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WO2025107649A1 - Procédé de communication et dispositif associé - Google Patents

Procédé de communication et dispositif associé Download PDF

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Publication number
WO2025107649A1
WO2025107649A1 PCT/CN2024/103512 CN2024103512W WO2025107649A1 WO 2025107649 A1 WO2025107649 A1 WO 2025107649A1 CN 2024103512 W CN2024103512 W CN 2024103512W WO 2025107649 A1 WO2025107649 A1 WO 2025107649A1
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WO
WIPO (PCT)
Prior art keywords
model
communication device
information
communication
models
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/CN2024/103512
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English (en)
Chinese (zh)
Inventor
杨璐
刘哲
卢嘉勋
彭程晖
王君
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Huawei Technologies Co Ltd
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Huawei Technologies Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Huawei Technologies Co Ltd filed Critical Huawei Technologies Co Ltd
Publication of WO2025107649A1 publication Critical patent/WO2025107649A1/fr
Pending legal-status Critical Current
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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network

Definitions

  • the present application relates to the field of communication technology, and in particular to a communication method and related equipment.
  • Wireless communication can be the transmission communication between two or more communication nodes without propagation through conductors or cables.
  • the communication nodes generally include network equipment and terminal equipment.
  • AI artificial intelligence
  • the present application provides a communication method and related equipment that can realize the deployment of AI models and implement the operation of the models.
  • the first aspect of the present application provides a communication method, which is performed by a first communication device, which may be a communication device (such as a network device), or the first communication device may be a partial component in the communication device (such as a processor, a chip or a chip system, etc.), or the first communication device may also be a logic module or software that can implement all or part of the functions of the communication device.
  • the first communication device generates a first artificial intelligence AI model and sends first information, which is used to determine the first AI model.
  • the first communication device After the first communication device generates or updates the first AI model, it sends the first information used to determine the first AI model to the recipient to deploy the first AI model to the recipient, thereby realizing functions such as AI model deployment, model operation, generation or update.
  • update may be replaced by other terms, such as modification, iteration, optimization, processing, or receiving from other devices.
  • AI model neural network model
  • AI neural network model AI neural network model
  • machine learning model AI processing model
  • the first AI model group includes a first AI model and a second AI model
  • the function of the first AI model group is implemented at least through the model processing of the first AI model and the model processing of the second AI model.
  • the first communication device can deploy the first AI model to the first communication device through the model parameters of the first AI model group or the model parameters of the first AI model contained in the second information, and perform model processing on the first AI model; accordingly, the second communication device can perform model processing on the second AI model deployed on the second communication device.
  • the model processing may include one or more of model update processing, model training processing, and model inference processing.
  • the second communication device can be implemented in many ways.
  • the second communication device may be a terminal device, and accordingly, the first communication device and the second communication device may communicate on a sidelink (SL).
  • the first AI model and the second AI model may be referred to as an end-to-end model, or an end-to-end collaborative model, etc.
  • the second communication device may be a network device (e.g., an access network device), and accordingly, the first communication device and the second communication device may communicate on uplink and downlink communication links.
  • the first AI model and the second AI model may be referred to as an edge-end model, an edge-end collaborative model, an edge-end model, an edge-end collaborative model, etc.
  • the first AI model group is regarded as one AI model
  • the first AI model and the second AI model may be understood as two AI sub-models in the one AI model.
  • an AI model is deployed on a communication device (for example, a first AI model is deployed on a first communication device, a second AI model is deployed on a second communication device, etc.), which can be understood as that after the communication device obtains the model parameters of the AI model, based on the AI model
  • the model parameters of an AI model are used to obtain/generate/construct the AI model, and subsequently the communication device can perform model processing on the AI model.
  • the model parameters may include one or more of the model's hyperparameters, the model's data set (including the model's input data and label data corresponding to the input data), and the model's structural parameters.
  • an AI model group may include two or more AI models.
  • the first AI model group may include other AI models in addition to the first AI model and the second AI model.
  • the other AI models may be deployed on other communication devices different from the first communication device and the second communication device. This is not limited here.
  • the AI model involved in the present application can be used to manage the wireless communication signal (including at least one of configuration, update, and optimization).
  • the AI model may include an AI model for modulation and/or demodulation, an AI model for channel prediction, an AI model for beam management, an AI model for assisted positioning, an AI model for channel compression, an AI model for resource scheduling, and one or more of the AI models for replacing one or more modules in a transmitter and/or receiver.
  • the AI model involved in the present application may also be an AI model for other AI tasks, such as an AI model for image recognition, an AI model for natural language processing, an AI model for computer vision, etc.
  • the first communication device is a functional entity used to generate a first AI model.
  • the first communication device can be used to generate/obtain/determine/update one or more AI models.
  • the first communication device is a functional entity used to generate a first AI model and deploy the first AI model on a second communication device through first information.
  • the first communication device can communicate with one or more second communication devices, and the first communication device can send one or more information of the first communication device (for example, one or more first information) to the second communication device to deploy the first AI model in one or more second communication devices.
  • one or more information of the first communication device for example, one or more first information
  • the first AI model is a dedicated AI model.
  • the second communication device can be a terminal device.
  • the AI model deployed on the terminal device can be a dedicated AI model, and the first information sent by the first communication device to the second communication device can be used to determine the dedicated AI model. Since different terminal devices may have different terminal-side characteristics (such as different local data, different local computing power, different channel characteristics, etc.), by deploying a dedicated AI model in the terminal device, the AI model deployed on the terminal device can adapt to the terminal-side characteristics of the terminal device, in order to improve the model processing performance of the AI model. In addition, compared with the large model, deploying a dedicated AI model in the second communication device can effectively improve the model accuracy of personalized scenarios.
  • the general AI model can be called a basic model, a large model or an L0 model.
  • the dedicated AI model can be called a small model, an L1 model, an L2 model, etc.
  • the big model can refer to a machine learning model with a large number of parameters and complex structure, which can process massive data and complete various complex tasks, such as natural language processing, computer vision, speech recognition, etc.
  • the large model can be designed to improve the model's expressiveness and predictive performance, and to be able to handle more complex tasks and data.
  • large models can learn complex patterns and features by training on massive amounts of data, have stronger generalization capabilities, and can make accurate predictions on unprocessed data.
  • a small model can refer to a model with fewer parameters and shallower layers.
  • a large model usually has more parameters and deeper layers, has stronger expressive power and higher accuracy, but also requires more computing resources and time for training and reasoning. It is suitable for scenarios with large data volumes and sufficient computing resources, such as cloud computing, high-performance computing, artificial intelligence, etc.
  • the small model has the advantages of being lightweight, efficient, and easy to deploy, and is suitable for scenarios with small data volumes and limited computing resources, such as mobile applications, embedded devices, and the Internet of Things.
  • the first communication device before generating the first AI model, includes: the first communication device receives second information from the second communication device; the first communication device generates the first AI model, including: the first communication device generates the first AI model according to the second information.
  • the first communication device can receive information (such as second information) from one or more second communication devices, and generate/obtain/determine/update one or more dedicated AI models based on the information of the one or more second communication devices.
  • the information of the second communication device can be used to indicate user demand information, user data information, channel state information, etc., so that the first communication device generates/obtains/determines/updates a dedicated AI model based on the information of the second communication device, thereby effectively improving the model accuracy of personalized scenarios.
  • the second information includes at least one of the following: channel state information between the first communication device and the second communication device, and local computing power state of the second communication device.
  • the first communication device can generate/obtain/determine/update one or more dedicated AI models based on the channel state information between the first communication device and the second communication device and/or the local computing power state of the second communication device, thereby generating a dedicated AI model in combination with actual scenario requirements and improving the model accuracy in personalized scenarios.
  • the channel state information may include channel information between the first communication device and the second communication device, and/or channel information between the second communication device and the first communication device.
  • the channel information between the first communication device and the second communication device can be understood as uplink channel information
  • the channel information between the second communication device and the first communication device can be understood as downlink channel information.
  • the channel state information between the first communication device and the second communication device may be obtained based on a reference signal.
  • the reference signal may include a channel state information reference signal (CSI-RS), a sounding reference signal (SRS), etc.
  • CSI-RS channel state information reference signal
  • SRS sounding reference signal
  • the reference signal may include a sidelink-synchronization signal/physical broadcast channel block (sidelink synchronization signal/physical broadcast channel block, sidelink SSB, SL-SSB, or S-SS/PSBCH block), a sidelink-channel state information reference signal (sidelink channel state information reference signal, SL-CSI-RS), etc.
  • sidelink synchronization signal/physical broadcast channel block sidelink SSB, SL-SSB, or S-SS/PSBCH block
  • sidelink-channel state information reference signal sidelink channel state information reference signal, SL-CSI-RS
  • the second information may further include at least one of the following: location information of the second communication device, behavior information of the second communication device, local data information and tag information of the second communication device.
  • the second information may include at least one of the above items.
  • the first communication device may determine the first AI model group based on the above at least one item of information to enhance the flexibility of the solution implementation.
  • the method also includes: the first communication device receives third information from the third communication device; the first communication device sends the third information to the second communication device, the third information is used to determine a third AI model, and the third AI model is a general AI model.
  • the second communication device deploys the general AI model based on the third information of the first communication device before or after deploying the dedicated AI model, so that the dedicated AI model deployed on the second communication device can be switched to a general AI model, or the general AI model can be switched to a dedicated AI model, thereby realizing the coordinated deployment of the dedicated AI model and the general AI model, that is, the first communication device can generate or update a matching AI model based on the information feedback from itself, the second communication device or other communication devices, and deploy an AI model that meets the scenario requirements on itself, the second communication device or other communication devices based on the actual scenario, thereby realizing the joint deployment of the dedicated AI model and the general AI model, effectively reducing the application cost of the model and improving the model accuracy in personalized scenarios.
  • the method after the first communication device sends the first information to the second communication device, the method also includes: the first communication device receives fourth information from the second communication device, and the fourth information is used to indicate one of the AI models in the list of one or more AI models.
  • the second communication device can send fourth information to the first communication device to feedback the recommended AI model.
  • the AI model list may include one or more AI models, and each AI model may include two or more AI models.
  • the relationship between the AI model group and the AI model can also be understood as the relationship between the AI model and the AI sub-model.
  • the AI model list can also be replaced by an AI model group list, that is, the AI model group list can include one or more AI models.
  • list can be replaced with other terms such as set, dictionary, combination, space, etc.
  • the list of one or more AI models includes a list of one or more dedicated AI models and/or a list of general AI models.
  • the list of one or more AI models contains list information of different AI models, so that the first communication device and/or the second communication device can select a matching AI model based on actual needs to meet the accuracy requirements of different scenarios.
  • the first AI model is a general AI model.
  • the first AI model can be either a dedicated AI model or a general AI model to meet the accuracy requirements of different scenarios.
  • a first communication device generates a first AI model, including: the first communication device receives fifth information from a third communication device, the fifth information is used to determine the first AI model, the first AI model is deployed on the first communication device and the second communication device, or the first AI model is deployed on the second communication device.
  • the first communication device can generate or update the first AI model through information from one or more third communication devices (for example, one or more fifth information), and subsequently multiple first communication devices and second communication devices connected to each first communication device can deploy a general AI model with higher generalization and better versatility.
  • the method before the second communication device receives the first information from the first communication device, the method also includes: the first communication device sends a first message to the second communication device, where the first message is used to inform the second communication device to use the first AI model.
  • the first communication device after receiving the fifth information, can generate or update the first AI model based on the fifth information, and before the first communication device sends the first information to the second communication device, the first communication device can send a first message to the second communication device to inform the second communication device that the first AI model (general AI model) can be used. It should be understood that the first message can also be used to inform the second communication device not to use the first AI model.
  • the first AI model general AI model
  • the first message may be a broadcast, unicast or multicast message.
