Information configuration method and communication apparatus

Abstract

Embodiments of the present application provide an information configuration method and a communication apparatus, applied to the technical field of wireless communications. In the method, an access network device sends first configuration information and second configuration information to a terminal device, the first configuration information being used for configuring a measurement reference signal (RS), and the second configuration information being used for instructing to collect, on the basis of the first configuration information, data associated with an artificial intelligence (AI) model or an AI function; and the terminal device collects the data on the basis of the first configuration information and / or the second configuration information. By implementing the embodiments of the present application, the first configuration information can be reused to collect the data, and there is no need to send a separate set of detailed configuration information used for collecting the data, thereby effectively reducing signaling overheads.

Description

Information configuration method and communication device

[0001] This application claims priority to Chinese Patent Application No. 202510388141.3, filed on March 28, 2025, with the China National Intellectual Property Administration, entitled "Information Configuration Method and Communication Device", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of communication technology, and in particular to an information configuration method and a communication device. Background Technology

[0003] Release 17 of the 3rd Generation Partnership Project (3GPP) proposes applying artificial intelligence (AI) technology to the field of communication networks to optimize network performance and improve user experience. For example, several scenarios have been designed for the application of AI technology in the radio access network (RAN): energy saving, load balancing, mobility optimization, enhanced channel state information reference signal (CSI-RS) feedback, beam scanning enhancement, and positioning enhancement.

[0004] When applying AI technology to the aforementioned scenarios, it is necessary to first collect training data to obtain a trained AI model or AI function, and then perform AI inference based on the trained AI model or AI function. For AI models deployed on terminal devices, it is often necessary to configure the terminal device to collect training data and perform AI inference. However, the current configuration signaling overhead for terminal devices is relatively large. Summary of the Invention

[0005] This application provides an information configuration method and a communication device, which can effectively reduce the signaling overhead of configuring terminal devices.

[0006] Firstly, embodiments of this application provide an information configuration method, which can be applied to the terminal device side, such as the terminal device itself, or modules within the terminal device (e.g., processors, chips, or chip systems; specifically, it can be a modem chip, also known as a baseband chip, or a system-on-a-chip (SoC) chip containing a modem core, or a system-in-package (SIP) chip), or it can be a logical node, logical module, or software capable of implementing all or part of the terminal device's functions.

[0007] The method includes: receiving first configuration information and second configuration information, wherein the first configuration information is used to configure a measurement reference signal RS, and the second configuration information is used to instruct the collection of data associated with an artificial intelligence (AI) model or AI function based on the first configuration information; and collecting the data based on the first configuration information and / or the second configuration information.

[0008] In this embodiment, the first configuration information is the configuration information received by the terminal device in the traditional measurement configuration. This first configuration information is used to configure the measurement RS. Simultaneously, data associated with the AI ​​model or AI function, such as the input and / or output data of the AI ​​model, are also data related to RS measurement. For example, RS can be a channel state information reference signal (CSI-RS), a synchronizing signal / physical broadcast channel block (SSB), a sounding reference signal (SRS), a positioning reference signal (DL PRS), or a demodulation reference signal (DMRS), or a reference signal that may appear in the future. RS can be used for channel estimation, demodulation, synchronization, beam management, or positioning, etc. This application does not limit the type or use of RS.

[0009] Therefore, by implementing the method described in the first aspect, the terminal device can collect data related to the AI ​​model or AI function based on the first configuration information, that is, reuse the first configuration information to collect data, without having to receive a separate set of detailed configuration information for data collection, which can effectively reduce signaling overhead.

[0010] In one possible implementation, the aforementioned second configuration information is used to indicate the method of collecting data associated with an artificial intelligence (AI) model or AI function based on the first configuration information, specifically including: the second configuration information is used to indicate the method of collecting data associated with an AI model or AI function based on the first information in the first configuration information.

[0011] In this approach, the terminal device can specify which information in the first configuration information to collect data based on, as indicated by the second configuration information. For example, the first information can indicate all or part of the information in the first configuration information, and the terminal device can collect data based on that information.

[0012] In one possible implementation, the second configuration information includes second information; the second configuration information is used to indicate the method of collecting data related to artificial intelligence (AI) models or AI functions based on the first information in the first configuration information, specifically including: the second configuration information is used to indicate the method of collecting data related to artificial intelligence (AI) models or AI functions based on the second information and the first information in the first configuration information.

[0013] In this approach, the second information differs from the first configuration information. The terminal device can use the second information, along with the first information indicated by the second configuration information, to clearly define which information from the second configuration information is used for data collection. This allows for flexible configuration of data collection, rather than simply reusing the first configuration information for data collection.

[0014] In one possible implementation, the aforementioned first information is used to indicate one or more of the following: RS resource configuration information, RS measurement time information, RS measurement area information, or RS measurement data volume.

[0015] For example, the first information may include detailed information about one or more of the following: RS resource configuration information, RS measurement time information, RS measurement area information, or RS measurement data volume. For instance, detailed information about RS resource configuration information may include, but is not limited to, an index of RS resource configuration information, RS resource set, and / or the type of RS resource configuration set. Alternatively, the first information may include an index of one or more of these items, such as an index of RS resource configuration information. Compared to including detailed information in the first information, this can further reduce signaling overhead.

[0016] In one possible implementation, the second configuration information includes second information; the second configuration information is used to indicate the method of collecting data related to artificial intelligence (AI) models or AI functions based on the first configuration information, specifically including: the second configuration information is used to indicate the method of collecting data related to artificial intelligence (AI) models or AI functions based on the first configuration information and the second information.

[0017] In this approach, the second information differs from the first configuration information. The terminal device can use both the second information and the second configuration information to explicitly determine whether to collect data based on the second information and reuse the first configuration information. This allows for flexible configuration of data collection, rather than simply reusing the first configuration information for data collection. Furthermore, the terminal device can use the second information to determine which information in the first configuration information to reuse or not reuse. For example, if the second information includes the data collection period, and the first configuration information includes the RS measurement period and other information, then the terminal device can collect data based on the second information and the remaining information. In this way, the second configuration information does not need to specify in detail which information in the first configuration information is reused for data collection, further reducing signaling overhead.

[0018] In one possible implementation, the aforementioned second information is used to indicate one or more of the following: data collection time information, data collection area information, or data collection volume.

[0019] For example, the second information may include detailed information about one or more of the following: data collection time information, data collection area information, or data collection volume. For instance, detailed information about the data collection time may include, but is not limited to, the data collection cycle, data collection duration, data collection start time, data collection end time, and / or data collection interval. Alternatively, the second information may include an index of these one or more of the data collection time information, such as an index of the data collection time information. Compared to including detailed information, this can further reduce signaling overhead. Or, the second information may include an associated identifier of one or more of the following: data collection time information, data collection area information, or data collection volume.

[0020] In one possible implementation, the second configuration information is also used to instruct data to be reported based on the first configuration information.

[0021] In this method, the second configuration information is also used to instruct the first configuration information to report data. In this way, the reporting configuration information in the first configuration information can be reused, without having to receive a separate set of detailed configuration information for reporting the collected data, thereby further reducing signaling overhead.

[0022] In one possible implementation, the second configuration information mentioned above includes data reporting configuration information.

[0023] In this approach, the data reporting configuration information in the second configuration information is specifically designed for data reporting and differs from that in the first configuration information. This allows for flexible configuration of data reporting, rather than simply reusing the reporting configuration information from the first configuration information.

[0024] In one possible implementation, the above data reporting configuration information is used to indicate one or more of the following: the time information for data reporting or the amount of data to be reported.

[0025] For example, the data reporting configuration information may include detailed information about one or more of the following: the data reporting time information or the data volume being reported. For instance, detailed information about the data reporting time information may include, but is not limited to, the data reporting cycle, the duration of the data reporting, the start time of the data reporting, and / or the end time of the data reporting. Alternatively, the data reporting configuration information may include an index of these one or more items, such as an index of the data reporting time information. Compared to including detailed information in the data reporting configuration information, this can further reduce signaling overhead.

[0026] Secondly, embodiments of this application provide an information configuration method, which can be applied to the access network device side, such as the access network device itself, or modules within the access network device (e.g., processors, chips, or chip systems, specifically, a modem chip, also known as a baseband chip, or a SoC chip or SIP chip containing a modem core), or logical nodes, logical modules, or software capable of implementing all or part of the functions of the access network device.

