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, the first configuration message indicating a first association ID; the first configuration message is a dedicated RRC message, the first association ID indicated by the first configuration message is public; the first association ID is associated to a first RS resource set, the first RS resource set is common to a cell; the first configuration message is a configuration for inference. The application is based on functional layering in the introduction of AI / ML process, improves the AI function pertinence and AI model work efficiency, optimizes the computing resources, and further improves the system transmission reliability.

Description

Technical Field

[0001] This application relates to signal transmission methods and apparatus in wireless communication systems, and more particularly to methods and apparatus for the integration of artificial intelligence and communication. 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 for the data collection mechanism of the AI / ML model on the UE side during the update, training and inference process. The basic principle is to identify beams, beam lists or beam sets with similar characteristics through the same association ID in order to optimize the training, inference and update of the AI ​​model. Therefore, how to determine the relationship between the association ID and the reference signal resource set is a key problem that needs to be solved.

[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 the first reported configuration;

[0013] Receive a first configuration message, which indicates a first associated ID;

[0014] Wherein, the first configuration message is a dedicated RRC message, the first association ID indicated by the first configuration message is public; the first association ID is associated with a first RS resource set, the first RS resource set is public to the cell; the first configuration message is a configuration for inference.

[0015] As an example, the problem this application aims to solve includes: how the first node determines whether the first RS resource set and the first associated ID are cell-common or UE-specific.

[0016] As an example, the problem this application aims to solve includes: how to configure inference parameters to support the deep integration of AI and communication.

[0017] As an example, the features of the above method include: In this application, the base station configures the first association ID through a dedicated RRC message, and the first node determines whether the RS resource set associated with the first association ID is cell-common according to the indication of the dedicated RRC message, thereby solving the above problem.

[0018] As an example, the features of the above method include: in this application, the association ID indicated by the dedicated RRC message can be public, and the RS resource set associated with the association ID can also be public.

[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 configuration message configures the higher-level parameters configured for inference, the higher-level parameters including the first association ID.

[0021] As an example, the features of the above method include: the first configuration message configures CSI reporting or measurement reporting, and the first configuration message indicates the first RS resource while also indicating the associated ID.

[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: simplified protocol implementation, convenient resource management under the CSI framework, and good compatibility.

[0024] As an example, the advantages of the above method include: the method of configuring associated IDs and RS resource sets allows the network to flexibly expand the functionality of AI models, while separating tasks to reduce coupling.

[0025] According to one aspect of this application, the above method is characterized in that the first RS resource set includes at least one SSB.

[0026] As an example, the features of the above method include: the set of public RS resources associated with the public association ID includes the cell's public RS signals.

[0027] As an example, the features of the above method include: the first RS resource set includes at least one SSB in an SSB burst set.

[0028] As an example, the features of the above method include: the first RS resource set includes at least one synchronization signal.

[0029] As an example, the advantages of the above method include: it facilitates cell synchronization signal management.

[0030] As an example, the advantages of the above method include: good compatibility.

[0031] According to one aspect of this application, the above method is characterized in that the first associated ID being public means at least one of the following:

[0032] - The first associated ID is indicated as Common;

[0033] - The first associated ID is cell-specific, or the first associated ID is cell-group-specific;

[0034] - The first node considers the first associated ID to be unique within the cell or cell group.

[0035] As an example, the features of the above method include: higher-layer signaling indicating that the first associated ID is cell public.

[0036] As an example, the characteristics of the above method include: the associated ID is common to the cell or cell group.

[0037] As an example, the advantages of the above method include: reducing signaling complexity.

[0038] As an example, the advantages of the above method include: supporting efficient management of AI entities at the cell level and allowing unified management of training data within the cell.

[0039] As an example, the advantages of the above method include: simplifying terminal operation and improving terminal performance.

[0040] According to one aspect of this application, the above method is characterized in that the first association ID is associated with a first entity, the first entity corresponding to a model or the first entity corresponding to a function; the first RS resource set is used for at least the former in inference or training based on the first entity; and the first node does not perform performance monitoring for the first entity.

[0041] As an example, the features of the above method include: the first entity is an AI entity.

[0042] As an example, the characteristics of the above method include: the first entity is functionality-specific or the first entity is AI / ML model-specific.

[0043] As an example, the characteristics of the above method include: the first entity is public.

[0044] As an example, the characteristics of the above method include: the first node does not directly calculate the performance parameters used to evaluate the first entity.

[0045] As an example, the advantages of the above method include: the first entity is used for common inference or training in the cell, without requiring the terminal to perform performance monitoring, thereby reducing the requirements on the terminal, reducing deployment costs, and saving terminal power.

[0046] As an example, the advantages of the above method include: terminals within the cell can report measurements for the first entity, the base station calculates performance parameters for the first entity, and configures or instructs operations such as activation / deactivation / rollback / switching of the AI ​​entity based on the performance parameters, thereby improving the real-time performance and accuracy of performance monitoring.

[0047] As an example, the advantages of the above method include: the base station is able to obtain cooperation gains from the cooperation of multiple terminals and cells.

[0048] According to one aspect of this application, the above method is characterized in that the first association ID is also associated with a second RS resource set, the second RS resource set being dedicated; the first association ID is also associated with a second entity, the second entity corresponding to a model or the second entity corresponding to a function; the second RS resource set is used for at least the former in inference or training based on the second entity; and the first node performs performance monitoring for the second entity.

[0049] As an example, the characteristics of the above method include: the second entity is an AI entity.

[0050] As an example, the characteristics of the above method include: the second entity is functionality-specific or the second entity is AI / ML model-specific.

[0051] As an example, the characteristics of the above method include: the second entity is exclusive to the first node.

[0052] As an example, the features of the above method include: the first node directly calculates the performance parameters used to evaluate the second entity.

[0053] As an example, the advantages of the above method include: the terminal can promptly obtain performance changes of the AI ​​entity by performing performance monitoring on the dedicated AI entity.

[0054] As an example, the advantages of the above method include: the second entity can be deployed on the UE side or in a UE-specific module of the gNB, allowing the second entity to optimize using terminal-personalized data during communication.

[0055] As an example, the advantages of the above method include:

[0056] According to one aspect of this application, the above method is characterized in that the first entity is used for at least one of the following:

[0057] - Determine the CSI for Layer 3;

[0058] - Select a cell or reselect a cell;

[0059] - Switch;

[0060] -position.

[0061] As an example, the features of the above method include: the first entity is used for cell-level mobility management.

[0062] As an example, the features of the above method include: the first entity is used for wireless resource management.

[0063] As an example, the advantages of the above method include: improving the efficiency of cell handover and reducing the probability of handover failure.

[0064] As an example, the benefits of the above method include: optimizing inter-cell load balancing and improving network capacity.

[0065] According to one aspect of this application, the above method is characterized in that the first entity makes predictions for the first node when it is in a disconnected state.

[0066] As an example, the features of the above method include: a non-connected terminal can still affect random access resources, paging, etc. in network resources, and the first entity in this application can predict the behavior of the non-connected terminal based on historical statistics and environmental information.

[0067] As an example, the characteristics of the above method include: the non-connected state refers to a state other than RRC-CONNECTED.

[0068] As an example, the advantages of the above method include: the first entity can dynamically adjust the resource allocation and energy consumption strategy of the cell by predicting the activity trajectory of the terminal, which helps to reduce resource conflicts.

[0069] As an example, the benefits of the above method include: reducing business interruption time and improving business continuity.

[0070] As an example, the advantages of the above method include: reducing access latency and improving user experience.

[0071] According to one aspect of this application, the above method is characterized in that the second entity is used for CSI determination of layer 1.

[0072] As an example, the features of the above method include: the second entity is used for beam-level mobility management.

[0073] As an example, the features of the above method include: the second entity is used for physical layer channel characteristic capture.

[0074] As an example, the advantages of the above method include: AI models can capture complex channel characteristics that are difficult to represent by traditional methods, thereby improving the accuracy of CSI reports.

[0075] As an example, the advantages of the above method include: improving the real-time performance of feedback.

[0076] As an example, the advantages of the above method include: enhancing the intelligence level of the network.

[0077] According to one aspect of this application, the above method is characterized in that the first association ID is associated with two training data sets, the two training data sets being determined based on two different RS resource sets.

[0078] As an example, the features of the above method include: the two training data sets are training data collected by the first node and training data collected by the second node, respectively.

[0079] As an example, the features of the above method include: the two training data sets are a cell-wide common training data set and a UE-specific training data set, respectively.

[0080] As an example, the features of the above method include: the two RS resource sets respectively provide different types of channel information.

[0081] As an example, the benefits of the above method include: increasing data diversity, enabling the model to learn from data of different dimensions, and gaining a more comprehensive understanding of channel characteristics.

[0082] As an example, the advantages of the above method include: improving the generalization performance of the model while ensuring the accuracy of inference.

[0083] As an example, the advantages of the above method include: supporting joint optimization and improving the prediction accuracy of the model.

[0084] According to one aspect of this application, the above method is characterized in that the first node is a user equipment.

[0085] According to one aspect of this application, the above method is characterized in that the first node is a terminal.

