Method and apparatus used in nodes for wireless communications and artificial intelligence

Abstract

Disclosed in the present application are a method and apparatus used in nodes for wireless communications and artificial intelligence. The method comprises: a first node receiving a first configuration message set, wherein in the first configuration message set, K1 configurations for reasoning are configured, K1 being a positive integer; and sending a first reporting message, wherein the first reporting message indicates whether the K1 configurations for reasoning are applicable, and whether the K1 configurations for reasoning are applicable indicated by means of the first reporting message depends on one of the number of activated serving cells of the first node or the frequency bandwidth corresponding to the activated serving cells. In the present application, nodes can determine, on the basis of an activation status of a serving cell, whether a complex AI model or AI entity needs to be configured, thereby ensuring the accuracy of reasoning and also avoiding resource waste.

Description

A method and apparatus for use in nodes for wireless communication and artificial intelligence

[0001] This application claims priority to Chinese Patent Application No. 202411814914.1, filed on December 10, 2024, entitled "A Method and Apparatus Used in a Node for Wireless Communication and Artificial Intelligence", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to signal transmission methods and apparatus in wireless communication systems, and more particularly to methods and apparatus for configuring AI / ML. Background Technology

[0003] 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 into use cases, lifecycle management (LCM), simulation verification, and data collection.

[0004] 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

[0005] The applicant's research revealed that, for terminal-side AI / ML models, after functional identification is completed, the applicable functions of the terminal may still be updated due to channel changes or configuration changes. In this case, the terminal and the base station need to have a consistent understanding of the applicable functions on the terminal side. Therefore, how the terminal determines the update of the applicable functions, as well as the necessity and mechanism for the terminal to report the update of the applicable functions, are key issues that need to be addressed.

[0006] 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.

[0007] 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.

[0008] As an example, the interpretation of terms in this application is based on the definitions in the 3GPP specification protocol TS38 series.

[0009] As an example, the interpretation of terms in this application is based on the definitions in the 3GPP specification protocol TS37 series.

[0010] As an example, the interpretation of the terms used in this application is based on the definitions in 3GPP specification protocol Rel-17.

[0011] As an example, the interpretation of the terms used in this application is based on the definitions in 3GPP specification protocol Rel-18.

[0012] This application discloses a method for a first node in wireless communication and artificial intelligence, comprising:

[0013] Receive a first set of configuration messages, which configures K1 configurations for inference, where K1 is a positive integer;

[0014] Send a first reporting message, the first reporting message indicating whether the K1 configurations for inference are applicable;

[0015] Whether the K1 configurations indicated by the first reporting message are applicable depends on one of the number of active serving cells of the first node or the bandwidth corresponding to the active serving cells.

[0016] As an example, the problem this application aims to solve includes: how a terminal determines whether multiple configurations indicated by a base station for inference are applicable.

[0017] As an example, the problem this application aims to solve includes: how a base station obtains changes in terminal applicability functions.

[0018] As an example, the features of the above method include: in this application, the base station first sends multiple sets of inference configurations, the terminal sends a first reporting message, the first reporting message indicates the inference configuration applicable to the terminal from the multiple sets of inference configurations, and then the base station obtains the inference configuration applicable to the terminal according to the first reporting message, thereby solving the above problem.

[0019] As an example, the features of the above method include: Generally speaking, the more serving cells a terminal activates, the more spectrum resources it occupies. The more dispersed the spectrum resources are, the more complex AI configuration the terminal needs to support technologies such as multi-point collaboration and cross-cell joint transmission. Therefore, changes in the number or frequency band of activated serving cells in this application will trigger updates to the applicable configuration for inference, thereby solving the above problems.

[0020] As an example, the features of the above method include: the first node is a terminal.

[0021] As an example, the features of the above method include: the applicable configuration for reasoning includes applicable functions or applicable groups of functions.

[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: better adaptability to various terminal capabilities and terminal conditions.

[0024] As an example, the advantages of the above method include: adapting to diverse service needs and enhancing the ability of terminals to collaborate across cells.

[0025] As an example, the advantages of the above method include: when a configuration for inference is associated with an AI entity or an AI model, the terminal can determine whether a complex AI model or AI entity needs to be configured based on the activation status of the serving cell, thereby ensuring the accuracy of inference while avoiding resource waste.

[0026] According to one aspect of this application, the above method is characterized in that the upper limit of the number of applicable configurations for inference indicated by the first reporting message depends on the number of active serving cells of the first node.

[0027] As an example, the characteristics of the above method include: the upper limit of the number of applicable configurations for inference indicated by the first reporting message depends on the number of active serving cells of the first node and the capabilities of the first node.

[0028] As an example, the features of the above method include: the first reporting message indicates an upper limit on the number of applicable configurations for inference.

[0029] As an example, the advantages of the above method include: reducing the requirements for the terminal, thereby reducing deployment costs.

[0030] As an example, the advantages of the above method include: limiting the upper limit of the number of applicable configurations for inference can avoid unnecessary storage resource consumption due to excessive configurations.

[0031] As an example, the advantages of the above method include: reduced configuration complexity.

[0032] According to one aspect of this application, the above method is characterized in that the first inference configuration is one of the K1 configurations for inference, the first inference configuration is for multiple cells, and whether the first inference configuration indicated by the first reporting message is applicable depends on one of the number of active serving cells of the first node or the frequency bandwidth corresponding to the active serving cells.

[0033] As an example, the features of the above method include: the multi-cell includes multiple SCells.

[0034] As an example, the features of the above method include: the multi-cell comprises multiple active SCells.

[0035] As an example, the features of the above method include: whether the first inference configuration indicated by the first reporting message is applicable.

[0036] As an example, the advantages of the above method include: it fully considers the capabilities of the terminal, better adapts to various different terminal capabilities and terminal situations, and is highly flexible and adaptable.

[0037] As an example, the advantages of the above method include: more flexible configuration.

[0038] As an example, the advantages of the above method include: supporting the sharing of AI / ML models or AI / ML entities across multiple cells, reducing the number of AI / ML models or AI / ML entities configured, and enhancing cross-cell collaboration capabilities.

[0039] According to one aspect of this application, the above method is characterized in that whether the K1 configurations indicated by the first reporting message are applicable for inference depends on the number of deactivated serving cells of the first node.

[0040] As an example, the features of the above method include: the inference-for-the-inference configuration applicable to the K1 inference-for-the-inference configurations indicated by the first reporting message depends on the number of deactivated serving cells of the first node.

[0041] As an example, the features of the above method include: when the number of deactivated serving cells of the first node is large, the terminal can instruct a lightweight AI / ML model to reduce operational complexity.

[0042] As an example, the features of the above method include: when the number of deactivated serving cells of the first node is small, the terminal can instruct a complex AI / ML model to ensure reduced multi-cell cooperation, reduced inter-cell interference, and optimized channel transmission.

[0043] As an example, the advantages of the above method include reducing unnecessary configuration operations in the deactivated state.

[0044] As an example, the advantages of the above method include: saving power consumption and extending the battery life of the terminal.

[0045] According to one aspect of this application, the above method is characterized in that the first reporting message is used to determine at least one of the following:

[0046] - The number of applicable configurations for reasoning;

[0047] - Whether a configuration for reasoning is applicable, wherein the given configuration for reasoning is one of the K1 configurations for reasoning.

[0048] As an example, the characteristics of the above method include: the number of applicable configurations for inference depends on the number of serving cells activated by the first node and the capabilities of the first node.

[0049] As an example, the features of the above method include: the given configuration for inference depends on the serving cell activated by the first node.

[0050] As an example, the advantages of the above method include: reducing resource waste.

[0051] As an example, the advantages of the above method include: after the terminal explicitly reports the number of applicable configurations, the base station can limit or optimize the amount of tasks allocated to the terminal, thereby preventing the terminal from overloading computing power or increasing power consumption due to too many inference tasks.

[0052] As an example, the advantages of the above method include: reducing network coordination complexity and improving system flexibility and adaptability.

[0053] According to one aspect of this application, the above method is characterized by comprising:

[0054] Receive the first signaling set;

[0055] The first signaling set is used to determine the active serving cell of the first node.

[0056] As an example, the features of the above method include: the first signaling set configures and indicates the serving cell activated by the first node.

[0057] As an example, the features of the above method include: the first signaling set indicates the serving cell activated and the serving cell deactivated by the first node.

[0058] As an example, the features of the above method include: the first signaling set triggers a change in the serving cell activated by the first node.

[0059] As an example, the advantages of the above method include maintaining the real-time performance and accuracy of the configuration.

[0060] As an example, the advantages of the above method include: the first signaling set indicates the change of the serving cell activated by the first node, thereby triggering the first node to send a first reporting message; the base station can select a lightweight scheduling strategy according to the state of the SCell, reducing the occupation of resources.

[0061] As an example, the benefits of the above method include improved network performance and quality of service.

[0062] According to one aspect of this application, the above method is characterized by comprising:

[0063] Determine if the timers in the first timer set have expired;

[0064] The timer expiration in the first timer set is used to determine the number of active serving cells of the first node.

[0065] As an example, the features of the above method include: the timers in the first timer set correspond one-to-one with the serving cell or serving cell group activated by the first node, and the expiration of the timers in the first timer set is used to determine the deactivation of the corresponding activated serving cell or serving cell group.

[0066] As an example, the features of the above method include: the first timer set includes multiple timers, which are multiple sCellDeactivationTimer.

[0067] As an example, the advantages of the above method include maintaining the real-time performance and accuracy of the configuration.

[0068] As an example, the advantages of the above method include: after the timer in the first timer set expires and triggers the first node to send the first reporting message, the base station can then select a lightweight scheduling strategy based on the status of the SCell, thereby reducing the occupation of resources.

[0069] As an example, the advantages of the above method include: supporting the intelligent evolution of the network.

[0070] According to one aspect of this application, the above method is characterized in that the K1 configurations for inference are K1 CSI-ReportConfigs respectively.

[0071] As an example, the features of the above method include: this application is applicable to AI / ML-based beam management.

[0072] As an example, the characteristics of the above method include: all K1 CSI-ReportConfigs carry parameters for model inference.

[0073] As an example, the advantages of the above method include: reducing the impact on standards and having good compatibility.

[0074] As an example, the advantages of the above method include: configuring AI / ML-based beam management based on the CSI framework can simplify signaling requirements.

[0075] As an example, the advantages of the above method include: improved accuracy and flexibility of beam management.

[0076] According to one aspect of this application, the above method is characterized in that the serving cell is all the serving cells configured for the first node, or the serving cell is all the serving cells in a cell group configured for the first node.

