Communication method, communication apparatus, system, and storage medium

Abstract

Disclosed in embodiments of the present application are a communication method, a communication apparatus, a system, and a storage medium, applied to the technical field of wireless communications. In the embodiments of the present application, first indication information indicates that a first network device supports AI function configuration, so that a first terminal device can enter, by sending first request information, an RRC connected state when there is no data transmission with the first network device. Because the first indication information is used for indicating that the first network device supports the AI function configuration, which indicates that the first network device can provide the AI function configuration for data collection of the first terminal device, the first terminal device may send the first request information to request to establish an RRC connection, so that the first terminal device can establish the RRC connection to the first network device when there is no data transmission, thereby implementing data collection.

Description

Communication methods, communication devices, systems and storage media

[0001] This application claims priority to Chinese Patent Application No. CN202411982104.7, filed on December 27, 2024, entitled "Communication Method, Communication Device, System and Storage Medium", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of wireless communication technology, and in particular to a communication method, communication device, system and storage medium. Background Technology

[0003] Currently, artificial intelligence (AI) is being applied to communication systems to improve communication performance. Machine learning (ML) refers to extracting identifiable features from a series of raw data and then learning from these features to ultimately generate a model. AI / ML technologies can be applied to new radio (NR) to improve network performance and user experience through intelligent data collection and analysis.

[0004] Specifically, when a terminal device is in a connected state, it will receive requests for data collection. A terminal device in an idle or inactive state may move from the coverage area of ​​a traditional base station to the coverage area of ​​a base station that supports AI functions. However, a terminal device in an idle or inactive state will not establish a radio resource control (RRC) connection with the base station and therefore cannot collect data. Summary of the Invention

[0005] This application provides a communication method, communication device, system, and storage medium, which instructs a first network device to support AI function configuration through a first instruction information, enabling a first terminal device to enter the RRC connection state by sending a first request information, even when there is no data transmission with the first network device, thereby collecting data.

[0006] The first aspect of this application provides a communication method. Optionally, the executing entity of this method may be a first device, which may be a terminal device, a component or device applied to the terminal device (e.g., a processor, circuit, chip, or chip system), or a logic module or software capable of implementing all or part of the functions of the terminal device. Taking the first device as a first terminal device as an example, in this method, the first terminal device receives first indication information, which is used to indicate that the first network device supports AI function configuration; the first terminal device sends first request information, which is used by the first terminal device to request the establishment of a Radio Resource Control (RRC) connection. The first request information includes a request reason, which is the reason why the first terminal device requests to establish an RRC connection, and the request reason is used to indicate that the first terminal device is a terminal device used for data collection.

[0007] Based on the first aspect of this application, since the first instruction information is used to instruct the first network device to support AI function configuration, it indicates that the first network device can provide AI function configuration for the data collection of the first terminal device. Therefore, the first terminal device can send a first request information to request the establishment of an RRC connection, so that the first terminal device can establish an RRC connection with the first network device without data transmission, thereby realizing data collection.

[0008] In some possible implementations, the first indication information is also used to indicate a scenario in which the first network device supports AI function configuration, including one or more of beam management scenarios, mobility scenarios, or channel state information (CSI) prediction scenarios.

[0009] In this embodiment of the application, by instructing the first network device to support the configuration of AI functions, the first terminal device can determine whether the AI ​​model or function that needs to be collected is the model or function in the corresponding scenario, so that the first terminal device can establish an RRC connection with the first network device according to the specific needs of data collection.

[0010] In some possible implementations, the first terminal device may also send a second request message, which is used by the first terminal device to request data collection. The first and second request messages are carried in the same message.

[0011] In some possible implementations, the second request information includes preset conditions that instruct the first terminal device to stop data collection.

[0012] In this embodiment of the application, by carrying preset conditions in the second request information, the first network device can instruct the first terminal device to stop data collection and clear the configuration related to data collection when the preset conditions are met, thereby enabling the first terminal device and the first network device to align the configuration related to data collection.

[0013] In some possible implementations, the first terminal device may also receive second indication information, which includes at least one identifier. The at least one identifier is used to indicate at least one configuration condition, and the at least one identifier is used by the first terminal device to determine whether there is a function or model that meets at least one configuration condition.

[0014] In this embodiment of the application, by receiving the second instruction information, the first terminal device can determine whether the first network device can provide the corresponding configuration for the AI ​​model or function that the first terminal device needs to collect data.

[0015] In some possible implementations, the first indication information includes at least one identifier, which is used to indicate at least one configuration condition. The first terminal device is used to collect data for a first function or a first model. If the first function or the first model satisfies any one of the at least one configuration condition, the first terminal device sends a first request information.

[0016] In this embodiment of the application, by carrying an identifier in the first instruction information, the first terminal device can send the first request information when it determines that the first network device can provide AI configuration for the first model or the first function. This avoids the problem of the first terminal device sending invalid request information due to the lack of relevant configuration by the first network device, and reduces the waste of signaling resources.

[0017] In some possible implementations, the first terminal device may also send third indication information, which includes at least one first identifier among at least one identifier. The third indication information is used to indicate that the data collected by the first terminal device is applied to a second function or a second model, and the second function or the second model satisfies the configuration conditions corresponding to the first identifier.

[0018] In this embodiment of the application, by carrying a first identifier in the third information, the first terminal device can indicate to the first network device which set of configuration parameters is available, so that the first network device can issue an AI model or function to activate the corresponding configuration parameters according to the first identifier.

[0019] In some possible implementations, the first terminal device may also send a fourth instruction message, which is used to instruct the first terminal device to stop data collection.

[0020] In this embodiment of the application, the first terminal device can actively report the fourth instruction information, thereby instructing the first network device to clear the configuration related to data collection of the first terminal device, so that the first terminal device and the first network device can synchronize the configuration regarding data collection.

[0021] In some possible implementations, the first terminal device may also send a third request message, which is used to request the cessation of data collection.

[0022] In this embodiment of the application, the first terminal device can send a third request message to request the cessation of data collection. Therefore, the first network device can clear the configuration related to data collection based on the third request message and then instruct the first terminal device to stop data collection.

[0023] In some possible implementations, the first terminal device may receive a fifth instruction message, which is used to instruct the first terminal device to stop data collection.

[0024] In this embodiment of the application, the first terminal device stops data collection by receiving the fifth instruction information, thereby enabling the first terminal device and the first network device to synchronize their configurations regarding data collection.

[0025] In some possible implementations, the first terminal device may also send a stop reason, which is the reason why the first terminal device stops data collection.

[0026] In this embodiment of the application, the first terminal device sends a stop reason, enabling the first network device to send a fifth instruction message to the first terminal device based on the stop reason, indicating that data collection should be stopped.

[0027] A second aspect of this application provides a communication method. Optionally, the execution subject of this method may be a second device, which may be a network device, a component or device applied to the network device (e.g., a processor, circuit, chip, or chip system), or a logic module or software capable of implementing all or part of the functions of the network device (e.g., a central unit (CU), a distributed unit (DU), or a radio unit (RU)). Taking a first network device as an example, the first network device sends first indication information, which is used to indicate that the first network device supports AI function configuration; the first network device receives first request information, which is used by a first terminal device to request the establishment of an RRC connection. The first request information includes a request reason, which is the reason why the first terminal device requests the establishment of an RRC connection, and the request reason is used to indicate that the first terminal device is a terminal device used for data collection.

[0028] In some possible implementations, the first indication information is also used to indicate a scenario in which the first network device supports AI function configuration, including one or more of beam management scenarios, mobility scenarios, or channel state information (CSI) prediction scenarios.

[0029] In some possible implementations, the first network device may also send a second request message, which instructs the first terminal device to request data collection.

[0030] In some possible implementations, the second request information includes preset conditions that instruct the first terminal device to stop data collection.

[0031] In some possible implementations, the first network device may also receive second indication information, which includes at least one identifier for indicating at least one configuration condition, and the first terminal device may use the at least one identifier to determine whether there is a function or model that satisfies at least one configuration condition.

[0032] In some possible implementations, the first indication information includes at least one identifier, the at least one identifier is used to indicate at least one configuration condition, the first terminal device is used to collect data for the first function or the first model, and the first request information is used to indicate that the first function or the first model meets any one of the at least one configuration condition.

[0033] In some possible implementations, the first network device may also send third indication information, which includes at least one first identifier among at least one identifier. The third indication information is used to indicate that the data collected by the first terminal device is applied to a second function or a second model, and the second function or the second model satisfies the configuration conditions corresponding to the first identifier.

[0034] In some possible implementations, the first network device may also send a fourth instruction message, which instructs the first terminal device to stop data collection.

[0035] In some possible implementations, the first network device may also send a third request message to request a halt to data collection.

[0036] In some possible implementations, the first network device may also receive a fifth instruction message, which is used to instruct the first terminal device to stop data collection.

[0037] In some possible implementations, the first network device may also send a stop reason, which is the reason why the first terminal device stops collecting data.

[0038] In some possible implementations, the first network device may also send first configuration information, which is used to instruct the first terminal device to configure data collection.

[0039] In some possible implementations, the first configuration information includes measurement resources for beam management data collection, data collection duration, data collection volume, reference signal configuration for data collection, and measurement results of the data collection.

[0040] A third aspect of this application provides a communication device, which may be the first device described above. The communication device includes modules or units for performing the methods described in the first aspect and any possible implementation thereof.

[0041] A fourth aspect of this application provides a communication device, which may be the second device described above. The communication device includes modules or units for performing the methods described in the second aspect and any possible implementation thereof.

[0042] A fifth aspect of this application provides a communication device, which may be a first device or a second device, or a component applied to the first device or the second device (e.g., a processor, circuit, chip, or chip system), or a logic module or software (e.g., CU, DU, or RU) capable of implementing all or part of the functions of the first device or the second device. The communication device includes:

[0043] A processor for executing a program that causes the communication device to perform the method as described in the first or second aspect and any possible implementation thereof.

[0044] Optionally, the communication device further includes a memory, and the processor is coupled to the memory; the memory is used to store programs.

[0045] The sixth aspect of this application provides a chip or chip system including at least one processor and a communication interface, the communication interface and at least one processor being interconnected via a line, the at least one processor being used to run computer programs or instructions to perform the communication method described in any of the possible implementations of the first or second aspect.

[0046] The communication interface in the chip can be an input / output interface, pins, or circuits.

[0047] In one possible implementation, the chip or chip system described above in this application further includes at least one memory storing instructions. The memory can be an internal storage unit of the chip, such as a register or cache, or it can be a storage unit of the chip itself, such as a read-only memory or random access memory.

[0048] The seventh aspect of this application provides a communication system, including a communication device that performs the first aspect and any possible implementation thereof, and a communication device that performs the second aspect and any possible implementation thereof.

[0049] An eighth aspect of this application provides a computer-readable storage medium including instructions that, when executed on a computer, cause the computer to perform the method described in the first aspect above, or cause the computer to perform the method described in the second aspect above.