  • the second aspect of the present application provides a communication method, which is performed by a second communication device, which may be a communication device (such as a network device or a terminal device), or the second communication device may be a partial component in a communication device (such as a processor, a chip or a chip system, etc.), or the second communication device may also be a logic module or software that can implement all or part of the functions of the communication device.
  • the second communication device receives first information from the first communication device; the second communication device determines a first AI model based on the first information.
  • the second communication device can determine the first AI model based on the first information.
  • the second communication device can determine the first AI model based on the first information from the first communication device to deploy the first AI model, thereby supporting the deployment of the model and realizing the operation of the model.
  • the first AI model is a dedicated AI model.
  • the method before the second communication device receives the first information from the first communication device, the method further includes: the second communication device sends second information to the first communication device, and the second information is used to generate the first AI model.
  • the second information includes at least one of the following: channel state information between the first communication device and the second communication device, and local computing power state of the second communication device.
  • the second information further includes at least one of the following: location information of the second communication device, behavior information of the second communication device, local data information and tag information of the second communication device.
  • the method further includes: the second communication device receives third information from the first communication device, the third information is used to determine a third AI model, and the third AI model is a general AI model.
  • the method further includes: the second communication device sends fourth information to the first communication device, and the fourth information is used to indicate one of the AI models in the list of one or more AI models.
  • the list of one or more AI models includes a list of one or more dedicated AI models and/or a list of general AI models.
  • the first AI model is a general AI model.
  • the method also includes: the second communication device receives fifth information from the first communication device, the fifth information is used to determine the first AI model, the first AI model is deployed in the first communication device and the second communication device, or the first AI model is deployed in the second communication device.
  • the method before the second communication device receives the first information from the first communication device, the method also includes: the second communication device receives a first message from the first communication device, the first message being used to inform the second communication device to use the first AI model.
  • the first message may be a broadcast, unicast or multicast message.
  • the second aspect of the embodiment of the present application reference may be made to the description of various possible implementations in the first aspect, and will not be repeated here.
  • the first information is used to determine a category to which the second communication device belongs.
  • the first communication device can classify the one or more second communication devices based on the second information, and include the relevant information of the category in the first information, so as to determine the AI model corresponding to each category according to the category. Subsequently, the first communication device will send the first information to the one or more second communication devices respectively. After the one or more second communication devices receive the first information from the first communication device, each second communication device can determine the category to which the second communication device belongs according to the first information, and filter out the AI model corresponding to the category from the first information according to the category to which it belongs, so as to implement the deployment of the AI model in the second communication device and implement the operation of the model.
  • the first AI model is included in the first AI model group, the first AI model group also includes the second AI model, the first AI model is deployed on the first communication device, and the second AI model is deployed on the second communication device; wherein, the input of the second AI model includes the output of the first AI model, or the input of the first AI model includes the output of the second AI model.
  • the first communication device can be a network device
  • the second communication device can be a terminal device. Since different terminal devices may have different terminal characteristics (such as different local data, different local computing power, different channel characteristics, etc.), different network devices may have different edge characteristics.
  • the second AI model deployed in the terminal device can be adapted to the terminal characteristics of the terminal device
  • the first AI model deployed in the network device can be adapted to the edge characteristics of the network device, so as to improve the model processing performance of the AI model.
  • the second communication device can be implemented in many ways.
  • the second communication device may be a terminal device, and accordingly, the first communication device and the second communication device may communicate on a sidelink (SL).
  • the first AI model and the second AI model may be referred to as an end-to-end model, or an end-to-end collaborative model, etc.
  • the second communication device may be a network device (e.g., an access network device), and accordingly, the first communication device and the second communication device may communicate on uplink and downlink communication links.
  • the first AI model and the second AI model may be referred to as an edge-end model, an edge-end collaborative model, an edge-end model, an edge-end collaborative model, etc.
  • the cloud can be understood as the central node of traditional cloud computing and the control end of edge computing.
  • the edge can be understood as the edge side of cloud computing, which is divided into infrastructure edge and device edge. In wireless networks, it refers to edge servers or base stations.
  • the end can be understood as terminal devices, such as mobile phones, tablets, sensors and other types of terminals.
  • the first information is also used to determine the second AI model in the first AI model group.
  • the second communication device determines the AI models deployed in different devices respectively by receiving the first information from the first communication device.
  • the first information includes an identifier of the first AI model, and the identifier is used to determine the first AI model in a list including one or more dedicated AI models.
  • the first communication device can send the identifier of the first AI model to the second communication device to determine the first AI model deployed in the second communication device, thereby reducing the amount of data transmission between the first communication device and the second communication device.
  • the method before sending the first information to the second communication device, the method further includes: the first communication device sending a list of one or more dedicated AI models to the second communication device.
  • the prerequisite for the first communication device to send the identifier of the first AI model is that the second communication device has stored a list of one or more dedicated AI models.
  • the first communication device sends a list of one or more dedicated AI models to the second communication device in advance so that when the type of model needs to be changed later, the identifier of the model can be sent, thereby reducing the amount of data transmission.
  • the first information includes model parameters and/or model structure information of the first AI model.
  • the first information also includes an identifier of the first AI model.
  • the first information includes model parameters and/or model structure information of the first AI model, it may also include an identifier of the first AI model, so that the second communication device determines the deployed AI model according to the identifier.
  • the first information is periodically sent information.
  • the third aspect of the present application provides a communication method, which is performed by a third communication device, which may be a network device (such as an access network device, a core network device, a cloud server, etc.), or the third communication device may be a partial component in a network device (such as a processor, a chip or a chip system, etc.), or the third communication device may also be a logic module or software that can implement all or part of the functions of the communication device.
  • a third communication device may be a network device (such as an access network device, a core network device, a cloud server, etc.), or the third communication device may be a partial component in a network device (such as a processor, a chip or a chip system, etc.), or the third communication device may also be a logic module or software that can implement all or part of the functions of the communication device.
  • the third communication device receives the sixth information from the first communication device; the third communication device generates the fifth information based on the sixth information; the third communication device sends the fifth information to the first communication device, and the fifth information is used to determine the general AI model, the general AI model is deployed in the first communication device and the second communication device, or the general AI model is deployed in the second communication device.
  • the third communication device can generate a general AI model based on the sixth information from the first communication device, and send the fifth information to the first communication device to deploy the general AI model on the first communication device.
  • the general AI model can also be deployed on the second communication device, so that the functions of supporting the deployment of the general AI model and realizing the operation of the model are realized at both the first communication device and the second communication device.
  • the sixth information includes the second information.
  • the sixth information is determined based on the processed second information.
  • the sixth information includes at least one of the following:
  • the channel status information between the first communication device and the second communication device, and the local computing power status of the second communication device is the channel status information between the first communication device and the second communication device, and the local computing power status of the second communication device.
  • the sixth information further includes at least one of the following:
  • the location information of the second communication device The location information of the second communication device, the behavior information of the second communication device, the local data information and the tag information of the second communication device.
  • the present application provides a communication device, which is a first communication device, comprising a processing unit and a transceiver unit; the processing unit is used to generate a first artificial intelligence AI model; the transceiver unit is used to send first information, and the first information is used to determine the first AI model.
  • the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the first aspect and achieve corresponding technical effects.
  • the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the first aspect and achieve corresponding technical effects.
  • the present application provides a communication device, which is a second communication device, comprising a transceiver unit and a processing unit, wherein the transceiver unit is used to receive first information from a first communication device; and the processing unit is used to determine a first AI model based on the first information.
  • the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the second aspect and achieve corresponding technical effects.
  • the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the second aspect and achieve corresponding technical effects.
  • the sixth aspect of the present application provides a communication device, which is a third communication device, and includes a transceiver unit and a processing unit; the transceiver unit is used to receive sixth information from the first communication device; the processing unit is used to generate fifth information based on the sixth information; the transceiver unit is used to send fifth information to the first communication device, and the fifth information is used to determine a general AI model, which is deployed in the first communication device and the second communication device, or the general AI model is deployed in the second communication device.
  • the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the third aspect and achieve corresponding technical effects.
  • the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the third aspect and achieve corresponding technical effects.
  • the seventh aspect of the present application provides a communication device, comprising at least one processor, at least one processor coupled to a memory; the memory is used to store programs or instructions; the at least one processor is used to execute the program or instructions so that the device implements a method of any possible implementation method of any aspect of the first to third aspects mentioned above.
  • the communication device further includes a memory.
  • the processor and the memory are integrated together.
  • the present application provides a communication device, comprising at least one logic circuit and an input/output interface; the logic circuit is used to execute a method as any possible implementation method in any one of the first to third aspects described above.
  • a ninth aspect of the present application provides a communication system, the communication system comprising the first communication device and the second communication device.
  • the communication system comprises the first communication device, the second communication device and the third communication device.
  • the tenth aspect of the present application provides a computer-readable storage medium, which is used to store one or more computer-executable instructions.
  • the processor executes a method as any possible implementation method of any aspect of the first to third aspects mentioned above.
  • a computer program product (or computer program) is provided.
  • the processor executes a method of any possible implementation of any aspect of the first to third aspects above.
  • the twelfth aspect of the present application provides a chip system, which includes at least one processor, and is used to support a communication device to implement a method of any possible implementation of any aspect of the first to third aspects above.
  • the chip system may also include a memory for storing program instructions and data necessary for the communication device.
  • the chip system may be composed of a chip, or may include a chip and other discrete devices.
  • the chip system also includes an interface circuit, which provides program instructions and/or data for at least one processor.
  • the technical effects brought about by any design method in the fourth to twelfth aspects can refer to the technical effects brought about by the different design methods in the above-mentioned first to third aspects, and will not be repeated here.
  • FIGS. 1a to 1d are schematic diagrams of a communication system provided by the present application.
  • FIGS. 2a to 2g are schematic diagrams of the AI processing process involved in this application.
  • FIG3 is a schematic diagram of an implementation of a communication method provided in an embodiment of the present application.
  • FIG4 is a schematic diagram of another implementation of the communication method provided in an embodiment of the present application.
  • FIGS 5a to 5b are interactive schematic diagrams of the communication method provided by the present application.
  • FIG6a is a schematic diagram of a collaborative deployment of a dedicated AI model and a general AI model provided in an embodiment of the present application;
  • 6b to 6d are schematic diagrams of another implementation of the communication method provided in an embodiment of the present application.
  • FIG. 7 to 11 are schematic diagrams of the communication device provided in the present application.
  • Terminal device It can be a wireless terminal device that can receive network device scheduling and instruction information.
  • the wireless terminal device can be a device that provides voice and/or data connectivity to users, or a handheld device with wireless connection function, or other processing devices connected to a wireless modem.
  • the terminal device can communicate with one or more core networks or the Internet via a radio access network (RAN).
  • RAN radio access network
  • the terminal device can be a mobile terminal device, such as a mobile phone (or "cellular" phone, mobile phone), a computer and a data card, for example, a portable, pocket-sized, handheld, computer-built-in or vehicle-mounted mobile device, which exchanges voice and/or data with the radio access network.
  • PCS personal communication service
  • SIP session initiation protocol
  • WLL wireless local loop
  • PDAs personal digital assistants
  • Pads computers with wireless transceiver functions, and other devices.
  • the wireless terminal device can also be called a system, a subscriber unit, a subscriber station, a mobile station, a mobile station (MS), a remote station, an access point (AP), a remote terminal device (remote terminal), an access terminal device (access terminal), a user terminal device (user terminal), a user agent (user agent), a subscriber station (SS), a customer premises equipment (CPE), a terminal, a user equipment (UE), a mobile terminal (MT), etc.
  • a system a subscriber unit, a subscriber station, a mobile station, a mobile station (MS), a remote station, an access point (AP), a remote terminal device (remote terminal), an access terminal device (access terminal), a user terminal device (user terminal), a user agent (user agent), a subscriber station (SS), a customer premises equipment (CPE), a terminal, a user equipment (UE), a mobile terminal (MT), etc.
  • the terminal device may also be a wearable device.
