Communication method and related equipment

Abstract

The invention discloses a communication method and related equipment, which are used for enabling a communication device in a communication system to serve as a data acquisition node so as to realize data acquisition. In the method, after a first communication device sends first information used for indicating state information of the first communication device, the first communication device can receive second information used for configuring first data acquisition and acquire data based on the second information. Wherein the second information is determined based on state information of the first communication device. In other words, the first communication device can perform data acquisition based on the configuration of the second communication device. Therefore, the communication device in the communication system can be used as a data acquisition node to realize data acquisition.

Description

Technical Field

[0001] The present application relates to the field of communications, and in particular to a communication method and related equipment. Background Art

[0002] Wireless communication can be the transmission communication between two or more communication nodes without propagation through conductors or cables. The communication nodes generally include network equipment and terminal equipment.

[0003] At present, in wireless communication systems, communication nodes generally have signal transceiving capabilities and computing capabilities. Taking network devices with computing capabilities as an example, the computing capabilities of network devices mainly provide computing power support for signal transceiving capabilities (for example: sending and receiving signals) to achieve communication between network devices and other communication nodes.

[0004] However, in a communication network, the computing power of communication nodes may have surplus computing power in addition to providing computing power support for the above communication tasks. Therefore, how to utilize this computing power is a technical problem that needs to be solved urgently. Summary of the invention

[0005] The present application provides a communication method and related equipment, which are used to enable a communication device in a communication system to serve as a data collection node to achieve data collection.

[0006] The first aspect of the present application provides a communication method, which is performed by a first communication device, which may be a communication device (such as a network device or a terminal device), or the first communication device may be a partial component in a communication device (such as a processor, a chip or a chip system, etc.), or the first communication device may also be a logic module or software that can implement all or part of the functions of the communication device. In this method, the first communication device sends a first message, which is used to indicate the status information of the first communication device; the first communication device receives a second message, which is used to configure the first data collection; wherein the second information is determined based on the status information of the first communication device; and the first communication device collects data based on the second information.

[0007] Based on the above technical solution, after the first communication device sends the first information indicating the status information of the first communication device, the first communication device can receive the second information for configuring the first data collection, and collect data based on the second information. The second information is determined based on the status information of the first communication device. In other words, the first communication device can collect data based on the configuration of the second communication device. Thus, the communication device in the communication system can serve as a data collection node to achieve data collection.

[0008] In addition, in the above technical solution, the second information for configuring the first data collection may be determined based on the status information of the first communication device. In this way, the configuration of the first data collection can be adapted to the status of the first communication device, thereby improving the success rate of data collection.

[0009] Optionally, the sending of the second information for configuring the first data collection may be triggered by an artificial intelligence (AI) task functionality ID. For example, when the AI task functionality ID is enabled, the second communication device may send the second information, enabling the first communication device to perform data collection based on the second information.

[0010] In this application, terms such as data collection, data gathering, data collection, data acquisition, data capture, etc. may be used interchangeably.

[0011] In a possible implementation manner of the first aspect, the second information includes indication information indicating N collection conditions for the first data collection, where N is a positive integer; wherein, the status indicated by the status information satisfies the N collection conditions.

[0012] Optionally, the indication information in the second information may indicate the N conditions in various ways. For example, the indication information may include the N conditions themselves, or may include the indexes / identifications of the N conditions, etc.

[0013] Based on the above technical solution, the second information for configuring the first data collection may include N collection conditions for the first data collection, and the status indicated by the status information satisfies the N collection conditions. In this way, the first communication device can perform data collection based on the collection conditions that meet the status of the first communication device.

[0014] In a possible implementation manner of the first aspect, the second information includes indication information for indicating to perform the first data collection based on the N collection conditions.

[0015] Based on the above technical solution, through the above indication information, the first communication device can clarify that the N collection conditions are used for the first data collection of the first communication device, and subsequently the first communication device can perform data collection based on the N collection conditions.

[0016] In a possible implementation manner of the first aspect, the second information further includes indication information indicating M collection conditions, where the M collection conditions are conditions for a second data collection, and M is a positive integer.

[0017] Optionally, the second information further includes indication information for indicating not to perform the first data collection based on the M collection conditions.

[0018] Optionally, the M acquisition conditions may be determined based on the status information of the first communication device sent by the first communication device, or may be determined based on the status information of other communication devices, or may be determined based on the status information of multiple communication devices (including the first communication device and other communication devices).

[0019] Optionally, the second data acquisition is different from the first data acquisition configured for the second information. The second data acquisition may be used for other data acquisition processes of the first communication device or for data acquisition processes of other communication devices, which is not limited herein.

[0020] Based on the above technical solution, the second information received by the first communication device may further include conditions indicating other data acquisitions. In this way, the second communication device can configure conditions for other data acquisition processes in the second information, and can flexibly indicate whether to perform data acquisition based on the M acquisition conditions through additional indication information, which can reduce the implementation complexity.

[0021] In a possible implementation manner of the first aspect, the second information includes at least one of the following: the AI task identifier corresponding to the first data acquisition, the AI function identifier corresponding to the first data acquisition, and the dataset size information of the first data acquisition.

[0022] Based on the above technical solution, the second information for configuring the first data acquisition may include at least one of the above to implement the indication of the task and / or dataset size corresponding to the data acquisition.

[0023] In a possible implementation manner of the first aspect, the status information includes at least one of the following: system status information, AI status information, dataset status information, and scenario status information.

[0024] Based on the above technical solution, the status information of the first communication device may include at least one of the above, so that the second communication device can determine at least one of the above statuses of the first communication device based on the first information, thereby improving the flexibility of the solution implementation.

[0025] Optionally, each of the above statuses satisfies one or more of the following:

[0026] The system status information includes at least one of a system parameter set and a signal processing configuration;

[0027] The AI status information includes at least one of AI model parameters and AI computing power resources;

[0028] The scenario status information includes at least one of signal to noise ratio (SNR) status information, motion status information, reference signal configuration information, and antenna configuration information;

[0029] The dataset status information includes at least one of the acquisition time information of the dataset, the geographical location information of the dataset, the acquisition phase information corresponding to the dataset, the indication information indicating whether the dataset is existing data, and the indication information indicating the reporting method of the dataset.

[0030] In a possible implementation manner of the first aspect, the second information is determined based on the status information of the first communication device, including: the second information is determined based on the status information of the first communication device and the task requirements of the AI task.

[0031] Based on the above technical solution, the determination basis of the second information may further include the task requirements of the AI task in addition to the status information of the first communication device. In this way, the data collected by the first communication device based on the second information can meet the task requirements.

[0032] Optionally, the data collected by the first communication device can be used for model processing of the AI model (such as at least one of model training, model inference, and model monitoring). In this way, the communication devices in the communication system can serve as data collection nodes for AI-related data to achieve the collection of AI-related data.

[0033] Optionally, wireless communication signals can be transmitted between different communication devices (such as the transceiver of communication resource configuration information, the transceiver of reference signals, etc.). The AI models involved in the embodiments of the present application (such as the first AI model, the second AI model, etc. mentioned later) can be used to process the wireless communication signals (including at least one of management, configuration, update, and optimization). For example, the AI model can include an AI model for modulation and / or demodulation, an AI model for channel prediction, an AI model for beam management, an AI model for assisted positioning, an AI model for channel compression, an AI model for resource scheduling, an AI model for mobility management, an AI model for load balancing, an AI model for network energy saving, and an AI model for replacing one or more modules in the transmitter and / or receiver. Or, the AI models involved in the embodiments of the present application can also be AI models for other AI tasks, such as an AI model for image recognition, an AI model for natural language processing, an AI model for computer vision, etc.

[0034] In this application, terms such as AI model, neural network model, AI neural network model, machine learning model, and AI processing model can be used interchangeably.

[0035] In a possible implementation of the first aspect, part or all of the data is used for the processing of the first AI model, and the first AI model is deployed in the first communication device.

[0036] Based on the above technical solution, in the data obtained by the first communication device through data collection based on the second information, part or all of the data can be used for the processing of the first AI model deployed in the first communication device. In this way, the first communication device can realize the collection of the data used for the model processing of the AI model deployed in the first communication device based on the first data collection configured by the second information.

[0037] In a possible implementation of the first aspect, part or all of the data is also used for the processing of the second AI model, and the second AI model is deployed in the second communication device; wherein, the input of the first AI model includes the output of the second AI model, or the input of the second AI model includes the output of the first AI model; the method further includes: the first communication device sending part or all of the data.

[0038] Based on the above technical solution, in the data obtained by the first communication device through data collection based on the second information, part or all of the data can also be used for the processing of the second AI model deployed in the second communication device. In this way, the first communication device can realize the collection of the data used for the model processing of the AI models deployed on both sides based on the first data collection configured by the second information.

[0039] In a possible implementation of the first aspect, part or all of the data is used for the processing of the second AI model, and the second AI model is deployed in the second communication device; the method further includes: the first communication device sending part or all of the data.

[0040] Based on the above technical solution, in the data obtained by the first communication device through data collection based on the second information, part or all of the data can be used for the processing of the second AI model deployed in the second communication device. In this way, the first communication device can realize the collection of the data used for the model processing of the AI model deployed in the second communication device based on the first data collection configured by the second information.

[0041] The second aspect of the present application provides a communication method, which is executed by a second communication device. The second communication device may be a communication device (such as a network device or a terminal device), or the second communication device may be some components in the communication device (such as a processor, a chip, or a chip system, etc.), or the second communication device may also be a logical module or software that can implement all or part of the functions of the communication device. In this method, the second communication device receives first information, which is used to indicate the status information of the first communication device; the second communication device sends second information, which is used to configure data collection; wherein, the second information is determined based on the status information of the first communication device.

[0042] Based on the above technical solution, after the second communication device receives the first information for indicating the status information of the first communication device, the second communication device may send the second information for configuring the first data collection, so that the first communication device can collect data based on the second information after receiving the first information. Wherein, the second information is determined based on the status information of the first communication device. In other words, the first communication device can perform data collection based on the configuration of the second communication device. Thus, the communication devices in the communication system can serve as data collection nodes to achieve data collection.

[0043] In addition, in the above technical solution, the second information for configuring the first data collection may be determined based on the status information of the first communication device. In this way, the configuration of the first data collection can be adapted to the status of the first communication device, thereby improving the success rate of data collection.

[0044] In a possible implementation manner of the second aspect, the second information includes indication information indicating N collection conditions for the data collection, where N is a positive integer; wherein, the status indicated by the status information satisfies the N collection conditions.

[0045] Optionally, the indication information in the second information may indicate the N conditions in various ways. For example, the indication information may include the N conditions themselves, or may include the indexes / identifications of the N conditions, etc.

[0046] Based on the above technical solution, the second information for configuring the first data collection may include N collection conditions for the first data collection, and the status indicated by the status information satisfies the N collection conditions. In this way, the first communication device can perform data collection based on the collection conditions that meet the status of the first communication device.

[0047] In a possible implementation manner of the second aspect, the second information includes indication information for indicating data collection based on the N collection conditions.

[0048] Based on the above technical solution, through the above indication information, the first communication device can clarify that the N acquisition conditions are for the first data acquisition of the first communication device. Subsequently, the first communication device can perform data acquisition based on the N acquisition conditions.

