WO2023098860A1 - 通信方法和通信装置 - Google Patents
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- WO2023098860A1 WO2023098860A1 PCT/CN2022/136138 CN2022136138W WO2023098860A1 WO 2023098860 A1 WO2023098860 A1 WO 2023098860A1 CN 2022136138 W CN2022136138 W CN 2022136138W WO 2023098860 A1 WO2023098860 A1 WO 2023098860A1
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Definitions
- the present disclosure relates to the field of communication, and more particularly, to a communication method and a communication device.
- a wireless communication network such as a mobile communication network
- services supported by the network are becoming more and more diverse, and therefore requirements to be met are becoming more and more diverse.
- the network needs to be able to support ultra-high speeds, ultra-low latency, and/or very large connections.
- This feature makes network planning, network configuration and/or resource scheduling more and more complex.
- functions of the network become more and more powerful, such as supporting higher and higher spectrum, supporting high-order multiple input multiple output (MIMO) technology, supporting beamforming, and/or supporting new technologies such as beam management, etc. technology, making network energy saving a hot research topic.
- MIMO multiple input multiple output
- These new requirements, new scenarios, and new features have brought unprecedented challenges to network planning, O&M, and efficient operations.
- artificial intelligence technology can be introduced into the wireless communication network to realize network intelligence. Based on this, how to effectively implement artificial intelligence in the network is a problem worth studying.
- the present disclosure provides a communication method and a communication device, which can obtain an intelligent model meeting communication performance requirements while reducing air interface resource overhead.
- a communication method is provided, and the method may be executed by a terminal or a module (such as a chip) configured (or used) in the terminal.
- the method includes: receiving first information, the first information being used to indicate a training policy of the first intelligent model; performing model training on the first intelligent model according to the training policy.
- the network can inform the terminal of the training strategy of the smart model, and the terminal executes the training of the smart model based on the training strategy provided by the network, which reduces the need for the access network node to send the model parameters of the smart model to the terminal (such as the weight value, activation function, bias, etc.) can make the model obtained by terminal training match the model adopted by the network without affecting the transmission efficiency of service data as much as possible, and meet the requirements of the application Performance requirements in actual communication.
- the model parameters of the smart model such as the weight value, activation function, bias, etc.
- the training strategy includes one or more of the following:
- Model training method loss function information, model initialization method, type of model optimization algorithm, or parameters of the optimization algorithm.
- the above model training method may be, but not limited to, one of supervised learning, unsupervised learning or reinforcement learning.
- the above loss function information may indicate a loss function used to train the first intelligent model, and the loss function may be but not limited to a cross-entropy loss function or a square loss function. Or the loss function can also be a machine learning intelligent model.
- the above model initialization manner may be an initialization manner of the weight of each neuron of the first intelligent model.
- the first information may indicate that the model is initialized in such a way that the initial weight of each neuron is a random value within a preset range.
- the first information includes a start value and an end value of the preset range.
- the first information may indicate that the initial weights of neurons are all 0.
- the first information may indicate that the initial weight of the neuron is generated using a preset function.
- the first information may indicate identification information of one of multiple predefined initialization manners.
- model optimization algorithm may be an adaptive momentum estimation (adaptive momentum estimation, ADAM) algorithm, a stochastic gradient descent algorithm (stochastic gradient descent, SGD) or a batch gradient descent algorithm (batch gradient descent, BGD).
- ADAM adaptive momentum estimation
- SGD stochastic gradient descent
- BGD batch gradient descent
- parameters of the above optimization algorithm may include but not limited to one or more of the following:
- the method further includes: acquiring second information, where the second information is used to indicate the structure of the first smart model.
- the access network node notifies the terminal of the structure of the first intelligent model
- the terminal generates an initial first intelligent model with the structure based on the instruction of the access network node, and trains the first intelligent model based on the indicated training strategy
- the first intelligent model obtained by the terminal training can be matched with the model adopted by the network device to meet the performance requirements of the access network nodes, so that the first intelligent model can be used to improve the performance of wireless communication.
- the second information is specifically used to indicate one or more of the following structural information of the first smart model:
- Network layer structure information information, dimensions of input data or dimensions of output data.
- the access network node can notify the terminal of one or more of the network layer structure information (such as the number of neural network layers, type, etc.), the dimension of input data, and the dimension of output data through the second information.
- the amount of information is far less than the amount of information such as the weight value, activation function, and bias of each neuron in the neural network, and the resource occupancy rate can be reduced to a small extent, so that terminal training can obtain the first intelligent model that meets performance requirements.
- the network layer structure information includes one or more of the following:
- the number of neural network layers contained in the first intelligent model the type of the neural network layer, the use method of the neural network layer, the cascading relationship between the neural network layers, the dimension of the input data of the neural network layer or the neural network Dimensions of the layer's output data.
- the method further includes: receiving third information, where the third information is used to indicate information of a training data set, and the training data set is used to train the first intelligence Model.
- the training data set may include training samples, or training samples and labels.
- the information of the training data set may indicate the type of the training sample, or the type of the training sample and the type of the label.
- the information of the training data set may indicate the usage of the first intelligent model.
- the third information may include a training data set.
- the access network node may also notify the terminal of the training data set used when training the first intelligent model.
- the first intelligent model obtained by the terminal training can be closer to the requirement of the access network node, or in other words, better match the intelligent model of the access network node.
- the method further includes: sending capability information, where the capability information is used to indicate the capability of running the smart model.
- the terminal can use the capability information to inform the access network node terminal device of the capability of running the smart model, so that the access network node can indicate to the terminal the training strategy and/or model structure that meets the terminal capability requirements based on the terminal capability.
- the capability information is specifically used to indicate one or more of the following capabilities: whether to support running the smart model, the type of smart model that supports running, data processing capability or storage ability.
- the method further includes: receiving fourth information, where the fourth information is used to indicate test information, and the test information is used to test the performance of the first smart model.
- the access network node may also send test information to the terminal, so that the terminal can test the first intelligent model based on the test information.
- the test information includes one or more of the following:
- Test data information For evaluation methods or performance evaluation parameters.
- the method further includes: sending fifth information, where the fifth information is used to indicate a test result of the first smart model.
- the terminal After testing the first intelligent model based on the test information, the terminal determines whether the trained first intelligent model meets the performance requirement, and notifies the access network node of the test result through the fifth information.
- the sixth information is sent, the sixth information is used to indicate inference data, the inference data is obtained by reasoning test data of the first intelligent model, and the test information includes The test data.
- the terminal can reason the test data to obtain the reasoning data, and send the reasoning data to the access network node through the sixth information, so that the access network node can determine whether the first intelligent model trained by the terminal meets the performance requirement based on the reasoning data .
- the method further includes: receiving seventh information, where the seventh information is used to indicate the updated training strategy of the first intelligent model, and/or, using Indicates the updated structure of the first smart model.
- the access network node determines that the first intelligent model trained by the terminal does not meet the performance requirements, the access network node notifies the terminal of the updated training strategy and/or the updated model structure through the seventh information, and the terminal can base on The seventh information trains the first intelligent model again.
- the seventh information is specifically used to indicate at least one change amount of the training strategy, and/or at least one change amount of the structure of the first intelligent model.
- the seventh information may specifically indicate at least one variation of the training strategy and/or at least one variation of the model structure, which can reduce information overhead.
- a communication method is provided, and the method may be executed by an access network node or a module (such as a chip) configured (or used) in the access network node.
- the method includes: sending first information, where the first information is used to indicate a training policy of the first intelligent model.
- the training strategy is the same as the training strategy introduced in the first aspect, and for the sake of brevity, details are not repeated here.
- the method further includes: sending second information, where the second information is used to indicate the structure of the first smart model.
