurements in the third RS resource set and the fourth RS resource set respectively and obtain a first measurement result and a second measurement result; when at least one of the first measurement result or the second measurement result satisfies a first condition, the first information in this application is sent; at least one of {the antenna 420, the receiver 418, the receiver processor 470, the multi-antenna receiver processor 472, the controller / processor 475, and the memory 476} is used to monitor the first information in this application.

[0560] Example 5

[0561] Example 5 illustrates a first flowchart of transmission between a first node and a second node according to an embodiment of this application, as shown in the attached diagram. Figure 5 As shown. In the appendix Figure 5In this embodiment, the first node U1 and the second node N2 communicate via a wireless link. It should be noted that the order in this embodiment does not limit the signal transmission order or the order of implementation in this application.

[0562] For the first node U1, a first configuration message is received in step S510; and a first reporting message is sent in step S511.

[0563] For the second node N2, a first configuration message is sent in step S520; and a first reporting message is received in step S522.

[0564] In Embodiment 5, the first configuration message indicates a first association ID and a second association ID; the first association ID and the second association ID are respectively associated with a first RS resource set and a second RS resource set; both the first RS resource set and the second RS resource set are configured to a first entity, the first entity corresponding to a model or a function; the first reporting message depends on the reasoning of the first entity, and the input of the reasoning of the first entity depends on measurements on at least one of the first RS resource set or the second RS resource set.

[0565] As an example, the first node U1 is the first node in this application.

[0566] As an example, the second node N2 is the second node in this application.

[0567] As one embodiment, the air interface between the second node N2 and the first node U1 includes a wireless interface between the base station equipment and the user equipment.

[0568] As one embodiment, the air interface between the second node N2 and the first node U1 includes a wireless interface between the relay node device and the user equipment.

[0569] As one embodiment, the air interface between the second node N2 and the first node U1 includes a wireless interface between user equipment and user equipment.

[0570] As one example, the second node N2 and the first node U1 communicate via the Uu interface.

[0571] As one example, the second node N2 is the maintenance base station of the serving cell of the first node U1.

[0572] As an example, the logical channel occupied by the first configuration message includes DCCH (Dedicated Control Channel).

[0573] As an example, the first configuration message is carried by SRB1 (Signalling Radio Bearer 1).

[0574] As an example, the transmission channel occupied by the first configuration message includes DL-SCH (DownLink-Shared Channel).

[0575] As an example, the physical layer channel occupied by the first configuration message includes PDSCH (Physical Downlink Shared Channel).

[0576] As an example, the transmission channel occupied by the first reporting message includes UL-SCH (UpLink-Shared Channel).

[0577] As an example, the physical layer channel occupied by the first reporting message includes PUSCH (Physical Uplink Shared Channel).

[0578] As an example, the physical layer channel occupied by the first reporting message includes PUCCH (Physical Uplink Control Channel).

[0579] As an example, step S511 is after step S510; step S521 is after step S520.

[0580] Example 6

[0581] Example 6 illustrates a second flowchart of transmission between a first node and a second node according to an embodiment of this application, as shown in the attached diagram. Figure 6 As shown. In the appendix Figure 6 In this embodiment, the first node U3 and the second node N4 communicate via a wireless link. It should be noted that the order in this embodiment does not limit the signal transmission order or the order of implementation in this application.

[0582] For the first node U3, the first information is sent in step S630.

[0583] For the second node N4, the first information is received in step S640.

[0584] In Example 6, the first node U3 performs measurements in the third RS resource set and the fourth RS resource set respectively and obtains a first measurement result and a second measurement result; at least one of the first measurement result or the second measurement result satisfies a first condition; the first association ID and the second association ID are respectively associated with the third RS resource set and the fourth RS resource set, and the first information indicates that the first entity is not applicable.

[0585] As an example, the first node U3 is the first node in this application.

[0586] As an example, the second node N4 is the second node in this application.

[0587] As one embodiment, the air interface between the second node N4 and the first node U3 includes a wireless interface between the base station equipment and the user equipment.

[0588] As one embodiment, the air interface between the second node N4 and the first node U3 includes a wireless interface between the relay node device and the user equipment.

[0589] As one embodiment, the air interface between the second node N4 and the first node U3 includes a wireless interface between user equipment and user equipment.

[0590] As one example, the second node N4 and the first node U3 communicate via the Uu interface.

[0591] As one example, the second node N4 is the sustaining base station for the serving cell of the first node U3.

[0592] As one embodiment, the third RS resource set includes an antenna port.

[0593] As one embodiment, the third RS resource set includes a reference signal port.

[0594] As an example, the third RS resource set includes RS.

[0595] As an example, the third RS resource set includes at least one RS resource.

[0596] As an example, the third RS resource set includes downlink RS resources.

[0597] As an example, the third RS resource set includes broadcast RS resources.

[0598] As an example, the third RS resource set includes multicast RS resources.

[0599] As an example, the third RS resource set includes cell-specific RS resources.

[0600] As an example, the third RS resource set includes RS resources specific to the cell group.

[0601] As an example, the third RS resource set includes RS resources specific to the cell set.

[0602] As an example, the third RS resource set includes RS resources for location.

[0603] As an example, the third RS resource set includes PRS resources.

[0604] As an example, the third RS resource set includes RS resources for sensing.

[0605] As an example, the third RS resource set includes ISAC sensing signal resources.

[0606] As an example, the third RS resource set includes ISAC-RS resources.

[0607] As an example, the third RS resource set includes IRS resources.

[0608] As one example, the third RS resource set includes RS resources for mobility management.

[0609] As one embodiment, the third RS resource set includes RS resources for cell-level mobility management.

[0610] As an example, the third RS resource set includes RS resources used for cell synchronization.

[0611] As one embodiment, the third RS resource set includes synchronization signals in at least 5G systems and systems after 5G systems.

[0612] As an example, the third RS resource set includes synchronization signals from at least a 6G system.

[0613] As an example, the third RS resource set includes at least a synchronization signal.

[0614] As an example, the third RS resource set includes at least a master synchronization signal.

[0615] As one embodiment, the third RS resource set includes at least a secondary synchronization signal.

[0616] As an example, the third RS resource set includes at least PBCH.

[0617] As an example, the third RS resource set includes SSB.

[0618] As an example, the third RS resource set includes SSB resources.

[0619] As an example, the third RS resource set includes at least one SSB.

[0620] As an example, the third RS resource set includes an SSB.

[0621] As an example, the third RS resource set includes multiple SSBs.

[0622] As an example, the third RS resource set includes at least one SSB in the SSB burst set.

[0623] As an example, the third RS resource set is the first RS resource set.

[0624] As one embodiment, the third RS resource set includes the first RS resource set.

[0625] As an example, the first RS resource set includes the third RS resource set.

[0626] As an example, the third RS resource set and the first RS resource set are QCLs.

[0627] As an example, the third RS resource set and the first RS resource set are both QCLs, and the QCL type is QCL typeE.

[0628] As an example, the third RS resource set and the first RS resource set are both QCLs, and the QCL type is QCL typeD.

[0629] As an example, the third RS resource set and the first RS resource set are QCL, and the QCL type is a QCL type other than QCL typeA, QCL typeB, QCL typeC and QCL typeD.

[0630] As an example, the third RS resource set corresponds to the same spatial transmission parameters as the first RS resource set.

[0631] As an example, the third RS resource set corresponds to the same network-side attachment conditions as the first RS resource set.

[0632] As an example, the third RS resource set is associated with the first association ID.

[0633] As an example, the first node receives RS resources from the third RS resource set.

[0634] As an example, the RS resources in the third RS resource set are aperiodic.

[0635] As an example, the RS resources in the third RS resource set are on-demand.

[0636] As an example, the RS resources in the third RS resource set are periodic, and the set of RS resources in the RS resource set is not less than the RS resources in the first RS resource set.