[0050] The ninth aspect of this application provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the method described in the first aspect above, or cause the computer to perform the method described in the second aspect above. Attached Figure Description

[0051] Figures 1a to 1f are schematic diagrams of the communication system provided in the embodiments of this application;

[0052] Figures 2a to 2g are schematic diagrams of the AI ​​processing procedures involved in the embodiments of this application;

[0053] Figure 3 is a schematic diagram of the terminal establishing an RRC connection in an embodiment of this application;

[0054] Figure 4 is a schematic diagram of the terminal restoring the RRC connection in an embodiment of this application;

[0055] Figure 5 shows the application framework of AI / ML in NR in the embodiments of this application;

[0056] Figures 6 to 9 are schematic diagrams of the communication methods in the embodiments of this application;

[0057] Figures 10 to 12 are schematic diagrams of the communication device in the embodiments of this application;

[0058] Figures 13 and 14 are schematic diagrams of the O-RAN architecture in the embodiments of this application. Detailed Implementation

[0059] First, some terms used in the embodiments of this application will be explained to facilitate understanding by those skilled in the art.

[0060] (1) Terminal device: can be a wireless terminal device that can receive network device scheduling and instruction information. The wireless terminal device can be a device that provides voice and / or data connectivity to the user, or a handheld device with wireless connection function, or other processing device connected to a wireless modem.

[0061] Terminal devices can communicate with one or more core networks or the Internet via a radio access network (RAN). Terminal devices can be mobile terminal devices, such as mobile phones (or "cellular" phones), computers, and data cards. For example, they can be portable, pocket-sized, handheld, computer-embedded, or vehicle-mounted mobile devices that exchange voice and / or data with the RAN. Examples include personal communication service (PCS) phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), tablets, and computers with wireless transceiver capabilities. Wireless terminal equipment can also be called subscriber unit, subscriber station, mobile station (MS), remote station, access point (AP), remote terminal, access terminal, user terminal, user agent, subscriber station (SS), customer premises equipment (CPE), terminal, user equipment (UE), mobile terminal (MT), etc.

[0062] By way of example and not limitation, in this embodiment, the terminal device can also be a wearable device. Wearable devices, also known as wearable smart devices or smart wearable devices, are a general term for devices that utilize wearable technology to intelligently design and develop everyday wearables, such as glasses, gloves, watches, clothing, and shoes. Wearable devices are portable devices that are worn directly on the body or integrated into the user's clothing or accessories. Wearable devices are not merely hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are feature-rich, large in size, and can achieve complete or partial functions without relying on a smartphone, such as smartwatches or smart glasses, as well as those that focus on a specific type of application function and require the use of other devices such as smartphones, such as various smart bracelets, smart helmets, and smart jewelry for vital sign monitoring.

[0063] Terminals can also be drones, robots, devices in device-to-device (D2D) communication, vehicles to everything (V2X) communication, virtual reality (VR) terminal devices, augmented reality (AR) terminal devices, wireless terminals in industrial control, wireless terminals in self-driving, wireless terminals in telemedicine or telehealth services, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, etc.

[0064] Furthermore, terminal devices can also be terminal devices in future communication systems beyond the fifth generation (5G) (such as 5G Advanced communication systems) or in future evolved public land mobile networks (PLMNs). For example, 5G Advanced networks can further expand the form and function of 5G communication terminals, including but not limited to vehicles, cellular network terminals (integrating satellite terminal functions), drones, and Internet of Things (IoT) devices.

[0065] In this embodiment, the terminal device can also obtain AI services provided by the network device. Optionally, the terminal device can also have AI processing capabilities.

[0066] (2) Network equipment: This can be equipment within a wireless network. For example, network equipment can be a RAN node (or device) that connects terminal devices to the wireless network, and can also be called a base station. Currently, some examples of RAN equipment include: base station, evolved NodeB (eNodeB), gNB (gNodeB) in 5G communication systems, transmission reception point (TRP), evolved Node B (eNB), radio network controller (RNC), Node B (NB), home base station (e.g., home evolved Node B, or home Node B, HNB), base band unit (BBU), or wireless fidelity (Wi-Fi) access point (AP), etc. In addition, in a network architecture, network equipment can include central unit (CU) nodes, distributed unit (DU) nodes, or RAN equipment including both CU and DU nodes.

[0067] Optionally, RAN nodes can also be macro base stations, micro base stations, indoor stations, relay nodes, donor nodes, or radio controllers in cloud radio access network (CRAN) scenarios. RAN nodes can also be servers, wearable devices, vehicles, or in-vehicle equipment. For example, the access network equipment in vehicle-to-everything (V2X) technology can be a roadside unit (RSU).

[0068] In another possible scenario, multiple RAN nodes collaborate to assist the terminal in achieving wireless access, with different RAN nodes each implementing some of the base station's functions. For example, RAN nodes can be CUs, DUs, CUs (control plane, CP), CUs (user plane, UP), or radio units (RUs). CUs and DUs can be configured separately or included in the same network element, such as a baseband unit (BBU). RUs can be included in radio equipment or radio units, such as remote radio units (RRUs), active antenna units (AAUs), radio heads (RHs), or remote radio heads (RRHs).

[0069] In different systems, CU (or CU-CP and CU-UP), DU, or RU may have different names, but those skilled in the art will understand their meaning. For example, in an open access network (open RAN, O-RAN, or ORAN) system, CU can also be called O-CU (open CU), DU can also be called O-DU, CU-CP can also be called O-CU-CP, CU-UP can also be called O-CU-UP, and RU can also be called O-RU. For ease of description, this application uses CU, CU-CP, CU-UP, DU, and RU as examples. Any of the units among CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented through software modules, hardware modules, or a combination of software modules and hardware modules.

[0070] Communication between access network devices and terminal devices follows a specific protocol layer structure. This protocol layer may include a control plane protocol layer and a user plane protocol layer. The control plane protocol layer may include at least one of the following: radio resource control (RRC) layer, packet data convergence protocol (PDCP) layer, radio link control (RLC) layer, media access control (MAC) layer, or physical (PHY) layer, etc. The user plane protocol layer may include at least one of the following: service data adaptation protocol (SDAP) layer, PDCP layer, RLC layer, MAC layer, or physical layer, etc.

[0071] The correspondence between network elements and their achievable protocol layer functions in the ORAN system can be found in Table 1 below.

[0072] Table 1

[0073] Network devices can be other devices that provide wireless communication functions for terminal devices. The embodiments of this application do not limit the specific technology or form of the network device. For ease of description, the embodiments of this application are not limited.

[0074] Network equipment may also include core network equipment, such as the Mobility Management Entity (MME), Home Subscriber Server (HSS), Serving Gateway (S-GW), Policy and Charging Rules Function (PCRF), and Public Data Network Gateway (PDN gateway or P-GW) in 4th generation (4G) networks; and access and mobility management function (AMF), user plane function (UPF), or session management function (SMF) in 5G networks. Furthermore, this core network equipment may also include other core network equipment in 5G networks and next-generation networks of 5G networks.

[0075] In this embodiment of the application, the network device may also have network nodes with AI capabilities, which can provide AI services to terminals or other network devices. For example, it may be an AI node, computing node, RAN node with AI capabilities, or core network element with AI capabilities on the network side (access network or core network).

[0076] In this application embodiment, the device for implementing the function of the network device can be the network device itself, or it can be a device capable of supporting the network device in implementing the function, such as a chip system. This device can be disposed within the network device. In the technical solutions provided in this application embodiment, the example of a network device being used to implement the function of the network device is used to describe the technical solutions provided in this application embodiment.

[0077] (3) RRC state switching: There are three RRC states for terminal devices: connected state, idle state and inactive state.

[0078] In connected mode, the UE establishes an RRC connection with the network and can transmit data.

[0079] In idle state, the UE has not established an RRC connection with the network, and the base station does not have the UE's context. If the UE needs to enter the connected state from idle state, it needs to initiate an RRC connection establishment process.

[0080] In the inactive state, since the UE previously entered the connected state and then the base station released the RRC connection, both the base station and the UE saved the context. If the UE needs to enter the connected state from the inactive state, it needs to initiate an RRC connection restoration process. Compared to the RRC establishment process, the RRC restoration process has shorter latency and lower signaling overhead, but the base station needs to save the UE's context, which consumes more storage overhead.

[0081] (4) Configuration and Pre-configuration: In this application, both configuration and pre-configuration are used. Configuration refers to the network device / server sending configuration information or parameter values ​​to the terminal via messages or signaling, so that the terminal can determine communication parameters or resources for transmission based on these values ​​or information. Pre-configuration is similar to configuration; it can be parameter information or parameter values ​​negotiated in advance between the network device / server and the terminal device, or it can be parameter information or parameter values ​​used by the base station / network device or terminal device as specified in standard protocols, or it can be parameter information or parameter values ​​pre-stored in the base station / server or terminal device. This application does not limit this.

[0082] Furthermore, these values ​​and parameters can be changed or updated.

[0083] (5) The terms "system" and "network" in the embodiments of this application can be used interchangeably. "Multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, or B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the related objects before and after are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, "at least one of A, B and C" includes A, B, C, AB, AC, BC or ABC. And, unless otherwise specified, the ordinal numbers such as "first" and "second" mentioned in the embodiments of this application are used to distinguish multiple objects and are not used to limit the order, sequence, priority or importance of multiple objects.

[0084] (6) In the embodiments of this application, "send" and "receive" indicate the direction of signal transmission. For example, "send information to XX" can be understood as the destination of the information being XX, which may include sending directly through the air interface or sending indirectly through the air interface by other units or modules. "Receive information from YY" can be understood as the source of the information being YY, which may include receiving directly from YY through the air interface or receiving indirectly from YY through the air interface by other units or modules. "Send" can also be understood as the "output" of the chip interface, and "receive" can also be understood as the "input" of the chip interface.

[0085] In other words, sending and receiving can occur between devices, such as between network devices and terminal devices, or within a device, such as between components, modules, chips, software modules, or hardware modules within the device via buses, wiring, or interfaces.

[0086] It is understandable that information may undergo necessary processing, such as encoding and modulation, between the source and destination, but the destination can understand the valid information from the source. Similar statements in this application can be interpreted in a similar way and will not be elaborated further.

[0087] (7) In the embodiments of this application, "instruction" may include direct instruction and indirect instruction, as well as explicit instruction and implicit instruction. The information indicated by a certain piece of information (as described below, the instruction information) is called the information to be instructed. In the specific implementation process, there are many ways to indicate the information to be instructed, such as, but not limited to, directly indicating the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly indicate the information to be instructed by indicating other information, where there is an association between the other information and the information to be instructed; or it can only indicate a part of the information to be instructed, while the other parts of the information to be instructed are known or pre-agreed upon. For example, the instruction can be implemented by using a pre-agreed (e.g., protocol predefined) arrangement order of various information, thereby reducing the instruction overhead to a certain extent. This application does not limit the specific method of instruction. It is understood that for the sender of the instruction information, the instruction information can be used to indicate the information to be instructed; for the receiver of the instruction information, the instruction information can be used to determine the information to be instructed.