- the structural information specifically indicated by the second information is the same as that introduced in the first aspect, and for the sake of brevity, details are not repeated here.
- the method further includes: sending third information, where the third information is used to indicate information used for a training data set, and the training data set is used to train the first An intelligent model.
- the method further includes: receiving capability information, where the capability information is used to indicate the capability of running the smart model.
- the training strategy and/or the structure of the first intelligent model can be determined according to the capability information.
- the capabilities specifically indicated by the capability information are the same as those described in the first aspect, and will not be repeated here for brevity.
- determining the training strategy and/or the structure of the first intelligent model according to the capability information includes: determining the first intelligent model according to the capability information and/or, train the first intelligent model according to the training strategy.
- the method further includes:
- test information is the same as that introduced in the first aspect, and will not be repeated here for brevity.
- the method further includes:
- the fifth information is used to indicate the test result of the first intelligent model.
- the sixth information is received, the sixth information is used to indicate reasoning data, the reasoning data is obtained by reasoning the test data of the first intelligent model, and the test information includes the test data.
- the seventh information is specifically used to indicate at least one parameter change amount of the training strategy, and/or at least one parameter change amount of the structure of the first intelligent model .
- a communication method is provided.
- the method can be executed by a terminal or a module (such as a chip) configured on (or used for) the terminal.
- the method is executed by the terminal as an example for description below.
- the method includes: receiving second information, the second information is used to indicate the structure of the first intelligent model, the second information includes network layer structure information of the first intelligent model, dimensions of input data or dimensions of output data One or more types of structural information; according to the second information, determine the structure of the first intelligent model; perform model training on the first intelligent model.
- the information about the network layer structure is the same as that introduced in the first aspect, and for the sake of brevity, details are not repeated here.
- the method further includes: acquiring first information, the first information being used to indicate the training strategy of the first intelligent model; and, the first intelligent model Performing model training includes: performing model training on the first intelligent model according to the training strategy.
- the training strategy is the same as that introduced in the first aspect, and for the sake of brevity, details are not repeated here.
- the method further includes: receiving third information, where the third information is used to indicate information of a training data set, and the training data set is used to train the first intelligence Model.
- the method further includes: sending capability information, where the capability information is used to indicate the capability of running the smart model.
- the capability information is the same as that introduced in the first aspect, and will not be repeated here for brevity.
- the method further includes: receiving fourth information, where the fourth information is used to indicate test information, and the test information is used to test the performance of the first smart model.
- test information is the same as that introduced in the first aspect, and will not be repeated here for brevity.
- the method further includes: sending fifth information, where the fifth information is used to indicate the test result of the first intelligent model; and/or, sending sixth information , the sixth information is used to indicate reasoning data, the reasoning data is obtained by reasoning the test data of the first intelligent model, and the test information includes the test data.
- the method further includes: receiving seventh information, where the seventh information is used to indicate the updated training strategy of the first intelligent model, and/or, using Indicates the updated structure of the first smart model.
- the seventh information is specifically used to indicate at least one change amount of the training strategy, and/or at least one change amount of the structure of the first intelligent model.
- a communication method is provided, and the method may be executed by an access network node or a module (such as a chip) configured (or used) in the access network node.
- the method includes: sending second information, the second information is used to indicate the structure of the first intelligent model, the second information includes network layer structure information of the first intelligent model, dimensions of input data or dimensions of output data One or more structural information.
- the information about the network layer structure is the same as that introduced in the first aspect, and for the sake of brevity, details are not repeated here.
- the method further includes: sending first information, where the first information is used to indicate a training policy of the first intelligent model.
- the training strategy is the same as that introduced in the first aspect, and for the sake of brevity, details are not repeated here.
- the method further includes: sending third information, the third information is used to indicate the information of the training data set, and the training data set is used to train the first intelligence Model.
- the method further includes: receiving capability information, where the capability information is used to indicate the capability of running the smart model.
- the capability information is the same as that introduced in the first aspect, and will not be repeated here for brevity.
- the method further includes: sending fourth information, where the fourth information is used to indicate test information, and the test information is used to test the performance of the first smart model.
- test information is the same as that introduced in the first aspect, and will not be repeated here for brevity.
- the method further includes: receiving fifth information, where the fifth information is used to indicate the test result of the first intelligent model; and/or, receiving sixth information , the sixth information is used to indicate reasoning data, the reasoning data is obtained by reasoning the test data of the first intelligent model, and the test information includes the test data.
- the method further includes: sending seventh information, where the seventh information is used to indicate the updated training strategy of the first intelligent model, and/or, using Indicates the updated structure of the first smart model.
- the seventh information is specifically used to indicate at least one change amount of the training strategy, and/or at least one change amount of the structure of the first intelligent model.
- a communication device may include a one-to-one corresponding module for performing the method/operation/step/action described in the first aspect.
- the module may be a hardware circuit, or a However, software may also be realized by combining hardware circuits with software.
- the device includes: a transceiver unit, configured to receive first information, the first information being used to indicate a training strategy of the first intelligent model; a processing unit, configured to, according to the training strategy, Do model training.
- the sixth aspect provides a communication device.
- the device may include a one-to-one corresponding module for executing the method/operation/step/action described in the second aspect.
- the module may be a hardware circuit or a However, software may also be realized by combining hardware circuits with software.
- the device includes: a processing unit, configured to determine the training strategy of the first intelligent model; a transceiver unit, configured to send first information, the first information being used to indicate the training strategy of the first intelligent model.
- a seventh aspect provides a communication device.
- the device may include a one-to-one corresponding module for executing the method/operation/step/action described in the third aspect.
- the module may be a hardware circuit or However, software may also be realized by combining hardware circuits with software.
- the device includes: a transceiver unit, configured to receive second information, the second information is used to indicate the structure of the first intelligent model, the second information includes network layer structure information of the first intelligent model, input One or more structural information in the dimensions of the data or the dimensions of the output data; the processing unit is used to determine the structure of the first intelligent model according to the second information; Model for model training.
- a communication device may include a one-to-one corresponding module for executing the method/operation/step/action described in the fourth aspect.
- the module may be a hardware circuit or However, software may also be realized by combining hardware circuits with software.
- the device includes: a processing unit, configured to determine the structure of the first intelligent model; a transceiver unit, configured to send second information, the second information is used to indicate the structure of the first intelligent model, and the second information It includes one or more structural information of the network layer structure information of the first intelligent model, the dimension of input data or the dimension of output data.
- a communication device including a processor.
- the processor may implement the first aspect or the third aspect and the method in any possible implementation manner of the first aspect or the third aspect.
- the communication device further includes a memory, and the processor is coupled to the memory, and can be used to execute instructions in the memory, so as to realize any possibility in the first aspect or the third aspect and the first aspect or the third aspect method in the implementation.
- the communication device further includes a communication interface, and the processor is coupled to the communication interface.
- the communication interface may be a transceiver, a pin, a circuit, a bus, a module or other types of communication interfaces, without limitation.
- the communication device is a terminal.
- the communication interface may be a transceiver, or an input/output interface.
- the communication device is a chip configured in a terminal.
- the communication interface may be an input/output interface.
- the transceiver may be a transceiver circuit.
- the input/output interface may be an input/output circuit.
- a communication device including a processor.
- the processor may implement the method in the second aspect or the fourth aspect and any possible implementation manner of the second aspect or the fourth aspect.
- the communication device further includes a memory, the processor is coupled to the memory, and can be used to execute instructions in the memory, so as to realize the second aspect or the fourth aspect and any possibility in the second aspect or the fourth aspect method in the implementation.
- the communication device further includes a communication interface, and the processor is coupled to the communication interface.
- the communication device is an access network node.
- the communication interface may be a transceiver, or an input/output interface.
- the communication device is a chip configured in an access network node.