  • Wearable devices may also be referred to as wearable smart devices or smart wearable devices, etc., which are a general term for the application of wearable technology to intelligently design and develop wearable devices for daily wear, such as glasses, gloves, watches, clothing and shoes.
  • a wearable device is a portable device that is worn directly on the body or integrated into the user's clothes or accessories. Wearable devices are not only hardware devices, but also powerful functions achieved through software support, data interaction, and cloud interaction.
  • wearable smart devices include full-featured, large-size, and independent of smartphones to achieve complete or partial functions, such as smart watches or smart glasses, etc., as well as those that only focus on a certain type of application function and need to be used in conjunction with other devices such as smartphones, such as various types of smart bracelets, smart helmets, and smart jewelry for vital sign monitoring.
  • the terminal can also be a drone, a robot, a terminal in device-to-device (D2D) communication, a terminal in vehicle to everything (V2X), a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal in industrial control, a wireless terminal in self driving, a wireless terminal in remote medical, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city, a wireless terminal in a smart home, etc.
  • D2D device-to-device
  • V2X vehicle to everything
  • VR virtual reality
  • AR augmented reality
  • the terminal device may also be a terminal device in a communication system that evolves after the fifth generation (5th generation, 5G) communication system (e.g., a sixth generation (6th generation, 6G) communication system, etc.) or a terminal device in a public land mobile network (PLMN) that evolves in the future, etc.
  • 5G fifth generation
  • 6G sixth generation
  • PLMN public land mobile network
  • the 6G network can further expand the form and function of the 5G communication terminal
  • the 6G terminal includes but is not limited to a car, a cellular network terminal (with integrated satellite terminal function), a drone, and an Internet of Things (IoT) device.
  • IoT Internet of Things
  • the terminal device may also obtain AI services provided by the network device.
  • the terminal device may also have AI processing capabilities.
  • the network equipment can be a RAN node (or device) that connects a terminal device to a wireless network, which can also be called a base station.
  • RAN equipment are: base station, evolved NodeB (eNodeB), gNB (gNodeB) in a 5G communication system, transmission reception point (TRP), evolved Node B (eNB), radio network controller (RNC), Node B (NB), home base station (e.g., home evolved Node B, or home Node B, HNB), baseband unit (BBU), or wireless fidelity (Wi-Fi) access point AP, etc.
  • the network equipment may include a centralized unit (CU) node, a distributed unit (DU) node, or a RAN device including a CU node and a DU node.
  • CU centralized unit
  • DU distributed unit
  • RAN device including a CU node and a DU node.
  • the RAN node can also be a macro base station, a micro base station or an indoor station, a relay node or a donor node, or a wireless controller in a cloud radio access network (CRAN) scenario.
  • the RAN node can also be a server, a wearable device, a vehicle or an onboard device, etc.
  • the access network device in the vehicle to everything (V2X) technology can be a road side unit (RSU).
  • the RAN node can be a central unit (CU), a distributed unit (DU), a CU-control plane (CP), a CU-user plane (UP), or a radio unit (RU).
  • the CU and DU can be set separately, or can also be included in the same network element, such as a baseband unit (BBU).
  • BBU baseband unit
  • the RU can be included in a radio frequency device or a radio frequency unit, such as a remote radio unit (RRU), an active antenna unit (AAU) or a remote radio head (RRH).
  • CU or CU-CP and CU-UP
  • DU or RU may also have different names, but those skilled in the art can understand their meanings.
  • O-CU open CU
  • DU may also be called O-DU
  • CU-CP may also be called O-CU-CP
  • CU-UP may also be called O-CU-UP
  • RU may also be called O-RU.
  • CU, CU-CP, CU-UP, DU and RU are used as examples for description in this application.
  • Any unit of CU (or CU-CP, CU-UP), DU and RU in this application may be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.
  • the communication between the access network device and the terminal device follows a certain protocol layer structure.
  • the protocol layer may include a control plane protocol layer and a user plane protocol layer.
  • the control plane protocol layer may include at least one of the following: a radio resource control (RRC) layer, Packet data convergence protocol (PDCP) layer, radio link control (RLC) layer, media access control (MAC) layer, or physical (PHY) layer, etc.
  • the user plane protocol layer may include at least one of the following: service data adaptation protocol (SDAP) layer, PDCP layer, RLC layer, MAC layer, or physical layer, etc.
  • SDAP service data adaptation protocol
  • the network device may be any other device that provides wireless communication functions for the terminal device.
  • the embodiments of the present application do not limit the specific technology and specific device form used by the network device. For the convenience of description, the embodiments of the present application do not limit.
  • the network equipment may also include core network equipment, such as mobility management entity (MME), home subscriber server (HSS), serving gateway (S-GW), policy and charging rules function (PCRF), public data network gateway (PDN gateway, P-GW) in the fourth generation (4G) network; access and mobility management function (AMF), user plane function (UPF) or session management function (SMF) in the 5G network.
  • MME mobility management entity
  • HSS home subscriber server
  • S-GW serving gateway
  • PDN gateway public data network gateway
  • P-GW public data network gateway
  • AMF access and mobility management function
  • UPF user plane function
  • SMF session management function
  • SMF session management function
  • 5G network equipment may also include other core network equipment in the 5G network and the next generation network of the 5G network.
  • the above-mentioned network device may also have a network node with AI capabilities, which can provide AI services for terminals or other network devices.
  • a network node with AI capabilities can provide AI services for terminals or other network devices.
  • it may be an AI node on the network side (access network or core network), a computing node, a RAN node with AI capabilities, a core network element with AI capabilities, etc.
  • the device for realizing the function of the network device may be a network device, or may be a device capable of supporting the network device to realize the function, such as a chip system, which may be installed in the network device.
  • the technical solution provided in the embodiment of the present application is described by taking the device for realizing the function of the network device as an example that the network device is used as the device.
  • Configuration and pre-configuration are used at the same time.
  • Configuration refers to the network device/server sending some parameter configuration information or parameter values to the terminal through messages or signaling, so that the terminal can determine the communication parameters or resources during transmission based on these values or information.
  • Pre-configuration is similar to configuration, and can be parameter information or parameter values pre-negotiated between the network device/server and the terminal device, or parameter information or parameter values used by the base station/network device or terminal device specified by the standard protocol, or parameter information or parameter values pre-stored in the base station/server or terminal device. This application does not limit this.
  • system and “network” in the embodiments of the present application can be used interchangeably.
  • “Multiple” refers to two or more.
  • “And/or” describes the association relationship of associated objects, indicating that three relationships may exist.
  • a and/or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural.
  • the character “/” generally indicates that the objects associated with each other are in an "or” relationship.
  • At least one of the following” or similar expressions refers to any combination of these items, including any combination of single items or plural items.
  • “at least one of A, B and C” includes A, B, C, AB, AC, BC or ABC.
  • the ordinal numbers such as “first” and “second” mentioned in the embodiments of the present application are used to distinguish multiple objects, and are not used to limit the order, timing, priority or importance of multiple objects.
  • Send and “receive” in the embodiments of the present application indicate the direction of signal transmission.
  • send information to XX can be understood as the destination of the information is XX, which can include direct sending through the air interface, and also include indirect sending through the air interface by other units or modules.
  • Receiveive information from YY can be understood as the source of the information is YY, which can include direct receiving from YY through the air interface, and also include indirect receiving from YY through the air interface from other units or modules.
  • Send can also be understood as the "output” of the chip interface, and “receive” can also be understood as the "input” of the chip interface.
  • sending and receiving can be performed between devices, for example, between a network device and a terminal device, or can be performed within a device, for example, sending or receiving between components, modules, chips, software modules, or hardware modules within the device through a bus, wiring, or interface.
  • information may be processed between the source and destination of information transmission, such as coding, modulation, etc., but the destination can understand the valid information from the source. Similar expressions in this application can be understood similarly and will not be repeated.
  • indication may include direct indication and indirect indication, and may also include explicit indication and implicit indication.
  • the information indicated by a certain information is called information to be indicated.
  • information to be indicated In the specific implementation process, there are many ways to indicate the information to be indicated, such as but not limited to, directly indicating the information to be indicated, such as the information to be indicated itself or the index of the information to be indicated.
  • the information to be indicated may also be indirectly indicated by indicating other information, wherein the other information is associated with the information to be indicated; or only a part of the information to be indicated may be indicated, while the other part of the information to be indicated is known or agreed in advance.
  • the indication of specific information may be realized by means of the arrangement order of each information agreed in advance (such as predefined by the protocol), thereby reducing the indication overhead to a certain extent.
  • the present application does not limit the specific method of indication. It is understandable that, for the sender of the indication information, the indication information may be used to indicate the information to be indicated, and for the receiver of the indication information, the indication information may be used to determine the information to be indicated.
  • the present application can be applied to a long term evolution (LTE) system, a new radio (NR) system, or a communication system evolved after 5G (such as Beyond 5G (B5G), 6G, etc.).
  • LTE long term evolution
  • NR new radio
  • 5G such as Beyond 5G (B5G), 6G, etc.
  • the communication system includes at least one network device and/or at least one terminal device.
  • FIG. 1a is a schematic diagram of the architecture of a communication system 1000 used in an embodiment of the present application.
  • the communication system includes a radio access network (RAN) 100 and a core network 200.
  • the communication system 1000 may also include the Internet 300.
  • the RAN 100 includes at least one RAN node (such as 110a and 110b in FIG. 1a, collectively referred to as 110), and may also include at least one terminal (such as 120a-120j in FIG. 1a, collectively referred to as 120).
  • the RAN 100 may also include other RAN nodes, for example, a wireless relay device and/or a wireless backhaul device (not shown in FIG. 1a).
  • the terminal 120 is connected to the RAN node 110 in a wireless manner, and the RAN node 110 is connected to the core network 200 in a wireless or wired manner.
  • the core network device in the core network 200 and the RAN node 110 in the RAN 100 may be independent and different physical devices, or may be the same physical device that integrates the logical functions of the core network device and the logical functions of the RAN node. Terminals and terminals as well as RAN nodes and RAN nodes may be connected to each other via wired or wireless means.
  • RAN100 may be an evolved universal terrestrial radio access (E-UTRA) system, a new radio (NR) system, and a future radio access system defined in the 3rd generation partnership project (3GPP). RAN100 may also include two or more of the above different radio access systems. RAN100 may also be an open RAN (O-RAN).
  • E-UTRA evolved universal terrestrial radio access
  • NR new radio
  • 3GPP 3rd generation partnership project
  • RAN100 may also include two or more of the above different radio access systems.
  • RAN100 may also be an open RAN (O-RAN).
  • a base station is taken as an example of a RAN node for description below.
  • Base stations and terminals can be fixed or movable. Base stations and terminals can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on the water surface; they can also be deployed on airplanes, balloons, and artificial satellites. The embodiments of this application do not limit the application scenarios of base stations and terminals.
  • the helicopter or drone 120i in FIG. 1a can be configured as a mobile base station.
  • the terminal 120j that accesses the wireless access network 100 through 120i the terminal 120i is a base station; but for the base station 110a, 120i is a terminal, that is, 110a and 120i communicate through the wireless air interface protocol.
  • 110a and 120i can also communicate through the interface protocol between base stations.
  • relative to 110a, 120i is also a base station. Therefore, base stations and terminals can be collectively referred to as communication devices.
  • 110a and 110b in FIG. 1a can be referred to as communication devices with base station functions
  • 120a-120j in FIG. 1a can be referred to as communication devices with terminal functions.
  • Base stations and terminals, base stations and base stations, and terminals and terminals can communicate through licensed spectrum, unlicensed spectrum, or both. They can communicate through 6 gigahertz (Gigahertz), The present invention can communicate using a spectrum below 6 GHz, a spectrum above 6 GHz, or a spectrum below 6 GHz and a spectrum above 6 GHz at the same time.
  • the embodiments of the present application do not limit the spectrum resources used for wireless communication.
  • the functions of the base station may also be performed by a module (such as a chip) in the base station, or by a control subsystem including the base station function.