[0637] As an example, the third RS resource set is a time-domain sample of the RS resources in the first RS resource set.

[0638] As an example, the unit of the first measurement result is dB (decibel).

[0639] As an example, the first measurement result is a real number.

[0640] As an example, the first measurement result is a vector.

[0641] As an example, the first measurement result corresponds to a performance metric.

[0642] As an example, the first measurement result corresponds to a KPI (Key Performance Indicator).

[0643] As an example, the first measurement result is an intermediate KPI.

[0644] As an example, the first measurement result is the final KPI (eventual KPI).

[0645] As an example, the first measurement result was used for performance monitoring.

[0646] As an example, the candidates for the first measurement result include one or more of GCS (Generalized Cosine Similarity), SGSC (Squared Generalized Cosine Similarity), NMSE (Normalized Mean Squared Error), truth ground CSI, equivalent MSE (equivalent Mean Squared Error), and numerical spectral efficiency gap.

[0647] As an example, the candidates for the first measurement result include one or more of throughput, BLER (Block Error Rate), and hypothetical BLER.

[0648] As an example, the first measurement result is NMSE.

[0649] As an example, the first measurement result is SGCS.

[0650] As an example, the first measurement result is truth ground CSI.

[0651] As an example, the meaning of the first association ID being associated with the third RS resource set includes: the first association ID is configured to be associated with the third RS resource set.

[0652] As an example, the meaning of the first association ID being associated with the third RS resource set includes: the RRC signaling that configures the first association ID is also used to configure the third RS resource set.

[0653] As an example, the meaning of the first association ID being associated with the third RS resource set includes: the RRC signaling configuring the first association ID is also used to indicate the third RS resource set.

[0654] As an example, the meaning of the first associated ID being associated with the third RS resource set includes: the RRC signaling that configures the first associated ID is also used to indicate the ID corresponding to the third RS resource set.

[0655] As an example, the meaning of the first association ID being associated with the third RS resource set includes: the first association ID being used to characterize the spatial characteristics of the third RS resource set.

[0656] As an example, the meaning of the first association ID being associated with the third RS resource set includes: the first association ID being used to indicate the spatial characteristics of the third RS resource set.

[0657] As an example, the meaning of the first association ID being associated with the third RS resource set includes: the AI / ML model corresponding to the first association ID depends on the third RS resource set.

[0658] As an example, the meaning of the first association ID being associated with the third RS resource set includes: the third RS resource set is used for performance monitoring of the AI / ML model corresponding to the first association ID.

[0659] As an example, the meaning of the first association ID being associated with the third RS resource set includes: the entity corresponding to the first association ID depends on the third RS resource set.

[0660] As an example, the meaning of the first association ID being associated with the third RS resource set includes: the third RS resource set is used for performance monitoring of the entity corresponding to the first association ID.

[0661] As one embodiment, the fourth RS resource set includes an antenna port.

[0662] As one embodiment, the fourth RS resource set includes a reference signal port.

[0663] As an example, the fourth RS resource set includes RS.

[0664] As an example, the fourth RS resource set includes at least one RS resource.

[0665] As an example, the fourth RS resource set includes downlink RS resources.

[0666] As an example, the fourth RS resource set is dedicated.

[0667] As an example, the fourth RS resource set is UE-specific.

[0668] As an example, the fourth RS resource set is not community-shared.

[0669] As an example, the fourth RS resource set includes UE-specific RS resources.

[0670] As an example, the fourth RS resource set is exclusive to the first node.

[0671] As an example, the fourth RS resource set is not a cell-common RRC signaling configuration.

[0672] As an example, the fourth RS resource set includes RS resources used for measurement.

[0673] As an example, the fourth RS resource set includes CSI-RS resources.

[0674] As an example, the fourth RS resource set includes at least one CSI-RS resource.

[0675] As an example, the fourth RS resource set includes NZP CSI-RS resources.

[0676] As an example, the fourth RS resource set corresponds to an NZP-CSI-RS-ResourceSetId.

[0677] As an example, the fourth RS resource set corresponds to a CSI-ResourceConfigId.

[0678] As one embodiment, the fourth RS resource set includes the second RS resource set.

[0679] As an example, the fourth RS resource set is the second RS resource set.

[0680] As one embodiment, the second RS resource set includes the fourth RS resource set.

[0681] As an example, the fourth RS resource set corresponds to the same spatial transmission parameters as the second RS resource set.

[0682] As an example, the fourth RS resource set corresponds to the same network-side attachment conditions as the second RS resource set.

[0683] As an example, the fourth RS resource set is associated with the second association ID.

[0684] As an example, the first node receives RS resources from the fourth RS resource set.

[0685] As an example, the RS resources in the fourth RS resource set are aperiodic.

[0686] As an example, the RS resources in the fourth RS resource set are on-demand.

[0687] As an example, the RS resources in the fourth RS resource set are periodic, and the set of RS resources in the RS resource set is not less than the RS resources in the second RS resource set.

[0688] As an example, the fourth RS resource set is a time-domain sample of the RS resources in the second RS resource set.

[0689] As an example, the unit of the second measurement result is dB.

[0690] As an example, the second measurement result is a real number.

[0691] As an example, the second measurement result is a vector.

[0692] As an example, the second measurement result corresponds to a performance index.

[0693] As an example, the second measurement result corresponds to a KPI.

[0694] As an example, the second measurement result is an intermediate KPI.

[0695] As an example, the second measurement result is the final KPI.

[0696] As an example, the second measurement result was used for performance monitoring.

[0697] As an example, the candidates for the second measurement result include one or more of GCS, SGSC, NMSE, truth ground CSI, equivalent MSE, and numerical spectral efficiency gap.

[0698] As an example, the candidates for the second measurement result include one or more of throughput, BLER, and hypothetical BLER.

[0699] As an example, the second measurement result is NMSE.

[0700] As an example, the second measurement result is SGCS.

[0701] As an example, the second measurement result is the truth ground CSI.

[0702] As an example, the meaning of the second association ID being associated with the fourth RS resource set includes: the second association ID is configured to be associated with the fourth RS resource set.

[0703] As an example, the meaning of the second association ID being associated with the fourth RS resource set includes: the RRC signaling that configures the second association ID is also used to configure the fourth RS resource set.

[0704] As one example, the meaning of the second association ID being associated with the fourth RS resource set includes: the RRC signaling configuring the second association ID is also used to indicate the fourth RS resource set.

[0705] As an example, the meaning of the second association ID being associated with the fourth RS resource set includes: the RRC signaling that configures the second association ID is also used to indicate the ID corresponding to the fourth RS resource set.

[0706] As an example, the meaning of the second association ID being associated with the fourth RS resource set includes: the second association ID being used to characterize the spatial characteristics of the fourth RS resource set.

[0707] As an example, the meaning of the second association ID being associated with the fourth RS resource set includes: the second association ID being used to indicate the spatial characteristics of the fourth RS resource set.

[0708] As an example, the meaning of the second association ID being associated with the fourth RS resource set includes: the AI / ML model corresponding to the second association ID depends on the fourth RS resource set.

[0709] As an example, the meaning of the second association ID being associated with the fourth RS resource set includes: the fourth RS resource set is used for performance monitoring of the AI / ML model corresponding to the second association ID.

[0710] As an example, the meaning of the second association ID being associated with the fourth RS resource set includes: the entity corresponding to the second association ID depends on the fourth RS resource set.

[0711] As an example, the meaning of the second association ID being associated with the fourth RS resource set includes: the fourth RS resource set is used for performance monitoring of the entity corresponding to the second association ID.

[0712] As an example, when at least one of the first measurement result or the second measurement result satisfies the first condition, the first node sends the first information.

[0713] As an example, when the first measurement result meets the first condition, the first node sends the first information.

[0714] As a sub-implementation of this embodiment, the first information includes the first measurement result.