- the communication interface may be an input/output interface.
- the transceiver may be a transceiver circuit.
- the input/output interface may be an input/output circuit.
- a processor including: an input circuit, an output circuit, and a processing circuit.
- the processing circuit is configured to receive a signal through the input circuit and transmit a signal through the output circuit, so that the processor executes the method in any possible implementation manner of the first aspect to the fourth aspect and the first aspect to the fourth aspect .
- the above-mentioned processor can be one or more chips
- the input circuit can be an input pin
- the output circuit can be an output pin
- the processing circuit can be a transistor, a gate circuit, a flip-flop and various logic circuits, etc. .
- the input signal received by the input circuit may be received and input by, for example but not limited to, the receiver
- the output signal of the output circuit may be, for example but not limited to, output to the transmitter and transmitted by the transmitter
- the circuit may be the same circuit, which is used as an input circuit and an output circuit respectively at different times.
- the present disclosure does not limit the specific implementation manners of the processor and various circuits.
- a computer program product includes: a computer program (also referred to as code, or an instruction), which, when the computer program is executed, causes the computer to perform the above-mentioned first to fourth aspects. aspect and the method in any possible implementation manner of the first aspect to the fourth aspect.
- a computer-readable storage medium stores a computer program (also referred to as code, or instruction) when it is run on a computer, so that the computer executes the above-mentioned first aspect to the fourth aspect and the method in any possible implementation manner of the first aspect to the fourth aspect.
- a computer program also referred to as code, or instruction
- a communication system including the aforementioned at least one device for implementing the method for a terminal and at least one device for implementing the method for an access network node.
- FIG. 1 is a schematic diagram of a communication system provided by the present disclosure
- FIG. 2A is an example diagram of an application framework of AI in a communication system provided by the present disclosure
- FIGS. 2B to 2E are schematic diagrams of the network architecture provided by the present disclosure.
- Fig. 2F is a schematic diagram of the layer relationship of the neural network provided by the present disclosure.
- Fig. 3 is a schematic diagram of the training process of the intelligent model provided by the present disclosure
- Fig. 4 is a schematic flowchart of the communication method provided by the present disclosure.
- FIG. 5A is a schematic diagram of the input-output relationship of neurons when the dropout method is not used in the present disclosure
- FIG. 5B is a schematic diagram of the input-output relationship of neurons when the dropout method is adopted in the present disclosure
- FIG. 5C is another schematic flowchart of the communication method provided by the present disclosure.
- Fig. 6 is another schematic flowchart of the communication method provided by the present disclosure.
- Fig. 7 is a schematic block diagram of an example of a communication device provided by the present disclosure.
- Fig. 8 is a schematic structural diagram of an example of a terminal device provided by the present disclosure.
- Fig. 9 is a schematic structural diagram of an example of a network device provided by the present disclosure.
- At least one (item) can also be described as one (item) or multiple (items), and multiple (items) can be two (items), three (items), four (items) or more Multiple (items), without limitation.
- “/" can indicate that the associated objects are an "or” relationship, for example, A/B can indicate A or B; "and/or” can be used to describe that there are three relationships between associated objects, for example, A and / Or B, can mean: A alone exists, A and B exist at the same time, and B exists alone, where A and B can be singular or plural.
- words such as “first”, “second”, “A”, or “B” may be used to distinguish technical features with the same or similar functions.
- the words “first”, “second”, “A”, or “B” do not limit the quantity and execution order.
- words such as “first”, “second”, “A”, or “B” are not necessarily different.
- Words such as “exemplary” or “such as” are used to indicate examples, illustrations or illustrations, and any design described as “exemplary” or “such as” should not be construed as being more preferred or better than other design solutions.
- Advantage. The use of words such as “exemplary” or “for example” is intended to present related concepts in a specific manner for easy understanding.
- FIG. 1 is a schematic structural diagram of a communication system 1000 to which the present disclosure can be applied.
- the communication system includes a radio access network (radio access network, RAN) 100 and a core network (core network, CN) 200.
- the communication system 1000 may also include the Internet 300 .
- the radio access network 100 may include at least one access network node (such as 110a and 110b in FIG. 1 ), and may also include at least one terminal (such as 120a-120j in FIG. 1 ).
- the terminal is connected to the access network node through wireless communication.
- the access network nodes are connected to the core network through wireless communication or wired communication.
- the core network device and the access network node may be independent and different physical devices; or may be the same physical device that integrates the functions of the core network device and the access network node; or may be other possible situations, such as a
- the function of the access network node and some functions of the core network equipment can be integrated on the physical device, and another physical device realizes the rest of the functions of the core network equipment.
- the present disclosure does not limit the physical existence forms of core network devices and access network nodes. Terminals may be connected to each other in a wired or wireless manner.
- the access network nodes may be connected to each other in a wired or wireless manner.
- FIG. 1 is only a schematic diagram, and the communication system may also include other network devices, such as wireless relay devices and wireless backhaul devices.
- the access network node may be an access network device, such as a base station (base station), a node B (Node B), an evolved node B (evolved NodeB, eNodeB or eNB), a transmission reception point (transmission reception point, TRP), a The next generation Node B (next generation NodeB, gNB) in the fifth generation (5th generation, 5G) mobile communication system, the access network node in the open radio access network (open radio access network, O-RAN or open RAN), The next-generation base station in the sixth generation (6th generation, 6G) mobile communication system, the base station in the future mobile communication system, or the access node in the wireless fidelity (wireless fidelity, WiFi) system, etc.
- base station base station
- Node B node B
- eNodeB or eNB evolved node B
- TRP transmission reception point
- the access network node may be a module or unit that completes some functions of the base station, for example, it may be a centralized unit (central unit, CU), a distributed unit (distributed unit, DU), a centralized unit control plane (CU control plane, CU-CP) module, or centralized unit user plane (CU user plane, CU-UP) module.
- the access network node may be a macro base station (such as 110a in Figure 1), a micro base station or an indoor station (such as 110b in Figure 1), or a relay node or a donor node.
- the 5G system can also be called a new radio (new radio, NR) system.
- the device for implementing the function of the access network node may be an access network node; it may also be a device capable of supporting the access network node to realize the function, such as a chip system, a hardware circuit, a software module, or a hardware A circuit plus a software module, the device can be installed in the access network node or can be matched with the access network node.
- the system-on-a-chip may be composed of chips, and may also include chips and other discrete devices.
- the technical solution provided by the present disclosure is described below by taking the device for realizing the function of the access network node as the access network node, and optionally taking the access network node as the base station as an example.
- a terminal may also be called terminal equipment, user equipment (user equipment, UE), mobile station, mobile terminal, and so on.
- Terminals can be widely used in various scenarios for communication.
- the scenario includes, but is not limited to, at least one of the following scenarios: enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), large-scale machine type communication ( massive machine-type communications (mMTC), device-to-device (D2D), vehicle to everything (V2X), machine-type communication (MTC), Internet of Things (Internet of Things) , IOT), virtual reality, augmented reality, industrial control, automatic driving, telemedicine, smart grid, smart furniture, smart office, smart wear, smart transportation, or smart city, etc.
- eMBB enhanced mobile broadband
- URLLC ultra-reliable low-latency communication
- mMTC massive machine-type communications
- D2D device-to-device
- V2X vehicle to everything
- MTC Internet of Things
- IOT Internet of Things
- virtual reality augmented reality
- the terminal can be a mobile phone, a tablet computer, a computer with wireless transceiver function, a wearable device, a vehicle, a drone, a helicopter, an airplane, a ship, a robot, a mechanical arm, or a smart home device, etc.
- the present disclosure does not limit the specific technology and specific equipment form adopted by the terminal.