[0077] As an example, the features of the above method include: the cell group is a set of cells configured to support AI / ML.

[0078] As an example, the features of the above method include: the cell group is an MCG or an SCG.

[0079] As an example, the characteristics of the above method include: all serving cells include all SCells and SpCells.

[0080] As an example, the advantages of the above method include: facilitating the integration of heterogeneous networks and reducing conflicts.

[0081] As an example, the benefits of the above method include: grouping serving cells helps to focus on specific functions, optimize the dimensions of AI model input, reduce redundant information, and improve inference efficiency.

[0082] As an example, the advantages of the above method include: allowing for tiered management of base stations and optimizing deployment.

[0083] According to one aspect of this application, the above method is characterized in that the first node is a user equipment.

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

[0085] This application discloses a method for a second node in wireless communication and artificial intelligence, comprising:

[0086] Send a first set of configuration messages, which configures K1 configurations for inference, where K1 is a positive integer;

[0087] Receive a first reporting message, which indicates whether the K1 configurations for inference are applicable;

[0088] Whether the K1 configurations indicated by the first reporting message are applicable depends on one of the number of active serving cells or the bandwidth corresponding to the active serving cells, as determined by the sender of the first reporting message.

[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 upper limit of the number of applicable configurations for inference indicated by the first reporting message depends on the number of active serving cells of the sender of the first reporting message.

[0104] According to one aspect of this application, the above method is characterized in that the first inference configuration is one of the K1 configurations for inference, the first inference configuration is for multiple cells, and whether the first inference configuration indicated by the first reporting message is applicable depends on one of the number of active serving cells or the bandwidth corresponding to the active serving cells of the sender of the first reporting message.

[0105] According to one aspect of this application, the above method is characterized in that whether the K1 configurations indicated by the first reporting message are applicable for inference depends on the number of deactivated serving cells of the sender of the first reporting message.

[0106] According to one aspect of this application, the above method is characterized in that the first reporting message is used to determine at least one of the following:

[0107] - The number of applicable configurations for reasoning;

[0108] - Whether a configuration for reasoning is applicable, wherein the given configuration for reasoning is one of the K1 configurations for reasoning.

[0109] According to one aspect of this application, the above method is characterized by comprising:

[0110] Send the first signaling set;

[0111] The first signaling set is used to determine the active serving cell of the sender of the first reporting message.

[0112] According to one aspect of this application, the method is characterized in that the sender of the first reporting message determines that a timer in a first timer set has expired; the timer expiration in the first timer set is used to determine the number of serving cells activated by the sender of the first reporting message.

[0113] According to one aspect of this application, the above method is characterized in that the K1 configurations for inference are K1 CSI-ReportConfigs respectively.

[0114] According to one aspect of this application, the above method is characterized in that the serving cell is all the serving cells configured by the sender of the first reporting message, or the serving cell is all the serving cells in a cell group configured by the sender of the first reporting message.

[0115] According to one aspect of this application, the method described above is characterized in that the second node is a base station.

[0116] This application discloses a device for a first node in wireless communication and artificial intelligence, comprising:

[0117] A first receiver receives a first configuration message set, which configures K1 configurations for inference, where K1 is a positive integer;

[0118] The first transmitter sends a first reporting message, which indicates whether the K1 configurations for inference are applicable;

[0119] Whether the K1 configurations indicated by the first reporting message are applicable depends on one of the number of active serving cells of the first node or the bandwidth corresponding to the active serving cells.

[0120] This application discloses a device for a second node in wireless communication and artificial intelligence, comprising:

[0121] The second transmitter sends a first configuration message set, which configures K1 configurations for inference, where K1 is a positive integer;

[0122] The second receiver receives a first reporting message, which indicates whether the K1 configurations for inference are applicable;

[0123] Whether the K1 configurations indicated by the first reporting message are applicable depends on one of the number of active serving cells or the bandwidth corresponding to the active serving cells, as determined by the sender of the first reporting message.

[0124] As an example, compared with conventional solutions, this application has the following advantages, but is not limited to:

[0125] 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.

[0126] The terminal can determine whether it needs to configure complex AI models or AI entities based on the activation status of the serving cell, thereby ensuring the accuracy of inference while avoiding waste of resources;

[0127] Maintain the real-time and accuracy of the configuration;

[0128] It supports the sharing of AI / ML models or AI / ML entities across multiple cells, reducing the number of AI / ML models or AI / ML entities required and enhancing cross-cell collaboration capabilities. Attached Figure Description

[0129] 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:

[0130] Figure 1 illustrates a flowchart of the first node transmission according to an embodiment of this application;

[0131] Figure 2 shows a schematic diagram of a network architecture according to an embodiment of this application;

[0132] Figure 3 illustrates a schematic diagram of an embodiment of a wireless protocol architecture for the user plane and control plane according to an embodiment of this application;

[0133] Figure 4 shows a schematic diagram of a first communication device and a second communication device according to an embodiment of this application;

[0134] Figure 5 shows a first flowchart of the transmission between a first node and a second node according to an embodiment of this application;

[0135] Figure 6 illustrates a second flowchart of the transmission between a first node and a second node according to an embodiment of this application;

[0136] Figure 7 illustrates a schematic diagram of a first reporting message indicating whether K1 configurations for inference are applicable, according to an embodiment of this application;

[0137] Figure 8 shows a schematic diagram of a first reporting message according to an embodiment of this application;

[0138] Figure 9 illustrates a schematic diagram of the number of applicable configurations for inference indicated by a first reporting message according to an embodiment of this application;

[0139] Figure 10 shows a schematic diagram of a first inference configuration according to an embodiment of this application;

[0140] Figure 11 shows a schematic diagram of RAN domain AI / ML function deployment according to an embodiment of this application;

[0141] Figure 12 shows a schematic diagram of the deployment of AI / ML functions of a UE according to an embodiment of this application;

[0142] Figure 13 shows a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of this application;

[0143] Figure 14 illustrates a schematic diagram of artificial intelligence or machine learning according to an embodiment of this application;

[0144] Figure 15 shows a structural block diagram of a processing apparatus for a first node according to an embodiment of this application;

[0145] Figure 16 shows a structural block diagram of a processing apparatus for a second node according to an embodiment of the present application. 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 embodiments in Figure 1 and the embodiments in Figures 5-16, the embodiments in Figure 5 and the embodiments in Figures 6-16, etc.

[0147] Example 1

[0148] Example 1 illustrates a flowchart of the first node transmission according to an embodiment of this application, as shown in Figure 1. In Figure 1, each block represents a step. In particular, the order of the steps in the blocks does not represent a specific temporal relationship between the steps.

[0149] In step 101, the first node receives a first configuration message set, which configures K1 configurations for inference, where K1 is a positive integer; in step 102, it sends a first reporting message, which indicates whether the K1 configurations for inference are applicable.

[0150] In Embodiment 1, whether the K1 configurations indicated by the first reporting message are applicable depends on one of the number of active serving cells of the first node or the bandwidth corresponding to the active serving cells.

[0151] As one 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 one embodiment, the first node receives the first set of configuration messages.

[0155] As one embodiment, the first configuration set is transmitted via higher layer signaling.

[0156] As an example, the first set of configuration messages is transmitted via RRC (Radio Resource Control) signaling.

[0157] As an example, the first set of configuration messages is transmitted via RRC messages.

[0158] As an example, the first configuration message set includes one or more RRC IEs (Information Elements).

[0159] As an example, the first configuration message set includes one or more fields in an RRC IE.

[0160] As one embodiment, the first configuration message set includes one or more fields of each of the plurality of RRC IEs.

[0161] As an example, the first set of configuration messages indicates higher-level parameters.

[0162] As one example, the first configuration message set includes one or more RRCReconfiguration IEs.

[0163] As one embodiment, the first configuration message set includes one or more domains of each RRCReconfiguration IE in one or more RRCReconfiguration IEs.

[0164] As one example, the first configuration message set includes one or more ServingCellConfig IEs.

[0165] As one example, the first configuration message set includes one or more domains of each ServingCellConfig IE in one or more ServingCellConfig IEs.

[0166] As one example, the first configuration message set includes one or more CSI-MeasConfig IEs.

[0167] As one embodiment, the first configuration message set includes one or more domains of each CSI-MeasConfig IE in one or more CSI-MeasConfig IEs.

[0168] As one example, the first configuration message set includes one or more CSI-ReportConfig IEs.

[0169] As one embodiment, the first configuration message set includes one or more fields of each CSI-ReportConfig IE in one or more CSI-ReportConfig IEs.

[0170] As one example, the first configuration message set includes one or more CSI-AperiodicTriggerStateList IEs.

[0171] As one embodiment, the first configuration message set includes one or more domains of each CSI-AperiodicTriggerStateList IE in one or more CSI-AperiodicTriggerStateList IEs.

[0172] As one example, the first configuration message set includes one or more CSI-AperiodicTriggerState IEs.

[0173] As one embodiment, the first configuration message set includes one or more domains of each CSI-AperiodicTriggerState IE in one or more CSI-AperiodicTriggerState IEs.

[0174] As one example, the first configuration message set includes one or more CSI-SemiPersistentOnPUSCH-TriggerStateList IEs.

[0175] As one embodiment, the first configuration message set includes one or more domains of each CSI-SemiPersistentOnPUSCH-TriggerStateList IE in one or more CSI-SemiPersistentOnPUSCH-TriggerStateList IEs.

[0176] As one example, the first configuration message set includes one or more CSI-SemiPersistentOnPUSCH-TriggerState IEs.

[0177] As one embodiment, the first configuration message set includes one or more domains of each CSI-SemiPersistentOnPUSCH-TriggerState IE in one or more CSI-SemiPersistentOnPUSCH-TriggerState IEs.

[0178] As one example, the first configuration message set includes one or more CSI-ReportSubConfigTriggerList IEs.

[0179] As one embodiment, the first configuration message set includes one or more domains of each CSI-ReportSubConfigTriggerList IE in one or more CSI-ReportSubConfigTriggerList IEs.

[0180] As one example, the first configuration message set includes one or more CSI-ReportSubConfig IEs.

[0181] As one embodiment, the first configuration message set includes one or more domains of each CSI-ReportSubConfig IE in one or more CSI-ReportSubConfig IEs.

[0182] As one example, the first configuration message set includes one or more CSI-ResourceConfig IEs.

[0183] As one embodiment, the first configuration message set includes one or more domains of each CSI-ResourceConfig IE in one or more CSI-ResourceConfig IEs.

[0184] As one example, the first configuration message set includes one or more CSI-IM-Resource IEs.