[0715] As a sub-implementation of this embodiment, the first information does not include the first measurement result.

[0716] As a sub-implementation of this embodiment, the message carried by the first information depends on the first measurement result.

[0717] As a sub-example of this embodiment, the first information indicates that the first entity is not applicable to the first RS resource set, and the first node determines that the first entity is not applicable based on the first measurement result.

[0718] As an example, when the second measurement result meets the first condition, the first node sends the first information.

[0719] As a sub-implementation of this embodiment, the first information includes the second measurement result.

[0720] As a sub-example of this embodiment, the first information does not include the second measurement result.

[0721] As a sub-implementation of this embodiment, the message carried by the first information depends on the second measurement result.

[0722] As a sub-example of this embodiment, the first information indicates that the first entity is not applicable to the second RS resource set.

[0723] As an example, both the first measurement result and the second measurement result satisfying the first condition are used to trigger the transmission of the first information.

[0724] As a sub-implementation of this embodiment, the first information includes the first measurement result and the second measurement result.

[0725] As a sub-example of this embodiment, the first information does not include the first measurement result and the second measurement result.

[0726] As a sub-implementation of this embodiment, the message carried by the first information depends on the first measurement result and the second measurement result.

[0727] As a sub-example of this embodiment, the first information indicates that the first entity is not applicable to the first RS resource set and the second RS resource set.

[0728] As one embodiment, the first information includes layer 1 messages.

[0729] As an example, the first information is transmitted via UCI.

[0730] As an example, the first information includes 1 bit, which directly indicates that the first entity is not applicable.

[0731] As one embodiment, the first information includes 2 bits, which directly indicate that the first entity is not applicable to the first RS resource set, or the 2 bits directly indicate that the first entity is not applicable to the second RS resource set, or the 2 bits directly indicate that the first entity is not applicable.

[0732] As an example, the first information includes 2 bits, which directly indicate which set of resources or resources the first entity is not applicable to.

[0733] As an example, after receiving the first information, the second node determines that the first entity is not applicable.

[0734] As an example, after receiving the first information, the second node determines which RS resource set the first entity is not applicable to.

[0735] As an example, the first node implicitly indicates that the first entity is not applicable by sending the first information.

[0736] As an example, the first node sends a monitoring result for the first entity, the monitoring result indicating that the first entity is not applicable.

[0737] As an example, the first information indicates that the first entity is not applicable.

[0738] As an example, the first information is transmitted via PUCCH.

[0739] As an example, the first information is transmitted via PUSCH.

[0740] As an example, the first information is transmitted via UL-SCH.

[0741] As one embodiment, step S630 is in the appendix Figure 5 After step S510 described above; step S640 is in the appendix Figure 5After step S520 described above.

[0742] As one embodiment, step S630 is in the appendix Figure 5 Before step S511; step S640 is in the appendix Figure 5 Before step S520 described above.

[0743] As a sub-implementation of this embodiment, the first information indicates that the first entity is not applicable to the first RS resource set; the first reporting message depends on the measurement of the first node U3 on the second RS resource set.

[0744] As a sub-example of this embodiment, the first information indicates that the first entity is not applicable to the second RS resource set; the first reporting message depends on the measurement of the first node U3 on the first RS resource set.

[0745] Example 7

[0746] Example 7 illustrates a first schematic diagram of a first condition according to an embodiment of this application, as shown in the attached diagram. Figure 7 As shown. In the appendix Figure 7 In the diagram, the horizontal axis represents time, and the vertical axis represents one of the first measurement results or the second measurement result, wherein the measurement results are all below a first threshold in the first time window.

[0747] In Example 7, satisfying the first condition means that the measurement results are all below the first threshold in the first time window.

[0748] As an example, satisfying the first condition means that at least one of the first measurement result or the second measurement result is lower than the first threshold in the first time window.

[0749] As an example, satisfying the first condition means that the first measurement result is lower than the first threshold in the first time window.

[0750] As an example, satisfying the first condition means that the second measurement result is lower than the first threshold in the first time window.

[0751] As an example, satisfying the first condition means that either the first measurement result or the second measurement result is lower than the first threshold in the first time window.

[0752] As an example, satisfying the first condition means that both the first measurement result and the second measurement result are below the first threshold in the first time window.

[0753] As an example, the first threshold is predefined.

[0754] As an example, the first threshold is fixed.

[0755] As an example, the first threshold is configured through higher-level parameters.

[0756] As an example, the higher-layer signaling of the third RS resource set is configured while the first threshold is configured.

[0757] As an example, the higher-layer signaling of the fourth RS resource set is configured while the first threshold is configured.

[0758] As an example, the first measurement result and the second measurement result correspond to different first thresholds.

[0759] As an example, the first measurement result and the second measurement result correspond to the same first threshold.

[0760] As an example, the first threshold is a real number.

[0761] As an example, Appendix Figure 7 The measurement result mentioned is a real number, and the measurement result being lower than the first threshold includes: the measurement result being worse than the first threshold.

[0762] As an example, Appendix Figure 7 The measurement result mentioned is a real number, and the measurement result being lower than the first threshold includes: the measurement result being less than the first threshold.

[0763] As an example, Appendix Figure 7 The measurement result is a real number, and the measurement result being lower than the first threshold includes: the absolute value of the measurement result being less than the first threshold.

[0764] As an example, Appendix Figure 7 The measurement result is a vector, and the measurement result being lower than the first threshold includes: the similarity between the measurement result and the corresponding prediction result being lower than the first threshold.

[0765] As an example, Appendix Figure 7 The measurement result is a vector, and the measurement result being lower than the first threshold includes: the Euclidean distance between the measurement result and the corresponding prediction result is lower than the first threshold.

[0766] As an example, the meaning of the measurement result being below the first threshold throughout the first time window includes: the time during which the measurement result is below the first threshold is not less than the duration of the first time window.

[0767] As an example, the meaning of the measurement result being lower than the first threshold in the first time window includes: the first node calculates the measurement result multiple times in the first time window, and the measurement result obtained from the multiple calculations is lower than the first threshold.

[0768] As an example, the first time window is continuous in the time domain.

[0769] As an example, the duration of the first time window is predefined.

[0770] As an example, the duration of the first time window is configured through higher-level parameters.

[0771] As an example, the higher-layer signaling of the third RS resource set is configured while the first time window is configured.

[0772] As an example, the higher-layer signaling of the fourth RS resource set is configured while the first time window is configured.

[0773] As an example, the first measurement result and the second measurement result correspond to different first time windows.

[0774] As an example, the first measurement result and the second measurement result correspond to the same first time window.

[0775] As an example, the duration of the first time window is fixed.

[0776] As an example, the duration of the first time window is equal to K1 milliseconds.

[0777] As a sub-implementation of this embodiment, K1 is a positive integer greater than 1.

[0778] As a sub-example of this embodiment, K1 is a positive real number greater than 1.

[0779] As an example, the first node maintains a first timer. When the measurement result calculated by the first node is lower than the first threshold and the first timer is not running, the first node starts the first timer.

[0780] As a sub-implementation of this embodiment, the duration of the first timer is equal to the expiration value of the first time window. When the first timer expires, the first node considers that the measurement result satisfies the first condition.

[0781] As a sub-example of this embodiment, when the measurement result is not lower than the first threshold, the first node stops the first timer.

[0782] As a sub-implementation of this embodiment, the first node maintains a first timer for the first measurement result and the second measurement result, respectively.

[0783] Example 8

[0784] Example 8 illustrates a second schematic diagram of a first condition according to an embodiment of this application, as shown in the attached diagram. Figure 8 As shown. In the appendix Figure 8 In the diagram, the horizontal axis represents time, and the vertical axis represents one of the first measurement result or the second measurement result; the first node monitors the measurement result as being lower than the first threshold between time T0 and time T1 and between time T2 and time T3, and monitors the measurement result as not being lower than the first threshold between time T1 and time T2 and between time T3 and time T4.