- the device for realizing the function of the terminal may be a terminal; it may also be a device capable of supporting the terminal to realize the function, such as a chip system, a hardware circuit, a software module, or a hardware circuit plus a software module. Installs in the terminal or can be used with the terminal.
- the technical solution provided by the present disclosure is described below by taking the terminal as an example of the device for realizing the function of the terminal, and optionally taking the UE as an example.
- Base stations and/or terminals may be fixed or mobile. Base stations and/or terminals can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; or can be deployed on water; or can be deployed on aircraft, balloons, and satellites in the air.
- the present disclosure does not limit the environment/scene where the base station and the terminal are located.
- the base station and the terminal can be deployed in the same or different environments/scenarios, for example, the base station and the terminal are deployed on land at the same time; or, the base station is deployed on land, and the terminal is deployed on water, etc., no more examples are given here.
- base station and terminal may be relative.
- the helicopter or UAV 120i in FIG. 1 can be configured as a mobile base station.
- the terminal 120i is a base station; but for the base station 110a, 120i It may be a terminal, that is, communication between 110a and 120i may be performed through a wireless air interface protocol.
- 110a and 120i communicate through an interface protocol between base stations.
- relative to 110a, 120i is also a base station. Therefore, both base stations and terminals can be collectively referred to as communication devices (or communication devices), 110a and 110b in FIG. 1 can be referred to as communication devices with base station functions, and 120a-120j in FIG. 1 can be referred to as communication devices with terminal functions. device.
- the protocol layer structure between the access network node and the terminal may include an artificial intelligence (AI) layer, which is used to transmit data related to the AI function.
- AI artificial intelligence
- an independent network element such as an AI entity, AI network element, AI node, or AI device, etc.
- the AI network element can be directly connected to the base station, or can be indirectly connected to the base station through a third-party network element.
- the third-party network element may be a core network element such as an access and mobility management function (access and mobility management function, AMF) network element or a user plane function (user plane function, UPF) network element.
- AI entities or may be called AI modules or other names
- the network element configured with the AI entity may be a base station, a core network device, or a network management (operation, administration and maintenance, OAM), etc.
- OAM is used to operate, manage and/or maintain core network equipment (network management of core network equipment), and/or is used to operate, manage and/or maintain access network nodes (network management of access network nodes).
- an AI entity may be integrated in a terminal or a terminal chip.
- the AI entity may also be called by other names, such as AI module or AI unit, etc., which are mainly used to implement AI functions (or called AI-related operations), and this disclosure does not limit its specific name.
- an AI model is a specific method for realizing an AI function.
- the AI model represents the mapping relationship between the input and output of the model.
- the AI model can be a neural network, linear regression model, decision tree model, clustering SVD model or other machine learning models. Among them, the AI model may be referred to as an intelligent model, a model or other names for short, without limitation.
- AI-related operations may include at least one of the following: data collection, model training, model information release, model testing (or called model verification), model inference (or called model reasoning, reasoning, or prediction, etc.), or reasoning Result release etc.
- FIG. 2A is an example diagram of the first application framework of AI in a communication system.
- the data source is used to store training data and inference data.
- the model training host (model training host) obtains the AI model by analyzing or training the training data provided by the data source, and deploys the AI model in the model inference host (model inference host).
- the AI model represents the mapping relationship between the input and output of the model. Learning the AI model through the model training node is equivalent to using the training data to learn the mapping relationship between the input and output of the model.
- the model inference node uses the AI model to perform inference based on the inference data provided by the data source, and obtains the inference result.
- the model inference node inputs the inference data into the AI model, and obtains an output through the AI model, and the output is the inference result.
- the inference result may indicate: configuration parameters used (executed) by the execution object, and/or operations performed by the execution object.
- the reasoning result can be uniformly planned by the execution (actor) entity, and sent to one or more execution objects (for example, network elements) for execution.
- the model inference node can feed back its inference results to the model training node. This process can be called model feedback.
- the parameters fed back are used for the model training node to update the AI model, and the updated AI model is deployed on the model inference node. in the node.
- the execution object can feed back the collected network parameters to the data source. This process can be called performance feedback, and the fed-back parameters can be used as training data or inference data.
- the application framework shown in FIG. 2A can be deployed on the network element shown in FIG. 1 .
- the application framework in FIG. 2A may be deployed on at least one of the terminal device, access network device, core network device, or independently deployed AI network element (not shown) in FIG. 1 .
- the AI network element (which can be regarded as a model training node) can analyze or train the training data (training data) provided by the terminal device and/or the access network device to obtain a model.
- At least one of the terminal device, the access network device, or the core network device (which can be regarded as a model reasoning node) can use the model and reasoning data to perform reasoning and obtain the output of the model.
- the reasoning data may be provided by the terminal device and/or the access network device.
- the input of the model includes inference data
- the output of the model is the inference result corresponding to the model.
- At least one of the terminal device, the access network device, or the core network device (which may be regarded as an execution object) may perform a corresponding operation according to the inference data and/or the inference result.
- the model inference node and the execution object may be the same or different, without limitation.
- the network architecture to which the communication solution provided by the present disclosure can be applied is introduced below with reference to FIGS. 2B to 2E .
- the access network device includes a near real-time access network intelligent controller (RAN intelligent controller, RIC) module for model learning and reasoning.
- RAN intelligent controller RIC
- near real-time RIC can be used to train an AI model and use that AI model for inference.
- the near real-time RIC can obtain network-side and/or terminal-side information from at least one of CU, DU, or RU, and the information can be used as training data or inference data.
- the near real-time RIC may submit the reasoning result to at least one of CU, DU or RU.
- the inference results can be exchanged between the CU and the DU.
- the reasoning results can be exchanged between the DU and the RU, for example, the near real-time RIC submits the reasoning result to the DU, and the DU forwards it to the RU.
- a non-real-time RIC is included outside the access network (optionally, the non-real-time RIC can be located in the OAM or in the core network device) for model learning and reasoning.
- non-real-time RIC is used to train an AI model and use that model for inference.
- the non-real-time RIC can obtain network-side and/or terminal-side information from at least one of CU, DU, or RU, which can be used as training data or inference data, and the inference results can be submitted to CU, DU, or RU At least one of the .
- the inference results can be exchanged between the CU and the DU.
- the reasoning results can be exchanged between the DU and the RU, for example, the non-real-time RIC submits the reasoning result to the DU, and the DU forwards it to the RU.
- the access network equipment includes a near-real-time RIC, and the access network equipment includes a non-real-time RIC (optionally, the non-real-time RIC can be located in the OAM or the core network equipment middle).
- non-real-time RIC can be used for model learning and reasoning; and/or, like the first possible implementation above, near real-time RIC can be used for model learning and reasoning; and/or, Near-real-time RIC can obtain AI model information from non-real-time RIC, and obtain network-side and/or terminal-side information from at least one of CU, DU or RU, and use this information and the AI model information to obtain inference results, optional Yes, the near-real-time RIC can submit the inference results to at least one of CU, DU, or RU.
- the inference results can be exchanged between CU and DU.
- the inference results can be exchanged between DU and RU.
- the near real-time RIC submits the inference result to the DU, and the DU forwards it to the RU.
- near real-time RIC is used to train model A and use model A for inference.
- non-real-time RIC is used to train Model B and utilize Model B for inference.
- the non-real-time RIC is used to train the model C, and the model C is submitted to the near-real-time RIC, and the near-real-time RIC uses the model C for inference.
- FIG. 2C is an example diagram of a network architecture to which the method provided by the present disclosure can be applied. Compared with FIG. 2B , in FIG. 2B CU is separated into CU-CP and CU-UP.
- FIG. 2D is an example diagram of a network architecture to which the method provided by the present disclosure can be applied.
- the access network device includes one or more AI entities, and the functions of the AI entities are similar to the near real-time RIC described above.