  • the control subsystem including the base station function here may be a control center in the above-mentioned application scenarios such as smart grid, industrial control, smart transportation, and smart city.
  • the functions of the terminal may also be performed by a module (such as a chip or a modem) in the terminal, or by a device including the terminal function.
  • FIG1b is another schematic diagram of a communication system provided by an embodiment of the present application.
  • the network device is a base station as an example for explanation, and both device 1 and device 2 are terminal devices.
  • the communication link between device 1 and device 2 can be called a sidelink (SL), and the communication link between device 1 (or device 2) and the base station can be called an uplink and a downlink, including an uplink and a downlink; it can be seen that the sidelink is a communication mechanism for different terminal devices to communicate directly without going through a network device.
  • SL sidelink
  • the communication link between device 1 (or device 2) and the base station can be called an uplink and a downlink, including an uplink and a downlink; it can be seen that the sidelink is a communication mechanism for different terminal devices to communicate directly without going through a network device.
  • the transmitting device and the receiving device can be a terminal device or a network device of the same type, or a road side unit (RSU) and a terminal device, wherein the RSU is a road side station or a road side unit from a physical entity point of view, and from a functional point of view, the RSU can be a terminal device or a network device, and this application does not impose any restrictions on this. That is, the transmitting device is a terminal device, and the receiving device is also a terminal device; or, the transmitting device is a road side station, and the receiving device is also a terminal device; or, the transmitting device is a terminal device, and the receiving device is also a road side station.
  • the sidelink can also be a base station device of the same type or different types. At this time, the function of the sidelink is similar to that of the relay link, but the air interface technology used can be the same or different.
  • broadcast, unicast, and multicast are supported on the sidelink.
  • Broadcast communication is similar to network equipment broadcasting system information, that is, the terminal device sends broadcast service data to the outside without encryption. Any other terminal device within the effective receiving range can receive the data of the broadcast service if it is interested in the broadcast service.
  • Unicast communication is similar to data communication after establishing an RRC connection between a terminal device and a network device. It requires a unicast connection to be established between two terminal devices. After the unicast connection is established, the two terminal devices can communicate data based on the negotiated identifier. The data can be encrypted or unencrypted. Compared with broadcasting, in unicast communication, only two terminal devices that have established a unicast connection can communicate unicast data.
  • a unicast communication on the sidelink corresponds to a pair of source layer-2 identifier (source L2 ID) and destination layer-2 identifier (destination L2 ID).
  • source L2 ID source layer-2 identifier
  • destination L2 ID destination layer-2 identifier
  • the subheader of the media access control protocol data unit (MAC PDU) in the sidelink will include the source L2 ID and the destination L2 ID so that the data can be transmitted to the correct receiving end.
  • MAC PDU media access control protocol data unit
  • Multicast communication refers to the communication between all terminal devices in a communication group. Any terminal device in the group can send and receive data of the multicast service.
  • the communication link between the two terminal devices can be called a sidelink, or the two terminal devices communicate based on the proximity-based services communication 5 (PC5) port.
  • PC5 proximity-based services communication 5
  • V2X communication technology utilizes and enhances the current cellular network functions and elements to achieve low-latency and high-reliability communication between various nodes in the vehicle network, including vehicle-to-vehicle communication (V2V), vehicle-to-pedestrian communication (V2P), vehicle-to-infrastructure communication (V2I), and vehicle-to-network communication (V2N).
  • V2V vehicle-to-vehicle communication
  • V2P vehicle-to-pedestrian communication
  • V2I vehicle-to-infrastructure communication
  • V2N vehicle-to-network communication
  • LTE Long Term Evolution
  • C-V2X has evolved from LTE-V2X to NR-V2X (New Radio V2X, NR-V2X).
  • V2X communication has great potential to reduce vehicle collision accidents, thereby reducing the corresponding number of casualties.
  • the advantages of V2X are not limited to improving safety.
  • Vehicles that can perform V2X communication can help to better manage traffic, further promote green transportation and lower energy consumption.
  • Intelligent Transportation System (ITS) is an application combined with V2X.
  • vehicle users Vehicle UE, referred to as V-UE
  • V-UE vehicle users
  • V-UE vehicle users
  • V-UE vehicle users
  • V-UE vehicle users
  • V-UE vehicle users
  • V-UE vehicle users
  • V-UE will also receive information from surrounding users in real time.
  • 5G NR V2X can support lower transmission latency and more reliable communication transmission. transmission, higher throughput, better user experience, and meeting the needs of a wider range of application scenarios.
  • the vehicle-to-vehicle communication technology supported by V2X can be extended to device-to-device (D2D) communication under any system.
  • D2D device-to-device
  • an AI network element can be introduced into the communication system provided in the present application to implement some or all AI-related operations.
  • the AI network element may also be referred to as an AI node, an AI device, an AI entity, an AI module, an AI model, or an AI unit, etc.
  • the AI network element may be a network element built into a communication system.
  • the AI network element may be an AI module built into: a terminal device, an access network device, a core network device, a cloud server, or a network management (operation, administration and maintenance, OAM) to implement AI-related functions.
  • the OAM may be a network management device for a core network device and/or a network management device for an access network device.
  • the AI network element may also be a network element independently set up in a communication system.
  • an AI entity may also be included in a terminal or a chip built into the terminal to implement AI-related functions.
  • AI artificial intelligence
  • AI Artificial intelligence
  • machines human intelligence for example, it can allow machines to use computer hardware and software to simulate certain intelligent behaviors of humans.
  • machine learning methods can be used.
  • machines use training data to learn (or train) a model.
  • the model represents the mapping from input to output.
  • the learned model can be used for reasoning (or prediction), that is, the model can be used to predict the output corresponding to a given input. Among them, the output can also be called the reasoning result (or prediction result).
  • Machine learning can include supervised learning, unsupervised learning, and reinforcement learning. Among them, unsupervised learning can also be called unsupervised learning.
  • Supervised learning uses machine learning algorithms to learn the mapping relationship from sample values to sample labels based on the collected sample values and sample labels, and uses AI models to express the learned mapping relationship.
  • the process of training a machine learning model is the process of learning this mapping relationship.
  • the sample values are input into the model to obtain the model's predicted values, and the model parameters are optimized by calculating the error between the model's predicted values and the sample labels (ideal values).
  • the learned mapping can be used to predict new sample labels.
  • the mapping relationship learned by supervised learning can include linear mapping or nonlinear mapping. According to the type of label, the learning task can be divided into classification task and regression task.
  • Unsupervised learning uses algorithms to discover the inherent patterns of samples based on the collected sample values.
  • One type of algorithm in unsupervised learning uses the samples themselves as supervisory signals, that is, the model learns the mapping relationship from sample to sample, which is called self-supervised learning.
  • the model parameters are optimized by calculating the error between the model's predicted value and the sample itself.
  • Self-supervised learning can be used in applications such as signal compression and decompression recovery.
  • Common algorithms include autoencoders and adversarial generative networks.
  • Reinforcement learning is different from supervised learning. It is a type of algorithm that learns problem-solving strategies by interacting with the environment. Unlike supervised and unsupervised learning, reinforcement learning problems do not have clear "correct" action label data.
  • the algorithm needs to interact with the environment to obtain reward signals from the environment, and then adjust the decision-making actions to obtain a larger reward signal value. For example, in downlink power control, the reinforcement learning model adjusts the downlink transmission power of each user according to the total system throughput fed back by the wireless network, and then expects to obtain a higher system throughput.
  • the goal of reinforcement learning is also to learn the mapping relationship between the state of the environment and the better (e.g., optimal) decision action.
  • the network cannot be optimized by calculating the error between the action and the "correct action”. Reinforcement learning training is achieved through iterative interaction with the environment.
  • Neural network is a specific model in machine learning technology. According to the universal approximation theorem, neural network can theoretically approximate any continuous function, so that neural network has the ability to learn any mapping.
  • Traditional communication systems require rich expert knowledge to design communication modules, while deep learning communication systems based on neural networks can automatically discover implicit pattern structures from a large number of data sets, establish mapping relationships between data, and obtain performance that is superior to traditional modeling methods.
  • each neuron performs a weighted sum operation on its input values and outputs the operation result through an activation function.
  • FIG. 2a it is a schematic diagram of a neuron structure.
  • w i is used as the weight of xi to weight xi .
  • the bias for weighted summation of input values according to the weights is, for example, b.
  • the activation function can take many forms.
  • the output of the neuron is:
  • the output of the neuron is:
  • b can be a decimal, an integer (such as 0, a positive integer or a negative integer), or a complex number. Can be the same or different.
  • a neural network generally includes multiple layers, each of which may include one or more neurons.
  • the expressive power of the neural network can be improved, providing a more powerful information extraction and abstract modeling capability for complex systems.
  • the depth of a neural network may refer to the number of layers included in the neural network, and the number of neurons included in each layer may be referred to as the width of the layer.
  • the neural network includes an input layer and an output layer. The input layer of the neural network processes the received input information through neurons, passes the processing results to the output layer, and the output layer obtains the output result of the neural network.
  • the neural network includes an input layer, a hidden layer, and an output layer.
  • the input layer of the neural network processes the received input information through neurons, passes the processing results to the middle hidden layer, the hidden layer calculates the received processing results, obtains the calculation results, and the hidden layer passes the calculation results to the output layer or the next adjacent hidden layer, and finally the output layer obtains the output result of the neural network.
  • a neural network may include one hidden layer, or include multiple hidden layers connected in sequence, without limitation.
  • a neural network is, for example, a deep neural network (DNN).
  • DNNs can include feedforward neural networks (FNN), convolutional neural networks (CNN), and recurrent neural networks (RNN).
  • FNN feedforward neural networks
  • CNN convolutional neural networks
  • RNN recurrent neural networks
  • FIG2b is a schematic diagram of an FNN network.
  • the characteristic of an FNN network is that neurons in adjacent layers are fully connected to each other. This characteristic makes FNN usually require a large amount of storage space and leads to high computational complexity.
  • CNN is a neural network that is specifically designed to process data with a grid-like structure. For example, time series data (discrete sampling on the time axis) and image data (discrete sampling on two dimensions) can be considered to be data with a grid-like structure.
  • CNN does not use all the input information for calculations at once, but uses a fixed-size window to intercept part of the information for convolution operations, which greatly reduces the amount of calculation of model parameters.
  • each window can use different convolution kernel operations, which enables CNN to better extract the features of the input data.
  • RNN is a type of DNN network that uses feedback time series information. Its input includes the new input value at the current moment and its own output value at the previous moment. RNN is suitable for obtaining sequence features that are correlated in time, and is particularly suitable for applications such as speech recognition and channel coding.
  • a loss function can be defined.
  • the loss function describes the gap or difference between the output value of the model and the ideal target value.
  • the loss function can be expressed in many forms, and there is no restriction on the specific form of the loss function.
  • the model training process can be regarded as the following process: by adjusting some or all parameters of the model, the value of the loss function is less than the threshold value or meets the target requirements.
  • Models can also be referred to as AI models, rules or other names.
  • AI models can be considered as specific methods for implementing AI functions.
  • AI models characterize the mapping relationship or function between the input and output of a model.
  • AI functions may include one or more of the following: data collection, model training (or model learning), model information publishing, model inference (or model reasoning, inference, or prediction, etc.), model monitoring or model verification, or reasoning result publishing, etc.
  • AI functions can also be referred to as AI (related) operations, or AI-related functions.
  • Fully connected neural network also called multilayer perceptron (MLP).
  • an MLP consists of an input layer (left), an output layer (right), and multiple hidden layers (middle).
  • Each layer of the MLP contains several nodes, called neurons. The neurons in two adjacent layers are connected to each other.
  • w is the weight matrix
  • b is the bias vector
  • f is the activation function
  • a neural network can be understood as a mapping relationship from an input data set to an output data set.
  • neural networks are randomly initialized, and the process of obtaining this mapping relationship from random w and b using existing data is called neural network training.
  • a specific method of training is to use a loss function to evaluate the output results of the neural network.