[0027] The method includes: sending first configuration information and second configuration information, wherein the first configuration information is used to configure a measurement reference signal RS, and the second configuration information is used to instruct the collection of data associated with an artificial intelligence (AI) model or AI function based on the first configuration information.

[0028] In one possible implementation, the aforementioned second configuration information is used to indicate the method of collecting data associated with an artificial intelligence (AI) model or AI function based on the first configuration information, specifically including: the second configuration information is used to indicate the method of collecting data associated with an AI model or AI function based on the first information in the first configuration information.

[0029] In one possible implementation, the second configuration information includes second information; the second configuration information is used to indicate the method of collecting data related to artificial intelligence (AI) models or AI functions based on the first information in the first configuration information, specifically including: the second configuration information is used to indicate the method of collecting data related to artificial intelligence (AI) models or AI functions based on the second information and the first information in the first configuration information.

[0030] In one possible implementation, the aforementioned first information is used to indicate one or more of the following: RS resource configuration information, RS measurement time information, RS measurement area information, or RS measurement data volume.

[0031] In one possible implementation, the second configuration information includes second information; the second configuration information is used to indicate the method of collecting data related to artificial intelligence (AI) models or AI functions based on the first configuration information, specifically including: the second configuration information is used to indicate the method of collecting data related to artificial intelligence (AI) models or AI functions based on the first configuration information and the second information.

[0032] In one possible implementation, the aforementioned second information is used to indicate one or more of the following: data collection time information, data collection area information, or data collection volume.

[0033] In one possible implementation, the second configuration information is also used to instruct data to be reported based on the first configuration information.

[0034] In one possible implementation, the second configuration information mentioned above includes data reporting configuration information.

[0035] In one possible implementation, the above data reporting configuration information is used to indicate one or more of the following: the time information for data reporting or the amount of data to be reported.

[0036] The beneficial effects in the second aspect can be found in the beneficial effects in the first aspect, and will not be repeated here.

[0037] Thirdly, embodiments of this application provide an information configuration method, which can be applied to the terminal device side, such as the terminal device itself, or modules within the terminal device (e.g., processors, chips, or chip systems; specifically, it can be a modem chip, also known as a baseband chip, or a SoC chip or SIP chip containing a modem core), or a logical node, logical module, or software capable of implementing all or part of the terminal device's functions.

[0038] The method includes: receiving second configuration information, which is used to instruct the collection of data associated with an artificial intelligence (AI) model or AI function; and receiving third configuration information, which is used to instruct AI inference to be performed based on the second configuration information.

[0039] In this embodiment, the second configuration information is used to instruct the collection of data associated with the AI ​​model or AI function during the training phase, for example, including the input data and / or output data of the AI ​​model. The second configuration information is also used to instruct the terminal device to perform AI inference during the application phase. Since the formats of the input data and / or output data of the AI ​​model are similar in both the training and application phases, the same configuration information can be configured for the terminal device in both phases.

[0040] In this scenario, by implementing the method described in the second aspect, the terminal device can perform AI inference based on the second configuration information, that is, reuse the second configuration information to collect data related to AI inference, without needing to receive a separate set of detailed configuration information for AI inference, which can effectively reduce signaling overhead.

[0041] In one possible implementation, the aforementioned data is used to train AI models or AI functions.

[0042] In this approach, the data collected based on the second configuration information can be used to train an AI model or AI function to obtain a trained AI model or AI function. During the application phase, the AI ​​inference-related data collected using the second configuration information is reused. This AI inference-related data can match the trained AI model or AI function to the greatest extent possible, thereby further improving the performance of AI inference.

[0043] In one possible implementation, the aforementioned third configuration information is also used to indicate the configuration information of the access network device associated with the second configuration information.

[0044] In this approach, the terminal device can accurately determine the second configuration information based on the configuration information of the access network device associated with the second configuration information, thereby reusing the second configuration information to collect data related to AI inference.

[0045] In one possible implementation, the configuration information of the access network device includes the beamwidth information and / or the downtilt angle information of the access network device.

[0046] Fourthly, embodiments of this application provide an information configuration method, which can be applied to the access network device side, such as the access network device itself, or modules within the access network device (e.g., processors, chips, or chip systems, specifically, a modem chip, also known as a baseband chip, or a SoC chip or SIP chip containing a modem core), or logical nodes, logical modules, or software capable of implementing all or part of the functions of the access network device.

[0047] The method includes: sending second configuration information, which is used to instruct the collection of data associated with an artificial intelligence (AI) model or AI function; and sending third configuration information, which is used to instruct AI inference to be performed based on the second configuration information.

[0048] In one possible implementation, the aforementioned data is used to train AI models or AI functions.

[0049] In one possible implementation, the aforementioned third configuration information is also used to indicate the configuration information of the access network device associated with the second configuration information.

[0050] In one possible implementation, the configuration information of the access network device includes the beamwidth information and / or the downtilt angle information of the access network device.

[0051] The beneficial effects of the fourth aspect can be found in the beneficial effects of the third aspect, and will not be elaborated here.

[0052] Fifthly, embodiments of this application provide a communication device that has the function of implementing any one of the first to fourth aspects, or any possible implementation thereof. For example, the communication device includes a module, unit, or means corresponding to the operation described in the method of executing any one of the first to fourth aspects, or any possible implementation thereof. The module, unit, or means can be implemented by software, or by hardware, or by a combination of software and hardware.

[0053] Sixthly, embodiments of this application provide a communication device including an interface circuit and one or more processors. The one or more processors are coupled to a memory. The memory stores part or all of the necessary computer program or instructions for implementing the functions involved in the methods described in any of the first to fourth aspects, or any possible implementations thereof. The one or more processors can execute the computer program or instructions, which, when executed, cause the communication device to implement the methods described in any of the first to fourth aspects, or any possible implementations thereof. The interface circuit is used to implement communication functions within the communication device and / or communication functions between the communication device and other devices or components.

[0054] In one possible implementation, the processor is used to communicate with other devices or components through the interface circuit.

[0055] In one possible implementation, the communication device may also include the memory.

[0056] The aforementioned communication device can be an access network device, or a module within an access network device (e.g., a processor, chip, or chip system; specifically, it can be a modem chip, also known as a baseband chip, or a SoC chip or SIP chip containing a modem core), or a logical node, logical module, or software capable of implementing all or part of the functions of the access network device. Alternatively, the aforementioned communication device can be a terminal device, or a module within a terminal device (e.g., a processor, chip, or chip system; specifically, it can be a modem chip, also known as a baseband chip, or a SoC chip or SIP chip containing a modem core), or a logical node, logical module, or software capable of implementing all or part of the functions of the terminal device.

[0057] In a seventh aspect, embodiments of this application provide a communication system including a terminal device and an access network device. The terminal device can execute the methods described in the first or third aspect above, and the access network device can execute the methods described in the second or fourth aspect above.

[0058] Eighthly, embodiments of this application provide a computer-readable storage medium storing computer-readable instructions that, when read and executed by a computer, cause the computer to perform the method described in any of the first to fourth aspects above, or any possible implementation thereof.

[0059] Ninthly, embodiments of this application provide a computer program product that, when read and executed by a computer, causes the computer to perform the method described in any of the first to fourth aspects above, or any possible implementation thereof.

[0060] The computer described in the eighth or ninth aspect may include, but is not limited to, terminal equipment or access network equipment.