[0088] In this application, unless otherwise specified, the same or similar parts between the various embodiments can be referred to each other. In the various embodiments of this application, and in the various methods / designs / implementations within each embodiment, unless otherwise specified or logically conflicting, the terminology and / or descriptions between different embodiments and between the various methods / designs / implementations within each embodiment are consistent and can be mutually referenced. The technical features in different embodiments and the various methods / designs / implementations within each embodiment can be combined to form new embodiments, methods, or implementations based on their inherent logical relationships. The following descriptions of the embodiments of this application do not constitute a limitation on the scope of protection of this application.

[0089] This application can be applied to long-term evolution (LTE) systems, new radio (NR) systems, or future communication systems beyond 5G. These communication systems include at least one network device and / or at least one terminal device.

[0090] Please refer to Figure 1a, which is a schematic diagram of the architecture of the communication system 10 used in the embodiments of this application. As shown in Figure 1a, the communication system may include a radio access network (RAN) 100. Optionally, the communication system 10 may also include a core network 200 and an Internet 300. The RAN 100 includes at least one RAN node (110a and 110b in Figure 1a, collectively referred to as 110) and at least one terminal (120a-120j in Figure 1a, collectively referred to as 120). The RAN 100 may also include other RAN nodes, such as wireless relay devices and / or wireless backhaul devices (not shown in Figure 1a). The terminal 120 is wirelessly connected to the RAN node 110, and the RAN node 110 is wirelessly or wiredly connected to the core network 200. The core network equipment in the core network 200 and the RAN node 110 in the RAN 100 may be independent and different physical devices, or they may be the same physical device integrating the logical functions of the core network equipment and the logical functions of the RAN node. Terminals can be connected to each other, as can RAN nodes, via wired or wireless means.

[0091] Taking the communication system shown in Figure 1a as an example, in addition to performing communication-related services, different devices (including network devices and network devices, network devices and terminal devices, and / or terminal devices and terminal devices) may also perform AI-related services.

[0092] As shown in Figure 1b, taking a network device as a base station as an example, the base station can perform communication-related services and AI-related services with one or more terminal devices, and different terminal devices can also perform communication-related services and AI-related services.

[0093] As shown in Figure 1c, taking terminal devices including televisions and mobile phones as an example, communication-related services and AI-related services can also be performed between televisions and mobile phones.

[0094] The technical solutions provided in this application can be applied to wireless communication systems (such as the systems shown in Figures 1a, 1b, or 1c). For example, AI network elements can be introduced into the communication system provided in this application to realize some or all AI-related operations. AI network elements can also be called AI nodes, AI devices, AI entities, AI modules, AI models, or AI units, etc. The AI ​​network element can be built into a network element within the communication system. For example, the AI ​​network element can be an AI module built into: access network equipment, core network equipment, cloud server, or operation, administration, and maintenance (OAM) to realize AI-related functions. The OAM can act as the network management system for the core network equipment and / or the access network equipment. Alternatively, the AI ​​network element can also be an independently set network element in the communication system. Optionally, the terminal or its built-in chip can also include an AI entity to realize AI-related functions.

[0095] Optionally, in communication systems, AI application cases may include, but are not limited to: channel state information (CSI) feedback enhancement, beam management enhancement, positioning accuracy enhancement, network energy saving, load balancing, and mobility optimization. These will be explained below.

[0096] 1. Enhanced CSI feedback:

[0097] Channel quality information (CSI) is the channel attribute of a communication link, reported by the terminal device to the network device. By reporting this information, the terminal device can select an appropriate modulation and coding scheme (MCS) to adapt to changing wireless channels. For example, the terminal device might perform channel estimation based on the received channel state information-reference signal (CSI-RS) and then feed back the CSI-RS to the network device. This information serves as input to the network device's model, enabling AI model training. Applying AI to CSI feedback enhancement can reduce overhead, improve accuracy, and enhance predictive capabilities.

[0098] CSI-RS feedback enhancement may include at least one sub-function, such as: CSI compression, CSI prediction, and CSI-RS configuration signaling reduction. CSI compression may further include CSI compression in at least one domain: spatial, time, and frequency.

[0099] 2. Enhanced beam management (BM):

[0100] With the advancement of wireless communication technology, communication systems face increasingly diverse service demands, which place higher requirements on system capacity and communication latency. To address these challenges, 5G mobile communication systems have introduced high-frequency bands above 6 GHz. These high-frequency bands offer advantages in both bandwidth and frequency compared to mid- and low-frequency bands below 6 GHz, thus providing higher transmission rates and system capacity. However, the weaker penetration and stronger path fading effects of high-frequency signals limit their propagation distance and coverage. Thanks to massive MIMO (Massively Multi-Track Antenna) technology, high-frequency communication systems typically employ numerous antennas for beamforming, thereby achieving considerable beam gain to compensate for the limited propagation distance caused by the characteristics of high-frequency propagation.

[0101] To achieve beamforming gain, effective beam management becomes crucial. To achieve beam management, existing technologies employ methods such as layered scanning to reduce beam scanning overhead. This involves scanning a wide beam first, followed by scanning a small portion of narrow beams within the wide beam, thus reducing overhead. A schematic diagram of wide and narrow beams is shown in Figure 1d. Beam selection is primarily accomplished through reference signals and corresponding beam measurements. Specifically, the reference signals mainly include the synchronization signal block (SSB) and the channel state information-reference signal (CSI-RS). The SSB is a cell broadcast signal, comprising the primary synchronization signal (PSS), secondary synchronization signal (SSS), physical broadcast channel (PBCH), and demodulation reference signal (DMRS). The SSB is transmitted periodically according to the cell configuration, and its function extends beyond beam management, also including initial access and time-frequency synchronization. Simply put, the SSB signal can be considered a wide-beam signal. Correspondingly, CSI-RS signals are user-level signals, and the network side configures one or more CSI-RS resources for users based on actual conditions. Similarly, CSI-RS signals are not only used for beam management but also for channel quality measurement, etc. Simply put, CSI-RS signals are narrow-beam signals.

[0102] Traditional beam management systems perform a two-step beam scan during the serving beam selection phase: The first phase scans the SSB (wide beam), during which the UE measures and sends the reference signal received power (RSRP) of the SSB to the network. The second phase, based on the RSRP reported by the UE, the network selects the SSB with the highest RSRP and configures CSI-RS resource scanning to scan the narrow beams covered by this SSB to determine the optimal beam. During the narrow beam scanning process configured by the network, the network sends a TCI status indication message containing QCL information to instruct the UE to receive data using a fixed wide beam. The UE feeds back the measurement results to the network, which then determines the optimal beam for subsequent data transmission based on the measurement results. Each TCI status may include a reference signal resource identifier. The reference signal resource identifier can be, for example, at least one of the following: a non-zero power (NZP) CSI-RS resource identifier (NZP-CSI-RS-ResourceId) or an SSB index (SSB-Index).

[0103] AI plays a significant role in beam management, primarily in two aspects: spatial prediction (BM-Case 1) and temporal prediction (BM-Case 2). In the BM-Case 1 scenario, as shown in Figure 1e, the input to the AI ​​model is the beam information (usually RSRP values) of a specific pattern scanned at a certain time. This set of beam information is called set B. The AI ​​model predicts the complete beam set set A and selects the top-K beams and related information to report to the network.

[0104] In the BM-Case2 scenario, as shown in Figure 1f, a sliding time window will collect the model's input information. The figure shows the set B beam RSRP from time (t-N+1) to time (t). The AI ​​model processes the input data and outputs the prediction results set A (regression model) or top-K beam IDs (classification model) for future time windows. Finally, it selects the top-k beams and related information to report to the network.

[0105] 3. Enhanced positioning accuracy:

[0106] In line-of-sight (LOS) or non-line-of-sight (NLOS) scenarios, AI-based positioning can improve positioning accuracy with a smaller number of TRP antennas. Positioning enhancement can include at least one sub-function, such as: positioning enhancement based on access network devices, positioning enhancement based on positioning management function network elements, and positioning enhancement based on terminal devices.

[0107] 4. Network energy saving:

[0108] Network energy conservation can be achieved through cell activation / deactivation, load reduction, coverage improvement, or other RAN setting adjustments. AI technology can be used to optimize energy-saving decisions by leveraging data collected within the RAN network. AI algorithms can predict energy efficiency and load status for the next cycle, which can be used to assist in cell activation / deactivation decisions to save energy. Based on the predicted load, the system can dynamically configure energy-saving strategies to maintain a balance between system performance and energy efficiency, and reduce energy consumption.

[0109] 5. Load balancing:

[0110] Load balancing can distribute the load evenly between cells and across different areas within a cell, or transfer some traffic from congested cells, or offload users across a single cell, carrier, or access standard, thereby improving network performance. Using AI models to enhance load balancing performance—such as inputting various measurements and feedback from terminal devices and network nodes, as well as historical data—can provide a higher quality user experience and increase system capacity.

[0111] 6. Mobility Management:

[0112] Mobility management is a solution that ensures service continuity for mobile devices by minimizing dropped calls, radio link failures (RLFs), unnecessary handovers, and ping-pong effects. AI can enhance mobility management by, for example, reducing the probability of unexpected events, predicting device location / mobility / performance, and routing traffic.

[0113] It should be understood that the definitions of the above technical terms are merely illustrative. For example, as technology continues to develop, the scope of the above definitions may also change, and the embodiments of this application are not intended to limit the scope.

[0114] It should be understood that the definitions of the above technical terms are merely illustrative. For example, as technology continues to develop, the scope of the above definitions may also change, and the embodiments of this application are not intended to limit the scope.

[0115] For example, an AI function may include multiple AI sub-functions.

[0116] Optionally, AI application cases are also called AI application scenarios or AI functions.

[0117] As described above regarding AI application examples, AI can be widely used to improve network performance in areas such as CSI feedback enhancement, beam management, positioning accuracy enhancement, energy saving, mobility enhancement, and load balancing. AI models can typically be deployed on the network side and / or the terminal device side. The training of AI models relies on the collection of training data, which can come from measurements and feedback from the terminal devices.

[0118] The following is a brief introduction to the concepts that may be involved in this application.

[0119] 1) AI can enable machines to possess human-like intelligence, for example, allowing machines to use computer hardware and software to simulate certain intelligent human behaviors. To achieve artificial intelligence, machine learning methods can be employed. In machine learning, machines learn (or train) a model using training data. This model represents the mapping between input and output. The learned model can be used for reasoning (or prediction), that is, it can be used to predict the output corresponding to a given input. This output can also be called the reasoning result (or prediction result).

[0120] Machine learning (ML) can include supervised learning, unsupervised learning, and reinforcement learning. Unsupervised learning can also be called learning without supervision.

