Communication methods, communication devices, systems and storage media

Abstract

This application discloses a communication method, communication device, system, and storage medium, applied in the field of wireless communication technology, for use by a terminal device to determine available AI functions or AI models. The method includes: receiving a first configuration parameter, the first configuration parameter including a first prediction range, the first prediction range being used to determine whether at least one AI model satisfies the first prediction range; and sending first indication information, the first indication information being used to indicate that at least one AI model satisfies the first prediction range. Because the first configuration parameter includes the first prediction range, the terminal device can determine whether there is an AI model that satisfies the first prediction range, clarifying the method by which the terminal device determines whether there is a usable model. If the terminal device has at least one AI model that satisfies the first prediction range, the terminal device can report that the first configuration parameter is available, thereby obtaining the performance benefits brought by the AI ​​function.

Description

Technical Field

[0001] 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

[0002] With the development of communication technology, communication equipment in communication systems can now perform not only traditional communication services but also other new types of services, such as artificial intelligence (AI) services. Generally, a communication system capable of handling AI services can also be called an AI system.

[0003] The configuration of AI models on the terminal device side can be completed through a two-stage reporting process. The terminal device reports its supported AI capabilities to the network device. This AI capability information indicates the AI ​​functions or models the terminal device can support. Based on the AI ​​capability information reported by the terminal device, the network device sends a reporting configuration to the terminal device. The terminal device then reports the available AI functions or models based on the reporting configuration.

[0004] However, how terminal devices determine the available AI functions or AI models is a problem that urgently needs to be solved. Summary of the Invention

[0005] This application provides a communication method, communication device, system, and storage medium for a terminal device to determine available AI functions or AI models.

[0006] The first aspect of this application provides a communication method. Optionally, the execution subject 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 a terminal device as an example, the terminal device receives first configuration parameters from a network device. The first configuration parameters include a first prediction range, which is used to determine whether at least one AI model satisfies the first prediction range. The terminal device sends first indication information to the network device, which is used to indicate that at least one AI model satisfies the first prediction range.

[0007] Based on the first aspect of this application, since the first configuration parameter includes a first prediction range, the terminal device can determine whether there is an AI model that meets the first prediction range, thus clarifying the method by which the terminal device determines whether there is a usable model. If the terminal device has at least one AI model that meets the first prediction range, the terminal device can report that the first configuration parameter is available, thereby obtaining the performance benefits brought by the AI ​​function.

[0008] In some possible implementations, the first prediction range is used to indicate that the number of prediction slots is greater than or equal to M, and / or to indicate that the number of prediction slots is less than or equal to N, where M is a positive integer and N is a positive integer greater than M.

[0009] In this embodiment of the application, by defining a first prediction range, the terminal device is able to determine whether there is an available AI model based on the first prediction range, thus defining a way for the terminal device to determine the AI ​​model.

[0010] In some possible implementations, the terminal device may also receive a second configuration parameter, which includes a second prediction range that is different from the first prediction range.

[0011] In this embodiment of the application, since the terminal device can also receive a second configuration parameter, the terminal device can determine whether there is an AI model that satisfies any one of the configuration parameters based on multiple sets of configuration parameters, thus clarifying the way the terminal device determines the AI ​​model.

[0012] In some possible implementations, the first indication information is used to indicate that the first model meets the first prediction range, and the terminal device may also send first information to indicate that the first model is used to predict X time slots.

[0013] In this embodiment of the application, the terminal device sends first information to the network device to indicate the specific configuration of the AI ​​model that meets the first prediction range, thereby enabling the network device to activate the corresponding AI model on the terminal device according to the specific configuration reported by the terminal device.

[0014] In some possible implementations, the terminal device may also receive first reporting configuration information, which indicates that the reporting time slot includes a first time slot among X time slots.

[0015] In this embodiment of the application, since the first reporting configuration information is used to indicate that the reporting time slot includes the first time slot among X time slots, the terminal device can determine which specific time slot(s) needs to be predicted based on the first reporting configuration information, so that the prediction result reported by the terminal device can meet the needs of the network device.

[0016] In some possible implementations, the terminal device may also receive second reporting configuration information, which indicates that the reporting time slot includes Y time slots out of X time slots, the first time slot out of the Y time slots is the i-th time slot out of the X time slots, i is a positive integer less than or equal to X-Y+1, and Y is a positive integer less than or equal to X.

[0017] In this embodiment of the application, since the first reporting configuration information is used to indicate Y time slots and the first time slot among the Y time slots, the terminal device can report Y time slots starting from the i-th time slot according to the first reporting configuration information, thereby reducing the indication overhead of the network device.

[0018] In some possible implementations, the terminal device may also receive a monitoring configuration, which indicates the method for reporting monitoring results, wherein the monitoring results are the monitoring results of Z time slots, and Z time slots are the time slots reported by the terminal device.

[0019] In this embodiment, the terminal device determines the method of reporting monitoring results by receiving monitoring configuration, so that the monitoring configuration of the terminal device can meet the needs of the network device.

[0020] In some possible implementations, the monitoring configuration is used to indicate the monitoring results reported for Z time slots, or to indicate the statistical values ​​of the monitoring results reported for Z time slots.

[0021] In this embodiment of the application, by specifying the method of reporting monitoring results by the terminal device, the monitoring configuration of the terminal device can meet the needs of the network device.

[0022] In some possible implementations, the terminal device may receive a first identifier, which is used to indicate a first configuration parameter.

[0023] In this embodiment of the application, the first configuration parameter is indicated by the first identifier, so that when the terminal device reports that the configuration is available, the first configuration parameter is clearly indicated by the first identifier, thereby aligning the information about the available model between the terminal device and the network device.

[0024] In some possible implementations, the terminal device may receive a second identifier, which is used to indicate a second configuration parameter.

[0025] In this embodiment of the application, the second identifier indicates the second configuration parameter, so that when the terminal device reports that the configuration is available, the second identifier can clearly indicate that the first configuration parameter is available, thereby aligning the information about the available model between the terminal device and the network device.

[0026] In some possible implementations, the first indication information includes a first identifier and / or a second identifier.

[0027] In this embodiment of the application, by reporting a first identifier and / or a second identifier, the terminal device and the network device can align information about the available models.

[0028] 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 network device as an example, the network device sends a first configuration parameter to a terminal device. The first configuration parameter is used to indicate a prediction range, and the prediction range is used to determine whether at least one AI model satisfies the prediction range. The network device receives first indication information from the terminal device, which indicates that at least one AI model satisfies the first prediction range.

[0029] In some possible implementations, the first prediction range is used to indicate that the number of prediction slots is greater than or equal to M, and / or to indicate that the number of prediction slots is less than or equal to N, where M is a positive integer and N is a positive integer greater than M.

[0030] In some possible implementations, the network device may also send a second configuration parameter, which includes a second prediction range that is different from the first prediction range.

[0031] In some possible implementations, the first indication information is used to indicate that the first model meets the first prediction range, and the network device may also receive first information, which is used to indicate that the first model is used to predict X time slots.

[0032] In some possible implementations, the network device may also send first reporting configuration information, which indicates that the reporting time slot includes a first time slot out of X time slots.