[0185] As one embodiment, the first configuration message set includes one or more domains of each CSI-IM-Resource IE in one or more CSI-IM-Resource IEs.

[0186] As one example, the first configuration message set includes one or more CSI-IM-ResourceSet IEs.

[0187] As one embodiment, the first configuration message set includes one or more fields of each CSI-IM-ResourceSet IE in one or more CSI-IM-ResourceSet IEs.

[0188] As one example, the first configuration message set includes one or more NZP-CSI-RS-ResourceSet IEs.

[0189] As one embodiment, the first configuration message set includes one or more domains of each NZP-CSI-RS-ResourceSet IE in one or more NZP-CSI-RS-ResourceSet IEs.

[0190] As one example, the first configuration message set includes one or more NZP-CSI-RS-Resource IEs.

[0191] As one embodiment, the first configuration message set includes one or more domains of each NZP-CSI-RS-Resource IE in one or more NZP-CSI-RS-Resource IEs.

[0192] As one example, the first configuration message set includes one or more MeasObjectNR IEs.

[0193] As one embodiment, the first configuration message set includes one or more fields of each MeasObjectNR IE in one or more MeasObjectNR IEs.

[0194] As one example, the first configuration message set includes one or more CSI-RS-ResourceConfigMobility IEs.

[0195] As one embodiment, the first configuration message set includes one or more domains of each CSI-RS-ResourceConfigMobility IE in one or more CSI-RS-ResourceConfigMobility IEs.

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

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

[0198] As an example, the name of the RRC signaling used to transmit the first set of configuration messages includes Report.

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

[0200] As an example, the name of the RRC signaling used to transmit the first set of configuration messages includes CSI-ReportConfig.

[0201] As an example, the name of the RRC signaling used to transmit the first set of configuration messages includes Resource.

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

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

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

[0205] As one example, the name of the RRC signaling used to transmit the first set of configuration messages includes Model.

[0206] As an example, the name of the RRC signaling used to transmit the first set of configuration messages includes Functionality.

[0207] As one example, the name of the RRC signaling used to transmit the first set of configuration messages includes Inference.

[0208] As an example, the name of the RRC signaling used to transmit the first configuration message set includes Infer.

[0209] As one example, the name of the RRC signaling used to transmit the first configuration message set includes Monitoring.

[0210] As an example, the first configuration message set configures the K1 configurations for inference, where K1 is a positive integer.

[0211] As an example, K1 is equal to 1.

[0212] As an example, K1 is a positive integer greater than 1.

[0213] As an example, the reasoning described in this application includes prediction.

[0214] As an example, the reasoning described in this application includes computation based on AI / ML models.

[0215] As an example, the reasoning mentioned in this application refers to infer.

[0216] As an example, the reasoning described in this application corresponds to: inference.

[0217] As an example, the reasoning mentioned in this application refers to prediction.

[0218] As an example, the reasoning mentioned in this application refers to: prediction.

[0219] As an example, the inference described in this application includes AI / ML inference.

[0220] As an example, the reasoning described in this application is based on training or AI.

[0221] As an example, the reasoning described in this application includes an AI entity used for reasoning.

[0222] As an example, the reasoning described in this application includes at least a portion of an AI entity.

[0223] As an example, the reasoning described in this application includes a portion of an AI entity used for reasoning.

[0224] As an example, the configuration for inference is configured as parameters for inference.

[0225] As an example, the configuration for inference indicates the parameters for inference.

[0226] As an example, the configuration for inference is associated with an associated ID.

[0227] As a sub-example of this embodiment, the association ID is an identifier associated with the AI ​​model.

[0228] As a sub-example of this embodiment, the association ID is an identifier associated with Functionality.

[0229] As one embodiment, the configuration for reasoning includes at least one of: reasoning input, reasoning output, reasoning purpose, and associated ID.

[0230] As a sub-implementation of this embodiment, the purpose of the inference includes at least one of CSI (Channel State Information) prediction, beam prediction, and CSI compression.

[0231] As a sub-implementation of this embodiment, the uses of the inference include at least one of: CSI prediction, beam prediction, CSI compression, and RLF (Radio Link Failure).

[0232] As one embodiment, the inference configuration includes at least one of the following: information related to a resource set for prediction, information related to an 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.

[0233] As an example, the configuration for inference means that the configuration for inference indicates the parameters for the AI / ML model.

[0234] As an example, the configuration for inference means that the configuration for inference indicates parameters for Functionality.

[0235] As an example, the configuration for inference includes: the configuration for inference indicates the parameters corresponding to the training data set.

[0236] As an example, the configuration for inference means that the configuration for inference indicates the parameters corresponding to the inference data set.

[0237] As an example, the configuration for inference means that the configuration for inference includes parameters for AI / ML.

[0238] As an example, the configuration for inference means that the configuration for inference indicates the parameters for monitoring the model used for inference.

[0239] As an example, the configuration for inference means that the configuration for inference includes parameters for model monitoring.

[0240] As an example, the first configuration message set includes K1 configuration messages, each of which corresponds to one of the K1 configurations used for inference.

[0241] As a sub-implementation of this embodiment, the K1 configuration messages respectively indicate K1 higher-level parameters.

[0242] As a sub-implementation of this embodiment, the K1 configuration messages are K1 RRC IEs respectively.

[0243] As a sub-example of this embodiment, the K1 configuration messages are respectively K1 fields in an RRC IE.

[0244] As a sub-implementation of this embodiment, the K1 configuration messages are K1 CSI-ReportConfig IEs.

[0245] As a sub-implementation of this embodiment, the K1 configuration messages each correspond to K1 CSI-ReportConfigId IEs.

[0246] As a sub-implementation of this embodiment, the first configuration message set is carried by an RRCReconfiguration IE.

[0247] As a sub-implementation of this embodiment, the first configuration message set is carried by an RRCReconfiguration message.

[0248] As an example, the K1 configurations for inference each include K1 inference parameter groups, and each of the K1 inference parameter groups includes at least one inference parameter.

[0249] As an example, the K1 configurations for inference are K1 CSI reporting configurations used for inference.

[0250] As an example, the K1 configurations for inference are K1 inference-based channel state reporting configurations.

[0251] As an example, the K1 configurations for inference are K1 CSI-ReportConfigs.

[0252] As an example, the K1 configurations for inference correspond to K1 CSI-ReportConfigIds respectively.

[0253] Send the first reporting message.

[0254] As an example, the first node sends the first reporting message.

[0255] As one example, the first reporting message includes a baseband signal.

[0256] As one example, the first reporting message includes a radio frequency signal.

[0257] As one example, the first reporting message includes a wireless signal.

[0258] As one example, the first reporting message includes higher-level messages.

[0259] As an example, the first reported message belongs to UAI (UE Assistance Information).

[0260] As an example, the first reporting message belongs to the UEAssistanceInformation message.

[0261] As an example, the first reporting message includes a UAI report.

[0262] As an example, the first reporting message includes a UEAssistanceInformation message.

[0263] As an example, the first reporting message includes an RRC message.

[0264] As an example, the first reporting message includes an RRCReconfigurationComplete message.

[0265] As one example, the first reporting message includes dynamic signaling.

[0266] As one example, the first reporting message includes a sub-header.

[0267] As an example, the first reported message includes MAC (Medium Access Control) layer signaling.

[0268] As an example, the first reporting message includes MAC CE (Control Element).

[0269] As an example, the name of the MAC layer signaling carrying the first reporting message includes: non-applicable.

[0270] As an example, the name of the MAC layer signaling carrying the first reporting message includes: applicable.

[0271] As an example, the name of the MAC layer signaling carrying the first reporting message includes: functionalities.

[0272] As an example, the name of the MAC layer signaling carrying the first reporting message includes: functionality.

[0273] As an example, the name of the MAC layer signaling carrying the first reporting message includes: model.

[0274] As an example, the first reported message includes UCI (Uplink Control Information).

[0275] As an example, the first reporting message includes Layer 1 (L1) information.

[0276] As one example, the first reporting message includes physical layer information.

[0277] As one example, the first reporting message includes channel state information.

[0278] As a sub-implementation of this embodiment, the channel state information includes LI (Layer Indicator).

[0279] As a sub-implementation of this embodiment, the channel state information includes RI (Rank Indicator).

[0280] As a sub-example of this embodiment, the channel state information includes CQI (Channel Quality Indicator).

[0281] As a sub-example of this embodiment, the channel state information includes PMI (Precoding Matrix Indicator).

[0282] As a sub-example of this embodiment, the channel state information includes CRI (CSI-RS Resource Indicator).

[0283] As a sub-example of this embodiment, the channel state information includes SSBRI (SS / PBCH Block Resource indicator).

[0284] As a sub-example of this embodiment, the channel state information includes L1-RSRP (Layer 1 Reference Signal Received Power).

[0285] As one example, the first reporting message includes a prediction report.

[0286] As an example, the first reporting message includes a beam indication, which includes at least one of SSBRI and CRI.

[0287] As an example, the first reporting message includes the Associated ID.

[0288] As an example, the first reporting message is an applicability report.

[0289] As an example, the first reporting message is used to identify one or more CSI-ReportConfigs configured for inference.

[0290] As an example, the first reporting message is used to determine one or more sets of inference-related parameters.

[0291] As an example, the logical channel occupied by the first reporting message includes DCCH (UpLink-Shared Channel).

[0292] As an example, the first reporting message is carried by SRB1 (Signalling Radio Bearer 1).

[0293] As an example, the first reporting message is carried by SRB3.

[0294] As an example, the first reporting message indicates whether the K1 configurations for inference are applicable.

[0295] As an example, the first reporting message indicates the inference-for-the-inference configuration applicable to the first node from the K1 inference-for-the-inference configurations.

[0296] As an example, the first reporting message indicates all applicable inference configurations among the K1 inference configurations.

[0297] As an example, the first reporting message indicates all inapplicable inference configurations among the K1 inference configurations.

[0298] As an example, the first reporting message indicates that one of the K1 configurations for inference has become inapplicable for inference.

[0299] As an example, the first reporting message indicates which of the K1 configurations for inference has become applicable for inference.

[0300] As an example, the first reporting message indicates the number of all applicable inference-for-inference configurations among the K1 inference-for-inference configurations.

[0301] As an example, the first reporting message indicates the number of all inapplicable inference configurations among the K1 inference configurations.

[0302] As an example, the first reporting message indicates whether any of the K1 configurations for inference is applicable.

[0303] As a sub-implementation of this embodiment, the first reporting message includes K1 sub-messages, each of which indicates whether the K1 configurations for prediction are applicable.

[0304] As an example, the first reporting message indicates the number of prediction configurations applicable to the K1 prediction configurations.