[0785] In Example 8, satisfying the first condition means that the number of times the measurement result is below the first threshold in the first time window is greater than the first integer.

[0786] As an example, satisfying the first condition means that the number of times the first measurement result is below the first threshold in the first time window is greater than the first integer.

[0787] As an example, satisfying the first condition means that the number of times the second measurement result is below the first threshold in the first time window is greater than the first integer.

[0788] As an example, satisfying the first condition means that the number of times either the first measurement result or the second measurement result is lower than the first threshold in the first time window is greater than the first integer.

[0789] As an example, satisfying the first condition means that the number of times the first measurement result and the second measurement result are below the first threshold in the first time window is greater than the first integer.

[0790] As an example, the first threshold is predefined.

[0791] As an example, the first threshold is fixed.

[0792] As an example, the first threshold is configured through higher-level parameters.

[0793] As an example, the higher-layer signaling of the third RS resource set is configured while the first threshold is configured.

[0794] As an example, the higher-layer signaling of the fourth RS resource set is configured while the first threshold is configured.

[0795] As an example, the first measurement result and the second measurement result correspond to different first thresholds.

[0796] As an example, the first measurement result and the second measurement result correspond to the same first threshold.

[0797] As an example, the first threshold is a real number.

[0798] As an example, the measurement result is a real number, and the measurement result being lower than the first threshold includes: the measurement result being worse than the first threshold.

[0799] As an example, the measurement result is a real number, and the measurement result being lower than the first threshold includes: the measurement result being less than the first threshold.

[0800] As an example, the measurement result is a real number, and the measurement result being lower than the first threshold includes: the absolute value of the measurement result being less than the first threshold.

[0801] As an example, the measurement result is a vector, and the measurement result being lower than the first threshold includes: the similarity between the measurement result and the corresponding prediction result being lower than the first threshold.

[0802] As an example, the first integer is a positive integer greater than 1.

[0803] As an example, the value of the first integer is fixed.

[0804] As an example, the value of the first integer is predefined.

[0805] As an example, the value of the first integer is configured through higher-level parameters.

[0806] As an example, the higher-layer signaling of the third RS resource set is configured while the first integer is configured.

[0807] As an example, the higher-layer signaling of the fourth RS resource set is configured while the first integer is configured.

[0808] As an example, the first measurement result and the second measurement result correspond to different first integers.

[0809] As an example, the first measurement result and the second measurement result correspond to the same first integer.

[0810] As an example, the first time window is continuous in the time domain.

[0811] As an example, the duration of the first time window is predefined.

[0812] As an example, the duration of the first time window is configured through higher-level parameters.

[0813] As an example, the higher-layer signaling of the third RS resource set is configured while the first time window is configured.

[0814] As an example, the higher-layer signaling of the fourth RS resource set is configured while the first time window is configured.

[0815] As an example, the first measurement result and the second measurement result correspond to different first time windows.

[0816] As an example, the first measurement result and the second measurement result correspond to the same first time window.

[0817] As an example, the duration of the first time window is fixed.

[0818] As an example, the duration of the first time window is equal to K1 milliseconds.

[0819] As a sub-implementation of this embodiment, K1 is a positive integer greater than 1.

[0820] As a sub-example of this embodiment, K1 is a positive real number greater than 1.

[0821] As an example, the meaning of the measurement result being lower than the first threshold more than a first integer in the first time window includes: the first node calculates the measurement result multiple times in the first time window, and the number of times the measurement result obtained from the multiple calculations is lower than the first threshold is greater than the first integer.

[0822] As an example, the meaning of the measurement result being lower than the first threshold more than the first integer in the first time window includes: the first node calculates the measurement result multiple times in the first time window, and at least one of the measurement results obtained from the multiple calculations is not lower than the first integer.

[0823] As an example, the first node maintains a first timer. When the measurement result calculated by the first node is lower than the first threshold and the first timer is not running, the first node starts the first timer.

[0824] As a sub-example of this embodiment, when the first timer is running, the first node maintains a first counter. When the measurement result calculated by the first node is lower than the first threshold, the first counter is incremented by 1.

[0825] As an additional embodiment of this sub-example, when the first timer expires, the first node resets or clears the first counter.

[0826] As an additional embodiment of this sub-example, when the first counter is not less than the first integer, the first node stops the first timer.

[0827] As an additional embodiment of this sub-example, when the first counter is not less than the first integer, the first node considers that the measurement result satisfies the first condition.

[0828] As an additional embodiment of this sub-example, the duration of the first timer is equal to the expiration value of the first time window. When the first timer expires, the first node considers that the measurement result does not meet the first condition.

[0829] As a sub-example of this embodiment, when the first timer is running, the first node maintains the first counter, and the measurement result calculated by the first node is not lower than the first threshold, the first counter is incremented by 1.

[0830] As an additional embodiment of this sub-example, when the first timer expires, the first node resets or clears the first counter.

[0831] As an additional embodiment of this sub-example, when the first counter is not less than the second integer, the first node stops the first timer.

[0832] As an additional embodiment of this sub-example, when the first counter is not less than the second integer, the first node considers that the measurement result does not meet the first condition.

[0833] As an additional embodiment of this sub-example, the duration of the first timer is equal to the expiration value of the first time window. When the first timer expires, the first node considers that the measurement result satisfies the first condition.

[0834] As a sub-implementation of this embodiment, the first node maintains a first timer for the first measurement result and the second measurement result, respectively.

[0835] As a sub-implementation of this embodiment, the first node maintains a first counter for the first measurement result and the second measurement result, respectively.

[0836] As an example, satisfying the first condition means that the measurement result is lower than the first threshold in the first time window, and the number of times the measurement result is lower than the first threshold in the first time window is greater than a first integer.

[0837] As an example, the first node periodically calculates the measurement results.

[0838] As an example, the first node performs the relevant calculation of the measurement results.

[0839] Example 9

[0840] Example 9 illustrates a schematic diagram of a first associated ID and a second associated ID according to an embodiment of this application, as shown in the attached diagram. Figure 9 As shown. In the appendix Figure 9 In this context, the first associated ID is public, while the second associated ID is private.

[0841] As an example, the first association ID is public, and the second association ID is private.

[0842] As an example, the first associated ID is public.

[0843] As an example, the first associated ID being public means at least one of the following:

[0844] - The first associated ID is indicated as Common;

[0845] - The first associated ID is cell-specific, or the first associated ID is cell-group-specific;

[0846] - The first node considers the first associated ID to be unique within the cell or cell group.

[0847] As an example, the first association ID being public means that the first association ID is a parameter of Common.

[0848] As an example, the meaning of "the first associated ID is public" includes: the first associated ID is indicated as "Common".

[0849] As an example, the meaning of "the first association ID is public" includes: the first association ID is a CellSpecific parameter.

[0850] As an example, the meaning of "the first associated ID is public" includes: the first associated ID is cell-specific.

[0851] As an example, the meaning of "the first associated ID is public" includes: the first associated ID is cell group exclusive.

[0852] As an example, the meaning of "the first associated ID is public" includes that the first associated ID is unique within the serving cell of the first node.

[0853] As an example, the meaning of "the first associated ID is public" includes: the first node considers the first associated ID to be unique within the cell.

[0854] As an example, the meaning of "the first association ID is public" includes: the first association ID is unique within the serving cell group corresponding to the serving cell of the first node.

[0855] As an example, the meaning of "the first associated ID is public" includes: the first node considers the first associated ID to be unique within the cell group.

[0856] As an example, the first association ID being public means that the first association ID is the same for every terminal in the serving cell of the first node.

[0857] As an example, the meaning of "the first association ID is public" includes that the first association ID is the same for every terminal in the serving cell group corresponding to the serving cell of the first node.