- the OAM includes one or more AI entities, and the functions of the AI entities are similar to the non-real-time RIC described above.
- the core network device includes one or more AI entities, and the functions of the AI entities are similar to the above-mentioned non-real-time RIC.
- both the OAM and the core network equipment include AI entities, the models trained by their respective AI entities are different, and/or the models used for reasoning are different.
- the different models include at least one of the following differences: the structural parameters of the model (such as the number of layers of the model, the width of the model, the connection relationship between layers, the weight of neurons, the activation function of neurons, or the at least one of the bias), the input parameters of the model (such as the type of the input parameter and/or the dimension of the input parameter), or the output parameters of the model (such as the type of the output parameter and/or the dimension of the output parameter).
- the structural parameters of the model such as the number of layers of the model, the width of the model, the connection relationship between layers, the weight of neurons, the activation function of neurons, or the at least one of the bias
- the input parameters of the model such as the type of the input parameter and/or the dimension of the input parameter
- the output parameters of the model such as the type of the output parameter and/or the dimension of the output parameter.
- FIG. 2E is an example diagram of a network architecture to which the method provided by the present disclosure can be applied.
- the access network devices in FIG. 2E are separated into CU and DU.
- the CU may include an AI entity, and the function of the AI entity is similar to the above-mentioned near real-time RIC.
- the DU may include an AI entity, and the function of the AI entity is similar to the above-mentioned near real-time RIC.
- both the CU and the DU include AI entities, the models trained by their respective AI entities are different, and/or the models used for reasoning are different.
- the CU in FIG. 2E may be further split into CU-CP and CU-UP.
- one or more AI models may be deployed in the CU-CP.
- one or more AI models can be deployed in CU-UP.
- the OAM of the access network device and the OAM of the core network device may be deployed separately and independently.
- the core network equipment may include one or more modules, for example including
- a model can infer one parameter, or infer multiple parameters.
- the learning process of different models can be deployed in different devices or nodes, and can also be deployed in the same device or node.
- the inference process of different models can be deployed in different devices or nodes, or can be deployed in the same device or node.
- Artificial intelligence can make machines have human intelligence, for example, it can make machines use computer software and hardware to simulate certain intelligent behaviors of humans.
- machine learning methods or other methods may be adopted without limitation.
- Neural network is a specific implementation of machine learning. According to the general approximation theorem, the neural network can theoretically approximate any continuous function, so that the neural network has the ability to learn any mapping. Therefore, neural networks can accurately and abstractly model complex high-dimensional problems. That is, intelligent models can be realized through neural networks.
- the idea of a neural network is derived from the neuronal structure of brain tissue.
- Each neuron performs a weighted sum operation on its input values, and passes the weighted sum result through an activation function to generate an output.
- the weighted And the bias is b.
- the form of the activation function can be varied.
- the output of the neuron is:
- the element x i of the input x of the neuron, the element w i of the weight value w, or the bias b can be various possible values such as decimals, integers (including 0, positive integers or negative integers, etc.), or complex numbers.
- the activation functions of different neurons in a neural network can be the same or different.
- a neural network generally includes a multi-layer structure, and each layer may include one or more neurons.
- Increasing the depth and/or width of a neural network can improve the expressive power of the neural network, providing more powerful information extraction and abstract modeling capabilities for complex systems.
- the depth of the neural network refers to the number of layers included in the neural network, and the number of neurons included in each layer can be called the width of the layer.
- FIG. 2F it is a schematic diagram of the layer relationship of the neural network.
- a neural network includes an input layer and an output layer.
- the input layer of the neural network processes the input received by neurons, and then passes the result to the output layer, and the output layer obtains the output result of the neural network.
- a neural network in another implementation, includes an input layer, a hidden layer, and an output layer.
- the input layer of the neural network processes the input received by neurons, and then passes the result to the hidden layer in the middle, and the hidden layer then passes the calculation result to the output layer or the adjacent hidden layer, and finally the output layer obtains the result of the neural network. Output the result.
- a neural network may include one or more sequentially connected hidden layers without limitation.
- a loss function can be defined.
- the loss function describes the gap or difference between the output value of the AI model and the ideal target value, and the present disclosure does not limit the specific form of the loss function.
- the training process of the AI model is the process of adjusting some or all parameters of the AI model so that the value of the loss function is less than the threshold value or meets the target requirements. If the AI model is a neural network, one or more of the following parameters can be adjusted during training: the number of layers of the neural network, the width of the neural network, the connection relationship between layers, the weight value of neurons, the activation function of neurons, or A bias in the activation function such that the difference between the output of the neural network and the ideal target value is as small as possible.
- Fig. 3 is an example diagram of the training process of the intelligent model.
- the intelligent model is taken as an example of a neural network model, and the neural network f ⁇ ( ⁇ ) can include a 4-layer convolutional layer network and a 4-layer fully connected layer network as an example for introduction.
- the AI entity can obtain training data from the basic data set (such as a collection of channel data).
- the training data can include training samples and labels.
- the sample x is used as input and processed by the neural network f ⁇ ( ⁇ ) to output the inference result f ⁇ (x) , the loss function calculates the error between the inference result and the label, and the AI entity can use the backpropagation optimization algorithm (which can be called the model optimization algorithm) to optimize the network parameter ⁇ based on the error obtained by the loss function.
- the backpropagation optimization algorithm which can be called the model optimization algorithm
- a large amount of training data is used to train the neural network, so that the difference between the output of the neural network and the label is less than a preset value, and then the training of the neural network is completed.
- the training process shown in Figure 3 adopts a supervised learning training method, that is, based on samples and labels, the loss function is used to realize the training of the model.
- the training process of the intelligent model can also use unsupervised learning, using the algorithm to learn the internal mode of the sample, and realize the training of the intelligent model based on the sample.
- the training process of the intelligent model can also use reinforcement learning to obtain the excitation signal of environmental feedback through interaction with the environment, so as to learn the strategy to solve the problem and realize the optimization of the model.
- the disclosure does not limit the training method of the model and the type of the model.
- the trained AI model can perform reasoning tasks. After the actual data is input into the AI model for processing, the corresponding inference results are obtained. Optionally, an AI model can infer one parameter, or infer multiple parameters.
- the application of artificial intelligence in wireless communication systems can significantly improve the performance of communication systems.
- the network side and the terminal need to use matching artificial intelligence models to improve wireless communication performance.
- the terminal can compress and encode the uplink information by using a compression encoder model and send it to the network side, and the network side can decode the uplink information sent by the terminal by using a matching decoder model.
- the application of artificial intelligence in the field of wireless communication often involves complex nonlinear function fitting.
- the scale of the intelligent model is often large, for example, the number of model layers is large, and the number of model parameters is large.
- a convolutional neural network (CNN) for an encoder model implementing CSI feedback compression might include 15 neural network layers.
- the intelligent model used by the terminal is usually specified by the network.
- the network side may select a model from multiple predefined models, and notify the terminal of the identifier of the model, so that the terminal device can determine the adopted model based on the identifier.
- the network side can also deliver the intelligent model to the terminal, but the parameters of the intelligent model are huge and need to occupy a large amount of air interface resources, which will affect the transmission efficiency of service data.
- this disclosure proposes that the network can inform the terminal of the training strategy of the intelligent model, and the terminal executes the training of the intelligent model based on the training strategy provided by the network, so that the model obtained by the terminal training can match the model adopted by the network and achieve the expected performance need.
- the communication method provided by the present disclosure will be described below with reference to the accompanying drawings. It should be noted that this article takes the interaction between a terminal and an access network node as an example, but this application is not limited thereto.
- the communication method provided in this disclosure can be used between any two nodes, and one node communicates with another node Obtain a training policy and/or model structure of the smart model, and perform model training based on the training policy and/or model structure.