[0033] In some possible implementations, the network device may also send a second reporting configuration information, which indicates that the reporting time slot includes Y time slots out of X time slots, the first time slot out of the Y time slots is the i-th time slot out of the X time slots, i is a positive integer less than or equal to X-Y+1, and Y is a positive integer less than or equal to X.

[0034] In some possible implementations, the network device may also send a monitoring configuration that indicates how to report monitoring results, wherein the monitoring results are the monitoring results of Z time slots, and Z time slots are the time slots reported by the terminal device.

[0035] In some possible implementations, the monitoring configuration is used to indicate the monitoring results reported for Z time slots, or to indicate the statistical values ​​of the monitoring results reported for Z time slots.

[0036] In some possible implementations, the network device may send a first identifier, which is used to indicate a first configuration parameter.

[0037] In some possible implementations, the network device may send a second identifier, which is used to indicate a second configuration parameter.

[0038] In some possible implementations, the first indication information includes a first identifier and / or a second identifier.

[0039] 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.

[0040] 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.

[0041] 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:

[0042] A processor for executing a program that causes the communication device to perform the method as described in the first or second aspect of the foregoing and any possible implementation thereof.

[0043] Optionally, the communication device further includes a memory, and the processor is coupled to the memory; the memory is used to store programs.

[0044] 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.

[0045] The communication interface in the chip can be an input / output interface, pins, or circuits.

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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

[0050] Figures 1a to 1f A schematic diagram of the communication system provided in the embodiments of this application;

[0051] Figures 2a to 2g This is a schematic diagram illustrating the AI ​​processing procedure involved in the embodiments of this application;

[0052] Figure 3 This is a schematic diagram of the terminal-side model configuration process in an embodiment of this application;

[0053] Figures 4 to 5 This is an interactive schematic diagram of the communication method in the embodiments of this application;

[0054] Figures 6 to 9 This is a schematic diagram of the communication device in an embodiment of this application;

[0055] Figures 10 to 11 This is a schematic diagram of the O-RAN architecture in an embodiment of this application. Detailed Implementation

[0056] First, some terms used in the embodiments of this application will be explained to facilitate understanding by those skilled in the art.

[0057] (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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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; 5G Advanced terminals include, but are not limited to, vehicles, cellular network terminals (integrating satellite terminal functions), drones, and Internet of Things (IoT) devices.

[0062] 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.

[0063] (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.

[0064] 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).

[0065] 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 set up 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).

[0066] 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.

[0067] 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.

[0068] The correspondence between network elements and their achievable protocol layer functions in the ORAN system can be found in Table 1 below.

[0069] Table 1

[0070] ORAN network elements 3GPP protocol layer functions O-CU-CP RRC+PDCP-Control Plane (PDCP-C) O-CU-UP SDAP+PDCP - User Plane (PDCP-U) O-DU RLC+MAC+PHY-high O-RU PHY-low

[0071] 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.

[0072] 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.

[0073] 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).

[0074] 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.

[0075] (3) 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 transmission resources 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.

[0076] Furthermore, these values ​​and parameters can be changed or updated.

[0077] (4) 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.

[0078] (5) 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.

[0079] 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.

[0080] 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.

[0081] (6) 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, and for the receiver of the instruction information, the instruction information can be used to determine the information to be instructed.

[0082] 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.

[0083] 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.

[0084] Please see Figure 1a This is a schematic diagram of the architecture of the communication system 1000 used in an embodiment of this application. Figure 1aAs shown, the communication system may include a radio access network (RAN) 100. Optionally, the communication system 1000 may also include a core network 200 and an Internet 300. The RAN 100 includes at least one RAN node (e.g., Figure 1a 110a and 110b, collectively referred to as 110, may also include at least one terminal (such as...). Figure 1a RAN100, denoted as RAN100, comprises RAN nodes 120a-120j, collectively referred to as RAN120. RAN100 may also include other RAN nodes, such as wireless relay equipment and / or wireless backhaul equipment. Figure 1a (Not shown in the image). Terminal 120 connects wirelessly to RAN node 110, and RAN node 110 connects wirelessly or via a wired connection to core network 200. The core network equipment in core network 200 and RAN node 110 in RAN 100 can be independent physical devices, or they can be the same physical device integrating the logical functions of core network equipment and RAN nodes. Terminals can connect to each other, and RAN nodes can connect to each other, via wired or wireless connections.

[0085] by Figure 1a Taking the communication system shown as an example, in addition to performing communication-related services, different devices (including network devices and terminal devices, and / or terminal devices and terminal devices) may also perform AI-related services.

[0086] like Figure 1b As shown, taking a network device as a base station as an example, a 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.

[0087] like Figure 1c As shown, taking terminal devices including TVs and mobile phones as an example, TVs and mobile phones can also perform communication-related services and AI-related services.

[0088] The technical solution provided in this application can be applied to wireless communication systems (e.g.) Figure 1a , Figure 1b or Figure 1cThe system shown, for example, the communication system provided in this application, can incorporate AI network elements to implement 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, an 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 implement 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 a network element independently set up in the communication system. Optionally, the terminal or its built-in chip can also include an AI entity to implement AI-related functions.

[0089] 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.

[0090] 1. Enhanced CSI feedback:

[0091] 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.

[0092] 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.

[0093] 2. Enhanced beam management (BM):

[0094] 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.

[0095] 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 first scanning a wide beam, and then scanning a small portion of a narrow beam within that wide beam, thereby reducing overhead. A schematic diagram of wide and narrow beams is shown below. Figure 1dAs shown. 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, the CSI-RS signal is a user-level signal, with the network configuring one or more CSI-RS resources for users based on actual conditions. Similarly, the CSI-RS signal is not only used for beam management but also for channel quality measurements; it can be simply understood as a narrow-beam signal.

[0096] 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 involves the network filtering the SSB with the highest RSRP based on the RSRP reported by the UE, and configuring 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 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).

[0097] AI plays a significant role in beam management, primarily in two aspects: spatial domain prediction (BM-Case 1) and temporal domain prediction (BM-Case 2). In the BM-Case 1 scenario, such as... Figure 1e As shown, the input to the AI ​​model is the beam information (usually RSRP value) of a specific pattern scanned at a certain time. The set of this 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.

[0098] In the BM-Case2 scenario, such as Figure 1f As shown, 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.

[0099] 3. Enhanced positioning accuracy:

[0100] 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.

[0101] 4. Network energy saving:

[0102] 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.

[0103] 5. Load balancing:

[0104] 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.

[0105] 6. Mobility Management:

[0106] 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.

[0107] 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.

[0108] 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.

[0109] For example, an AI function may include multiple AI sub-functions.

[0110] Optionally, AI application cases are also called AI application scenarios or AI functions.

[0111] 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.

[0112] The following is a brief introduction to the concepts that may be involved in this application.

[0113] AI can endow machines with human-like intelligence, for example, allowing them 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 inputs and outputs. 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).

[0114] Machine learning (ML) can include supervised learning, unsupervised learning, and reinforcement learning. Unsupervised learning can also be called learning without supervision.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] like Figure 2a The diagram shown is a schematic representation of a neuron structure. Assume the neuron's input 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.

[0121] 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.

[0122] 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).

[0123] Figure 2b This is a schematic diagram of a Free-Nearest Neural Network (FNN). A key characteristic of FNNs 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The implementation process of the neural network will be described below with reference to the accompanying drawings.