[0061] The implementation methods or beneficial effects of aspects five through nine can be found in aspects one through two, and will not be elaborated here. Attached Figure Description

[0062] Figure 1 is a schematic diagram of the architecture of a communication system 10 provided in an embodiment of this application;

[0063] Figure 2 is a schematic diagram of an application framework for AI technology provided in an embodiment of this application;

[0064] Figure 3 is a flowchart illustrating an information configuration method proposed in an embodiment of this application;

[0065] Figure 4 is a schematic diagram of the format of a first configuration information and a second configuration information provided in an embodiment of this application;

[0066] Figure 5 is a flowchart illustrating another information configuration method provided in an embodiment of this application;

[0067] Figure 6 is a schematic diagram of the structure of a communication device provided in an embodiment of this application;

[0068] Figure 7 is a schematic diagram of another communication device provided in an embodiment of this application. Detailed Implementation

[0069] Figure 1 is a schematic diagram of the architecture of a communication system 10 provided in an embodiment of this application. As shown in Figure 1, the communication system 10 includes a radio access network (RAN) 100, wherein the RAN 100 includes at least one RAN node (110a and 110b in Figure 1, collectively referred to as 110), and may also include at least one terminal (120a-120j in Figure 1, collectively referred to as 120). The RAN 100 may also include other RAN nodes, such as wireless relay devices and / or wireless backhaul devices (not shown in Figure 1). The terminal 120 is wirelessly connected to the RAN node 110. Terminals and RAN nodes can be interconnected via wired or wireless means. The communication system 10 may also include a core network 200. The RAN node 110 is connected to the core network 200 via wireless or wired means. The core network equipment in the core network 200 and the RAN node 110 in the RAN 100 may be independent and different physical devices, or they may be the same physical device integrating the logical functions of the core network equipment and the logical functions of the RAN node. Communication system 10 may also include Internet 300.

[0070] RAN100 can be an evolved universal terrestrial radio access (E-UTRA) system, a new radio (NR) system, or a future radio access system as defined in the 3rd generation partnership project (3GPP), or it can be a WiFi system. RAN100 can also include two or more of the above-mentioned different radio access systems. RAN100 can also be an open RAN (O-RAN).

[0071] RAN nodes, also known as radio access network devices, RAN entities, or access nodes, are used to help terminals access communication systems wirelessly. In one application scenario, an RAN node can be a base station, an evolved NodeB (eNodeB), a transmission reception point (TRP), a next-generation NodeB (gNB) in a 5G mobile communication system, a base station in a future mobile communication system, or an access node in a WiFi system. RAN nodes can be macro base stations (as shown in Figure 1, 110a), micro base stations or indoor stations (as shown in Figure 1, 110b), relay nodes, or donor nodes.

[0072] In another application scenario, multiple RAN nodes can collaborate to help terminals achieve wireless access, with different RAN nodes implementing different functions of the base station. For example, a RAN node can be a central unit (CU), a distributed unit (DU), or a radio unit (RU). Here, the CU performs the functions of the base station's Radio Resource Control (RRC) and Packet Data Convergence Protocol (PDCP), and can also perform the functions of the Service Data Adaptation Protocol (SDAP). The DU performs the functions of the base station's Radio Link Control (RANC) and Medium Access Control (MAC) layers, and can also perform some or all of the physical layer functions. For specific descriptions of these protocol layers, refer to the relevant 3GPP technical specifications. The RU can be used to implement radio frequency signal transmission and reception. The CU and DU can be two independent RAN nodes or integrated into the same RAN node, such as within a baseband unit (BBU). The RU can be included in radio frequency equipment, such as in a remote radio unit (RRU) or an active antenna unit (AAU). The CU can be further divided into two types of RAN nodes: CU-control plane and CU-user plane.

[0073] In different systems, RAN nodes may have different names. For example, in an O-RAN system, a CU can be called an open CU (O-CU), a DU can be called an open DU (O-DU), and an RU can be called an open RU (O-RU). The RAN nodes in the embodiments of this application can be implemented through software modules, hardware modules, or a combination of software and hardware modules. For example, a RAN node can be a server loaded with the corresponding software modules. The embodiments of this application do not limit the specific technology or device form used in the RAN nodes. For ease of description, a base station is used as an example of a RAN node in the following description.

[0074] A terminal is a device with wireless transceiver capabilities, capable of sending signals to or receiving signals from a base station. Terminals can also be called terminal equipment, user equipment (UE), mobile station, mobile terminal, etc. Terminals can be widely used in various scenarios, such as device-to-device (D2D), vehicle-to-everything (V2X) communication, machine-type communication (MTC), Internet of Things (IoT), ambient IoT (AIoT), virtual reality, augmented reality, industrial control, autonomous driving, telemedicine, smart grids, smart furniture, smart offices, smart wearables, smart transportation, smart cities, etc. Terminals can be mobile phones, tablets, computers with wireless transceiver capabilities, wearable devices, vehicles, airplanes, ships, robots, robotic arms, smart home devices, etc. The embodiments of this application do not limit the specific technology or device form used in the terminal.

[0075] Base stations and terminals can be fixed or mobile. They can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on water; and they can be deployed on aircraft, balloons, and satellites. The embodiments of this application do not limit the application scenarios of the base stations and terminals.

[0076] The roles of base stations and terminals can be relative. For example, the helicopter or drone 120i in Figure 1 can be configured as a mobile base station. For terminals 120j that access the wireless access network 100 through 120i, terminal 120i is a base station; however, for base station 110a, 120i is a terminal, meaning that 110a and 120i communicate via a wireless air interface protocol. Of course, 110a and 120i can also communicate via a base station-to-base station interface protocol. In this case, relative to 110a, 120i is also a base station. Therefore, both base stations and terminals can be collectively referred to as communication devices. 110a and 110b in Figure 1 can be called communication devices with base station functions, and 120a-120j in Figure 1 can be called communication devices with terminal functions.

[0077] Communication between base stations and terminals, between base stations, and between terminals can be conducted using licensed spectrum, unlicensed spectrum, or both simultaneously. Communication can be conducted using spectrum below 6 GHz, spectrum above 6 GHz, or both simultaneously. The embodiments of this application do not limit the spectrum resources used for wireless communication.

[0078] In the embodiments of this application, the functions of the base station can be executed by modules (such as chips) within the base station, or by a control subsystem that includes base station functions. This control subsystem, including base station functions, can be a control center in the aforementioned application scenarios such as smart grids, industrial control, intelligent transportation, and smart cities. Similarly, the functions of the terminal can be executed by modules (such as chips or modems) within the terminal, or by a device that includes terminal functions.

[0079] In this application, the base station sends downlink signals or downlink information to the terminal, with the downlink information carried on the downlink channel; the terminal sends uplink signals or uplink information to the base station, with the uplink information carried on the uplink channel. To communicate with the base station, the terminal needs to establish a radio connection on a cell controlled by the base station. The cell with which the terminal has established a radio connection is called the terminal's serving cell. When the terminal communicates with this serving cell, it is also susceptible to interference from signals from neighboring cells.

[0080] The following explanations of some terms used in the embodiments of this application are provided to facilitate understanding by those skilled in the art.

[0081] This section is for ease of understanding only and should not be regarded as a disclosure or specific limitation of the technical solution of this application.

[0082] I. Framework for the Application of Artificial Intelligence Technology

[0083] Artificial intelligence (AI) technology refers to the technology of performing complex calculations by simulating the human brain. With the improvement of data storage and computing power, AI technology is gradually being applied to the field of communications to improve network performance and user experience.

[0084] Figure 2 illustrates a schematic diagram of an AI technology application framework, which includes, but is not limited to, the following modules: data collection, model training, model inference, and actor.

[0085] In the embodiments of this application, "model" refers to a model built based on AI technology, i.e., an AI Model. Optionally, an AI Model can be used to implement one or more AI functions, or one or more AI Models can be used to implement one AI function.

[0086] Optionally, one or more of the modules shown in Figure 2 may be deployed in the same physical device or in different physical devices, and this application does not limit this.

[0087] Specifically, these modules implement the following functions:

[0088] The Data collection module is used to collect data inputs from various physical devices and use this data as a database for training or inference of AI Models or AI Functionality. For example, the Data collection module can collect data from one or more of the following: terminal devices, access network devices, or core network devices. The data used for training the AI ​​Model or AI Functionality can be simply referred to as Training Data. After collecting the Training Data, the Data collection module can send it to the Model Training module for AI Model or AI Functionality training. The data used for AI Model or AI Functionality inference can be simply referred to as Inference Data. After collecting the Inference Data, the Data collection module can send it to the Model Inference module for AI Model or AI Functionality inference (or simply AI inference).

[0089] The Model Training module is used to train the AI ​​Model or AI Functionality based on Training Data, and to evaluate the AI ​​Model or AI Functionality during the training process, resulting in a trained AI Model or AI Functionality. The Model Training module can then send the relevant parameters of the trained AI Model or AI Functionality to the Model Inference module for model application or updates (Model Deployment / Update).