[0121] Supervised learning, based on collected sample values ​​and labels, uses machine learning algorithms to learn the mapping relationship between sample values ​​and labels, and then expresses this learned mapping relationship using an AI model. The process of training the machine learning model is the process of learning this mapping relationship. During training, sample values ​​are input into the model to obtain the model's predicted values, and the model parameters are optimized by calculating the error between the model's predicted values ​​and the sample labels (ideal values). After the mapping relationship is learned, it can be used to predict new sample labels. The mapping relationship learned in supervised learning can include linear or non-linear mappings. Based on the type of label, the learning task can be divided into classification tasks and regression tasks.

[0122] Unsupervised learning relies on collected sample values ​​to discover inherent patterns within the samples themselves. One type of unsupervised learning algorithm uses the samples themselves as supervisory signals, meaning the model learns the mapping relationship from sample to sample; this is called self-supervised learning. During training, model parameters are optimized by calculating the error between the model's predictions and the samples themselves. Self-supervised learning can be used for signal compression and decompression recovery applications; common algorithms include autoencoders and generative adversarial networks.

[0123] Reinforcement learning, unlike supervised learning, is a type of algorithm that learns problem-solving strategies through interaction with the environment. Unlike supervised and unsupervised learning, reinforcement learning problems do not have explicit "correct" action labels. The algorithm needs to interact with the environment to obtain reward signals from the environment, and then adjust its decision actions to obtain a larger reward signal value. For example, in downlink power control, the reinforcement learning model adjusts the downlink transmission power of each user based on the total system throughput feedback from the wireless network, aiming to achieve a higher system throughput. The goal of reinforcement learning is also to learn the mapping relationship between the environment state and a better (e.g., optimal) decision action. However, because the label of the "correct action" cannot be obtained in advance, the network cannot be optimized by calculating the error between the action and the "correct action." Reinforcement learning training is achieved through iterative interaction with the environment.

[0124] Neural networks (NNs) are a specific model in machine learning techniques. According to the general approximation theorem, neural networks can theoretically approximate any continuous function, thus enabling them to learn arbitrary mappings. Traditional communication systems rely on extensive expert knowledge to design communication modules, while deep learning communication systems based on neural networks can automatically discover hidden pattern structures from large datasets, establish mapping relationships between data, and achieve performance superior to traditional modeling methods.

[0125] The idea behind neural networks comes from the neuronal structure of the brain. For example, each neuron performs a weighted summation of its input values ​​and outputs the result through an activation function.

[0126] Figure 2a shows a schematic diagram of a neuron structure. Assume the input to the neuron is x = [x0, x1, ..., x...]. n The weights corresponding to each input are w = [w0, w1, ..., w] n ], where n is a positive integer, w i and x i It can be any possible type, such as a decimal, an integer (e.g., 0, a positive integer, or a negative integer), or a complex number. i As x i The weights are used to assign weights to x. i Weighting is applied. The bias for the weighted sum of the input values ​​is, for example, b. Activation functions can take many forms. Suppose the activation function of a neuron is: y = f(z) = max(0, z), then the output of that neuron is: For example, if the activation function of a neuron is y = f(z) = z, then the output of that neuron is: Here, b can be any possible type, such as a decimal, an integer (e.g., 0, a positive integer, or a negative integer), or a complex number. The activation functions of different neurons in a neural network can be the same or different.

[0127] Furthermore, neural networks generally consist of multiple layers, each of which may include one or more neurons. Increasing the depth and / or width of a neural network can improve its expressive power, providing more powerful information extraction and abstract modeling capabilities for complex systems. The depth of a neural network can refer to the number of layers it includes, and the number of neurons in each layer can be called the width of that layer. In one implementation, a neural network includes an input layer and an output layer. The input layer processes the received input information through neurons and passes the processing result to the output layer, which then obtains the output of the neural network. In another implementation, a neural network includes an input layer, hidden layers, and an output layer. The input layer processes the received input information through neurons and passes the processing result to the hidden layer. The hidden layer calculates the received processing result and passes the calculation result to the output layer or the next adjacent hidden layer, ultimately obtaining the output of the neural network. A neural network may include one hidden layer or multiple sequentially connected hidden layers, without limitation.

[0128] Neural networks, for example, are deep neural networks (DNNs). Depending on how the network is constructed, DNNs can include feedforward neural networks (FNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs).

[0129] Figure 2b is a schematic diagram of an FNN network. A characteristic of FNN networks is that neurons in adjacent layers are completely connected pairwise. This characteristic makes FNNs typically require a large amount of storage space, leading to high computational complexity.

[0130] CNNs are neural networks specifically designed to process data with a grid-like structure. For example, time-series data (e.g., discrete sampling along a time axis) and image data (e.g., two-dimensional discrete sampling) can both be considered grid-like data. CNNs do not use all the input information at once for computation; instead, they use a fixed-size window to extract a portion of the information for convolution operations, which significantly reduces the computational cost of model parameters. Furthermore, depending on the type of information extracted by the window (e.g., people and objects in an image represent different types of information), each window can use different convolution kernels, allowing CNNs to better extract features from the input data.

[0131] Recurrent Neural Networks (RNNs) are a type of neural network that utilizes feedback time-series information. The input to an RNN includes the current input value and its own output value from the previous time step. RNNs are suitable for acquiring temporally correlated sequence features, and are applicable to applications such as speech recognition and channel coding / decoding.

[0132] In the model training process described above, a loss function can be defined. The loss function describes the difference between the model's output value and the ideal target value. The loss function can be expressed in various forms, and there are no restrictions on its specific form. The model training process can be viewed as follows: by adjusting some or all of the model's parameters, the value of the loss function is made to be less than a threshold or to meet the target requirement.

[0133] A model can also be called an AI model, a rule, or other names. An AI model can be considered a specific method for implementing AI functions. An AI model represents the mapping relationship or function between the model's input and output. AI functions can include one or more of the following: data collection, model training (or model learning), model information dissemination, model inference (or model reasoning, inference, or prediction, etc.), model monitoring or model validation, or inference result publication, etc. AI functions can also be called AI (related) operations or AI-related functions.

[0134] The implementation process of the neural network will be described below with reference to the accompanying drawings.

[0135] 1. Fully connected neural network, also known as multilayer perceptron (MLP).

[0136] As shown in Figure 2c, an MLP consists of an input layer (left side), an output layer (right side), and multiple hidden layers (middle). Each layer of an MLP contains several nodes, called neurons. Neurons in adjacent layers are connected pairwise.

[0137] Optionally, considering neurons in two adjacent layers, the output h of the next layer's neurons is the weighted sum of all neurons x in the previous layer connected to it, processed by an activation function, and can be expressed as: h = f(wx + b).

[0138] Where w is the weight matrix, b is the bias vector, and f is the activation function.

[0139] Alternatively, the output of the neural network can be recursively expressed as: y = f z (w z f z-1 (…)+b z ).

[0140] Where z is the index of the neural network layer, z is greater than or equal to 1 and z is less than or equal to Z, where Z is the total number of layers in the neural network.

[0141] In other words, a neural network can be understood as a mapping from an input data set to an output data set. Neural networks are typically initialized randomly; the process of obtaining this mapping from random values ​​w and b using existing data is called training the neural network.

[0142] Optionally, the training method involves using a loss function to evaluate the output of the neural network.

[0143] As shown in Figure 2d, the error can be backpropagated, and the neural network parameters (including w and b) can be iteratively optimized using gradient descent until the output of the loss function reaches its minimum value, which is the "better point (e.g., the optimal point)" in Figure 2d. It can be understood that the neural network parameters corresponding to the "better point (e.g., the optimal point)" in Figure 2d can be used as the neural network parameters in the trained AI model information.

[0144] Alternatively, the gradient descent process can be represented as:

[0145] Where θ represents the parameters to be optimized (including w and b), L is the loss function, and η is the learning rate, controlling the step size of gradient descent. This represents the differentiation operation. This indicates taking the derivative of θ with respect to L.

[0146] Alternatively, the backpropagation process can utilize the chain rule for partial derivatives.

[0147] As shown in Figure 2e, the gradient of the parameters in the previous layer can be recursively calculated from the gradient of the parameters in the next layer, and can be expressed as:

[0148] Among them, w ij Let s be the weight of the connection between node j and node i. i The weighted sum of the inputs at node i.

[0149] 2. Federated Learning (FL).

[0150] The concept of federated learning effectively addresses the current challenges in the development of artificial intelligence. While fully protecting user data privacy and security, it enables various edge devices and central servers to collaborate efficiently to complete the model's learning task.

[0151] As shown in Figure 2f, the FL architecture is the most widely used training architecture in the current FL field, and the FedAvg algorithm is the basic algorithm of FL. The FedAvg algorithm flow is roughly as follows:

[0152] (1) Initialize the model to be trained at the center end. And broadcast it to all clients.

[0153] (2) In the t∈[1,T] round, the client k∈[1,K] is based on the local dataset. For the received global model Perform E epochs of training to obtain the local training results. This is then reported to the central node. In the example shown in Figure 2f, the local training results sent by distributed nodes n, k, and m are denoted as G, respectively. n G k G m .

[0154] (3) The central node collects local training results from all (or some) clients. Assume the set of clients uploading local models in round t is... The central server will use the number of samples from the corresponding client as weights to calculate the new global model. The specific update rule is as follows: Then the central end will send the latest version of the global model. The broadcast is sent to all clients for a new round of training.

[0155] (4) Repeat steps (2) and (3) until the model finally converges or the number of training rounds reaches the upper limit.

[0156] Optionally, in addition to reporting the local model, the client can also... It can also train local gradients The central node averages the local gradients reported by all clients and updates the global model based on this average gradient.

[0157] As can be seen in the FL framework, the dataset resides on distributed nodes (such as clients). These distributed nodes collect their local datasets, perform local training, and report the local results (model or gradients) to the central node. The central node itself may not have a dataset; it can be responsible for fusing the training results from the distributed nodes to obtain a global model, which is then distributed back to the distributed nodes.

[0158] 3. Decentralized learning.

[0159] Figure 2g illustrates a fully distributed system without a central node. The design goal f(x) of a decentralized learning system is generally the goal f of each node. iThe mean of (x), i.e. Where n is the number of distributed nodes, and x is the parameter to be optimized; in machine learning, x is the parameter of the machine learning model (such as a neural network). Each node utilizes local data and its local target f. i (x) Calculate the local gradient Then it is sent to its communicatively reachable neighboring nodes. Upon receiving the gradient information from its neighbor, any node can update the parameters x of its local model according to the following formula:

[0160] in, This represents the parameters of the local model after the (k+1)th update (k is a natural number) in the i-th node. This represents the parameters of the local model for the i-th node after the k-th update (if k is 0, then it represents...). (where α is the parameter of the local model of the i-th node that is not involved in the update) k N represents the tuning coefficient. i It is the set of neighboring nodes of node i, |N i | represents the number of elements in the set of neighboring nodes of node i, that is, the number of neighboring nodes of node i. Through information interaction between nodes, the decentralized learning system will eventually learn a unified model.