[0129] 1. Fully connected neural networks, also known as multilayer perceptrons (MLP).

[0130] like Figure 2c As shown, 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.

[0131] Optionally, considering neurons in two adjacent layers, the output h of a neuron in the next layer 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:

[0132] h = f(wx + b).

[0133] Where w is the weight matrix, b is the bias vector, and f is the activation function.

[0134] Alternatively, the output of the neural network can be recursively expressed as:

[0135] y = f z (w z f z-1 (…)+b z ).

[0136] 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.

[0137] 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.

[0138] Optionally, the training method involves using a loss function to evaluate the output of the neural network.

[0139] like Figure 2d As shown, 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. Figure 2d The term "relative advantage (e.g., optimal advantage)" is used. This is understandable. Figure 2d The neural network parameters corresponding to the "better points (e.g., the best points)" in the text can be used as neural network parameters in the trained AI model information.

[0140] Alternatively, the gradient descent process can be represented as:

[0141]

[0142] 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.

[0143] Alternatively, the backpropagation process can utilize the chain rule for partial derivatives.

[0144] like Figure 2e As shown, 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:

[0145]

[0146] 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.

[0147] 2. Federated Learning (FL).

[0148] 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.

[0149] like Figure 2f As shown, the FL architecture is currently the most widely used training architecture in the FL field, and the FedAvg algorithm is the foundational algorithm of FL. The FedAvg algorithm flow is roughly as follows:

[0150] (1) Initialize the model to be trained at the center end. And broadcast it to all clients.

[0151] (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. Report it to the central node. Figure 2f In the example shown, the local training results sent by distributed nodes n, k, and m are denoted as G, respectively. n G k G m .

[0152] (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.

[0153] (4) Repeat steps (2) and (3) until the model finally converges or the number of training rounds reaches the upper limit.

[0154] 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.

[0155] 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.

[0156] 3. Decentralized learning.

[0157] like Figure 2g The diagram shows 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. i The 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:

[0158]

[0159] 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...). (The parameters of the local model that did not participate in the update for the i-th node), Δ 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.

[0160] The technical solution provided in this application can be applied to communication systems (e.g.) Figure 1a or Figure 1b or Figure 1cIn a communication system (as shown in the diagram), communication nodes typically possess both signal transmission and reception capabilities and computational capabilities. Taking a network device with computational capabilities as an example, the network device's computational capabilities primarily provide computing power support for signal transmission and reception capabilities (e.g., processing signals for transmission and reception) to enable the network device to perform communication tasks with other communication nodes.

[0161] 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. Currently, in AI systems, model configuration on the terminal device side is completed through a two-stage reporting process, the specific steps of which are as follows: Figure 3 As shown:

[0162] 301. The network device sends a terminal capability request to the terminal device.

[0163] Network devices send request messages to terminal devices, requesting the terminal devices to report information on supported AI capabilities. For example, this could indicate AI / ML functions or models in beam management scenarios, or AI / ML functions or models in positioning scenarios. In some possible cases, the UE can report indications of AI / ML function support.

[0164] 302. The terminal device sends terminal capability information to the network device.

[0165] In response to a request message from a network device, the terminal device reports its supported AI capabilities to the network device.

[0166] 303. Network devices send configuration information to terminal devices.

[0167] Based on the AI / ML functions or models supported by the terminal device, the network device sends configuration information to the terminal device according to the network's requirements. This information instructs the terminal device to report available AI / ML functions or models. The configuration information includes one or more CSI-ReportConfig files related to inference configuration, and one or more sets of inference-related configuration parameters. These parameters are used by the terminal device to select whether a corresponding AI / ML function or model is available. For example, in BM-Case1 or BM-Case2, the configuration parameters might be information related to setA and setB. In the BM-Case2 scenario, they might be information related to prediction / measurement time slots.

[0168] 304. The terminal device sends auxiliary information to the network device.

[0169] The terminal device reports available AI / ML functions or models to the network device using auxiliary information. This reporting can be an AI model, an AI / ML function, or configuration parameters issued by the network device in step 303; the specific type is not limited here. For example, the terminal device can report bit information to the network device, indicating whether the configuration parameter is available. For instance, a bit of "0" indicates that the configuration parameter is unavailable, while a bit of "1" indicates that the configuration parameter is available.

[0170] 305. Network devices send configuration information to terminal devices.

[0171] Configuration information sent from network devices to terminal devices can be used to activate available AI models or AI functions deployed on the terminal devices.

[0172] 306. Network device activation / deactivation / inference / monitoring of AI / ML functions or models.

[0173] Network devices can also activate / deactivate AI / ML functions or models deployed on terminal devices in subsequent actions, use these functions or models for inference, or monitor them. For example, a network device can send configurations for monitoring AI models to a terminal device, thereby enabling monitoring of the AI ​​models.

[0174] However, in step 304, how the terminal device determines the available AI / ML functions or models based on the configuration information sent by the network device is a problem that urgently needs to be solved. Assume that the terminal device performs a strict match based on the configuration parameters sent by the network device, that is, the terminal device determines whether each configuration sent by the network corresponds one-to-one with the functions / models supported by the terminal device. For example, in the BM-Case2 scenario, if the configuration sent by the network device in step 303 requires the prediction of 3 time slots, then in step 304, the terminal device will identify the functions / models that can clearly predict 3 time slots based on this configuration and inform the network that this set of configurations is available; otherwise, it will not report it.

[0175] Based on the current assumptions, when a network device sends a configuration request to predict the function / model of 3 future time slot beams, if the terminal device finds that only the function / model of predicting 5 time slots is available according to the configuration sent by the network device, the terminal device will not report the availability of the function or model to the network device (reporting the availability of the function or model can also be understood as indicating that the configuration sent by the network device is available), causing the terminal device to miss the prediction in AI-related scenarios and be unable to obtain the benefits brought by AI.

[0176] Based on this, an embodiment of this application provides a method. Please refer to... Figure 4 , Figure 4 This is a schematic diagram of an embodiment of a communication method provided in this application. Figure 4 The method shown is executed interactively by the network device and the terminal device. This method can be applied to, for example... Figures 1a to 1c In the system architecture shown, network devices can refer to network equipment, components or devices applied to network equipment (e.g., processors, circuits, chips, or chip systems), or logical modules or software that can implement all or part of the functions of network equipment (e.g., central unit (CU), distributed unit (DU), or radio unit (RU)). Terminal devices can refer to terminal equipment, components or devices applied to terminal equipment (e.g., processors, circuits, chips, or chip systems), or circuits or chips in terminal equipment responsible for communication functions (e.g., modem chips, also known as baseband chips, or system-on-chip (SoC) chips containing modem cores, or system-in-package (SIP) chips). The method includes:

[0177] 401. The terminal device receives the first configuration parameters from the network device. Accordingly, the network device sends the first configuration parameters to the terminal device.

[0178] The terminal device receives a first configuration parameter, which includes a first prediction range. The first prediction range is used to determine whether there is at least one AI model on the terminal device that satisfies the first prediction range.

[0179] It should be noted that, in the embodiments of this application, the AI ​​model may refer to an AI model deployed on a terminal device, an AI / ML function deployed on a terminal device, or an ML model deployed on a terminal device; the specific meaning is not limited here. The model / function deployed on the terminal device can also be understood as a pre-trained model / function downloaded by the terminal device from a network device or server.