[0305] As an example, the first reporting message indicates the associated ID corresponding to the prediction configuration applicable to the K1 prediction configurations.

[0306] As an example, the first reporting message indicates the index or identifier of the configuration applicable to the first node for reasoning.

[0307] As an example, the first reporting message includes a first field, which includes a bitmap of K1 bits, wherein each of the K1 bits corresponds one-to-one with a K1 configuration for inference, and the K1 bits indicate whether the K1 configurations for inference are applicable.

[0308] As a sub-implementation of this embodiment, when the m-th bit of the first field is equal to 1, the first reporting message indicates that the m-th configuration for reasoning is applicable, where m is a positive integer not greater than K1.

[0309] As a supplementary embodiment of this example, the K1 configurations for inference correspond to K1 CSI-ReportConfigIds, and the K1 CSI-ReportConfigIds are arranged in ascending order.

[0310] As a supplementary embodiment of this example, the K1 configurations for inference correspond to K1 CSI-ReportConfigIds, which are arranged in descending order.

[0311] As a sub-implementation of this embodiment, the K1 configurations for inference correspond to K1 CSI-ReportConfigIds respectively, and the highest bit of the K1 bits corresponds to the maximum value among the K1 CSI-ReportConfigIds.

[0312] As a sub-implementation of this embodiment, the K1 configurations for inference correspond to K1 CSI-ReportConfigIds respectively, and the highest bit of the K1 bits corresponds to the minimum value among the K1 CSI-ReportConfigIds.

[0313] As a sub-implementation of this embodiment, when the m-th bit of the first field is equal to 0, the first reporting message indicates that the m-th configuration for reasoning is not applicable, where m is a positive integer not greater than K1.

[0314] As an example, whether the K1 configurations indicated by the first reporting message are applicable depends on one of the number of active serving cells of the first node or the bandwidth corresponding to the active serving cells.

[0315] As an example, whether the K1 configurations indicated by the first reporting message are applicable for inference depends on the number of active serving cells of the first node.

[0316] As a sub-example of this embodiment, the serving cell refers to a serving cell that can support AI / ML.

[0317] As an example, a change in the number of active serving cells of the first node is used to trigger the sending of the first reporting message.

[0318] Typically, the number of active serving cells of the first node is used to determine the number of applicable configurations for inference indicated by the first reporting message.

[0319] As an example, the relationship between the number of activated serving cells and the number of applicable configurations for inference indicated by the first reporting message is determined by a predefined table.

[0320] As an example, for a given number of active serving cells, the first node can determine the corresponding number of applicable configurations for inference.

[0321] As an example, the number of applicable configurations for inference is linearly related to the number of activated serving cells.

[0322] As an example, the number of applicable configurations for inference is linearly related to the inverse of the number of active serving cells.

[0323] As an example, the number of applicable configurations for inference depends on the reciprocal of the number of active serving cells.

[0324] As an example, the more active serving cells there are, the fewer applicable configurations for inference there are.

[0325] As an example, the fewer the number of activated serving cells, the more applicable configurations are used for inference.

[0326] As an example, among the K1 inference configurations indicated by the first reporting message, at least one inference configuration is applicable depending on the number of active serving cells of the first node.

[0327] As an example, whether any of the K1 inference-for-reasoning configurations indicated by the first reporting message is applicable depends on the number of active serving cells of the first node.

[0328] As an example, whether the K1 configurations indicated by the first reporting message are applicable for inference depends on the frequency bandwidth corresponding to the active serving cell of the first node.

[0329] As a sub-example of this embodiment, the bandwidth corresponding to the activated serving cell refers to the sum of the bandwidths corresponding to the active BWP (BandWidth Part) in the activated serving cell.

[0330] As a sub-example of this embodiment, the bandwidth corresponding to the activated serving cell refers to the sum of the bandwidths corresponding to the activated serving cells.

[0331] As a sub-example of this embodiment, the bandwidth corresponding to the activated serving cell refers to the sum of the bandwidths of the downlink carriers corresponding to the activated serving cell.

[0332] As a sub-example of this embodiment, the bandwidth corresponding to the activated serving cell refers to the sum of the bandwidths of the uplink carriers corresponding to the activated serving cell.

[0333] As a sub-example of this embodiment, the bandwidth corresponding to the activated serving cell refers to whether the activated serving cell is configured with an SUL (Supplimentary UpLink) carrier.

[0334] As a sub-example of this embodiment, the bandwidth corresponding to the activated serving cell refers to whether the activated serving cell is configured as an uplink-only cell.

[0335] As a sub-example of this embodiment, the bandwidth corresponding to the activated serving cell refers to the sum of the bandwidths corresponding to the active downlink BWPs in the activated serving cell.

[0336] As a sub-example of this embodiment, the bandwidth corresponding to the activated serving cell refers to the sum of the bandwidths corresponding to the active uplink BWPs in the activated serving cell.

[0337] As a sub-example of this embodiment, the bandwidth corresponding to the activated serving cell refers to the sum of the bandwidths measured in L1 within the activated serving cell.

[0338] As a sub-example of this embodiment, the bandwidth corresponding to the activated serving cell refers to the sum of the bandwidths measured in L3 (Layer 3) measurements within the activated serving cell.

[0339] As an example, the change in the bandwidth corresponding to the activated serving cell of the first node is used to trigger the sending of the first reporting message.

[0340] As an example, whether the active BWP corresponding to the activated serving cell of the first node is switched to a dormant BWP is used to trigger the sending of the first reporting message.

[0341] As an example, whether the active BWP corresponding to the activated serving cell of the first node is switched to a non-dormant BWP is used to trigger the sending of the first reporting message.

[0342] As one embodiment, the serving cell is all the serving cells configured for the first node, or the serving cell is all the serving cells in a cell group configured for the first node.

[0343] As one example, the serving cell includes a PCell (Primary Cell).

[0344] As one example, the serving cell includes SCell (Secondary Cell).

[0345] As one example, the serving cell includes SpCell (Special Cell).

[0346] As one example, the serving cell includes cells in an MCG (Master Cell Group).

[0347] As an example, the serving cell includes cells in the SCG (Secondary Cell Group).

[0348] As an example, the serving cell is all the serving cells in a cell group configured for the first node.

[0349] As one example, the serving cell is all the serving cells in the MCG configured for the first node.

[0350] As an example, the serving cell is all the serving cells in the SCG configured for the first node.

[0351] As an example, the cell group includes cells that support AI / ML.

[0352] As an example, the cell group is a group of cells configured to support AI / ML.

[0353] As an example, the cell group is a set of cells consisting of all SCells.

[0354] As an example, the cell group is a set of cells associated with the same TAG (Time Advance Group).

[0355] As an example, the cell group is a collection of cells associated with the same AI entity.

[0356] As an example, the cell group is a set of cells that are configured to be scheduled simultaneously.

[0357] As one example, the serving cell is all the serving cells configured for the first node.

[0358] As an example, all serving cells include cells in the SCG and cells in the MCG.

[0359] As an example, all serving cells include all SCells and SpCells.

[0360] As an example, all the serving cells include cells that support AI / ML.

[0361] As an example, all the serving cells include cells that support AI / ML and cells that do not support AI / ML.

[0362] Example 2

[0363] Example 2 illustrates a schematic diagram of a network architecture according to an embodiment of this application, as shown in Figure 2.

[0364] Figure 2 illustrates network architecture 200. Network architecture 200 is the network architecture for LTE (Long-Term Evolution), LTE-A (Long-Term Evolution Advanced), 5G systems, 5G-Advanced, and future 6G systems. The network architectures for 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.

[0365] 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.

[0366] As shown in Figure 2, 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, radio base station, radio 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.

[0367] As an example, the first node in this application includes the UE 201.

[0368] As an example, the second node in this application includes node B 203.

[0369] As an example, node B 203 is a macrocell base station.

[0370] As an example, node B 203 is a microcell base station.

[0371] As an example, node B 203 is a pico cell base station.

[0372] As an example, node B 203 is a femtocell.

[0373] As an example, node B 203 is a base station device that supports large latency differences.

[0374] As an example, node B 203 is a flight platform device.

[0375] As an example, node B 203 is a satellite device.

[0376] 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).

[0377] As an example, the UE 201 includes a mobile phone.

[0378] As an example, the UE 201 is a vehicle including a car.

[0379] 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.

[0380] 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.

[0381] As an example, the wireless link between the node B 203 and the UE 201 includes a cellular link.

[0382] As an example, the node B 203 and the UE 201 are connected via the Uu air interface.

[0383] As an example, the sender of the first configuration message set in this application includes the node B 203.

[0384] As an example, the recipient of the first configuration message set in this application includes the UE 201.

[0385] As an example, the sender of the first reporting message in this application includes the UE 201.

[0386] As an example, the recipient of the first reporting message in this application includes the node B 203.

[0387] As an example, the sender of the first signaling set in this application includes the node B 203.

[0388] As an example, the recipient of the first signaling set in this application includes the UE 201.

[0389] As an example, the node B 203 supports the deployment of network-side (NW-side) AI / ML models.

[0390] As an example, the UE 201 supports the deployment of UE-side AI / ML models.

[0391] As an example, the UE 201 supports a 5G system.

[0392] As an example, the node B 203 supports a 5G system.

[0393] As an example, the UE 201 supports at least a 6G system.

[0394] As an example, the node B 203 supports at least a 6G system.

[0395] Example 3

[0396] Example 3 illustrates a schematic diagram of an embodiment of a wireless protocol architecture for the user plane and control plane according to an embodiment of this application, as shown in Figure 3.

[0397] Figure 3 is a schematic diagram illustrating an embodiment of the wireless protocol architecture for the user plane 350 and the control plane 300. Figure 3 shows the wireless protocol architecture for the control plane 300 between a first communication node device (UE or RSU in V2X, onboard equipment or onboard communication module) and a second node device (gNB, RSU in UE or V2X, onboard equipment or onboard communication module), or between two UEs, 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 herein as PHY 301. L2 305 is above PHY 301 and is responsible for the link between the first node device and the second node device, or between two UEs, through PHY 301. L2 305 includes a MAC (Medium Access Control) sublayer 302, an RLC (Radio Link Control) sublayer 303, and a PDCP (Packet Data Convergence Protocol) sublayer 304, which terminate at the second 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 reQuest). 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 between 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 L2 355, RLC sublayer 353 in L2 355, and MAC sublayer 352 in L2 355. However, PDCP sublayer 354 also provides header compression for upper-layer packets to reduce wireless transmission overhead. L2 355 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 Bearers (DRBs) to support service diversity. Although not illustrated, the first communication node device may have several upper layers above L2 355, 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.).