[0086] This application discloses a method for a second node in wireless communication and artificial intelligence, comprising:

[0087] Send a first configuration message, which indicates a first associated ID;

[0088] Wherein, the first configuration message is a dedicated RRC message, the first association ID indicated by the first configuration message is public; the first association ID is associated with a first RS resource set, the first RS resource set is public to the cell; the first configuration message is a configuration for inference.

[0089] As an example, the features of the above method include: the second node includes a base station and a core network.

[0090] As an example, the features of the above method include: the second node includes a core network.

[0091] As an example, the features of the above method include: the second node includes an entity for deploying AI / ML models.

[0092] As an example, the features of the above method include: the second node includes a node for deploying AI / ML models.

[0093] As an example, the features of the above method include: the second node includes a base station.

[0094] As an example, the features of the above method include: the second node is a base station.

[0095] As an example, the features of the above method include: the second node is an eNB.

[0096] As an example, the features of the above method include: the second node is a gNB.

[0097] 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.

[0098] 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.

[0099] As an example, the features of the above method include: the base station in this application includes a core network.

[0100] As an example, the features of the above method include: the base station in this application includes core network equipment.

[0101] As an example, the features of the above method include: the base station in this application includes an entity for deploying AI / ML models.

[0102] As an example, the features of the above method include: the base station in this application includes nodes for deploying AI / ML models.

[0103] According to one aspect of this application, the above method is characterized in that the first RS resource set includes at least one SSB.

[0104] According to one aspect of this application, the above method is characterized in that the first associated ID being public means at least one of the following:

[0105] - The first associated ID is indicated as Common;

[0106] - The first associated ID is cell-specific, or the first associated ID is cell-group-specific;

[0107] - The recipient of the first configuration message assumes that the first associated ID is unique within the cell or cell group.

[0108] According to one aspect of this application, the above method is characterized in that the first association ID is associated with a first entity, the first entity corresponding to a model or the first entity corresponding to a function; the first RS resource set is used for at least the former in inference or training based on the first entity; and the recipient of the first configuration message does not perform performance monitoring for the first entity.

[0109] According to one aspect of this application, the above method is characterized in that the first association ID is also associated with a second RS resource set, the second RS resource set being dedicated; the first association ID is also associated with a second entity, the second entity corresponding to a model or the second entity corresponding to a function; the second RS resource set is used for at least the former in inference or training based on the second entity; and the recipient of the first configuration message performs performance monitoring for the second entity.

[0110] According to one aspect of this application, the above method is characterized in that the first entity is used for at least one of the following:

[0111] - Determine the CSI for Layer 3;

[0112] - Select a cell or reselect a cell;

[0113] - Switch;

[0114] -position.

[0115] According to one aspect of this application, the above method is characterized in that the first entity makes a prediction regarding the recipient of the first configuration message being in a disconnected state.

[0116] According to one aspect of this application, the above method is characterized in that the second entity is used for CSI determination of layer 1.

[0117] According to one aspect of this application, the above method is characterized in that the first association ID is associated with two training data sets, the two training data sets being determined based on two different RS resource sets.

[0118] According to one aspect of this application, the above method is characterized in that the second node is a base station.

[0119] This application discloses a device for a first node in wireless communication and artificial intelligence, comprising:

[0120] A first receiver receives a first configuration message, the first configuration message indicating a first associated ID;

[0121] Wherein, the first configuration message is a dedicated RRC message, the first association ID indicated by the first configuration message is public; the first association ID is associated with a first RS resource set, the first RS resource set is public to the cell; the first configuration message is a configuration for inference.

[0122] This application discloses a device for a second node in wireless communication and artificial intelligence, comprising:

[0123] The first transmitter sends a first configuration message, which indicates a first associated ID;

[0124] Wherein, the first configuration message is a dedicated RRC message, the first association ID indicated by the first configuration message is public; the first association ID is associated with a first RS resource set, the first RS resource set is public to the cell; the first configuration message is a configuration for inference.

[0125] As an example, compared with conventional solutions, this application has the following advantages, but is not limited to:

[0126] 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.

[0127] Introducing a first entity and a second entity can optimize system performance and better adapt to different levels of needs, achieving efficient resource utilization and functional separation.

[0128] In the process of introducing AI / ML, we will improve the targeting of AI functions and the working efficiency of AI models by using functional layering, and optimize computing resources.

[0129] The method of configuring associated IDs and RS resource sets allows the network to flexibly expand the functionality of AI models, while separating tasks to reduce coupling. Attached Figure Description

[0130] 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:

[0131] Figure 1 A flowchart of the first node transmission according to an embodiment of this application is shown;

[0132] Figure 2 A schematic diagram of a network architecture according to an embodiment of this application is shown;

[0133] 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;

[0134] Figure 4 A schematic diagram of a first communication device and a second communication device according to an embodiment of this application is shown;

[0135] 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;

[0136] Figure 6 A first schematic diagram of a first associated ID according to an embodiment of this application is shown;

[0137] Figure 7 A second schematic diagram of a first associated ID according to an embodiment of this application is shown;

[0138] Figure 8 A third schematic diagram of a first associated ID according to an embodiment of this application is shown;

[0139] Figure 9 A schematic diagram of an AI entity according to an embodiment of this application is shown;

[0140] Figure 10 A schematic diagram illustrating the deployment of RAN domain AI / ML functionality according to an embodiment of this application is shown;

[0141] Figure 11 A schematic diagram illustrating the deployment of AI / ML functions in a UE according to an embodiment of this application is shown;

[0142] Figure 12 A schematic diagram of an artificial intelligence or machine learning-based processing system according to an embodiment of this application is shown;

[0143] Figure 13 A schematic diagram illustrating artificial intelligence or machine learning according to an embodiment of this application is shown;

[0144] Figure 14 A structural block diagram of a processing apparatus for a first node according to an embodiment of this application is shown;

[0145] Figure 15 A structural block diagram of a processing apparatus for a second node according to an embodiment of this application is shown. Detailed Implementation

[0146] 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 15 The embodiments in the appendix Figure 5 Examples and appendices Figure 6 -Appendix Figure 15 Examples, etc.

[0147] Example 1

[0148] 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.

[0149] The first node receives a first configuration message in step 101, the first configuration message indicating a first association ID.

[0150] In Example 1, the first configuration message is a dedicated RRC message, and the first association ID indicated by the first configuration message is public; the first association ID is associated with a first RS resource set, which is cell public; the first configuration message is a configuration for inference.

[0151] As an example, the first node is a user equipment (UE).

[0152] As one example, the first node is a terminal.

[0153] As an example, the first node is the first node in this application.

[0154] As an example, the ID refers to: IDentify, proof.

[0155] As an example, the ID refers to: IDentification, identity verification.

[0156] As an example, the ID refers to: IDentity, identity, or identifier.

[0157] As an example, the ID refers to: Identifier, identifier.

[0158] As an example, the ID refers to: InDex, index.

[0159] As an example, the ID refers to: InDicator, indicator.

[0160] As an example, RRC refers to Radio Resource Control.

[0161] As an example, RS stands for Reference Signal.

[0162] As an example, the first node receives the first configuration message.

[0163] As one example, the first configuration message is transmitted via higher-layer signaling.

[0164] As an example, the first configuration message is transmitted via RRC signaling.

[0165] As an example, the first configuration message includes one or more RRC IEs (Information Elements).

[0166] As an example, the first configuration message includes one or more domains in an RRC IE.

[0167] As one example, the first configuration message includes one or more domains for each of the plurality of RRC IEs.

[0168] As an example, the first configuration message indicates higher-level parameters.

[0169] As an example, the first configuration message is a dedicated RRC message.

[0170] As an example, the meaning of "the first configuration message is a dedicated RRC message" includes: the first configuration message is a dedicated RRC message.

[0171] As an example, the meaning of "the first configuration message is a dedicated RRC message" includes: the first configuration message is a UE-specific RRC message.

[0172] As an example, the meaning of "the first configuration message is a dedicated RRC message" includes: the first configuration message is an RRC message other than the common RRC message.

[0173] As an example, the meaning of "the first configuration message is a dedicated RRC message" includes: the first configuration message is an RRC message other than a cell-specific RRC message.

[0174] As an example, the meaning of "the first configuration message is a dedicated RRC message" includes: at least the configuration parameters in the first configuration message are specific to the first node.

[0175] As an example, the meaning of the first configuration message being a dedicated RRC message includes: at least the configuration parameters in the first configuration message are configured only for the first node.

[0176] As an example, the meaning of the first configuration message being a dedicated RRC message includes: the first configuration message includes multiple configuration parameters, and at least one of the multiple configuration parameters is dedicated to the first node.

[0177] As an example, the meaning of the first configuration message being a dedicated RRC message includes: the first configuration message is transmitted through DCCH (Dedicated Control Channel).

[0178] As one example, the first configuration message includes RRCReconfiguration IE.

[0179] As one example, the first configuration message includes one or more domains in the RRCReconfiguration IE.

[0180] As an example, the first configuration message includes ServingCellConfig IE.

[0181] As one example, the first configuration message includes one or more domains in the ServingCellConfig IE.

[0182] As an example, the first configuration message includes CSI-MeasConfig IE.

[0183] As one example, the first configuration message includes one or more domains in the CSI-MeasConfig IE.

[0184] As one example, the first configuration message includes CSI-ReportConfig IE.

