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WO2025237119A1 - Procédé de communication et appareil associé - Google Patents

Procédé de communication et appareil associé

Info

Publication number
WO2025237119A1
WO2025237119A1 PCT/CN2025/093003 CN2025093003W WO2025237119A1 WO 2025237119 A1 WO2025237119 A1 WO 2025237119A1 CN 2025093003 W CN2025093003 W CN 2025093003W WO 2025237119 A1 WO2025237119 A1 WO 2025237119A1
Authority
WO
WIPO (PCT)
Prior art keywords
information
data
model
training
receiving end
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/CN2025/093003
Other languages
English (en)
Chinese (zh)
Inventor
谢俊文
马梦瑶
李佳徽
杨讯
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
Original Assignee
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 WO2025237119A1 publication Critical patent/WO2025237119A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • 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
    • H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence

Definitions

  • This application relates to the field of communication technology, and in particular to a communication method and related apparatus.
  • Wireless communication can be a transmission communication between two or more communication devices that does not propagate through conductors or cables.
  • the two or more communication devices include network devices and terminal devices, or the two or more communication devices include different terminal devices.
  • AI models (which can also be simply referred to as models in this application embodiment) suitable for various scenarios or tasks have emerged.
  • AI models are usually trained on one side of the network device or the terminal device, and then the trained model is sent to the other end for deployment.
  • This application provides a communication method for collaboratively training a neural network model between a sending end and a receiving end. This method is flexible and applicable to various scenarios and tasks, thus improving compatibility.
  • inventions of this application propose a communication method applied to a sending end.
  • the sending end can be a communication device (such as a terminal device or network device), or it can be a component within the communication device (such as a processor, chip, or chip system), or it can be a logic module or software capable of implementing all or part of the functions of the communication device.
  • the method includes: first, the sending end sending first information to a receiving end, the first information including one or more first data and one or more first training label data, the first data including data output from the intermediate layer of a first model, the first training label data being used to update the first model; second, the sending end receiving second information sent by the receiving end, the second information indicating model update data output in reverse by the intermediate layer of the first model.
  • model may be replaced with other terms, such as neural network model, artificial intelligence (AI) model, AI network, AI neural network model, neural network, machine learning model, or AI processing model, etc.
  • AI artificial intelligence
  • processing of updating the first model in the embodiments of this application includes, but is not limited to, one or more of the following: training, iteration, optimization, fine-tuning, tuning, or improvement.
  • the first model in the embodiments of this application may include one or more models.
  • the intermediate layers of the first model may also be referred to as the hidden layers of the first model, which refers to a series of processing layers connecting the input layer and the output layer in the first model.
  • the sending end sends first information to the receiving end.
  • the first information includes one or more first data points and one or more first training label data points.
  • the first data points include data output from the intermediate layers of the first model, and the first training label data is used to update the first model.
  • the sending end receives second information sent by the receiving end.
  • the second information instructs the intermediate layers of the first model to output model update data in reverse order.
  • the first information includes a first training label.
  • the first information includes multiple first training labels.
  • the first information sent by the sending end to the receiving end may be first information obtained after data compression processing.
  • the sending end compresses and sends the first information to the receiving end.
  • the second information sent from the receiving end to the sending end may be second information obtained through data compression.
  • the receiving end compresses and sends the second information to the sending end.
  • the sending end can also directly send the first information
  • the receiving end can also directly send the second information
  • the first training label data includes: index information of the first training label in the training label set, or the first training label, and the training label set includes one or more training labels.
  • the first training label can be implemented in multiple ways, which improves the flexibility of the solution.
  • the first information further includes: grouping indication information, which indicates the grouping method of one or more first data included in the first information; and/or, group number information, which indicates the number of one or more first data included in the first information.
  • the first information can be realized through various grouping methods, thus effectively improving the generalization of the model.
  • the second information includes: a plurality of second data, wherein each second data corresponds to a first data; or, the second information includes: a weighted value of the plurality of second data, and the first information further includes the weight of the first data.
  • the second information can carry the second data in a variety of ways, thus effectively improving the generalization of the model.
  • the method further includes: the sending end acquiring a first training sample, the training label corresponding to the first training sample being a first training label; the sending end using the first training sample to process the first model multiple times, outputting one or more first data, wherein the processing method and/or processing parameters are different each time.
  • the method further includes:
  • the sending end acquires multiple first training samples, and the training label corresponding to the first training sample is the first training label.
  • the sending end uses multiple first training samples to process the first model once or multiple times, and outputs one or more first data, wherein the first training samples used in each processing are different.
  • the sending end acquires multiple first training samples, including:
  • the sending end obtains public training samples
  • the sending end processes the common training samples to generate multiple first training samples, and the multiple first training samples are different from each other.
  • the sending end can obtain multiple first training samples in various ways, which improves the flexibility of the solution implementation.
  • the method further includes: the sending end sending third information to the receiving end, the third information including any one or more of the following:
  • the third data, the input location information of the third data in the first model, the dimension information of the third data, the identification information of the first model, or the first type indication information, the first type indication information is used to indicate the data type of the third data, wherein the third data includes any one or more of the following information: first channel information, first environment information, or multimodal information.
  • the third information sent by the sending end to the receiving end may be third information obtained through data compression.
  • the sending end compresses and sends the third information to the receiving end.
  • the sending end can also directly send this third information.
  • the third information includes: third data, input location information of the third data in the first model, dimension information of the third data, identification information of the first model, and first type indication information, wherein the third data includes: first channel information, first environment information, and multimodal information.
  • the sending end in addition to sending the first information to the receiver, can also send other information, which improves the generalization ability of the model and enhances the flexibility of implementation. Furthermore, by defining a unified interaction format for the data exchanged between the sending and receiving ends, the two ends can be flexibly trained for different training scenarios and tasks, improving compatibility.
  • the method further includes:
  • the sending end receives a fourth message sent by the receiving end, which includes any one or more of the following:
  • the fourth data, the second type indication information, the termination interaction instruction of the first model, the dimension information of the fourth data, or the identification information of the first model, the second type indication information is used to indicate the data type of the fourth data
  • the termination interaction instruction of the first model is used to instruct the sender to stop sending information related to the first model.
  • the fourth data includes any one or more of the following information: second channel information, second environment information, multimodal information, training residual information generated by the receiver during the training of the first model, or the performance information of the updated first model.
  • the fourth information sent from the receiving end to the sending end may be fourth information obtained through data compression.
  • the receiving end compresses and sends the fourth information to the sending end.
  • the receiving end can also directly send this fourth piece of information.
  • the fourth information includes: fourth data, second type indication information, termination interaction instruction of the first model, dimension information of the fourth data, and identification information of the first model, wherein the fourth data includes: second channel information, second environment information, multimodal information, training residual information generated by the receiver during the training of the first model, and performance information of the updated first model.
  • the receiving end can send other information besides the second information to the sending end, improving the model's generalization ability and implementation flexibility. Furthermore, by defining a unified interaction format for the data exchanged between the sending and receiving ends, the two ends can be flexibly trained for different training scenarios and tasks, improving compatibility.
  • the first model includes one or more splitting positions; the first data includes: data output by intermediate layers corresponding to one or more splitting positions in the first model; and/or, the second data includes: model update data output in reverse from one or more splitting positions in the first model.
  • the first model since the first model can output feature data or update data at multiple segmentation positions, it enriches the amount of data exchanged between the sender and receiver and improves the generalization of the model.
  • the method further includes: the sending end sending configuration information to the receiving end, the configuration information being used to configure the training task for training the first model, the configuration information including any one or more of the following:
  • Model information wherein the model information includes any one or more of the following: the identifier of the first model, the structural information of the first model, the training task information of the first model, the initialization parameters of the first model, or the parameters of the first model to be updated;
  • Segmentation information wherein the segmentation information includes: the number of segmentation positions in the first model, and/or, the position information of the segmentation positions in the first model;
  • training information which includes any one or more of the following: training label set information, validation set information, number of training iterations, learning rate, or loss function, wherein the training label set includes one or more training labels;
  • the sending end receives configuration information sent by the receiving end.
  • the sending end and the receiving end can support the adjustment of the trained model within a training cycle, thereby improving training efficiency.
  • the configuration information further includes: first compression configuration information, and/or, second compression configuration information, wherein: the first compression configuration information is used to indicate the compression method and compression parameters of the first information and/or the third information, and the second compression configuration information is used to indicate the compression method and compression parameters of the second information and/or the fourth information.
  • the data exchanged between the sending end and the receiving end supports multiple compression methods, which can effectively reduce the amount of data transmitted and improve communication efficiency.
  • the first information is carried in a radio resource control (RRC) message or a media access control (MAC) message.
  • RRC radio resource control
  • MAC media access control
  • the second information is carried in a Radio Resource Control (RRC) message, or a Media Access Control (MAC) message.
  • RRC Radio Resource Control
  • MAC Media Access Control
  • the third information is carried in a Radio Resource Control (RRC) message or a Media Access Control (MAC) message;
  • RRC Radio Resource Control
  • MAC Media Access Control
  • the fourth information is carried in a Radio Resource Control (RRC) message, or a Media Access Control (MAC) message.
  • RRC Radio Resource Control
  • MAC Media Access Control
  • the receiving end can be a communication device (such as a terminal device or network device), or it can be a component within the communication device (such as a processor, chip, or chip system), or it can be a logic module or software capable of implementing all or part of the functions of the communication device.
  • the method includes: the receiving end receiving first information sent by a sending end, the first information including one or more first data items and one or more first training label data items, the first data including data output from the intermediate layer of a first model, the first training label data being used to update the first model; the receiving end sending second information to the sending end, the second information indicating model update data output in reverse from the intermediate layer of the first model.
  • model may be replaced with other terms, such as neural network model, artificial intelligence (AI) model, AI network, AI neural network model, neural network, machine learning model, or AI processing model, etc.
  • AI artificial intelligence
  • processing of updating the first model in the embodiments of this application includes, but is not limited to, one or more of the following: training, iteration, optimization, fine-tuning, tuning, or improvement.
  • first model in the embodiments of this application may include one or more models.
  • the sending end sends first information to the receiving end.
  • the first information includes one or more first data points and one or more first training label data points.
  • the first data points include data output from the intermediate layers of the first model, and the first training label data is used to update the first model.
  • the sending end receives second information sent by the receiving end.
  • the second information instructs the intermediate layers of the first model to output model update data in reverse order.