- one or more modules of the access network node (such as RU, DU, CU, CU-CP, CU-UP or near real-time RIC) can realize the corresponding method of the access network node or operate.
- Fig. 4 is a schematic flowchart of a communication method 400 provided in the present disclosure.
- the access network node sends first information to the terminal, where the first information is used to indicate a training policy of the first intelligent model.
- the terminal receives the first information from the access network node.
- the terminal determines a training strategy for the first smart model according to the first information.
- the training strategy includes one or more of the following:
- Model training method loss function information, model initialization method, type of model optimization algorithm, or parameters of the optimization algorithm.
- the first information may indicate a training manner for the terminal to train the first smart model, and the terminal may use the model training manner indicated by the first information to train the first smart model.
- the terminal may use the model training manner indicated by the first information to train the first smart model.
- the trained first intelligent model match the intelligent model on the network side. Achieve the effect of improving wireless communication performance.
- the first information may instruct the terminal to train the first intelligent model in one of supervised learning, unsupervised learning, or reinforcement learning. But the present disclosure is not limited thereto.
- the first information may include loss function information. After receiving the first information, the terminal may obtain a loss function for training the first intelligent model based on the loss function information.
- the access network node may notify the terminal of the loss function adopted by the loss function information.
- the loss function information may directly indicate the loss function, or the loss function information may indicate identification information of a loss function among a plurality of predefined loss functions.
- the terminal may train the first intelligent model by means of supervised learning, for example, it may be a pre-defined training method using the supervised learning, or the access network node indicates that the model training method is supervised learning through the first information. Way.
- the training goal of the supervised learning model training method is to minimize the value of the loss function.
- the model training can be completed when the value of the loss function obtained through training is less than or equal to the first threshold.
- the first threshold may be predefined or indicated by the loss function information.
- the loss function can be the following cross-entropy loss function:
- log(A) means to calculate the logarithm of A
- ⁇ n B n means to sum B n based on the value range of n.
- the loss function can be a squared loss function as follows:
- x n is the sample
- y n is the label of the nth sample
- f ⁇ (x n ) is the output result of the model for the input data x n .
- the terminal may train the first smart model in an unsupervised learning manner, and for unsupervised learning, the loss function may be a function used to evaluate model performance.
- the training goal of the unsupervised learning model training method is to maximize the value of the loss function.
- the model training can be completed when the value of the loss function obtained through training is greater than or equal to the second threshold.
- the second threshold may be predefined or indicated by the loss function information.
- the first intelligent model is used to infer the transmit power based on the channel response, then the input of the first intelligent model is the channel response h n , and the reasoning result of the first intelligent model is the transmit power P n , then the loss function can be calculated data throughput
- the function, such as the loss function can be as follows:
- log(A) means to calculate the logarithm of A
- N 0 is the noise power
- the loss function may be the above mathematical expression, and optionally, the loss function may also be a machine learning intelligent model.
- the complexity and parameter quantity of the loss function model are much smaller than the complexity and parameter quantity of the first intelligent model, and the loss function information may include structural information and/or parameter information of the loss function, so that the terminal can obtain the first information after receiving the first information
- a loss function model is used to train the first intelligent model based on the loss function, which can improve the effect of terminal model training.
- the loss function may be a nonlinear performance measurement model such as system throughput, signal bit error rate, and signal-to-interference-noise ratio.
- the first information may indicate a model initialization manner, and the terminal may initialize the first smart model based on the model initialization manner indicated by the first information.
- the first intelligent model may be a neural network model
- the first information may indicate an initialization manner of neuron weights in the first intelligent model.
- the access network node may instruct the terminal that the weight of each neuron is randomly selected, or that the weight values of the neurons are all 0, or that the weight values of the neurons are generated by a preset function, and the like.
- the access network node may use the first information to instruct the terminal to randomly select a value within the interval [z min , z max ] as the weight of the neuron, for example, the first information may indicate z min and z max .
- the terminal selects a value for each neuron within the interval [z min , z max ] as the initial weight value of the neuron.
- the first information indicates the identification information of one of the predefined initialization methods
- the first information includes a 2-bit indication field, which is used to indicate the initialization method of the neuron weight, and the 2 bits indicate "00" Indicates that the initial value of the weight of the neuron is randomly selected.
- the terminal can select a weight value for each neuron within a preset value range, or the first information indicates this method.
- the terminal is the weight value of the neuron within the value interval indicated by the first information;
- the 2-bit indication "01” means that the initial value of the neuron is set to 0;
- the 2-bit indication "10” Indicates that the preset function is used to generate a sequence of initial values, and each value in the sequence is used as the initial value of a neuron.
- the training device can use the model optimization algorithm based on the output value of the loss function to obtain the optimized parameters of the intelligent model, and after updating the intelligent model based on the optimized parameters, use the intelligent model after the updated parameters to perform the following steps: A model training.
- the access network node may notify the terminal of the optimization algorithm of the first intelligent model through the first information.
- the first information may indicate one type of optimization algorithm among predefined types of model optimization algorithms, and the terminal may use the model optimization algorithm indicated by the first information to perform model optimization when training the first intelligent model.
- the model optimization algorithm may include but not limited to one or more of the following algorithms:
- ADAM Adaptive momentum estimation
- SGD stochastic gradient descent
- BGD batch gradient descent
- the communication protocol may predefine multiple types of model optimization algorithms, and the parameters of each type of model optimization algorithm may also be predefined, and the terminal may, based on the identification information of the type of model optimization algorithm indicated by the first information, The type of the optimization algorithm and the predefined parameters of the optimization algorithm are determined.
- the terminal uses the model optimization algorithm to update the model weights.
- the parameters of the optimization algorithm may also be indicated by the first information, and reference may be made to the description below.
- the first information may indicate parameters of the model optimization algorithm, where the type of the model optimization algorithm may be indicated by the access network node through the first information, or the type of the model optimization algorithm may be preconfigured.
- the parameters of the model optimization algorithm may include but not limited to one or more of the following parameters:
- the learning rate is a parameter that characterizes the step size of gradient descent in model training.
- the learning rate may be a fixed value, and the access network node may notify the terminal of the value of the learning rate through the first information.
- the learning rate can be set to 0.001.
- the learning rate can also be set to a value that gradually decreases with the iterative steps, and the first information can indicate the learning rate and the value that the learning rate decreases for each iteration.
- the first information can indicate that the initial value is 0.1, and each iteration decreases Small 0.01.
- the number of iterations refers to the number of times the training data set is traversed.
- the amount of batch processed data refers to the amount of training data selected in the training data set for each model training and used for gradient descent update.
- the training strategy of the first intelligent model indicated by the first information mentioned above may include but not limited to one or more strategies in (1) to (5) above. So that the terminal device can train the first intelligent model based on the training strategy, and obtain an intelligent model matching the network.
- the terminal can use but not limited to the following methods to determine the training Strategy.
- the terminal may be pre-configured with one or more of the above training strategies. If the access network node does not indicate one or more of the strategies, the terminal performs model training based on the pre-configured training strategy. train.
- the pre-configured training strategy may be defined by the protocol, or the pre-configured training strategy may be configured by the manufacturer based on terminal production and implementation.
- the terminal may determine one or more training strategies not indicated by the access network node based on the one or more training strategies indicated by the access network node. That is to say, multiple training strategies may have an association relationship, and the access network node implicitly indicates other training strategies related to the training strategy by indicating a training strategy.
- the association relationship may be stipulated in the agreement, or the access network node notifies the terminal in advance, and is not limited.
- the access network node may use the first information to indicate the model training mode without indicating the loss function information, and the terminal determines the loss function based on the model training mode indicated by the access network node. If the first information indicates that the model training method is a supervised learning method, the terminal may determine that the loss function is a cross-entropy loss function, or the terminal may determine that the loss function is a square loss function. But the present application is not limited thereto.