[0185] As one example, the first configuration message includes one or more domains in the CSI-ReportConfig IE.

[0186] As one example, the first configuration message includes a CSI-AperiodicTriggerStateList IE.

[0187] As one example, the first configuration message includes one or more domains in the CSI-AperiodicTriggerStateList IE.

[0188] As an example, the first configuration message includes a CSI-AperiodicTriggerState IE.

[0189] As one example, the first configuration message includes one or more domains in the CSI-AperiodicTriggerState IE.

[0190] As an example, the first configuration message includes CSI-SemiPersistentOnPUSCH-TriggerStateListIE.

[0191] As one example, the first configuration message includes one or more domains in CSI-SemiPersistentOnPUSCH-TriggerStateListIE.

[0192] As an example, the first configuration message includes CSI-SemiPersistentOnPUSCH-TriggerState IE.

[0193] As an example, the first configuration message includes one or more domains in the CSI-SemiPersistentOnPUSCH-TriggerState IE.

[0194] As one example, the first configuration message includes a CSI-ReportSubConfigTriggerList IE.

[0195] As one example, the first configuration message includes one or more domains in the CSI-ReportSubConfigTriggerList IE.

[0196] As one example, the first configuration message includes CSI-ReportSubConfig IE.

[0197] As one example, the first configuration message includes one or more domains in a CSI-ReportSubConfig IE.

[0198] As one example, the first configuration message includes CSI-ResourceConfig IE.

[0199] As one example, the first configuration message includes one or more domains in the CSI-ResourceConfig IE.

[0200] As an example, the first configuration message includes a CSI-IM-Resource IE.

[0201] As one example, the first configuration message includes one or more domains in the CSI-IM-Resource IE.

[0202] As one example, the first configuration message includes CSI-IM-ResourceSetIE.

[0203] As one example, the first configuration message includes one or more domains in CSI-IM-ResourceSetIE.

[0204] As an example, the first configuration message includes NZP-CSI-RS-ResourceSet IE.

[0205] As one example, the first configuration message includes one or more domains in the NZP-CSI-RS-ResourceSet IE.

[0206] As an example, the first configuration message includes NZP-CSI-RS-Resource IE.

[0207] As one example, the first configuration message includes one or more domains in the NZP-CSI-RS-Resource IE.

[0208] As one example, the first configuration message includes MeasObjectNR IE.

[0209] As one example, the first configuration message includes one or more domains in the MeasObjectNR IE.

[0210] As an example, the first configuration message includes CSI-RS-ResourceConfigMobility IE.

[0211] As one example, the first configuration message includes one or more domains in the CSI-RS-ResourceConfigMobility IE.

[0212] As an example, the name of the RRC signaling used to transmit the first configuration message includes CSI.

[0213] As an example, the name of the RRC signaling used to transmit the first configuration message includes CSI-RS.

[0214] As an example, the name of the RRC signaling used to transmit the first configuration message includes Report.

[0215] As an example, the name of the RRC signaling used to transmit the first configuration message includes Config.

[0216] As an example, the name of the RRC signaling used to transmit the first configuration message includes CSI-ReportConfig.

[0217] As an example, the name of the RRC signaling used to transmit the first configuration message includes Resource.

[0218] As an example, the name of the RRC signaling used to transmit the first configuration message includes Associated.

[0219] As an example, the name of the RRC signaling used to transmit the first configuration message includes AI.

[0220] As an example, the name of the RRC signaling used to transmit the first configuration message includes ML.

[0221] As an example, the name of the RRC signaling used to transmit the first configuration message includes Model.

[0222] As an example, the name of the RRC signaling used to transmit the first configuration message includes Functionality.

[0223] As an example, the name of the RRC signaling used to transmit the first configuration message includes Inference.

[0224] As an example, the name of the RRC signaling used to transmit the first configuration message includes Infer.

[0225] As an example, the name of the RRC signaling used to transmit the first configuration message includes Monitoring.

[0226] As an example, the first configuration message indicates the first associated ID.

[0227] As an example, the first configuration message explicitly indicates the first associated ID.

[0228] As an example, the first configuration message implicitly indicates the first associated ID.

[0229] As an example, the first configuration message implicitly indicates the first association ID by indicating whether the two RS resource sets are associated.

[0230] As an example, the first configuration message implicitly indicates the first associated ID by whether two RS resource sets are configured.

[0231] As an example, the first configuration message directly indicates the first associated ID.

[0232] As an example, the first configuration message directly configures the first associated ID.

[0233] As an example, the first configuration message indirectly indicates the first associated ID.

[0234] As an example, the first configuration message indirectly indicates the first associated ID by instructing other configuration messages.

[0235] As an example, one of the fields included in the first configuration message indicates the first associated ID.

[0236] As an example, at least one field included in the first configuration message indicates the first associated ID.

[0237] As an example, the first associated ID is a non-negative integer.

[0238] As an example, the first associated ID is a positive integer.

[0239] As an example, the first associated ID is a string.

[0240] As an example, the first associated ID is an associated ID.

[0241] As an example, the first associated ID is an Associated ID.

[0242] As an example, the first associated ID is an Associated-Id.

[0243] As an example, the first associated ID identifies a Functionality.

[0244] 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.

[0245] As an example, the first associated ID is associated with an AI (Artificial Intelligence) model.

[0246] As one example, the first association ID is associated with multiple AI models, which are trained using the same training dataset.

[0247] As an example, the first association ID is associated with a training dataset.

[0248] As an example, the first association ID is associated with two different training datasets.

[0249] As an example, the first association ID is associated with at least one RS resource set.

[0250] As an example, the first association ID is associated with multiple CSI-ResourceConfigIds.

[0251] As an example, the first association ID is associated with two CSI-ResourceConfigIds, and the two CSI-ResourceConfigIds are associated with the same CSI-ReportConfigId.

[0252] As an example, the first association ID is associated with at least one SSB-Index.

[0253] As an example, the first associated ID is associated with at least one CSI-SSB-ResourceSetId.

[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, the at least two beams include at least the first RS resource set and the second RS resource set in this application.

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

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

[0262] As an example, the beam described in this application includes a downlink (DL) beam.

[0263] As an example, the beam described in this application includes an uplink (UL) beam.

[0264] As an example, the beam described in this application includes: RS resources.

[0265] As an example, the features described in this application include: spatial relation.

[0266] As an example, the features described in this application include: spatial domain filtering.

[0267] As an example, the features described in this application include: spatial filtering.

[0268] As one embodiment, the features described in this application include: spatial receiving features.

[0269] As one embodiment, the features described in this application include: spatial Rxparameter.

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

[0271] As an example, the feature described in this application includes: spatialRxparameter.

[0272] As one example, the features described in this application include: channel reciprocity.

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

[0274] As an example, the feature described in this application includes: beamwidth.

[0275] As an example, the features described in this application include: side lobe levels.

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

[0277] As an example, the features described in this application include: codeword.

[0278] As one embodiment, the features described in this application include: beam indexing.

[0279] As one embodiment, the features described in this application include: beam set index.

[0280] As one embodiment, the features described in this application include: beam list index.

[0281] As one example, the features described in this application include: RS resource index.

[0282] As an example, the features described in this application include: RS resource collection index.

[0283] As an example, the features described in this application include: an RS resource list index.

[0284] As an example, the features described in this application include: TCI (Transmission Configuration Indicator) state.

[0285] As an example, the features described in this application include: qcl-info.

[0286] As an example, the features described in this application include: QCL relationships.

[0287] As an example, the features described in this application include: QCL type.

[0288] As an example, the features described in this application include at least one of QCL typeA, QCL typeB, QCL typeC, and QCL typeD.

[0289] As an example, the features described in this application include QCL type E.

[0290] 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.

[0291] As an example, QCL in this application refers to Quasi Co-Location.

[0292] As an example, QCL in this application refers to Quasi Co-Located.

[0293] As an example, the QCL types described in this application include typeA, typeB, typeC, and typeD.

[0294] As an example, the QCL types described in this application include QCL types other than typeA, typeB, typeC, and typeD.

[0295] As an example, the QCL type described in this application includes typeE.

[0296] 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.

[0297] 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.

[0298] As an example, the QCL parameters of type E described in this application include at least the spatial transmission parameter (Tx parameter).

[0299] As an example, the first association ID indicated by the first configuration message is public.

[0300] As an example, the first associated ID configured in the first configuration message is public.

[0301] As an example, the first associated ID being public means at least one of the following:

[0302] - The first associated ID is indicated as Common;

[0303] - The first associated ID is cell-specific, or the first associated ID is cell-group-specific;

[0304] - The first node considers the first associated ID to be unique within the cell or cell group.

[0305] As an example, the first association ID being public means that the first association ID is a parameter of Common.

[0306] As an example, the meaning of "the first associated ID is public" includes: the first associated ID is indicated as "Common".

[0307] As an example, the meaning of "the first association ID is public" includes: the first association ID is a CellSpecific parameter.

[0308] As an example, the meaning of "the first associated ID is public" includes: the first associated ID is cell-specific.

[0309] As an example, the meaning of "the first associated ID is public" includes: the first associated ID is cell group exclusive.

[0310] 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.

[0311] 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.

[0312] 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.

[0313] 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.

[0314] 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.

[0315] 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.