[0049] In a possible implementation manner of the second aspect, the second information further includes indication information indicating M acquisition conditions, where the M acquisition conditions are conditions for second data acquisition, and M is a positive integer.

[0050] Optionally, the second information further includes indication information for indicating not to perform data acquisition based on the M acquisition conditions.

[0051] Optionally, the M acquisition conditions can be determined based on the status information of the first communication device sent by the first communication device, or can be determined based on the status information of other communication devices, or can be determined based on the status information of multiple communication devices (including the first communication device and other communication devices).

[0052] Optionally, the second data acquisition is different from the first data acquisition configured by the second information. The second data acquisition can be used for other data acquisition processes of the first communication device or for data acquisition processes of other communication devices, which is not limited here.

[0053] Based on the above technical solution, the second information sent by the second communication device can further include indication information for other data acquisition conditions. In this way, the second communication device can configure conditions for other data acquisition processes in the second information and flexibly indicate whether to perform data acquisition based on the M acquisition conditions through additional indication information, which can reduce the implementation complexity.

[0054] In a possible implementation manner of the second aspect, the second information further includes at least one of the following: the AI task identifier corresponding to the data acquisition, the dataset size information of the data acquisition.

[0055] Based on the above technical solution, the second information for configuring the first data acquisition can include at least one of the above to implement the indication of the task and / or dataset size corresponding to the data acquisition.

[0056] In a possible implementation manner of the second aspect, the status information includes at least one of the following: system status information, AI status information, dataset status information, scenario status information.

[0057] Based on the above technical solution, the status information of the first communication device can include at least one of the above, enabling the second communication device to determine at least one of the above statuses of the first communication device based on the first information, so as to improve the flexibility of the scheme implementation.

[0058] Optionally, each of the above states satisfies one or more of the following:

[0059] The system status information includes at least one of a system parameter set and a signal processing configuration;

[0060] The AI status information includes at least one of AI model parameters and AI computing power resources;

[0061] The scenario status information includes at least one of SNR status information, motion status information, reference signal configuration information, and antenna configuration information;

[0062] The dataset status information includes at least one of the acquisition time information of the dataset, the geographical location information of the dataset, the acquisition stage information corresponding to the dataset, the indication information indicating whether the dataset is existing data, and the indication information indicating the reporting method of the dataset.

[0063] In a possible implementation manner of the second aspect, the second information is determined based on the status information of the first communication device, including: the second information is determined based on the status information of the first communication device and the task requirements of the AI task, and the data satisfies the task requirements.

[0064] Based on the above technical solution, the basis for determining the second information may further include the task requirements of the AI task in addition to the status information of the first communication device. In this way, the data collected by the first communication device based on the second information can satisfy the task requirements.

[0065] Optionally, the data collected by the first communication device can be used for model processing of the AI model (such as at least one of model training, model inference, and model monitoring). In this way, the communication device in the communication system can be used as a data collection node for AI-related data to implement the collection of AI-related data.

[0066] Optionally, wireless communication signals can be transmitted between different communication devices (e.g., transceiver of configuration information of communication resources, transceiver of reference signals, etc.). The AI models involved in the embodiments of the present application (e.g., the first AI model, the second AI model, etc. mentioned hereinafter) can be used to process the wireless communication signals (including at least one of management, configuration, update, optimization). For example, the AI model can include one or more of an AI model for modulation and / or demodulation, an AI model for channel prediction, an AI model for beam management, an AI model for assisting positioning, an AI model for channel compression, an AI model for resource scheduling, an AI model for mobility management, an AI model for load balancing, an AI model for network energy saving, and an AI model for replacing one or more modules in a transmitter and / or a receiver. Alternatively, the AI models involved in the embodiments of the present application can also be AI models for other AI tasks, such as an AI model for image recognition, an AI model for natural language processing, an AI model for computer vision, etc.

[0067] In the present application, terms such as AI model, neural network model, AI neural network model, machine learning model, AI processing model, etc. can be replaced with each other.

[0068] In a possible implementation manner of the second aspect, part or all of the data is used for processing by the first AI model, and the first AI model is deployed in the first communication device.

[0069] Based on the above technical solution, in the data obtained by the first communication device through data collection based on the second information, part or all of the data can be used for processing by the first AI model deployed in the first communication device. In this way, the first communication device can realize the collection of data used for model processing of the AI model deployed in the first communication device based on the first data collection configured by the second information.

[0070] In a possible implementation manner of the second aspect, part or all of the data is also used for processing by the second AI model, and the second AI model is deployed in the second communication device; wherein, the input of the first AI model includes the output of the second AI model, or the input of the second AI model includes the output of the first AI model; the method further includes: the second communication device receives part or all of the data.

[0071] Based on the above technical solution, in the data obtained by the first communication device through data collection based on the second information, part or all of the data can also be used for processing by the second AI model deployed in the second communication device. In this way, the first communication device can realize the collection of data used for model processing of the AI models deployed on both sides based on the first data collection configured by the second information.

[0072] In a possible implementation of the second aspect, part or all of the data is used for the processing of the second AI model, and the second AI model is deployed in the second communication device; the method further includes: the second communication device receives part or all of the data.

[0073] Based on the above technical solution, in the data collected by the first communication device based on the second information, part or all of the data can be used for the processing of the second AI model deployed in the second communication device. In this way, the first communication device can implement the collection of the data used for the model processing of the AI model deployed in the second communication device based on the first data collection configured by the second information.

[0074] A third aspect of the present application provides a communication device, which is the first communication device, and the device includes a transceiver unit and a processing unit; the transceiver unit is used to send the first information, and the first information is used to indicate the status information of the first communication device; the transceiver unit is further used to receive the second information, and the second information is used to configure data collection; wherein, the second information is determined based on the status information of the first communication device; the processing unit collects data based on the second information.

[0075] In the third aspect of the present application, the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation of the first aspect and achieve the corresponding technical effects. Specifically, reference can be made to the first aspect, and details are not described here again.

[0076] A fourth aspect of the present application provides a communication device, which is the second communication device, and the device includes a transceiver unit and a processing unit. The transceiver unit is used to receive the first information, and the first information is used to indicate the status information of the first communication device; the processing unit is used to determine the second information; the transceiver unit is further used to send the second information, and the second information is used to configure data collection; wherein, the second information is determined based on the status information of the first communication device.

[0077] In the fourth aspect of the present application, the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation of the second aspect and achieve the corresponding technical effects. Specifically, reference can be made to the second aspect, and details are not described here again.

[0078] A fifth aspect of the present application provides a communication device, including at least one processor, and the at least one processor is coupled to a memory; the memory is used to store programs or instructions; the at least one processor is used to execute the programs or instructions so that the device implements the method described in any one of the possible implementations in any one of the first aspect to the second aspect.

[0079] The sixth aspect of the present application provides a communication device, including at least one logic circuit and an input / output interface; the logic circuit is configured to execute the method described in any possible implementation manner of any one of the foregoing first aspect to second aspect.

[0080] The seventh aspect of the present application provides a communication system, which includes the above-mentioned first communication device and a second communication device.

[0081] The eighth aspect of the present application provides a computer-readable storage medium, which is used to store one or more computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes the method described in any possible implementation manner of any one of the above first aspect to second aspect.

[0082] The ninth aspect of the present application provides a computer program product (or computer program). When the computer program in the computer program product is executed by the processor, the processor executes the method described in any possible implementation manner of any one of the above first aspect to second aspect.

[0083] The tenth aspect of the present application provides a chip system, which includes at least one processor, and is configured to support a communication device to implement the method described in any possible implementation manner of any one of the above first aspect to second aspect.

[0084] In a possible design, the chip system may further include a memory, which is used to store necessary program instructions and data of the communication device. The chip system may be composed of chips, or may include chips and other discrete devices. Optionally, the chip system further includes an interface circuit, and the interface circuit provides program instructions and / or data for the at least one processor.

[0085] Among them, the technical effects brought by any one of the design manners from the third aspect to the tenth aspect can refer to the technical effects brought by different design manners of the above first aspect to second aspect, and will not be elaborated here. Description of the Drawings

[0086] Figures 1a to 1c It is a schematic diagram of the communication system provided by the present application;

[0087] Figures 2a to 2h It is a schematic diagram of the AI processing process involved in the present application;

[0088] Figure 3 It is an interaction schematic diagram of the communication method provided by the present application;

[0089] Figures 4 to 5 It is a schematic diagram of the second information provided by the present application;

[0090] Figure 6 Another interaction schematic diagram of the communication method provided by this application;

[0091] Figures 7 to 11 Schematic diagram of the communication device provided by this application. Detailed implementation manners

[0092] First, some terms in the embodiments of this application are explained to facilitate the understanding of those skilled in the art.

[0093] (1) Terminal device: It can be a wireless terminal device capable of receiving scheduling and indication information from a network device. The wireless terminal device can be a device that provides voice and / or data connectivity to a user, or a handheld device with a wireless connection function, or other processing devices connected to a wireless modem.

[0094] A terminal device can communicate with one or more core networks or the Internet via a radio access network (RAN). The terminal device can be a mobile terminal device, such as a mobile phone (or a "cellular" phone, mobile phone), a computer, and a data card. For example, it can be a portable, pocket-sized, handheld, computer-integrated, or vehicle-mounted mobile device that exchanges voice and / or data with the radio access network. For example, devices such as personal communication service (PCS) phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), tablets (Pads), and computers with wireless transceiver functions. The wireless terminal device can also be referred to as a system, subscriber unit, subscriber station, mobile 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.

[0095] By way of example and not limitation, in the embodiments of the present application, the terminal device may also be a wearable device. A wearable device may also be referred to as a wearable intelligent device or a smart wearable device, etc. It is a general term for devices developed by applying wearable technology to the intelligent design of daily wear, such as glasses, gloves, watches, clothing, and shoes. A wearable device is a portable device that is either directly worn on the body or integrated into the user's clothes or accessories. A wearable device is not only a hardware device, but also realizes powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable intelligent devices include those with complete functions and large sizes that can realize complete or partial functions without relying on a smart phone, such as smart watches or smart glasses, etc., and those that only focus on a certain type of application function and need to cooperate with other devices such as smart phones, such as various smart bracelets for vital sign monitoring, smart helmets, and smart jewelry.

[0096] The terminal may also be a drone, a robot, a terminal in device-to-device (D2D) communication, a terminal in vehicle-to-everything (V2X) communication, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal in industrial control, a wireless terminal in self-driving, a wireless terminal in remote medical, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, etc.

[0097] In addition, the terminal device may also be a terminal device in a communication system evolved after the fifth-generation (5G) communication system (such as the sixth-generation (6G) communication system, etc.) or a terminal device in a future evolved public land mobile network (PLMN). Exemplarily, the 6G network can further expand the form and function of 5G communication terminals. 6G terminals include, but are not limited to, vehicles, cellular network terminals (integrating satellite terminal functions), drones, and internet of things (IoT) devices.

[0098] In the embodiments of the present application, the above terminal device may also obtain AI services provided by a network device. Optionally, the terminal device may also have AI processing capabilities.

[0099] (2) Network device: It can be a device in a wireless network. For example, the network device can be a RAN node (or device) that connects a terminal device to a wireless network, and can also be called a base station. Currently, some examples of RAN devices are: base station, evolved NodeB (eNodeB), gNB (gNodeB) in a 5G communication system, 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. Additionally, in a network structure, the network device can include a centralized unit (CU) node, or a distributed unit (DU) node, or a RAN device including a CU node and a DU node.