[0180] In this application embodiment, "model / function available" can be understood as the terminal device having a trained model / function, and the configuration parameters being able to match the model / function.

[0181] The first configuration parameter can also be called the inference configuration parameter, and its naming is not limited in this application embodiment. The first configuration parameter can be understood as the inference configuration parameter used to select available functions / models. The first prediction range can be a range of features. Taking the BM-Case2 scenario as an example, the first configuration parameter is used to indicate the range of the required number of time slots to be predicted. For example, the first prediction range is used to indicate that the number of prediction time slots needs to be 3 to 6.

[0182] It should be noted that the first configuration parameter is a set of configurations, meaning that in addition to the first prediction range, the first configuration parameter also includes other configuration parameters. For example, the first configuration parameter is used to indicate that the number of prediction time slots needs to be 3 to 6, and also to indicate whether prediction is performed through the SetA16 beam or the SetB8 beam; the specifics are not limited here. In other words, if the terminal device is to report AI / ML functions or models according to the first configuration parameter, then the AI / ML function or model, in addition to meeting the first prediction range, also needs to meet other configuration parameters, i.e., it needs to meet a set of configuration parameters.

[0183] The first prediction range can be the intersection of multiple prediction ranges. For example, the first configuration parameter indicates that the number of prediction time slots needs to be greater than or equal to 3 time slots. At the same time, the first configuration parameter also indicates that the number of prediction time slots needs to be less than or equal to 6 time slots. Therefore, the first prediction range is used to indicate that the number of prediction time slots needs to be between 3 and 6 time slots.

[0184] Optionally, the terminal device receives a first identifier from the network device, which is used to indicate a first configuration parameter.

[0185] For example, there is an association between configuration parameters and identifiers. For instance, a first identifier corresponds to a first configuration parameter, and a second identifier corresponds to a second configuration parameter; the specific association is not limited here. The association between configuration parameters and identifiers can be shown in Table 2 below:

[0186] Table 2: Relationship between configuration parameters and identifiers

[0187] Configuration parameters set1 set2 set3 set4 logo ID1 ID2 ID3 ID4

[0188] As shown in Table 2, set1 is the first configuration parameter, and ID1 is the first identifier. When the terminal device receives the first identifier, it can determine the first configuration parameter according to the association shown in Table 2. The specific details are not limited here.

[0189] In this embodiment of the application, a first identifier is sent to indicate the first configuration parameter, so that the terminal device can determine the first configuration parameter according to the association between the configuration parameter and the identifier, thereby reducing signaling overhead.

[0190] Optional, Figure 4The illustrated embodiment also includes step 401a. Step 401a may be performed before step 402.

[0191] 401a. The terminal device receives a second configuration parameter from the network device. Accordingly, the network device sends the second configuration parameter to the terminal device.

[0192] The second configuration parameter includes a second prediction range, which differs from the first prediction range. For example, the second prediction range is used to indicate that the number of prediction slots needs to be greater than four.

[0193] Specifically, network devices can send multiple sets of configuration parameters to terminal devices, and when reporting the first indication information, the terminal devices can indicate which set of configuration parameters is available.

[0194] The first configuration parameter and the second configuration parameter can be carried in the same message or in different messages.

[0195] It should be noted that the timing of steps 401 and 401a is not limited in the embodiments of this application. That is, step 401a can be executed after step 401 or before step 401, and the specific timing is not limited here.

[0196] 402. The terminal device sends a first instruction message to the network device. Correspondingly, the network device receives the first instruction message from the terminal device.

[0197] The terminal device determines whether there is at least one AI model that satisfies the first configuration parameters based on the first configuration parameters. If so, the terminal device sends a first indication message, which indicates that at least one AI model satisfies the first prediction range.

[0198] It should be noted that the first indication information is actually used to indicate that at least one AI model meets the first configuration parameter, and if the first configuration parameter is met, then the first prediction range must be met.

[0199] In this embodiment of the application, since the first configuration parameter includes the first prediction range, the terminal device can determine whether there is an AI model that meets the first prediction range based on the first prediction range, thereby enabling the terminal device to report that the first configuration parameter is available, and thus obtain the performance benefits brought by the AI ​​function.

[0200] In one possible implementation, if at least one AI model on the terminal device satisfies the first configuration parameter, the terminal device sends a first indication message; if no AI model on the terminal device satisfies the first configuration parameter, the terminal device sends a third indication message, which indicates that no AI model satisfies the first prediction range or the first configuration parameter.

[0201] In another possible implementation, if the terminal device has at least one AI model that meets the first configuration parameter, the terminal device sends the first indication information; if none of the AI ​​models on the terminal device meet the first configuration parameter, the terminal device does not send the first indication information.

[0202] Optionally, the terminal device may carry a first identifier and / or a second identifier in the first indication information. Specifically, if the network device indicates the first configuration parameter through the first identifier, the terminal device may carry the first identifier in the first indication information to indicate that the first configuration parameter is available when reporting that the first configuration parameter is available.

[0203] Optional, Figure 4 The illustrated embodiment also includes step 403. Step 403 may be performed after step 402.

[0204] 403. The terminal device sends the first information to the network device. Correspondingly, the network device receives the first information from the terminal device.

[0205] In addition to reporting which AI / ML functions or models are available or which configuration parameters are available, terminal devices can also send first information to indicate the specific configuration values ​​of AI / ML functions or models.

[0206] For example, a terminal device has a model that meets a first configuration, i.e., a first model. A first indication message indicates that the first model meets the first configuration parameters, meaning the first model is available. The terminal device sends a first message indicating that the first model can predict four time slots. Alternatively, it can be said that the function of the first model is to predict four time slots. It can also be said that the first model can predict four time slots; the specific number is not limited here.

[0207] For example, the first indication information is used to indicate that the first configuration parameter is available. The terminal device indicates in the first information that the AI ​​model that meets the first configuration parameter can predict 3 time slots and 4 time slots, respectively.

[0208] It should be noted that the first information can be carried in the same message as the first instruction information, or it can be carried in different messages; no specific limitation is made here.

[0209] Optionally, if the network device indicates the first configuration parameters through the first identifier, the terminal device may indicate the model or functional configuration that satisfies the first configuration parameters corresponding to the first identifier in the first information. For example, the association between identifiers and features is shown in Table 3 below:

[0210] Table 3: Relationship between Identifiers and Features

[0211] logo feature ID1 Predict 3 time slots, predict 4 time slots ID2 Predict 3 time slots ID3 Predict 4 time slots, predict 5 time slots, predict 6 time slots

[0212] As shown in Table 3 above, the first identifier is ID1, corresponding to the first configuration parameters. Assume the first configuration parameters are: the number of predicted time slots must be greater than or equal to 3 time slots and less than 6 time slots; SetA16 beam; SetB8 beam. The terminal device can use the first information indication to satisfy the function / model of ID1, which involves predicting the AI ​​model of the next 3 or 4 time slots in the SetA16 beam via the SetB8 beam. The terminal device can also use the first information indication to satisfy the function / model of ID2, which involves predicting the AI ​​model of the next 3 time slots in the SetA16 beam via the SetB16 beam. Specific details are not limited here.

[0213] In this embodiment of the application, the first information may also be referred to as auxiliary information, and the naming of this information is not limited in this embodiment of the application.