[0316] As an example, the first association ID being public means that NW ensures that the corresponding parameters of other terminals are aligned with the first node as necessary.

[0317] 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.

[0318] 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.

[0319] As an example, the first association ID is associated with the first RS resource set.

[0320] 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.

[0321] 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.

[0322] 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.

[0323] 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.

[0324] 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.

[0325] 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.

[0326] 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.

[0327] 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.

[0328] 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.

[0329] 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.

[0330] 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.

[0331] 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.

[0332] 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.

[0333] 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.

[0334] As an example, the entity described in this application corresponds to a Functionality.

[0335] As an example, the entity described in this application corresponds to a Functionality ID.

[0336] As an example, the entity described in this application corresponds to an Entity.

[0337] As an example, the entity described in this application corresponds to an AI / ML model.

[0338] As an example, the entity described in this application corresponds to an AI / ML model ID.

[0339] As an example, the AI / ML model identified by the AI / ML model ID described in this application may be logical. The mapping relationship from the logical AI / ML model to the physical AI / ML model is usually implemented by the device manufacturer. Furthermore, the AI / ML model ID corresponding to the same AI / ML model may be different at different stages of LCM (Life Cycle Management), that is, the AI / ML model ID described in this application may not be globally unique.

[0340] As an example, the associated ID described in this application is used to ensure the consistency of the same AI / ML model corresponding to different AI / ML model IDs in the LCM phase, at least during the training and inference phases.

[0341] As an example, the first node deploys a terminal-side AI model, and the first node performs training and updating of the AI ​​model, assuming that all RS resources in the first RS resource set are used for training or updating the AI ​​model.

[0342] As an example, the first node is associated ID specific when performing training data collection.

[0343] As an example, when the first node reports the collected dataset for model training, it will also report the associated ID corresponding to the dataset for model training.

[0344] As an example, the first node does not want datasets associated with the same association ID to be reported with different reporting information.

[0345] As an example, the first node assumes that the reported information corresponding to datasets associated with the same association ID has the same priority.

[0346] As an example, the first RS resource set is community-shared.

[0347] As an example, the meaning of "the first RS resource set is common to the cell" includes: the first node considers the first RS resource set to be common to the cell.

[0348] As an example, the statement that the first RS resource set is cell-common means that the first node assumes that all RS resources in the first RS resource set are cell-common.

[0349] As one embodiment, the first RS resource set includes antenna ports(s).

[0350] As one embodiment, the first RS resource set includes a reference signal port.

[0351] As an example, the first RS resource set includes RS.

[0352] As an example, the first RS resource set includes at least one RS resource.

[0353] As one embodiment, the first RS resource set includes downlink RS resources.

[0354] As an example, the first RS resource set includes broadcast RS resources.

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

[0356] As one example, the first RS resource set includes cell-specific RS resources.

[0357] As an example, the first RS resource set includes RS resources specific to the cell group.

[0358] As one embodiment, the first RS resource set includes RS resources specific to the cell set.

[0359] As an example, the first RS resource set includes positioning RS resources.

[0360] As an example, the first RS resource set includes PRS (Positioning Reference Signal) resources.

[0361] As an example, the first RS resource set includes sensing RS resources.

[0362] As an example, the first RS resource set includes ISAC (Integrated Sensing and Communication) sensing signal resources.

[0363] As an example, the first RS resource set includes ISAC-RS resources.

[0364] As an example, the first RS resource set includes IRS (ISAC Reference Signal) resources.

[0365] As one embodiment, the first RS resource set includes RS resources for mobility management.

[0366] As one embodiment, the first RS resource set includes RS resources for cell-level mobility management.

[0367] As one embodiment, the first RS resource set includes RS resources used for cell synchronization.

[0368] As one embodiment, the first RS resource set includes synchronization signals in at least 5G systems and systems after 5G systems.

[0369] As an example, the first RS resource set includes at least a synchronization signal in a 6G system.

[0370] As one embodiment, the first RS resource set includes at least a synchronization signal (SS).

[0371] As one embodiment, the first RS resource set includes at least a Primary Synchronization Signal (PSS).

[0372] As one embodiment, the first RS resource set includes at least a Secondary Synchronization Signal (SSS).

[0373] As an example, the first RS resource set includes at least PBCH (Physical Broadcast Channel).

[0374] Typically, the PBCH, PSS, and SSS are received in consecutive symbols and form an SS / PBCH block.

[0375] As an example, the first RS resource set includes SSB.

[0376] As an example, the first RS resource set includes SSB resources.

[0377] As an example, the first RS resource set includes at least one SSB.

[0378] As an example, the first RS resource set includes an SSB.

[0379] As one embodiment, the first RS resource set includes multiple SSBs.

[0380] As an example, the first RS resource set includes at least one SSB in an SSB burst set.

[0381] As an example, SSB in this application refers to Synchronization Signal Block.

[0382] As an example, the SSB mentioned in this application refers to the SS / PBCH block.

[0383] As an example, the cell described in this application is a camping cell.

[0384] As an example, the cell described in this application is a serving cell.

[0385] As an example, the cell described in this application is an additional cell.

[0386] As an example, the cell described in this application is a PCell (Primary Cell).

[0387] As an example, the cell described in this application is a SCell (Secondary Cell).

[0388] As an example, the cell described in this application is a SpCell (Special Cell).

[0389] As an example, the cell described in this application is an MCG (Master Cell Group) cell.

[0390] As an example, the cell described in this application is an SCG (Secondary Cell Group) cell.

[0391] As an example, the first configuration message is a configuration for inference.

[0392] As an example, the reasoning mentioned in this application refers to infer.

[0393] As an example, the reasoning mentioned in this application refers to prediction.

[0394] As an example, the inference described in this application includes AI / ML inference.

[0395] As an example, the reasoning described in this application is based on training or AI.

[0396] As an example, the reasoning described in this application includes an AI entity used for reasoning.

[0397] As an example, the reasoning described in this application includes at least a portion of an AI entity.

[0398] As an example, the reasoning described in this application includes a portion of an AI entity used for reasoning.

[0399] As an example, the reasoning model described in this application is obtained through training.

[0400] As an example, the meaning of the first configuration message being a configuration for inference includes: the first configuration message indicating parameters for an AI / ML model.

[0401] As an example, the meaning of the first configuration message as a configuration for inference includes: the first configuration message indicates parameters for Functionality.

[0402] As one embodiment, the meaning of the first configuration message as a configuration for inference includes: the first configuration message indicates the parameters corresponding to the training data set.

[0403] As an example, the meaning of the first configuration message as a configuration for inference includes: the first configuration message indicates the parameters corresponding to the inference data set.

[0404] As an example, the meaning of the first configuration message being a configuration for inference includes: the first configuration message includes parameters for AI / ML.

[0405] As an example, the meaning of the first configuration message as a configuration for inference includes: the first configuration message indicates the parameters for monitoring the model used for inference.

[0406] As an example, the meaning of the first configuration message being a configuration for inference includes: the first configuration message includes parameters for model monitoring.

[0407] As an example, the first configuration message configures the parameters configured for inference.

[0408] As an example, the first configuration message indicates the parameters configured for inference.

[0409] As an example, the parameters configured for inference include the first associated ID.

[0410] As one embodiment, the parameters configured for inference include the first association ID, which is the associated identifier.

[0411] As an example, the parameters configured for inference include the first association ID, which is an identifier associated with the AI ​​model.

[0412] As an example, the parameters configured for inference include the first association ID, which is an identifier associated with inference.

[0413] As an example, the parameters configured for inference include at least one of the inference input, inference output, purpose of inference, and the first associated ID.

[0414] As an example, the purpose of the inference includes at least one of CSI (Channel State Information) prediction, beam prediction, or CSI compression.

[0415] As an example, the purpose of the inference includes at least one of CSI prediction, beam prediction, CSI compression, or RLF (Radio Link Failure).

[0416] As an example, the parameters configured for inference include at least one of the following: information related to the resource set for prediction, information related to the RS resource set for measurement, report content related information, time instance related information for measurement, time instance related information for prediction, and a first association ID.

[0417] Example 2

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] As an example, the first node in this application includes the UE 201.

[0423] As an example, the second node in this application includes node B 203.

[0424] As an example, node B 203 is a macrocell base station.

[0425] As an example, node B 203 is a microcell base station.

[0426] As an example, node B 203 is a pico cell base station.

[0427] As an example, node B 203 is a femtocell.

[0428] As an example, node B 203 is a base station device that supports large latency differences.

[0429] As an example, node B 203 is a flight platform device.

[0430] As an example, node B 203 is a satellite device.

[0431] 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).

[0432] As an example, the UE 201 includes a mobile phone.

[0433] As an example, the UE 201 is a vehicle including a car.

[0434] 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.

[0435] 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.

[0436] As an example, the wireless link between the node B 203 and the UE 201 includes a cellular link.

[0437] As an example, the node B 203 and the UE 201 are connected via the Uu air interface.

[0438] As an example, the sender of the first configuration message in this application includes the node B 203.

[0439] As an example, the recipient of the first configuration message in this application includes the UE 201.

[0440] As an example, node B 203 supports the deployment of network-side AI / ML models.

[0441] As an example, the UE 201 supports the deployment of UE-side AI / ML models.