[0100] Optionally, the RAN node can also be a macro base station, a micro base station or an indoor station, a relay node or a donor node, or a radio controller in a cloud radio access network (CRAN) scenario. The RAN node can also be a server, a wearable device, a vehicle or an in-vehicle device, etc. For example, the access network device in vehicle to everything (V2X) technology can be a road side unit (RSU).

[0101] In another possible scenario, multiple RAN nodes cooperate to assist a terminal in achieving wireless access, and different RAN nodes respectively implement partial functions of a base station. For example, the RAN nodes can be a central unit (CU), a distributed unit (DU), a CU-control plane (CP), a CU-user plane (UP), or a radio unit (RU), etc. The CU and the DU can be separately provided, or can also be included in the same network element, such as a baseband unit (BBU). The RU can be included in a radio frequency device or a radio frequency unit, such as included in a remote radio unit (RRU), an active antenna unit (AAU), or a remote radio head (RRH).

[0102] In different systems, the CU (or CU-CP and CU-UP), the DU, or the RU can also have different names, but those skilled in the art can understand their meanings. For example, in an open RAN (O-RAN or ORAN) system, the CU can also be called an O-CU (open CU), the DU can also be called an O-DU, the CU-CP can also be called an O-CU-CP, the CU-UP can also be called an O-CU-UP, and the RU can also be called an O-RU. For the convenience of description, in this application, the CU, CU-CP, CU-UP, DU, and RU are used as examples for description. Any one of the CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

[0103] The communication between the access network device and the terminal device follows a certain protocol layer structure. The 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.

[0104] For the correspondence between the network elements in the ORAN system and their achievable protocol layer functions, refer to Table 1 below.

[0105] Table 1

[0106] ORAN network element Protocol layer functions of 3GPP O-CU-CP RRC+PCDP - Control Plane (PDCP-C) O-CU-UP SDAP+PCDP - User Plane (PDCP-U) O-DU RLC+MAC+PHY-high O-RU PHY-low

[0107] The network device may be other devices that provide wireless communication functions for the terminal device. The specific technologies and specific device forms adopted by the network device are not limited in the embodiments of the present application. For the convenience of description, the embodiments of the present application do not limit.

[0108] The network device may further include core network devices, such as a mobility management entity (MME), a home subscriber server (HSS), a serving gateway (S-GW), a policy and charging rules function (PCRF), and a public data network gateway (PDN gateway, P-GW) in a 4th generation (4G) network; network elements such as an access and mobility management function (AMF), a user plane function (UPF), or a session management function (SMF) in a 5G network. In addition, the core network device may further include other core network devices in a 5G network and the next-generation network of the 5G network.

[0109] In the embodiments of the present application, the above-mentioned network device may further be a network node with AI capabilities, which can provide AI services for terminals or other network devices. For example, it can be an AI node, a computing power node, a RAN node with AI capabilities, or a core network element with AI capabilities on the network side (access network or core network).

[0110] In the embodiments of the present application, the device for implementing the functions of the network device may be the network device or a device capable of supporting the network device to implement such functions, such as a chip system, and this device may be installed in the network device. In the technical solutions provided in the embodiments of the present application, the case where the device for implementing the functions of the network device is the network device is taken as an example to describe the technical solutions provided in the embodiments of the present application.

[0111] (3) Configuration and pre-configuration: In the present application, both configuration and pre-configuration are used. Among them, configuration means that the network device / server sends the configuration information or value of some parameters to the terminal through messages or signaling, so that the terminal can determine the communication parameters or resources during transmission according to these values or information. Pre-configuration is similar to configuration, and it can be parameter information or parameter values pre-negotiated between the network device / server and the terminal device, or parameter information or parameter values specified by standard protocols for base stations / network devices or terminal devices, or parameter information or parameter values pre-stored in base stations / servers or terminal devices. The present application does not limit this.

[0112] Furthermore, these values and parameters can be changed or updated.

[0113] (4) The terms "system" and "network" in the embodiments of the present application may be used interchangeably. "A plurality of" means two or more. "And / or" describes the association relationship of associated objects and indicates that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after. "At least one (item)" or similar expressions thereof refer to any combination of these items, including any combination of single item (item) or plural items (items). For example, "at least one of A, B, and C" includes A, B, C, AB, AC, BC, or ABC. Also, unless otherwise specified, the ordinal numbers such as "first" and "second" mentioned in the embodiments of the present application are used to distinguish multiple objects and are not used to limit the order, time sequence, priority, or importance of multiple objects.

[0114] (5) "Sending" and "receiving" in the embodiments of the present application represent the direction of signal transmission. For example, "sending information to XX" can be understood as the destination of the information is XX, which may include directly sending through the air interface, and also include indirectly sending through the air interface by other units or modules. "Receiving information from YY" can be understood as the source of the information is YY, which may include directly receiving from YY through the air interface, and may also include indirectly receiving from YY through the air interface by other units or modules. "Sending" can also be understood as the "output" of the chip interface, and "receiving" can also be understood as the "input" of the chip interface.

[0115] In other words, sending and receiving can be carried out between devices. For example, between a network device and a terminal device, or can be carried out within a device. For example, sending or receiving between components, modules, chips, software modules, or hardware modules within a device through a bus, trace, or interface.

[0116] It can be understood that necessary processing may be performed on the information between the source end and the destination end of the information transmission, such as encoding, modulation, etc., but the destination end can understand the valid information from the source end. Similar expressions in the present application can be understood similarly and will not be elaborated further.

[0117] (6) In the embodiments of the present application, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. If the information indicated by a certain piece of information (such as the indication information described below) is called the information to be indicated, then in the specific implementation process, there are many ways to indicate the information to be indicated. For example, but not limited to, the information to be indicated can be directly indicated, such as the information to be indicated itself or the index of the information to be indicated, etc. It is also possible to indirectly indicate the information to be indicated by indicating other information, where there is an association relationship between the other information and the information to be indicated; it is also possible to only indicate a part of the information to be indicated, while the other parts of the information to be indicated are known or pre-agreed. For example, the arrangement order of each piece of information pre-agreed (such as protocol pre-definition) can be used to implement the indication of specific information, thereby reducing the indication overhead to a certain extent. The present application does not limit the specific manner of indication. It can be understood that for the sender of the indication information, the indication information can be used to indicate the information to be indicated, and for the receiver of the indication information, the indication information can be used to determine the information to be indicated.

[0118] In the present application, unless otherwise specified, the same or similar parts between various embodiments can be referred to each other. In each embodiment of the present application, as well as in each method / design / implementation manner in each embodiment, if there is no special specification and logical conflict, the terms and / or descriptions between different embodiments, as well as between each method / design / implementation manner in each embodiment, are consistent and can be mutually referred to. The technical features in different embodiments, as well as in each method / design / implementation manner in each embodiment, can be combined according to their internal logical relationships to form new embodiments, methods, or implementation manners. The embodiments of the present application described below do not constitute a limitation on the protection scope of the present application.

[0119] The present application can be applied to a long term evolution (LTE) system, a new radio (NR) system, or a communication system evolved after 5G (such as 6G, etc.). Among them, the communication system includes at least one network device and / or at least one terminal device.

[0120] Please refer to Figure 1a , which is a schematic diagram of the communication system in the present application. Figure 1a shows, by way of example, one network device and six terminal devices, namely terminal device 1, terminal device 2, terminal device 3, terminal device 4, terminal device 5, and terminal device 6, etc. In Figure 1a the example shown, terminal device 1 is taken as an intelligent teacup, terminal device 2 as an intelligent air conditioner, terminal device 3 as an intelligent fuel dispenser, terminal device 4 as a means of transportation, terminal device 5 as a mobile phone, and terminal device 6 as a printer for illustration.

[0121] As shown Figure 1a in the figure, the AI configuration information sending entity can be a network device. The AI configuration information receiving entities can be terminal devices 1 - 6. At this time, the network device and terminal devices 1 - 6 form a communication system. In this communication system, terminal devices 1 - 6 can send data to the network device, and the network device needs to receive the data sent by terminal devices 1 - 6. At the same time, the network device can send configuration information to terminal devices 1 - 6.

[0122] Exemplarily, in Figure 1a , terminal devices 4 - 6 can also form a communication system. Among them, terminal device 5 acts as a network device, that is, the AI configuration information sending entity; terminal devices 4 and 6 act as terminal devices, that is, the AI configuration information receiving entities. For example, in a vehicle - to - everything (V2X) system, terminal device 5 sends AI configuration information to terminal devices 4 and 6 respectively, and receives the data sent by terminal devices 4 and 6; correspondingly, terminal devices 4 and 6 receive the AI configuration information sent by terminal device 5 and send data to terminal device 5.

[0123] Taking the Figure 1a shown communication system as an example, in addition to performing communication - related services, different devices (including between network devices, between network devices and terminal devices, and / or between terminal devices) may also perform AI - related services.

[0124] As Figure 1b shown, taking the network device as a base station as an example, the base station can perform communication - related services and AI - related services with one or more terminal devices, and different terminal devices can also perform communication - related services and AI - related services.

[0125] As Figure 1c shown, taking the terminal devices including a TV and a mobile phone as an example, the TV and the mobile phone can also perform communication - related services and AI - related services.

[0126] The technical solution provided by this application can be applied to a wireless communication system (such as Figure 1a , Figure 1b or Figure 1cIn the system shown, for example, an AI network element can be introduced in the communication system provided in this application to implement some or all of the AI-related operations. The AI network element can also be referred to as an AI node, an AI device, an AI entity, an AI module, an AI model, or an AI unit, etc. The AI network element can be built into the network elements of the communication system. For example, the AI network element can be an AI module built into an access network device, a core network device, a cloud server, or a network management (operation, administration and maintenance, OAM), to implement AI-related functions. The OAM can be the network management of the core network device and / or the network management of the access network device. Alternatively, the AI network element can also be an independently set network element in the communication system. Optionally, an AI entity can also be included in the terminal or the chip built into the terminal to implement AI-related functions.

[0127] The following briefly introduces the artificial intelligence (AI) that may be involved in this application.

[0128] Artificial intelligence (AI) can enable machines to have human intelligence. For example, it can enable machines to apply computer software and hardware to simulate certain intelligent behaviors of humans. To achieve artificial intelligence, machine learning methods can be used. In machine learning methods, machines learn (or train) a model using training data. This model represents the mapping from input to output. The learned model can be used for inference (or prediction), that is, the output corresponding to a given input can be predicted using this model. Among them, this output can also be referred to as an inference result (or a prediction result).

[0129] Machine learning can include supervised learning, unsupervised learning, and reinforcement learning. Among them, unsupervised learning can also be referred to as non-supervised learning.

[0130] Supervised learning is based on the collected sample values and sample labels, uses machine learning algorithms to learn the mapping relationship from sample values to sample labels, and expresses the learned mapping relationship with an AI model. The process of training a machine learning model is the process of learning this mapping relationship. During the training process, the sample values are input into the model to obtain the predicted values of the model, and the model parameters are optimized by calculating the error between the predicted values of the model and the sample labels (ideal values). After the mapping relationship learning is completed, the learned mapping can be used to predict new sample labels. The mapping relationship learned by supervised learning can include linear mapping or non-linear mapping. According to the type of labels, the learning tasks can be divided into classification tasks and regression tasks.