[0161] The technical solution provided in this application can be applied to communication systems (such as the systems shown in Figure 1a, 1b, or 1c). In a communication system, communication nodes generally possess signal transmission and reception capabilities as well as computing capabilities. Taking a network device with computing capabilities as an example, the computing capabilities of the network device mainly provide computational support for the signal transmission and reception capabilities (e.g., processing the transmission and reception of signals) to realize the communication tasks between the network device and other communication nodes.

[0162] The technical solution provided in this application can be applied to communication systems (such as the systems shown in Figure 1a, 1b, or 1c). In a communication system, communication nodes generally possess signal transmission and reception capabilities as well as computing capabilities. Taking a network device with computing capabilities as an example, the computing capabilities of the network device mainly provide computational support for the signal transmission and reception capabilities (e.g., processing the transmission and reception of signals) to realize the communication tasks between the network device and other communication nodes.

[0163] 2) When the UE transitions from idle state to connected state, the UE needs to establish an RRC connection. The specific steps are shown in Figure 3:

[0164] 301. The UE sends an RRC setup request message to the base station.

[0165] The UE sends an RRCSetupRequest message to the base station. The message content in step 301 may vary depending on the UE state and the application scenario. For example, in an initial random access scenario, the RRCSetupRequest message in step 3 carries the reason for RRC establishment and the UE identifier, requesting the establishment of an RRC connection.

[0166] 302. The base station sends an RRC connection establishment message (RRCSetup) to the UE.

[0167] The base station replies to the UE with an RRCSetup message, which carries detailed information on the signaling radio bearer (SRB) resource configuration for establishing SRB1.

[0168] 303. The UE sends an RRC connection establishment complete message (RRCSetupComplete) to the base station.

[0169] The UE configures radio resources according to the SRB1 resource information indicated in the RRCSetup message, and then sends an RRCSetupComplete message to the base station. Upon receiving the RRCSetupComplete message, the RRC connection is established.

[0170] 3) In the inactive state, the UE disconnects from the network via RRC and does not need to receive downlink data, thus achieving the same power-saving effect as the idle state. However, unlike the idle state, in the inactive state, the UE and the access network device (when entering the inactive state) retain the UE's context. When it needs to enter the connected state (for example, when the UE has uplink data to send, or when the network pagees the UE to enter the connected state), the UE initiates the RRC connection recovery process, as shown in Figure 4.

[0171] 401. The UE sends an RRC recovery request message (RRCResumeRequest) to the base station.

[0172] This message carries the reason for RRC recovery and the UE identifier, requesting the recovery of the RRC connection.

[0173] 402. The base station sends an RRC connection recovery message (RRCResume) to the UE.

[0174] 403. The UE sends an RRC connection restoration complete message (RRCResumeComplete) to the base station.

[0175] The UE initiates the RRC connection recovery process and enters the connected state based on the saved UE context, thereby reducing latency and saving signaling overhead.

[0176] With the development of communication technology, communication systems can now handle not only traditional communication services but also new types of services, such as AI services. Generally, systems capable of processing AI services, such as communication systems, can also be called AI systems. However, improving user experience within AI systems remains a pressing technical challenge.

[0177] By intelligently collecting and analyzing data in AI systems, network performance and user experience can be improved. The framework for the application of AI / ML in NR is shown in Figure 5.

[0178] The data collection entity stores data inputs from network devices, terminal devices, or other management entities, serving as a database for AI model training and data analysis inference. The model training entity analyzes the training data provided by the data collection entity to produce the optimal AI model. The model inference entity uses the AI ​​model, based on the data provided by the data collection entity, to make reasonable AI-based predictions about network operation or guide the network to make policy adjustments. These policy adjustments are planned uniformly by the actor entity and sent to multiple network entities for execution. Simultaneously, the network's performance after applying the relevant policies is again input into the database for storage.

[0179] When the UE is in connected mode, it receives a data collection request from its upper layer and performs the data collection. When the UE moves from connected mode to idle / inactive mode, it may move from the coverage area of ​​a traditional base station that does not support AI functionality to the coverage area of ​​a base station that does support AI functionality. Since the UE has a data collection requirement but no data transmission requirement, the UE in idle / inactive mode will not establish an RRC connection with the base station, thus preventing data collection.

[0180] Based on this, embodiments of this application provide a communication method, communication device, and storage medium. When a terminal device is in an idle or inactive state, if there is a need for data collection, it sends a first request message to request to enter the RRC connection state, thereby performing the data collection action.

[0181] Please refer to Figure 6. The embodiment shown in Figure 6 is executed interactively by a first network device and a first terminal device, wherein the network device supports AI functions. An embodiment of this application includes a communication method comprising:

[0182] 601. The first network device sends a first instruction message to the first terminal device. Correspondingly, the first terminal device receives the first instruction message from the first network device.

[0183] The first network device sends a first instruction message to the first terminal device, which instructs the first network device to support AI function configuration. Here, "the first network device supports AI function configuration" can be understood as the first network device providing the first terminal device with a configuration matching an AI model, which is deployed on the first terminal device. "The first terminal device deploys an AI model" can be understood as the first terminal device downloading a pre-trained AI model from the network device or server, or the first terminal device training an AI model using AI.

[0184] Optionally, the first indication information can also be used to indicate scenarios in which one or more first network devices support AI function configuration. In other words, the first indication information indicates which scenarios the first network device can provide corresponding configurations for AI models. For example, the first indication information may indicate that the first network device can support AI function configuration in a beam management scenario. Another example is that the first indication information may indicate that the first network device can support AI function configuration in a mobility scenario. Yet another example is that the first indication information may indicate that the first network device can support AI function configuration in a CSI prediction scenario. Specific limitations are not specified here.

[0185] The first terminal device determines whether data collection is possible based on the first indication information. For example, if the first indication information indicates that the first network device supports AI function configuration, then the first terminal device can collect data, and the first terminal device can execute step 602. As another example, if the first indication information indicates that the first network device supports AI function configuration in a beam management scenario, then the first terminal device can collect data for AI models in a beam management scenario, and the first terminal device can execute step 602. However, if the first indication information indicates that the first network device supports AI function configuration in a beam management scenario, but the first terminal device's requirement is to collect data for AI models in a mobile scenario, then the first terminal device does not execute step 602.

[0186] Optionally, the first network device may send a first indication message via a system message, wherein the first message may be a system information block (SIB), such as SIB1.

[0187] Optionally, the first indication information may include at least one identifier, which indicates at least one configuration condition. This can also be understood as at least one identifier indicating at least one network-side configuration parameter; the specific meaning is not limited here. The first terminal device has identifiers for functions / models that require data collection. If the first function or the first model satisfies any one of the at least one configuration condition, the first terminal device executes step 602. Alternatively, if the identifier on the first terminal device corresponds to any one of the at least one identifiers, the first terminal device executes step 602.

[0188] Specifically, the at least one identifier may be referred to as an associated ID, and the naming of this application embodiment is not limited.

[0189] It should be noted that the identifiers of the functions / models requiring data collection can be obtained from the data collection needs of the terminal device before it switches to an idle or inactive state. The data collection needs correspond to the functions / models that require data collection.

[0190] 602. The first terminal device sends a first request message to the first network device. Correspondingly, the first network device receives the first request message from the first terminal device.

[0191] The first terminal device needs to collect data for the AI ​​model, so it sends a first request message to the first network device. The first request message is used to request the establishment of an RRC connection. The first request message includes a request reason, which is the reason why the first terminal device requests to establish an RRC connection. The request reason is used to indicate that the first terminal device is a terminal device used for data collection.

[0192] The first request information, used to request the establishment of an RRC connection, can also be understood as a request to restore an RRC connection. Specifically, when the first terminal device is in an idle state, the first request information is used to request the establishment of an RRC connection, switching from the idle state to the connected state. In this case, the first request information can be carried in the RRC connection establishment request message (RRCSetupRequest). When the first terminal device is in an inactive state, the first request information is used to request the restoration of the RRC connection, switching from the inactive state to the connected state. In this case, the first request information can be carried in the RRC connection restoration request message (RRCResumeRequest).

[0193] Correspondingly, when the first request information is carried in an RRC connection establishment request message, the request reason can be carried in the establishmentCause field to indicate the reason for establishing the RRC connection. When the first request information is carried in an RRC connection recovery message, the request reason can be carried in the resumeCause field to indicate the reason for recovering the RRC connection.

[0194] The request reason is used to indicate that the first terminal device is a terminal device used for data collection. It can be understood that the request reason is that the first terminal device needs to collect data.

[0195] Optionally, the embodiment shown in FIG6 further includes step 603. Step 603 may be performed after step 601.

[0196] 603. The first terminal device sends a second request message to the first network device. Correspondingly, the first network device receives the second request message from the first terminal device.

[0197] The second request information is used by the first terminal device to request data collection; in other words, the second request information is used by the first terminal device to request the initiation of data collection.

[0198] Optionally, the second request information may include preset conditions that instruct the first terminal device to stop data collection. For example, the preset conditions may indicate that when the first terminal device collects data for a preset duration, the first network device instructs the first terminal device to stop data collection. Or, for another example, the preset conditions may indicate that when the amount of data collected by the first terminal device reaches a preset threshold, the first network device instructs the first terminal device to stop data collection. Specific details are not limited here.

[0199] It should be noted that the first request information and the second request information can be carried in the same message, that is, both the first request information and the second request information can be carried in the RRC connection establishment request message or the RRC connection recovery request message.

[0200] Optionally, the embodiment shown in FIG6 further includes step 604. Step 604 may be performed after step 602.

[0201] 604. The first network device sends a second instruction message to the first terminal device. Correspondingly, the first terminal device receives the second instruction message from the first network device.

[0202] The second indication information includes at least one identifier, which is used to indicate at least one configuration condition. The at least one identifier is used by the first terminal device to determine whether there is at least one identifier on the first terminal device that corresponds to any one of the at least one identifiers in the second indication information.

[0203] The first terminal device determines whether the model for which data needs to be collected corresponds to the identifier provided by the first network device. If so, the first terminal device executes step 605.

[0204] It should be noted that if step 601 includes at least one identifier, then step 604 may not be performed.

[0205] Optionally, the second indication information can be carried in the RRC connection establishment message (RRCSetup) or the RRC connection recovery message (RRCResume), and the specifics are not limited here.

[0206] Optionally, the embodiment shown in FIG6 further includes step 605. Step 605 may be performed after step 604.

[0207] 605. The first terminal device sends a third instruction message to the first network device. Correspondingly, the first network device receives the third instruction message from the first terminal device.

[0208] The third indication information includes at least one first identifier in the identifier, and the third indication information is used to indicate that the data collected by the first terminal device is applied to the second function or the second model, and the second function or the second model satisfies the configuration conditions corresponding to the first identifier.