  • the first training label data includes: index information of the first training label in the training label set, or the first training label, and the training label set includes one or more training labels.
  • the first training label can be implemented in multiple ways, which improves the flexibility of the solution.
  • the first information further includes: grouping indication information, which indicates the grouping method of one or more first data included in the first information; and/or, group number information, which indicates the number of one or more first data included in the first information.
  • the first information can be realized through various grouping methods, thus effectively improving the generalization of the model.
  • the second information includes: a plurality of second data, wherein each second data corresponds to a first data; or, the second information includes: a weighted value of a plurality of second data, and the first information further includes the weight of the first data.
  • the second information can carry the second data in a variety of ways, thus effectively improving the generalization of the model.
  • the method further includes: the receiving end receiving third information sent by the sending end, the third information including any one or more of the following:
  • the third data the input location information of the third data in the first model, the dimension information of the third data, the identification information of the first model, or the first type indication information, wherein the first type indication information is used to indicate the data type of the third data, wherein the third data includes any one or more of the following information: first channel information, first environment information, or multimodal information.
  • the sending end in addition to sending the first information to the receiver, can also send other information, which improves the generalization ability of the model and enhances the flexibility of implementation. Furthermore, by defining a unified interaction format for the data exchanged between the sending and receiving ends, the two ends can be flexibly trained for different training scenarios and tasks, improving compatibility.
  • the method further includes:
  • the receiving end can send other information besides the second information to the sending end, improving the model's generalization ability and implementation flexibility. Furthermore, by defining a unified interaction format for the data exchanged between the sending and receiving ends, the two ends can be flexibly trained for different training scenarios and tasks, improving compatibility.
  • the first model includes one or more segmentation positions;
  • the first data includes: data output from intermediate layers corresponding to one or more segmentation positions in the first model;
  • the second data includes: model update data output in reverse from one or more split positions in the first model.
  • the first model since the first model can output feature data or update data at multiple segmentation positions, it enriches the amount of data exchanged between the sender and receiver and improves the generalization of the model.
  • the method further includes: receiving configuration information from the receiver, the configuration information being used to configure the training task for training the first model, the configuration information including any one or more of the following:
  • Model information wherein the model information includes any one or more of the following: the identifier of the first model, the structural information of the first model, the training task information of the first model, the initialization parameters of the first model, or the parameters of the first model to be updated;
  • Segmentation information wherein the segmentation information includes: the number of segmentation positions in the first model, and/or, the position information of the segmentation positions in the first model;
  • training information which includes any one or more of the following: training label set information, validation set information, number of training iterations, learning rate, or loss function, wherein the training label set includes one or more training labels;
  • the receiving end sends configuration information to the sending end.
  • the sending end and the receiving end can support the adjustment of the trained model within a training cycle, thereby improving training efficiency.
  • the configuration information also includes:
  • First compression configuration information and/or, second compression configuration information, wherein:
  • the first compression configuration information is used to indicate the compression method and compression parameters of the first information and/or the third information.
  • the second compression configuration information is used to indicate the compression method and compression parameters of the second and/or fourth information.
  • the data exchanged between the sending end and the receiving end supports multiple compression methods, which can effectively reduce the amount of data transmitted and improve communication efficiency.
  • the first information is carried in a Radio Resource Control (RRC) message or a Media Access Control (MAC) message.
  • RRC Radio Resource Control
  • MAC Media Access Control
  • the second information is carried in a Radio Resource Control (RRC) message, or a Media Access Control (MAC) message.
  • RRC Radio Resource Control
  • MAC Media Access Control
  • the third information is carried in a Radio Resource Control (RRC) message or a Media Access Control (MAC) message.
  • RRC Radio Resource Control
  • MAC Media Access Control
  • the fourth information is carried in a Radio Resource Control (RRC) message, or a Media Access Control (MAC) message.
  • RRC Radio Resource Control
  • MAC Media Access Control
  • the third aspect of this application provides a communication device, which is a transmitting end or a receiving end.
  • the device includes a transceiver unit and a processing unit.
  • the constituent modules of the communication device can also be used to perform the steps performed in various possible implementations of the first aspect and achieve the corresponding technical effects. For details, please refer to the first aspect, which will not be repeated here.
  • the fourth aspect of this application provides a communication device, which is a transmitting end or a receiving end.
  • the device includes a transceiver unit and a processing unit.
  • the constituent modules of the communication device can also be used to perform the steps performed in various possible implementations of the second aspect and achieve the corresponding technical effects. For details, please refer to the second aspect, which will not be repeated here.
  • the fifth aspect of this application provides a communication device including at least one processor coupled to a memory; the memory is used to store a program or instructions; the at least one processor is used to execute the program or instructions to enable the device to implement the method described in any possible implementation of the first or second aspect.
  • the sixth aspect of this application provides a communication device including at least one logic circuit and an input/output interface; the logic circuit is used to perform the method described in any of the possible implementations of the first or second aspect described above.
  • the seventh aspect of this application provides a communication system that includes the aforementioned transmitting end or receiving end.
  • the communication system may also include other communication devices that communicate with the transmitting end or the receiving end.
  • An eighth aspect of this application provides a computer-readable storage medium for storing one or more computer-executable instructions that, when executed by a processor, perform the method as described in any possible implementation of the first or second aspect above.
  • the ninth aspect of this application provides a computer program product (or computer program) that, when executed by a processor, performs the method described in any possible implementation of the first or second aspect described above.
  • the tenth aspect of this application provides a chip or chip system including at least one processor for supporting a communication device in implementing the method described in any possible implementation of the first or second aspect above.
  • the chip or chip system may further include a memory for storing program instructions and data necessary for the communication device.
  • the chip system may be composed of chips or may include chips and other discrete devices.
  • the chip system may also include interface circuitry that provides program instructions and/or data to the at least one processor.
  • FIGS 1a to 1c are schematic diagrams of the communication system provided in this application.
  • FIGS. 2a to 2e are schematic diagrams of the AI processing involved in this application.
  • Figure 3 is a schematic diagram of a communication scenario in an embodiment of this application.
  • FIG. 4 is a schematic diagram of another communication scenario in the embodiments of this application.
  • FIG. 5 is a flowchart illustrating one embodiment of the communication method in this application.
  • Figure 6 is a schematic diagram of the structure of the first information in an embodiment of this application.
  • Figure 7 is a schematic diagram of another structure of the first information in an embodiment of this application.
  • Figure 8 is a schematic diagram of a structure of the second information in an embodiment of this application.
  • Figure 9 is a schematic diagram of another structure of the second information in an embodiment of this application.
  • Figure 10 is a schematic diagram of the segmentation position of a first model in an embodiment of this application.
  • Figure 11 is a schematic diagram of the segmentation position of another first model in an embodiment of this application.
  • Figure 12 is a schematic diagram of another structure of the first model in the embodiments of this application.
  • Figure 13 is a schematic diagram of a forward interaction data structure in an embodiment of this application.
  • Figure 14 is a schematic diagram of another structure of forward interaction data in an embodiment of this application.
  • Figure 15 is a schematic diagram of a structure of the second information in an embodiment of this application.
  • Figure 16 is a schematic diagram of the structure of MAC message carrying forward interaction data in an embodiment of this application.
  • Figure 17 is a schematic diagram of the structure of MAC messages carrying reverse interaction data in an embodiment of this application.
  • Figure 18 is a structural schematic diagram of a communication device 1800 according to an embodiment of this application.
  • Figure 19 is another schematic structural diagram of the communication device 1900 provided in this application.
  • Figure 20 is a schematic diagram of the structure of the communication device 2000 provided in an embodiment of this application.
  • Figure 21 is a schematic diagram of the structure of the communication device 2100 provided in an embodiment of this application.
  • Terminal device can be a wireless terminal device that can receive network device scheduling and instruction information.
  • the wireless terminal device can be a device that provides voice and/or data connectivity to the user, or a handheld device with wireless connection function, or other processing device connected to a wireless modem.
  • Terminal devices can communicate with one or more core networks or the Internet via a radio access network (RAN).
  • Terminal devices can be mobile terminal devices, such as mobile phones (or "cellular" phones), computers, and data cards.
  • mobile phones or "cellular" phones
  • computers and data cards.
  • they can be portable, pocket-sized, handheld, computer-embedded, or vehicle-mounted mobile devices that exchange voice and/or data with the RAN.
  • Examples include personal communication service (PCS) phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), tablets, and computers with wireless transceiver capabilities.
  • PCS personal communication service
  • SIP session initiation protocol
  • WLL wireless local loop
  • PDAs personal digital assistants
  • tablets and computers with wireless transceiver capabilities.
  • Wireless terminal equipment can also be referred to as a system, subscriber unit, subscriber station, mobile station, mobile station (MS), remote station, access point (AP), remote terminal, access terminal, user terminal, user agent, subscriber station (SS), customer premises equipment (CPE), terminal, user equipment (UE), mobile terminal (MT), etc.
  • the terminal device can also be a wearable device.
  • Wearable devices also known as wearable smart devices or smart wearable devices, are a general term for devices that utilize wearable technology to intelligently design and develop everyday wearables, such as glasses, gloves, watches, clothing, and shoes.
  • Wearable devices are portable devices that are worn directly on the body or integrated into the user's clothing or accessories.
  • Wearable devices are not merely hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction.
  • wearable smart devices include those that are feature-rich, large in size, and can achieve complete or partial functions without relying on a smartphone, such as smartwatches or smart glasses, as well as those that focus on a specific type of application function and require the use of other devices such as smartphones, such as various smart bracelets, smart helmets, and smart jewelry for vital sign monitoring.
  • Terminals can also be drones, robots, devices in device-to-device (D2D) communication, vehicles to everything (V2X) communication, virtual reality (VR) terminal devices, augmented reality (AR) terminal devices, wireless terminals in industrial control, wireless terminals in self-driving, wireless terminals in remote medical care, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, and wireless terminals in smart homes, etc.
  • D2D device-to-device
  • V2X vehicles to everything
  • VR virtual reality
  • AR augmented reality
  • wireless terminals in industrial control wireless terminals in self-driving
  • wireless terminals in remote medical care wireless terminals in smart grids
  • wireless terminals in transportation safety wireless terminals in smart cities, and wireless terminals in smart homes, etc.
  • terminal devices can also be terminal devices in communication systems evolved from fifth-generation (5G) communication systems (such as 5G Advanced or sixth-generation (6G) communication systems), or terminal devices in future public land mobile networks (PLMNs).