[0131] Unsupervised learning discovers the intrinsic patterns of samples by itself using algorithms based on the collected sample values. In unsupervised learning, there is a type of algorithm that uses the samples themselves as the supervision signal, that is, the model learns the mapping relationship from samples to samples, which is called self-supervised learning. During training, the model parameters are optimized by calculating the error between the predicted value of the model and the samples themselves. Self-supervised learning can be used in applications such as signal compression and decompression recovery. Common algorithms include autoencoders and generative adversarial networks, etc.

[0132] Reinforcement learning is different from supervised learning. It is a type of algorithm that learns strategies to solve problems by interacting with the environment. Different from supervised and unsupervised learning, there is no clear "correct" action label data in reinforcement learning problems. The algorithm needs to interact with the environment to obtain the reward signal feedback from the environment, and then adjust the decision-making actions to obtain a larger numerical value of the reward signal. For example, in downlink power control, the reinforcement learning model adjusts the downlink transmission power of each user according to the total system throughput rate feedback by the wireless network, and then expects to obtain a higher system throughput rate. The goal of reinforcement learning is also to learn the mapping relationship between the environmental state and the optimal (such as the best) decision-making actions. However, because the "correct action" labels cannot be obtained in advance, the network cannot be optimized by calculating the error between the action and the "correct action". The training of reinforcement learning is achieved through iterative interaction with the environment.

[0133] Neural network (NN) is a specific model in machine learning technology. According to the universal approximation theorem, neural networks can theoretically approximate any continuous function, enabling neural networks to have the ability to learn any mapping. Traditional communication systems need to rely on rich 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 obtain performance superior to traditional modeling methods.

[0134] The idea of neural networks comes from the neuron structure of the brain tissue. For example, each neuron performs a weighted sum operation on its input values and outputs the operation result through an activation function.

[0135] As Figure 2a shown, it is a schematic diagram of a neuron structure. Assume the input of the neuron is x = [x0, x1, …, x n , and the weights corresponding to each input are w = [w, w1, …, w n , where n is a positive integer, w i and x i can be various possible types such as decimals, integers (such as 0, positive integers, or negative integers, etc.), or complex numbers. w i as the weight of x i is used to weight xi Weighting is performed. The bias for weighted summation of the input values according to the weights is, for example, b. There can be various forms of activation functions. Assuming the activation function of a neuron is: y = f(z) = max(0, z), then the output of this neuron is: For another example, the activation function of a neuron is: y = f(z) = z, then the output of this neuron is: Among them, b can be various possible types such as decimals, integers (such as 0, positive integers or negative integers), or complex numbers. The activation functions of different neurons in the neural network can be the same or different.

[0136] In addition, a neural network generally includes multiple layers, and each layer can include one or more neurons. By increasing the depth and / or width of the neural network, the expressive ability of the neural network can be improved, providing a more powerful information extraction and abstract modeling ability for complex systems. Among them, the depth of the neural network can refer to the number of layers included in the neural network, and the number of neurons included in each layer can be called the width of that layer. In one implementation, the neural network includes an input layer and an output layer. The input layer of the neural network processes the received input information through neurons and passes the processing result to the output layer, and the output layer obtains the output result of the neural network. In another implementation, the neural network includes an input layer, a hidden layer, and an output layer. The input layer of the neural network processes the received input information through neurons and passes the processing result to the intermediate hidden layer. The hidden layer calculates the received processing result to obtain a calculation result, and the hidden layer passes the calculation result to the output layer or the next adjacent hidden layer, and finally the output layer obtains the output result of the neural network. Among them, a neural network can include one hidden layer, or include multiple sequentially connected hidden layers, without limitation.

[0137] The neural network is, for example, a deep neural network (DNN). According to the construction method of the network, DNN can include a feedforward neural network (FNN), a convolutional neural network (CNN), and a recurrent neural network (RNN).

[0138] Figure 2b Fig. is a schematic diagram of an FNN network. The characteristic of the FNN network is that neurons between adjacent layers are completely connected pairwise. This characteristic makes FNN usually require a large amount of storage space and result in a high computational complexity.

[0139] A CNN is a neural network specifically designed to process data with a similar grid structure. For example, time series data (discretely sampled along the time axis) and image data (two-dimensionally discretely sampled) can both be considered data with a similar grid structure. Instead of using all the input information for computation at once, a CNN uses a window of a fixed size to intercept partial information for convolution operations, which greatly reduces the computational amount of model parameters. Additionally, depending on the type of information intercepted by the window (such as people and objects in the same picture being different types of information), different convolution kernels can be used for each window, enabling the CNN to better extract the features of the input data.

[0140] An RNN is a type of DNN network that utilizes feedback time series information. Its input includes the new input value at the current moment and its own output value at the previous moment. RNNs are suitable for obtaining sequential features that are relevant in time and are particularly applicable to applications such as speech recognition and channel coding and decoding.

[0141] During the model training process of the above-mentioned machine learning, a loss function can be defined. The loss function describes the gap or difference between the output value of the model and the ideal target value. The loss function can be embodied in various forms, and there is no restriction on the specific form of the loss function. The model training process can be regarded as the following process: by adjusting some or all of the parameters of the model, the value of the loss function is made less than the threshold value or meets the target requirements.

[0142] The model can also be referred to as an AI model, rule, or other names, etc. An AI model can be considered as a specific method for implementing AI functions. An AI model represents the mapping relationship or function between the input and output of the model. AI functions can include one or more of the following: data collection, model training (or model learning), model information release, model inference (or also known as model reasoning, reasoning, or prediction, etc.), model monitoring or model verification, or release of inference results, etc. AI functions can also be referred to as AI (related) operations or AI-related functions.

[0143] Next, the implementation process of the neural network will be described exemplarily in conjunction with the accompanying drawings.

[0144] 1. Fully connected neural network, also known as multilayer perceptron (MLP).

[0145] As Figure 2c shown, an MLP contains an input layer (left side), an output layer (right side), and multiple hidden layers (in the middle). Among them, each layer of the MLP contains several nodes, called neurons. Among them, the neurons in adjacent layers are pairwise connected.

[0146] Optionally, considering neurons in adjacent layers, the output h of a neuron in the next layer is the weighted sum of all the neurons x in the previous layer connected to it, followed by an activation function, and can be expressed as:

[0147] h = f(wx + b).

[0148] Where w is the weight matrix, b is the bias vector, and f is the activation function.

[0149] Further optionally, the output of the neural network can be recursively expressed as:

[0150] y = f n (w n f n-1 (…)+b n ).

[0151] Where n is the index of the neural network layer, 1 <= n <= N, where N is the total number of layers in the neural network.

[0152] In other words, a neural network can be understood as a mapping relationship from an input data set to an output data set. Usually, neural networks are randomly initialized, and the process of obtaining this mapping relationship from random w and b using existing data is called the training of the neural network.

[0153] Optionally, the specific way of training is to evaluate the output result of the neural network using a loss function.

[0154] As Figure 2d shown, the error can be backpropagated, and the neural network parameters (including w and b) can be iteratively optimized by the method of gradient descent until the loss function reaches the minimum value, that is, Figure 2d the "preferred point (e.g., the optimal point)" in Figure 2d . It can be understood that the neural network parameters corresponding to the "preferred point (e.g., the optimal point)" in

[0155] can be used as the neural network parameters in the trained AI model information.

[0156]

[0157] Where θ is the parameter to be optimized (including w and b), L is the loss function, η is the learning rate, which controls the step size of gradient descent, represents the derivative operation, represents the derivative of L with respect to θ.

[0158] Further optionally, the process of backpropagation utilizes the chain rule of partial derivatives.

[0159] AsFigure 2e As shown, the gradient of the parameters of the previous layer can be recursively calculated from the gradient of the parameters of the next layer, which can be expressed as:

[0160]

[0161] where w ij is the weight connecting node j to node i, and s i is the weighted sum of inputs on node i.

[0162] 2. Federated Learning (FL).

[0163] The concept of federated learning effectively solves the dilemmas faced by the current development of artificial intelligence. On the premise of fully ensuring the privacy and security of user data, it enables each edge device and the central server to cooperate efficiently to complete the model learning task.

[0164] As Figure 2f shown, the FL architecture is the most widely used training architecture in the current FL field. The FedAvg algorithm is the basic algorithm of FL, and its algorithm process is roughly as follows:

[0165] (1) The central server initializes the model to be trained and broadcasts it to all client devices.

[0166] (2) In the t-th round where t ∈ [1, T], client k ∈ [1, K] trains the received global model on the local dataset for E epochs to obtain the local training result and reports it to the central node.

[0167] (3) The central node aggregates the local training results from all (or part of) the clients. Suppose the set of clients uploading local models in the t-th round is The central server will calculate the weighted average with the sample numbers of the corresponding clients as weights to obtain a new global model. The specific update rule is After that, the central server broadcasts the latest version of the global model to all client devices for a new round of training.

[0168] (4) Repeat steps (2) and (3) until the model finally converges or the number of training rounds reaches the upper limit.

[0169] In addition to reporting the local model it is also possible to report the local gradients of the training The central node will average the local gradients and update the global model according to the direction of this average gradient.

[0170] As can be seen, in the FL framework, the dataset exists at the distributed nodes. That is, the distributed nodes collect the local datasets and perform local training, and report the local results (models or gradients) obtained from the training to the central node. The central node itself does not have a dataset and is only responsible for fusing the training results of the distributed nodes to obtain the global model and distributing it to the distributed nodes.

[0171] 3. Decentralized learning. Different from federated learning, another distributed learning architecture is decentralized learning.

[0172] As Figure 2g shown, consider a fully distributed system without a central node. The design goal f(x) of the decentralized learning system is generally the mean of the node goals f i (x), that is where n is the number of distributed nodes, x is the parameter to be optimized, and in machine learning, x is the parameter of the machine learning (such as neural network) model. Each node uses the local data and the local goal f i (x) to calculate the local gradient and then sends it to the neighbor nodes that can communicate. After any node receives the gradient information sent by its neighbor, it can update the parameter x of the local model according to the following formula:

[0173]

[0174] where represents the parameter of the local model after the (k + 1)th (k is a natural number) update in the i-th node, represents the parameter of the local model after the k-th update in the i-th node (if k is 0, it means is the parameter of the local model of the i-th node that has not participated in the update), α k represents the tuning coefficient, N i is the set of neighbor nodes of node i, and |N i | represents the number of elements in the set of neighbor nodes of node i, that is, the number of neighbor nodes of node i. Through the information interaction between nodes, the decentralized learning system will ultimately learn a unified model.

[0175] 4. Split learning.

[0176] As Figure 2h shown, in split learning, the complete neural network model is divided into two parts (i.e., two sub-networks), and one part is deployed at the distributed nodes (for example Figure 2hOn nodes 1, 2, and 3), and the other part is deployed on the central node. The place where the complete neural network is split is called the "split layer". During forward inference, the distributed nodes input local data into the local sub-network. When inferring to the split layer, the result Fk of the split layer (such as F1 / F2 / F3 in the figure) is sent to the central node through the communication link. The central node inputs the received Fk into another sub-network deployed by itself and continues forward inference to obtain the final inference result. During the gradient backpropagation of model training, the gradient is passed backward through the sub-network of the central node to the split layer to obtain the backward propagation result Gk (such as G1 / G2 / G3 in the figure). Then the central node sends Gk to the distributed nodes and continues the gradient backpropagation on the sub-networks of the distributed nodes.