- the access network node may also send second information to the terminal, where the second information is used to indicate the structure of the first intelligent model.
- the access network node In addition to notifying the terminal of the training policy of the first intelligent model, the access network node also notifies the terminal of the structure of the first intelligent model through the second information. After receiving the second information, the terminal generates an initial first intelligent model with this structure (or called the first intelligent model before training) based on the second information, and adopts the training strategy indicated by the first information to the first intelligent model. The intelligent model is trained, so that the trained first intelligent model can match the model adopted by the access network node.
- the access network node may indicate the identification information of a structure among the structures of multiple predefined intelligent models, and notify the terminal to adopt the intelligent model of the structure.
- the access network node may indicate the structural parameters of the first intelligent model.
- the second information may specifically be used to indicate one or more of the following structural information of the first smart model:
- Network layer structure information information, dimensions of input data or dimensions of output data.
- the second information may indicate the dimension of the input data of the first intelligent model, that is, the dimension of a training sample.
- the first intelligent model is a neural network model, including an input layer and an output layer
- the dimension of the input data is the dimension of the input data of the input layer of the first intelligent model.
- the second information may indicate the dimension of the output data of the first intelligent model, that is, the dimension of the inference data output by the first intelligent model. If the first intelligent model is a neural network model, the dimension of the output data is the dimension of the output data of the output layer of the first intelligent model.
- the second information may include the network layer structure information of the first intelligent model, and the network layer structure information may include but not limited to one or more of the following:
- the number of neural network layers contained in the first intelligent model the type of neural network layers, the use of neural network layers, the cascading relationship between neural network layers, the dimension of input data of neural network layers or the output of neural network layers Dimensions of the data.
- the access network node may indicate the number of neural network layers included in the first intelligent model, and may indicate the type of each neural network layer, such as CNN, FNN, dimension transformation neural network layer, and the like.
- CNN may be implemented by a filter.
- the access network node may also notify the terminal of the type of filter (Filter) implementing the CNN through the network layer structure information.
- the type of Filter can be the dimension of the filter.
- the network layer structure information may indicate that the dimension of the filter is 3 ⁇ 3, 5 ⁇ 5 or 3 ⁇ 3 ⁇ 3, etc.
- the dimension transformation neural network layer is used for the transformation of the data dimension.
- the dimension transformation neural network layer can be located between two neural network layers, and the dimension transformation neural network layer is used to transform the input data (ie, the output data of the previous neural network)
- the dimension of the input data of a neural network layer is obtained after the dimension transformation, and the data after the dimension transformation is output to the next neural network layer.
- the dimension of the output data of the neural network layer A is 3 ⁇ 3 ⁇ 10
- the dimension of the input data of the neural network layer B is required to be 30 ⁇ 3.
- a dimension transformation neural network can be set between the two neural networks to convert the 3 ⁇ 3 ⁇ 10 data output by the neural network layer A into 30 ⁇ 3 data, and output the data to the neural network layer B.
- the data can be processed by the neural network layer B after dimension transformation.
- the access network node may also notify the terminal of the use mode of the neural network layer through the second information, for example, may notify the terminal of whether one or more neural network layers in the neural network use a temporary dropout mode.
- the dropout method means that during model training, one or more neurons in the neural network layer can be randomly selected not to participate in this training, and the one or more neurons can be restored to participate in model training in the next training.
- the input-output relationship of neurons in the neural network layer may be as shown in FIG. 5A .
- the dropout method is adopted, the input-output relationship of the neurons in the neural network layer may be as shown in FIG. 5B .
- the terminal can randomly select multiple neurons in the CNN neural network layer not to participate in model training, the output of the neurons is set to 0, and the neurons not participating in model training will not be finer in this training. , continue to use this weight in the next participating model training.
- the access network node may also notify the terminal of the dimension of the input data and/or the dimension of the output data of the neural network layer in the first intelligent model. And/or, the access network node may also notify the terminal of the cascading relationship between the neural network layers, for example, the cascading relationship between the neural network layers may be an arrangement order of the neural network layers in the first smart model.
- Table 1 shows an example of a neural network structure, and the access network node may notify the terminal of the neural network structure shown in Table 1, but the disclosure is not limited thereto.
- the second information indicates that the number of neural network layers of the first intelligent model is 6, and notify the type of each neural network layer, such as the type of neural network layer includes input layer, CNN, shape transformation, FNN and output layer
- the second information may also indicate the cascading relationship between the neural network layers.
- the second information may indicate that the arrangement sequence of the six neural network layers may be input layer, CNN, CNN, shape transformation, FNN, and output layer.
- the second information may also indicate the input dimension and output dimension of each neural network layer as shown in Table 2, wherein NT is the number of antennas, and N sc is the number of subcarriers.
- the second information may also notify the terminal to implement a 3 ⁇ 3 filter in the second and third CNN layers, and the 2nd, 3rd, 4th, and 5th neural network layers may use dropout.
- the present disclosure is not limited thereto, and the structure of the first intelligent model may be partly predefined and indicated by the access network node through the second information.
- the access network node may notify the terminal of some or all of the structural parameters shown in Table 1. Wherein, N/A shown in Table 1 indicates that this row is not applicable (or not configured) for this parameter.
- the terminal may generate an initial first smart model based on the structure of the first smart model shown in Table 1 indicated by the second information.
- the terminal can first determine that the first intelligent model includes 6 neural network layers based on the second information, and indicate the type, input dimension, and output dimension of each of the 6 neural network layers based on the second information, and generate a satisfying type and Dimensionally required 6 neural network layers, namely input layer, CNN layer, CNN layer, shape transformation layer, FNN layer and output layer.
- the terminal implements two CNN layers therein through a 3 ⁇ 3 filter based on the indication of the second information.
- the terminal determines the arrangement order of the 6 neural network layers based on the cascading relationship indicated by the second information, and obtains the initial first intelligent model.
- the 2, 3, 4, and 5 neural network layers are used in the way of dropout, and the neurons in the network layer are randomly selected in each training to not participate in this training.
- the access network node sends third information to the terminal, where the third information is used to indicate information of a training data set, and the training data set is used to train the first intelligent model.
- the third information may indicate the identification information of the preset training data set, so that the terminal uses the training data set corresponding to the identification information to train the first smart model after receiving the third information.
- the protocol can predefine multiple training data sets, and each training data set corresponds to a piece of identification information, and the access network node can indicate the identification information of one training data set among the multiple training data sets through the third information, and the terminal according to The identification information determines that the training data set corresponding to the identification information is used to train the first intelligent model.
- the information of the training data set may indicate the type of the training sample, or the type of the training sample and the type of the label.
- the training method of the first intelligent model is an unsupervised learning method
- the third information may indicate the type of training samples, for example, the first intelligent model may infer the transmission power based on the input channel data. Then the third information may indicate that the type of the training sample is channel data, and the terminal may train the first intelligent model according to the channel data.
- the channel data may be obtained by the terminal through measurement based on a reference signal sent by the access network node, or may be pre-configured on the terminal. But the present application is not limited thereto.
- the training method of the first intelligent model is an unsupervised learning method
- the third information may indicate the type of the training sample and the type of the label
- the terminal may determine the training data set based on the type of the training sample and the type of the label.
- the data contained in the data set may be data collected during communication between the terminal and the access network node, or may be data of a corresponding type preconfigured on the terminal.
- the first intelligent model implements the channel coding function of the data, that is, infers and outputs coded data based on the input data before coding
- the third information can indicate that the type of training samples is data before channel coding, and the type of label is after channel coding The data.