  • 5G Advanced or 6G networks can further expand the form and function of 5G communication terminals; 6G terminals include, but are not limited to, vehicles, cellular network terminals (integrating satellite terminal functions), drones, and Internet of Things (IoT) devices.
  • 5G fifth-generation
  • 6G sixth-generation
  • PLMNs public land mobile networks
  • 5G Advanced or 6G networks can further expand the form and function of 5G communication terminals
  • 6G terminals include, but are not limited to, vehicles, cellular network terminals (integrating satellite terminal functions), drones, and Internet of Things (IoT) devices.
  • IoT Internet of Things
  • the terminal device can also obtain artificial intelligence (AI) services provided by the network device.
  • AI artificial intelligence
  • the terminal device can also have AI processing capabilities.
  • Network equipment This can be equipment in a wireless network.
  • network equipment can be a RAN node (or device) that connects terminal devices to the wireless network, and can also be called a base station.
  • RAN equipment include: base station, evolved NodeB (eNodeB), gNB (gNodeB) in 5G communication systems, transmission reception point (TRP), evolved Node B (eNB), radio network controller (RNC), Node B (NB), home base station (e.g., home evolved Node B, or home Node B, HNB), base band unit (BBU), or wireless fidelity (Wi-Fi) access point (AP), etc.
  • network devices may include centralized unit (CU) nodes, distributed unit (DU) nodes, or RAN devices that include both CU and DU nodes.
  • RAN nodes can also be macro base stations, micro base stations or indoor stations, relay nodes or donor nodes, or radio controllers in cloud radio access network (CRAN) scenarios.
  • RAN nodes can also be servers, wearable devices, vehicles, or in-vehicle equipment.
  • the access network equipment in vehicle-to-everything (V2X) technology can be a roadside unit (RSU).
  • V2X vehicle-to-everything
  • RSU roadside unit
  • RAN nodes collaborate to assist the terminal in achieving wireless access, with different RAN nodes each implementing some of the base station's functions.
  • RAN nodes can be CUs, DUs, CUs (control plane, CP), CUs (user plane, UP), or radio units (RUs).
  • CUs and DUs can be set up separately or included in the same network element, such as a baseband unit (BBU).
  • RUs can be included in radio frequency equipment or radio frequency units, such as remote radio units (RRUs), active antenna units (AAUs), radio heads (RHs), or remote radio heads (RRHs).
  • RRUs remote radio units
  • AAUs active antenna units
  • RHs radio heads
  • RRHs remote radio heads
  • CU or CU-CP and CU-UP
  • DU or RU
  • RU may have different names, but those skilled in the art will understand their meaning.
  • O-CU open CU
  • DU can also be called O-DU
  • CU-CP can also be called O-CU-CP
  • CU-UP can also be called O-CU-UP
  • RU can also be called O-RU.
  • this application uses CU, CU-CP, CU-UP, DU, and RU as examples.
  • Any of the units among CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented through software modules, hardware modules, or a combination of software modules and hardware modules.
  • This protocol layer may include a control plane protocol layer and a user plane protocol layer.
  • the control plane protocol layer may include at least one of the following: radio resource control (RRC) layer, packet data convergence protocol (PDCP) layer, radio link control (RLC) layer, media access control (MAC) layer, or physical (PHY) layer, etc.
  • the user plane protocol layer may include at least one of the following: service data adaptation protocol (SDAP) layer, PDCP layer, RLC layer, MAC layer, or physical layer, etc.
  • SDAP service data adaptation protocol
  • Network devices can be other devices that provide wireless communication functions for terminal devices.
  • the embodiments of this application do not limit the specific technology or form of the network device. For ease of description, the embodiments of this application are not limited.
  • Network equipment may also include core network equipment, such as the Mobility Management Entity (MME), Home Subscriber Server (HSS), Serving Gateway (S-GW), Policy and Charging Rules Function (PCRF), and Public Data Network Gateway (PDN Gateway) in 4G networks; and access and mobility management function (AMF), user plane function (UPF), or session management function (SMF) in 5G networks.
  • MME Mobility Management Entity
  • HSS Home Subscriber Server
  • S-GW Serving Gateway
  • PCRF Policy and Charging Rules Function
  • PDN Gateway Public Data Network Gateway
  • AMF access and mobility management function
  • UPF user plane function
  • SMF Public Data Network Gateway
  • the network device may also have network nodes with AI capabilities, which can provide AI services to terminals or other network devices.
  • network nodes with AI capabilities can provide AI services to terminals or other network devices.
  • it may be an AI node, computing node, RAN node with AI capabilities, or core network element with AI capabilities on the network side (access network or core network).
  • the device for implementing the function of the network device can be the network device itself, or it can be a device capable of supporting the network device in implementing that function, such as a chip system, which can be installed in the network device.
  • a network device being used to implement the function of the network device is used to describe the technical solutions provided in this application embodiment.
  • Configuration and Pre-configuration In this application, both configuration and pre-configuration are used. Configuration refers to the network device/server sending configuration information or parameter values to the terminal via messages or signaling, so that the terminal can determine communication parameters or resources for transmission based on these values or information. Pre-configuration is similar to configuration; it can be parameter information or parameter values pre-negotiated between the network device/server and the terminal device, parameter information or parameter values specified by standard protocols for use by the base station/network device or terminal device, or parameter information or parameter values pre-stored in the base station/server or terminal device. This application does not limit this.
  • “send” and “receive” indicate the direction of signal transmission.
  • “send information to XX” can be understood as the destination of the information being XX, which may include sending directly through the air interface or sending indirectly through the air interface by other units or modules.
  • “Receive information from YY” can be understood as the source of the information being YY, which may include receiving directly from YY through the air interface or receiving indirectly from YY through the air interface by other units or modules.
  • “Send” can also be understood as the "output” of the chip interface, and “receive” can also be understood as the "input” of the chip interface.
  • sending and receiving can occur between devices, such as between network devices and terminal devices, or within a device, such as between components, modules, chips, software modules, or hardware modules within the device via buses, wiring, or interfaces.
  • instruction may include direct instruction and indirect instruction, as well as explicit instruction and implicit instruction.
  • the information indicated by a certain piece of information (hereinafter referred to as instruction information) is called the information to be instructed.
  • instruction information The information indicated by a certain piece of information (hereinafter referred to as instruction information) is called the information to be instructed.
  • instruction information indicates the information to be instructed.
  • there are many ways to indicate the information to be instructed such as, but not limited to, directly indicating the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly indicate the information to be instructed by indicating other information, where there is an association between the other information and the information to be instructed; or it can only indicate a part of the information to be instructed, while the other parts of the information to be instructed are known or pre-agreed upon.
  • the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol predefined) arrangement order of various information, thereby reducing the instruction overhead to a certain extent.
  • a pre-agreed e.g., protocol predefined
  • This application does not limit the specific method of instruction. It is understood that for the sender of the instruction information, the instruction information can be used to indicate the information to be instructed, and for the receiver of the instruction information, the instruction information can be used to determine the information to be instructed.
  • the communication system includes at least one network device and/or at least one terminal device.
  • Figure 1a is a schematic diagram of a communication system according to this application.
  • Figure 1a exemplarily shows one network device and six terminal devices, namely terminal device 1, terminal device 2, terminal device 3, terminal device 4, terminal device 5, and terminal device 6.
  • terminal device 1 is a smart teacup
  • terminal device 2 is a smart air conditioner
  • terminal device 3 is a smart gas pump
  • terminal device 4 is a vehicle
  • terminal device 5 is a mobile phone
  • terminal device 6 is a printer.
  • the entity sending the AI configuration information can be a network device.
  • the entity receiving the AI configuration information can be terminal devices 1-6.
  • the network device and terminal devices 1-6 form a communication system.
  • terminal devices 1-6 can send data to the network device, and the network device needs to receive the data sent by terminal devices 1-6.
  • the network device can send configuration information to terminal devices 1-6.
  • terminal devices 4 and 6 can also form a communication system.
  • Terminal device 5 acts as a network device, i.e., the entity sending AI configuration information
  • terminal devices 4 and 6 act as terminal devices, i.e., the entities receiving AI configuration information.
  • V2X vehicle-to-everything
  • terminal device 5 sends AI configuration information to terminal devices 4 and 6 respectively, and receives data sent by terminal devices 4 and 6; correspondingly, terminal devices 4 and 6 receive the AI configuration information sent by terminal device 5 and send data back to terminal device 5.
  • V2X vehicle-to-everything
  • different devices may also perform AI-related services.
  • the base station can perform communication-related services and AI-related services with one or more terminal devices, and different terminal devices can also perform communication-related services and AI-related services.
  • communication-related services and AI-related services can also be performed between televisions and mobile phones.
  • AI network elements can be introduced into the communication system provided in this application to implement some or all AI-related operations.
  • AI network elements can also be called AI nodes, AI devices, AI entities, AI modules, AI models, or AI units, etc.
  • AI network elements can be built into network elements within the communication system.
  • an AI network element can be an AI module built into: access network equipment, core network equipment, cloud servers, or operation, administration, and maintenance (OAM) systems to implement AI-related functions.
  • OAM can act as the network management system for core network equipment and/or access network equipment.
  • AI network elements can also be independently configured network elements within the communication system.
  • the terminal or its built-in chip can also include an AI entity to implement AI-related functions.
  • AI can endow machines with human-like intelligence, for example, allowing them to use computer hardware and software to simulate certain intelligent human behaviors.
  • machine learning methods can be employed.
  • machines learn (or train) a model using training data. This model represents the mapping between inputs and outputs.
  • the learned model can be used for reasoning (or prediction), that is, it can be used to predict the output corresponding to a given input. This output can also be called the reasoning result (or prediction result).
  • Machine learning can include supervised learning, unsupervised learning, and reinforcement learning. Unsupervised learning can also be called learning without supervision.
  • Supervised learning based on collected sample values and labels, uses machine learning algorithms to learn the mapping relationship between sample values and labels, and then expresses this learned mapping relationship using an AI model.
  • the process of training the machine learning model is the process of learning this mapping relationship.
  • 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 mapping relationship learned in supervised learning can include linear or non-linear mappings.
  • the learning task can be divided into classification tasks and regression tasks.
  • Unsupervised learning relies on collected sample values to discover inherent patterns within the samples themselves.
  • One type of unsupervised learning algorithm uses the samples themselves as supervisory signals, meaning the model learns the mapping relationship from sample to sample; this is called self-supervised learning.
  • model parameters are optimized by calculating the error between the model's predictions and the samples themselves.