[0442] As an example, the UE 201 supports a 5G system.

[0443] As an example, the node B 203 supports a 5G system.

[0444] As an example, the UE 201 supports at least a 6G system.

[0445] As an example, the node B 203 supports at least a 6G system.

[0446] Example 3

[0447] 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.

[0448] 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.).

[0449] As an example, Appendix Figure 3 The wireless protocol architecture described herein is applicable to the first node in this application.

[0450] As an example, Appendix Figure 3 The wireless protocol architecture described herein is applicable to the second node in this application.

[0451] As an example, in this application, the first configuration message is generated in the RRC 306.

[0452] As an example, the higher layer mentioned in this application refers to the layer above the physical layer.

[0453] As an example, the higher layer described in this application includes the RRC layer.

[0454] As an example, the higher-layer signaling described in this application includes RRC IE.

[0455] As an example, the higher-level signaling described in this application includes RRC messages.

[0456] As an example, the higher layer described in this application includes the MAC layer.

[0457] As an example, the higher-layer signaling described in this application includes MAC CE.

[0458] Example 4

[0459] 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 the access network.

[0460] 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.

[0461] 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.

[0462] 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.

[0463] 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.

[0464] 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.

[0465] 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.

[0466] 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 as described in this application, the first configuration message indicating a first association ID; the first configuration message is a dedicated RRC message, the first association ID indicated by the first configuration message is public; the first association ID is associated with a first RS resource set, the first RS resource set being cell-public; the first configuration message is a configuration for inference.

[0467] As one embodiment, the second communication device 450 includes: a memory storing a computer-readable instruction program that produces an action when executed by at least one processor, the action including: receiving the first configuration message in this application.

[0468] 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 apparatus at least sends the first configuration message as described in this application, the first configuration message indicating a first association ID; the first configuration message is a dedicated RRC message, the first association ID indicated by the first configuration message is public; the first association ID is associated with a first RS resource set, the first RS resource set being cell-public; the first configuration message is a configuration for inference.

[0469] As one embodiment, the first communication device 410 includes: a memory storing a computer-readable instruction program that produces an action when executed by at least one processor, the action including: sending the first configuration message in this application.

[0470] As an example, the first node in this application includes the second communication device 450.

[0471] As an example, the second node in this application includes the first communication device 410.

[0472] 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.

[0473] Example 5

[0474] Example 5 illustrates a flowchart of the 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 5 In 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.

[0475] For the first node U1, the first configuration message is received in step S510.

[0476] For the second node N2, a first configuration message is sent in step S520.

[0477] In Example 5, the first configuration message indicates a first association ID; the first configuration message is a dedicated RRC message, and the first association ID indicated by the first configuration message is public; the first association ID is associated with a first RS resource set, and the first RS resource set is cell-public; the first configuration message is a configuration for inference.

[0478] As an example, the first node U1 is the first node in this application.

[0479] As an example, the second node N2 is the second node in this application.

[0480] 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.

[0481] 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.

[0482] 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.

[0483] As one example, the second node N2 and the first node U1 communicate via the Uu interface.

[0484] As one example, the second node N2 is the maintenance base station of the serving cell of the first node U1.

[0485] As an example, the logical channel occupied by the first configuration message includes DCCH (Dedicated Control Channel).

[0486] As an example, the first configuration message is carried by SRB1 (Signalling Radio Bearer 1).

[0487] As an example, the transmission channel occupied by the first configuration message includes DL-SCH (DownLink-Shared Channel).

[0488] As an example, the physical layer channel occupied by the first configuration message includes PDSCH.

[0489] Example 6

[0490] Example 6 illustrates a first schematic diagram of a first associated ID according to an embodiment of this application, as shown in the attached diagram. Figure 6 As shown. In the appendix Figure 6 In this context, the first association ID is associated with a first entity, which corresponds to either a model or a function; the first association ID is associated with a first RS resource set; and the first RS resource set is used for at least the former in inference or training based on the first entity.

[0491] In Example 6, the first node does not perform performance monitoring on the first entity.

[0492] As an example, the first association ID is configured to be associated with the first entity.

[0493] As an example, the high-level parameters of the first entity are configured to indicate the first associated ID.

[0494] As an example, the first configuration message in this application configures higher-level parameters of the first entity, and the higher-level parameters include the first association ID.

[0495] As an example, the first entity may correspond to a model or a function.

[0496] As an example, the first entity is an Entity.

[0497] As an example, the first entity is an AI entity.

[0498] As an example, the first entity is an AI entity in Embodiment 9 of this application.

[0499] As an example, the first entity is part of an AI entity in Embodiment 9 of this application.

[0500] As an example, the first entity corresponds to a model.

[0501] As an example, the first entity corresponds to a model ID.

[0502] As an example, the first entity corresponds to a function.

[0503] As an example, the first entity corresponds to a function ID.

[0504] As an example, the first entity corresponds to multiple AI / ML models under a Functionality.

[0505] As an example, the first entity corresponds to multiple functions of an AI / ML model being applied.

[0506] As an example, this application does not limit whether the AI / ML model and Functionality are in a one-to-one correspondence.

[0507] As an example, the first node will not perform Rel-18 and previous CSI calculations and reports for RS resources in the first RS resource set.

[0508] As an example, the first RS resource set is used for at least the former in inference or training based on the first entity.

[0509] As an example, the first RS resource set is used for reasoning based on the first entity.

[0510] As one embodiment, the use of the first RS resource set for reasoning based on the first entity means that the first node receives the first RS resource set and uses it as input for reasoning about the first entity.

[0511] As one embodiment, the use of the first RS resource set for reasoning based on the first entity means that the first node measures the first RS resource set and uses it as input for reasoning about the first entity.

[0512] As an example, the first RS resource set is used for training based on the first entity.

[0513] As an example, the use of the first RS resource set for training based on the first entity means that the first RS resource set serves as the label for training the first entity.

[0514] As an example, the use of the first RS resource set for training based on the first entity means that the first RS resource set serves as input for training the first entity.

[0515] As an example, the first RS resource set is used for training and inference based on the first entity.

[0516] As an example, the meaning of "the first RS resource set is used for training and inference based on the first entity" includes: a portion of the RS resources in the first RS resource set are used for training the first entity, and another portion of the RS resources in the first RS resource set are used for inference of the first entity.

[0517] As an example, the first RS resource set being used for training and inference based on the first entity means that the first RS resource set includes multiple RS resources, and the RS resources used for training and inference of the first entity are orthogonal.

[0518] As an example, the first node does not perform performance monitoring on the first entity.

[0519] As an example, the meaning of "the first node does not perform performance monitoring for the first entity" includes: the first node does not periodically report on the first entity.

[0520] As a sub-example of this embodiment, the reporting is used for performance monitoring.

[0521] As a sub-implementation of this embodiment, the reporting is used for performance evaluation.

[0522] As a sub-example of this embodiment, the reporting includes the calculation results of the performance parameters of the first entity.

[0523] As an example, the meaning of "the first node does not perform performance monitoring on the first entity" includes: the first entity is not configured with RS resources or RS resource sets for performance monitoring.

[0524] As an example, the statement that the first node does not perform performance monitoring on the first entity means that performance monitoring of the first entity is only performed on the side where the first entity is configured.

[0525] As an example, the statement that the first node does not perform performance monitoring on the first entity means that the performance monitoring of the first entity is only performed on the network side or the base station side.

[0526] As an example, the statement that the first node does not perform performance monitoring for the first entity means that the second node in this application performs performance monitoring for the first entity.

[0527] As an example, the meaning of "the first node does not perform performance monitoring on the first entity" includes: in this application, the second node performs performance monitoring based on the measurement results reported by the first node.

[0528] As an example, the meaning of "the first node does not perform performance monitoring for the first entity" includes: the first node does not configure a set of RS resources for performance monitoring of the first entity.

[0529] As an example, the meaning of "the first node does not perform performance monitoring for the first entity" includes: the first node does not calculate performance parameters for the first entity.

[0530] As an example, the performance parameters described in this application include: performance metrics.

[0531] As an example, the performance parameters described in this application include: KPI (Key Performance Indicator).

[0532] As an example, the performance parameters described in this application include: intermediate KPI.

[0533] As an example, the performance parameters described in this application include: the final KPI (eventual KPI).

[0534] As an example, the candidate performance parameters described in this application include one or more of the following: 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.

[0535] As an example, the candidate performance parameters described in this application include one or more of throughput, BLER (Block Error Rate), and hypothetical BLER.

[0536] As an example, the performance parameter described in this application is NMSE.

[0537] As an example, the performance parameter described in this application is SGCS.

[0538] As an example, the performance parameter described in this application is the truth ground CSI.

[0539] As one embodiment, the first entity is used for at least one of the following:

[0540] - Determination of CSI for Layer 3 (L3);

[0541] - Select a cell or reselect a cell;

[0542] - Toggle (HandOver, HO);

[0543] -position.

[0544] As an example, the first entity is used for CSI determination of layer 3.

[0545] As an example, the first entity is used for CSI prediction of layer 3.

[0546] As an example, the first entity is used for CSI measurements of layer 3.

[0547] As an example, the first entity is used for CSI reporting of layer 3.

[0548] As an example, the first entity is used for cell-level mobility management.