[0090] The Model Inference module processes the Inference Data using the trained AI Model or AI Functionality to obtain output. For example, this output might be AI-based predictions (such as predicted signal quality over future times) and / or parameters guiding network policy adjustments (such as instructions for cell handover). Optionally, the Model Inference module can send policy adjustment parameters to the Actor module. Alternatively, the Model Inference module can also collect performance feedback from the AI ​​Model or AI Functionality and feed it back to the Model Training module so that the Model Training module can determine whether retraining is necessary.

[0091] The Actor module receives relevant parameters for policy adjustments and performs unified planning to guide relevant network entities in executing the policy adjustment behavior.

[0092] II. AI Use Cases

[0093] Based on the above application framework, Release 17 of the 3rd generation partnership project (3GPP) proposes several use cases for AI technology on the radio access network (RAN) side: energy saving, load balancing, mobility optimization, channel state information reference signal feedback enhancement (CSI-RS feedback enhancement), beam management enhancement, positioning accuracy enhancements, etc., and is not limited to these.

[0094] The basic principles of these use cases are briefly introduced below:

[0095] Energy Saving: Access network equipment collects load, energy consumption, and energy efficiency information of its own cell and neighboring cells, mobile path information of terminal devices accessing the cell, and / or measurement results, etc., and processes this information using an AI Model or AI Functionality to predict the load trend of the cell. Based on the load prediction results and other information (such as the cell's purpose, KPIs, etc.), energy-saving measures can be taken in a timely and appropriate manner without affecting network coverage or user access. For example, energy-saving measures include, but are not limited to, cell deactivation, carrier shutdown, channel shutdown, time slot shutdown, and / or reduced transmit power. Optionally, if network coverage is affected after the energy-saving measures are implemented, or if access or service requirements cannot be met, the access network equipment needs to modify the energy-saving strategy, either by directly reverting to normal operation or by re-predicting the load. Optionally, the previously used AI Model or AI Functionality can be modified / retrained for re-prediction.

[0096] Load Balancing: Access network devices collect load, energy consumption, and energy efficiency information of their own cell and neighboring cells, as well as the movement path information of terminal devices accessing the cell, and / or measurement results. They then process this information using an AI Model or AI Functionality to predict the load trend of the cell. Based on the load prediction results and other information (such as the cell's purpose, KPIs, etc.), they can rationally select whether to switch some terminal devices accessing the cell to a neighboring cell, or receive new terminal devices from a neighboring cell. This helps to make the load levels of the cell and neighboring cells more similar, reducing situations where some cells are overloaded and affect normal services, or where some cells have idle resources. Optionally, if the handover fails, or the services of the terminal devices are affected after the handover, or the load prediction is inaccurate leading to poor load balancing, or if a temporary abnormal load change renders the original load balancing strategy inapplicable, the access network device can exit or modify the current load balancing strategy, or re-predict the load. Optionally, the previously used AI Model or AI Functionality can be modified / retrained for re-prediction.

[0097] Mobility Optimization: Access network equipment collects historical mobility path information and current signal measurement information of terminal devices, and processes this information using an AI Model or AI Functionality to predict the future mobility path of the terminal devices. Further, based on the predicted mobility path, the access network equipment pre-determines whether the terminal device needs to handover. Optionally, if it is determined that the terminal device can handover in the future, the access network equipment can issue handover configurations in advance and notify the target cell to prepare access resources, reducing latency during the handover process and lowering the probability of handover or access failure. Optionally, when the predicted mobility path is incorrect, it may lead to handover failure or service interruption. In this case, the access network equipment needs to consider re-predicting the mobility path based on the abnormal situation. Optionally, the previously used AI Model or AI Functionality can be modified / retrained for re-prediction, etc.

[0098] CSI-RS Feedback Enhancement: The access network device, based on requirements and / or the capabilities of the terminal equipment, obtains an AI Model or AI Functionality for the channel matrix. This AI Model or AI Functionality is used to compress, encode, and / or quantize the channel matrix, thereby reducing signaling overhead. The access network device sends the relevant parameters of this AI Model or AI Functionality to the terminal equipment. After performing channel measurements, the terminal equipment can compress, encode, and / or quantize the channel matrix according to the AI ​​Model or AI Functionality, and then transmit it back to the access network device. The access network device then performs inverse processing on the received information according to the AI ​​Model or AI Functionality to obtain the original channel matrix.

[0099] Beam Management Enhancement: The access network device sends relevant parameters of the beam's AI Model or AI Functionality to the terminal device. This AI Model or AI Functionality is used to predict the beam measurement results. The access network device performs a P1 phase beam scan. The terminal device obtains the P1 phase beam scan measurement results based on the AI ​​Model or AI Functionality and reports them to the access network device. The access network device performs a P2 phase beam scan based on the P1 phase beam scan measurement results. The terminal device obtains the P2 phase beam scan measurement results, determines the optimal beam based on the P2 phase beam scan measurement results, and feeds them back to the access network device.

[0100] Positioning Accuracy Enhancements: The terminal device collects raw data and processes it based on an AI Model or AI Functionality to perform positioning prediction. Optionally, the terminal device sends the raw data to a location management function (LMF) or access network device, which processes the raw data and performs positioning prediction.

[0101] III. AI use cases for terminal devices

[0102] Optionally, in the AI ​​use cases mentioned above, the AI ​​models or AI functionalities of some AI use cases are deployed on the end device. Examples include the CSI-RS Feedback Enhancement, Beam Management Enhancement, and Positioning Accuracy Enhancements use cases.

[0103] For this type of AI case, the terminal device needs to collect Training Data during the training phase to obtain a trained AI Model or AI Functionality; and then, during the application phase, the terminal device collects Inference Data to perform AI inference using the trained AI Model or AI Functionality. This process often requires the access network device to configure the terminal device first, so that the terminal device can collect the corresponding Training Data and Inference Data. However, the current configuration signaling overhead for terminal devices is relatively large.

[0104] In this embodiment, the Training Data and Inference Data acquired by the terminal device can be data measured based on the reference signal (RS). Furthermore, the access network device also configures the terminal device to measure RS in a traditional measurement configuration. Therefore, this embodiment provides an information configuration method that can reuse traditional measurement configurations to collect data for AI Models or AI Functionality, thereby reducing configuration signaling overhead for the terminal device. Moreover, in some scenarios, the formats of Training Data and Inference Data are similar. Therefore, this embodiment provides another information configuration method that can reuse data collection configurations to obtain Inference Data and perform AI inference, thereby reducing configuration signaling overhead for the terminal device.

[0105] This application does not limit the types of RS or the data obtained based on RS measurements.

[0106] For example, RS can be a channel state information reference signal (CSI-RS), a synchronizing signal / physical broadcast channel block (SSB), a sounding reference signal (SRS), a positioning reference signal (DL PRS), or a demodulation reference signal (DMRS), or a reference signal that may appear in the future.

[0107] For example, Training Data and Inference Data include, but are not limited to, one or more of the following: reference signal receiving quality (RSRQ), reference signal receiving power (RSRP), received signal strength indicator (RSSI), signal-noise ratio (SNR), signal to interference plus noise ratio (SINR), channel quality indicator (CQI), precoding matrix indicator (PMI), time difference of arrival (TDOA), and angle of arrival (AOA).

[0108] The information configuration method proposed in the embodiments of this application will be described in detail below:

[0109] Figure 3 shows a flowchart of an information configuration method proposed in an embodiment of this application. The execution subject of this method can be a terminal device and an access network device, or the subject can be a chip in the terminal device and a chip in the access network device, or the subject can be other types of products. Those skilled in the art can make further extensions based on the content disclosed in the specification. The execution subject of the method shown in Figure 3 and the following methods are executors of a terminal device and an access network device as examples. As shown in Figure 3, the method includes steps 301 to 302. Wherein:

[0110] Step 301: The access network device sends first configuration information and second configuration information to the terminal device. The first configuration information is used to configure the measurement RS, and the second configuration information is used to instruct the collection of data related to the AI ​​model or AI function based on the first configuration information.

[0111] Accordingly, the terminal device receives the first configuration information and the second configuration information.