  • Self-supervised learning can be used for signal compression and decompression recovery applications; common algorithms include autoencoders and generative adversarial networks.
  • Reinforcement learning unlike supervised learning, is a type of algorithm that learns problem-solving strategies through interaction with the environment. Unlike supervised and unsupervised learning, reinforcement learning problems do not have explicit "correct" action labels.
  • the algorithm needs to interact with the environment to obtain reward signals from the environment, and then adjust its decision actions to obtain a larger reward signal value. For example, in downlink power control, the reinforcement learning model adjusts the downlink transmission power of each user based on the total system throughput feedback from the wireless network, aiming to achieve a higher system throughput.
  • the goal of reinforcement learning is also to learn the mapping relationship between the environment state and a better (e.g., optimal) decision action.
  • 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 networks are a specific model in machine learning techniques. According to the general approximation theorem, neural networks can theoretically approximate any continuous function, thus enabling them to learn arbitrary mappings.
  • Traditional communication systems rely on extensive expert knowledge to design communication modules, while deep learning communication systems based on neural networks can automatically discover hidden pattern structures from large datasets, establish mapping relationships between data, and achieve performance superior to traditional modeling methods.
  • each neuron performs a weighted summation of its input values and outputs the result through an activation function.
  • Figure 2a shows a schematic diagram of a neuron structure.
  • w ⁇ sub>i ⁇ /sub> is used as the weight for xi , and is used to weight xi .
  • the bias for the weighted summation of the input values based on the weights is, for example, b.
  • b can be any possible type, such as a decimal, an integer (e.g., 0, a positive integer, or a negative integer), or a complex number.
  • the activation functions of different neurons in a neural network can be the same or different.
  • neural networks generally consist of multiple layers, each of which may include one or more neurons. Increasing the depth and/or width of a neural network can improve its expressive power, providing more powerful information extraction and abstract modeling capabilities for complex systems.
  • the depth of a neural network can refer to the number of layers it includes, and the number of neurons in each layer can be called the width of that layer.
  • a neural network includes an input layer and an output layer. The input layer processes the received input information through neurons and passes the processing result to the output layer, which then obtains the output of the neural network.
  • a neural network includes an input layer, hidden layers, and an output layer. The input layer processes the received input information through neurons and passes the processing result to the hidden layer. The hidden layer calculates the received processing result and passes the calculation result to the output layer or the next adjacent hidden layer, ultimately obtaining the output of the neural network.
  • a neural network may include one hidden layer or multiple sequentially connected hidden layers, without limitation.
  • DNNs deep neural networks
  • DNNs can include feedforward neural networks (FNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs).
  • FNNs feedforward neural networks
  • CNNs convolutional neural networks
  • RNNs recurrent neural networks
  • Figure 2b is a schematic diagram of an FNN network.
  • a characteristic of FNN networks is that neurons in adjacent layers are completely connected pairwise. This characteristic makes FNNs typically require a large amount of storage space, leading to high computational complexity.
  • CNNs are neural networks specifically designed to process data with a grid-like structure. For example, time-series data (e.g., discrete sampling along a time axis) and image data (e.g., two-dimensional discrete sampling) can both be considered grid-like data.
  • CNNs do not use all the input information at once for computation; instead, they use a fixed-size window to extract a portion of the information for convolution operations, which significantly reduces the computational cost of model parameters.
  • each window can use different convolution kernels, allowing CNNs to better extract features from the input data.
  • RNNs are a type of distributed neural network (DNN) that utilizes feedback time-series information.
  • the input to an RNN includes the current input value and its own output value from the previous time step.
  • RNNs are well-suited for acquiring temporally correlated sequence features, and are particularly applicable to applications such as speech recognition and channel coding/decoding.
  • a loss function can be defined.
  • the loss function describes the difference or discrepancy between the model's output value and the ideal target value.
  • the loss function can be expressed in various forms, and there are no restrictions on its specific form.
  • the model training process can be viewed as follows: by adjusting some or all of the model's parameters, the value of the loss function is made to be less than a threshold value or to meet the target requirement.
  • a model can also be called an AI model, a rule, or other names.
  • An AI model can be considered a specific method for implementing AI functions.
  • An AI model represents the mapping relationship or function between the model's input and output.
  • AI functions can include one or more of the following: data collection, model training (or model learning), model information dissemination, model inference (or model reasoning, inference, or prediction, etc.), model monitoring or model validation, or inference result publication, etc.
  • AI functions can also be called AI (related) operations or AI-related functions.
  • a fully connected neural network is also called a multilayer perceptron (MLP).
  • MLP multilayer perceptron
  • an MLP consists of an input layer (left side), an output layer (right side), and multiple hidden layers (middle).
  • Each layer of an MLP contains several nodes, called neurons. Neurons in adjacent layers are connected pairwise.
  • w is the weight matrix
  • b is the bias vector
  • f is the activation function
  • n is the index of the neural network layer
  • n is greater than or equal to 1 and less than or equal to N, where N is the total number of layers in the neural network.
  • a neural network can be understood as a mapping from an input data set to an output data set.
  • Neural networks are typically initialized randomly; the process of obtaining this mapping from random values w and b using existing data is called training the neural network.
  • the training process can involve using a loss function to evaluate the output of the neural network.
  • the error can be backpropagated, and the neural network parameters (including w and b) can be iteratively optimized using gradient descent until the loss function reaches its minimum, which is the "better point (e.g., the optimal point)" in Figure 2d.
  • the neural network parameters corresponding to the "better point (e.g., the optimal point)" in Figure 2d can be used as the neural network parameters in the trained AI model information.
  • the gradient descent process can be represented as:
  • ⁇ represents the parameters to be optimized (including w and b)
  • L is the loss function
  • ⁇ is the learning rate, controlling the step size of gradient descent. This represents the differentiation operation. This indicates taking the derivative of ⁇ with respect to L.
  • the backpropagation process can utilize the chain rule for partial derivatives.
  • the gradient of the parameters in the previous layer can be recursively calculated from the gradient of the parameters in the next layer, and can be expressed as:
  • w ⁇ sub> ij ⁇ /sub> is the weight connecting node j to node i
  • s ⁇ sub>i ⁇ /sub> is the weighted sum of the inputs at node i.
  • AI models are typically trained on one side of the network device or the terminal device, and then the trained model is sent to the other end for deployment.
  • one-sided training may not be feasible.
  • models trained on one side may suffer from poor generalization. Therefore, how to achieve two-sided training of models has become an urgent problem to be solved.
  • this application proposes a communication method and related apparatus.
  • the sending end sends first information to the receiving end.
  • the first information includes one or more first data and one or more first training label data, wherein the first data includes data output from the intermediate layer of the first model, and the first training label data is used to update the first model.
  • the sending end receives second information sent by the receiving end, the second information indicating model update data output in reverse from the intermediate layer of the first model.
  • the sending end and receiving end can collaboratively train a neural network model, which can be flexibly applied to various different scenarios and tasks, improving compatibility.
  • the first information sent by the sending end to the receiving end includes one or more first data and one or more first training label data; by sending rich training-related data to the receiving end, the generalization ability of the model is improved.
  • FIG. 3 is a schematic diagram of a communication scenario according to an embodiment of this application.
  • the communication scenario involved in this embodiment includes: a sending end and a receiving end, wherein the direction from the sending end to the receiving end is defined as forward, and the direction from the receiving end to the sending end is defined as reverse (or backward).
  • the sending end sends forward interaction data to the receiving end, the forward interaction data including first information; the receiving end sends reverse interaction data (or backward interaction data) to the sending end, the reverse interaction data including second information.
  • the sending end can be a terminal device
  • the receiving end can be a network device, as shown in Figure 4, which is a schematic diagram of another communication scenario in an embodiment of this application.
  • the terminal device as the sending end, sends forward interaction data to the network device, which is the receiving end, and the network device sends reverse interaction data to the terminal device.
  • the sending end can be a network device, and the receiving end can be a terminal device.
  • the sender can be a network device
  • the receiver can be another network device.
  • the sending end can be a terminal device, and the receiving end can be another terminal device.
  • the communication method proposed in this application includes:
  • the sending end sends configuration information to the receiving end, and/or the receiving end sends configuration information to the sending end.
  • step S1 when the sending end and the receiving end need to collaboratively train the neural network model, the sending end can send configuration information to the receiving end.
  • the neural network model to be trained is referred to as the first model, which may include one or more neural network models.
  • the sending end can send configuration information to the receiving end, or the receiving end can send the configuration information to the sending end.
  • both the sending and receiving ends can send configuration information to each other, ensuring that the configuration information at both ends remains consistent. Through these methods, it is ensured that the sending and receiving ends are training the same neural network model (i.e., the first model).
  • the configuration information related to the first model may also change.
  • either the sending or receiving end can send the changed configuration information to the other end to ensure consistency between the configuration information at both ends.
  • This method ensures that the sending and receiving ends are training the same neural network model (i.e., the first model). For example, if the segmentation information (segmentation position and/or the number of segmentation positions) of the first model changes during training, the sending and receiving ends need to exchange configuration information (including the updated segmentation information) to ensure consistency between their first models.
  • the execution order of step S1 and subsequent steps S2-S3 is not restricted here.
  • the first model may include two parts: a transmitter model and a receiver model, wherein the transmitter trains the transmitter model in the first model, and the receiver trains the receiver model in the first model.
  • this configuration information is used to configure the training task for training the first model, and it includes one or more of the following:
  • Model information wherein the model information includes any one or more of the following: the identifier of the first model, the structural information of the first model, the training task information of the first model, the initialization parameters of the first model, or the parameters of the first model to be updated;
  • Segmentation information wherein the segmentation information includes: the number of segmentation positions in the first model, and/or, the position information of the segmentation positions in the first model;
  • training information which includes any one or more of the following: training label set information, validation set information, number of training iterations, learning rate, or loss function, wherein the training label set includes one or more training labels.
  • the identification information of the first model may include multiple identification information, each of which indicates a first model.
  • the identification information of the first model included in the configuration information needs to be updated. For example, before the update, if the sending end and the receiving end train first model #1 and first model #2, then the identification information of the first model in the configuration information before the update includes: the identifier (ID) of first model #1 and the ID of first model #2. After the update, if the sending end and the receiving end train first model #3 and first model #4, then the identification information of the first model in the updated configuration information includes: the ID of first model #3 and the ID of first model #4.