[0209] Specifically, based on at least one identifier sent in step 604, the first terminal device determines that there is a matching model or function that needs to be collected. The first terminal device then sends the identifier corresponding to the model or function to the first network device, thereby requesting the configuration conditions corresponding to the second function or the second model from the first network device.

[0210] Optionally, the third indication information can be carried in the RRC connection establishment completion message (RRCSetupComplete) or the RRC connection recovery completion message (RRCSetupComplete), and the specifics are not limited here.

[0211] Optionally, the first network device can send configuration parameters or conditions corresponding to the second function or second model to the first terminal device via RRC configuration messages (RRCConfiguration). For example, the first network device sends data collection configuration for AI functions in a beam management scenario to the first terminal device, where SETA = 16 beams and SETB = 8 beams.

[0212] Optionally, the first network device may also instruct the first terminal device to start data collection via an RRC configuration message (RRCConfiguration).

[0213] In this embodiment of the application, the first network device is instructed to support AI function configuration by the first instruction information, so that the first terminal device can enter the RRC connection state by sending the first request information when there is no data transmission with the first network device, thereby collecting data.

[0214] The embodiment shown in Figure 6 describes how the first terminal device starts data collection. The following describes how the first terminal device stops data collection.

[0215] Please refer to Figure 7. One communication method in this embodiment includes:

[0216] 701. The first terminal device sends a fourth instruction message to the first network device. Correspondingly, the first network device receives the fourth instruction message from the first terminal device.

[0217] The first terminal device sends a fourth instruction message to the first network device, which instructs the first terminal device to stop data collection. In other words, the first terminal device can stop data collection by actively reporting the fourth instruction message.

[0218] Optionally, the fourth indication information may include a stop reason, which is used to indicate why the first terminal device has stopped collecting data. For example, the stop reason may indicate that the amount of data collected by the first terminal device has exceeded a preset threshold. Another example is that the stop reason may indicate that the duration of data collection by the first terminal device has reached a preset duration. Yet another example is that the stop reason may indicate that the first terminal device has insufficient memory.

[0219] 702. The first network device sends a first message to the first terminal device. Correspondingly, the first terminal device receives the first message from the first network device.

[0220] Based on the fourth instruction, the first network device stops sending configuration information related to data collection. In other words, the first network device clears the configuration related to data collection.

[0221] In one possible implementation, if there is data transmission between the first terminal device and the first network device before the first terminal device stops data collection, then the first message is an RRC configuration message, and the first network device sends an RRC configuration message to the first terminal device after clearing the data collection-related configuration.

[0222] In another possible implementation, if there is no data transmission between the first terminal device and the first network device before data collection stops, the first message is an RRC release message (RRCRelease). The first network device clears the configuration related to data collection, retains the measurement results used for data collection, releases the first terminal device, and switches the first terminal device from the connected state to the idle state or the inactive state.

[0223] In this embodiment of the application, the first terminal device actively reports the cessation of data collection, enabling the first terminal device and the first network device to align the release of relevant configurations.

[0224] The embodiment shown in Figure 7 illustrates the method by which the first terminal device actively reports the cessation of data collection. The following describes another method by which the first terminal device stops data collection.

[0225] Please refer to Figure 8. One communication method in this embodiment includes:

[0226] 801. The first terminal device sends a third request message to the first network device. Correspondingly, the first network device receives the third request message from the first terminal device.

[0227] The first terminal device sends a third request message to the first network device to request the cessation of data collection.

[0228] Optionally, the third request information may include a stop reason, which is used to indicate why the first terminal device has stopped collecting data. For example, the stop reason may indicate that the amount of data collected by the first terminal device has exceeded a preset threshold. Another example is that the stop reason may indicate that the duration of data collection by the first terminal device has reached a preset duration. Yet another example is that the stop reason may indicate that the first terminal device has insufficient memory.

[0229] 802. The first network device sends a fifth instruction message to the first terminal device. Correspondingly, the first terminal device receives the fifth instruction message from the first network device.

[0230] In response to the third request information, the first network device sends a fifth instruction message to the first terminal device, instructing the first terminal device to stop data collection. The fifth instruction message also instructs the first terminal device to clear data collection-related configurations.

[0231] Optionally, the fifth indication information is carried in the RRC reconfiguration message (RRCReConfiguration).

[0232] Optionally, if the second request information in step 603 of the embodiment shown in FIG6 includes preset conditions, then step 801 may not be executed. That is, if the preset conditions are met, the first network device sends a fifth instruction message to the first terminal device, instructing the first terminal device to stop data collection.

[0233] 803. The first terminal device sends a second message to the first network device. Correspondingly, the first network device receives the second message from the first terminal device.

[0234] Specifically, the first terminal device stops data collection according to the fifth instruction information and clears the relevant configurations for data collection. After clearing, the first terminal device sends a second message to the first network device to indicate that the clearing is complete.

[0235] Optionally, the second message can be an RRC reconfiguration complete message (RRCReConfigurationComplete).

[0236] The above describes how the first terminal device starts or stops data collection. The following describes how the first terminal device ensures the continuity and integrity of data collection during the switching process.

[0237] Please refer to Figure 9. If the target network device (i.e., the second network device) supports AI function configuration, the steps for the first terminal device to switch from the source network device (i.e., the first network device) to the target network device include:

[0238] 901. The first network device sends a handover request to the second network device. Correspondingly, the second network device receives the handover request from the first network device.

[0239] The first network device sends a handover request (HO request) to the second network device. This handover request carries the data collection configuration and measurement information of the first terminal device. For example, the measurement information may include measurement resources for data collection in beam management scenarios, the duration of data collection by the first terminal device, the amount of data required for data collection by the first terminal device, the CSI-RS configuration for data collection, or measurement results reported by the first terminal device. The data collection configuration and measurement information of the first terminal device can also be referred to as the first configuration information.

[0240] Optionally, the switching request may also include at least one identifier. The description of the at least one identifier can be referred to in step 603 of the embodiment shown in FIG6, and will not be repeated here.

[0241] 902. The second network device sends a handover request response to the first network device. Correspondingly, the first network device receives the handover request response from the second network device.

[0242] Specifically, if the second network device supports AI function configuration, the second network device will send the configuration for data collection under the second network device to the first network device through the switch request response, based on the switch request.

[0243] If the second network device does not support AI function configuration, the second network device will reconfigure all parameters of the first terminal device and send a handover request response in the form of fullconfig.

[0244] Optionally, the switch request response may include instructions to delete the relevant configurations for data collection on the first terminal device.

[0245] 903. The first network device sends an RRC reconfiguration message to the first terminal device. Correspondingly, the first terminal device receives the RRC reconfiguration message from the first network device.

[0246] The first network device sends the updated RRC configuration to the first terminal device. Specifically, if the second network device supports AI function configuration, the first network device sends the configuration for data collection under the second network device to the first terminal device.

[0247] If the second network device does not support AI function configuration, the first network device instructs the first terminal device to stop data collection, report and save the measurement results. The method by which the first network device instructs the first terminal device to stop data collection can be referred to the embodiment shown in Figure 8, and will not be elaborated further here.

[0248] Optionally, the first network device can instruct the first terminal device to store the measurement results of the data collection via an RRC reconfiguration message, and can also instruct the first terminal device to report the measurement results of the data collection to the first network device.

[0249] 904. The first terminal device sends an RRC reconfiguration complete message to the second network device. Correspondingly, the second network device receives the RRC reconfiguration message from the first terminal device.

[0250] The first terminal device sends an RRC reconfiguration complete message to the second network device, thereby completing the handover.

[0251] Optionally, the embodiment shown in FIG9 further includes step 900. Step 900 may be performed before step 901.

[0252] 900. The first network device determines whether the second network device supports AI function configuration.

[0253] In addition to determining whether the second network device supports AI function configuration through the switch request response, the first network device can also determine whether the second network device supports AI function configuration through other means.

[0254] In one possible implementation, before triggering the handover request, the first network device and the second network device can exchange capability information (e.g., send their own AI capability information via the Xn interface). If the first network device determines that the second network device does not support the AI ​​function configuration, step 901 may not be executed. The first network device instructs the first terminal device to stop data collection.

[0255] In another possible implementation, the measurement results reported by the first terminal device carry indication information to indicate whether the second network device supports AI function configuration.

[0256] In this embodiment of the application, when the first terminal device in the data collection process moves from a first network device that supports AI configuration function to a network device that does not support AI configuration function, or moves to another network device that supports AI configuration function, the continuity of data collection and the integrity of the process are ensured by clarifying the interaction between the first network device and the second network device, as well as the interaction between the first terminal device and the first network device.

[0257] The communication method in the embodiments of this application has been described above. The communication device in the embodiments of this application is described below. Referring to Figure 10, the communication device 1000 can be used to execute the process performed by the first terminal device in the embodiments shown in Figures 6 to 9. For details, please refer to the relevant descriptions in the foregoing method embodiments. The communication device 1000 can be a terminal device, or a component or device applied to a terminal device (e.g., a processor, circuit, chip, or chip system), or a logic module or software capable of implementing all or part of the functions of the terminal device.

[0258] The communication device 1000 includes an interface module 1001 and a processing module 1002.

[0259] The processing module 1002 is used for data processing. The interface module 1001 can implement corresponding communication functions. The interface module 1001 can also be called a communication interface or a communication module.

[0260] Optionally, the communication device 1000 may further include a storage module, which can be used to store program code, program instructions and / or data. The processing module 1002 can read the instructions and / or data in the storage module so that the communication device 1000 can implement the aforementioned method embodiments.

[0261] The communication device 1000 can be used to perform the actions performed by the first terminal device in the above method embodiments. For example, it can be the first terminal device, a communication module within the first terminal device, or a circuit or chip in the first terminal device responsible for communication functions. The communication device 1000 can be the first terminal device or a component configurable on the first terminal device. The processing module 1002 is used to perform processing-related operations on the first terminal device side in the above method embodiments. The interface module 1001 is used to perform receiving-related operations on the first terminal device side in the above method embodiments.

[0262] Optionally, the interface module 1001 may include a sending module and a receiving module. The sending module is used to perform the sending operation in the above method embodiments. The receiving module is used to perform the receiving operation in the above method embodiments.

[0263] It should be noted that the communication device 1000 may include a transmitting module but not a receiving module. Alternatively, the communication device 1000 may include a receiving module but not a transmitting module. Specifically, it depends on whether the above-described scheme executed by the communication device 1000 includes both transmitting and receiving actions. For example, the communication device 1000 is used to execute the actions performed by the first terminal device in the embodiments shown in Figures 6 to 9. For details, please refer to the relevant descriptions in the embodiments shown in Figures 6 to 9; these will not be elaborated upon here.