[0398] As an example, the wireless protocol architecture in Figure 3 is applicable to the first node in this application.

[0399] As an example, the wireless protocol architecture in Figure 3 is applicable to the second node in this application.

[0400] As an example, the first configuration message set in this application is generated in the RRC 306.

[0401] As an example, in this application, the first reporting message is generated in the RRC 306.

[0402] As an example, in this application, the first reporting message is generated in the MAC sublayer 302 or the MAC sublayer 352.

[0403] As an example, in this application, the first reporting message is generated in the PHY 301 or the PHY 351.

[0404] As an example, the first signaling set in this application is generated in the RRC 306.

[0405] As an example, in this application, the first signaling set is generated in the MAC sublayer 302 or the MAC sublayer 352.

[0406] As an example, in this application, the first signaling set is generated in the PHY 301 or the PHY 351.

[0407] As an example, the higher layer mentioned in this application refers to the layer above the physical layer.

[0408] As an example, the higher layer described in this application includes the RRC layer.

[0409] As an example, the higher-layer signaling described in this application includes RRC IE.

[0410] As an example, the higher-level signaling described in this application includes RRC messages.

[0411] As an example, the higher layer described in this application includes the MAC layer.

[0412] As an example, the higher-layer signaling described in this application includes MAC CE.

[0413] Example 4

[0414] Example 4 illustrates a schematic diagram of a first communication device and a second communication device according to an embodiment of this application, as shown in Figure 4. Figure 4 is a block diagram of a first communication device 410 and a second communication device 450 communicating with each other in an access network.

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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 apparatus at least receives the first configuration message set in this application, the first configuration message set configuring K1 configurations for inference, where K1 is a positive integer; sends the first reporting message in this application, the first reporting message indicating whether the K1 configurations for inference indicated by the first reporting message are applicable; whether the K1 configurations for inference indicated by the first reporting message are applicable depends on one of the number of active serving cells of the second communication device 450 or the bandwidth corresponding to the active serving cells.

[0422] As one embodiment, the second communication device 450 includes: a memory storing a computer-readable instruction program that produces actions when executed by at least one processor, the actions including: receiving the first configuration message set in this application; and sending the first reporting message in this application.

[0423] As one embodiment, the first communication device 410 includes: at least one processor and at least one memory, the at least one memory including computer program code; the at least one memory and the computer program code are configured to be used with the at least one processor. The first communication device 410 at least sends the first configuration message set in this application, the first configuration message set configuring K1 configurations for inference, where K1 is a positive integer; receives the first reporting message in this application, the first reporting message indicating whether the K1 configurations for inference indicated by the first reporting message are applicable; whether the K1 configurations for inference indicated by the first reporting message are applicable depends on one of the number of active serving cells of the second communication device 450 or the bandwidth corresponding to the active serving cells.

[0424] As one embodiment, the first communication device 410 includes: a memory storing a computer-readable instruction program, which generates actions when executed by at least one processor, the actions including: sending the first configuration message set in this application; and receiving the first reporting message in this application.

[0425] As an example, the first node in this application includes the second communication device 450.

[0426] As an example, the second node in this application includes the first communication device 410.

[0427] 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 set of configuration messages 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 set of configuration messages in this application.

[0428] As an example, at least one of {the antenna 452, the transmitter / receiver 454, the transmission processor 468, the multi-antenna transmission processor 457, the controller / processor 459, the memory 460, and the data source 467} is used to send the first reporting message in this application; at least one of {the antenna 420, the receiver 418, the receiving processor 470, the multi-antenna receiving processor 472, the controller / processor 475, and the memory 476} is used to receive the first reporting message in this application.

[0429] 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 signaling set 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 signaling set in this application.

[0430] As an example, 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 determine the expiration of a timer in the first timer set in this application.

[0431] Example 5

[0432] Example 5 illustrates a first flowchart of transmission between a first node and a second node according to an embodiment of this application, as shown in Figure 5. In Figure 5, the first node U1 and the second node N2 communicate via a wireless link, and the steps in block F51 are optional. 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.

[0433] For the first node U1, in step S510, a first configuration message set is received; in step S5110, a first signaling set is received; and in step S511, a first reporting message is sent.

[0434] For the second node N2, a first configuration message set is sent in step S520; a first signaling set is sent in step S5210; and a first reporting message is received in step S521.

[0435] In Embodiment 5, the first configuration message set configures K1 configurations for inference, where K1 is a positive integer; the first reporting message indicates whether the K1 configurations for inference are applicable; whether the K1 configurations for inference indicated by the first reporting message are applicable depends on one of the number of active serving cells of the first node U1 or the bandwidth corresponding to the active serving cells.

[0436] As an example, the first node U1 is the first node in this application.

[0437] As an example, the second node N2 is the second node in this application.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] As one example, the second node N2 and the first node U1 communicate via the Uu interface.

[0442] As one example, the second node N2 is the maintenance base station of the serving cell of the first node U1.

[0443] As one example, the second node N2 is a maintenance base station of the serving cell of the first node U1.

[0444] As an example, step S511 is after step S510; step S521 is after step S520.

[0445] As an example, the logical channel occupied by the first configuration message includes DCCH.

[0446] As an example, the first configuration message is carried by SRB1.

[0447] As an example, the first configuration message is carried by SRB3.

[0448] As an example, the transmission channel occupied by the first configuration message includes DL-SCH (DownLink-Shared Channel).

[0449] As an example, the physical layer channel occupied by the first configuration message includes PDSCH (Physical Downlink Shared Channel).

[0450] As an example, the transmission channel occupied by the first reporting message includes UL-SCH (UpLink-Shared Channel).

[0451] As an example, the physical layer channel occupied by the first reporting message includes PUSCH (Physical Uplink Shared Channel).

[0452] As an example, the physical layer channel occupied by the first reporting message includes PUCCH (Physical Uplink Control Channel).

[0453] As an embodiment, the steps in box 51 of Figure 5 exist, and the method applied to the first node in this application includes: receiving a first signaling set; the first signaling set is used to determine the active serving cell of the first node.

[0454] As an example, in response to receiving the first signaling set, the first node sends the first reporting message.

[0455] As an example, the first signaling set indicates that the number of active serving cells of the first node has changed.

[0456] As an example, the first signaling set implicitly indicates a change in the number of active serving cells of the first node by instructing the first node to activate the serving cells.

[0457] As an example, the first signaling set implicitly indicates a change in the number of active serving cells of the first node by instructing the first node to deactivate the serving cells.

[0458] As an example, in response to a change in the number of serving cells activated by the first node, the first node sends the first reporting message.

[0459] As an example, in response to a change in the serving cell activated by the first node, the first node sends the first reporting message.

[0460] As an example, if the activation status of the serving cell given by the first node changes, the first node sends the first reporting message.

[0461] As one embodiment, the first signaling set may include only one signaling, or the first signaling set may include multiple signaling.

[0462] As one embodiment, the first signaling set configures the serving cell of the first node.

[0463] As an example, the first signaling set configures the SCell of the first node.

[0464] As an example, the first signaling set instructs the configuration and activation of the SCell of the first node.

[0465] As an example, the first signaling set indicates the configuration and activation of the SCG of the first node.

[0466] As one embodiment, the first signaling set includes RRC messages.

[0467] As one embodiment, the first signaling set includes RRCReconfiguration messages.

[0468] As one embodiment, the first signaling set includes RRC messages, and the RRC messages include the scg-State field.

[0469] As one embodiment, the first signaling set includes the first configuration message set.

[0470] As one embodiment, the first configuration message set includes the first signaling set.

[0471] As one embodiment, the first signaling set includes RRC signaling.

[0472] As an example, the first signaling set includes an RRC IE.

[0473] As one embodiment, the first signaling set includes multiple RRC IEs.

[0474] As an example, the first signaling set includes a CellGroupConfig IE.

[0475] As one embodiment, the first signaling set includes two CellGroupConfig IEs, which are used to configure the MCG and SCG of the first node.

[0476] As an example, the first signaling set includes one or more RRC IEs, and one of the one or more RRC IEs includes an sCellToAddModList field.

[0477] As an example, the first signaling set includes one or more RRC IEs, and one of the one or more RRC IEs includes an sCellToReleaseList field.

[0478] As an example, the first signaling set includes one or more RRC IEs, and one of the one or more RRC IEs includes an sCellState field.

[0479] As one example, the first signaling set includes one or more SCellConfig IEs.

[0480] As one embodiment, the first signaling set indicates the serving cell activated by the first node.

[0481] As one embodiment, the first signaling set instructs the first node to deactivate the serving cell.

[0482] As an example, the first signaling set indicates the SCell activated by the first node.

[0483] As an example, the first signaling set instructs the first node to deactivate the SCell.

[0484] As an example, the first signaling set includes at least one MAC layer signaling.

[0485] As an example, the first signaling set includes at least one MAC CE.

[0486] As an example, the first signaling set includes SCell Activation / Deactivation MAC CE.

[0487] As an example, the first signaling set includes Enhanced SCell Activation / Deactivation MAC CE.

[0488] As an example, the first signaling set is used to determine the SCellIndex corresponding to the active serving cell of the first node.

[0489] As an example, the first signaling set is used to determine the number of active serving cells of the first node.

[0490] As an example, the first signaling set is jointly carried by RRC layer signaling and MAC layer signaling.

[0491] As an example, the logical channel occupied by the first signaling set includes DCCH.

[0492] As an example, the first signaling set is carried by SRB1.

[0493] As an example, the first signaling set is carried by SRB3.

[0494] As an example, the transmission channel occupied by the first signaling set includes DL-SCH.

[0495] As an example, the physical layer channels occupied by the first signaling set include PDSCH.

[0496] As an example, step S5110 is after step S510; step S5210 is after step S520.

[0497] As an example, step S5110 precedes step S511; step S5210 precedes step S521.

[0498] As an example, the step in box 51 of Figure 5 is not present.

[0499] As a sub-implementation of this embodiment, the first configuration message set includes the first signaling set.

[0500] As a sub-implementation of this embodiment, the first configuration message set is the first signaling set.

[0501] Example 6

[0502] Example 6 illustrates a second flowchart of transmission between a first node and a second node according to an embodiment of this application, as shown in Figure 6. In Figure 6, the first node U3 and the second node N4 communicate via a wireless link, and the steps in block F61 are optional. 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.

[0503] For the first node U3, in step S630, the first configuration message set is received; in step S6310, the timers in the first timer set are determined to have expired; and in step S631, the first reporting message is sent.