  • the error can be back-propagated, and the neural network parameters (including w and b) can be iteratively optimized by the gradient descent method until the loss function reaches the minimum value, that is, the "better point (e.g., optimal point)" in Figure 2d.
  • the neural network parameters corresponding to the "better point (e.g., optimal point)" in Figure 2d can be used as the neural network parameters in the trained AI model information.
  • the gradient descent process can be expressed as:
  • ⁇ is the parameter to be optimized (including w and b)
  • L is the loss function
  • ⁇ is the learning rate, which controls the step size of gradient descent.
  • ⁇ is the learning rate, which controls the step size of gradient descent.
  • the back-propagation process utilizes the chain rule for partial derivatives.
  • the gradient of the previous layer parameters can be recursively calculated from the gradient of the next layer parameters, which can be expressed as:
  • w ij is the weight of node j connecting node i
  • si is the weighted sum of inputs on node i.
  • the FL architecture is a training architecture in the current FL field.
  • the FedAvg algorithm is the basic algorithm of FL, and its algorithm flow is roughly as follows:
  • the center initializes the model to be trained And broadcast it to all client devices.
  • the central node aggregates and collects local training results from all (or some) clients. Assume that the client set that uploads the local model in round t is The center will use the number of samples of the corresponding client as the weight to perform weighted averaging to obtain a new global model. The specific update rule is: The center then sends the latest version of the global model Broadcast to all client devices for a new round of training.
  • the central node In addition to reporting local models You can also use the local gradient of training After reporting, the central node averages the local gradients and updates the global model according to the direction of the average gradient.
  • the data set exists in the distributed nodes, that is, the distributed nodes collect local data sets, perform local training, and report the local results (models or gradients) obtained from the training to the central node.
  • the central node itself does not have a data set, and is only responsible for fusing the training results of the distributed nodes to obtain the global model and send it to the distributed nodes.
  • Decentralized learning Different from federated learning, there is another distributed learning architecture - decentralized learning.
  • the design goal f(x) of a decentralized learning system is generally the mean of the goals fi (x) of each node, that is, Where n is the number of distributed nodes, x is the parameter to be optimized. In machine learning, x is the parameter of the machine learning (such as neural network) model.
  • Each node uses local data and local target fi (x) to calculate the local gradient Then it is sent to the neighboring nodes that can be communicated with. After any node receives the gradient information sent by its neighbor, it can update the parameter x of the local model according to the following formula:
  • ⁇ k represents the tuning coefficient
  • Ni is the set of neighbor nodes of node i
  • represents the number of elements in the set of neighbor nodes of node i, that is, the number of neighbor nodes of node i.
  • the technical solution provided in this application can be applied to wireless communication systems (such as the system shown in FIG. 1a or FIG. 1b).
  • wireless communication systems such as the system shown in FIG. 1a or FIG. 1b.
  • AI artificial intelligence
  • wireless network architectures need to support a large number of AI functions. Therefore, how to support the deployment of models in wireless networks is an urgent problem to be solved.
  • the first type of deployment solution is:
  • AI/ML model training is located in OAM (operation, administrator and maintenance), and AL/ML model reasoning is located in gNB.
  • model training is located in OAM and model inference is located in the next generation radio access network (NG-RAN).
  • NG-RAN next generation radio access network
  • the gNB can adopt a separated structure
  • the gNB consists of a control unit (CU) and one or more distributed units (DU), and the interface between the gNB CU and the gNB DU is called F1.
  • the second type of deployment scheme is:
  • AI/ML model training is located in OAM, and AL/ML model inference is located in gNB-CU.
  • both model training and model inference are located in NG-RAN.
  • Figure 3 is a schematic diagram of an implementation of the communication method provided by the embodiment of the present application. The method includes the following steps.
  • the method is illustrated by taking the first communication device and the second communication device as the execution subject of the interaction diagram as an example, but the present application does not limit the execution subject of the interaction diagram.
  • the execution subject of the method can be replaced by a chip, a chip system, a processor, a logic module or software in a communication device.
  • the first communication device can be a network device and the second communication device can be a terminal device, or the first communication device and the second communication device are both terminal devices (for example, the method can be applied to the communication process of different terminal devices in a sidelink communication scenario).
  • the first communication device generates a first AI model.
  • the first communication device sends first information, where the first information is used to determine a first AI model.
  • the second communication device receives the first information.
  • the second communication device determines the first AI model according to the first information.
  • AI model neural network model
  • AI neural network model AI neural network model
  • machine learning model AI processing model
  • wireless communication signals (such as the transmission and reception of configuration information of communication resources, the transmission and reception of reference signals, etc.) can be transmitted between different communication devices (such as the first communication device and the second communication device).
  • the AI model involved in the present application can be used to manage the wireless communication signal (including at least one of configuration, update, and optimization).
  • the AI model may include an AI model for modulation and/or demodulation, an AI model for channel prediction, an AI model for beam management, and an AI model for One or more of the AI models for assisting positioning, the AI model for channel compression, the AI model for resource scheduling, and the AI model for replacing one or more modules in a transmitter and/or a receiver.
  • the AI model involved in this application may also be an AI model for other AI tasks, such as an AI model for image recognition, an AI model for natural language processing, an AI model for computer vision, etc.
  • the first AI model group is regarded as one AI model
  • the first AI model and the second AI model may be understood as two AI sub-models in the one AI model.
  • an AI model is deployed on a communication device (for example, the first AI model is deployed on the first communication device, the second AI model is deployed on the second communication device, etc.). It can be understood that after the communication device obtains the model parameters of the AI model, it obtains/generates/constructs the AI model based on the model parameters of the AI model, and subsequently the communication device can perform model processing on the AI model.
  • the model parameters may include one or more of the model's hyperparameters, the model's data set (including the model's input data and label data corresponding to the input data), and the model's structural parameters.
  • the first communication device is a functional entity for generating or updating a first AI model, that is, generating/obtaining/determining/updating one or more AI models.
  • the first communication device is a functional entity for generating or updating a first AI model, and deploying the first AI model on a second communication device through first information.
  • the first communication device can communicate with one or more second communication devices, and the first communication device can send one or more information of the first communication device (e.g., one or more first information) to the second communication device to deploy the first AI model on one or more second communication devices.
  • the first AI model is taken as an example in which a dedicated AI model and a general AI model are respectively used.
  • the general AI model can be called a basic model, a large model or an L0 model.
  • the dedicated AI model can be called a small model, an L1 model, an L2 model, etc.
  • the big model can refer to a machine learning model with a large number of parameters and complex structure, which can process massive data and complete various complex tasks, such as natural language processing, computer vision, speech recognition, etc.
  • the large model can be designed to improve the model's expressiveness and predictive performance, and to be able to handle more complex tasks and data.
  • large models can learn complex patterns and features by training on massive amounts of data, have stronger generalization capabilities, and can make accurate predictions on unprocessed data.
  • a small model can refer to a model with fewer parameters and shallower layers.
  • a large model usually has more parameters and deeper layers, has stronger expressive power and higher accuracy, but also requires more computing resources and time for training and reasoning. It is suitable for scenarios with large data volumes and sufficient computing resources, such as cloud computing, high-performance computing, artificial intelligence, etc.
  • the small model has the advantages of being lightweight, efficient, and easy to deploy, and is suitable for scenarios with small data volumes and limited computing resources, such as mobile applications, embedded devices, and the Internet of Things.
  • the first communication device generates a dedicated AI model in step S301
  • the second communication device determines the dedicated AI model according to the first information in step S303.
  • the second communication device may be a terminal device
  • the AI model deployed on the terminal device may be a dedicated AI model
  • the first information sent by the first communication device to the second communication device may be used to determine the dedicated AI model.
  • different terminal devices may have different end-side characteristics (such as different local data, different local computing power, different channel characteristics, etc.)
  • the AI model deployed on the terminal device can be adapted to the end-side characteristics of the terminal device, in order to improve the model processing performance of the AI model.
  • deploying a dedicated AI model in the second communication device can effectively improve the model accuracy of personalized scenarios.
  • the first communication device generates a dedicated AI model in step S301, including: receiving second information from a second communication device; generating a dedicated AI model based on the second information.
  • the first communication device may receive second information from one or more second communication devices, and generate a dedicated AI model based on the second information.
  • FIG4 is another schematic diagram of an implementation of the communication method provided in an embodiment of the present application, including the following steps:
  • the first communication device receives second information from a first second communication device.
  • the first communication device receives second information from a second communication device.
  • the first second communication device and the second communication device may respectively feed back the second information to the first communication device.
  • the first communication device generates corresponding dedicated AI models according to the second information of the two second communication devices.
  • the first communication device sends first information to the first second communication device, where the first information is used to determine a dedicated AI model.
  • the first communication device sends first information to the second communication device, where the first information is used to determine a dedicated AI model.
  • the first communication device After the first communication device generates first information corresponding to two second communication devices respectively according to the second information, it sends the corresponding first information to one or more second communication devices respectively to deploy the dedicated AI model in the corresponding second communication devices respectively.
  • Step S404 and step S405 may be understood as an implementation example of the aforementioned step S302.
  • the first communication device generates a dedicated AI model according to the second information in step S403, including: the first communication device performs at least one of migration, fine-tuning, distillation, and pruning on the general AI model.
  • a large model can learn complex patterns and features by training massive amounts of data, has stronger generalization capabilities, and can make accurate predictions on unprocessed data.
  • a small model can refer to a model with fewer parameters and shallower layers.
  • the first communication device can obtain a lightweight, specialized AI model with fewer parameters by migrating, fine-tuning, distilling, and pruning the general AI model.
  • list can be replaced with other terms such as set, dictionary, combination, space, etc.
  • the first communication device can generate a list of dedicated AI models based on the information bottleneck (IB) theory.
  • IB information bottleneck
  • the second information may include one or more of the following information A to information E.
  • Information A Channel state information between the first communication device and the second communication device.
  • the channel state information between the first communication device and the second communication device may be obtained based on a reference signal.
  • the reference signal may include a channel state information reference signal (CSI-RS), a sounding reference signal (SRS), etc.
  • CSI-RS channel state information reference signal
  • SRS sounding reference signal
  • the reference signal may include a sidelink-synchronization signal/physical broadcast channel block (sidelink synchronization signal/physical broadcast channel block, sidelink SSB, SL-SSB, or S-SS/PSBCH block), a sidelink-channel state information reference signal (sidelink channel state information reference signal, SL-CSI-RS), etc.
  • sidelink synchronization signal/physical broadcast channel block sidelink SSB, SL-SSB, or S-SS/PSBCH block
  • sidelink-channel state information reference signal sidelink channel state information reference signal, SL-CSI-RS
  • the channel state information may include channel information between the first communication device and the second communication device, and/or channel information between the second communication device and the first communication device.
  • the channel information between the first communication device and the second communication device can be understood as uplink channel information
  • the channel information between the second communication device and the first communication device can be understood as downlink channel information.
  • the complexity requirement of the model processing of the AI model may be related to the local computing power status of the second communication device. Therefore, for the first communication device, the dedicated AI model determined by the first communication device based on the local computing power status information can be adapted to the local computing power status of the second communication device, so as to provide the second communication device with a dedicated AI model that meets the local computing power status, thereby improving the processing performance of the first communication device in model processing based on the dedicated AI model.
  • the category to which the second communication device belongs may change when the location of the second communication device changes.
  • the dedicated AI model determined by the location information can be adapted to the location of the second communication device to generate a dedicated AI model that meets the accuracy requirements.
  • the parameters or structure of the dedicated AI model may change when the behavior of the second communication device changes. Therefore, for the first communication device, the first communication device determines the dedicated AI model based on the location information to generate a dedicated AI model that meets the accuracy requirements.
  • the behavior information includes one or more of the user's movement trajectory, RRC state, and access or switching related behaviors.