[0112] In one possible implementation, the first configuration information includes, but is not limited to, one or more of the following: an index of the first configuration information, resource configuration information of the RS, time information of the RS measurement, area information of the RS measurement, or data volume of the RS measurement.

[0113] For example, the resource configuration information of an RS includes, but is not limited to, one or more of the following: an index of the RS's resource configuration information, the RS resource set, or the type of RS resource. An RS resource set may include one or more of the following: an NZP CSI-RS resource set, an SSB resource set, or a CSI-IM resource set. The type of RS resource may be periodic, semi-persistent, or aperiodic.

[0114] For example, the time information of RS measurement includes, but is not limited to, one or more of the following: the index of the time information of RS measurement, the measurement period of RS, the duration of RS measurement, the start time of RS measurement, the end time of RS measurement, or the interval of RS measurement, etc.

[0115] For example, the area information for RS measurement includes, but is not limited to, one or more of the following: the index of the area information for RS measurement, the area identifier for RS measurement, the area range for RS measurement, or the priority of the area for RS measurement.

[0116] (1) In a first possible implementation, the second configuration information is used to indicate the method of collecting data related to the AI ​​model or AI function based on the first configuration information, specifically including: the second configuration information includes a first identifier, and when the first identifier is a first value, it indicates that data related to the artificial intelligence AI model or AI function is collected based on the first configuration information.

[0117] For example, the first identifier occupies one bit. If the bit is 1, the first identifier indicates that data associated with the AI ​​model or AI function is collected based on the first configuration information; conversely, if the bit is 0, the first identifier indicates that data associated with the AI ​​model or AI function is not collected based on the first configuration information. Alternatively, conversely, if the bit is 0, the first identifier indicates that data associated with the AI ​​model or AI function is collected based on the first configuration information; conversely, if the bit is 1, the first identifier indicates that data associated with the AI ​​model or AI function is not collected based on the first configuration information.

[0118] Optionally, the second configuration information may further include second information, that is, the second configuration information includes a first identifier and second information; the aforementioned second configuration information is used to indicate the method of collecting data related to artificial intelligence AI models or AI functions based on the first configuration information, specifically including: the second configuration information is used to indicate the method of collecting data related to artificial intelligence AI models or AI functions based on the first configuration information and the second information.

[0119] The second information is additional configuration provided by the access network device to the terminal device for data collection. This allows for additional configuration of data collection through the second information, enabling the terminal device to collect data based on the additional second configuration and by reusing the first configuration information, thus achieving more flexible configuration.

[0120] Optionally, the second information is used to indicate one or more of the following: the time information of data collection, the area information of data collection, or the amount of data collected.

[0121] For example, the time information for data collection includes, but is not limited to, one or more of the following: the index of the time information for data collection, the period of data collection, the duration of data collection, the start time of data collection, the end time of data collection, or the interval between data collections.

[0122] For example, the area information for data collection includes, but is not limited to, one or more of the following: an index of the area information for data collection, an area identifier for data collection, the area scope for data collection, or the priority of the area for data collection.

[0123] In the first example, the second information may include one or more of the following: the time of data collection, the area of ​​data collection, or the amount of data collected.

[0124] In the second example, the second information may include an associated identifier for one or more of the following: data collection time information, data collection area information, or the amount of data collected. This associated identifier may be predefined, such as that specified by a communication protocol or pre-configured by the access network device to the terminal device. For example, if the associated identifier for the data collection time information and the data collection area information is predefined as "01", then the second information may include "01" to indicate the data collection time information and the data collection area information. Compared to the first example, this example can further reduce signaling overhead. In the first example, the second information needs to include the data collection time information and the data collection area information, such as including an index of the data collection time information and an index of the data collection area information.

[0125] Optionally, the two examples above can also be combined, meaning the second information can include the content of the time information of data collection and the associated identifier of the area information of data collection.

[0126] Optionally, the second information may also indicate which information in the first configuration information should be reused or not reused. That is, the terminal device can determine which information in the first configuration information should be reused or not reused based on the second information.

[0127] For example, if the second information includes the data collection period, and the first configuration information includes the RS measurement period and other information, then the terminal device can collect data based on the second information and reuse the remaining information from the first configuration information except for the RS measurement period. This eliminates the need for the second configuration information to specify which information from the first configuration information to reuse, further reducing signaling overhead.

[0128] (2) In the second possible implementation, the second configuration information is used to indicate the method of collecting data related to the artificial intelligence AI model or AI function based on the first configuration information, specifically including: the second configuration information is used to indicate the method of collecting data related to the artificial intelligence AI model or AI function based on the first information in the first configuration information.

[0129] The first information refers to the information in the first configuration information.

[0130] In the first example, the second configuration information may include the first information. In this way, the terminal device can use the first information carried by the second configuration information to clearly determine which information in the first configuration information to reuse for collecting data related to AI models or AI functions.

[0131] In the second example, the second configuration information may include an association identifier for the first information. For instance, the association identifier for the first information is predefined, such as by a communication protocol or pre-configured by the access network device to the terminal device. For example, if the association identifier occupies at least one bit, and that at least one bit is a second value, the association identifier indicates that data is collected based on the first information; conversely, if that at least one bit is a third value, the association identifier indicates that data is not collected based on the first information. Thus, the terminal device can first determine the first information using the association identifier carried in the second configuration information, and then, based on the first information, determine which information in the first configuration information should be reused to collect data associated with the AI ​​model or AI function. Optionally, the association identifiers for different combinations of information in the first configuration information can be predefined. For example, if the association identifier corresponding to the resource configuration information of RS and the time information of RS measurement in the first configuration information is "10", and the association identifier corresponding to the resource configuration information of RS and the area information of RS measurement in the first configuration information is "11", then when the second configuration information includes "10", it indicates that data related to the AI ​​model or AI function is collected based on the resource configuration information of RS and the time information of RS measurement in the first configuration information; when the second configuration information includes "11", it indicates that data related to the AI ​​model or AI function is collected based on the resource configuration information of RS and the area information of RS measurement in the first configuration information.

[0132] Optionally, the first information may indicate all the information in the first configuration information, or the first information may indicate part of the information in the first configuration information.

[0133] For example, the first information may include an index of the first configuration information. Thus, when the second configuration information includes the first information, or an associated identifier of the first information, it can instruct the collection of data associated with the AI ​​model or AI function based on all the information in the first configuration information, thereby saving the signaling overhead of the second configuration information.

[0134] For example, the first information may include detailed information about the remaining portion of the first configuration information, excluding the index of the first configuration information. Thus, when the second configuration information includes the first information or its associated identifier, data collection based on a portion of the first configuration information can be instructed, thereby saving signaling overhead.

[0135] For example, the first information may include indexes of the remaining information in the first configuration information, excluding the index of the first configuration information itself. For instance, the first information may include one or more of the following: an index of the resource configuration information of the RS, an index of the time information measured by the RS, an index of the area information measured by the RS, or an index of the data volume measured by the RS. Thus, when the second configuration information includes the first information or its associated identifier, it can instruct the collection of data based on all or part of the information in the first configuration information. Since the second configuration information only includes indexes or their associated identifiers, and not detailed information content, the signaling overhead of the second configuration information can be further reduced.

[0136] Optionally, the content of the aforementioned first information can also be combined, such as the first information including the specific content of information A in the first configuration information and the index of information B. This application does not limit this.

[0137] Optionally, the second configuration information also includes second information, that is, the second configuration information includes the first information or the associated identifier of the first information, and the second information; the above-mentioned second configuration information is used to indicate the method of collecting data related to the artificial intelligence AI model or AI function based on the first configuration information, specifically including: the second configuration information is used to indicate the collection of data related to the AI ​​model or AI function based on the second information and the first information in the first configuration information.

[0138] The second information is additional information configured by the access network device for data collection by the terminal device. The second information can be referred to the corresponding content in (1) above, and will not be repeated here. In this way, additional configuration for data collection can be achieved through the second information, so that the terminal device can collect data based on the additional configured second information and reuse the first configuration information, and can achieve more flexible configuration.

[0139] Furthermore, regarding (1) or (2) above, optionally, the second configuration information is also used to indicate data to be reported based on the first configuration information. In this way, the data reported using the first configuration information can be reused, eliminating the need to configure an additional set of detailed reporting configuration information, which can effectively save signaling overhead.