[0264] For example, the communication device 1000 is used to execute the following scheme:

[0265] Interface module 1001 is used to receive first indication information, which is used to indicate that the first network device supports AI function configuration;

[0266] Processing module 1002 is used to generate the first request information;

[0267] The interface module 1001 is also used to send a first request information, which is used by the first terminal device to request the establishment of a Radio Resource Control (RRC) connection. The first request information includes a request reason, which is the reason why the first terminal device requests to establish an RRC connection. The request reason is used to indicate that the first terminal device is a terminal device used for data collection.

[0268] In one possible implementation, the first indication information is also used to indicate a scenario in which the first network device supports AI function configuration, including one or more of beam management scenarios, mobility scenarios, or channel state information (CSI) prediction scenarios.

[0269] In another possible implementation, the interface module 1001 is also used to send a second request message, which is used by the first terminal device to request data collection.

[0270] In another possible implementation, the second request information includes preset conditions, which are used to instruct the first terminal device to stop data collection.

[0271] In another possible implementation, the interface module 1001 is also used to receive second indication information, the second indication information including at least one identifier, the at least one identifier being used to indicate at least one configuration condition, and the at least one identifier being used by the first terminal device to determine whether there is a function or model that satisfies at least one configuration condition.

[0272] In another possible implementation, the first indication information includes at least one identifier, which is used to indicate at least one configuration condition, and the first terminal device is used to collect data for the first function or the first model;

[0273] Interface module 1001 is used to send the first request information, including:

[0274] The interface module 1001 is specifically used to send a first request message if the first function or the first model satisfies any one of the at least one configuration conditions.

[0275] In another possible implementation, the interface module 1001 is also used to send third indication information, which includes at least one first identifier among at least one identifier. The third indication information is used to indicate that the data collected by the first terminal device is applied to a second function or a second model, and the second function or the second model satisfies the configuration conditions corresponding to the first identifier.

[0276] In another possible implementation, the interface module 1001 is also used to send a fourth instruction message, which is used to instruct the first terminal device to stop data collection.

[0277] In another possible implementation, interface module 1001 is also used to send a third request message, which is used to request the cessation of data collection.

[0278] In another possible implementation, the interface module 1001 is also used to receive a fifth instruction message, which is used to instruct the first terminal device to stop data collection.

[0279] In another possible implementation, the interface module 1001 is also used to send a stop reason, which is the reason why the first terminal device stops collecting data.

[0280] It should be understood that the specific procedures for each module to perform the above-mentioned corresponding processes have been described in detail in the above method embodiments, and will not be repeated here for the sake of brevity.

[0281] Optionally, when the communication device 1000 is a terminal device or a communication module within a terminal device, the processing module 1002 in the above embodiments can be implemented by at least one processor or processor-related circuitry. Specifically, the processor may include a modem chip, or a system-on-chip (SoC) chip containing a modem core, or a system-in-package (SIP) chip. The interface module 1001 can be implemented by a transceiver or transceiver-related circuitry. The interface module 1001 may also be referred to as a communication module or communication interface. The storage module can be implemented by at least one memory.

[0282] Optionally, when the communication device 1000 is a circuit or chip in a terminal device responsible for communication functions, such as a modem chip or a SoC chip or SIP chip containing a modem core, the function of the processing module 1002 can be implemented by a circuit system in the aforementioned chip that includes one or more processors or processing cores. The function of the interface module 1001 can be implemented by the interface circuit or data transceiver circuit on the aforementioned chip.

[0283] The communication device shown in Figure 10 can also be used to execute the process performed by the first network device in the embodiments shown in Figures 6 to 9. For details, please refer to the relevant descriptions in the foregoing method embodiments.

[0284] The communication device 1000 can be used to perform the actions performed by the first network device in the above method embodiments. For example, it can be the first network device, a communication module within the first network device, or a circuit or chip in the first network device responsible for communication functions. The communication device 1000 can be the first network device or a component configurable within the first network device. The processing module 1002 is used to perform processing-related operations on the first network device side in the above method embodiments. The interface module 1001 is used to perform reception-related operations on the first network device side in the above method embodiments.

[0285] Optionally, the interface module 1001 may include a sending module and a receiving module. The sending module is used to perform the sending operation in the above method embodiments. The receiving module is used to perform the receiving operation in the above method embodiments. The communication device 1000 may be a network device, or a component or device applied to a network device (e.g., a processor, circuit, chip, or chip system), or a logic module or software capable of implementing all or part of the functions of the network device.

[0286] It should be noted that the communication device 1000 may include a transmitting module but not a receiving module. Alternatively, the communication device 1000 may include a receiving module but not a transmitting module. Specifically, it depends on whether the above-described scheme executed by the communication device 1000 includes both transmitting and receiving actions. For example, the communication device 1000 is used to execute the actions performed by the first network device in the embodiments shown in Figures 6 to 9. For details, please refer to the relevant descriptions in the embodiments shown in Figures 6 to 9; these will not be elaborated upon here.

[0287] For example, the communication device 1000 is used to execute the following scheme:

[0288] Processing module 1002 is used to generate first indication information;

[0289] Interface module 1001 is used to send first indication information, which is used to indicate that the first network device supports AI function configuration;

[0290] The interface module 1001 is also used to receive first request information, which is used by the first terminal device to request the establishment of an RRC connection. The first request information includes a request reason, which is the reason why the first terminal device requests the establishment of an RRC connection. The request reason is used to indicate that the first terminal device is a terminal device used for data collection.

[0291] In one possible implementation, the first indication information is also used to indicate a scenario in which the first network device supports AI function configuration, including one or more of beam management scenarios, mobility scenarios, or channel state information (CSI) prediction scenarios.

[0292] In another possible implementation, the interface module 1001 is also used to send a second request message, which is used to instruct the first terminal device to request data collection.

[0293] In another possible implementation, the second request information includes preset conditions, which are used to instruct the first terminal device to stop data collection.

[0294] In another possible implementation, the interface module 1001 is also used to receive second indication information, the second indication information including at least one identifier, the at least one identifier being used to indicate at least one configuration condition, and the at least one identifier being used by the first terminal device to determine whether there is a function or model that satisfies at least one configuration condition.

[0295] In another possible implementation, the first indication information includes at least one identifier, which is used to indicate at least one configuration condition. The first terminal device is used to collect data for the first function or the first model. The first request information is used to indicate that the first function or the first model meets any one of the at least one configuration condition.

[0296] In another possible implementation, the interface module 1001 is also used to send third indication information, which includes at least one first identifier among at least one identifier. The third indication information is used to indicate that the data collected by the first terminal device is applied to a second function or a second model, and the second function or the second model satisfies the configuration conditions corresponding to the first identifier.

[0297] In another possible implementation, the interface module 1001 is also used to send a fourth instruction message, which is used to instruct the first terminal device to stop data collection.

[0298] In another possible implementation, interface module 1001 is also used to send a third request message, which is used to request the cessation of data collection.

[0299] In another possible implementation, the interface module 1001 is also used to receive a fifth instruction message, which is used to instruct the first terminal device to stop data collection.

[0300] In another possible implementation, the interface module 1001 is also used to send a stop reason, which is the reason why the first terminal device stops collecting data.

[0301] In another possible implementation, the interface module 1001 is also used to send first configuration information, which is used to instruct the first terminal device to perform data collection configuration.

[0302] In another possible implementation, the first configuration information includes measurement resources for beam management data collection, data collection duration, data collection volume, reference signal configuration for data collection, and measurement results of data collection.

[0303] It should be understood that the specific procedures for each module to perform the above-mentioned corresponding processes have been described in detail in the above method embodiments, and will not be repeated here for the sake of brevity.

[0304] The processing module 1002 in the above embodiments can be implemented by at least one processor or processor-related circuitry. The interface module 1001 can be implemented by a transceiver or transceiver-related circuitry. The interface module 1001 can also be referred to as a communication module or communication interface. The storage module can be implemented by at least one memory.

[0305] The following describes a communication device provided in an embodiment of this application. Please refer to Figure 11, which is a schematic diagram of the structure of a communication device provided in an embodiment of this application. The communication device may be a first terminal device or a first network device in the above method embodiments, or it may be a chip, chip system, or processor that supports the first terminal device or the first network device in implementing the above methods. This communication device can be used to implement the methods described in the above method embodiments, and for details, please refer to the description in the above method embodiments.

[0306] The communication device may include one or more processors 1101, which are connected to a memory 1102, an input / output unit 1103, and a bus 1104. The processor 1101 may be a general-purpose processor or a dedicated processor, such as a baseband processor or a central processing unit (CPU). The baseband processor can be used to process communication protocols and communication data, while the CPU can be used to control the communication device (e.g., base station, baseband chip, terminal, terminal chip, DU or CU, etc.), execute software programs, and process data from the software programs.

[0307] Optionally, the communication device may include one or more memories 1102, which may store instructions that can be executed on the processor 1101, causing the communication device to perform the methods described in the above method embodiments. Optionally, the memories 1102 may also store data. The processor 1101 and the memories 1102 may be configured separately or integrated together.

[0308] Optionally, the communication device may also include a transceiver and an antenna. A transceiver, also called a transceiver unit, transceiver, or transceiver circuit, is used to implement transmission and reception functions. A transceiver may include a receiver and a transmitter; the receiver, also called a receiver circuit, is used to implement the receiving function; the transmitter, also called a transmitter or transmitting circuit, is used to implement the transmitting function.

[0309] In another possible design, the processor 1101 may include a transceiver for implementing receive and transmit functions. For example, the transceiver may be a transceiver circuit, an interface, or an interface circuit. The transceiver circuit, interface, or interface circuit for implementing receive and transmit functions may be separate or integrated. The aforementioned transceiver circuit, interface, or interface circuit may be used for reading and writing code / data, or it may be used for transmitting or relaying signals.

[0310] In another possible design, the processor 1101 may optionally store instructions that, when executed, cause the communication device to perform the methods described in the above method embodiments. The instructions may be stored in the processor 1101; in this case, the processor 1101 may be implemented in hardware.

[0311] In another possible design, the communication device may include a circuit that can perform the sending or receiving or communication functions of the first terminal device or the first network device in the aforementioned method embodiments. The processor and transceiver described in this application embodiment can be implemented on integrated circuits (ICs), analog ICs, radio frequency integrated circuits (RFICs), mixed-signal ICs, application-specific integrated circuits (ASICs), printed circuit boards (PCBs), electronic devices, etc. The processor and transceiver can also be manufactured using various IC process technologies, such as complementary metal oxide semiconductors (CMOS), n-type metal-oxide-semiconductor (NMOS), p-type metal oxide semiconductors (PMOS), bipolar junction transistors (BJTs), bipolar CMOS (BiCMOS), silicon germanium (SiGe), gallium arsenide (GaAs), etc.