  • the training task information of the first model indicates the scenario (or environment) in which the first model is applicable, such as: reconstruction scenario, detection scenario, outdoor scenario, or indoor scenario.
  • splitting position in this embodiment is used to indicate which intermediate layer in the first model outputs data as the first data, and the data output at that output position has been processed by that intermediate layer and other layers (e.g., other intermediate layers) before that intermediate layer.
  • the number of segmentation positions of the first model in the segmentation information is 2.
  • the position information of the segmentation positions in the first model in the segmentation information are: ReLU activation layer #1 and ReLU activation layer #4.
  • the segmentation positions indicated by the segmentation information are: segmentation position #1 and segmentation position #2, as shown in Figures 10 and 11.
  • Figure 10 is a schematic diagram of the segmentation position of the first model in an embodiment of this application
  • Figure 11 is a schematic diagram of the segmentation position of the first model in another embodiment of this application.
  • the first model includes multiple convolutional layers and multiple activation function layers.
  • the weights (W) of the convolutional layer #1 are a matrix of size 192x192x1x7, and the bias (B) of the convolutional layer #1 is 192.
  • Figures 10 and 11 show a partial structure of the first model, where the split position #1 is specifically the output of the activation function layer (ReLU) #1 in the first model, and the split position #2 is specifically the output of the activation function layer (ReLU) #4 in the first model.
  • partial structure of the model shown in Figure 10 and the partial structure of the model shown in Figure 11 belong to the same first model. That is, one branch of the first model is shown in Figure 10, and another branch of the first model is shown in Figure 11.
  • the configuration information may further include compression-related configuration information.
  • the configuration information may also include: first compression configuration information, and/or second compression configuration information, wherein: the first compression configuration information indicates the compression method and compression parameters of the first and/or third information, and the second compression configuration information indicates the compression method and compression parameters of the second and/or fourth information.
  • the sending end sends the first and/or third information to the receiving end, i.e., the forward interaction data sent by the sending end to the receiving end includes: the first and/or third information; the receiving end sends the second and/or fourth information to the sending end, i.e., the reverse interaction data sent by the receiving end to the sending end includes: the second and/or fourth information.
  • the first compression configuration information indicates the compression method and compression parameters of the forward interaction data
  • the second compression configuration information indicates the compression method and compression parameters of the reverse interaction data.
  • the forward interaction data and the reverse interaction data use the same compression method and compression parameters.
  • the first compression configuration information and the second compression configuration information are the same compression configuration information, which can also be called common compression configuration information.
  • the forward and reverse interaction data use different compression methods and parameters.
  • the configuration information sent from the sending end to the receiving end may include first compressed configuration information and second compressed configuration information; or, the configuration information sent from the receiving end to the sending end may include first compressed configuration information and second compressed configuration information; or, the configuration information sent from the sending end to the receiving end includes first compressed configuration information, and the configuration information sent from the receiving end to the sending end includes second compressed configuration information; or, the configuration information sent from the sending end to the receiving end includes second compressed configuration information, and the configuration information sent from the receiving end to the sending end includes first compressed configuration information.
  • first compression configuration information and/or second compression configuration information may be carried in configuration information; or the first compression configuration information may be carried in first information and/or third information, and the second compression configuration information may be carried in second information and/or fourth information; or it may be carried in other independent information, and the embodiments of this application do not limit this.
  • the embodiments of this application include, but are not limited to: no compression, fixed quantization, entropy coding, fixed quantization and entropy coding, dictionary compression, or dictionary compression and entropy coding, etc.
  • embodiments of this application include, but are not limited to: quantization parameters, such as quantization boundaries or quantization bits, entropy coding parameters, or codebook dimensions, etc.
  • the first compression configuration information and/or the second compression configuration information can be indicated using index information. Based on this index information, the corresponding compression configuration information can be determined from the compression configuration information table. The sending end and the receiving end configure this compression information table respectively.
  • the compression configuration information table is shown in Table 2, for example.
  • the codebook dimension in the compression parameters can also be indicated by index information.
  • the corresponding dictionary compression parameters i.e., codebook dimensions
  • the dictionary compression parameter table is shown in Table 3.
  • the aforementioned compressed configuration information can be carried within the configuration information, or it can be sent together with the first, second, third, and/or fourth information.
  • This application embodiment does not impose any limitations on this.
  • the forward interaction data sent from the sending end to the receiving end includes the first and/or third information, and this forward interaction data also includes the first compressed configuration information.
  • the reverse interaction data sent from the receiving end to the sending end includes the second and/or fourth information, and this reverse interaction data also includes the second compressed configuration information.
  • the sending end sends the first information and/or the third information to the receiving end.
  • step S2 after the sending end and receiving end configure the first model according to the configuration information, the sending end trains the first model and then sends the data output by the intermediate layer of the first model during the training process to the receiving end.
  • the data output by the intermediate layer of the first model is referred to as the first data.
  • the first information also includes first training label data, which is used to update the first model.
  • the first training label data includes: the index information of the first training label in the training label set, or the first training label, wherein the training label set includes one or more training labels.
  • This training label set is the training label set indicated by the training label set information in the configuration information.
  • the sending end can determine the training label set based on the training label set information in the configuration information. Then, the sending end trains the first model based on the first training label in the training label set. The sending end uses the data output from the intermediate layer of the first model as the first data.
  • the first training label data includes: the index information of the first training label in the training label set.
  • the sending and receiving ends train by collecting training samples in real time. These training samples are called the first training samples.
  • the training label corresponding to the first training sample is called the first training label.
  • the first training label data includes: the first training label.
  • the sending end can send one or more first data points to the receiving end.
  • the segmentation information included in the configuration information indicates that the first model includes multiple segmentation positions
  • the multiple feature data (or other data) output by the multiple segmentation positions are considered as one set of first data.
  • the multiple data points included in a single set of first data can be ordered according to the hierarchical size of the segmentation position that outputs the first data.
  • the sending end acquires multiple distinct first training samples. Then, the sending end trains a first model using these multiple first training samples, with each training iteration using a different first training sample. During the training process of the first model using multiple first training samples, multiple sets of first data are output.
  • the sending end can acquire multiple first training samples by directly acquiring multiple different first training samples.
  • the sending end can acquire multiple different first training samples through multiple acquisitions by a sensor.
  • the sending end can acquire a common training sample and then perform different processing on that common training sample to obtain multiple different first training samples.
  • different processing on the common training sample includes, but is not limited to: data cleaning, data standardization, data normalization, feature scaling, encoding, feature selection, feature extraction, data augmentation, or resampling.
  • the sending end after obtaining a common training sample, performs different processing on the common training sample to obtain multiple first data.
  • the sending end uses this first training sample to process the first model one or more times.
  • This processing can be training processing.
  • the sending end outputs one or more first data points.
  • the processing method and/or processing parameters are inconsistent each time. For example, the initialization parameters of the first model are different in each processing step, other input information of the first model is different in each processing step, or the sampling operation of the first model is different in each processing step.
  • the sending end After acquiring one or more first data points and one or more first training label data points at the sending end, the sending end sends first information to the receiving end.
  • the first information includes the one or more first data points and one or more first training label data points.
  • the one or more first data points and one or more first training label data points in the first information can be grouped in various different ways to improve generalization. The different grouping methods of the first information are described below.
  • the first information includes one or more first data and one first training label data, wherein the first training label data includes a first training label (or index information indicating the first training label) corresponding to the one or more first data.
  • Figure 6 is a schematic diagram of the structure of the first information in an embodiment of this application.
  • the first information includes n first data and one first training label data, where n is a positive integer greater than or equal to 1.
  • the first information includes one or more first data points and multiple first training label data points, wherein each first data point corresponds to one first training label data point.
  • the first information includes n first data points and n first training label data points, where n is a positive integer greater than or equal to 1.
  • Each first data point and its corresponding first training label data point are divided into a group of data, meaning the first information includes n groups of data, each group including one first data point and its corresponding first training label data point.
  • the nth group of data includes first data point n and first training label data point n.
  • the sending end may also inform the receiving end of the grouping method of the first information and the number of one or more first data points included in the first information.
  • the first information may further include: grouping indication information, which indicates the grouping method of the one or more first data points included in the first information; and/or, group number information, which indicates the number of one or more first data points included in the first information.
  • grouping indication information indicates whether the first information is grouped using grouping method A or grouping using grouping method B.
  • the group number information indicates that the first information includes n groups of data (n first data points and n first training label data points, each group including one first data point and one first training label data point).
  • the first information can be supplemented with indication information to indicate whether the first information adopts grouping method A or grouping method B. For example, when the indication information is 0, it indicates that the first information adopts grouping method A; when the indication information is 1, it indicates that the first information adopts grouping method B.
  • the sending end may also send third information to the receiving end.
  • the third information includes, but is not limited to: third data, input location information of the third data in the first model, dimension information of the third data, first type indication information, or identification information of the first model.
  • the first type indication information is used to indicate the data type of the third data.
  • the third data includes any one or more of the following information: first channel information, first environment information, or multimodal information.
  • the first type indication information is related to the channel between the sending end and the receiving end, and the first environment information is related to the environment in which the sending end and the receiving end are located.
  • the channel information in the embodiments of this application includes, but is not limited to: channel capacity information, channel bandwidth information, channel noise information, channel modulation method, channel coding method, channel state information, or channel bit error rate, etc.
  • the environmental information in the embodiments of this application includes, but is not limited to: terrain information, climate condition information, electromagnetic environment information, network topology information, network load information, signal quality information, or wireless spectrum information.
  • the multimodal information in the embodiments of this application includes, but is not limited to, image information, point cloud information, text information, or audio information.
  • the first type of indication information is shown in Table 4.
  • the dimension information of the third data refers to the data dimension of the third data.
  • the third data includes the first channel information, and the first channel information is a J*K dimension matrix
  • the dimension information of the third data is J*K, where J is a positive integer and K is a positive integer.
  • the identification information of the first model is used to indicate which models and/or the types of models included in the first model.
  • the identification information of the first model is shown in Table 5, for example.
  • the input position information for inputting third data in the first model is used to instruct the receiving end to input the third data at that input position in the first model after receiving the third information.
  • Figure 12 is another structural schematic diagram of the first model in an embodiment of this application.
  • the input position information for inputting third data in the first model instructs the input of third data at the output position of the activation function layer (ReLU) #6 of the first model.
  • the third data also includes first channel information and first environment information, after receiving the third information (including the third data), the receiving end inputs the first channel information and first environment information included in the third data at the output position of the activation function layer (ReLU) #6 of the first model.