[0177] Optionally, in split learning, the models deployed on different distributed nodes can be the same or different, which can be determined according to the own requirements and capabilities of different distributed nodes and is not limited here.

[0178] Optionally, a distributed node can also send local model-related parameters to other distributed nodes. For example, Figure 2h node 1 in can send the local model-related parameters to node 2 and node 3 respectively. In this example, node 1 first trains the local model (denoted as model 1). Optionally, node 1 can send the model-related parameters of model 1 to other nodes so that other nodes can continue training based on model 1 and can obtain the local models of other nodes faster.

[0179] It can be seen that during the forward inference and gradient backpropagation processes of split learning, it may involve a distributed node and a central node. The sub-network on the trained distributed node can be saved locally on the distributed node or on a specific model storage server. When a new distributed node joins the learning system, it can first download the trained distributed node sub-network and then use local data for further training.

[0180] The technical solution provided by this application can be applied to a wireless communication system (such as Figure 1a or Figure 1b or Figure 1c the system shown). In a wireless communication system, communication nodes generally have signal transceiver capabilities and computing capabilities. Taking a network device with computing capabilities as an example, the computing capabilities of the network device mainly provide computing power support for the signal transceiver capabilities (for example: performing transmission processing and reception processing on signals) to achieve the communication tasks between the network device and other communication nodes.

[0181] However, in a communication network, the computing power of a communication node may have surplus computing power in addition to providing computing power support for the above communication tasks. Therefore, how to utilize this computing power, for example, how to utilize this computing power to perform data collection, is a technical problem that needs to be solved urgently.

[0182] In order to solve the above problems, the present application provides a communication method and related equipment, which are used to enable a communication device in a communication system to serve as a data collection node to achieve data collection.

[0183] See also Figure 3 , is a schematic diagram of an implementation of the communication method provided in this application, and the method includes the following steps.

[0184] It should be noted that in Figure 3 The method shown (or Figure 6 In the method shown in the figure, the first communication device and the second communication device are used as the execution subjects of the interactive indication as an example to illustrate the method, but the present application does not limit the execution subjects of the interactive indication. Figure 3 The method shown (or Figure 6 In the method shown in the figure, the execution subject of the method can be replaced by a chip, a chip system, a processor, a logic module or software in the communication device. The first communication device can be a terminal device and the second communication device can be a network device, or the first communication device and the second communication device are both terminal devices (for example, the method can be applied to the communication process of different terminal devices in the side link communication scenario).

[0185] Exemplarily, taking the second communication device as a network device as an example, the network device may be implemented by the ORAN network element shown in the above Table 1. For example, the process of receiving and sending by the second communication device in the following steps S301 and S302 may be implemented by one or more of O-CU-CP, O-CU-UP, O-DU, and O-RU in the ORAN network element.

[0186] S301. A first communication device sends first information, and correspondingly, a second communication device receives the first information, wherein the first information is used to indicate status information of the first communication device.

[0187] S302. The second communication device sends second information, and correspondingly, the first communication device receives the second information, wherein the second information is used to configure the first data collection, and the second information is determined based on the state information of the first communication device.

[0188] S303. The first communication device collects data based on the second information.

[0189] In this application, terms such as data acquisition, data collection, data gathering, data obtaining, and data capture can be used interchangeably.

[0190] In a possible implementation, in step S301, the status information indicated by the first information sent by the first communication device includes at least one of the following information 1 to information 4.

[0191] Information 1. System status information.

[0192] Exemplarily, the system status information in Information 1 may include at least one of a system parameter set and a signal processing configuration. For example, the system parameter set (numerology) may include subcarrier spacing, frame structure, system bandwidth, etc. Also, for example, the signal processing configuration may include modulation order, coding rate, waveform, precoding, resource mapping method, multi-antenna configuration, etc.

[0193] Information 2. AI status information.

[0194] Exemplarily, the AI status information in Information 2 includes at least one of AI model parameters and AI computing power resources. For example, AI model parameters may include hyperparameters of the model, structural parameters of the model, etc. Also, for example, AI computing power resources may include floating-point numbers of a graphics processing unit (GPU), frequency of a central processing unit (CPU), etc.

[0195] Information 3. Dataset status information.

[0196] Exemplarily, the dataset status information in Information 4 includes at least one of the acquisition time information of the dataset, the geographical location information of the dataset, the acquisition stage information corresponding to the dataset, the indication information indicating whether the dataset is existing data, and the indication information indicating the reporting method of the dataset.

[0197] Information 4. Scenario status information.

[0198] Exemplarily, the scenario status information in Information 4 includes at least one of signal to noise ratio (SNR) status information, motion status information, reference signal configuration information, and antenna configuration information.

[0199] Thus, the status information of the first communication device may include at least one of the above, enabling the second communication device to determine at least one of the above statuses of the first communication device based on the first information, thereby enhancing the flexibility of the solution implementation.

[0200] Optionally, one or more pieces of content included in the first information may be transmitted through one message or multiple messages, which is not limited herein.

[0201] Optionally, the sending of the first information may be triggered by the signaling of the second communication device, such as scheduling signaling, request signaling, indication signaling, etc. In this way, the first communication device can clearly know the status information that needs to be reported in the first information based on this signaling, which is beneficial for the second communication device to obtain the status information it expects.

[0202] In a possible implementation manner, in step S302, the second information sent by the second communication device includes indication information indicating N acquisition conditions for the first data acquisition, where N is a positive integer; and the status indicated by the status information satisfies the N acquisition conditions. Specifically, the second information for configuring the first data acquisition may include the N acquisition conditions for the first data acquisition, and the status indicated by the status information satisfies the N acquisition conditions. In this way, the first communication device can perform data acquisition based on the acquisition conditions that meet the status of the first communication device.

[0203] Optionally, the indication information in the second information may indicate the N conditions in various ways. For example, the indication information may include the N conditions themselves, or may include the indexes / identifications of the N conditions, etc.

[0204] As an example, taking the first information including information 1 as an example, for example, the information 1 may include the system bandwidth of the first communication device. Correspondingly, one or more of the N acquisition conditions configured by the second information may configure the first communication device to acquire communication information (such as signal reception quality information, signal demodulation performance information, etc.) under the system bandwidth.

[0205] As another example, taking the first information including information 2 as an example, for example, the information 2 may include the AI model parameters of the first communication device. Correspondingly, one or more of the N acquisition conditions configured by the second information may configure the first communication device to acquire model processing information (such as the data dimension of the model input data, the data dimension of the model output data, etc.) of the AI model corresponding to the AI model parameters.

[0206] As another example, taking the first information including information 3 as an example, for example, the information 3 may include the acquisition phase information corresponding to the data set of the first communication device. Correspondingly, one or more of the N acquisition conditions configured by the second information may configure the first communication device to acquire the model processing information of the AI model corresponding to the acquisition phase information corresponding to the data set. The acquisition phase information may indicate at least one of the model training phase, the model inference phase, and the model monitoring phase.

[0207] As another example, taking the first information including information 4 as an example, for instance, the information 4 may include the SNR status information of the first communication device. Correspondingly, one or more of the N acquisition conditions configured by the second information may configure the first communication device to acquire communication information in the SNR status (such as high SNR status, low SNR status) indicated by the SNR status information.

[0208] As another example, taking the first information including information 4 as an example, for instance, the information 4 may include the motion state information of the first communication device. Correspondingly, one or more of the N acquisition conditions configured by the second information may configure the first communication device to acquire communication information in the motion state (such as high-speed motion state, low-speed motion state, etc.) indicated by the motion state information.

[0209] As another example, taking the first information including information 4 as an example, for instance, the information 4 may include the reference signal configuration information of the first communication device. Correspondingly, one or more of the N acquisition conditions configured by the second information may configure the first communication device to acquire communication information in the configuration (such as reference signal configuration with a larger number of resources, reference signal configuration with a smaller number of resources) indicated by the reference signal configuration information.

[0210] As another example, taking the first information including information 4 as an example, for instance, the information 4 may include the antenna configuration information of the first communication device. Correspondingly, one or more of the N acquisition conditions configured by the second information may configure the first communication device to acquire communication information in the configuration (such as the number of ports in the horizontal direction, the number of ports in the vertical direction) indicated by the antenna configuration information.

[0211] Optionally, the second information further includes indication information for indicating the first data acquisition based on the N acquisition conditions. Specifically, through the above indication information, the first communication device can clarify that the N acquisition conditions are for the first data acquisition of the first communication device, and subsequently, the first communication device can perform data acquisition based on the N acquisition conditions.

[0212] Optionally, the indication information for indicating the first data acquisition based on the N acquisition conditions may include the specific acquisition parameter ranges of the N acquisition conditions (such as SNR intervals, motion speed intervals, etc.), or may include the indexes of the specific acquisition parameter ranges of the N acquisition conditions (the specific acquisition parameter ranges can be configured through other signaling, such as RRC messages).

[0213] In a possible implementation, the second information further includes indication information indicating M acquisition conditions, where the M acquisition conditions are conditions for second data acquisition, and M is a positive integer. Optionally, the second information further includes indication information for indicating that the first data acquisition is not performed based on the M acquisition conditions. Specifically, the second information received by the first communication device may further include indication information for indicating conditions for other data acquisitions. In this way, the second communication device can configure conditions for other data acquisition processes in the second information and flexibly indicate whether to perform data acquisition based on the M acquisition conditions through additional indication information, which can reduce the implementation complexity.

[0214] Optionally, the M acquisition conditions may be determined based on the status information of the first communication device sent by the first communication device, or may be determined based on the status information of other communication devices, or may be determined based on the status information of multiple communication devices (including the first communication device and other communication devices).

[0215] In a possible implementation, the second information includes at least one of the following: the AI task identifier corresponding to the first data acquisition, the AI function identifier corresponding to the first data acquisition, and the dataset size information of the first data acquisition. Specifically, the second information for configuring the first data acquisition may include at least one of the above to implement the indication of the task and / or dataset size corresponding to the data acquisition.

[0216] Optionally, the second data acquisition is different from the first data acquisition configured by the second information. The second data acquisition may be used for other data acquisition processes of the first communication device or for data acquisition processes of other communication devices, which is not limited here.

[0217] It should be noted that, as described above, the second information may include multiple pieces of information (such as indication information indicating N acquisition conditions for the first data acquisition, indication information indicating that the first data acquisition is performed based on the N acquisition conditions, indication information indicating M acquisition conditions, the artificial intelligence AI task identifier corresponding to the first data acquisition, the AI function identifier corresponding to the first data acquisition, and the dataset size information of the first data acquisition). Among them, the multiple pieces of information may be carried by one message / signaling or by multiple messages / signaling. In other words, the second information may be carried by one message / signaling or by multiple messages / signaling. For example, taking the second information being carried by one signaling, the signaling may be as follows Figure 4The MAC CE in the example. For another example, the second information is carried by a first message (such as an RRC message) and a second message (such as MAC CE / DCI, etc.). The first message includes N collection conditions and M collection conditions, and the second message includes indication information indicating to perform the first data collection based on the N collection conditions and indication information indicating not to perform the first data collection based on the M collection conditions.