[0504] For the second node N4, a first configuration message set is sent in step S640; and a first reporting message is received in step S641.

[0505] In Example 6, the first configuration message set configures K1 configurations for inference, where K1 is a positive integer; the first reporting message indicates whether the K1 configurations for inference are applicable; whether the K1 configurations for inference indicated by the first reporting message are applicable depends on one of the number of active serving cells of the first node U3 or the bandwidth corresponding to the active serving cells.

[0506] As an example, the first node U3 is the first node in this application.

[0507] As an example, the second node N4 is the second node in this application.

[0508] As one embodiment, the air interface between the second node N4 and the first node U3 includes a wireless interface between the base station equipment and the user equipment.

[0509] As one embodiment, the air interface between the second node N4 and the first node U3 includes a wireless interface between the relay node device and the user equipment.

[0510] As one embodiment, the air interface between the second node N4 and the first node U3 includes a wireless interface between user equipment and user equipment.

[0511] As one example, the second node N4 and the first node U3 communicate via the Uu interface.

[0512] As one example, the second node N4 is the sustaining base station for the serving cell of the first node U3.

[0513] As one example, the second node N4 is a maintenance base station of the serving cell of the first node U3.

[0514] As an example, step S631 is after step S630; step S641 is after step S640.

[0515] As an example, the logical channel occupied by the first configuration message includes DCCH.

[0516] As an example, the first configuration message is carried by SRB1.

[0517] As an example, the first configuration message is carried by SRB3.

[0518] As an example, the transmission channel occupied by the first configuration message includes DL-SCH.

[0519] As an example, the physical layer channel occupied by the first configuration message includes PDSCH.

[0520] As an example, the transmission channel occupied by the first reporting message includes UL-SCH.

[0521] As an example, the physical layer channel occupied by the first reporting message includes PUSCH.

[0522] As an example, the physical layer channel occupied by the first reporting message includes PUCCH.

[0523] As an embodiment, the steps in box 61 of Figure 6 exist, and the method applied to the first node in this application includes: determining that timers in a first timer set have expired; the expired timers in the first timer set are used to determine the number of active serving cells of the first node.

[0524] As one embodiment, the first timer set may include only one timer, or the first timer set may include multiple timers.

[0525] As an example, the first timer set includes a timer that corresponds to the SCG of the first node U3.

[0526] As one embodiment, the first timer set includes multiple timers, each of which corresponds to a multiple serving cell.

[0527] As one embodiment, the first timer set includes multiple timers, each of which corresponds to a multiple SCell.

[0528] As one embodiment, the first timer set includes multiple timers, which are multiple sCellDeactivationTimer.

[0529] As an example, the first node U3 determines that the timers in the first timer set have expired.

[0530] As an example, the first node U3 determines that at least one timer in the first timer set has expired.

[0531] As an example, if at least one timer in the first timer set expires, the first node U3 sends the first reporting message.

[0532] As an example, if at least one timer in the first timer set expires, the first node U3 sends the first reporting message.

[0533] As an example, when a given timer in the first timer set expires, the first node U3 sends the first reporting message.

[0534] As an example, when a given timer in the first timer set expires, the first node U3 sends the first reporting message.

[0535] As an example, the expiration of the timers in the first timer set is used to determine the number of active serving cells of the first node.

[0536] As an example, the given timer is any timer in the first timer set, and the given timer corresponds to a serving cell of the first node U3. When the given timer expires, the serving cell corresponding to the given timer is deactivated.

[0537] As an example, the given timer is any timer in the first timer set, and the given timer corresponds to a cell group of the first node U3. When the given timer expires, the cell group corresponding to the given timer is deactivated.

[0538] As an example, step S6310 is performed after step S630.

[0539] As an example, step S6310 is performed before step S631.

[0540] Example 7

[0541] Example 7 illustrates a schematic diagram of a first reporting message indicating whether K1 configurations for inference are applicable, according to an embodiment of this application, as shown in Figure 7. In Figure 7, whether the K1 configurations for inference indicated by the first reporting message are applicable depends on the number of deactivated serving cells of the first node.

[0542] As an example, whether the K1 configurations indicated by the first reporting message are applicable for inference depends on the number of deactivated serving cells of the first node.

[0543] As an example, the number of inapplicable inference configurations among the K1 inference configurations indicated by the first reporting message depends on the number of deactivated serving cells of the first node.

[0544] As an example, the number of inapplicable inference configurations among the K1 inference configurations indicated by the first reporting message is equal to the number of deactivated serving cells of the first node.

[0545] As an example, the number of inapplicable inference configurations among the K1 inference configurations indicated by the first reporting message depends on the number of serving cells of the first node that are not configured to be directly activated.

[0546] As an example, the K1 configurations for inference correspond to the K1 serving cells of the first node, and a given serving cell among the K1 serving cells is deactivated, and the configuration for inference corresponding to the given serving cell is indicated as inapplicable.

[0547] As an example, the K1 configurations for inference correspond to the K1 serving cell groups of the first node, and a given serving cell group in the K1 serving cell groups is deactivated, and the configuration for inference corresponding to the given serving cell group is indicated as not applicable.

[0548] Example 8

[0549] Example 8 illustrates a schematic diagram of a first reporting message according to an embodiment of this application, as shown in Figure 8. In Figure 8, the first reporting message is used to determine at least one of the number of applicable configurations for inference and whether a given configuration for inference is applicable.

[0550] In Example 8, the given configuration for reasoning is one of the K1 configurations for reasoning.

[0551] As one embodiment, the first reporting message is used to determine at least one of the following:

[0552] - The number of applicable configurations for reasoning;

[0553] - Whether a configuration for reasoning is applicable, wherein the given configuration for reasoning is one of the K1 configurations for reasoning.

[0554] As an example, the first reporting message is used to determine the number of applicable configurations for inference.

[0555] As an example, the first reporting message indicates the number of applicable configurations for inference.

[0556] As an example, the first reporting message indicates the maximum number of applicable configurations for inference.

[0557] As an example, the first reporting message implicitly indicates the number of applicable configurations for inference by indicating whether the K1 configurations for inference are applicable.

[0558] As an example, the first reporting message is used to determine whether a given configuration for inference is applicable, wherein the given configuration for inference is one of the K1 configurations for inference.

[0559] As an example, the first reporting message indicates K2 inference configurations applicable to the K1 inference configurations, where K2 is a positive integer not greater than K1.

[0560] As an example, the first reporting message indicates whether each of the K1 configurations for inference is applicable.

[0561] As an example, the first reporting message indicates whether any of the K1 inference configurations is applicable.

[0562] As an example, the first reporting message is used to determine the number of applicable configurations for inference and whether a given configuration for inference is applicable, wherein the given configuration for inference is one of the K1 configurations for inference.

[0563] As an example, the given configuration for inference is one of the K1 configurations for inference for multi-cell inference.

[0564] As an example, the given configuration for reasoning is one of the K1 configurations for reasoning for SCell.

[0565] As an example, the given configuration for inference is one of the K1 configurations for inference, specifically the configuration for beam management on the secondary cell.

[0566] As an example, the given configuration for reasoning is one of the K1 configurations for reasoning, specifically the configuration for mobility management.

[0567] As an example, the given configuration for inference is one of the K1 configurations for inference for LTM.

[0568] Example 9

[0569] Example 9 illustrates a schematic diagram of the applicable number of inference-for-the-fact configurations indicated by a first reporting message according to an embodiment of this application, as shown in Figure 9. In Figure 9, the upper limit of the applicable number of inference-for-the-fact configurations indicated by the first reporting message depends on the number of active serving cells of the first node.

[0570] As an example, the upper limit of the number of applicable configurations for inference indicated by the first reporting message depends on the number of active serving cells of the first node.

[0571] As an example, the maximum number of applicable configurations for inference indicated by the first reporting message.

[0572] As an example, the first reporting message indicates the maximum number of configurations applicable to the first node for inference.

[0573] As an example, the upper limit of the number of applicable configurations for inference indicated by the first reporting message depends on the number of active serving cells of the first node and the capability of the first node.

[0574] As an example, the upper limit of the number of applicable configurations for inference indicated by the first reporting message is indicated by UAI.

[0575] As an example, the upper limit of the number of applicable configurations for inference indicated by the first reporting message depends on the number of serving cells configured for the first node.

[0576] As an example, the upper limit of the number of applicable configurations for inference indicated by the first reporting message depends on the number of configured SCells of the first node.

[0577] As an example, the upper limit of the number of applicable configurations for inference indicated by the first reporting message is no greater than the number of active serving cells of the first node.

[0578] As a sub-example of this embodiment, the configuration for inference is configured per cell.

[0579] As a sub-example of this embodiment, the configuration for inference is configured per cell group.

[0580] As a sub-example of this embodiment, the upper limit of the number of applicable configurations for inference indicated by the first reporting message is equal to the number of active serving cells of the first node.

[0581] As an example, the upper limit of the number of applicable configurations for inference indicated by the first reporting message is no greater than the product of the number of active serving cells of the first node and the first factor.

[0582] As a sub-implementation of this embodiment, the upper limit of the number of applicable configurations for inference indicated by the first reporting message is equal to the product of the number of active serving cells of the first node and a first factor, rounded down, where the first factor is a positive real number not greater than 1.

[0583] As a sub-implementation of this embodiment, the first factor is predefined or the first factor is fixed.

[0584] As a sub-implementation of this embodiment, the first factor is configured via RRC signaling.

[0585] As a sub-implementation of this embodiment, the first factor is a positive integer greater than 1.

[0586] Example 10

[0587] Example 10 illustrates a schematic diagram of a first inference configuration according to an embodiment of this application, as shown in Figure 10. In Figure 10, the first inference configuration is one of the K1 configurations for inference, and the first inference configuration is a multi-cell configuration, wherein the multi-cell configuration is respectively represented as serving cell #1, ..., serving cell #m, ... serving cell #M, where M is a positive integer greater than 1, and m is a positive integer not greater than M.

[0588] In Example 10, whether the first inference configuration indicated by the first reporting message is applicable depends on one of the number of active serving cells of the first node or the bandwidth corresponding to the active serving cells.

[0589] As an example, the first inference configuration is one of the K1 configurations for inference, the first inference configuration is for multiple cells, and whether the first inference configuration indicated by the first reporting message is applicable depends on one of the number of active serving cells of the first node or the frequency bandwidth corresponding to the active serving cells.

[0590] As an example, the term "multiple cells" refers to multiple serving cells.

[0591] As an example, the term "multiple cells" refers to multiple SCells.

[0592] As an example, the first inference is configured for multiple cells.