[0214] Optional, Figure 4 The illustrated embodiment also includes step 404. Step 404 may be performed after step 402.

[0215] 404. The terminal device receives configuration information reported from the network device. Correspondingly, the network device sends configuration information to the terminal device.

[0216] The network device sends configuration information to the terminal device based on the available functions / models or available configuration parameters reported by the terminal device. This configuration information instructs the terminal device on the configuration for reporting prediction results.

[0217] In one possible implementation, the reported configuration information is used to indicate that the prediction result is the first time slot out of X time slots. For example, in the BM-Case2 scenario, the network side requires the prediction of 3 time slots, while the terminal device reports that it can predict 5 time slots. In this case, the network device instructs the terminal device to report the prediction results of the 2nd, 3rd, and 4th time slots out of the 5 time slots by reporting the configuration information.

[0218] In another possible implementation, configuration information is reported to indicate the starting timeslot. For example, if the network requires prediction of 3 timeslots, and the terminal device reports 5 predictable timeslots, then the network device instructs the terminal device to report the first timeslot of the 5 timeslots by reporting configuration information. In other words, the network device requires the terminal device to report the prediction results of the first 3 timeslots starting from the second timeslot.

[0219] In this embodiment of the application, by clearly defining the prediction results reported by the terminal device, the terminal device can meet the network side's requirements based on its own deployed AI model or function, thereby obtaining the benefits brought by the AI ​​function.

[0220] Optional, Figure 4 The illustrated embodiment also includes step 405. Step 405 may be performed after step 404.

[0221] 405. The terminal device sends the prediction result to the network device. Correspondingly, the network device receives the prediction result from the terminal device.

[0222] The terminal device uses AI functions / models to perform inference based on the configuration information reported by the network device and reports the prediction results according to the network side's requirements.

[0223] Optional, Figure 4 The illustrated embodiment also includes step 406. Step 406 may be performed after step 405.

[0224] 406. The terminal device receives the monitoring configuration from the network device. Correspondingly, the network device sends the monitoring configuration to the terminal device.

[0225] Once the AI ​​function or AI model of a terminal device is activated, performance monitoring of that AI function or AI model is required. The network device sends a monitoring configuration to the terminal device, which instructs the terminal device on how to report monitoring results. In other words, the monitoring configuration instructs the terminal device on how to report monitoring results.

[0226] In one possible implementation, the monitoring configuration is used to instruct the terminal device to report monitoring results when reporting prediction results. For example, each time a prediction result is reported, the terminal device reports the prediction result for the current time slot. For instance, in the BM-Case2 scenario, if the network device instructs the terminal device to report the prediction results for the 2nd, 3rd, and 4th time slots, then the terminal device reports the monitoring result for the 2nd time slot in the 2nd time slot, reports the monitoring result for the 3rd time slot in the 3rd time slot, and so on.

[0227] In another possible implementation, the monitoring configuration is used to instruct the terminal device to report the statistical values ​​of the monitoring results for each time slot. For example, in the BM-Case2 scenario, if the network device instructs the terminal device to report the prediction results for the 2nd, 3rd, and 4th time slots, the terminal device will statistically analyze the monitoring results for the 2nd, 3rd, and 4th time slots and report them to the network device. The statistical method could be to calculate the error between the prediction and the actual measurement for each time slot and then take the average; or it could be to calculate the error rate between all prediction results and the actual measurement results. The specific method is not limited here.

[0228] Optional, Figure 4 The illustrated embodiment also includes step 407. Step 407 may be performed after step 406.

[0229] 407. The terminal device sends the monitoring results to the network device. Correspondingly, the network device receives the monitoring results from the terminal device.

[0230] The terminal device monitors the AI ​​function / model according to the monitoring configuration issued by the network device and reports the monitoring results as required by the network side.

[0231] Please see Figure 5 , Figure 5 This is a schematic diagram of another embodiment of a communication method provided in this application. The method includes:

[0232] 501. The terminal device receives the second information from the network device. Correspondingly, the network device sends the second information to the terminal device.

[0233] The terminal device receives a terminal capability request from the network device, which includes second information. This second information inquires whether the terminal device is willing to use multiple functions / models to support a set of configuration parameters. Alternatively, the second information inquires whether the terminal device is willing to determine multiple functions / models based on a set of configuration parameters.

[0234] It should be noted that whether a terminal device is willing to use multiple functions / models to support a set of configuration parameters can be understood as the terminal device having the ability to determine multiple functions or models based on a set of configuration parameters, and the terminal device accepting reporting requests from network devices. In other words, if a terminal device has the ability to determine multiple functions or models based on a set of configuration parameters, but does not accept reporting requests from network devices, i.e., the terminal device does not report, it means that the terminal device is unwilling to use multiple functions / models to support a set of configuration parameters.

[0235] Optionally, the second information is a 1-bit indication. For example, if the second information is "0", it means that the network device does not require the terminal device to use multiple functions / models to support a set of configuration parameters; if the second information is "1", it means that the network device requires the terminal device to use multiple functions / models to support a set of configuration parameters. For another example, if the second information is "0", it means that the network device requires the terminal device to use multiple functions / models to support a set of configuration parameters; if the second information is "1", it means that the network device does not require the terminal device to use multiple functions / models to support a set of configuration parameters. Specific details are not limited here.

[0236] 502. The terminal device sends a second instruction message to the network device. Correspondingly, the network device receives the second instruction message from the terminal device.

[0237] The terminal device indicates through the second indication information that it is willing to determine multiple functions / models based on a set of configuration parameters.

[0238] Optionally, the second indication information is a 1-bit indication. For example, if the second indication information is "0", it indicates that the terminal device does not want to use multiple functions / models to support a set of configuration parameters; if the second indication information is "1", it indicates that the terminal device is willing to use multiple functions / models to support a set of configuration parameters. For another example, if the second indication information is "0", it indicates that the terminal device is willing to use multiple functions / models to support a set of configuration parameters; if the second indication information is "1", it indicates that the terminal device is unwilling to use multiple functions / models to support a set of configuration parameters. Specific details are not limited here.

[0239] Optionally, the second indication information can also be used to indicate that the terminal device is willing to use multiple functions / models to support a set of configuration parameters in a specific AI scenario. For example, if the second indication information is "0", it means that the terminal device is unwilling to use multiple functions / models to support a set of configuration parameters in a beam management scenario; if the second indication information is "1", it means that the terminal device is willing to use multiple functions / models to support a set of configuration parameters in a beam management scenario. As another example, if the second indication information is "0", it means that the terminal device is unwilling to use multiple functions / models to support a set of configuration parameters in a mobility management scenario; if the second indication information is "1", it means that the terminal device is willing to use multiple functions / models to support a set of configuration parameters in a mobility management scenario. Specific details are not limited here.

[0240] 503. The network device sends third configuration parameters to the terminal device. Correspondingly, the terminal device receives the third configuration parameters from the network device.

[0241] The third configuration parameter includes bit information and reporting configuration. The bit information is used to instruct the terminal device to report at least one AI model or AI function that meets the reporting configuration.

[0242] Specifically, network devices can use 1 bit of information to instruct terminal devices to report all AI models or AI functions that meet the reporting configuration. For example, if the reporting configuration requires predicting the next 3 time slots, then if the terminal device has an AI model or AI function that can predict 3 or more time slots, the terminal device will report that the reporting configuration is available to the network device.