  • first and third information mentioned above can be carried in the same message, that is, the sending end sends the first and third information at the same time; or the first and third information mentioned above can be carried in different messages, that is, the sending end sends the first and third information separately. This application embodiment does not limit this.
  • the first and/or third information is carried in a radio resource control (RRC) message.
  • RRC radio resource control
  • the DataInfo field in RRC sequence format, indicates the specific type of the first and/or third information.
  • the data type is implicitly indicated by the presence of an element representing the data type in the DataInfo field.
  • the data type of the data included in the forward interaction data is indicated as follows: "DataInfo::SEQUENCE ⁇
  • the data type of the data included in the forward interaction data is indicated as follows: "DataInfo::SEQUENCE ⁇
  • This RRC message carries AI data, which specifically includes forward interaction data and model-related data.
  • a DataType field indicates whether a certain data type exists. If it exists, the data type is "true”; otherwise, if it does not exist, the data type is "false”.
  • the data type of the data included in the forward interaction data is indicated in the following way:
  • the ⁇ includeForwardInterData ENUMERATED ⁇ true ⁇ indicates that the RRC message carries forward interactive data.
  • the "forwardInterData” field indicates one or more of the following information: the identifier of the first model (modelIndicate), data configuration information (dataConfig), grouping indication information (groupMode), number of groups information (groupNum), feature data (featureData), or other data (otherData).
  • the configuration information in step S1 can also be carried in an RRC message.
  • the specific carrying method is similar to that of the first information and/or the third information carried in an RRC message, and will not be elaborated here.
  • Implementation method B The first and/or third information is carried in a media access control (MAC) message.
  • MAC media access control
  • the data type carried by the MAC message is indicated in the MAC subheader using a Logical Channel ID (LCID) or an Extended Logical Channel ID (eLCID). Therefore, when first and/or third information is carried in a MAC message, the data type of the first and/or third information can be indicated by the LCID or eLCID in the MAC subheader.
  • LCID Logical Channel ID
  • eLCID Extended Logical Channel ID
  • the receiving end receives a MAC message and finds that the value of the LCID field of the MAC message is 35, it determines that the MAC message carries the first and/or third information.
  • LCID or eLCID is used to indicate that the MAC message carries AI data.
  • AI data includes forward interaction data (first information and/or third information) and reverse interaction data (second information and/or fourth information). Therefore, in order to distinguish whether the AI data specifically carries forward interaction data or reverse interaction data, the type field of the MAC message is used to indicate this.
  • both the type field and the payload mentioned above can be carried on the MAC Control Element (MAC CE).
  • the payload can be used to carry AI data.
  • Figure 16 is a schematic diagram of the structure of a MAC message carrying forward interaction data in an embodiment of this application.
  • the LCID of the MAC message is 35, indicating that the MAC message carries AI data; the type of the MAC message is 0, indicating that the AI data carried by the MAC message specifically includes forward interaction data.
  • Figure 17 is a schematic diagram of the structure of a MAC message carrying reverse interaction data in an embodiment of this application.
  • the LCID of the MAC message is 35, indicating that the MAC message carries AI data; the type of the MAC message is 1, indicating that the AI data carried by the MAC message specifically includes reverse interaction data.
  • the configuration information in step S1 can also be carried in a MAC message.
  • the specific carrying method is similar to that of the first information and/or the third information carried in a MAC message, and will not be elaborated here.
  • the forward interaction data sent from the sending end to the receiving end includes: first information and third information.
  • the first information includes n first data points (first data 1 to first data n) and first training label data.
  • the forward interaction data shown in Figure 13 may also include configuration information (see step S1) and/or compressed configuration information (e.g., first compressed configuration information).
  • the forward interaction data sent from the sending end to the receiving end includes: first information and third information.
  • the first information includes n first data items (first data 1 to first data n), n first training label data items (first training label data 1 to first training label data n), and n third information items (third information 1 to third information n).
  • the first data items correspond to the first training label data and the third information items.
  • the forward interaction data shown in Figure 14 may also include configuration information (see step S1) and/or compressed configuration information (e.g., first compressed configuration information).
  • the first information may include only one such configuration information.
  • the first information includes multiple configuration information.
  • the receiving end sends the second and/or fourth information to the sending end.
  • the receiving end receives forward interaction data (including first information and/or third information) from the sending end.
  • the receiving end obtains the training loss function (loss) of the first model based on the forward interaction data and updates the decoding model (i.e., the receiving end model) in the first model.
  • the receiving end obtains second data based on the first model.
  • the second data includes model update data output from the reverse of the intermediate layers of the first model.
  • the second data includes, but is not limited to, gradients.
  • the gradient can be the gradient of the bias or the gradient of the weights, etc.
  • the second information includes multiple second data, where each second data corresponds to a first data.
  • Figure 8 is a schematic diagram of the structure of the second information in an embodiment of this application.
  • the sending end sends the second information to the receiving end, which includes m second data, where m is a positive integer.
  • the second information includes weighted values of multiple second-number data points (N), wherein the first information also includes the weights of the first data points.
  • the receiving end determines multiple second-number data points based on one or more first data points. Then, the receiving end performs weighted processing on the multiple second-number data points according to the weights of the first data points corresponding to each second-number data point, to obtain a weighted value for the multiple second-number data points. For example, if the weights of each first data point are equal, then the second information includes the average value of the multiple second-number data points.
  • Figure 9 is another structural schematic diagram of the second information in an embodiment of this application.
  • the sending end sends the second information to the receiving end, which includes weighted values of m second-number data points (i.e., a total of m second-number data points from second-number data 1 to second-number data m), where m is a positive integer.
  • the receiving end can also send a fourth information to the sending end.
  • the fourth information includes, but is not limited to:
  • the fourth data includes: second type indication information, termination interaction instruction of the first model, dimension information of the fourth data, or identification information of the first model.
  • the second type indication information is used to indicate the data type of the fourth data.
  • the termination interaction instruction of the first model is used to instruct the sending end to stop sending information related to the first model.
  • the second channel information is related to the channel between the sending end and the receiving end.
  • the second environment information is related to the environment in which the sending end and the receiving end are located.
  • the fourth data includes any one or more of the following information: second channel information, second environment information, multimodal information, training residual information generated by the receiving end during the training of the first model, or performance information of the updated first model.
  • the second channel information is similar to the first channel information in step S2 above, and will not be described in detail here.
  • the second environmental information is similar to the first environmental information in step S2 above, and will not be described in detail here.
  • the multimodal information is similar to the multimodal information in step S2 above, and will not be described in detail here.
  • the second type of indication information, the dimension information of the fourth data, or the identification information of the first model are similar to the first type of indication information, the dimension information of the third data, or the identification information of the first model in the aforementioned step S2, and will not be elaborated here.
  • the sending end can use this training residual information to train the first model.
  • the sending end Upon receiving a termination interaction command from the first model, the sending end ceases exchanging data related to the first model with the receiving end, such as first information and/or third information.
  • the sending end may also stop training the first model.
  • the sending end can determine whether to continue exchanging data related to the first model, such as first information and/or third information, based on this performance information. If the sending end determines that the performance of the first model meets the requirements based on the performance information, it can stop exchanging data related to the first model with the receiving end. Optionally, the sending end can also stop training the first model.
  • the performance information of the first model includes: test loss function, test accuracy, precision, and/or recall.
  • the sending end sends the second information and the fourth information to the receiving end.
  • the second information includes the weighted values of m second data (i.e., the weighted values of a total of m second data from second data 1 to second data m), where m is a positive integer. Since each of the m second data corresponds to one fourth information, the reverse interaction data also includes m fourth information (i.e., the m fourth information from fourth information 1 to fourth information m), where fourth information 1 corresponds to second data 1, and so on, with fourth information m corresponding to second data m.
  • the weighted value of the m second data points can be the average of the m second data points.
  • the reverse interaction data shown in Figure 15 may also include configuration information (see step S1) and/or compressed configuration information (e.g., second compressed configuration information).
  • configuration information see step S1
  • compressed configuration information e.g., second compressed configuration information
  • the second information includes the weighted values of the second data 1 to the second data m and a fourth information corresponding to the m second data from the second data 1 to the second data m.
  • the second information may include only one such configuration information.
  • the second information includes multiple configuration information.
  • steps S2 to S3 can be repeated until the performance of the first model meets the service requirements.
  • the sending end can send the configuration information of the model to the receiving end (similar to step S1). Then, the sending end and the receiving end exchange the relevant data of the model, similar to steps S2 to S3 above, which will not be elaborated here.
  • the sending end can send the updated configuration information to the receiving end (similar to step S1), or the receiving end can send the updated configuration information to the sending end (similar to step S1). Then, the sending end and the receiving end exchange the updated relevant data of the first model, similar to steps S2 to S3 above, which will not be elaborated here.
  • the sending end sends first information to the receiving end.
  • the first information includes one or more first data points and one or more first training label data points.
  • the first data points include data output from the intermediate layers of the first model, and the first training label data is used to update the first model.
  • the sending end receives second information sent by the receiving end.
  • the second information instructs the intermediate layers of the first model to output model update data in reverse.
  • the sending end and receiving end can collaboratively train a neural network model, which can be flexibly applied to various different scenarios and tasks, improving compatibility.
  • the first information sent by the sending end to the receiving end includes one or more first data points and one or more first training label data points.
  • the generalization ability of the model is improved.
  • the sending end and the receiving end can flexibly train for different training scenarios and tasks, improving compatibility.
  • the sending end and the receiving end can support training multiple models within a single training cycle, improving training efficiency.
  • the sending end and the receiving end can also adjust the trained model within a single training cycle, further improving training efficiency. Because the data exchanged between the sending and receiving ends supports multiple compression methods, the amount of data transmitted can be effectively reduced, thus improving communication efficiency.
  • FIG 18 is a schematic diagram of the structure of a communication device 1800 in an embodiment of this application.
  • This communication device 1800 can realize the functions of a transmitting end or a receiving end in the above method embodiments, and therefore can also achieve the beneficial effects of the above method embodiments.
  • the communication device 1800 can be a transmitting end or a receiving end, or it can be an integrated circuit or component inside the transmitting end or receiving end, such as a chip, baseband chip, modem chip, SoC chip containing a modem core, system-in-package (SIP) chip, communication module, chip system, processor, etc.
  • SIP system-in-package
  • the transceiver unit 1802 may include a transmitting unit and a receiving unit, which are used to perform transmitting and receiving respectively.