  • the second information when the second information includes local data information and label information of the second communication device, in order to ensure data security of the second communication device or prevent privacy leakage, for AI tasks where the second communication device needs to upload original local data, the data can be encrypted, such as homomorphic encryption.
  • the second communication device can send the local data to the first communication device in a compressed manner, in an embedding or other non-one-to-one mapping form, to ensure the security of the data.
  • the second communication device may determine the category to which the second communication device belongs according to the first information in step S303.
  • the first information includes the category to which the second communication device belongs, and the first communication device sends the first information to the second communication device so that the second communication device determines the category to which it belongs according to the first information.
  • the second communication device moves, the channel and position of the second communication device will change, and the category to which it belongs may change.
  • the second communication device periodically reports the second information to the first communication device, so the first communication device periodically updates the category to which the second communication device belongs, and sends the type identifier and dedicated AI model of the category to the second communication device. Since the first communication device needs to send a new model category (and/or) model to the second communication device after the category of the second communication device changes, the solution described in Figure 4 is suitable for scenarios where the second communication device is relatively fixed or moves slowly. In this scenario, the frequency of the first communication device sending the model is low, so the overhead is small.
  • the content of the list of one or more AI models included in the first information is described below.
  • the content of the first information is related to the first AI model, wherein the first AI model is a single AI model or the first AI model is included in the first AI model group.
  • the first AI model is a single AI model
  • the first information can be used to determine the category to which the second communication device belongs.
  • the first information includes an identifier of the first AI model, and the identifier type is used to determine the first AI model in a list containing one or more dedicated AI models.
  • the content of the list of dedicated AI models is shown in Table 2 below.
  • the first information includes an identifier, that is, the first communication device sends an identifier type to the second communication device.
  • a list of one or more dedicated AI models is sent to the second communication device.
  • the first communication device sends a list of one or more dedicated AI models to the second communication device in advance so that when the type of the model needs to be changed later, the model identifier type can be sent down, thereby reducing the amount of data transmission.
  • the second communication device can determine the AI model corresponding to the identifier from Table 2 by looking up a table or other methods.
  • the situation where the first communication device sends the identifier of the category to which the second communication device belongs is more suitable for the scenario where the second communication device moves at a relatively high speed.
  • the signaling overhead is relatively small.
  • list can be replaced with other terms such as set, dictionary, combination, space, etc.
  • the first information includes model parameters and/or model structure information of the first AI model, that is, the first communication device sends the model parameters and/or model structure information of the first AI model to the second communication device.
  • the second communication device can directly determine the deployed first AI model based on the model parameters and/or model structure information of the first AI model without performing the above-mentioned table lookup operation.
  • the first information also includes an identifier of the first AI model.
  • the first communication device may also send the identifier of the first AI model to the second communication device, so that the second communication device determines the first AI model based on the identifier or model parameters.
  • the first AI model is included in the first AI model group.
  • the first AI model is included in a first AI model group, the first AI model group also includes a second AI model, the first AI model is deployed on a first communication device, and the second AI model is deployed on a second communication device; wherein the input of the second AI model includes the output of the first AI model, or the input of the first AI model includes the output of the second AI model.
  • the first communication device can be a network device and the second communication device can be a terminal device. Since different terminal devices may have different terminal characteristics (such as different local data, different local computing power, different channel characteristics, etc.), and different network devices may have different edge characteristics, by deploying the second AI model in the terminal device and the first AI model in the network device, the second AI model deployed in the terminal device can be adapted to the terminal characteristics of the terminal device, and the first AI model deployed in the network device can be adapted to the edge characteristics of the network device, so as to improve the model processing performance of the AI model.
  • terminal characteristics such as different local data, different local computing power, different channel characteristics, etc.
  • different network devices may have different edge characteristics
  • an AI model group may include two or more AI models.
  • the first AI model group may include other AI models in addition to the first AI model and the second AI model.
  • the other AI models may be deployed on other communication devices different from the first communication device and the second communication device. This is not limited here.
  • the AI model group list may include one or more AI model groups, and each AI model group may include two or more AI models.
  • the relationship between the AI model group and the AI model can also be understood as the relationship between the AI model and the AI sub-model.
  • the AI model group list can also be replaced by an AI model list, that is, the AI model list can include one or more AI models.
  • list can be replaced with other terms such as set, dictionary, combination, space, etc.
  • the first AI model group is regarded as one AI model
  • the first AI model and the second AI model may be understood as two AI sub-models in the one AI model.
  • the first AI model and the second AI model included in the first AI model group may be general AI models or special AI models to implement updates of different types of models.
  • the first information includes an identifier of the first AI model, where the identifier is used to determine the first AI model in a list including one or more dedicated AI models.
  • the method before sending the first information to the second communication device, the method also includes: sending a list of one or more dedicated AI models to the second communication device.
  • the content of the first information may include an identifier (type).
  • the second communication device Before receiving the first information, stores a list of one or more dedicated AI models in advance. After the second communication device receives the first information from the first communication device, it can find the corresponding AI model from the list according to the identifier in the first information.
  • the situation where the first communication device sends the identifier of the category to which the second communication device belongs is more suitable for the scenario where the second communication device moves at a relatively high speed.
  • the signaling overhead is relatively small.
  • the first information includes model parameters and/or model structure information of the first AI model.
  • the first information also includes an identifier (type) of the first AI model.
  • the content of the first information may be the model parameters and/or model structure information of the first AI model, or the content of the first information may be the model parameters and/or model structure information of the first AI model and the identifier (type and AI model) corresponding to the first AI model.
  • the first information sent by the first communication device to the second communication device may include information of most models in the first model group.
  • the first information is also used to determine the second AI model in the first AI model group.
  • the content of the first information may be as shown in Table 3 below.
  • Type (applicable to the case where a list has been saved, type can be sent down, so that the first communication device can find the corresponding AI model from the list according to type to reduce signaling overhead).
  • the first AI model is deployed on the first communication device
  • the second AI model is deployed on the second communication device
  • the input of the first AI model deployed on the first communication device includes the output of the second AI model deployed on the second communication device.
  • the second communication device taking the input data of the second AI model as X, after being processed by the second AI model, the second communication device can obtain and send data Z; after transmission through the wireless channel, the data received by the first communication device is represented as (It is understandable that due to the transmission path loss and noise interference on the wireless channel, It may not be the same as Z. It can be understood as an estimate of Z or a measured value of Z, etc.).
  • the first communication device can As the input of the first AI model, the data is processed by the first AI model to obtain
  • the first AI model is deployed on the first communication device
  • the second AI model is deployed on the second communication device
  • the input of the second AI model includes the output of the first AI model.
  • the first communication device taking the input data of the first AI model as X, after being processed by the first AI model, the first communication device can obtain and send data Z; after transmission through the wireless channel, the data received by the second communication device is represented as Thereafter, the second communication device may transmit the data As the input of the second AI model, the data is processed by the second AI model
  • first communication device and the second communication device may be implemented in many ways.
  • the second communication device may be a terminal device, and accordingly, the first communication device and the second communication device may communicate on a sidelink (SL).
  • the first AI model and the second AI model may be referred to as an end-to-end model, or an end-to-end collaborative model, etc.
  • the second communication device may be a network device (e.g., an access network device), and accordingly, the first communication device and the second communication device may communicate on uplink and downlink communication links.
  • the first AI model and the second AI model may be referred to as an edge-to-end model, an edge-to-end collaboration model, an end-to-end model, an end-to-end collaboration model, and the like.
  • the scenario shown in FIG5a may be understood as end-to-end collaboration based on a downlink scenario
  • the scenario shown in FIG5b may be understood as end-to-end collaboration based on an uplink scenario.
  • data Y may be label data corresponding to data X, and the label data Y and the processing results of the first AI model and the second AI model
  • the association relationship between the first AI model and the second AI model can be used to detect or determine the processing performance of the first AI model and the second AI model.
  • the association relationship can be determined by gradient information, loss function, etc.
  • Figure 6a is a schematic diagram of the collaborative deployment of a dedicated AI model and a general AI model provided in an embodiment of the present application.
  • the cloud can be understood as the central node of traditional cloud computing, which is the control end of edge computing, and the cloud/core network can correspond to the third communication device described in this embodiment.
  • the edge can be understood as the edge side of cloud computing, which is divided into the infrastructure edge and the device edge.
  • it refers to an edge server or a base station, which can correspond to the first communication device of this embodiment.
  • the end can be understood as a terminal device, such as various types of terminals such as mobile phones, tablets, sensors, etc., which can correspond to the second communication device of this embodiment.
  • the general AI model is mainly generated by the cloud or core network, or pre-configured by the first communication device, and the dedicated AI model is generated by the first communication device.
  • the above implementation method can achieve end-edge-cloud AI model collaboration.
  • FIG. 6b is another implementation diagram of the communication method provided in the embodiment of the present application.
  • FIG. 6b is mainly used to introduce the deployment scheme of the general AI model.
  • FIG. 6b is mainly used to illustrate that the user adopts the latest AI model issued by the base station, including the following steps:
  • the first communication device receives third information from the third communication device.
  • the first communication device After receiving the third information from the third communication device, the first communication device can determine the general AI model according to the third information and store the general AI model.
  • the general AI model can also be pre-configured locally by the first communication device without receiving third information generated and sent from the cloud/core network.
  • the first communication device sends third information to the second communication device, where the third information is used to determine a third AI model, and the third AI model is a general AI model.
  • the second communication device determines a third AI model according to the third information.
  • the second communication device determines a first AI model according to the first information.
  • step S604 for the sake of simplicity, only the second communication device determines the content of the first AI model according to the first information.
  • the second communication device can feedback other requirements to the first communication device, and the first communication device responds to the requirements and sends the first information corresponding to the requirements to the second communication device.
  • the first communication device sends the first information to the second communication device, so that the second communication device determines the first AI model according to the first information and switches the deployed AI model from the third AI model to the first AI model.
  • steps S301 to S303 of Figure 3 can also be executed, that is, the second communication device can first deploy or store a dedicated AI model, and then deploy or store a general AI model, so as to deploy a matching AI model according to actual needs, thereby realizing the joint deployment of a dedicated AI model and a general AI model.
  • steps S301 to S303 of Figure 3 can also be executed, that is, the second communication device can first deploy or store a common AI model, and then deploy or store a dedicated AI model, so as to deploy a matching AI model according to actual needs, thereby realizing the joint deployment of a dedicated AI model and a general AI model.
  • FIG. 6c is another schematic diagram of implementing the communication method provided in an embodiment of the present application.
  • the first communication device is a base station (BS) and the second communication device is a user equipment (UE).
  • the steps are as follows:
  • the base station sends the category of the general AI model (for example, type 0) and the terminal model to the UE (the UE has been informed through SIB that the general AI model needs to be used after access).
  • the category of the general AI model for example, type 0
  • the type 0 end-side model is used.
  • the UE feeds back channel state information, etc. (ie, second information) to the base station.
  • the base station classifies the UE and determines the category to which the UE belongs based on the list of dedicated AI models.
  • the base station sends the corresponding category (for example: type 1) and the terminal-side dedicated AI model to the UE.
  • UE uses the latest type 1 end-side model.
  • the base station can receive the third information sent from the cloud/core network to determine the general AI model, or the base station obtains the general AI model according to the configuration information.
  • Step 1 corresponds to step S602 in Figure 6b
  • step 2 corresponds to step S603 in Figure 6b
  • step 5 corresponds to step S604 in Figure 6b.
  • steps 3, 4, and 6 are all optional steps.
  • the base station can send the category corresponding to the UE and the terminal-side dedicated AI model to the UE based on the second information fed back by the UE, or it can send the category corresponding to the UE and the terminal-side dedicated AI model to the UE based on its own needs or the needs fed back by other communication devices.
  • the UE can choose to adopt and deploy the dedicated AI model, or it can first store the category (for example: type 1) and the dedicated AI model corresponding to the category, and deploy it when there is a need for the dedicated AI model.
  • the base station can notify the UE of the update of its category.