[0218] Optionally, the second information may be carried in RRC, MAC CE, DCI, sidelink control information (SCI), or other signaling / messages.

[0219] As an implementation example, as Figure 4 shown, taking the second information carried in MAC CE as an example, the second information may include the following fields:

[0220] "R" field: Reserved field.

[0221] "Task ID" field: Represents the task identifier, that is, the "AI task identifier corresponding to the first data collection" mentioned above.

[0222] "Dataset Size" field: Represents the size of the data subset required for data collection, that is, the "dataset size information of the first data collection" mentioned above.

[0223] "Data Collection Condition i" field: Represents the i-th condition for data collection, or the index / identifier of the i-th condition. For example, Figure 4 as shown, some or all of the fields in "Data Collection Condition 0 - n" may indicate the indication information of the N collection conditions for the first data collection. Optionally, Figure 4 as shown, at least one of the fields in "Data Collection Condition 0 - n" may indicate the indication information of the M collection conditions for the second data collection.

[0224] "Ci" field: Represents whether the i-th condition needs to be satisfied. Exemplarily, 0 indicates that the condition does not need to be satisfied, and 1 indicates to collect data that meets the requirements of Data Collection Condition i. For example, Figure 4 when the collection condition indicated by the "Ci" field as shown is any one of the N collection conditions, the value of the "Ci" field can be 1. For another example, Figure 4 when the collection condition indicated by the "Ci" field as shown is any one of the M collection conditions, the value of the "Ci" field can be 0.

[0225] As an implementation example, as Figure 5 shown Figure 4 The "Data Collection Condition i" field shown can be used to represent the i-th condition for data collection. Correspondingly, the mapping relationship between the "Data Collection Condition i" field and the specific data collection parameter range of the collection condition can refer to Figure 5 the manner shown. Among them, this mapping relationship can be carried in the second information, can also be pre-configured or predefined in the first communication device and / or the second communication device, or can be configured by the second communication device before step S302 (for example, configured by an RRC message), which is not limited here.

[0226] In a possible implementation manner, the second information is determined based on the status information of the first communication device, including: the second information is determined based on the status information of the first communication device and the task requirements of the AI task. Specifically, the determination basis of the second information can include, in addition to the status information of the first communication device, the task requirements of the AI task. In this way, it can be ensured that the data collected by the first communication device based on the second information can meet the task requirements.

[0227] Based on Figure 3 the technical solution, after the first communication device sends the first information for indicating the status information of the first communication device in step S301, the first communication device can receive the second information for configuring the first data collection in step S302, and collect data based on the second information in step S303. Among them, the second information is determined based on the status information of the first communication device. In other words, the first communication device can collect data based on the configuration of the second communication device. Thus, the communication devices in the communication system can act as data collection nodes to implement data collection.

[0228] In addition, in the above technical solution, the second information for configuring the first data collection can be determined based on the status information of the first communication device. In this way, the configuration of the first data collection can be adapted to the status of the first communication device, thereby improving the success rate of data collection.

[0229] In Figure 3 a possible implementation manner of the method shown, the data obtained by the first communication device performing data collection in step S303 can be used for model processing of the AI model (such as at least one of model training, model inference, and model monitoring). In this way, the communication devices in the communication system can act as data collection nodes for AI-related data to implement the collection of AI-related data.

[0230] Optionally, wireless communication signals can be transmitted between different communication devices (such as the transceiver of configuration information of communication resources, the transceiver of reference signals, etc.). The AI models involved in the embodiments of the present application (such as the first AI model, the second AI model, etc. mentioned later) can be used to process the wireless communication signals (including at least one of management, configuration, update, and optimization). For example, the AI model can include an AI model for modulation and / or demodulation, an AI model for channel prediction, an AI model for beam management, an AI model for assisting positioning, an AI model for channel compression, an AI model for resource scheduling, an AI model for mobility management, an AI model for load balancing, an AI model for network energy saving, and an AI model for replacing one or more modules in a transmitter and / or a receiver. Alternatively, the AI models involved in the embodiments of the present application can also be AI models for other AI tasks, such as an AI model for image recognition, an AI model for natural language processing, an AI model for computer vision, etc.

[0231] Optionally, the AI model can be a model strongly related to the acquisition conditions described above (such as N acquisition conditions), or a model corresponding to the AI task identifier or AI function identifier described above, or a model with an input data size that conforms to the "dataset size" described above.

[0232] For ease of understanding, various implementation manners of the AI model applied to the data collected by the first communication device in step S303 will be described by way of example below.

[0233] Implementation manner 1: After the first communication device executes data acquisition in step S303, part or all of the data is used for the processing of the first AI model, and the first AI model is deployed on the first communication device.

[0234] Through implementation manner 1, the first communication device can realize the acquisition of the data used for the model processing of the AI model deployed on the first communication device based on the first data acquisition configured by the second information.

[0235] In a possible implementation manner of implementation manner 1, part or all of the data is also used for the processing of the second AI model, and the second AI model is deployed on the second communication device; wherein, the input of the first AI model includes the output of the second AI model, or the input of the second AI model includes the output of the first AI model. Correspondingly, Figure 3The method shown also includes: after step S303, the first communication device sends part or all of the data to the second communication device. In this way, the first communication device can collect the data used for model processing of the AI models deployed on both sides based on the first data collection configured by the second information.

[0236] Implementation method 2: After the first communication device executes data collection in step S303, part or all of the data is used for processing of the second AI model, and the second AI model is deployed on the second communication device. Correspondingly, Figure 3 The method shown also includes: after step S303, the first communication device sends part or all of the data to the second communication device.

[0237] Through implementation method 2, the first communication device can collect the data used for model processing of the AI model deployed on the second communication device based on the first data collection configured by the second information.

[0238] Optionally, in addition to the above implementation method 1 and implementation method 2, the AI model can also be implemented in other ways.

[0239] For example, the first communication device and the second communication device can deploy the same AI model. Correspondingly, the first communication device can send part or all of the collected data to the second communication device with reference to the above implementation method, so that both the first communication device and the second communication device can perform processing of the AI model based on the data.

[0240] Another example is that the AI model can be deployed on other devices different from the first communication device and the second communication device. Correspondingly, the first communication device can send part or all of the collected data to the other device, so that the other device can perform processing of the AI model based on the data.

[0241] In addition, when the AI model is deployed on the first communication device or the second communication device, the AI model can be called a single-end deployed model; while when the AI model is deployed on the first communication device and the second communication device, the AI model can be called a dual-end deployed model.

[0242] In Figure 3In a possible implementation of the method shown, in step S301, the second communication device may receive first information of one or more first communication devices to obtain status information of the one or more first communication devices. Thereafter, the second communication device may divide data categories based on the status information of the one or more first communication devices to determine one or more second information of the one or more first communication devices, and in step S302, send the one or more second information to the one or more first communication devices respectively. Thereafter, in step S303, the one or more first communication devices may perform data collection based on the one or more second information. To facilitate understanding of this solution, hereinafter, taking the one or more first communication devices including UE 1 and UE 2, and the second communication device being a network device as an example, the following Figure 6 shown example will be described.

[0243] Please refer to Figure 6 , which is a schematic diagram of the communication method provided by this application. The method includes the following steps.

[0244] Exemplarily, in Figure 6 the method shown, the network device may be implemented by the ORAN network element shown in Table 1 above. For example, the processes of the network device receiving and sending in the following steps A / B / D / E may be implemented by one or more of O-CU-CP, O-CU-UP, O-DU, O-RU in the ORAN network element, and the process of the network device performing data category division in steps C / H / I may be implemented by one or more of O-CU-CP, O-CU-UP, O-DU, O-RU in the ORAN network element.

[0245] A. UE 1 sends first information_1. Correspondingly, the network device receives the first information_1. Wherein, the first information_1 is used to indicate the status information of UE 1.

[0246] B. UE 2 sends first information_2. Correspondingly, the network device receives the first information_2. Wherein, the first information_2 is used to indicate the status information of UE 2.

[0247] It should be noted that both step A and step B are implementation examples of the foregoing step S301. Correspondingly, the implementation processes of the first information_1 and the first information_2 may refer to the implementation process of the first information in the foregoing step S301.

[0248] C. The network device divides data categories based on the status information of each UE.

[0249] D. The network device sends the second information _1 to UE 1. Correspondingly, UE 1 receives the second information _1. Among them, the second information _1 is used to configure the data collection of UE 1. And the second information _1 is determined based on the first information _1.

[0250] E. The network device sends the second information _2 to UE 2. Correspondingly, UE 2 receives the second information _2. Among them, the second information _2 is used to configure the data collection of UE 2. And the second information _2 is determined based on the first information _2.

[0251] It should be noted that both step D and step E are implementation examples of the previous step S302. Correspondingly, the implementation processes of the second information _1 and the second information _2 can refer to the implementation process of the second information in the previous step S302.

[0252] F. UE 1 collects data _1 based on the second information _1.

[0253] G. UE 2 collects data _2 based on the second information _2.

[0254] It should be noted that both step F and step G are implementation examples of the previous step S303. Correspondingly, the implementation processes of collecting data _1 based on the second information _1 and collecting data _2 based on the second information _2 can refer to the implementation process of collecting data in the previous step S303.

[0255] H. UE_1 and / or the network device perform model processing of the AI model based on the data _1.

[0256] I. UE_2 and / or the network device perform model processing of the AI model based on the data _2.

[0257] It should be understood that step H and step I can refer to the implementation methods of the above single - end deployment or double - end deployment, and step H and step I are optional steps.

[0258] As an implementation example, in Figure 6 the method shown, taking the first information _1 in step A and the first information _2 in step B indicating different SNR status information as an example. Correspondingly, the network device can determine that the data collected by UE 1 and UE 2 can be used as data for the AI model strongly related to SNR. Among them, one of the conditions corresponding to the N collection conditions in the aforementioned Figure 4 shown "Data Collection Conditioni" can indicate the SNR status information or the index of the SNR status information. Correspondingly, the aforementioned Figure 5 shown "Data Collection Condition i" field can be represented as the implementation process in Table 2 below.

[0259] Table 2

[0260]

[0261] In other words, the SNR status information indicated by UE 1 through the first information _1 can be one of the rows of information in Table 2. Similarly, the SNR status information indicated by UE 2 through the first information _2 can be one of the rows of information in Table 2.

[0262] Exemplarily, AI models strongly related to SNR can include intelligent demodulation models, intelligent modulation models, etc. deployed in network devices. Taking the intelligent demodulation model as an example, the input of the intelligent demodulation model is bits and the output is symbols. In addition, after the symbols are added with noise, they will be sent to the UE through the data channel, and the UE needs to collect the received symbols in different SNR intervals and give them to the network device. That is, the network device can divide data categories based on the SNR status information of each UE in step C (for example, divide the SNR intervals shown in Table 2 according to the reported SNR), determine and send the second information _1 for configuring the data collection of UE 1, and the second information _2 for configuring the data collection of UE 2. After that, after UE 1 and UE 2 perform data collection in steps F and G, they can send the relevant information of the received symbols in different SNR intervals, so that the network device can train the models in different SNR intervals based on the relevant information of the received symbols in steps H and I.