[0858] As an example, the fact that the first association ID is public means that the network ensures that the corresponding parameters of other terminals are aligned with the first node as necessary.

[0859] As an example, the meaning of "the first association ID is public" includes: the first association ID indicates additional conditions on the network side during the inference process, and the additional conditions in the inference process indicated by the first association ID are consistent in the cell.

[0860] As an example, the meaning of "the first association ID is public" includes: the first association ID indicates additional conditions on the network side during the inference process, and the first node assumes that the additional conditions in the inference process associated with the first association ID are consistent with other terminals in the serving cell.

[0861] As an example, the second associated ID is proprietary.

[0862] As an example, the meaning of "the second associated ID is dedicated" includes: the second associated ID is indicated as dedicated.

[0863] As an example, the meaning of "the second association ID is exclusive" includes: the second association ID is exclusive to the UE.

[0864] As an example, the meaning of "the second associated ID is exclusive" includes: the first node considers the second associated ID to be configurable within the cell.

[0865] As an example, the meaning of "the second associated ID is exclusive" includes: the first node considers the second associated ID to be variable within the cell.

[0866] Example 10

[0867] Example 10 illustrates a schematic diagram of a first RS resource set and a second RS resource set according to an embodiment of this application, as shown in the attached diagram. Figure 10 As shown. In the appendix Figure 10 In this context, the first RS resource set is public, while the second RS resource set is private.

[0868] As an example, the first RS resource set is public.

[0869] As an example, the type of the first RS resource set is not proprietary.

[0870] As an example, the type of the first RS resource set is other than that specific to the first node.

[0871] As an example, the first RS resource set does not include the RS resources exclusive to the first node.

[0872] As an example, the first RS resource set includes only RS resources other than those specific to the first node.

[0873] As an example, the type of the first RS resource set is common.

[0874] As an example, the type of the first RS resource set is cell-specific.

[0875] As an example, the type of the first RS resource set is cell group-specific.

[0876] As an example, the type of the first RS resource set is area-specific.

[0877] As an example, the first RS resource set is a cell-specific RS resource set.

[0878] As an example, the first RS resource set is a Common RS resource set.

[0879] As an example, the first RS resource set is an SSB.

[0880] As an example, the first RS resource set is a PRS.

[0881] As an example, the second RS resource set is dedicated.

[0882] As one example, the second RS resource set is UE-specific.

[0883] As an example, the second RS resource set is not community-shared.

[0884] As an example, the second RS resource set is used only for the synchronization of the first node.

[0885] As one embodiment, the second RS resource set includes UE-specific RS resources.

[0886] As one example, the second RS resource set is exclusive to the first node.

[0887] As an example, the second RS resource set is not a cell-common RRC signaling configuration.

[0888] As one example, the second RS resource set is a user-specific RS resource set.

[0889] As an example, the second RS resource set is a Dedicated RS resource set.

[0890] Example 11

[0891] Example 11 illustrates a schematic diagram of a first type of result according to an embodiment of this application, as shown in the attached diagram. Figure 11 As shown. In the appendix Figure 11 In this context, the first reporting message includes a first type of result, which is used simultaneously in both Layer 1 and Layer 3.

[0892] As an example, the first type of result includes RSRP.

[0893] As an example, the first type of result includes SNR.

[0894] As an example, the first type of result includes SINR.

[0895] As an example, the first type of result includes BLER (Block Error Rate).

[0896] As an example, the first type of result includes transmission delay.

[0897] As an example, the first type of result includes distance information.

[0898] As an example, the first type of result is used simultaneously for the measurement of layer 1 and the measurement of layer 3.

[0899] As an example, the first type of result is used simultaneously for reporting by layer 1 and reporting by layer 3.

[0900] As an example, the first type of result is used simultaneously for the feedback of layer 1 and the feedback of layer 3.

[0901] As an example, the first type of result being used for layer 1 means that the first type of result includes the CSI of layer 1.

[0902] As an example, the first type of result is used for at least the former in the inference and training of CSI at layer 1.

[0903] As an example, the first type of result is used for beam management (BM).

[0904] As an example, the first type of result was used for CSI compression.

[0905] As an example, the first type of result is used for beam-level mobility management.

[0906] As an example, the CSI of layer 1 includes LI.

[0907] As an example, the CSI of layer 1 includes RI.

[0908] As an example, the CSI of layer 1 includes CQI.

[0909] As an example, the CSI of layer 1 includes PMI.

[0910] As an example, the CSI of layer 1 includes CRI.

[0911] As an example, the CSI of layer 1 includes SSBRI.

[0912] As an example, the CSI of layer 1 includes L1-RSRP.

[0913] As an example, the first type of result being used in Layer 3 means that the first type of result is used in at least one of Layer 3 CSI determination, cell selection or cell reselection, handover or location.

[0914] As an example, the first type of result was used for the CSI determination of layer 3.

[0915] As an example, the first type of result is used for CSI prediction of layer 3.

[0916] As an example, the first type of result was used for the CSI measurement of layer 3.

[0917] As an example, the first type of result is used for CSI reporting of layer 3.

[0918] As an example, the first type of result is used for cell-level mobility management.

[0919] As an example, the first type of result is used for event-triggered measurement reporting.

[0920] As an example, the first type of result was used for RLF (Radio Link Failure).

[0921] As an example, the first type of result is used for RRM (Radio Resource Management).

[0922] As an example, the first type of result is used for cell selection.

[0923] As an example, the first type of result is used for cell re-selection.

[0924] As an example, the first type of result is used for cell selection and cell reselection.

[0925] As an example, the first type of result is used for switching.

[0926] As an example, the first type of result was used for localization.

[0927] As an example, the first type of result was used for perception.

[0928] As an example, the first type of result is a prediction for the first node when it is in a disconnected state.

[0929] As an example, the first type of result is for inference when the first node is in a disconnected state.

[0930] As an example, the first type of result is for inference in the non-connected state of a terminal residing in the cell.

[0931] As an example, the first type of result is for the inference of cell reselection in the disconnected state of the first node.

[0932] As an example, the first type of result applies to cell reselection in the disconnected state of the first node.

[0933] As one example, the non-connected state includes the idle state.

[0934] As an example, the non-connected state includes the inactive state.

[0935] As an example, the first type of result was used for the CSI determination of layer 3.

[0936] As an example, the first type of result is used for CSI prediction of layer 3.

[0937] As an example, the first type of result was used for the CSI measurement of layer 3.

[0938] As an example, the first type of result is used for CSI reporting of layer 3.

[0939] As an example, the first type of result is used for cell selection.

[0940] As an example, the first type of result is used for the cell reselection.

[0941] As an example, the first type of result is used for cell selection and cell reselection.

[0942] As an example, the first type of result is used for switching.

[0943] As an example, the first type of result was used for localization.

[0944] As an example, the first type of result was used for perception.

[0945] Example 12

[0946] Example 12 illustrates a schematic diagram of an AI entity according to an embodiment of this application, as shown in the attached diagram. Figure 12 As shown. In the appendix Figure 12 In this embodiment, a communication system includes an AI entity 1201, a base station 1202, a terminal 1203, and a terminal 1204. Terminals 1203 and 1204 can respectively access the base station 1202 and communicate with it. It is worth noting that this application does not limit the specific implementation of the AI ​​entity. The AI ​​entity 1201 can be located on the network side, interacting with network devices such as base stations, or located within network devices; it can also be located on the user side, interacting with terminals, or located within terminals.

[0947] As an example, the AI ​​entity is an AI / ML model.

[0948] As an example, the AI ​​entity is a model.

[0949] As an example, the AI ​​entity is associated with an AI / ML model ID.

[0950] As an example, the AI ​​entity is associated with a Functionality ID.

[0951] As an example, the AI ​​entity is associated with a data set.

[0952] As an example, the AI ​​entity is associated with a training dataset.

[0953] As an example, the AI ​​entity corresponds to an Associated ID.