  • the device 1800 when the device 1800 is used to execute the method performed by the transmitting end or the receiving end in FIG5 and related embodiments, the device 1800 includes a processing unit 1801 and a transceiver unit 1802.
  • the communication device 1800 is used at a transmitting end, and the communication device 1800 includes:
  • the transceiver unit 1802 is used to send first information to the receiving end.
  • the first information includes one or more first data and one or more first training label data.
  • the first data includes data output from the intermediate layer of the first model.
  • the first training label data is used to update the first model.
  • the transceiver unit 1802 is also used to receive second information sent by the receiving end, the second information indicating the model update data output in reverse by the intermediate layer of the first model.
  • the first training label data includes:
  • the first training label or the index information of the first training label in the training label set, wherein the training label set includes one or more training labels.
  • the first information further includes:
  • Grouping indication information wherein the grouping indication information is used to indicate the grouping method of the plurality of first data included in the first information
  • group number information which indicates the number of the plurality of first data included in the first information.
  • each second data corresponding to one first data.
  • the first information may also include the weight of the first data.
  • the transceiver unit 1802 is further configured to acquire a first training sample, wherein the training label corresponding to the first training sample is the first training label.
  • the processing unit 1801 is used to process the first model multiple times using the first training samples and output the plurality of first data, wherein the processing method and/or processing parameters are different each time.
  • the transceiver unit 1802 is further configured to acquire multiple first training samples, wherein the training label corresponding to the first training sample is the first training label.
  • the processing unit 1801 is further configured to process the first model once or multiple times using the plurality of first training samples, and output the plurality of first data, wherein the first training samples used in each processing are different.
  • the transceiver unit 1802 is also used to acquire public training samples
  • the processing unit 1801 is further configured to process the common training samples to generate the plurality of first training samples, wherein the plurality of first training samples are different from each other.
  • the transceiver unit 1802 is further configured to send third information to the receiving end, the third information including any one or more of the following:
  • the third data the input location information of the third data in the first model, the dimension information of the third data, the identification information of the first model, or the first type indication information, wherein the first type indication information is used to indicate the data type of the third data, wherein the third data includes any one or more of the following information: first channel information, first environment information, or multimodal information.
  • the transceiver unit 1802 is further configured to receive fourth information sent by the receiving end, the fourth information including any one or more of the following:
  • the first model includes one or more segmentation locations
  • the first data includes: data output from intermediate layers corresponding to one or more segmentation positions in the first model.
  • the transceiver unit 1802 is further configured to send configuration information to the receiving end, the configuration information being used to configure the training task for training the first model, the configuration information including any one or more of the following:
  • Model information wherein the model information includes any one or more of the following: the identification information of the first model, the structural information of the first model, the training task information of the first model, the initialization parameters of the first model, or the parameters of the first model to be updated.
  • Segmentation information wherein the segmentation information includes: the number of segmentation positions of the first model, and/or, the position information of the segmentation positions in the first model;
  • training information includes any one or more of the following: training label set information, validation set information, number of training iterations, learning rate, or loss function, wherein the training label set includes one or more training labels;
  • the transceiver unit 1802 is also used to receive the configuration information sent by the receiving end.
  • the configuration information further includes:
  • First compression configuration information and/or, second compression configuration information, wherein:
  • the first compression configuration information is used to indicate the compression method and compression parameters of the first information and/or the third information.
  • the second compression configuration information is used to indicate the compression method and compression parameters of the second information and/or the fourth information.
  • the first information is carried in a Radio Resource Control (RRC) message or a Media Access Control (MAC) message.
  • RRC Radio Resource Control
  • MAC Media Access Control
  • the second information is carried in a Radio Resource Control (RRC) message, or a Media Access Control (MAC) message.
  • RRC Radio Resource Control
  • MAC Media Access Control
  • the third information is carried in a Radio Resource Control (RRC) message or a Media Access Control (MAC) message.
  • RRC Radio Resource Control
  • MAC Media Access Control
  • the fourth information is carried in a Radio Resource Control (RRC) message, or a Media Access Control (MAC) message.
  • RRC Radio Resource Control
  • MAC Media Access Control
  • the communication device 1800 is applied at the receiving end, and the communication device 1800 includes:
  • the transceiver unit 1802 is used to receive first information sent by the sender.
  • the first information includes one or more first data and one or more first training label data.
  • the first data includes data output from the intermediate layer of the first model.
  • the first training label data is used to update the first model.
  • the transceiver unit 1802 is also used to send second information to the sending end, the second information indicating the model update data output in reverse by the intermediate layer of the first model.
  • the first training label data includes:
  • the first training label or the index information of the first training label in the training label set, wherein the training label set includes one or more training labels.
  • the first information further includes:
  • Grouping indication information wherein the grouping indication information is used to indicate the grouping method of the plurality of first data included in the first information
  • group number information which indicates the number of the plurality of first data included in the first information.
  • the second information includes: the weighted value of the plurality of second data.
  • the first information may also include the weight of the first data.
  • the first information may also include the weight of the first data.
  • the transceiver unit 1802 is further configured to receive third information transmitted by the transmitting end, the third information including any one or more of the following:
  • the third data the input location information of the third data in the first model, the dimension information of the third data, the identification information of the first model, or the first type indication information, wherein the first type indication information is used to indicate the data type of the third data, wherein the third data includes any one or more of the following information: first channel information, first environment information, or multimodal information.
  • the transceiver unit 1802 is further configured to send fourth information to the sending end, the fourth information including any one or more of the following:
  • the first model includes one or more segmentation locations
  • the first data includes: data output from intermediate layers corresponding to one or more segmentation positions in the first model;
  • the second data includes: model update data output in reverse from one or more of the segmentation positions in the first model.
  • the transceiver unit 1802 is further configured to receive configuration information from the receiving end, the configuration information being used to configure the training task for training the first model, the configuration information including any one or more of the following:
  • Model information wherein the model information includes any one or more of the following: the identification information of the first model, the structural information of the first model, the training task information of the first model, the initialization parameters of the first model, or the parameters of the first model to be updated.
  • Segmentation information wherein the segmentation information includes: the number of segmentation positions of the first model, and/or, the position information of the segmentation positions in the first model;
  • training information includes any one or more of the following: training label set information, validation set information, number of training iterations, learning rate, or loss function, wherein the training label set includes one or more training labels;
  • the transceiver unit 1802 is also used to send the configuration information to the sending end.
  • the configuration information further includes:
  • First compression configuration information and/or, second compression configuration information, wherein:
  • the first compression configuration information is used to indicate the compression method and compression parameters of the first information and/or the third information.
  • the second compression configuration information is used to indicate the compression method and compression parameters of the second information and/or the fourth information.
  • the first information is carried in a Radio Resource Control (RRC) message or a Media Access Control (MAC) message.
  • RRC Radio Resource Control
  • MAC Media Access Control
  • the second information is carried in a Radio Resource Control (RRC) message, or a Media Access Control (MAC) message.
  • RRC Radio Resource Control
  • MAC Media Access Control
  • the third information is carried in a Radio Resource Control (RRC) message or a Media Access Control (MAC) message.
  • RRC Radio Resource Control
  • MAC Media Access Control
  • the fourth information is carried in a Radio Resource Control (RRC) message, or a Media Access Control (MAC) message.
  • RRC Radio Resource Control
  • MAC Media Access Control
  • the function of the processing unit 1801 can be implemented by one or more processors.
  • the processor may include a modem chip, or a SoC chip or SIP chip containing a modem core.
  • the function of the transceiver unit 1802 can be implemented by transceiver circuitry.
  • the communication device 1800 when the communication device 1800 is a circuit or chip responsible for communication functions in a terminal (or network device), such as a modem chip or a SoC chip or SIP chip containing a modem core, the function of the processing unit 1801 can be implemented by a circuit system in the aforementioned chip that includes one or more processors or processor cores.
  • the function of the transceiver unit 1802 can be implemented by the interface circuit or data transceiver circuit on the aforementioned chip.
  • the communication device 1900 includes a logic circuit 1901 and an input/output interface 1902.
  • the communication device 1900 can be a chip or an integrated circuit.
  • the transceiver unit 1802 can be a communication interface, which can be the input/output interface 1902 in Figure 19.
  • the input/output interface 1902 can include an input interface and an output interface.
  • the communication interface can also be a transceiver circuit, which can include an input interface circuit and an output interface circuit.
  • the logic circuit 1901 and the input/output interface 1902 can also perform other steps executed by the transmitting or receiving end in any embodiment and achieve corresponding beneficial effects, which will not be elaborated further here.
  • the processing unit 1801 shown in FIG18 can be the logic circuit 1901 in FIG19.
  • the logic circuit 1901 can be a processing device, the functions of which can be partially or entirely implemented in software.
  • the processing apparatus 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 the corresponding processing and/or steps in any of the method embodiments.
  • the processing device may consist of only a processor.
  • a memory for storing computer programs is located outside the processing device, and the processor is connected to the memory via circuitry/wires to read and execute the computer programs stored in the memory.
  • the memory and processor may be integrated together or 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 (FPGAs), application-specific integrated circuits (ASICs), system-on-chips (SoCs), central processing units (CPUs), network processors (NPs), digital signal processors (DSPs), microcontroller units (MCUs), programmable logic controllers (PLDs), or other integrated chips, or any combination of the above chips or processors.
  • FPGAs field-programmable gate arrays
  • ASICs application-specific integrated circuits
  • SoCs system-on-chips
  • CPUs central processing units
  • NPs network processors
  • DSPs digital signal processors
  • MCUs microcontroller units
  • PLDs programmable logic controllers
  • Figure 20 is a structural schematic diagram of the communication device 2000 involved in the above embodiments provided by the embodiments of this application.
  • the communication device 2000 can be the communication device as the sending end (or receiving end) in the above embodiments.
  • the example shown in Figure 20 is that the sending end is implemented by the sending end (or the component in the sending end), or the example shown in Figure 20 is that the receiving end is implemented by the receiving end (or the component in the receiving end).
  • the present invention is a possible logical structure diagram of the communication device 2000, which may include, but is not limited to, at least one processor 2001 and a communication port 2002.
  • the transceiver unit 1802 can be a communication interface, which can be the communication port 2002 in Figure 20.
  • the communication port 2002 can include an input interface and an output interface.
  • the communication port 2002 can also be a transceiver circuit, which can include an input interface circuit and an output interface circuit.
  • the device may also include at least one of a memory 2003 and a bus 2004.