[0243] In one possible implementation, this 1-bit information is used to indicate a set of configurations. For example, if the bit information is "1", it means that the network device requires the terminal device to report an AI model or AI function that meets configuration A; if the bit information is "0", it means that the network device does not require the terminal device to report an AI model or AI function that meets configuration A.

[0244] It should be noted that the third configuration parameter may include multiple 1-bit bits to indicate different configurations.

[0245] In another possible implementation, this 1-bit information is used to indicate a specific feature configuration parameter. For example, if the bit information is "1", it means that the network device requires the terminal device to report an AI model or AI function that can predict more than 3 time slots; if the bit information is "0", it means that the network device does not require the terminal device to report an AI model or AI function that can predict more than 3 time slots. Specific details are not limited here.

[0246] 504. The terminal device sends a third instruction message to the network device. Correspondingly, the network device receives the third instruction message from the terminal device.

[0247] The terminal device determines whether there is at least one AI model that satisfies the third configuration parameter based on the third configuration parameter. If so, the terminal device sends a third indication message, while the first indication message indicates that at least one AI model satisfies the third configuration parameter.

[0248] 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. Please refer to [link / reference]. Figure 6 The communication device 600 can be used to perform Figure 4 or Figure 5 The process executed by the terminal device in the illustrated embodiment can be specifically described in the relevant descriptions of the foregoing method embodiments. The communication device 600 may be a terminal device, or a component or device applied to the terminal device (e.g., a processor, circuit, chip, or chip system), or a logic module or software that can implement all or part of the functions of the terminal device.

[0249] The communication device 600 includes an interface module 601 and a processing module 602.

[0250] The processing module 602 is used for data processing. The interface module 601 can implement corresponding communication functions. The interface module 601 can also be called a communication interface or a communication module.

[0251] Optionally, the communication device 600 may further include a storage module, which can be used to store program code, program instructions and / or data. The processing module 602 can read the instructions and / or data in the storage module so that the communication device 600 can implement the aforementioned method embodiments.

[0252] The communication device 600 can be used to perform the actions performed by the terminal device in the above method embodiments. For example, it can be a terminal device, a communication module within a terminal device, or a circuit or chip within a terminal device responsible for communication functions. The communication device 600 can be a terminal device or a component configurable on a terminal device. The processing module 602 is used to perform processing-related operations on the terminal device side in the above method embodiments. The interface module 601 is used to perform receiving-related operations on the terminal device side in the above method embodiments.

[0253] Optionally, interface module 601 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.

[0254] It should be noted that the communication device 600 may include a transmitting module but not a receiving module. Alternatively, the communication device 600 may include a receiving module but not a transmitting module. Specifically, it depends on whether the above-described scheme performed by the communication device 600 includes both transmitting and receiving actions. For example, the communication device 600 is used to perform the above-described... Figure 4 or Figure 5 The actions performed by the terminal device in the illustrated embodiment are shown above. For details, please refer to the above. Figure 4 or Figure 5 The relevant descriptions in the illustrated embodiments will not be elaborated here.

[0255] For example, the communication device 600 is used to execute the following scheme:

[0256] Interface module 601 is used to receive a first configuration parameter, the first configuration parameter including a first prediction range, the first prediction range is used to determine whether there is at least one AI model that satisfies the first prediction range;

[0257] Processing module 602 is used to determine whether at least one AI model meets the prediction range;

[0258] The interface module 601 is also used to send first indication information, which indicates that at least one AI model meets the first prediction range.

[0259] In one possible implementation, the first prediction range is used to indicate that the number of prediction slots is greater than or equal to M, and / or to indicate that the number of prediction slots is less than or equal to N, where M is a positive integer and N is a positive integer greater than M.

[0260] In another possible implementation, the interface module 601 is also used to receive a second configuration parameter, which includes a second prediction range that is different from the first prediction range.

[0261] In another possible implementation, the first indication information is used to indicate that the first model meets the first prediction range;

[0262] The interface module 601 is also used to send first information, which is used to instruct the first model to predict X time slots.

[0263] In another possible implementation, the interface module 601 is also used to receive first reporting configuration information, which indicates that the reporting time slot includes the first time slot among X time slots.

[0264] In another possible implementation, the interface module 601 is also used to receive second reporting configuration information, which indicates that the reporting time slot includes Y time slots out of X time slots, the first time slot out of Y time slots is the i-th time slot out of X time slots, i is a positive integer less than or equal to X-Y+1, and Y is a positive integer less than or equal to X.

[0265] In another possible implementation, the interface module 601 is also used to receive monitoring configuration, which indicates the method of reporting monitoring results. The monitoring results are the monitoring results of Z time slots, and the Z time slots are the time slots reported by the terminal device.

[0266] In another possible implementation, the monitoring configuration is used to indicate the monitoring results reported for Z time slots, or to indicate the statistical values ​​of the monitoring results reported for Z time slots.

[0267] In another possible implementation, interface module 601 is used to receive the first configuration parameters, including:

[0268] The interface module 601 is specifically used to receive a first identifier, which is used to indicate a first configuration parameter.

[0269] In another possible implementation, interface module 601 is also used to receive a second configuration parameter, including:

[0270] The interface module 601 is specifically used to receive the second identifier, which is used to indicate the second configuration parameters.

[0271] In another possible implementation, the first indication information includes a first identifier and / or a second identifier.

[0272] 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.

[0273] Optionally, when the communication device 600 is a terminal device or a communication module within a terminal device, the processing module 602 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 601 can be implemented by a transceiver or transceiver-related circuitry. The interface module 601 may also be referred to as a communication module or communication interface. The storage module can be implemented by at least one memory.

[0274] Optionally, when the communication device 600 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 602 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 601 can be implemented by the interface circuit or data transceiver circuit on the aforementioned chip.

[0275] The following is another structural schematic diagram of the communication device according to an embodiment of this application. Please refer to... Figure 7 Communication devices can be used to perform Figure 4 or Figure 5 The process executed by the network device in the illustrated embodiment can be found in the relevant descriptions in the foregoing method embodiments.

[0276] The communication device 700 includes an interface module 701. Optionally, a processing module 702.

[0277] The processing module 702 is used for data processing. The interface module 701 can implement corresponding communication functions. The interface module 701 can also be called a communication interface or a communication module.

[0278] Optionally, the communication device 700 may further include a storage module, which can be used to store program code, program instructions and / or data. The processing module 702 can read the instructions and / or data in the storage module so that the communication device 700 can implement the aforementioned method embodiments.

[0279] The communication device 700 can be used to perform the actions performed by the network device in the above method embodiments. For example, it can be a network device or a communication module within a network device, or a circuit or chip within a network device responsible for communication functions. The communication device 700 can be a network device or a component configurable within a network device. The processing module 702 is used to perform processing-related operations on the network device side in the above method embodiments. The interface module 701 is used to perform reception-related operations on the network device side in the above method embodiments.

[0280] Optionally, interface module 701 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.