[0140] In one possible implementation, the first configuration information further includes RS measurement reporting configuration information, used to configure the terminal device to report data obtained based on RS measurements. The second configuration information indicates that the implementation method of reporting data based on the first configuration information can be implemented with reference to (1) or (2) above. For example, the second configuration information includes a first identifier, and when the first identifier takes a first value, it indicates that data related to the artificial intelligence AI model or AI function is collected and reported based on the first configuration information; or, the second configuration information includes first information or the associated identifier of the first information, and the first information includes one or more of the first configuration information mentioned above and the RS measurement reporting configuration information in the first configuration information.

[0141] For example, the reporting configuration information for RS measurements includes, but is not limited to, one or more of the following: the index of the RS measurement reporting configuration information, the reporting time information for RS measurements, or the reporting data volume for RS measurements.

[0142] For example, the reporting time information for RS measurements is used to configure one or more of the following: RS measurement reporting cycle, RS measurement reporting duration, RS measurement reporting start time, RS measurement reporting end time, or RS measurement reporting interval, etc.

[0143] Alternatively, for (1) or (2) above, the second configuration information may optionally include data reporting configuration information, or one or more association identifiers in the data reporting configuration information. In this way, additional configuration for data reporting can be implemented, instead of reusing the reporting configuration information in the first configuration information, thus enabling more flexible configuration.

[0144] For example, the data reporting configuration information includes, but is not limited to, one or more of the following: the index of the data reporting configuration information, the time information of data reporting, or the amount of data reported.

[0145] For example, the data reporting time information is used to configure one or more of the following: data reporting cycle, data reporting duration, data reporting start time, data reporting end time, or data reporting interval, etc.

[0146] In one possible implementation, the aforementioned first configuration information and second configuration information are carried in the same message and sent as a whole. For example, the second configuration information can be carried in a newly added or blank field within the message containing the first configuration information; this newly added or blank field can be before and / or after the first configuration information. For example, as shown in Figure 4, a newly added field after the first configuration information carries a first identifier, first information, or an associated identifier of the first information; optionally, a further added field can carry one or more of the following: second information, configuration information for data reporting, or associated identifiers of one or more of the configuration information for data reporting.

[0147] In another possible implementation, the first configuration information and the second configuration information can be sent separately, and the timing of their transmission can be the same or different. For example, regarding different transmission timings, the first configuration information can be sent first, followed by the second configuration information, or vice versa; this application does not limit this. The timing of the transmission of the first and second configuration information can be predefined, such as as specified in the communication protocol, or pre-configured by the access network device to the terminal device.

[0148] In one possible implementation, the aforementioned first configuration information or second configuration information may be carried in one or a combination of at least two of radio resource control (RRC) signaling, media access control (MAC) layer signaling, and physical layer signaling. The MAC layer signaling may include, for example, a MAC control element (CE); the physical layer signaling may include, for example, downlink control information (DCI).

[0149] Step 302: The terminal device collects data based on the first configuration information and / or the second configuration information.

[0150] In this embodiment of the application, the terminal device determines which information in the first configuration information needs to be reused to collect data based on the first configuration information and / or the second configuration information.

[0151] Optionally, the terminal device can also determine which information, other than the first configuration information, is needed to collect data, such as the second information mentioned above. Then, the terminal device measures the RS based on the determined information to obtain data.

[0152] Optionally, the terminal device may also determine whether to reuse the reporting configuration information in the first configuration information to report data; or, the terminal device may also determine not to reuse the reporting configuration information in the first configuration information but to report data based on the data reporting configuration information in the second configuration information. In this way, the terminal device can report the collected data to the access network device.

[0153] Furthermore, optionally, the terminal device can train the AI ​​model or AI function based on the data; or, the terminal device can send the data to the training device, which will determine whether to train based on the data. This application does not limit this. For example, the training device can be an access network device or other devices.

[0154] Based on the embodiment described in Figure 3, the access network device can send first configuration information and second configuration information to the terminal device. The second configuration information instructs the terminal device to collect data related to the AI ​​model or AI function based on the first configuration information, that is, instructs the terminal device to reuse the first configuration information to collect data. In this way, it is not necessary to configure a separate set of detailed configuration information for data collection to the terminal device, which can effectively reduce signaling overhead.

[0155] Figure 5 shows a flowchart of another information configuration method proposed in an embodiment of this application. As shown in Figure 4, the method includes steps 501 to 502. Wherein:

[0156] Step 501: The access network device sends second configuration information to the terminal device. The second configuration information is used to instruct the collection of data related to the AI ​​model or AI function.

[0157] Accordingly, the terminal device receives the second configuration information.

[0158] In this embodiment of the application, the access network device sends second configuration information to the terminal device during the training phase of the AI ​​model or AI function in order to collect data associated with the AI ​​model or AI function.

[0159] In one possible implementation, the second configuration information is a set of detailed configuration information for data collection, including but not limited to one or more of the following: an index of the second configuration information, resource configuration information of the RS for data collection, time information for data collection, area information for data collection, or the amount of data collected. The implementation methods for these information can be referred to the corresponding descriptions above, and will not be repeated here.

[0160] In another possible implementation, the second configuration information is the second configuration information in the embodiment corresponding to Figure 3 above, that is, the access network device also sends the first configuration information to the terminal device, and the second configuration information is used to instruct the collection of data related to the AI ​​model or AI function based on the first configuration information.

[0161] Optionally, the second configuration information is also used to indicate the reporting of collected data. For example, the second configuration information may include detailed configuration information for data reporting, or the second configuration information may be used to indicate data reporting based on the reporting configuration information in the first configuration information. Specific implementations can be found in the corresponding descriptions in the embodiments shown in Figure 3, and will not be repeated here.

[0162] In one possible implementation, the second configuration information is carried in one or a combination of at least two of RRC signaling, MAC layer signaling, and physical layer signaling.

[0163] Optionally, the terminal device may collect data related to the AI ​​model or AI function based on the second configuration information.

[0164] Optionally, the terminal device can train the AI ​​model or AI function based on the data; or, the terminal device can send the data to the training device, which will determine whether to train based on the data. This application does not limit this. For example, the training device can be an access network device or other devices.

[0165] Optionally, after training is complete, the terminal device can obtain the relevant parameters of the trained AI model or AI function.

[0166] Step 502: The access network device sends third configuration information to the terminal device. The third configuration information is used to instruct AI inference to be performed based on the second configuration information.

[0167] Accordingly, the terminal device receives the third configuration information.

[0168] In this embodiment, step 502 is optional. The terminal device has parameters related to a trained AI model or AI function. The terminal device can perform AI inference using the trained AI model or AI function based on the third configuration information. Alternatively, after step 501, the terminal device may, by default or according to a pre-defined communication protocol, perform AI inference based on the second configuration information.

[0169] (1) In the first possible implementation, the third configuration information may include a second identifier, and when the second identifier is a fourth value, it indicates that AI inference is performed based on the second configuration information.

[0170] For example, the second identifier occupies one bit. If the bit is 1, the second identifier indicates that AI inference is performed based on the second configuration information; conversely, if the bit is 0, the second identifier indicates that AI inference is not performed based on the second configuration information. Alternatively, conversely, if the bit is 0, the second identifier indicates that AI inference is performed based on the second configuration information; conversely, if the bit is 1, the first identifier indicates that AI inference is not performed based on the second configuration information.

[0171] (2) In the second possible implementation, the third configuration information may include one or more of the second configuration information, or one or more of the associated identifiers.

[0172] For example, the second configuration information is a set of detailed configuration information for data collection. The second configuration information may include one or more of the following: an index of the second configuration information, resource configuration information of the RS for data collection, time information for data collection, area information for data collection, amount of data collected, and data reporting configuration information; or, an association identifier for these one or more of these. This association identifier may be predefined, such as as specified in the communication protocol, or pre-configured by the access network device to the terminal device.

[0173] For example, for the second configuration information in the embodiment corresponding to Figure 3 above, the third configuration information may include one or more of the first identifier, second information, first information, the associated identifier of the first information, and the configuration information of data reporting in the embodiment of Figure 3 above.