[0263] As another implementation example, in Figure 6 the method shown, taking the first information _1 in step A and the first information _2 in step B as an example to indicate different motion state information. Correspondingly, the network device can determine that the data collected by UE 1 and UE 2 can be used as data for an AI model strongly related to the motion state. Among them, one of the conditions corresponding to the N collection conditions in the foregoing Figure 4 shown "Data CollectionCondition i" can indicate the motion state information or the index of the motion state information. Correspondingly, the foregoing Figure 5 shown "Data Collection Condition i" field can be expressed as the implementation process of Table 3 below.

[0264] Table 3

[0265]

[0266] In other words, the motion state information indicated by UE 1 through the first information _1 can be one of the rows of information in Table 3. Similarly, the motion state information indicated by UE 2 through the first information _2 can be one of the rows of information in Table 3.

[0267] Exemplarily, AI models strongly related to the motion state may include a channel prediction model, a positioning prediction model, a channel state information (CSI) feedback model, etc., deployed in a network device. Taking the channel prediction model as an example, the input data of the channel prediction model may include historical channel data (including channel data in a low-speed motion state, channel data in a high-speed motion state, etc.), and the output data may be predicted channel information. Correspondingly, the UE may collect corresponding channel data based on the configured motion state information and send it to the network device. That is, the network device may divide data categories in step C based on the motion state information of each UE, determine and send the second information_1 for configuring the data collection of UE 1, and the second information_2 for configuring the data collection of UE 2. After that, after UE 1 and UE 2 perform data collection in steps F and G, they may send channel data in different motion states, enabling the network device to train the channel prediction model based on the channel data in different motion states in steps H and I.

[0268] As another implementation example, in Figure 6 the method shown, taking the first information_1 in step A and the first information_2 in step B as an example to indicate different reference signal configuration information. Correspondingly, the network device may determine that the data collected by UE 1 and UE 2 can be used as data for an AI model related to the reference signal configuration information. Among them, one of the conditions corresponding to the N collection conditions in the aforementioned Figure 4 shown "DataCollection Condition i" may indicate the reference signal configuration information or the index of the reference signal configuration information. Correspondingly, taking the reference signal configuration information including the number of reference signals carried by one resource block (RB) as an example, the aforementioned Figure 5 shown "Data CollectionCondition i" field may be expressed as the implementation process of Table 4 below.

[0269] Table 4

[0270]

[0271] In other words, the reference signal configuration information indicated by UE 1 through the first information_1 may be one row of information in Table 4. Similarly, the reference signal configuration information indicated by UE 2 through the first information_2 may be one row of information in Table 4.

[0272] Exemplarily, AI models that are strongly correlated with reference signal configurations may include intelligent receiver models, intelligent transceiver models, etc., deployed in network devices. Taking the intelligent receiver model as an example, the input data of the intelligent receiver model may include signals received over the air interface, and the output data is bits. Correspondingly, the UE may collect corresponding data based on the configured reference signal configuration information and send it to the network device. That is, the network device may divide data categories in step C based on the reference signal configuration information of each UE, determine and send the second information_1 for configuring the data collection of UE 1, and the second information_2 for configuring the data collection of UE 2. After that, after UE 1 and UE 2 perform data collection in steps F and G, they may send signals with different reference signal configuration information, enabling the network device to train the intelligent receiver model based on the signals with different reference signal configuration information in steps H and I.

[0273] As another implementation example, in Figure 6 the method shown, taking the first information_1 in step A and the first information_2 in step B as indicating different antenna configuration information as an example. Correspondingly, the network device may determine that the data collected by UE 1 and UE 2 can be used as data for the AI model related to the antenna configuration information. Among them, one of the conditions corresponding to the N collection conditions in the aforementioned Figure 4 "Data CollectionCondition i" may indicate the antenna configuration information or the index of the antenna configuration information. Correspondingly, taking the antenna configuration information including the number of transceiver antennas in the horizontal or vertical direction as an example, the aforementioned Figure 5 "Data Collection Condition i" field may be represented as the implementation process in Table 5 below.

[0274] Table 5

[0275]

[0276]

[0277] In other words, the antenna configuration information indicated by UE 1 through the first information_1 may be one of the rows in Table 5. Similarly, the antenna configuration information indicated by UE 2 through the first information_2 may be one of the rows in Table 5.

[0278] Exemplarily, the AI models strongly related to the antenna configuration may include a beam prediction model, a precoding information determination model, etc. deployed in a network device. Taking the beam prediction model as an example, the input data of the beam prediction model may include a small number of scanned fixed beams, and the output is the optimal beam direction (including the horizontal direction and / or the vertical direction). Correspondingly, the UE may collect corresponding data based on the configured antenna configuration information and send it to the network device. That is, the network device may divide the data categories in step C based on the antenna configuration information of each UE, determine and send the second information_1 for configuring the data collection of UE 1, and the second information_2 for configuring the data collection of UE 2. After that, after UE 1 and UE 2 perform data collection in steps F and G, they may send signals with different antenna configuration information, so that the network device can train the beam prediction model based on the signals with different antenna configuration information in steps H and I.

[0279] Please refer to Figure 7 , an embodiment of the present application provides a communication device 700. The communication device 700 may implement the functions of the first communication device (or the second communication device) in the foregoing method embodiment, and thus can also achieve the beneficial effects possessed by the foregoing method embodiment. In the embodiment of the present application, the communication device 700 may be the first communication device (or the second communication device), or an integrated circuit or component inside the first communication device (or the second communication device), such as a chip.

[0280] It should be noted that the transceiver unit 702 may include a sending unit and a receiving unit, which are respectively used to perform sending and receiving.

[0281] In a possible implementation manner, when the device 700 is used to execute the method performed by the first communication device in the foregoing embodiment, the device 700 includes a processing unit 701 and a transceiver unit 702; the transceiver unit 702 is used to send the first information, and the first information is used to indicate the status information of the first communication device; the transceiver unit 702 is further used to receive the second information, and the second information is used to configure data collection; wherein, the second information is determined based on the status information of the first communication device; the processing unit 701 collects data based on the second information.

[0282] In a possible implementation manner, when the device 700 is used to execute the method performed by the second communication device in the foregoing embodiment, the device 700 includes a processing unit 701 and a transceiver unit 702; the transceiver unit 702 is used to receive the first information, and the first information is used to indicate the status information of the first communication device; the processing unit 701 is used to determine the second information; the transceiver unit 702 is further used to send the second information, and the second information is used to configure data collection; wherein, the second information is determined based on the status information of the first communication device.

[0283] It should be noted that for the information execution process and other content of the units of the communication device 700 above, please refer to the description in the method embodiments shown above in this application for details, and will not be elaborated here.

[0284] Please refer to Figure 8 , which is another schematic structural diagram of the communication device 800 provided by this application. The communication device 800 includes a logic circuit 801 and an input / output interface 802. Among them, the communication device 800 can be a chip or an integrated circuit.

[0285] Among them, Figure 7 the shown transceiver unit 702 can be a communication interface, and this communication interface can be Figure 8 the input / output interface 802 in , and this input / output interface 802 can include an input interface and an output interface. Alternatively, this communication interface can also be a transceiver circuit, and this transceiver circuit can include an input interface circuit and an output interface circuit.

[0286] Optionally, the input / output interface 802 is used to send a first piece of information, and this first piece of information is used to indicate the status information of the first communication device; the input / output interface 802 is further used to receive a second piece of information, and this second piece of information is used to configure data acquisition; among them, this second piece of information is determined based on the status information of the first communication device; the logic circuit 801 acquires data based on this second piece of information.

[0287] Optionally, the input / output interface 802 is used to receive a first piece of information, and this first piece of information is used to indicate the status information of the first communication device; the logic circuit 801 is used to determine a second piece of information; the input / output interface 802 is further used to send the second piece of information, and this second piece of information is used to configure data acquisition; among them, this second piece of information is determined based on the status information of the first communication device.

[0288] Among them, the logic circuit 801 and the input / output interface 802 can also execute other steps performed by the first communication device or the second communication device in any embodiment and achieve the corresponding beneficial effects, which will not be elaborated here.

[0289] In a possible implementation manner, Figure 7 the shown processing unit 701 can be Figure 8 the logic circuit 801 in .

[0290] Optionally, the logic circuit 801 can be a processing device, and the functions of the processing device can be partially or fully implemented by software. Among them, the functions of the processing device can be partially or fully implemented by software.

[0291] Optionally, the processing device may include a memory and a processor. The memory is used to store a computer program, and the processor reads and executes the computer program stored in the memory to perform the corresponding processing and / or steps in any of the method embodiments.

[0292] Optionally, the processing device may only include a processor. The memory for storing the computer program is located outside the processing device, and the processor is connected to the memory through a circuit / wire to read and execute the computer program stored in the memory. The memory and the processor may be integrated together or physically independent of each other.

[0293] Optionally, the processing device may be one or more chips or one or more integrated circuits. For example, the processing device may be one or more field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), system on chips (SoCs), central processor units (CPUs), network processors (NPs), digital signal processing circuits (DSPs), microcontroller units (MCUs), programmable logic devices (PLDs), or other integrated chips, or any combination of the above chips or processors, etc.

[0294] Please refer to Figure 9 the communication device 900 involved in the above embodiments provided for the embodiments of the present application. The communication device 900 may specifically be the communication device acting as a terminal device in the above embodiments. Figure 9 The example shown is implemented by the terminal device (or components in the terminal device).

[0295] A possible schematic diagram of the logical structure of the communication device 900. The communication device 900 may include, but is not limited to, at least one processor 901 and a communication port 902.

[0296] Among them, Figure 7 the shown transceiver unit 702 may be a communication interface, and this communication interface may be Figure 9 the communication port 902 in it. The communication port 902 may include an input interface and an output interface. Alternatively, the communication port 902 may also be a transceiver circuit, and this transceiver circuit may include an input interface circuit and an output interface circuit.

[0297] Further optionally, the device may further include at least one of a memory 903 and a bus 904. In an embodiment of the present application, the at least one processor 901 is configured to control and process the operations of the communication device 900.

[0298] In addition, the processor 901 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It can implement or execute various exemplary logical blocks, modules, and circuits described in connection with the disclosure of the present application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, and so on. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0299] It should be noted that Figure 9 the illustrated communication device 900 can specifically be used to implement the steps implemented by the terminal device in the foregoing method embodiments and achieve the corresponding technical effects of the terminal device. Figure 9 For the specific implementation manners of the illustrated communication device, reference can be made to the descriptions in the foregoing method embodiments and will not be elaborated herein one by one.

[0300] Please refer to Figure 10 , which is a schematic structural diagram of the communication device 1000 involved in the foregoing embodiments provided in the embodiments of the present application. The communication device 1000 can specifically be the communication device acting as a network device in the foregoing embodiments. Figure 10 The illustrated example is implemented by a network device (or a component in the network device). Among them, the structure of the communication device can refer to Figure 10 the structure shown.

[0301] The communication device 1000 includes at least one processor 1011 and at least one network interface 1014. Further optionally, the communication device further includes at least one memory 1012, at least one transceiver 1013, and one or more antennas 1015. The processor 1011, the memory 1012, the transceiver 1013, and the network interface 1014 are connected, for example, connected by a bus. In the embodiments of the present application, this connection may include various interfaces, transmission lines, or buses, etc., and this embodiment does not limit this. The antenna 1015 is connected to the transceiver 1013. The network interface 1014 is used to enable the communication device to communicate with other communication devices through a communication link. For example, the network interface 1014 may include a network interface between the communication device and core network equipment, such as an S1 interface. The network interface may include a network interface between the communication device and other communication devices (such as other network equipment or core network equipment), such as an X2 or Xn interface.