  • the third communication device can generate a third AI model based on simulator pre-training or existing network data, and send third information to the second communication device, where the third information is used to determine the third AI model.
  • the third AI model is included in the second AI model group, the second AI model group also includes a fourth AI model, the third AI model is deployed on the first communication device, and the fourth AI model is deployed on the second communication device; wherein, the input of the third AI model includes the output of the fourth AI model, or, the input of the fourth AI model includes the output of the third AI model.
  • the first communication device may optionally provide the third information by default to the first communication device. For example, the first communication device obtains the third information based on a prior configuration (such as a default configuration), stores the third information locally or in the cloud, and obtains the third information from the local or cloud when the first communication device needs to deploy a general AI model. After the first communication device receives the third information from the third communication device, the first communication device may receive the third information sent from the second communication device to determine the third AI model, i.e., the general AI model, and deploy the general AI model on the second communication device.
  • a prior configuration such as a default configuration
  • the first communication device may receive the third information sent from the second communication device to determine the third AI model, i.e., the general AI model, and deploy the general AI model on the second communication device.
  • the second communication device may also optionally provide the third information by default to the second communication device. For example, the second communication device obtains the third information based on a pre-configuration (such as a default configuration), stores the third information locally or in the cloud, and obtains the third information from the local or cloud when the second communication device needs to deploy a general AI model.
  • a pre-configuration such as a default configuration
  • the first communication device and/or the second communication device itself has or stores (for example, a default configuration) a general AI model
  • the AI model can be stored locally or in the cloud in the first communication device and/or the second communication device.
  • the third information can be understood as a list of general AI models, and the list of general AI models can be shown in Table 4 below, taking the general AI model as a single model as an example.
  • the list of general AI models is similar to the list of special AI models in terms of content and issuance method. For details, please refer to the above description of the list of special AI models, which will not be repeated here.
  • type 0 is used to represent a general AI model
  • type 1 is used to represent a special AI model.
  • the type corresponding to different AI models can be set according to specific needs, which is only used as an example and not limited here.
  • the second communication device determines the third AI model according to the third information in step S603 if the second communication device receives the first information from the first communication device, the second communication device determines the first AI model according to the first information and uses the first AI model, i.e., the dedicated AI model, for communication or transmission.
  • the first AI model i.e., the dedicated AI model
  • the method further includes: the first communication device receives fourth information from the second communication device, where the fourth information is used to indicate one of the AI models in the list of one or more AI models.
  • the second communication device can actively send fourth information to the first communication device to provide feedback on the recommended AI model (e.g., type 3).
  • the recommended AI model e.g., type 3
  • Category 1 The first communication device adopts the suggestion of the second communication device.
  • the second communication device feeds back to the first communication device the model type currently recommended by the second communication device.
  • the first communication device makes a decision and provides feedback to the second communication device: this type is optional.
  • the second communication device runs a terminal-side model of this type.
  • the second category the first communication device rejects the suggestion of the second communication device and provides a type selection for the first communication device.
  • the second communication device feeds back to the first communication device the model type currently recommended by the second communication device.
  • the first communication device decides not to recommend selecting this type and feeds back the type recommended by the UE (e.g. type 2).
  • the second communication device runs the terminal-side model of this type according to the instruction of the first communication device.
  • FIG. 6d is another implementation diagram of the communication method provided in an embodiment of the present application.
  • the first communication device as a base station (BS) and the second communication device as a user equipment (UE) as an example for illustration.
  • FIG. 6d is mainly used to illustrate that the UE has the right to feedback the model type of the local preference selection to the base station, and the UE's suggestion needs to interact with the decision of the base station.
  • the type 0 end-side model is used.
  • the UE feeds back channel state information, etc. (ie, second information) to the base station.
  • the base station generates a list of dedicated AI models.
  • the base station sends a list of dedicated AI models to the UE (if it is an end-side collaborative model group, you can choose to only send the category and end-side model).
  • the UE selects a model based on a list of dedicated AI models.
  • the UE feeds back the recommended model selection (for example: type 3) to the base station.
  • the base station determines the feasibility of the model selection suggested by the UE.
  • the base station decides that the decision is feasible, it will send a model selection confirmation (ACK) message to the UE. Otherwise, it will send a model selection rejection (NACK) message to the UE and provide the base station selection (for example: type 2).
  • ACK model selection confirmation
  • NACK model selection rejection
  • the UE receives confirmation information from the base station, it uses the selection type previously suggested to the base station (for example, type 3). If the UE receives rejection information from the base station, it uses the selection type provided by the base station (for example, type 2).
  • the base station can receive the third information sent from the cloud/core network to determine the general AI model, or the base station obtains the general AI model according to the configuration information.
  • Step 1 corresponds to step S602 in Figure 6b
  • step 2 corresponds to step S603 in Figure 6b
  • step 5 corresponds to step S604 in Figure 6b.
  • step 3, step 4, and step 6 to step 10 are all optional steps.
  • the second information fed back is used to send the category corresponding to the UE and the dedicated AI model on the terminal side to the UE, and the category corresponding to the UE and the dedicated AI model on the terminal side can also be sent to the UE according to its own needs or the needs fed back by other communication devices.
  • the UE can choose to adopt and deploy the dedicated AI model, or it can first store the category (for example: type1) and the dedicated AI model corresponding to the category, and deploy it when there is a demand for the dedicated AI model.
  • steps 7 to 10 whether the UE feeds back the recommended model selection to the base station is determined according to the needs of the UE during actual execution, which is an optional step.
  • the UE can feed back the model selection recommended by the UE side (for example: type 3) to the base station. If the base station decides that it is feasible, the UE can adopt the recommended model selection (for example: type3). If the base station rejects the suggestion, the UE adopts the model selection recommended by the base station (for example: type 2).
  • the priority of the base station selection is higher than the UE recommended selection.
  • the list of one or more AI models includes a list of one or more specialized AI models and/or a list of general AI models.
  • the list of one or more AI models may include the content of a dedicated AI model and/or the content of a general AI model, that is, any content in Table 2, Table 3, Table 4, or the combination of Table 2, Table 3, and Table 4, or Table 2, Table 3, and Table 4. Specifically, it can be set according to actual needs and is not limited here.
  • the first AI model is a general AI model.
  • the general AI model is generated by a third communication device.
  • the third communication device receives the sixth information from the first communication device, generates the fifth information based on the sixth information, and the third communication device sends the fifth information to the first communication device, and the fifth information is used to determine the general AI model, and the general AI model is deployed in the first communication device and the second communication device, or the general AI model is deployed in the second communication device.
  • the third communication device generates a general AI model based on the sixth information from the first communication device, and sends the fifth information to the first communication device to deploy the general AI model on the first communication device.
  • the first communication device can generate or update the general AI model through information from one or more third communication devices (such as one or more fifth information), and subsequently multiple first communication devices and second communication devices connected to each first communication device can deploy a general AI model with higher generalization and better versatility.
  • the sixth information includes the second information.
  • the first communication device receives the second information from the second communication device, and sends the sixth information to the third communication device, wherein the sixth information includes all the content of the second information, or the sixth information includes part of the content of the second information, for example, only sending data required by the third communication device, omitting other data, so as to reduce the amount of data transmission.
  • the sixth information is determined based on the processed second information. Specifically, after the first communication device receives the second information from the second communication device, the first communication device processes the second information according to its own AI requirements or those from the third communication device, and sends the processed information (i.e., the sixth information) to the third communication device. Alternatively, the first communication device extracts a list of general AI models from the second information, and sends the list of general AI models to the third communication device, etc.
  • the third communication device is a cloud side or a core network.
  • the method before the first communication device receives the first information from the first communication device, the method further includes: the first communication device sends a broadcast message to the second communication device, the broadcast message is used to inform the second communication device to use the first AI model.
  • the second communication device receives the broadcast message from the first communication device.
  • the first communication device can inform the second communication device whether it needs to use the general AI model after it accesses by sending a broadcast message (e.g., system information block (SIB)) to the second communication device. If necessary, the first communication device can send the first information to the second communication device after the second communication device accesses. If not necessary, the first communication device does not send the first information to the second communication device, but sends the first information based on a request from the second communication device when the second communication device has relevant needs.
  • SIB system information block
  • one or more of the first information, the second information, the third information, the fourth information, the fifth information, and the sixth information are periodically sent information.
  • the first information can be one of the bases for determining the AI model
  • the second information can deploy a dedicated AI model
  • the third information can deploy a general AI model
  • the fourth information can provide model suggestions
  • the fifth information can deploy a general AI model.
  • the first communication device and the second communication device can periodically send the first information and/or the second information and/or the third information and/or the fourth information to achieve periodic determination and/or periodic deployment and/or periodic suggestions of the AI model, so as to achieve multiple iterative updates of the AI model through a periodic process.
  • the first communication device and the third communication device can periodically send the fifth information and/or the sixth information to achieve periodic deployment of the AI model, so as to achieve multiple iterative updates of the AI model through a periodic process.
  • the embodiment of the present application provides a communication device 700, which can implement the functions of the second communication device or the first communication device in the above method embodiment, and thus can also achieve the beneficial effects of the above method embodiment.
  • the communication device 700 can be the first communication device (or the second communication device), or it can be an integrated circuit or component inside the first communication device (or the second communication device), such as a chip.
  • the transceiver unit 702 may include a sending unit and a receiving unit, which are respectively used to perform sending and receiving.
  • the device 700 when the device 700 is used to execute the method executed by the first communication device in the aforementioned embodiment, the device 700 includes a processing unit 701 and a transceiver unit 702; the processing unit 701 is used to generate a first artificial intelligence AI model; the transceiver unit 702 is used to send first information, and the first information is used to determine the first AI model.
  • the device 700 when the device 700 is used to execute the method executed by the second communication device in the aforementioned embodiment, the device 700 includes a processing unit 701 and a transceiver unit 702; the transceiver unit 702 is used to receive first information from the first communication device; and the processing unit 701 is used to determine a first AI model based on the first information.
  • the device 700 when the device 700 is used to execute the method executed by the third communication device in the aforementioned embodiment, the device 700 includes a processing unit 701 and a transceiver unit 702; the transceiver unit 702 is used to receive sixth information from the first communication device; the processing unit 701 is used to generate fifth information based on the sixth information; the transceiver unit 702 is used to send fifth information to the first communication device, and the fifth information is used to determine a general AI model, the general AI model is deployed in the first communication device and the second communication device, or the general AI model is deployed in the second communication device.
  • FIG. 8 is another schematic structural diagram of a communication device 800 provided in the present application.
  • the communication device 800 includes a logic circuit 801 and an input/output interface 802.
  • the communication device 800 may be a chip or an integrated circuit.
  • the transceiver unit 702 shown in Fig. 7 may be a communication interface, which may be the input/output interface 802 in Fig. 8, which may include an input interface and an output interface.
  • the communication interface may also be a transceiver circuit, which may include an input interface circuit and an output interface circuit.
  • the logic circuit 801 is used to generate a first artificial intelligence AI model; the input-output interface 802 is used to send first information, and the first information is used to determine the first AI model.
  • the input-output interface 802 is used to receive first information from a first communication device; and the logic circuit 801 is used to determine a first AI model according to the first information.
  • the input-output interface 802 is used to receive sixth information from the first communication device; the logic circuit 801 is used to generate fifth information based on the sixth information; the input-output interface 802 is used to send fifth information to the first communication device, and the fifth information is used to determine a common AI model, the common AI model is deployed in the first communication device and the second communication device, or the common AI model is deployed in the second communication device.
  • the logic circuit 801 and the input/output interface 802 may also execute other steps executed by the first communication device or the second communication device in any embodiment and achieve corresponding beneficial effects, which will not be described in detail here.
  • the processing unit 701 shown in FIG. 7 may be the logic circuit 801 in FIG. 8 .
  • the logic circuit 801 may be a processing device, and the functions of the processing device may be partially or completely implemented by software.
  • the functions of the processing device may be partially or completely implemented by software.