[0954] As an example, the base station forwards the AI / ML model-related data it collects or that is reported by the terminal to the AI ​​entity, which then performs AI / ML-related operations such as constructing the training dataset and training the model. The base station then forwards the output of the AI / ML-related operations, such as the trained AI / ML model, model evaluation, and test results, to each terminal.

[0955] As an example, the terminal forwards the AI / ML model-related data it collects or the data sent by the base station to the AI ​​entity, which then performs AI / ML-related operations such as constructing the training dataset and training the model. The terminal then forwards the output of the AI / ML-related operations, such as the trained AI / ML model, model evaluation, and test results, to the base station.

[0956] As an example, to support AI / ML functionality in a wireless network, AI / ML network elements or modules can be introduced into the network. If an AI / ML network element is introduced, it corresponds to an independent network element; if an AI / ML module is introduced, it can be located inside a network element, such as inside a terminal device or network device.

[0957] As an example, the AI ​​entity is located inside the base station.

[0958] As one example, the AI ​​entity is a module or function of the base station.

[0959] As one example, the AI ​​entity is located inside the terminal.

[0960] As one example, the AI ​​entity is a module or function of the terminal.

[0961] As an example, one possible implementation of the first entity in this application is that the AI ​​entity is deployed in a server or cloud device of an Over The Top (OTT) system. Optionally, the cloud device is located on one or more of the user equipment side, network equipment side, or core network side.

[0962] As an example, the AI ​​entity corresponds to the first entity in this application.

[0963] Example 13

[0964] Example 13 illustrates a schematic diagram of RAN domain AI / ML function deployment according to an embodiment of this application, as shown in the attached diagram. Figure 13 As shown. In the appendix Figure 13 In this context, gNB can be replaced with network equipment such as eNB or 6G base stations.

[0965] In Example 13, the management of the ML inference functions of multiple base stations is completed by the RAN domain management function 1302, that is, data interaction with the RAN domain MnS (Management Service) consumer / cross-domain management 1301 (as shown in the attached figure). Figure 13 (As shown by the dashed arrow in the diagram). The RAN domain ML training function 1303 is located in the RAN domain management function 1302; while the ML inference function is located in the base station, that is, the AI / ML inference function 1304 is located in gNB 1305, the AI / ML inference function 1306 is located in gNB 1307, and so on.

[0966] AI / ML related functions include ML training (also known as AI training or AI / ML training), ML testing, and ML inference (also known as AI inference or AI / ML inference), etc. ML training, ML testing, and ML inference functions can be deployed independently or co-located. Deployment of AI / ML related functions can be implemented through software, such as downloading and / or running executable files; or it can be implemented through a combination of software and hardware, such as accelerating specific computing units through hardware to improve computing speed or save power.

[0967] ML training functions can be deployed in a cross-domain management system or a domain-specific management system; the domain-specific management system is used to manage the RAN domain or the CN (Core Network) domain. For example, ML training functions for MDA (Management Data Analytics) can be deployed in MDAF (Management Data Analytic Function); ML training for network data analytics can be deployed in NWDAF (Network Data Analytics Function), meaning the ML training function is an MTLF (Model Training Logical Function).

[0968] The ML inference function can also be deployed in a cross-domain management system or a domain-specific management system; for example, the ML inference function is MDAF, or the ML inference function is AnLF (Analytics Logical Function) located in NWDAF.

[0969] Similarly, ML testing capabilities can also be deployed in cross-domain management systems or domain-specific management systems.

[0970] Optionally, the management of ML inference function can also be completed by the base station itself, that is, each base station can independently interact with the RAN domain MnS consumer / cross-domain management 1301.

[0971] It should be noted that Embodiment 13 is merely a non-limiting implementation method; optionally, the ML training function of the RAN domain may also be deployed in the base station; or optionally, some base stations may deploy both the ML inference function and the ML training function of the RAN domain, while some base stations may only deploy the ML inference function.

[0972] As an example, one of the gNBs (or base stations) in Example 13 is the second node of this application.

[0973] Example 14

[0974] Example 14 illustrates a schematic diagram of the deployment of AI / ML functions in a UE according to an embodiment of this application, as shown in the attached diagram. Figure 14 As shown. In the appendix Figure 14 In this context, the RAN domain ML training function 1404 is optional.

[0975] UE function 1403 is deployed in the first node of this application, and the UE function 1403 includes AI / ML inference function 1405; the AI / ML inference function 1405 uses an ML model (also called an AI model) for inference; an ML model is typically trained before being used for AI / ML inference.

[0976] As an example, the UE function 1403 includes a RAN domain ML training function 1404, which runs training data through an ML model to obtain a relevant loss and adjusts the parameters of the ML model based on the calculated loss; the ML training includes at least one of ML initial training, ML re-training, and reinforcement learning.

[0977] The above embodiments can reduce the complexity of the base station, or save air interface resources caused by reporting training data; however, the above embodiments place high demands on the processing capabilities of the UE side.

[0978] Optionally, the UE function 1403 also includes a CN domain ML training function ( Figure 14 (Not included in the text).

[0979] Optionally, the UE function 1403 also includes an AI / ML deployment function. Figure 14 It is not included in the list, which is used to load ML models and data.

[0980] As an example, the first node indicates whether it supports ML training function (RAN domain or CN domain) through capability reporting. The capability reporting is RRC signaling or NAS (Non-Access Stratum) signaling.

[0981] As an example, the ML model and the associated metadata are loaded by the first node from a network device or a remote server.

[0982] Optionally, the UE function 1403 is an MnS producer that provides data to the CN domain MnF (Management Function) and / or the RAN domain MnF and / or the cross-domain management system 1401 for management or analysis (as shown by the double arrow 1402).

[0983] Optionally, the UE function 1403 is an MnS consumer that loads data from the CN domain MnF and / or RAN domain MnF and / or cross-domain management system 1401 for AI / ML-related management, such as managing data requests, ML model activation, and / or ML training (as shown by double arrow 1402).

[0984] As an example, the ML model is based on NN (Neural Networks).

[0985] As an example, the ML model is based on ANN (Artificial Neural Networks).

[0986] As an example, the ML model is based on CNN (Convolutional Neural Networks).

[0987] As an example, the ML model is based on the LLM (Large Language Model) architecture.

[0988] As an example, the ML model is based on the Transformer architecture.

[0989] As an example, the ML model is based on the GPT (Generative Pre-Trained) architecture.

[0990] As an example, the ML model is based on LSTM (Long Short-Term Memory network).

[0991] As an example, the ML model is based on MLP (MultiLayer Perceptron).

[0992] As an example, the ML model is based on GAN (Generative Adversarial Networks).

[0993] As an example, the ML model is based on a lightweight neural network.

[0994] As a sub-example of this embodiment, the lightweight neural network includes one or more of MobileNet, ShuffleNet, and SqueezeNet.

[0995] Example 15

[0996] Example 15 illustrates a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of this application, as shown in the attached diagram. Figure 15 As shown. In the appendix Figure 15 In this context, the processing system based on artificial intelligence or machine learning includes a first processor, a second processor, a third processor, and a fourth processor.

[0997] In Example 15, the first processor sends a first dataset to the second processor and a second dataset to the third processor; the second processor generates a target first-class parameter set based on the first dataset, and sends the generated target first-class parameter set to the third processor; the third processor processes the second dataset using the target first-class parameter set to obtain a first-class output, optionally, the third processor sends the first-class output to the fourth processor. (See Appendix...) Figure 15 In this configuration, the first type of feedback and the second type of feedback are optional; the second processor includes ML training functionality; and the third processor includes ML inference functionality.

[0998] As one embodiment, the fourth processor includes ML testing functionality.

[0999] As one embodiment, the fourth processor includes performance monitoring / evaluation of the ML model.