[0302] Among them, Figure 7 The shown transceiver unit 702 may be a communication interface, and this communication interface may be Figure 10 the network interface 1014 in

[0303] The processor 1011 is mainly used to process communication protocols and communication data, and control the entire communication device, execute software programs, and process the data of software programs. For example, it is used to support the communication device to perform the actions described in the embodiments. The communication device may include a baseband processor and a central processor. The baseband processor is mainly used to process communication protocols and communication data, and the central processor is mainly used to control the entire terminal device, execute software programs, and process the data of software programs. Figure 10 The processor 1011 in

[0304] The memory is mainly used to store software programs and data. The memory 1012 can exist independently and be connected to the processor 1011. Optionally, the memory 1012 can be integrated with the processor 1011, for example, integrated within a single chip. Among them, the memory 1012 can store the program code for implementing the technical solution of the embodiments of the present application, and is controlled by the processor 1011 for execution. Various types of computer program codes being executed can also be regarded as the driver programs of the processor 1011.

[0305] Figure 10 Only one memory and one processor are shown. In an actual terminal device, there may be multiple processors and multiple memories. The memory can also be referred to as a storage medium or a storage device, etc. The memory can be a storage element on the same chip as the processor, that is, an on-chip storage element, or an independent storage element, and the embodiments of the present application do not make any limitations in this regard.

[0306] The transceiver 1013 can be used to support the reception or transmission of radio frequency signals between the communication device and the terminal. The transceiver 1013 can be connected to the antenna 1015. The transceiver 1013 includes a transmitter Tx and a receiver Rx. Specifically, one or more antennas 1015 can receive radio frequency signals. The receiver Rx of the transceiver 1013 is used to receive the radio frequency signals from the antenna, convert the radio frequency signals into digital baseband signals or digital intermediate frequency signals, and provide the digital baseband signals or digital intermediate frequency signals to the processor 1011, so that the processor 1011 can perform further processing on the digital baseband signals or digital intermediate frequency signals, such as demodulation processing and decoding processing. In addition, the transmitter Tx in the transceiver 1013 is also used to receive the modulated digital baseband signals or digital intermediate frequency signals from the processor 1011, convert the modulated digital baseband signals or digital intermediate frequency signals into radio frequency signals, and transmit the radio frequency signals through one or more antennas 1015. Specifically, the receiver Rx can selectively perform one-level or multi-level down-conversion processing and analog-to-digital conversion processing on the radio frequency signals to obtain digital baseband signals or digital intermediate frequency signals, and the order of the down-conversion processing and the analog-to-digital conversion processing can be adjusted. The transmitter Tx can selectively perform one-level or multi-level up-conversion processing and digital-to-analog conversion processing on the modulated digital baseband signals or digital intermediate frequency signals to obtain radio frequency signals, and the order of the up-conversion processing and the digital-to-analog conversion processing can be adjusted. The digital baseband signals and the digital intermediate frequency signals can be collectively referred to as digital signals.

[0307] The transceiver 1013 can also be referred to as a transceiver unit, a transceiver, a transceiver device, etc. Optionally, the devices used to implement the receiving function in the transceiver unit can be regarded as the receiving unit, and the devices used to implement the transmitting function in the transceiver unit can be regarded as the transmitting unit. That is, the transceiver unit includes a receiving unit and a transmitting unit. The receiving unit can also be referred to as a receiver, an input port, a receiving circuit, etc., and the transmitting unit can be referred to as a transmitter, a transmitter, or a transmitting circuit, etc.

[0308] It should be noted that Figure 10 The illustrated communication device 1000 can specifically be used to implement the steps implemented by the network device in the foregoing method embodiments and achieve the corresponding technical effects of the network device. Figure 10 For the specific implementation manners of the illustrated communication device 1000, reference can be made to the descriptions in the foregoing method embodiments, and details are not described herein again.

[0309] Please refer to Figure 11 for the structural schematic diagram of the communication device involved in the above embodiments provided in the embodiments of the present application.

[0310] It can be understood that the communication device 110 includes, for example, modules, units, components, circuits, or interfaces, etc., which are appropriately configured together to execute the technical solutions provided by the present application. The communication device 110 can be the terminal device or the network device described above, or a component (such as a chip) in these devices, for implementing the methods described in the following method embodiments. The communication device 110 includes one or more processors 111. The processor 111 can be a general-purpose processor or a dedicated processor, etc. For example, it can be a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control the communication device (such as, a RAN node, a terminal, or a chip, etc.), execute software programs, and process the data of the software programs.

[0311] Optionally, in one design, the processor 111 can include a program 113 (sometimes also referred to as code or instructions), and the program 113 can be run on the processor 111, so that the communication device 110 executes the methods described in the following embodiments. In another possible design, the communication device 110 includes a circuit ( Figure 11 not shown).

[0312] Optionally, the communication device 110 can include one or more memories 112, on which there is a program 114 (sometimes also referred to as code or instructions), and the program 114 can be run on the processor 111, so that the communication device 110 executes the methods described in the above method embodiments.

[0313] Optionally, the processor 111 and / or the memory 112 may include AI modules 117, 118 for implementing AI-related functions. The AI modules may be implemented in software, hardware, or a combination of both. For example, the AI module may include a radio intelligence control (RIC) module. For example, the AI module may be a near-real-time RIC or a non-real-time RIC.

[0314] Optionally, data may also be stored in the processor 111 and / or the memory 112. The processor and the memory may be provided separately or integrated together.

[0315] Optionally, the communication device 110 may further include a transceiver 115 and / or an antenna 116. The processor 111 is sometimes also referred to as a processing unit and controls the communication device (such as a RAN node or a terminal). The transceiver 115 is sometimes also referred to as a transceiver unit, a transceiver, a transceiver circuit, or a transceiver, etc., and is used to implement the transceiver function of the communication device through the antenna 116.

[0316] Among them, Figure 7 The shown processing unit 701 may be the processor 111. Figure 7 The shown transceiver unit 702 may be a communication interface, and this communication interface may be Figure 11 the transceiver 115 therein. The transceiver 115 may include an input interface and an output interface. Alternatively, the transceiver 115 may also be a transceiver circuit, and this transceiver circuit may include an input interface circuit and an output interface circuit.

[0317] The embodiment of the present application further provides a computer-readable storage medium for storing one or more computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes the method described in the possible implementation manners of the first communication device or the second communication device in the foregoing embodiments.

[0318] The embodiment of the present application further provides a computer program product (or referred to as a computer program). When the computer program product is executed by the processor, the processor executes the method in the possible implementation manners of the above first communication device or second communication device.

[0319] The embodiments of the present application further provide a chip system, which includes at least one processor for supporting a communication device to implement the functions involved in the possible implementation manners of the above communication device. Optionally, the chip system further includes an interface circuit, and the interface circuit provides program instructions and / or data for the at least one processor. In a possible design, the chip system may further include a memory for storing the necessary program instructions and data of the communication device. The chip system may be composed of chips or may include chips and other discrete devices, where the communication device may specifically be the first communication device or the second communication device in the foregoing method embodiments.

[0320] The embodiments of the present application further provide a communication system, and the network system architecture includes the first communication device and the second communication device in any of the above embodiments.

[0321] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other may be through some interfaces, and the indirect couplings or communication connections of devices or units may be in electrical, mechanical, or other forms.

[0322] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0323] In addition, each functional unit in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that makes a contribution, or all or part of the technical solution, may be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

Claims

1. A communication method, characterized in that, Including: Sending a first message, where the first message is used to indicate the status information of a first communication device; Receiving a second message, where the second message is used to configure first data collection; wherein, the second message is determined based on the status information of the first communication device; Collecting data based on the second message.

2. The method according to claim 1, characterized in that, Part or all of the collected data is used for the processing of a first AI model, and the first AI model is deployed on the first communication device.

3. The method according to claim 2, characterized in that Part or all of the data is also used for the processing of a second AI model, and the second AI model is deployed on a second communication device; wherein, the input of the first AI model includes the output of the second AI model, or the input of the second AI model includes the output of the first AI model; The method further includes: Sending part or all of the data to the second communication device.

4. The method according to claim 1, wherein Part or all of the data is used for the processing of a second AI model, and the second AI model is deployed on a second communication device; The method further includes: Sending part or all of the data to the second communication device.

5. A communication method, characterized in that, Including: Receiving a first message, where the first message is used to indicate the status information of a first communication device; Sending a second message, where the second message is used to configure first data collection; wherein, the second message is determined based on the status information of the first communication device.

6. The method according to claim 5, wherein Part or all of the collected data is used for the processing of a first AI model, and the first AI model is deployed on the first communication device.

7. The method according to claim 6, wherein Part or all of the collected data is also used for the processing of a second AI model, and the second AI model is deployed on a second communication device; wherein, the input of the first AI model includes the output of the second AI model, or the input of the second AI model includes the output of the first AI model; The method further includes: Receiving part or all of the data.

8. The method according to claim 5, wherein Part or all of the collected data is used for the processing of a second AI model, and the second AI model is deployed on a second communication device; The method further includes: Receiving part or all of the data.

9. The method according to any one of claims 1 to 8, characterized in that, The second message includes indication information indicating N collection conditions for the first data collection, where N is a positive integer; wherein, the status indicated by the status information of the first communication device satisfies the N collection conditions.

10. The method according to claim 9, characterized in that, The second message further includes indication information for indicating the first data collection based on the N collection conditions.

11. The method according to claim 9 or 10, characterized in that, The second message further includes indication information indicating M collection conditions, where the M collection conditions are conditions for second data collection, and M is a positive integer.

12. The method according to any one of claims 1 to 11, characterized in that The second message includes at least one of the following: The artificial intelligence (AI) task identifier corresponding to the first data collection, the AI function identifier corresponding to the first data collection, the dataset size information of the first data collection.

13. The method according to any one of claims 1 to 12, characterized in that, The status information includes at least one of the following: System status information, AI status information, dataset status information, scenario status information.

14. The method according to claim 13, wherein Satisfying one or more of the following: The system status information includes at least one of a system parameter set and a signal processing configuration; The AI status information includes at least one of AI model parameters and AI computing power resources; The scene status information includes at least one of signal-to-noise ratio (SNR) status information, motion status information, reference signal configuration information, and antenna configuration information; The dataset status information includes at least one of the acquisition time information of the dataset, the geographical location information of the dataset, the acquisition phase information corresponding to the dataset, the indication information indicating whether the dataset is existing data, and the indication information indicating the reporting method of the dataset.

15. The method according to any one of claims 1 to 14, characterized in that, The second information is determined based on the status information of the first communication device and includes: The second information is determined based on the status information of the first communication device and the task requirements of the AI task, and the collected data meets the task requirements.

16. A communication device, characterized in that, It includes a module for executing the method according to any one of claims 1 to 15.

17. A communication device, characterized in that, It includes at least one processor, and the at least one processor is coupled to a memory; the at least one processor is configured to execute the method according to any one of claims 1 to 15.

18. The communication device according to claim 17, wherein The communication device is a chip or a chip system.

19. A readable storage medium, characterized in that, The computer program or instruction is stored in the storage medium, and when the computer program or instruction is executed by the communication device, the method according to any one of claims 1 to 15 is implemented.

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