  • the at least one processor 2001 is used to control the operation of the communication device 2000.
  • the processor 2001 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 devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the 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 computing functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, etc.
  • the communication device 2000 shown in Figure 20 can be used to implement the steps implemented by the sending end or the receiving end in the aforementioned method embodiments, and to achieve the corresponding technical effects of the sending end or the receiving end.
  • the specific implementation of the communication device shown in Figure 20 can be referred to the description in the aforementioned method embodiments, and will not be repeated here.
  • Figure 21 is a schematic diagram of the communication device 2100 involved in the above embodiments provided by the present application.
  • the communication device 2100 can be a communication device serving as a receiving end (or a transmitting end) as described in the above embodiments.
  • the example shown in Figure 21 illustrates a receiving end implemented through a receiving end (or a component within a receiving end), or a transmitting end implemented through a transmitting end (or a component within a transmitting end).
  • the structure of this communication device can be referenced from the structure shown in Figure 21.
  • the communication device 2100 includes at least one processor 2111 and at least one network interface 2114. Further optionally, the communication device also includes at least one memory 2112, at least one transceiver 2113, and one or more antennas 2115.
  • the processor 2111, memory 2112, transceiver 2113, and network interface 2114 are connected, for example, via a bus. In this embodiment, the connection may include various interfaces, transmission lines, or buses, etc., and this embodiment is not limited thereto.
  • the antenna 2115 is connected to the transceiver 2113.
  • the network interface 2114 enables the communication device to communicate with other communication devices through a communication link.
  • the network interface 2114 may include a network interface between the communication device and core network equipment, such as an S1 interface; the network interface may also include a network interface between the communication device and other communication devices (e.g., other receivers, other transmitters, or core network equipment), such as an X2 or Xn interface.
  • core network equipment such as an S1 interface
  • other communication devices e.g., other receivers, other transmitters, or core network equipment
  • the transceiver unit 1802 can be a communication interface, which can be the network interface 2114 in Figure 21.
  • the network interface 2114 can include an input interface and an output interface.
  • the network interface 2114 can also be a transceiver circuit, which can include an input interface circuit and an output interface circuit.
  • the processor 2111 is primarily used to process communication protocols and communication data, control the entire communication device, execute software programs, and process data from the software programs, for example, to support the actions described in the embodiments of the communication device.
  • the communication device may include a baseband processor and a central processing unit (CPU).
  • the baseband processor is primarily used to process communication protocols and communication data, while the CPU is primarily used to control the entire communication device, execute software programs, and process data from the software programs.
  • the processor 2111 in Figure 21 can integrate the functions of both a baseband processor and a CPU. Those skilled in the art will understand that the baseband processor and CPU can also be independent processors interconnected via technologies such as buses.
  • the communication device may include multiple baseband processors to adapt to different network standards, and multiple CPUs to enhance its processing capabilities.
  • the various components of the communication device can be connected via various buses.
  • the baseband processor can also be described as a baseband processing circuit or a baseband processing chip.
  • the CPU can also be described as a central processing circuit or a central processing chip.
  • the function of processing communication protocols and communication data can be built into the processor or stored in memory as a software program, which is then executed by the processor to implement the baseband processing function.
  • the memory is primarily used to store software programs and data.
  • the memory 2112 can exist independently or be connected to the processor 2111.
  • the memory 2112 can be integrated with the processor 2111, for example, integrated within a single chip.
  • the memory 2112 can store program code that executes the technical solutions of the embodiments of this application, and its execution is controlled by the processor 2111.
  • the various types of computer program code being executed can also be considered as drivers for the processor 2111.
  • Figure 21 shows only one memory and one processor. In actual communication devices, there can be multiple processors and multiple memories. Memory can also be called storage medium or storage device, etc. Memory can be a storage element on the same chip as the processor, i.e., an on-chip storage element, or it can be a separate storage element; the embodiments of this application do not limit this.
  • Transceiver 2113 can be used to support the reception or transmission of radio frequency (RF) signals between a communication device and a terminal.
  • Transceiver 2113 can be connected to antenna 2115.
  • Transceiver 2113 includes a transmitter Tx and a receiver Rx. Specifically, one or more antennas 2115 can receive RF signals.
  • the receiver Rx of transceiver 2113 receives the RF signals from the antennas, converts the RF signals into digital baseband signals or digital intermediate frequency (IF) signals, and provides the digital baseband signals or IF signals to processor 2111 so that processor 2111 can perform further processing on the digital baseband signals or IF signals, such as demodulation and decoding.
  • IF intermediate frequency
  • the transmitter Tx in transceiver 2113 is also used to receive modulated digital baseband signals or IF signals from processor 2111, convert the modulated digital baseband signals or IF signals into RF signals, and transmit the RF signals through one or more antennas 2115.
  • the receiver Rx can selectively perform one or more stages of downmixing and analog-to-digital conversion on the radio frequency signal to obtain a digital baseband signal or a digital intermediate frequency (IF) signal.
  • IF digital intermediate frequency
  • the order of these downmixing and IF conversion processes is adjustable.
  • the transmitter Tx can selectively perform one or more stages of upmixing and digital-to-analog conversion on the modulated digital baseband signal or digital IF signal to obtain a radio frequency signal.
  • the order of these upmixing and IF conversion processes is also adjustable.
  • the digital baseband signal and the digital IF signal can be collectively referred to as digital signals.
  • the transceiver 2113 can also be called a transceiver unit, transceiver, transceiver device, etc.
  • the device in the transceiver unit that performs the receiving function can be regarded as the receiving unit
  • the device in the transceiver unit that performs the transmitting function can be regarded as the transmitting unit. That is, the transceiver unit includes a receiving unit and a transmitting unit.
  • the receiving unit can also be called a receiver, input port, receiving circuit, etc.
  • the transmitting unit can be called a transmitter, transmitter, or transmitting circuit, etc.
  • the communication device 2100 shown in Figure 21 can be used to implement the steps implemented by the receiving end or the sending end in the aforementioned method embodiments, and to achieve the corresponding technical effects of the receiving end or the sending end.
  • the specific implementation of the communication device 2100 shown in Figure 21 can be referred to the description in the aforementioned method embodiments, and will not be repeated here.
  • This application also provides a computer-readable storage medium for storing one or more computer-executable instructions.
  • the processor When the computer-executable instructions are executed by a processor, the processor performs the method described in the possible implementations of the sending end, receiving end, or second communication device in the foregoing embodiments.
  • This application also provides a computer program product (or computer program) that, when executed by the processor, executes the method described above for the possible implementation of the sending or receiving end.
  • This application also provides a chip system including at least one processor for supporting a communication device in implementing the functions involved in the possible implementations of the communication device described above.
  • the chip system further includes an interface circuit that provides program instructions and/or data to the at least one processor.
  • the chip system may further include a memory for storing the program instructions and data necessary for the communication device.
  • the chip system may be composed of chips or may include chips and other discrete devices, wherein the communication device may specifically be a transmitting end or a receiving end in the aforementioned method embodiments.
  • This application also provides a communication system, the network system architecture of which includes the sending end or receiving end in any of the above embodiments.
  • the communication system may also include other communication devices that communicate with the transmitting end or the receiving end.
  • the disclosed systems, apparatuses, and methods can be implemented in other ways.
  • the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods.
  • multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.
  • the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms. Whether a function is implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
  • the units described as separate components may or may not be physically separate.
  • the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
  • the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.
  • the integrated unit can be implemented in hardware or as a software functional unit. If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.
  • This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.
  • the aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

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Abstract

Des modes de réalisation de la présente demande divulguent un procédé de communication et un appareil associé. Le procédé comprend les étapes suivantes : un terminal de transmission transmet des premières informations à un terminal de réception, les premières informations comprenant un ou plusieurs éléments de premières données et un ou plusieurs éléments de premières données d'étiquette d'entraînement, les premières données comprenant des données délivrées en sortie par les couches intermédiaires d'un premier modèle, et les premières données d'étiquette d'entraînement étant utilisées pour mettre à jour le premier modèle ; et le terminal de transmission reçoit des secondes informations transmises par le terminal de réception, les secondes informations indiquant des données de mise à jour de modèle délivrées en sortie en sens inverse par les couches intermédiaires du premier modèle. Au moyen du procédé, le terminal de transmission et le terminal de réception entraînent en coopération un modèle de réseau neuronal, le procédé peut être adapté de manière flexible à diverses scènes et tâches différentes, et la compatibilité est améliorée. De plus, les premières informations transmises par le terminal de transmission au terminal de réception comprennent un ou plusieurs éléments de premières données et un ou plusieurs éléments de premières données d'étiquette d'entraînement, et des données liées à l'entraînement enrichies sont transmises au terminal de réception, de sorte que la généralisation d'un modèle soit améliorée.
PCT/CN2025/093003 2024-05-11 2025-05-07 Procédé de communication et appareil associé Pending WO2025237119A1 (fr)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2022052601A1 (fr) * 2020-09-10 2022-03-17 华为技术有限公司 Procédé d'apprentissage de modèle de réseau neuronal ainsi que procédé et dispositif de traitement d'image
WO2024007264A1 (fr) * 2022-07-07 2024-01-11 华为技术有限公司 Procédé d'apprentissage de modèle et dispositif de communication
CN117669688A (zh) * 2023-01-03 2024-03-08 脸萌有限公司 拆分学习防御中的标签推断
CN117764170A (zh) * 2023-12-22 2024-03-26 中国电信股份有限公司技术创新中心 信息处理方法、芯片、集群、装置、设备及存储介质
CN117829320A (zh) * 2024-03-05 2024-04-05 中国海洋大学 一种基于图神经网络和双向深度知识蒸馏的联邦学习方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2022052601A1 (fr) * 2020-09-10 2022-03-17 华为技术有限公司 Procédé d'apprentissage de modèle de réseau neuronal ainsi que procédé et dispositif de traitement d'image
WO2024007264A1 (fr) * 2022-07-07 2024-01-11 华为技术有限公司 Procédé d'apprentissage de modèle et dispositif de communication
CN117669688A (zh) * 2023-01-03 2024-03-08 脸萌有限公司 拆分学习防御中的标签推断
CN117764170A (zh) * 2023-12-22 2024-03-26 中国电信股份有限公司技术创新中心 信息处理方法、芯片、集群、装置、设备及存储介质
CN117829320A (zh) * 2024-03-05 2024-04-05 中国海洋大学 一种基于图神经网络和双向深度知识蒸馏的联邦学习方法

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