[0549] As an example, the first entity is used for event-triggered measurement reporting.

[0550] As an example, the first entity is used for RLF (Radio Link Failure).

[0551] As an example, the first entity is used for RRM (Radio Resource Management).

[0552] As an example, the first entity is used for cell selection.

[0553] As an example, the first entity is used for cell re-selection.

[0554] As an example, the first entity is used for cell selection and cell reselection.

[0555] As an example, the first entity is used for switching.

[0556] As an example, the first entity is used for positioning.

[0557] As an example, the first entity is used for sensing.

[0558] As one example, the first entity makes a prediction for the first node when it is in a disconnected state.

[0559] As an example, the first entity performs inference for the first node when it is in a disconnected state.

[0560] As an example, the first entity is for inference in the non-connected state of a terminal residing in the cell.

[0561] As an example, the first entity infers cell reselection in the disconnected state of the first node.

[0562] As an example, the first entity performs cell reselection for the first node in the disconnected state.

[0563] As one example, the non-connected state includes the idle state.

[0564] As an example, the non-connected state includes the inactive state.

[0565] Example 7

[0566] Example 7 illustrates a second schematic diagram of a first associated ID according to an embodiment of this application, as shown in the attached diagram. Figure 7 As shown. In the appendix Figure 7 In this context, the first association ID is also associated with a second RS resource set, and the first association ID is also associated with a second entity, which corresponds to a model or a function; the second RS resource set is used for at least the former in inference or training based on the second entity.

[0567] In Example 7, the second RS resource set is dedicated; the first node performs performance monitoring on the second entity.

[0568] As an example, the first association ID is also associated with a second RS resource set, which is dedicated; the first association ID is also associated with a second entity, which corresponds to a model or a function; the second RS resource set is used for at least the former in inference or training based on the second entity; and the first node performs performance monitoring for the second entity.

[0569] As an example, the first association ID is associated with the second RS resource set.

[0570] As an example, the meaning of the first association ID being associated with the second RS resource set includes: the first association ID is configured to be associated with the second RS resource set.

[0571] As an example, the meaning of the first association ID being associated with the second RS resource set includes: the RRC signaling that configures the first association ID is also used to configure the second RS resource set.

[0572] As an example, the meaning of the first association ID being associated with the second RS resource set includes: the RRC signaling that configures the first association ID is also used to instruct the second RS resource set.

[0573] As an example, the meaning of the first association ID being associated with the second RS resource set includes: the RRC signaling that configures the first association ID is also used to indicate the ID corresponding to the second RS resource set.

[0574] As an example, the meaning of the first association ID being associated with the second RS resource set includes: the first association ID being used to characterize the spatial characteristics of the second RS resource set.

[0575] As an example, the meaning of the first association ID being associated with the second RS resource set includes: the first association ID being used to indicate the spatial characteristics of the second RS resource set.

[0576] As an example, the meaning of the first association ID being associated with the second RS resource set includes: the AI / ML model corresponding to the first association ID depends on the second RS resource set.

[0577] As an example, the meaning of the first 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 first association ID.

[0578] As an example, the meaning of the first 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 first association ID.

[0579] As an example, the meaning of the first 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 first association ID.

[0580] As an example, the meaning of the first association ID being associated with the second RS resource set includes: the entity corresponding to the first association ID depends on the second RS resource set.

[0581] As an example, the meaning of the first 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 first association ID.

[0582] As an example, the meaning of the first association ID being associated with the second RS resource set includes: the second RS resource set is used for reasoning about the entity corresponding to the first association ID.

[0583] As an example, the meaning of the first 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 first association ID.

[0584] As one embodiment, the second RS resource set includes an antenna port.

[0585] As one embodiment, the second RS resource set includes a reference signal port.

[0586] As one embodiment, the second RS resource set includes RS.

[0587] As one embodiment, the second RS resource set includes at least one RS resource.

[0588] As one embodiment, the second RS resource set includes downlink RS resources.

[0589] As an example, the second RS resource set is dedicated.

[0590] As one example, the second RS resource set is UE-specific.

[0591] As an example, the second RS resource set is not community-shared.

[0592] As one embodiment, the second RS resource set includes UE-specific RS resources.

[0593] As an example, the second RS resource set is exclusive to the first node.

[0594] As an example, the second RS resource set is not a cell-common RRC signaling configuration.

[0595] As one embodiment, the second RS resource set includes RS resources used for measurement.

[0596] As one embodiment, the second RS resource set includes CSI-RS (Channel State Information-Reference Signal) resources.

[0597] As an example, the second RS resource set includes at least one CSI-RS resource.

[0598] As an example, the second RS resource set includes NZP (Non-Zero Power) CSI-RS resources.

[0599] As an example, the second RS resource set corresponds to an NZP-CSI-RS-ResourceSetId.

[0600] As an example, the second RS resource set corresponds to a CSI-ResourceConfigId.

[0601] As an example, the first association ID is associated with the second entity.

[0602] As an example, the first association ID is configured to be associated with the second entity.

[0603] As an example, the high-level parameters of the second entity are configured to indicate the first associated ID.

[0604] As an example, the first configuration message in this application configures higher-level parameters of the second entity, and the higher-level parameters include the first association ID.

[0605] As an example, the second entity corresponds to a model or the second entity corresponds to a function.

[0606] As an example, the second entity is an Entity.

[0607] As an example, the second entity is an AI entity.

[0608] As an example, the second entity is an AI entity in Embodiment 12 of this application.

[0609] As an example, the second entity is part of an AI entity in Embodiment 12 of this application.

[0610] As an example, the second entity corresponds to a model.

[0611] As an example, the second entity corresponds to a model ID.

[0612] As an example, the second entity corresponds to a function.

[0613] As an example, the second entity corresponds to a function ID.

[0614] As an example, the second entity corresponds to multiple AI / ML models under a Functionality.

[0615] As an example, the second entity corresponds to multiple functions of an AI / ML model being applied.

[0616] As an example, the second RS resource set is used for at least the former in inference or training based on the second entity.

[0617] As an example, the second RS resource set is used for reasoning based on the second entity.

[0618] As an example, the first node will not perform Rel-18 and previous CSI calculations and reporting for RS resources in the second RS resource set.

[0619] As an example, the first node will not perform Rel-18 and prior measurement reporting for RS resources in the second RS resource set.

[0620] As one embodiment, the second RS resource set being used for reasoning based on the second entity means that the first node receives the second RS resource set and uses it as input for reasoning about the second entity.

[0621] As one embodiment, the second RS resource set being used for reasoning based on the second entity means that the first node measures the second RS resource set and uses it as input for reasoning about the second entity.

[0622] As one example, the second RS resource set is used for training based on the second entity.

[0623] As one embodiment, the use of the second RS resource set for training based on the second entity means that the second RS resource set serves as a label for training the second entity.

[0624] As one embodiment, the use of the second RS resource set for training based on the second entity means that the second RS resource set serves as input for training the second entity.

[0625] As one example, the second RS resource set is used for training and inference based on the second entity.

[0626] As an example, the second RS resource set being used for training and inference based on the second entity means that: a portion of the RS resources in the second RS resource set are used for training the second entity, and another portion of the RS resources in the second RS resource set are used for inference of the second entity.

[0627] As an example, the second RS resource set being used for training and inference based on the second entity means that the second RS resource set includes multiple RS resources, and the RS resources used for training and inference of the second entity are orthogonal.

[0628] As an example, the first node performs performance monitoring on the second entity.

[0629] As an example, the first node performing performance monitoring on the second entity means that the second node periodically reports on the second entity.

[0630] As a sub-example of this embodiment, the reporting is used for performance monitoring.

[0631] As a sub-implementation of this embodiment, the reporting is used for performance evaluation.

[0632] As a sub-implementation of this embodiment, the reporting includes the performance parameters of the second entity.

[0633] As an example, the first node performing performance monitoring on the second entity means that the second entity is configured with RS resources or a set of RS resources for performance monitoring.

[0634] As an example, the first node performing performance monitoring on the second entity means that the performance monitoring of the second entity includes the operations on the first node side.

[0635] As an example, the first node performing performance monitoring on the second entity means that the first node calculates the performance parameters of the second entity.

[0636] As an example, the first node performing performance monitoring on the second entity means that the first node reports the performance parameters of the second entity.

[0637] As an example, the first node performing performance monitoring on the second entity means that the first node is configured with a set of RS resources for performance monitoring on the second entity.

[0638] As a sub-implementation of this embodiment, the RS resource set is a resource set other than the first RS resource set and the second RS resource set.

[0639] As a sub-implementation of this embodiment, the RS resource set is specific to the second entity.

[0640] As a sub-implementation of this embodiment, the RS resource set is configured for each second entity.

[0641] As an example, the second entity is used for CSI determination of Layer 1 (L1).

[0642] As an example, the second entity is used for at least the former in the inference and training of CSI in layer 1.

[0643] As an example, the second entity is used for beam management (BM).

[0644] As an example, the second entity is used for CSI compression.

[0645] As one example, the second entity is used for beam-level mobility management.

[0646] As an example, the CSI of layer 1 includes LI (Layer Indicator).

[0647] As an example, the CSI of layer 1 includes RI (Rank Indicator).

[0648] As an example, the CSI of layer 1 includes CQI (Channel Quality Indicator).