[1000] As an example, the third processor sends a first type of feedback to the second processor; the first type of feedback is used to trigger the recalculation or update of the target first type of parameter set, that is, to trigger ML initial training or ML retraining.

[1001] As one embodiment, the fourth processor sends a second type of feedback to the first processor; the second type of feedback is used to generate the first dataset or the second dataset, or the second type of feedback is used to trigger the sending of the first dataset or the sending of the second dataset.

[1002] As one embodiment, the first processor generates the first dataset and the second dataset based on the measurement of the reference signal.

[1003] As one embodiment, the third processor belongs to the first node, and the fourth processor belongs to the second node.

[1004] As an example, the third processor belongs to the first node.

[1005] As an example, the first dataset includes training data.

[1006] As one embodiment, the second processor is used to train an ML model, and the trained model is described by the target first class of parameter sets.

[1007] As an example, the second processor belongs to the first node; the above method avoids passing the first dataset to the second node.

[1008] As an example, the second processor belongs to the second node in this application; the above method supports joint training and optimizes system performance.

[1009] As an example, the second processor belongs to the core network; the above method supports network-wide joint training, further optimizing system performance.

[1010] As an example, the second dataset includes inference data.

[1011] As one embodiment, the second dataset includes measurements of the first node in this application on the first RS resource set.

[1012] As one embodiment, the second dataset includes measurements of the first node in this application on the second RS resource set.

[1013] As an example, the third processor constructs a model based on the target first type of parameter group, and then inputs the second dataset into the constructed model to obtain the first type of output.

[1014] As one embodiment, the output of the third processor includes the performance parameters described in this application.

[1015] As one embodiment, the input of the third processor includes measurements taken by the first node in this application on the third RS resource set.

[1016] As an example, the output of the third processor includes the first measurement result from this application.

[1017] As one embodiment, the input of the third processor includes the measurements taken by the first node in this application on the fourth RS resource set.

[1018] As one embodiment, the output of the third processor includes the second measurement result described in this application.

[1019] As an example, the third processor generates a recovery dataset based on the first type of output, and the error between the recovery dataset and the second dataset is used to generate the first type of feedback.

[1020] As an example, the first type of feedback is used to reflect the performance of the trained model; when the performance of the trained model fails to meet the requirements, the second processing opportunity will recalculate the target first type of parameter set.

[1021] 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.

[1022] 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 the pooling function, or parameters of the activation function.

[1023] 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.

[1024] Example 16

[1025] Example 16 illustrates a schematic diagram based on artificial intelligence or machine learning according to an embodiment of this application, as shown in the attached diagram. Figure 16 As shown. In the appendix Figure 16In this process, the first and second operations belong to the first stage, the third operation belongs to the second stage, the fourth operation belongs to the third stage, and the fifth operation belongs to the fourth stage; the arrowed lines indicate the sequence of the process.

[1026] As an example, the first operation includes AI / ML training, the second operation includes AI / ML testing, the third operation includes AI / ML emulation, the fourth operation includes AI / ML entity loading, and the fifth operation includes AI / ML inference.

[1027] 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 inference phase.

[1028] As an example, the first stage includes AI / ML model training.

[1029] As an example, the first stage includes AI / ML model training and AI / ML testing.

[1030] As an example, the AI / ML model training includes initial training and re-training of one or a group of AI / ML entities.

[1031] As an example, the training of the AI / ML model depends on training data.

[1032] As an example, the AI / ML model training includes AI / ML entity validation.

[1033] As an example, the AI / ML entity verification is used to evaluate the performance of the AI / ML entity.

[1034] As an example, the AI / ML entity verification relies on verification data.

[1035] As an example, if the AI / ML entity verification results do not meet expectations, the AI / ML model will be retrained.

[1036] As an example, the AI / ML testing includes testing the validated AI / ML entities to estimate the performance of the trained AI / ML model.

[1037] As an example, if the AI / ML test results meet expectations, the AI / ML entity proceeds to the next stage; otherwise, the AI / ML model will be retrained.

[1038] As an example, the AI / ML test relies on test data.

[1039] As one embodiment, the second stage includes AI / ML simulation, which performs AI / ML entity reasoning in a simulation environment.

[1040] As an example, the AI / ML simulation estimates the performance of AI / ML entity reasoning in a simulation environment before using AI / ML entities.

[1041] As one embodiment, the second stage is optional.

[1042] As an example, the third stage includes AI / ML entity loading, which is to obtain trained AI / ML entities to obtain the desired AI / ML inference function.

[1043] As an example, the third stage is optional.

[1044] As an example, the third stage is no longer needed when the training and inference functions are co-located.

[1045] As an example, the fourth stage includes AI / ML inference.

[1046] As an example, the first reporting message is generated in the fourth stage.

[1047] As an example, a portion of the first reported message is generated in the fourth stage.

[1048] As an example, the first reported message part relies on AI inference, and the part relying on AI inference is generated in the fourth stage.

[1049] Example 17

[1050] Example 17 illustrates a structural block diagram of a processing apparatus for a first node according to an embodiment of this application, as shown in the attached diagram. Figure 17 As shown. In the appendix Figure 17 In the first node, the processing device 1700 includes a first receiver 1701 and a first processor 1702.

[1051] In embodiment 17, the first receiver 1701 receives a first configuration message, which indicates a first association ID and a second association ID; the first processor 1702 sends first reporting information.

[1052] In Example 17, the first association ID and the second association ID are respectively associated with a first RS resource set and a second RS resource set; both the first RS resource set and the second RS resource set are configured to a first entity, the first entity corresponding to a model or a function; the first reporting message depends on the reasoning of the first entity, and the input of the reasoning of the first entity depends on the measurement on at least one of the first RS resource set or the second RS resource set.

[1053] As an example, the first association ID is public, and the second association ID is private.

[1054] As an example, the first RS resource set is public, and the second RS resource set is private.

[1055] As an example, the first reporting message includes a first type of result, which is used simultaneously in both layer 1 and layer 3.

[1056] As an example, the first processor 1702 performs measurements in the third RS resource set and the fourth RS resource set respectively and obtains a first measurement result and a second measurement result; when at least one of the first measurement result or the second measurement result satisfies a first condition, it sends a first message; the first association ID and the second association ID are respectively associated with the third RS resource set and the fourth RS resource set, and the first message indicates that the first entity is not applicable.

[1057] As an example, satisfying the first condition means at least one of the following:

[1058] - At least one of the first measurement result or the second measurement result is lower than the first threshold in the first time window;

[1059] - The number of times that at least one of the first measurement result or the second measurement result is below the first threshold within the first time window is greater than a first integer.

[1060] As an example, the first type of result being used for Layer 1 means that the first type of result includes CSI of Layer 1; the first type of result being used for Layer 3 means that the first type of result is used for at least one of CSI determination, cell selection or cell reselection, handover or location of Layer 3.

[1061] As an example, the associated ID described in this application is used to ensure the consistency of network-side (NW-side) additional conditions during model training and model inference.

[1062] As an example, the reasoning described in this application includes a portion of an AI entity used for reasoning.

[1063] As an example, the reasoning model described in this application is obtained through training.

[1064] As an example, the first node 1700 is a user equipment.

[1065] As an example, the first node 1700 is a terminal.

[1066] As an example, the first node 1700 is a relay node device.

[1067] As an example, the first receiver 1701 includes at least one of the following in embodiment 4: the antenna 452, the receiver 454, the receiver processor 456, the multi-antenna receiver processor 458, the controller / processor 459, the memory 460, and the data source 467.

[1068] As an example, the first processor 1702 includes at least one of the following in embodiment 4: the antenna 452, the transmitter 454, the transmitter processor 468, the multi-antenna transmitter processor 457, the controller / processor 459, the memory 460, and the data source 467.