  • the processing device may include a memory and a processor, wherein the memory is used to store a computer program, and the processor reads and executes the computer program stored in the memory to perform corresponding processing and/or steps in any one of the method embodiments.
  • the processing device may include a processor.
  • a memory for storing a computer program is located outside the processing device, and the processor is connected to the memory via a circuit/wire to read and execute the computer program stored in the memory.
  • the memory and the processor may be integrated together, or may be physically independent of each other.
  • the processing device may be one or more chips, or one or more integrated circuits.
  • the processing device may be one or more field-programmable gate arrays (FPGA), application specific integrated circuits (ASIC), system on chip (SoC), central processing unit (CPU), or a combination thereof.
  • FPGA field-programmable gate arrays
  • ASIC application specific integrated circuits
  • SoC system on chip
  • CPU central processing unit
  • CPU central processing unit
  • CPU central processing unit
  • processor unit CPU
  • NP network processor
  • DSP digital signal processor
  • MCU microcontroller unit
  • PLD programmable logic device
  • the communication device 900 may include but is not limited to at least one processor 901 and a communication port 902.
  • the transceiver unit 702 shown in Fig. 7 may be a communication interface, which may be the communication port 902 in Fig. 9, which may include an input interface and an output interface.
  • the communication port 902 may also be a transceiver circuit, which may include an input interface circuit and an output interface circuit.
  • the device may also include at least one of a memory 903 and a bus 904 .
  • the at least one processor 901 is used to control and process the actions of the communication device 900 .
  • the processor 901 can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a transistor logic device, a hardware component or any combination thereof. It can implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application.
  • the processor can also be a combination that implements a computing function, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, and the like.
  • the communication device 900 shown in Figure 9 can be specifically used to implement the steps implemented by the terminal device in the aforementioned method embodiment, and to achieve the corresponding technical effects of the terminal device.
  • the specific implementation methods of the communication device shown in Figure 9 can refer to the description in the aforementioned method embodiment, and will not be repeated here one by one.
  • FIG 10 is a structural diagram of the communication device 1000 involved in the above-mentioned embodiments provided in an embodiment of the present application.
  • the communication device 1000 can specifically be a communication device as a network device in the above-mentioned embodiments.
  • the example shown in Figure 10 is that the network device is implemented through the network device (or a component in the network device), wherein the structure of the communication device can refer to the structure shown in Figure 10.
  • the communication device 1000 includes at least one processor 1011 and at least one network interface 1014. Further optionally, the communication device also includes at least one memory 1012, at least one transceiver 1013 and one or more antennas 1015.
  • the processor 1011, the memory 1012, the transceiver 1013 and the network interface 1014 are connected, for example, through a bus. In an embodiment of the present application, the connection may include various interfaces, transmission lines or buses, etc., which are not limited in this embodiment.
  • the antenna 1015 is connected to the transceiver 1013.
  • the network interface 1014 is used to enable the communication device to communicate with other communication devices through a communication link.
  • the network interface 1014 may include a network interface between the communication device and the core network device, such as an S1 interface, and the network interface may include a network interface between the communication device and other communication devices (such as other network devices or core network devices), such as an X2 or Xn interface.
  • the transceiver unit 702 shown in Fig. 7 may be a communication interface, which may be the network interface 1014 in Fig. 10, and the network interface 1014 may include an input interface and an output interface.
  • the network interface 1014 may also be a transceiver circuit, and the transceiver circuit may include an input interface circuit and an output interface circuit.
  • the processor 1011 is mainly used to process the communication protocol and communication data, and to control the entire communication device, execute the software program, and process the data of the software program, for example, to support the communication device to perform the actions described in the embodiment.
  • the communication device may include a baseband processor and a central processor, the baseband processor is mainly used to process the communication protocol and communication data, and the central processor is mainly used to control the entire terminal device, execute the software program, and process the data of the software program.
  • the processor 1011 in Figure 10 can integrate the functions of the baseband processor and the central processor. It can be understood by those skilled in the art that the baseband processor and the central processor can also be independent processors, interconnected by technologies such as buses.
  • the terminal device can include multiple baseband processors to adapt to different network formats, the terminal device can include multiple central processors to enhance its processing capabilities, and the various components of the terminal device can be connected through various buses.
  • the baseband processor can also be described as a baseband processing circuit or a baseband processing chip.
  • the central processor can also be described as a central processing circuit or a central processing chip.
  • the function of processing the communication protocol and communication data can be built into the processor, or it can be stored in the memory in the form of a software program, and the processor executes the software program to realize the baseband processing function.
  • the memory is mainly used to store software programs and data.
  • the memory 1012 can be independent and connected to the processor 1011.
  • the memory 1012 can be integrated with the processor 1011, for example, integrated into a chip.
  • the memory 1012 can store program codes for executing the technical solutions of the embodiments of the present application, and the execution is controlled by the processor 1011.
  • the various computer program codes executed can also be regarded as the driver program of the processor 1011.
  • FIG10 shows only one memory and one processor.
  • the memory may also be referred to as a storage medium or a storage device, etc.
  • the memory may be a storage element on the same chip as the processor, i.e., an on-chip storage element, or an independent storage element, which is not limited in the embodiments of the present application.
  • the transceiver 1013 can be used to support the reception or transmission of radio frequency signals between the communication device and the terminal, and the transceiver 1013 can be connected to the antenna 1015.
  • the transceiver 1013 includes a transmitter Tx and a receiver Rx.
  • one or more antennas 1015 can receive radio frequency signals
  • the receiver Rx of the transceiver 1013 is used to receive the radio frequency signal from the antenna, convert the radio frequency signal into a digital baseband signal or a digital intermediate frequency signal, and provide the digital baseband signal or the digital intermediate frequency signal to the processor 1011, so that the processor 1011 further processes the digital baseband signal or the digital intermediate frequency signal, such as demodulation and decoding.
  • the transmitter Tx in the transceiver 1013 is also used to receive a modulated digital baseband signal or a digital intermediate frequency signal from the processor 1011, and convert the modulated digital baseband signal or the digital intermediate frequency signal into a radio frequency signal, and send the radio frequency signal through one or more antennas 1015.
  • the receiver Rx can selectively perform one or more stages of down-mixing and analog-to-digital conversion processing on the RF signal to obtain a digital baseband signal or a digital intermediate frequency signal, and the order of the down-mixing and analog-to-digital conversion processing is adjustable.
  • the transmitter Tx can selectively perform one or more stages of up-mixing and digital-to-analog conversion processing on the modulated digital baseband signal or digital intermediate frequency signal to obtain a RF signal, and the order of the up-mixing and digital-to-analog conversion processing is adjustable.
  • the digital baseband signal and the digital intermediate frequency signal can be collectively referred to as a digital signal.
  • the transceiver 1013 may also be referred to as a transceiver unit, a transceiver, a transceiver device, etc.
  • a device in the transceiver unit for implementing a receiving function may be regarded as a receiving unit
  • a device in the transceiver unit for implementing a sending function may be regarded as a sending unit, that is, the transceiver unit includes a receiving unit and a sending unit
  • the receiving unit may also be referred to as a receiver, an input port, a receiving circuit, etc.
  • the sending unit may be referred to as a transmitter, a transmitter, or a transmitting circuit, etc.
  • the communication device 1000 shown in Figure 10 can be specifically used to implement the steps implemented by the network device in the aforementioned method embodiment, and to achieve the corresponding technical effects of the network device.
  • the specific implementation method of the communication device 1000 shown in Figure 10 can refer to the description in the aforementioned method embodiment, and will not be repeated here one by one.
  • FIG. 11 is a schematic diagram of the structure of the communication device involved in the above-mentioned embodiment provided in an embodiment of the present application.
  • the communication device 110 includes, for example, modules, units, elements, circuits, or interfaces, etc., which are appropriately configured together to perform the technical solutions provided in the present application.
  • the communication device 110 may be the terminal device or network device described above, or a component (such as a chip) in these devices, to implement the method described in the following method embodiment.
  • the communication device 110 includes one or more processors 111.
  • the processor 111 may be a general-purpose processor or a dedicated processor, etc.
  • it may be a baseband processor or a central processing unit.
  • the baseband processor may be used to process communication protocols and communication data
  • the central processing unit may be used to control the communication device (such as a RAN node, a terminal, or a chip, etc.), execute software programs, and process data of software programs.
  • the processor 111 may include a program 113 (sometimes also referred to as code or instruction), and the program 113 may be executed on the processor 111 so that the communication device 110 performs the method described in the following embodiments.
  • the communication device 110 includes a circuit (not shown in FIG. 11 ).
  • the communication device 110 may include one or more memories 112 on which a program 114 (sometimes also referred to as code or instructions) is stored.
  • the program 114 can be executed on the processor 111 so that the communication device 110 executes the method described in the above method embodiment.
  • the processor 111 and/or the memory 112 may include an AI module 117, 118, and the AI module is used to implement AI-related functions.
  • the AI module may be implemented by software, hardware, or a combination of software and hardware.
  • the AI module may include a wireless intelligent control (radio intelligence control, RIC) module.
  • the AI module may be a near real-time RIC or a non-real-time RIC.
  • data may also be stored in the processor 111 and/or the memory 112.
  • the processor and the memory may be provided separately or integrated together.
  • the communication device 110 may further include a transceiver 115 and/or an antenna 116.
  • the processor 111 may also be sometimes referred to as a processing unit, which controls the communication device (e.g., a RAN node or a terminal).
  • the transceiver 115 may also be sometimes referred to as a transceiver unit, a transceiver, a transceiver circuit, or a transceiver, etc., which is used to implement the transceiver function of the communication device through the antenna 116.
  • the processing unit 701 shown in FIG. 7 may be the processor 111.
  • the transceiver unit 702 shown in FIG. 7 may be a communication interface.
  • the port may be the transceiver 115 in Figure 11, and the transceiver 115 may include an input interface and an output interface.
  • the transceiver 115 may also be a transceiver circuit, and the transceiver circuit may include an input interface circuit and an output interface circuit.
  • An embodiment of the present application further provides a computer-readable storage medium, which is used to store one or more computer-executable instructions.
  • the processor executes the method described in the possible implementation methods of the first communication device or the second communication device in the aforementioned embodiment.
  • An embodiment of the present application also provides a computer program product (or computer program).
  • the processor executes the method that may be implemented by the above-mentioned first communication device or second communication device.
  • An embodiment of the present application also provides a chip system, which includes at least one processor for supporting a communication device to implement the functions involved in the possible implementation methods of the above-mentioned communication device.
  • the chip system also includes an interface circuit, which provides program instructions and/or data for the at least one processor.
  • the chip system may also include a memory, which is used to store the necessary program instructions and data for the communication device.
  • the chip system can be composed of chips, and may also include chips and other discrete devices, wherein the communication device can specifically be the first communication device or the second communication device in the aforementioned method embodiment.
  • An embodiment of the present application also provides a communication system, and the network system architecture includes the first communication device and the second communication device in any of the above embodiments.
  • the disclosed systems, devices and methods can be implemented in other ways.
  • the device embodiments described above are only schematic.
  • the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.
  • Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.
  • the above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.
  • the technical solution of the present application is essentially or the part that contributes or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application.
  • the aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code.

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Abstract

La présente demande divulgue un procédé de communication et un dispositif associé. Le procédé consiste à : générer un premier modèle d'intelligence artificielle (IA) ; et envoyer des premières informations à un second appareil de communication, les premières informations étant utilisées pour déterminer le premier modèle d'IA. Après qu'un premier appareil de communication génère le premier modèle d'IA, les premières informations utilisées pour déterminer le premier modèle d'IA sont envoyées à un récepteur de façon à déployer le premier modèle d'IA dans le récepteur, ce qui permet d'obtenir un déploiement du modèle d'IA et des fonctions telles que le fonctionnement, la génération ou la mise à jour du modèle.
PCT/CN2024/103512 2023-11-24 2024-07-04 Procédé de communication et dispositif associé Pending WO2025107649A1 (fr)

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