[0649] As an example, the CSI of layer 1 includes PMI (Precoding Matrix Indicator).

[0650] As an example, the CSI of layer 1 includes CRI (CSI-RS Resource Indicator).

[0651] As an example, the CSI of layer 1 includes SSBRI (SS / PBCH Block Resource Indicator).

[0652] As an example, the CSI of Layer 1 includes L1-RSRP (Layer 1 Reference Signal Received Power).

[0653] Example 8

[0654] Example 8 illustrates a third schematic diagram of a first associated ID according to an embodiment of this application, as shown in the attached diagram. Figure 8 As shown. In the appendix Figure 8 In this context, the first association ID is associated with two training data sets, namely training data set #1 and training data set #2.

[0655] In Example 8, the two training data sets are determined based on two different RS resource sets.

[0656] As an example, the two training data sets are the training data collected by the first node and the training data collected by the second node, respectively.

[0657] As one example, the two training data sets are a common training data set for the cell and a UE-specific training data set, respectively.

[0658] As an example, the two training data sets are the training data obtained from the RS resource set of the first node's measurement period and the training data obtained from the RS resource set of the first node's measurement semi-persistent period.

[0659] As an example, the two training data sets are the training data obtained from the RS resource set of the first node during the measurement period and the training data obtained from the RS resource set of the first node during the measurement non-periodic period.

[0660] As an example, the two training datasets are respectively designed for beam management and CSI compression.

[0661] As an example, the two training datasets are respectively for terminal-side AI / ML model deployment and network-side AI / ML model deployment.

[0662] As an example, the two training datasets are training datasets for two different cells.

[0663] As an example, the two training datasets are the training data collected by the first node for SSB and the training data collected by the first node for CSI-RS resources, respectively.

[0664] As an example, the two training data sets are the training data collected by the first node for the two CSI-RS resource sets.

[0665] As an example, the RS resources on which the two training data sets are based are the first RS resource set and the second RS resource set in this application, respectively.

[0666] As an example, the RS resources on which the two training data sets are based are a portion of the RS resources in the first RS resource set of this application and a portion of the RS resources in the second RS resource set of this application, respectively.

[0667] Example 9

[0668] Example 9 illustrates a schematic diagram of an AI entity according to an embodiment of this application, as shown in the attached diagram. Figure 9 As shown. In the appendix Figure 9In this embodiment, a communication system includes an AI entity 901, a base station 902, a terminal 903, and a terminal 904. Terminals 903 and 904 can respectively access the base station 902 and communicate with it. It is worth noting that the embodiments of this application do not limit the specific implementation of the AI ​​entity. The AI ​​entity 901 can be located on the network side, interacting with network devices such as base stations, or located inside network devices; it can also be located on the user side, interacting with terminals, or located inside terminals.

[0669] As an example, the AI ​​entity is an AI / ML model.

[0670] As an example, the AI ​​entity is a model.

[0671] As an example, the AI ​​entity is associated with an AI / ML model ID.

[0672] As an example, the AI ​​entity is associated with a Functionality ID.

[0673] As an example, the AI ​​entity is associated with a dataset.

[0674] As an example, the AI ​​entity is associated with a training dataset.

[0675] As an example, the AI ​​entity corresponds to an Associated ID.

[0676] As an example, the AI ​​entity is associated with the first associated ID.

[0677] 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.

[0678] 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.

[0679] 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.

[0680] As an example, the AI ​​entity is located inside the base station.

[0681] As one example, the AI ​​entity is a module or function of the base station.

[0682] As one example, the AI ​​entity is located inside the terminal.

[0683] As one example, the AI ​​entity is a module or function of the terminal.

[0684] 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.

[0685] As an example, the AI ​​entity corresponds to the first entity in this application.

[0686] As an example, the AI ​​entity corresponds to the second entity in this application.

[0687] As an example, the AI ​​entity corresponds to the first entity and the second entity in this application.

[0688] Example 10

[0689] Example 10 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 10 As shown. In the appendix Figure 10 In this context, gNB can be replaced with network equipment such as eNB or 6G base stations.

[0690] In Example 10, the management of the ML inference functions of multiple base stations is completed by the RAN domain management function 1002, that is, data interaction with the RAN domain MnS (Management Service) consumer / cross-domain management 1001 (as shown in the attached document). Figure 10(As shown by the dashed arrow in the diagram). The RAN domain ML training function 1003 is located in the RAN domain management function 1002; while the ML inference function is located in the base station, that is, the AI / ML inference function 1004 is located in gNB 1005, the AI / ML inference function 1006 is located in gNB 1007, and so on.

[0691] 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.

[0692] 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).

[0693] 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.

[0694] Similarly, ML testing capabilities can also be deployed in cross-domain management systems or domain-specific management systems.

[0695] 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 1001.

[0696] It should be noted that Embodiment 10 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.

[0697] As an example, one of the gNBs (or base stations) in Example 10 is the second node of this application.

[0698] Example 11

[0699] Example 11 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 11 As shown. In the appendix Figure 11 In this context, the RAN domain ML training function 1104 is optional.

[0700] UE function 1103 is deployed in the first node of this application, and the UE function 1103 includes AI / ML inference function 1105; the AI / ML inference function 1105 uses an ML model (also called an AI model) for inference; an ML model is typically trained before being used for AI / ML inference.

[0701] As an example, the UE function 1103 includes a RAN domain ML training function 1104, 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.

[0702] 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.

[0703] Optionally, the UE function 1103 also includes a CN domain ML training function ( Figure 11 (Not included in the text).

[0704] Optionally, the UE function 1103 also includes an AI / ML deployment function. Figure 11It is not included in the list, which is used to load ML models and data.

[0705] 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.

[0706] As an example, the ML model and the associated metadata are loaded by the first node from a network device or a remote server.

[0707] Optionally, the UE function 1103 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 1101 for management or analysis (as shown by the double arrow 1102).

[0708] Optionally, the UE function 1103 is an MnS consumer that loads data from the CN domain MnF and / or RAN domain MnF and / or cross-domain management system 1101 for AI / ML-related management, such as managing data requests, ML model activation, and / or ML training (as shown by double arrow 1102).

[0709] As an example, the ML model is based on NN (Neural Networks).

[0710] As an example, the ML model is based on ANN (Artificial Neural Networks).

[0711] As an example, the ML model is based on CNN (Convolutional Neural Networks).

[0712] As an example, the ML model is based on the LLM (Large Language Model) architecture.

[0713] As an example, the ML model is based on the Transformer architecture.

[0714] As an example, the ML model is based on the GPT (Generative Pre-Trained) architecture.

[0715] As an example, the ML model is based on LSTM (Long Short-Term Memory network).

[0716] As an example, the ML model is based on MLP (MultiLayer Perceptron).

[0717] As an example, the ML model is based on GAN (Generative Adversarial Networks).

[0718] As an example, the ML model is based on a lightweight neural network.

[0719] As a sub-example of this embodiment, the lightweight neural network includes one or more of MobileNet, ShuffleNet, and SqueezeNet.

[0720] Example 12

[0721] Example 12 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 12 As shown. In the appendix Figure 12 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.

[0722] In Example 12, 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 12 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.

[0723] As one embodiment, the fourth processor includes ML testing functionality.

[0724] As one embodiment, the fourth processor includes performance monitoring / evaluation of the ML model.

[0725] 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.

[0726] 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.

[0727] As one embodiment, the first processor generates the first dataset and the second dataset based on the measurement of the reference signal.

[0728] As one embodiment, the third processor belongs to the first node, and the fourth processor belongs to the second node.

[0729] As an example, the third processor belongs to the first node.

[0730] As an example, the first dataset includes training data.

[0731] 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.

[0732] As an example, the second processor belongs to the first node; the above method avoids passing the first dataset to the second node.

[0733] As an example, the second processor belongs to the second node in this application; the above method supports joint training and optimizes system performance.

[0734] As an example, the second processor belongs to the core network; the above method supports network-wide joint training, further optimizing system performance.

[0735] As an example, the second dataset includes inference data.

[0736] 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.

[0737] As one embodiment, the output of the third processor includes the performance parameters described in this application.

[0738] As an example, the third processor belongs to the first node in this application, and the third processor monitors the second entity in this application.

[0739] As an example, the third processor belongs to the second node in this application, and the third processor monitors the first entity in this application.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] Example 13

[0746] Example 13 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 13 As shown. In the appendix Figure 13 In 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.

[0747] 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.

[0748] 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.

[0749] As an example, the first stage includes AI / ML model training.

[0750] As an example, the first stage includes AI / ML model training and AI / ML testing.

[0751] As an example, the AI / ML model training includes initial training and re-training of one or a group of AI / ML entities.

[0752] As an example, the training of the AI / ML model depends on training data.

[0753] As an example, the AI / ML model training includes AI / ML entity validation.

[0754] As an example, the AI / ML entity verification is used to evaluate the performance of the AI / ML entity.

[0755] As an example, the AI / ML entity verification relies on verification data.

[0756] As an example, if the AI / ML entity verification results do not meet expectations, the AI / ML model will be retrained.

[0757] As an example, the AI / ML testing includes testing the validated AI / ML entities to estimate the performance of the trained AI / ML model.

[0758] 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.