[0593] As an example, the first inference configuration for multiple cells means that the higher-level parameters included in the first inference configuration are shared by the multiple cells.

[0594] As an example, the first inference configuration for multiple cells means that the first inference configuration includes higher-level parameters of the multiple cells.

[0595] As an example, the first inference configuration for multiple cells means that the first inference configuration is associated with at least two cells.

[0596] As an example, the meaning of "the first inference configuration for multiple cells" includes: the first inference configuration is an inference configuration for multi-cell joint processing.

[0597] As an example, the meaning of "the first inference configuration for multiple cells" includes: the first inference configuration is an inference configuration for joint scheduling of multiple cells.

[0598] As an example, the meaning of "the first inference configuration for multiple cells" includes: the first inference configuration is an inference configuration for joint optimization of multiple cells.

[0599] As an example, the first inference configuration for multiple cells means that the first inference configuration is an inference configuration for AI / ML-based mobility management.

[0600] As an example, the first inference configuration for multiple cells means that the first inference configuration is for AI / ML-based LTM (L1 / L2 Triggered Mobility).

[0601] As an example, the first inference configuration for multiple cells means that the first inference configuration is an inference configuration for AI / ML-based positioning.

[0602] As an example, the first inference configuration for multiple cells means that the AI / ML model or AI entity configured in the first inference configuration is cross-cell.

[0603] As an example, whether the first inference configuration indicated by the first reporting message is applicable depends on the number of active serving cells of the first node.

[0604] As a sub-implementation of this embodiment, when the number of active serving cells of the first node is greater than a first threshold, the first inference configuration indicated by the first reporting message is not applicable; when the number of active serving cells of the first node is not greater than the first threshold, the first inference configuration indicated by the first reporting message is applicable; the first threshold is a positive integer; the first threshold is predetermined, or the first threshold is fixed, or the first threshold is configured through RRC signaling.

[0605] As an example, whether the first inference configuration indicated by the first reporting message is applicable depends on the frequency bandwidth corresponding to the active serving cell of the first node.

[0606] As a sub-implementation of this embodiment, when the bandwidth corresponding to the active serving cell of the first node is greater than the second threshold, the first inference configuration indicated by the first reporting message is not applicable; when the bandwidth corresponding to the active serving cell of the first node is not greater than the second threshold, the first inference configuration indicated by the first reporting message is applicable; the second threshold is a positive integer; the second threshold is predetermined, or the second threshold is fixed, or the second threshold is configured through RRC signaling.

[0607] As an example, whether the first inference configuration indicated by the first reporting message is applicable depends on the serving cell activated by the first node in the multi-cell network.

[0608] As a sub-example of this embodiment, when at least one cell in the multiple cells does not belong to the active serving cell of the first node, the first inference configuration indicated by the first reporting message is not applicable; when all cells in the multiple cells belong to the active serving cell of the first node, the first inference configuration indicated by the first reporting message is applicable.

[0609] As a sub-implementation of this embodiment, when the number of active serving cells that do not belong to the first node in the multi-cell network is greater than a third threshold, the first inference configuration indicated by the first reporting message is not applicable; when the number of active serving cells that do not belong to the first node in the multi-cell network is not greater than the third threshold, the first inference configuration indicated by the first reporting message is applicable; the third threshold is a positive integer greater than 1; the third threshold is predetermined, or the third threshold is fixed, or the third threshold is configured through RRC signaling.

[0610] As an example, whether the first inference configuration indicated by the first reporting message is applicable depends on the frequency bandwidth corresponding to the active serving cell of the first node.

[0611] As a sub-implementation of this embodiment, when the bandwidth corresponding to at least one cell in the multi-cell set is greater than the fourth threshold, the first inference configuration indicated by the first reporting message is not applicable; when the bandwidth corresponding to any cell in the multi-cell set is not greater than the fourth threshold, the first inference configuration indicated by the first reporting message is applicable; the fourth threshold is a positive integer; the fourth threshold is predetermined, or the fourth threshold is fixed, or the fourth threshold is configured through RRC signaling.

[0612] As a supplementary embodiment of this example, any one of the multiple cells is the serving cell activated by the first node.

[0613] As a supplementary embodiment of this example, at least one of the multiple cells is the serving cell activated by the first node.

[0614] As a supplementary embodiment of this example, the at least one cell is the serving cell activated by the first node.

[0615] Example 11

[0616] Example 11 illustrates a schematic diagram of RAN domain AI / ML function deployment according to one embodiment of this application, as shown in Figure 11. In Figure 11, the gNB can be replaced with, for example, an eNB, or a network device such as a 6G base station.

[0617] In Example 11, the management of ML inference functions of multiple base stations is completed by the RAN domain management function 1102, that is, data interaction with the RAN domain MnS (Management Service) consumer / cross-domain management 1101 (as shown by the dashed arrow in Figure 11). The RAN domain ML training function 1103 is located in the RAN domain management function 1102; while the ML inference function is located in the base station, that is, the AI / ML inference function 1104 is located in gNB 1105, the AI / ML inference function 1106 is located in gNB 1107, and so on.

[0618] 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.

[0619] 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).

[0620] 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.

[0621] Similarly, ML testing capabilities can also be deployed in cross-domain management systems or domain-specific management systems.

[0622] 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 1101.

[0623] It should be noted that Example 11 is merely a non-limiting implementation; optionally, the ML training function of the RAN domain may also be deployed at the base station; or optionally, some base stations may deploy both the ML inference function and the ML training function of the RAN domain, while some base stations may only deploy the ML inference function.

[0624] As an example, one of the gNBs (or base stations) in Example 11 is the second node of this application.

[0625] Example 12

[0626] Example 12 illustrates a schematic diagram of the deployment of AI / ML functions in a UE according to one embodiment of this application, as shown in Figure 12. In Figure 12, the RAN domain ML training function 1204 is optional.

[0627] UE function 1203 is deployed in the first node of this application, and the UE function 1203 includes AI / ML inference function 1205; the AI / ML inference function 1205 uses an ML model (also called an AI model) for inference; an ML model is typically trained before being used for AI / ML inference.

[0628] As an example, the UE function 1203 includes a RAN domain ML training function 1204, 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.

[0629] 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.

[0630] Optionally, the UE function 1203 also includes a CN domain ML training function (not shown in Figure 12).

[0631] Optionally, the UE function 1203 also includes an AI / ML deployment function—not shown in Figure 12—for loading ML models and data.

[0632] 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.

[0633] As an example, the first node instructs the AI / ML inference function 1205 for the applicable configuration for inference via UAI.

[0634] As an example, the ML model and the associated metadata are loaded by the first node from a network device or a remote server.

[0635] Optionally, the UE function 1203 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 1201 for management or analysis (as shown by the double arrow 1202).

[0636] Optionally, the UE function 1203 is an MnS consumer that loads data from the CN domain MnF and / or RAN domain MnF and / or cross-domain management system 1201 for AI / ML-related management, such as managing data requests, ML model activation, and / or ML training (as shown by double arrow 1202).

[0637] As an example, the ML model is based on NN (Neural Networks).

[0638] As an example, the ML model is based on ANN (Artificial Neural Networks).

[0639] As an example, the ML model is based on CNN (Convolutional Neural Networks).

[0640] As an example, the ML model is based on the LLM (Large Language Model) architecture.

[0641] As an example, the ML model is based on the Transformer architecture.

[0642] As an example, the ML model is based on the GPT (Generative Pre-Trained) architecture.

[0643] As an example, the ML model is based on LSTM (Long Short-Term Memory network).

[0644] As an example, the ML model is based on MLP (MultiLayer Perceptron).

[0645] As an example, the ML model is based on GAN (Generative Adversarial Nets).

[0646] As an example, the ML model is based on a lightweight neural network.

[0647] As a sub-example of this embodiment, the lightweight neural network includes one or more of MobileNet, ShuffleNet, and SqueezeNet.

[0648] Example 13

[0649] Example 13 illustrates a schematic diagram of a processing system based on artificial intelligence or machine learning according to an embodiment of this application, as shown in Figure 13. In Figure 13, the processing system based on artificial intelligence or machine learning includes a first processor, a second processor, a third processor, and a fourth processor.

[0650] In Example 13, 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 sending the first-class output to the fourth processor. In Figure 13, the first-class feedback and the second-class feedback are optional; the second processor includes ML training functionality; the third processor includes ML inference functionality.

[0651] As one embodiment, the fourth processor includes ML testing functionality.

[0652] As one embodiment, the fourth processor includes performance monitoring / evaluation of the ML model.

[0653] 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.

[0654] 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.

[0655] As one embodiment, the first processor generates the first dataset and the second dataset based on the measurement of the reference signal.

[0656] As one embodiment, the third processor belongs to the first node, and the fourth processor belongs to the second node.

[0657] As an example, the third processor belongs to the first node.

[0658] As an example, the first dataset includes training data.

[0659] 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.

[0660] As an example, the second processor belongs to the first node; the above method avoids passing the first dataset to the second node.

[0661] As an example, the second processor belongs to the second node in this application; the above method supports joint training and optimizes system performance.

[0662] As an example, the second processor belongs to the core network; the above method supports network-wide joint training, further optimizing system performance.

[0663] As an example, the second dataset includes inference data.

[0664] 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.

[0665] 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.

[0666] 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.

[0667] 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.

[0668] 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.

[0669] 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.

[0670] Example 14

[0671] Example 14 illustrates a schematic diagram based on artificial intelligence or machine learning according to an embodiment of this application, as shown in Figure 14. In Figure 14, the first and second operations belong to a first stage, the third operation belongs to a second stage, the fourth operation belongs to a third stage, and the fifth operation belongs to a fourth stage; the arrowed lines indicate the sequence of processes.

[0672] 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.

[0673] As an example, 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.

[0674] As an example, the first stage includes AI / ML model training.

[0675] As an example, the first stage includes AI / ML model training and AI / ML testing.

[0676] As an example, the AI / ML model training includes initial training and re-training of one or a group of AI / ML entities.

[0677] As an example, the training of the AI / ML model depends on training data.

[0678] As an example, the AI / ML model training includes AI / ML entity validation.

[0679] As an example, the AI / ML entity verification is used to evaluate the performance of the AI / ML entity.

[0680] As an example, the AI / ML entity verification relies on verification data.

[0681] As an example, if the AI / ML entity verification results do not meet expectations, the AI / ML model will be retrained.

[0682] As an example, the AI / ML testing includes testing the validated AI / ML entities to estimate the performance of the trained AI / ML model.

[0683] 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.

[0684] As an example, the AI / ML test relies on test data.

[0685] As one embodiment, the second stage includes AI / ML simulation, which performs AI / ML entity reasoning in a simulation environment.