[0174] Furthermore, in relation to (1) or (2) above, optionally, the third configuration information is also used to indicate the configuration information of the access network device associated with the second configuration information.

[0175] In this embodiment, the terminal device can collect data related to the AI ​​model or AI function under the configuration of the access network device corresponding to the second configuration information. This allows the terminal device to accurately determine the second configuration information based on the access network device's configuration information and reuse it for AI inference, thereby ensuring the performance of AI inference.

[0176] For example, the configuration information of the access network device includes, but is not limited to, the beamwidth information and / or the downtilt angle information of the access network device.

[0177] In one possible implementation, the third configuration information may also include the configuration information of the access network device associated with the second configuration information, or the third configuration information may also include the associated identifier of the access network device's configuration information.

[0178] Furthermore, optionally, the terminal device can perform AI inference based on third configuration information.

[0179] Optionally, prior to step 502, the access network device may also send capability query information to the terminal device. This capability query information is used to query the AI ​​functions supported by the terminal device. Accordingly, the terminal device may report the supported AI functions to the access network device.

[0180] The AI ​​functions supported by the terminal device can be those described in the AI ​​use cases above. These AI functions can be represented by the identifiers of AI models or AI functions. Specifically, the terminal device can report the identifiers of the supported AI models or AI functions. In this way, the access network device can send the third-party configuration information corresponding to the supported AI models or AI functions to the terminal device.

[0181] Optionally, after step 502, the terminal device can further determine whether it can currently support AI inference based on the third configuration information. For example, the terminal device can determine the application requirements of the AI ​​model or AI function corresponding to the third configuration information and judge whether the application requirements are currently met. Optionally, if it is determined that the application requirements are currently met, AI inference is performed based on the third configuration information; if it is determined that the application requirements are not currently met, an indication message is sent to the access network device to indicate that the application requirements are not met. Optionally, after receiving the indication message, the access network device can resend the third configuration information after a preset time period, or the access network device can select other configuration information (such as changing the access network device to the inference configuration information corresponding to another AI function or AI model), or the access network device can determine to retrain the AI ​​model or AI function, etc., which are not limited in this application.

[0182] For example, the application requirements include, but are not limited to, one or more of the following: the macroscopic physical attributes of the terminal device, the hardware and software attributes of the terminal device, the channel environment in which the terminal device operates, the communication configuration of the terminal device and the access network device, and the time-frequency domain resources for the terminal device to perform air interface communication. The macroscopic physical attributes of the terminal device include, but are not limited to, one or more of the following: the terminal device's moving speed, direction of movement, geographical location, and / or altitude. The hardware and software attributes of the terminal device include, but are not limited to, one or more of the following: the terminal device's remaining battery power, computing power, storage space, and the compilation environment for the AI ​​models or AI functions supported by the terminal device. The channel environment in which the terminal device operates includes, but is not limited to, one or more of the following: urban macrocell (UMa), urban microcell (UMi), or indoor hotspot (InH). The communication configuration of the terminal device and the access network device includes, but is not limited to, one or more of the following: the number of receiving antennas on the terminal device or the number of transmitting ports on the access network device. The time-frequency resources for the terminal device to perform air interface communication include, but are not limited to, one or more of the following: carrier frequency, subcarrier spacing, or bandwidth.

[0183] Based on the embodiment described in Figure 5, the access network device can send second configuration information and third configuration information to the terminal device. The third configuration information instructs the terminal device to perform AI inference based on the second configuration information, that is, instructs the terminal device to reuse the second configuration information for AI inference. In this way, it is not necessary to send a separate set of detailed configuration information for AI inference to the terminal device, which can effectively reduce signaling overhead.

[0184] It is understood that, in order to achieve the functions in the above embodiments, the access network device and the terminal device include hardware structures and / or software modules corresponding to perform each function. Those skilled in the art should readily recognize that, based on the units and method steps of the various examples described in conjunction with the embodiments disclosed in this application, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application scenario and design constraints of the technical solution.

[0185] Figures 6 and 7 are schematic diagrams of possible communication devices provided in embodiments of this application. These communication devices can be used to implement the functions of access network devices or terminal devices in the above method embodiments, and thus can also achieve the beneficial effects of the above method embodiments. In the embodiments of this application, the communication device can be a terminal device as shown in Figure 1, an access network device as shown in Figure 1, or a module (such as a chip) applied to an access network device or a terminal device.

[0186] As shown in Figure 6, the communication device 600 includes a processing unit 610 and a transceiver unit 620.

[0187] In one embodiment, the communication device 600 is used to implement the functions of the access network device or terminal device in the embodiment corresponding to FIG3 above.

[0188] When the communication device 600 is used to implement the functions of the terminal device in the embodiment corresponding to FIG3 above: the transceiver unit 620 is used to receive first configuration information and second configuration information, the first configuration information is used to configure the measurement reference signal RS, and the second configuration information is used to indicate that data related to the AI ​​model or AI function is collected based on the first configuration information; the processing unit 610 is used to collect the data based on the first configuration information and / or the second configuration information.

[0189] When the communication device 600 is used to implement the function of the access network device in the embodiment corresponding to FIG3 above: the transceiver unit 620 is used to send first configuration information and second configuration information, the first configuration information is used to configure the measurement reference signal RS, and the second configuration information is used to indicate that data related to the AI ​​model or AI function is collected based on the first configuration information; the processing unit 610 is used to generate the first configuration information and the second configuration information.

[0190] In another embodiment, the communication device 600 is used to implement the functions of the access network device or terminal device in the embodiment corresponding to FIG5 above.

[0191] When the communication device 600 is used to implement the functions of the terminal device in the embodiment corresponding to FIG5 above: the transceiver unit 620 is used to receive second configuration information, which is used to instruct the collection of data associated with AI models or AI functions; and to receive third configuration information, which is used to instruct AI inference based on the second configuration information; the processing unit 610 is used to collect data associated with AI models or AI functions based on the second configuration information, and to perform AI inference based on the third configuration information.

[0192] When the communication device 600 is used to implement the function of the access network device in the embodiment corresponding to FIG5 above: the transceiver unit 620 is used to send second configuration information, which is used to instruct the collection of data related to AI models or AI functions; send third configuration information, which is used to instruct AI inference based on the second configuration information; and the processing unit 610 is used to generate the second configuration information and the third configuration information.

[0193] For a more detailed description of the processing unit 610 and the transceiver unit 620, please refer to the relevant descriptions in the above method embodiments.

[0194] As shown in Figure 7, the communication device 700 includes a processor 710 and an interface circuit 720. The processor 710 and the interface circuit 720 are coupled to each other. It is understood that the interface circuit 720 can be a transceiver or an input / output interface. Optionally, the communication device 700 may also include a memory 730 for storing instructions executed by the processor 710, or storing input data required by the processor 710 to execute instructions, or storing data generated after the processor 710 executes instructions. Sometimes, the interface circuit 720 can also be understood as part of the processor 710, in which case the communication device 700 includes the processor 710.

[0195] When the communication device 700 is used to implement the above method embodiment, the processor 710 is used to implement the function of the processing unit 610, and the interface circuit 720 is used to implement the function of the transceiver unit 620.

[0196] When the aforementioned communication device is a chip applied to a terminal device, the terminal device chip implements the functions of the terminal device in the above method embodiments. The terminal device chip receives information from the access network device, which can be understood as the information being first received by other modules (such as radio frequency modules or antennas) in the terminal device, and then sent to the terminal device by these modules. The terminal device chip sends information to the access network device, which can be understood as the information being first sent to other modules (such as radio frequency modules or antennas) in the terminal device, and then sent to the access network device by these modules.

[0197] When the aforementioned communication device is a chip applied to an access network device, the access network device chip implements the functions of the access network device in the above method embodiments. The access network device chip receives information from the terminal device, which can be understood as the information being first received by other modules (such as radio frequency modules or antennas) in the access network device, and then sent to the access network device chip by these modules. The access network device chip sends information to the terminal device, which can be understood as the information being sent down to other modules (such as radio frequency modules or antennas) in the access network device, and then sent to the terminal device by these modules.