[0759] As an example, the AI / ML test relies on test data.

[0760] As one embodiment, the second stage includes AI / ML simulation, which performs AI / ML entity reasoning in a simulation environment.

[0761] As an example, the AI / ML simulation estimates the performance of AI / ML entity reasoning in a simulation environment before using AI / ML entities.

[0762] As one embodiment, the second stage is optional.

[0763] 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.

[0764] As an example, the third stage is optional.

[0765] As an example, the third stage is no longer needed when the training and inference functions are co-located.

[0766] As an example, the fourth stage includes AI / ML inference.

[0767] Example 14

[0768] Example 14 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 14 As shown. In the appendix Figure 14 In the first node, the processing device 1400 includes a first receiver 1401.

[0769] In embodiment 14, the first receiver 1401 receives a first configuration message, which indicates a first association ID.

[0770] In Example 14, the first configuration message is a dedicated RRC message, and the first association ID indicated by the first configuration message is public; the first association ID is associated with a first RS resource set, which is cell public; the first configuration message is a configuration for inference.

[0771] As an example, the first RS resource set includes at least one SSB.

[0772] As an example, the first associated ID being public means at least one of the following:

[0773] - The first associated ID is indicated as Common;

[0774] - The first associated ID is cell-specific, or the first associated ID is cell-group-specific;

[0775] - The first node considers the first associated ID to be unique within the cell or cell group.

[0776] As an example, the first association ID is associated with a first entity, which corresponds to a model or a function; the first RS resource set is used for at least the former in inference or training based on the first entity; the first node does not perform performance monitoring for the first entity.

[0777] As an example, the first association ID is also associated with a second RS resource set, which is dedicated; the first association ID is also associated with a second entity, which corresponds to a model or a function; the second RS resource set is used for at least the former in inference or training based on the second entity; and the first node performs performance monitoring for the second entity.

[0778] As one embodiment, the first entity is used for at least one of the following:

[0779] - Determine the CSI for Layer 3;

[0780] - Select a cell or reselect a cell;

[0781] - Switch;

[0782] -position.

[0783] As one example, the first entity makes a prediction for the first node when it is in a disconnected state.

[0784] As an example, the second entity is used for CSI determination of layer 1.

[0785] As an example, the first association ID is associated with two training data sets, which are determined based on two different RS resource sets.

[0786] As an example, the first entity is used for cell-level mobility management.

[0787] As one example, the second entity is used for beam management.

[0788] As an example, the two training data sets are the training data collected by the first node and the training data collected by the sender of the first configuration message.

[0789] As one example, the two training data sets are a common training data set for the cell and a UE-specific training data set, respectively.

[0790] As an example, the first node calculates the performance parameters of the second entity.

[0791] As an example, the sender of the first configuration message calculates the performance parameters of the first entity.

[0792] As an example, the first node 1400 is a user equipment.

[0793] As an example, the first node 1400 is a terminal.

[0794] As an example, the first node 1400 is a relay node device.

[0795] As an example, the first receiver 1401 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.

[0796] Example 15

[0797] Example 15 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 15 As shown. In the appendix Figure 15 In the second node, the processing device 1500 includes a first transmitter 1501.

[0798] In embodiment 15, the second transmitter 1501 sends a first reporting message; the first configuration message indicates a first association ID.

[0799] In Example 15, the first configuration message is a dedicated RRC message, and the first association ID indicated by the first configuration message is public; the first association ID is associated with a first RS resource set, which is cell public; the first configuration message is a configuration for inference.

[0800] As an example, the first RS resource set includes at least one SSB.

[0801] As an example, the first associated ID being public means at least one of the following:

[0802] - The first associated ID is indicated as Common;

[0803] - The first associated ID is cell-specific, or the first associated ID is cell-group-specific;

[0804] - The recipient of the first configuration message assumes that the first associated ID is unique within the cell or cell group.

[0805] As an example, the first association ID is associated with a first entity, which corresponds to a model or a function; the first RS resource set is used for at least the former in inference or training based on the first entity; and the recipient of the first configuration message does not perform performance monitoring for the first entity.

[0806] As an example, the first association ID is also associated with a second RS resource set, which is dedicated; the first association ID is also associated with a second entity, which corresponds to a model or a function; the second RS resource set is used for at least the former in inference or training based on the second entity; and the recipient of the first configuration message performs performance monitoring for the second entity.

[0807] As one embodiment, the first entity is used for at least one of the following:

[0808] - Determine the CSI for Layer 3;

[0809] - Select a cell or reselect a cell;

[0810] - Switch;

[0811] -position.

[0812] As one example, the first entity makes a prediction based on the recipient of the first configuration message being in a disconnected state.

[0813] As an example, the second entity is used for CSI determination of layer 1.

[0814] As an example, the first association ID is associated with two training data sets, which are determined based on two different RS resource sets.

[0815] As an example, the first entity is used for cell-level mobility management.

[0816] As one example, the second entity is used for beam management.

[0817] As an example, the two training data sets are the training data collected by the receiver of the first configuration message and the training data collected by the second node.

[0818] As one example, the two training data sets are a common training data set for the cell and a UE-specific training data set, respectively.

[0819] As an example, the recipient of the first configuration message calculates the performance parameters of the second entity.

[0820] As one example, the second node calculates the performance parameters of the first entity.

[0821] As an example, the second node 1500 is a base station device.

[0822] As one embodiment, the second node 1500 is a user equipment.

[0823] As an example, the second node 1500 is a TRP.

[0824] As an example, the first transmitter 1501 includes at least one of the following in embodiment 4: the antenna 420, the transmitter 418, the transmission processor 415, the multi-antenna transmission processor 471, the controller / processor 475, and the memory 476.

[0825] 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.

[0826] 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 associated ID; Wherein, the first configuration message is a dedicated RRC message, the first association ID indicated by the first configuration message is public; the first association ID is associated with a first RS resource set, the first RS resource set is public to the cell; the first configuration message is a configuration for inference.

2. The method in the terminal according to claim 1, characterized in that, The first RS resource group includes at least one SSB.

3. The method in the terminal according to claim 1 or 2, characterized in that, The first associated ID being public means at least one of the following: - The first associated ID is indicated as Common; - The first associated ID is cell-specific, or the first associated ID is cell-group-specific; The terminal considers the first associated ID to be unique within the cell or cell group.

4. The method in the terminal according to any one of claims 1 to 3, characterized in that, The first association ID is associated with a first entity, which corresponds to a model or a function; The first RS resource set is used for at least the former in inference or training based on the first entity; The terminal does not perform performance monitoring on the first entity.

5. The method in the terminal according to any one of claims 1 to 4, characterized in that, The first association ID is also associated with a second RS resource set, which is exclusive; the first association ID is also associated with a second entity, which corresponds to a model or a function. The second RS resource set is used for at least the former in inference or training based on the second entity; The terminal performs performance monitoring on the second entity.

6. The method in the terminal according to claim 4, characterized in that, The first entity is used for at least one of the following: - Determine the CSI for Layer 3; - Select a cell or reselect a cell; - Switch; -position.

7. The method in the terminal according to claim 4 or 6, characterized in that, The first entity makes a prediction based on the terminal being in a disconnected state.

8. The method in the terminal according to claim 5, characterized in that, The second entity was used for CSI determination of layer 1.

9. The method in the terminal according to any one of claims 1 to 8, characterized in that, The first association ID is associated with two training data sets, which are determined based on two different RS resource sets.

10. 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 1-9.

11. 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 associated ID; Wherein, the first configuration message is a dedicated RRC message, the first association ID indicated by the first configuration message is public; the first association ID is associated with a first RS resource set, the first RS resource set is public to the cell; the first configuration message is a configuration for inference.

12. The method in a base station according to claim 11, characterized in that, The first RS resource group includes at least one SSB.

13. The method in a base station according to claim 11 or 12, characterized in that, The first associated ID being public means at least one of the following: - The first associated ID is indicated as Common; - The first associated ID is cell-specific, or the first associated ID is cell-group-specific; - The recipient of the first configuration message assumes that the first associated ID is unique within the cell or cell group.

14. The method in a base station according to any one of claims 11 to 13, characterized in that, The first association ID is associated with a first entity, which corresponds to a model or a function; The first RS resource set is used for at least the former in inference or training based on the first entity; the recipient of the first configuration message does not perform performance monitoring for the first entity.

15. The method in a base station according to any one of claims 11 to 14, characterized in that, The first association ID is also associated with a second RS resource set, which is exclusive; the first association ID is also associated with a second entity, which corresponds to a model or a function. The second RS resource set is used for at least the former in inference or training based on the second entity; the recipient of the first configuration message performs performance monitoring for the second entity.

16. The method in a base station according to claim 14, characterized in that, The first entity is used for at least one of the following: - Determine the CSI for Layer 3; - Select a cell or reselect a cell; - Switch; -position.

17. The method in a base station according to claim 14 or 16, characterized in that, The first entity makes a prediction based on the recipient of the first configuration message being in a disconnected state.

18. The method in a base station according to claim 15, characterized in that, The second entity was used for CSI determination of layer 1.

19. The method in a base station according to any one of claims 11 to 18, characterized in that, The first association ID is associated with two training data sets, which are determined based on two different RS resource sets.

20. 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 11-19.

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