[0281] It should be noted that the communication device 700 may include a transmitting module but not a receiving module. Alternatively, the communication device 700 may include a receiving module but not a transmitting module. Specifically, it depends on whether the above-described scheme performed by the communication device 700 includes both transmitting and receiving actions. For example, the communication device 700 is used to perform the above-described... Figure 4 or Figure 5 The actions performed by the network device in the illustrated embodiment are shown above. For details, please refer to the above. Figure 4 or Figure 5 The relevant descriptions in the illustrated embodiments will not be elaborated here.

[0282] For example, the communication device 700 is used to execute the following scheme:

[0283] Processing module 702 is used to generate the first configuration parameters;

[0284] Interface module 701 is used to send a first configuration parameter, which is used to indicate the prediction range. The prediction range is used to determine whether there is at least one AI model that meets the prediction range.

[0285] The interface module 701 is also used to receive first indication information, which indicates that at least one AI model meets the first prediction range.

[0286] In one possible implementation, the first prediction range is used to indicate that the number of prediction slots is greater than or equal to M, and / or to indicate that the number of prediction slots is less than or equal to N, where M is a positive integer and N is a positive integer greater than M.

[0287] In another possible implementation, the interface module 701 is also used to send a second configuration parameter, which includes a second prediction range that is different from the first prediction range.

[0288] In another possible implementation, the first indication information is used to indicate that the first model meets the first prediction range;

[0289] The interface module 701 is also used to receive first information, which is used to instruct the first model to predict X time slots.

[0290] In another possible implementation, the interface module 701 is also used to send first reporting configuration information, which indicates that the reporting time slot includes the first time slot among X time slots.

[0291] In another possible implementation, the interface module 701 is also used to send second reporting configuration information, which indicates that the reporting time slot includes Y time slots out of X time slots, the first time slot out of Y time slots is the i-th time slot out of X time slots, i is a positive integer less than or equal to X-Y+1, and Y is a positive integer less than or equal to X.

[0292] In another possible implementation, interface module 701 is also used to send monitoring configuration, which indicates the method of reporting monitoring results. The monitoring results are the monitoring results of Z time slots, and Z time slots are the time slots reported by the terminal device.

[0293] In another possible implementation, the monitoring configuration is used to indicate the monitoring results reported for Z time slots, or to indicate the statistical values ​​of the monitoring results reported for Z time slots.

[0294] In another possible implementation, interface module 701 is used to send the first configuration parameters, including:

[0295] Interface module 701 is specifically used to send a first identifier, which is used to indicate the first configuration parameter.

[0296] In another possible implementation, interface module 701 is also used to send a second configuration parameter, including:

[0297] Interface module 701 is specifically used to send a second identifier, which is used to indicate a second configuration parameter.

[0298] In another possible implementation, the first indication information includes a first identifier and / or a second identifier.

[0299] 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.

[0300] The processing module 702 in the above embodiments can be implemented by at least one processor or processor-related circuitry. The interface module 701 can be implemented by a transceiver or transceiver-related circuitry. The interface module 701 can also be referred to as a communication module or communication interface. The storage module can be implemented by at least one memory.

[0301] The following describes a communication device provided in an embodiment of this application. Please refer to [link / reference]. Figure 8 , Figure 8This is a schematic diagram of a communication device provided in an embodiment of this application. The communication device can be a network device or a terminal device in the above method embodiments, or it can be a chip, chip system, or processor that supports the network device or terminal 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.

[0302] The communication device may include one or more processors 801, which are connected to a memory 802, an input / output unit 803, and a bus 804. The processor 801 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.

[0303] Optionally, the communication device may include one or more memories 802, which may store instructions that can be executed on the processor 801 to cause the communication device to perform the methods described in the above method embodiments. Optionally, the memories 802 may also store data. The processor 801 and the memories 802 may be configured separately or integrated together.

[0304] 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.

[0305] In another possible design, the processor 801 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 can be used for reading and writing code / data, or it can be used for transmitting or relaying signals.

[0306] In another possible design, the processor 801 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 801; in this case, the processor 801 may be implemented in hardware.

[0307] In another possible design, the communication device may include a circuit that can perform the sending or receiving or communication functions of the network device or terminal 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.

[0308] The communication device described in the above embodiments may be a network device or a terminal device, but the scope of the communication device described in the embodiments of this application is not limited to this, and the structure of the communication device may vary. Figure 8 The communication device can be a standalone device or part of a larger device. For example, the communication device can be:

[0309] (1) Independent integrated circuit IC, or chip, or chip system or subsystem;

[0310] (2) A collection of one or more ICs, optionally including a storage component for storing data and instructions;

[0311] (3) ASIC, such as modem;

[0312] (4) Modules that can be embedded in other devices;

[0313] (5) Receivers, terminals, smart terminals, cellular phones, wireless devices, handheld devices, mobile units, vehicle-mounted devices, network devices, cloud devices, artificial intelligence devices, etc.

[0314] (6) Others, etc.

[0315] For cases where the communication device can be a chip or a chip system, please refer to [link / reference]. Figure 9 The diagram shows the structure of the chip. Figure 9 The chip 900 shown includes a processor 901 and an interface 902. Optionally, it may also include a memory 903. The number of processors 901 can be one or more, and the number of interfaces 902 can be multiple.

[0316] For cases where the chip is used to implement the functions of the network device or terminal device in the embodiments of this application:

[0317] The interface 902 is used to receive or output signals;

[0318] The processor 901 is used to perform data processing operations on network devices or terminal devices.

[0319] Figure 10 An example diagram of an O-RAN architecture (CU-DU separation architecture) is shown. The O-RAN architecture may also include other components besides those shown in the diagram, which are not limited here.

[0320] In a communication system, network elements are connected via interfaces (e.g., NG, Xn) or air interfaces (Uu). These network element nodes, such as core network equipment, access network nodes (RAN nodes), and one or more devices in terminals, may also contain one or more AI modules (only one is shown in the figure for clarity). An access network node can be a single RAN node or can include multiple RAN nodes, for example, CU and DU. CU and / or DU may also contain one or more AI modules. Optionally, a CU may be further divided into CU-CP and CU-UP. CU-CP and / or CU-UP contain one or more AI models. AI modules are used to implement corresponding AI functions. AI modules deployed in different network elements can be the same or different. Depending on the parameter configuration, AI modules can implement different functions. An AI module can have one or more models. A model can infer an output, which includes one or more parameters. The learning, training, or inference processes of different models can be deployed in different nodes or devices, or they can be deployed in the same node or device.

[0321] Figure 11Another O-RAN architecture is presented: the RAN Intelligent Controller (RIC) architecture. RICs include near-real-time (near-RT) RICs and non-real-time (non-RT) RICs. Near-real-time RICs are used for model training and inference. For example, they are used to train AI models and then use those models for inference. Near-real-time RICs can obtain network-side and / or terminal-side information from RAN nodes (e.g., CUs, CU-CPs, CU-UPs, DUs, and / or RUs) and / or terminals. This information can be used as training data or inference data. Optionally, near-real-time RICs can deliver inference results to RAN nodes and / or terminals. Optionally, inference results can be exchanged between CUs and DUs, and / or between DUs and RUs. For example, a near-real-time RIC delivers an inference result to a DU, which then forwards it to an RU.