[0686] As an example, the AI / ML simulation estimates the performance of AI / ML entity reasoning in a simulation environment before using AI / ML entities.

[0687] As one embodiment, the second stage is optional.

[0688] 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.

[0689] As an example, the third stage is optional.

[0690] As an example, the third stage is no longer needed when the training and inference functions are co-located.

[0691] As an example, the fourth stage includes AI / ML inference.

[0692] Example 15

[0693] Example 15 illustrates a structural block diagram of a processing apparatus for a first node according to an embodiment of the present application, as shown in Figure 15. In Figure 15, the processing apparatus 1500 in the first node includes a first receiver 1501 and a first transmitter 1502.

[0694] In embodiment 15, the first receiver 1501 receives a first configuration message set, which configures K1 configurations for inference, where K1 is a positive integer; the first transmitter 1502 sends a first reporting message, which indicates whether the K1 configurations for inference are applicable.

[0695] In Example 15, whether the K1 configurations for inference indicated by the first reporting message are applicable depends on one of the number of active serving cells of the first node or the bandwidth corresponding to the active serving cells.

[0696] As an example, the upper limit of the number of applicable configurations for inference indicated by the first reporting message depends on the number of active serving cells of the first node.

[0697] As an example, the first inference configuration is one of the K1 configurations for inference, the first inference configuration is for multiple cells, and whether the first inference configuration indicated by the first reporting message is applicable depends on one of the number of active serving cells of the first node or the frequency bandwidth corresponding to the active serving cells.

[0698] As an example, whether the K1 configurations indicated by the first reporting message are applicable for inference depends on the number of deactivated serving cells of the first node.

[0699] As one embodiment, the first reporting message is used to determine at least one of the following:

[0700] - The number of applicable configurations for reasoning;

[0701] - Whether a configuration for reasoning is applicable, wherein the given configuration for reasoning is one of the K1 configurations for reasoning.

[0702] As one embodiment, the first receiver 1501 receives a first signaling set; the first signaling set is used to determine the active serving cell of the first node.

[0703] As an example, the first receiver 1501 determines that a timer in the first timer set has expired; the expired timer in the first timer set is used to determine the number of active serving cells of the first node.

[0704] As an example, the K1 configurations for inference are K1 CSI-ReportConfigs.

[0705] As one embodiment, the serving cell is all the serving cells configured for the first node, or the serving cell is all the serving cells in a cell group configured for the first node.

[0706] As an example, the cell group includes cells that support AI / ML.

[0707] As an example, the cell group is a group of cells configured to support AI / ML.

[0708] As an example, the cell group is a set of cells consisting of all SCells.

[0709] As an example, the cell group is a set of cells associated with the same TAG.

[0710] As an example, the cell group is a collection of cells associated with the same AI entity.

[0711] As an example, the cell group is a set of cells that are configured to be scheduled simultaneously.

[0712] As an example, the number of activated serving cells is used to determine the number of applicable configurations for inference indicated by the first reporting message.

[0713] As an example, the first node 1500 is a user equipment.

[0714] As an example, the first node 1500 is a terminal.

[0715] As an example, the first node 1500 is a relay node device.

[0716] As an example, the first receiver 1501 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.

[0717] As an example, the first transmitter 1502 includes at least one of the following in embodiment 4: the antenna 452, the transmitter 454, the transmission processor 468, the multi-antenna transmission processor 457, the controller / processor 459, the memory 460, and the data source 467.

[0718] Example 16

[0719] Example 16 illustrates a structural block diagram of a processing apparatus for a second node according to an embodiment of the present application, as shown in Figure 16. In Figure 16, the processing apparatus 1600 in the second node includes a second transmitter 1601 and a second receiver 1602.

[0720] In embodiment 16, the second transmitter 1601 sends a first configuration message set, which configures K1 configurations for inference, where K1 is a positive integer; the second receiver 1602 receives a first reporting message, which indicates whether the K1 configurations for inference are applicable.

[0721] In Embodiment 16, whether the K1 configurations indicated by the first reporting message are applicable depends on one of the number of active serving cells or the bandwidth corresponding to the active serving cells, as determined by the sender of the first reporting message.

[0722] As one embodiment, the upper limit of the number of applicable configurations for inference indicated by the first reporting message depends on the number of active serving cells of the sender of the first reporting message.

[0723] As an example, the first inference configuration is one of the K1 configurations for inference, the first inference configuration is for multiple cells, and whether the first inference configuration indicated by the first reporting message is applicable depends on one of the number of active serving cells or the bandwidth corresponding to the active serving cells of the sender of the first reporting message.

[0724] As an example, whether the K1 configurations indicated by the first reporting message are applicable for inference depends on the number of deactivated serving cells of the sender of the first reporting message.

[0725] As one embodiment, the first reporting message is used to determine at least one of the following:

[0726] - The number of applicable configurations for reasoning;

[0727] - Whether a configuration for reasoning is applicable, wherein the given configuration for reasoning is one of the K1 configurations for reasoning.

[0728] As one embodiment, the second transmitter 1601 sends a first signaling set; the first signaling set is used to determine the active serving cell of the sender of the first reporting message.

[0729] As one embodiment, the sender of the first reporting message determines that a timer in the first timer set has expired; the timer expiration in the first timer set is used to determine the number of serving cells activated by the sender of the first reporting message.

[0730] As an example, the K1 configurations for inference are K1 CSI-ReportConfigs.

[0731] As one embodiment, the serving cell is all the serving cells configured by the sender of the first reporting message, or the serving cell is all the serving cells in a cell group configured by the sender of the first reporting message.

[0732] As an example, the cell group is a set of cells consisting of all SCells.

[0733] As an example, the cell group is a set of cells associated with the same TAG.

[0734] As an example, the cell group is a collection of cells associated with the same AI entity.

[0735] As an example, the cell group is a set of cells that are configured to be scheduled simultaneously.

[0736] As an example, the number of activated serving cells is used to determine the number of applicable configurations for inference indicated by the first reporting message.

[0737] As one example, the second node 1600 is a base station device.

[0738] As one example, the second node 1600 is a user equipment.

[0739] As an example, the second node 1600 is a TRP.

[0740] As an example, the second transmitter 1601 includes at least one of the following in embodiment 4: the antenna 420, the transmitter 416, the transmission processor 416, the multi-antenna transmission processor 471, the controller / processor 475, and the memory 476.

[0741] As one embodiment, the second receiver 1602 includes at least one of the following in embodiment 4: the antenna 420, the receiver 416, the receiver processor 470, the multi-antenna receiver processor 472, the controller / processor 475, and the memory 476.

[0742] 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 station or system equipment in this application includes, but is 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.

[0743] 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 set of configuration messages, which configures K1 configurations for inference, where K1 is a positive integer; Send a first reporting message, the first reporting message indicating whether the K1 configurations for inference are applicable; The applicability of the K1 configurations for inference indicated by the first reporting message depends on one of the number of active serving cells of the terminal or the bandwidth corresponding to the active serving cells.

2. The method in the terminal according to claim 1, characterized in that, The upper limit of the number of applicable configurations for inference indicated by the first reporting message depends on the number of active serving cells of the terminal.

3. The method in the terminal according to claim 1, characterized in that, The first inference configuration is one of the K1 configurations for inference. The first inference configuration is for multiple cells. Whether the first inference configuration indicated by the first reporting message is applicable depends on one of the number of active serving cells of the terminal or the bandwidth corresponding to the active serving cells.

4. The method in the terminal according to claim 1, characterized in that, Whether the K1 configurations indicated by the first reporting message are applicable for inference depends on the number of deactivated serving cells of the terminal.

5. The method in the terminal according to any one of claims 1 to 4, characterized in that, The first reporting message is used to determine at least one of the following: - The number of applicable configurations for reasoning; - Whether a configuration for reasoning is applicable, wherein the given configuration for reasoning is one of the K1 configurations for reasoning.

6. The method in the terminal according to any one of claims 1 to 5, characterized in that, include: Receive the first signaling set; The first signaling set is used to determine the active serving cell of the terminal.

7. The method in the terminal according to any one of claims 1 to 5, characterized in that, include: Determine if the timers in the first timer set have expired; The timer expiration in the first timer set is used to determine the number of active serving cells of the terminal.

8. The method in the terminal according to any one of claims 1 to 7, characterized in that, The K1 configurations for inference are K1 CSI-ReportConfigs.

9. The method in the terminal according to any one of claims 1 to 8, characterized in that, The serving cell is all the serving cells configured for the terminal, or the serving cell is all the serving cells in a cell group configured for the terminal.

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 set of configuration messages, which configures K1 configurations for inference, where K1 is a positive integer; Receive a first reporting message, which indicates whether the K1 configurations for inference are applicable; Whether the K1 configurations indicated by the first reporting message are applicable depends on one of the number of active serving cells or the bandwidth corresponding to the active serving cells, as determined by the sender of the first reporting message.

12. The method in a base station according to claim 11, characterized in that, The upper limit of the number of applicable configurations for inference indicated by the first reporting message depends on the number of active serving cells of the sender of the first reporting message.

13. The method in a base station according to claim 11, characterized in that, The first inference configuration is one of the K1 configurations for inference. The first inference configuration is for multiple cells. Whether the first inference configuration indicated by the first reporting message is applicable depends on one of the number of active serving cells or the bandwidth corresponding to the active serving cells of the sender of the first reporting message.

14. The method in a base station according to claim 11, characterized in that, Whether the K1 configurations indicated by the first reporting message are applicable for inference depends on the number of deactivated serving cells of the sender of the first reporting message.

15. The method in a base station according to any one of claims 11 to 14, characterized in that, The first reporting message is used to determine at least one of the following: - The number of applicable configurations for reasoning; - Whether a configuration for reasoning is applicable, wherein the given configuration for reasoning is one of the K1 configurations for reasoning.

16. The method in a base station according to any one of claims 11 to 15, characterized in that, include: Send the first signaling set; The first signaling set is used to determine the active serving cell of the sender of the first reporting message.

17. The method in a base station according to any one of claims 11 to 15, characterized in that, The sender of the first reporting message determines that a timer in the first timer set has expired; the timer expiration in the first timer set is used to determine the number of active serving cells of the sender of the first reporting message.

18. The method in a base station according to any one of claims 11 to 17, characterized in that, The K1 configurations for inference are K1 CSI-ReportConfigs.

19. The method in a base station according to any one of claims 11 to 18, characterized in that, The serving cells are all the serving cells configured by the sender of the first reporting message, or the serving cells are all the serving cells in a cell group configured by the sender of the first reporting message.

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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