[0312] The communication device described in the above embodiments may be a first terminal device or a first network device, but the scope of the communication device described in the embodiments of this application is not limited thereto, and the structure of the communication device may not be limited to FIG11. The communication device may be a standalone device or may be part of a larger device. For example, the communication device may be:

[0313] (1) Independent integrated circuit IC, or chip, or chip system or subsystem;

[0314] (2) A collection of one or more ICs, optionally including a storage component for storing data and instructions;

[0315] (3) ASIC, such as modem;

[0316] (4) Modules that can be embedded in other devices;

[0317] (5) Receivers, terminals, smart terminals, cellular phones, wireless devices, handheld devices, mobile units, vehicle-mounted devices, network devices, cloud devices, artificial intelligence devices, etc.

[0318] (6) Others, etc.

[0319] For communication devices that can be chips or chip systems, please refer to the schematic diagram of the chip structure shown in Figure 12. The chip 1200 shown in Figure 12 includes a processor 1201 and an interface 1202. Optionally, it may also include a memory 1203. The number of processors 1201 can be one or more, and the number of interfaces 1202 can be multiple.

[0320] For cases where the chip is used to implement the functions of the first terminal device or the first network device in the embodiments of this application:

[0321] The interface 1202 is used to receive or output signals;

[0322] The processor 1201 is used to perform data processing operations on the first terminal device or the first network device.

[0323] As shown in Figure 13, network elements in a communication system are connected via interfaces (e.g., NG, Xn) or over-the-air interfaces. These network element nodes, such as core network equipment, access network nodes (RAN nodes), terminals, or one or more devices in operations administration and maintenance (OAM), are equipped with one or more AI modules (only one is shown in Figure 13 for clarity). An access network node can be a single RAN node or can comprise multiple RAN nodes, for example, including CUs and DUs. The CU and / or DU can also be equipped with one or more AI modules. A CU can also be divided into CU-CP and CU-UP, with one or more AI modules configured in the CU-CP and / or CU-UP.

[0324] As shown in Figure 14, the communication system includes a RAN intelligent controller (RIC). For example, the RIC can be the AI ​​module shown in Figure 13, used to implement AI-related functions. RICs include near-real-time RICs (near-RT RICs) and non-real-time RICs (non-RT RICs). Non-real-time RICs primarily process non-real-time information, such as data that is not sensitive to latency, with latency in the order of seconds. Real-time RICs primarily process near-real-time information, such as data that is relatively sensitive to latency, with latency in the order of tens of milliseconds.

[0325] Near real-time (NRT) RICs are used for model training and inference. For example, they are used to train AI models and then use those models for inference. NRT RICs can obtain network-side and / or end-device-side information from RAN nodes (e.g., CUs, CU-CPs, CU-UPs, DUs, compute nodes, and / or RUs) and / or end-devices. This information can be used as training data or inference data. NRT RICs can deliver inference results to RAN nodes and / or end-devices. Inference results can be exchanged between CUs and DUs, and / or between DUs and RUs. For example, a NRT RIC delivers an inference result to a DU, which then forwards it to an RU.

[0326] Non-real-time RICs are also used for model training and inference. For example, they can be used to train AI models and then use those models for inference. Non-real-time RICs can obtain network-side and / or end-device-side information from RAN nodes (e.g., CUs, CU-CPs, CU-UPs, DUs, compute nodes, and / or RUs) and / or end-devices. This information can be used as training data or inference data, and the inference results can be delivered to RAN nodes and / or end-devices. Inference results can be exchanged between CUs and DUs, and / or between DUs and RUs; for example, a non-real-time RIC delivers inference results to a DU, which then forwards them to an RU.

[0327] Near real-time RICs and non-real-time RICs can also be configured as separate network elements. Near real-time RICs and non-real-time RICs can also be part of other devices. For example, near real-time RICs can be set in RAN nodes (e.g., CU, DU, compute nodes), while non-real-time RICs can be set in OAM, cloud servers, core network devices, or other network devices.

[0328] In one possible implementation, the CU is used to perform the action of the first network device sending first indication information to the first terminal device in step 601 of the embodiment shown in FIG6. The DU is used to perform the actions of the first network device receiving first request information from the first terminal device in step 602, the first network device receiving second request information from the first terminal device in step 603, the first network device sending second indication information to the first terminal device in step 604, and the first network device receiving third indication information from the first terminal device in step 605, as shown in the embodiment. The RRC message sent by the DU to the CU carries the first indication information sent by the first terminal device to the DU. The DU transmits the establishment completion information reported by the first terminal device to the CU. The DU sends the measurement results reported by the first terminal device to the CU, and the DU sends the data collection time length or the data collection data volume to the CU.

[0329] It is understood that some optional features in the embodiments of this application can be implemented independently in certain scenarios without relying on other features, such as the current solution on which they are based, to solve the corresponding technical problems and achieve the corresponding effects. Alternatively, they can be combined with other features as needed in certain scenarios. Correspondingly, the communication device given in the embodiments of this application can also implement these features or functions, which will not be elaborated here.

[0330] It should be understood that the processor in the embodiments of this application can be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method embodiments can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor described above can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0331] It is understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAK are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memory used in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0332] This application also provides a computer-readable storage medium including instructions that, when executed on a computer, cause the computer to perform the methods described in the foregoing embodiments. The computer-readable storage medium may be a non-volatile storage medium.

[0333] This application also provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the methods described in the foregoing embodiments.

[0334] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0335] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0336] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0337] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0338] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0339] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

Claims

1. A communication method, characterized in that, The method is applied to a first terminal device, the first terminal device being in an idle state or an inactive state, the method comprising: Receive a first indication message, the first indication message being used to instruct the first network device to support AI function configuration; Send a first request message, the first request message being used by the first terminal device to request the establishment of a Radio Resource Control (RRC) connection, the first request message including a request reason, the request reason being the reason why the first terminal device requests to establish an RRC connection, the request reason being used to indicate that the first terminal device is a terminal device used for data collection.

2. The method according to claim 1, characterized in that, The first indication information is also used to indicate the scenario in which the first network device supports AI function configuration, the scenario including one or more of beam management scenario, mobility scenario or channel state information (CSI) prediction scenario.

3. The method according to claim 1 or 2, characterized in that, The method further includes: Send a second request message, which is used by the first terminal device to request data collection.

4. The method according to claim 3, characterized in that, The second request information includes preset conditions, which are used to instruct the first terminal device to stop data collection.

5. The method according to any one of claims 1 to 4, characterized in that, The method further includes: The first terminal device receives a second indication message, which includes at least one identifier. The at least one identifier is used to indicate at least one configuration condition. The at least one identifier is used by the first terminal device to determine whether there is a function or model that meets the at least one configuration condition.

6. The method according to claim 1 or 2, characterized in that, The first indication information includes at least one identifier, which is used to indicate at least one configuration condition, and the first terminal device is used to collect data for a first function or a first model; The sending of the first request information includes: If the first function or the first model satisfies any one of the at least one configuration conditions, then the first request information is sent.

7. The method according to any one of claims 1 to 6, characterized in that, The method further includes: Send a third indication message, the third indication message including a first identifier among the at least one identifier, the third indication message being used to indicate that the data collected by the first terminal device is applied to a second function or a second model, the second function or the second model satisfying the configuration conditions corresponding to the first identifier.

8. The method according to any one of claims 1 to 7, characterized in that, The method further includes: A fourth instruction message is sent, which instructs the first terminal device to stop data collection.

9. The method according to any one of claims 1 to 7, characterized in that, The method further includes: Send a third request message, which is used to request the cessation of data collection.

10. The method according to claim 4 or 9, characterized in that, The method further includes: The fifth instruction information is received, which is used to instruct the first terminal device to stop data collection.

11. The method according to claim 8 or 9, characterized in that, The method further includes: Send a stop reason, wherein the stop reason is the reason why the first terminal device stops collecting data.

12. A communication method, characterized in that, The method is applied to a first network device, and the method includes: Send a first indication message, which is used to indicate that the first network device supports AI function configuration; A first request message is received. The first request message is used by a first terminal device to request the establishment of an RRC connection. The first request message includes a request reason, which is the reason why the first terminal device requests the establishment of an RRC connection. The request reason is used to indicate that the first terminal device is a terminal device used for data collection.

13. The method according to claim 12, characterized in that, The first indication information is also used to indicate the scenario in which the first network device supports AI function configuration, the scenario including one or more of beam management scenario, mobility scenario or channel state information (CSI) prediction scenario.

14. The method according to claim 12 or 13, characterized in that, The method further includes: Send a second request message, which instructs the first terminal device to request data collection.

15. The method according to claim 14, characterized in that, The second request information includes preset conditions, which are used to instruct the first terminal device to stop data collection.

16. The method according to any one of claims 12 to 15, characterized in that, The method further includes: The first terminal device receives a second indication message, which includes at least one identifier. The at least one identifier is used to indicate at least one configuration condition. The at least one identifier is used by the first terminal device to determine whether there is a function or model that meets the at least one configuration condition.

17. The method according to claim 12 or 13, characterized in that, The first indication information includes at least one identifier, the at least one identifier being used to indicate at least one configuration condition, the first terminal device being used to collect data for a first function or a first model, and the first request information being used to indicate that the first function or the first model satisfies any one of the at least one configuration condition.

18. The method according to any one of claims 12 to 17, characterized in that, The method further includes: Send a third indication message, the third indication message including a first identifier among the at least one identifier, the third indication message being used to indicate that the data collected by the first terminal device is applied to a second function or a second model, the second function or the second model satisfying the configuration conditions corresponding to the first identifier.

19. The method according to any one of claims 12 to 18, characterized in that, The method further includes: A fourth instruction message is sent, which instructs the first terminal device to stop data collection.

20. The method according to any one of claims 12 to 18, characterized in that, The method further includes: Send a third request message, which is used to request the cessation of data collection.

21. The method according to claim 15 or 20, characterized in that, The method further includes: The fifth instruction information is received, which is used to instruct the first terminal device to stop data collection.

22. The method according to claim 19 or 20, characterized in that, The method further includes: Send a stop reason, wherein the stop reason is the reason why the first terminal device stops collecting data.

23. The method according to any one of claims 12 to 22, characterized in that, The method further includes: Send first configuration information, which is used to instruct the first terminal device to configure data collection.

24. The method according to claim 23, characterized in that, The first configuration information includes measurement resources for beam management data collection, data collection duration, data collection volume, reference signal configuration for data collection, and measurement results of data collection.

25. A communication device, characterized in that, Includes modules or units for performing the method as described in any one of claims 1 to 11, or the method as described in any one of claims 12 to 24.

26. A communication device, characterized in that, include: A processor for executing a program that causes the communication device to perform the method as claimed in any one of claims 1 to 11, or the method as claimed in any one of claims 12 to 24.

27. A computer-readable storage medium, characterized in that, Includes instructions that, when executed on a computer, cause the computer to perform the method as claimed in any one of claims 1 to 11, or cause the computer to perform the method as claimed in any one of claims 12 to 24.

28. A computer program product containing instructions, characterized in that, When it is run on a computer, it causes the computer to perform the method as described in any one of claims 1 to 11, or causes the computer to perform the method as described in any one of claims 12 to 24.

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