[0198] In this application, entity A sends information to entity B, either directly or indirectly through other entities. Similarly, entity B receives information from entity A, either directly or indirectly through other entities. Entities A and B can be RAN nodes or terminal devices, or modules within RAN nodes or terminal devices. Information transmission and reception can be between RAN nodes and terminal devices, such as between access network devices and terminal devices; between two RAN nodes, such as between a CU and a DU; or between different modules within a single device, such as between a terminal device chip and other modules of the terminal device, or between an access network device chip and other modules of the access network device.

[0199] It is understood that the processor in the embodiments of this application can be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, field-programmable gate arrays, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

[0200] The method steps in the embodiments of this application can be implemented in hardware or in software instructions executable by a processor. The software instructions can consist of corresponding software modules, which can be stored in random access memory, flash memory, read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, registers, hard disks, portable hard disks, read-only optical discs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. The storage medium can also be a component of the processor. The processor and the storage medium can reside in an application-specific integrated circuit (ASIC). Alternatively, the ASIC can reside in an access network device or a terminal device. The processor and the storage medium can also exist as discrete components in the access network device or the terminal device.

[0201] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this application are performed entirely or partially. The computer can be a general-purpose computer, a special-purpose computer, a computer network, an access network device, a user equipment, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a digital video optical disc; or it can be a semiconductor medium, such as a solid-state drive. The computer-readable storage medium may be a volatile or non-volatile storage medium, or may include both types of storage media.

[0202] The terms "first," "second," "third," and "fourth," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0203] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0204] It is understood that the various numerical designations used in the embodiments of this application are merely for descriptive convenience and are not intended to limit the scope of the embodiments of this application. The order of the process numbers described above does not imply the order of execution; the execution order of each process should be determined by its function and internal logic.

Claims

1. An information configuration method characterized by comprising: The method includes: Receive first configuration information and second configuration information, wherein the first configuration information is used to configure the measurement reference signal RS, and the second configuration information is used to instruct the collection of data associated with artificial intelligence AI models or AI functions based on the first configuration information; The data is collected based on the first configuration information and / or the second configuration information.

2. The method of claim 1, wherein, The second configuration information is used to instruct the collection of data related to artificial intelligence (AI) models or AI functions based on the first configuration information, including: The second configuration information is used to instruct the collection of data associated with artificial intelligence (AI) models or AI functions based on the first information in the first configuration information.

3. The method of claim 2, wherein, The second configuration information includes second information; The second configuration information is used to instruct the collection of data associated with artificial intelligence (AI) models or AI functions based on the first information in the first configuration information, including: The second configuration information is used to instruct the collection of data associated with artificial intelligence (AI) models or AI functions based on the second information and the first information in the first configuration information.

4. The method according to claim 2 or 3, characterized in that, The first information is used to indicate one or more of the following: RS resource configuration information, RS measurement time information, RS measurement area information, or RS measurement data volume.

5. The method of claim 1, wherein, The second configuration information includes second information; The second configuration information is used to instruct the collection of data related to artificial intelligence (AI) models or AI functions based on the first configuration information, including: The second configuration information is used to instruct the collection of data associated with artificial intelligence (AI) models or AI functions based on the first configuration information and the second information.

6. The method according to claim 3 or 5, characterized in that, The second information is used to indicate one or more of the following: Information on the time of data collection, the area of ​​data collection, or the amount of data collected.

7. The method according to any one of claims 1 to 6, characterized in that, The second configuration information is also used to instruct the data to be reported based on the first configuration information.

8. The method according to any one of claims 1-6, characterized in that, The second configuration information includes the data reporting configuration information.

9. The method of claim 8, wherein, The data reporting configuration information is used to indicate one or more of the following: The time information of the data being reported or the amount of data being reported.

10. An information configuration method characterized by comprising: The method includes: Send first configuration information and second configuration information. The first configuration information is used to configure the measurement reference signal RS, and the second configuration information is used to instruct the collection of data associated with artificial intelligence (AI) models or AI functions based on the first configuration information.

11. The method of claim 10, wherein, The second configuration information is used to instruct the collection of data related to artificial intelligence (AI) models or AI functions based on the first configuration information, including: The second configuration information is used to instruct the collection of data associated with artificial intelligence (AI) models or AI functions based on the first information in the first configuration information.

12. The method according to claim 11, characterized in that, The second configuration information includes second information; The second configuration information is used to instruct the collection of data associated with artificial intelligence (AI) models or AI functions based on the first information in the first configuration information, including: The second configuration information is used to instruct the collection of data associated with artificial intelligence (AI) models or AI functions based on the second information and the first information in the first configuration information.

13. The method according to claim 11 or 12, characterized in that, The first information is used to indicate one or more of the following: RS resource configuration information, RS measurement time information, RS measurement area information, or RS measurement data volume.

14. The method according to claim 10, characterized in that, The second configuration information includes second information; The second configuration information is used to instruct the collection of data related to artificial intelligence (AI) models or AI functions based on the first configuration information, including: The second configuration information is used to instruct the collection of data associated with artificial intelligence (AI) models or AI functions based on the first configuration information and the second information.

15. The method according to claim 12 or 14, characterized in that, The second information is used to indicate one or more of the following: Information on the time of data collection, the area of ​​data collection, or the amount of data collected.

16. The method according to any one of claims 10-15, characterized in that, The second configuration information is also used to instruct the data to be reported based on the first configuration information.

17. The method according to any one of claims 10-15, characterized in that, The second configuration information includes the data reporting configuration information.

18. The method according to claim 17, characterized in that, The data reporting configuration information is used to indicate one or more of the following: The time information of the data being reported or the amount of data being reported.

19. An information configuration method, characterized in that, The method includes: Receive second configuration information, which is used to instruct the collection of data associated with artificial intelligence (AI) models or AI functions; Receive third configuration information, which is used to instruct AI inference to be performed based on the second configuration information.

20. The method according to claim 19, characterized in that, The data is used to train the AI ​​model or the AI ​​function.

21. The method according to claim 19 or 20, characterized in that, The third configuration information is also used to indicate the configuration information of the access network device associated with the second configuration information.

22. The method according to claim 21, characterized in that, The configuration information of the access network device includes the beamwidth information and / or the downtilt angle information of the access network device.

23. An information configuration method, characterized in that, The method includes: Send a second configuration message, which is used to instruct the collection of data associated with artificial intelligence (AI) models or AI functions. Send third configuration information, which is used to instruct AI inference to be performed based on the second configuration information.

24. The method according to claim 23, characterized in that, The data is used to train the AI ​​model or the AI ​​function.

25. The method according to claim 23 or 24, characterized in that, The third configuration information is also used to indicate the configuration information of the access network device associated with the second configuration information.

26. The method according to claim 25, characterized in that, The configuration information of the access network device includes the beamwidth information and / or the downtilt angle information of the access network device.

27. A communication device, characterized in that, It includes a module for performing the method as described in any one of claims 1 to 9, or includes a module for performing the method as described in any one of claims 10 to 18, or includes a module for performing the method as described in any one of claims 19 to 22, or includes a module for performing the method as described in any one of claims 23 to 26.

28. A communication device, characterized in that, The device includes a processor and an interface circuit. The interface circuit is used to receive signals from other communication devices and transmit them to the processor, or to send signals from the processor to other communication devices. The processor implements the method as described in any one of claims 1 to 9 through logic circuits or executable code instructions; or, the processor implements the method as described in any one of claims 10 to 18 through logic circuits or executable code instructions; or, the processor implements the method as described in any one of claims 19 to 22 through logic circuits or executable code instructions; or, the processor implements the method as described in any one of claims 23 to 26 through logic circuits or executable code instructions.

29. A computer-readable storage medium, characterized in that, The storage medium stores a computer program or instructions that, when executed by a communication device, implement the method as described in any one of claims 1 to 9, or the method as described in any one of claims 10 to 18, or the method as described in any one of claims 19 to 22, or the method as described in any one of claims 23 to 26.

30. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by the communication device, they implement the method as described in any one of claims 1 to 9; or, when the computer program or instructions are executed by the communication device, they implement the method as described in any one of claims 10 to 18; or, when the computer program or instructions are executed by the communication device, they implement the method as described in any one of claims 19 to 22; or, when the computer program or instructions are executed by the communication device, they implement the method as described in any one of claims 23 to 26.

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