[1069] Example 18

[1070] Example 18 illustrates a structural block diagram of a processing apparatus for a second node according to an embodiment of this application, as shown in the attached diagram. Figure 18 As shown. In the appendix Figure 18 In the second node, the processing device 1800 includes a first transmitter 1801 and a second receiver 1702.

[1071] In embodiment 18, the first transmitter 1801 sends a first configuration message, which indicates a first association ID and a second association ID; the second receiver 1802 receives the first reporting information.

[1072] In Example 18, the first association ID and the second association ID are associated with a first RS resource set and a second RS resource set, respectively; both the first RS resource set and the second RS resource set are configured to a first entity, which corresponds to a model or a function; the first reporting message depends on the reasoning of the first entity, and the input of the reasoning of the first entity depends on a measurement on at least one of the first RS resource set or the second RS resource set.

[1073] As an example, the first association ID is public, and the second association ID is private.

[1074] As an example, the first RS resource set is public, and the second RS resource set is private.

[1075] As an example, the first reporting message includes a first type of result, which is used simultaneously in both layer 1 and layer 3.

[1076] As one embodiment, the second receiver 1802 monitors first information; the sender of the first information performs measurements in the third RS resource set and the fourth RS resource set respectively and obtains a first measurement result and a second measurement result; when at least one of the first measurement result or the second measurement result satisfies a first condition, the sender of the first information sends the first information; the first association ID and the second association ID are respectively associated with the third RS resource set and the fourth RS resource set, and the first information indicates that the first entity is not applicable.

[1077] As an example, satisfying the first condition means at least one of the following:

[1078] - At least one of the first measurement result or the second measurement result is lower than the first threshold in the first time window;

[1079] - The number of times that at least one of the first measurement result or the second measurement result is below the first threshold within the first time window is greater than a first integer.

[1080] As an example, the first type of result being used for Layer 1 means that the first type of result includes CSI of Layer 1; the first type of result being used for Layer 3 means that the first type of result is used for at least one of CSI determination, cell selection or cell reselection, handover or location of Layer 3.

[1081] As an example, the association ID described in this application is used to ensure the consistency of the second node-side additional conditions during model training and model inference.

[1082] As an example, the reasoning described in this application includes a portion of an AI entity used for reasoning.

[1083] As an example, the reasoning model described in this application is obtained through training.

[1084] As one example, the second node 1800 is a base station device.

[1085] As one embodiment, the second node 1800 is a user equipment.

[1086] As an example, the second node 1800 is a TRP.

[1087] As an example, the first transmitter 1801 includes at least one of the following in embodiment 4: the antenna 420, the transmitter 418, the transmission processor 416, the multi-antenna transmission processor 471, the controller / processor 475, and the memory 476.

[1088] As one embodiment, the second receiver 1802 includes at least one of the following in embodiment 4: the antenna 420, the receiver 418, the receiver processor 470, the multi-antenna receiver processor 472, the controller / processor 475, and the memory 476.

[1089] 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. Correspondingly, 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 cards, IoT terminals, RFID (Radio Frequency Identification) terminals, NB-IoT (Narrow Band Internet of Things) terminals, MTC (Machine Type Communication) terminals, eMTC (enhanced MTC) terminals, data cards, internet 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, eNB (evolved Node B), gNB, TRP, GNSS (Global Navigation Satellite System), relay satellites, satellite base stations, airborne base stations, RSUs, unmanned aerial vehicles, and test equipment, such as transceivers or signaling testers that simulate some functions of a base station, and other wireless communication equipment.

[1090] Those skilled in the art will understand that the present invention can be practiced in other specified forms without departing from its core or essential characteristics. Therefore, the embodiments disclosed herein should in any way be considered descriptive rather than restrictive. The scope of the invention is defined by the appended claims rather than the foregoing description, and all modifications within their equivalent meaning and scope are considered to be included therein.

Claims

1. A method for use in a terminal for wireless communication and artificial intelligence, characterized in that, include: Receive a first configuration message, which indicates a first association ID and a second association ID; Send the first report; Wherein, the first association ID and the second association ID are respectively associated with the first RS resource set and the second RS resource set; the first RS resource set and the second RS resource set are both configured to the first entity, the first entity corresponds to a model or the first entity corresponds to a function; the first reporting message depends on the reasoning of the first entity, and the input of the reasoning of the first entity depends on the measurement on at least one of the first RS resource set or the second RS resource set.

2. The method in the terminal according to claim 1, characterized in that, The first associated ID is public, and the second associated ID is private.

3. The method in the terminal according to claim 1 or 2, characterized in that, The first RS resource set is public, while the second RS resource set is private.

4. The method in the terminal according to any one of claims 1 to 3, characterized in that, The first reported message includes a first type of result, which is used simultaneously in both Layer 1 and Layer 3.

5. The method in the terminal according to any one of claims 1 to 4, characterized in that, include: Measurements were performed in the third RS resource set and the fourth RS resource set, and the first and second measurement results were obtained respectively. When at least one of the first measurement result or the second measurement result satisfies the first condition, first information is sent; The first association ID and the second association ID are respectively associated with the third RS resource set and the fourth RS resource set, and the first information indicates that the first entity is not applicable.

6. The method in the terminal according to any one of claims 1 to 5, characterized in that, Satisfying the first condition means at least one of the following: - At least one of the first measurement result or the second measurement result is lower than the first threshold in the first time window; - The number of times that at least one of the first measurement result or the second measurement result is below the first threshold within the first time window is greater than a first integer.

7. The method in the terminal according to claim 4, characterized in that, The first type of result being used for Layer 1 means that the first type of result includes Layer 1 CSI; the first type of result being used for Layer 3 means that the first type of result is used for at least one of Layer 3 CSI determination, cell selection or cell reselection, handover or location.

8. A terminal, characterized in that, The terminal includes: one or more processors and memory; The memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the terminal to perform the method as described in any one of claims 1-7.

9. A method used in a base station for wireless communication and artificial intelligence, characterized in that, include: Send a first configuration message, which indicates a first association ID and a second association ID; Receive the first reported information; Wherein, the first association ID and the second association ID are respectively associated with the first RS resource set and the second RS resource set; the first RS resource set and the second RS resource set are both configured to the first entity, the first entity corresponds to a model or the first entity corresponds to a function; the first reporting message depends on the reasoning of the first entity, and the input of the reasoning of the first entity depends on the measurement on at least one of the first RS resource set or the second RS resource set.

10. The method in a base station according to claim 9, characterized in that, The first associated ID is public, and the second associated ID is private.

11. The method in a base station according to claim 9 or 10, characterized in that, The first RS resource set is public, while the second RS resource set is private.

12. The method in a base station according to any one of claims 9 to 11, characterized in that, The first reported message includes a first type of result, which is used simultaneously in both Layer 1 and Layer 3.

13. The method in a base station according to any one of claims 9 to 12, characterized in that, include: Monitoring first information; Wherein, the sender of the first information performs measurements in the third RS resource set and the fourth RS resource set respectively and obtains a first measurement result and a second measurement result; when at least one of the first measurement result or the second measurement result satisfies a first condition, the sender of the first information sends the first information; The first association ID and the second association ID are respectively associated with the third RS resource set and the fourth RS resource set, and the first information indicates that the first entity is not applicable.

14. The method in a base station according to any one of claims 9 to 13, characterized in that, Satisfying the first condition means at least one of the following: - At least one of the first measurement result or the second measurement result is lower than the first threshold in the first time window; - The number of times that at least one of the first measurement result or the second measurement result is below the first threshold within the first time window is greater than a first integer.

15. The method in a base station according to claim 12, characterized in that, The first type of result being used for Layer 1 means that the first type of result includes Layer 1 CSI; the first type of result being used for Layer 3 means that the first type of result is used for at least one of Layer 3 CSI determination, cell selection or cell reselection, handover or location.

16. A base station, characterized in that, The base station includes: one or more processors and a memory; The memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the base station to perform the method as described in any one of claims 9-15.

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