[0322] Non-real-time RICs are used for model training and inference. For example, they are used to train AI models and then use those models for inference. Non-real-time RICs can obtain network-side and / or terminal-side information from RAN nodes (e.g., CUs, CU-CPs, CU-UPs, DUs, and / or RUs and / or terminals). This information can be used as training data or inference data, and the inference results can be delivered to RAN nodes and / or terminals. Optionally, inference results can be exchanged between CUs and DUs, and / or between DUs and RUs; for example, a non-real-time RIC delivers the inference result to a DU, which then forwards it to an RU. Near-real-time RICs and non-real-time RICs can also be configured as separate network elements. Optionally, near-real-time RICs and non-real-time RICs can also be part of other devices; for example, a near-real-time RIC can be located in a RAN node (e.g., in a CU or DU), while a non-real-time RIC can be located in an OAM, cloud server, core network device, or other network device.

[0323] In one possible implementation, the CU is used to perform actions such as Figure 4 The illustrated embodiment includes steps 401 (network device sending first configuration parameters to terminal device), 401a (network device sending second configuration parameters to terminal device), 402 (network device receiving second indication information from terminal device), and 403 (network device receiving first information from terminal device). The CU determines the reported configuration information for activating a model based on the available functions / models reported by the terminal device and the configuration sent by the CU. Figure 4In step 404 of the illustrated embodiment, the CU sends the reported configuration information to the terminal device, instructing the terminal device to activate the function / model under the corresponding configuration, so that the DU can perform corresponding processing after receiving the inference prediction report from the terminal device.

[0324] In another possible implementation, the CU is used to perform actions such as Figure 4 The illustrated embodiment includes steps 401 (network device sending first configuration parameters to terminal device), 401a (network device sending second configuration parameters to terminal device), 402 (network device receiving second indication information from terminal device), and 403 (network device receiving first information from terminal device). The CU determines the reported configuration information for activating a model based on the available functions / models reported by the terminal device and the configuration sent by the CU. Figure 4 In step 404 of the illustrated embodiment, the CU sends the reported configuration information to the DU. The DU decides whether to activate the corresponding function / model / configuration and instructs the CU accordingly. The DU can also provide the CU with auxiliary information required for configuration.

[0325] 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.

[0326] 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.

[0327] 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.

[0328] 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.

[0329] 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.

[0330] 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.

[0331] 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.

[0332] 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.

[0333] 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.

[0334] 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.

[0335] 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 includes: Receive a first configuration parameter, the first configuration parameter including a first prediction range, the first prediction range is used to determine whether there is at least one AI model that satisfies the first prediction range; Send a first indication message, which indicates that at least one AI model meets the first prediction range.

2. The method according to claim 1, characterized in that, The first prediction range is used to indicate that the number of prediction slots is greater than or equal to M, and / or to indicate that the number of prediction slots is less than or equal to N, where M is a positive integer and N is a positive integer greater than M.

3. The method according to claim 1 or 2, characterized in that, The method further includes: Receive a second configuration parameter, the second configuration parameter including a second prediction range, the second prediction range being different from the first prediction range.

4. The method according to any one of claims 1 to 3, characterized in that, The first indication information is used to indicate that the first model meets the first prediction range, and the method further includes: Send a first message, which instructs the first model to predict X time slots.

5. The method according to claim 4, characterized in that, The method further includes: Receive first reporting configuration information, which indicates that the reporting time slot includes the first time slot among the X time slots.

6. The method according to claim 4, characterized in that, The method further includes: Receive second reporting configuration information, which indicates that the reporting time slot includes Y time slots out of the X time slots, and the first time slot out of the Y time slots is the i-th time slot out of the X time slots, where i is a positive integer less than or equal to X-Y+1 and Y is a positive integer less than or equal to X.

7. The method according to claims 1 to 6, characterized in that, The method further includes: Receive monitoring configuration, the monitoring configuration is used to indicate the method of reporting monitoring results, the monitoring results are the monitoring results of Z time slots, and the Z time slots are the time slots reported by the terminal device.

8. The method according to claim 7, characterized in that, The monitoring configuration is used to indicate the monitoring results of the Z time slots to be reported, or to indicate the statistical values ​​of the monitoring results of the Z time slots to be reported.

9. The method according to any one of claims 1 to 8, characterized in that, The receiving of the first configuration parameter includes: Receive a first identifier, which is used to indicate the first configuration parameter.

10. The method according to any one of claims 3 to 9, characterized in that, The receiving of the second configuration parameter includes: Receive a second identifier, which is used to indicate the second configuration parameter.

11. The method according to claim 10, characterized in that, The first indication information includes the first identifier and / or the second identifier.

12. A communication method, characterized in that, The method includes: Send a first configuration parameter, which is used to indicate the prediction range, and the prediction range is used to determine whether there is at least one AI model that meets the prediction range; Receive first indication information, which indicates that at least one AI model satisfies the first prediction range.

13. The method according to claim 12, characterized in that, The first prediction range is used to indicate that the number of prediction slots is greater than or equal to M, and / or to indicate that the number of prediction slots is less than or equal to N, where M is a positive integer and N is a positive integer greater than M.

14. The method according to claim 12 or 13, characterized in that, The method further includes: Send a second configuration parameter, which includes a second prediction range that is different from the first prediction range.

15. The method according to any one of claims 12 to 14, characterized in that, The first indication information is used to indicate that the first model meets the first prediction range, and the method further includes: Receive first information, which is used to instruct the first model to predict X time slots.

16. The method according to claim 15, characterized in that, The method further includes: Send first reporting configuration information, which indicates that the reporting time slot includes the first time slot among the X time slots.

17. The method according to claim 15, characterized in that, The method further includes: Send second reporting configuration information, which is used to indicate that the reporting time slot includes Y time slots out of the X time slots, and the first time slot out of the Y time slots is the i-th time slot out of the X time slots, where i is a positive integer less than or equal to X-Y+1, and Y is a positive integer less than or equal to X.

18. The method according to claims 12 to 17, characterized in that, The method further includes: Send monitoring configuration, which is used to indicate the method of reporting monitoring results, wherein the monitoring results are the monitoring results of Z time slots, and the Z time slots are the time slots reported by the terminal device.

19. The method according to claim 18, characterized in that, The monitoring configuration is used to indicate the monitoring results reported for Z time slots, or to indicate the statistical values ​​of the monitoring results reported for Z time slots.

20. The method according to any one of claims 12 to 19, characterized in that, Sending the first configuration parameter includes: Send a first identifier, which is used to indicate the first configuration parameter.

21. The method according to any one of claims 14 to 20, characterized in that, Sending the second configuration parameter includes: Send a second identifier, which is used to indicate the second configuration parameter.

22. The method according to claim 21, characterized in that, The first indication information includes the first identifier and / or the second identifier.

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

24. A communication device, characterized in that, Includes modules or units for performing the method as described in any of claims 12 to 22.

25. A communication device, characterized in that, include: A processor for executing a program that causes the communication device to perform the method as described in any one of claims 1 to 11.

26. A communication device, characterized in that, include: A processor for executing a program that causes the communication device to perform the method as described in any one of claims 12 to 22.

27. A communication system, characterized in that, include: A communication device for performing any of the methods described in steps 1 to 11, and a communication device for performing any of the methods described in claims 12 to 22.

28. A computer-readable storage medium comprising 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 22.

29. A computer program product comprising instructions that, when run on a computer, causes the computer to perform the method as claimed in any one of claims 1 to 11, or causes the computer to perform the method as claimed in any one of claims 12 to 22.

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