- the information of the training data set may also indicate the use of the first intelligent model, for example, the information of the training data set indicates that the first intelligent model is used for compression coding, decoding or transmission power.
- the terminal determines the corresponding training data set according to the purpose of the first intelligent model.
- the third information may indicate that the purpose of the first intelligent model is compression coding, and the training method of the first intelligent model is supervised learning, then the terminal may determine the method for training the first intelligent model based on the purpose of the first intelligent model.
- the type of the training sample is information data before compression encoding, and the label is compressed data after compression encoding, so as to determine the training data set of the first intelligent model including the training sample and the label.
- the data included in the training data set may be data collected during communication between the terminal and the access network node, or may be data of a corresponding type preconfigured on the terminal. But the present application is not limited thereto.
- the third information may include a training data set
- the training data set may include training samples, or training samples and labels, that is to say, the terminal may obtain the training data set from the network.
- the training method of the first intelligent model is a supervised learning method
- the training data set in the third information includes training samples and labels.
- the training method of the first intelligent model is an unsupervised learning method, and the training data set in the third information includes training samples but does not include labels.
- the terminal can train the first intelligent model while downloading the training data in the training data set with a small resource bandwidth, which can reduce resource occupation and avoid affecting the transmission rate of service data.
- the access network node may determine the training strategy and model of the first intelligent model based on the capability information of the terminal, and/or, based on the environmental data (such as channel data, etc.) between the access network node and the terminal. One or more items in the structure or training data set.
- the access network node may acquire the training policy and/or model structure of the first smart model from a third-party node in the network, and forward it to the terminal. That is to say, the above information (including but not limited to the first information, second information, and third information) sent by the access network node to the terminal may be generated by the access network node, or may be obtained by the access network node from a third party. Obtained by the node and forwarded to the terminal.
- the third-party node may be a node in the network that has an AI function or is configured with an AI entity.
- the third-party node may be a non-real-time RIC, and the non-real-time RIC may be included in the OAM.
- the third-party node may actually be an OAM.
- the access network node receives capability information from the terminal, where the capability information is used to indicate the capability of the terminal to run the smart model.
- the capability information is specifically used to indicate one or more of the following capabilities of the terminal:
- the capability information may indicate whether the terminal supports running an intelligent model.
- the access network node After the access network node receives the capability information and determines that the terminal supports the intelligent model based on the capability information, the access network node sends the first information to the terminal, In order to notify the terminal of the strategy for training the first intelligent model.
- the access network node needs to notify the terminal of the structure of the first intelligent model through the second information, and the capability information may indicate the operator library information of the machine learning model supported by the terminal.
- the operator library information may indicate a set of basic intelligent model computing units that the terminal can support, or the operator library information may indicate basic intelligent model computing units not supported by the terminal.
- the access network node obtains the model operator database information supported by the terminal by using the capability information, so as to determine the structure of the first intelligent model.
- the intelligent model operation unit may include but not limited to one or more of the following:
- 1D CNN unit (conv1d), 2D CNN unit (conv2d), 3D CNN unit (conv3d), pooling layer, pooling operation, or sigmoid activation function.
- the capability information indicates the data processing capability of the terminal.
- the capability information may indicate the type of processor, the operation speed of the processor, the amount of data that the processor can process, and the like.
- the type of the processor may include a type of a graphics processing unit (graphics processing unit, GPU) and/or a type of a central processing unit (central processing unit, CPU).
- the access network node may determine one or more of the training strategy, model structure, or training data set of the first intelligent model based on the data processing capability of the terminal.
- the access network node may train to obtain terminal capabilities and/or channel conditions based on the capability information of the terminal, and/or, based on environmental data (such as channel data, etc.) between the access network node and the terminal. smart model.
- the access network node notifies the terminal of the training strategy for training the smart model, so that the terminal adopts the training strategy to train the first smart model, so that the first smart model trained by the terminal is as close as possible to the smart model trained by the access network node.
- the first intelligent model applied by the terminal and the access network node in the communication process can match the intelligent model used by the access network node, so as to achieve the effect of improving communication performance.
- the first intelligent model is a compression coding model
- the access network node can train a matching compression decoding model used by the access network node and a compression coding model used by the terminal based on the capability information and channel data of the terminal.
- the access network node notifies the terminal of the training strategy used by the access network node to train the compression coding model.
- the access network node notifies the terminal of the initialization method used by the access network node to randomly select values within the interval [z min , z max ]
- the model training method is supervised learning
- the loss function is cross-entropy loss function
- the optimization algorithm is stochastic gradient descent algorithm.
- the terminal After obtaining the training strategy of the access network node, the terminal trains the compression coding model based on the training strategy, so that the compression coding model trained by the terminal is as identical as possible to the compression coding model obtained by the training of the access network node, thereby realizing the
- the compression coding model used matches the compression decoding model used by the access network nodes in communication.
- the access network node may also notify the terminal of the structure of the intelligent model as the structure of the first intelligent model, and/or, the access network node may also instruct the terminal to train the intelligent model. Information about the dataset.
- a third-party node in the network may obtain terminal capability information from an access network node, and/or obtain environmental data between the access network node and the terminal, and obtain Match the intelligent model of the access network node application and the intelligent model of the terminal application, and send the intelligent model of the access network node application to the access network node by the third-party node, and notify the access network node terminal to train the intelligent model
- the training policy of is forwarded to the terminal by the access network node through the first information.
- the third-party node may also notify the terminal of the structure of the intelligent model and/or the training data set information through the access network node.
- the terminal performs model training on the first smart model according to the training strategy.
- the terminal can determine the training strategy of the first intelligent model, such as model training method, loss function, model initialization method, type of model optimization algorithm, parameters of the optimization algorithm, etc., part or all of the training strategy can be based on the terminal
- the training strategy not indicated by the access network node determined by the first information of the node may be obtained by the terminal based on pre-configured information or based on an associated training strategy indicated by the access network node.
- the terminal may acquire the structure of the first intelligent model, and determine the first intelligent model based on the structure.
- the terminal may receive second information from the access network node, the second information is used to indicate the structure of the first intelligent model, and the terminal determines the initial first intelligent model (or called first intelligent model before training).
- the terminal obtains the structure of the first smart model from the preconfigured information, and determines the initial first smart model.
- the terminal may receive third information from the access network node, where the third information is used to indicate the information of the training data set,
- the terminal acquires the training data set according to the third information, and performs model training on the first intelligent model by using the training strategy indicated by the first information based on the training data set.
- the present disclosure is not limited thereto, and the terminal may also perform model training on the first smart model based on the data set stored in the terminal.
- the terminal After the terminal acquires the training policy of the first intelligent model, the training data set, and the initial first intelligent model based on the structure of the first intelligent model, the terminal may start to perform model training of the first intelligent model.
- the terminal determines that the model training mode is a supervised learning mode, and the training data set includes training samples and labels.
- the terminal inputs the training samples into the first intelligent model in each training, and the first intelligent model outputs the inference result after processing the training samples, and the terminal uses the loss function to obtain the loss value output by the loss function based on the inference result and the label.
- the model optimization algorithm is used to optimize the parameters of the first intelligent model, and the first intelligent model after updating parameters is obtained, and the first intelligent model after updating parameters is applied to the next model training, after multiple iterations of training , when the loss value output by the loss function is less than or equal to the first threshold, the terminal determines that the training of the first intelligent model is completed, and obtains the trained first intelligent model.
- the terminal determines that the model training mode is an unsupervised learning mode, and the training data set includes training samples.
- the terminal inputs the training samples into the first intelligent model during each training, and the first intelligent model outputs an inference result after processing the training samples.
- the terminal uses the loss function to obtain the output value of the loss function.
- the model optimization algorithm is used to optimize the parameters of the first intelligent model, and the first intelligent model with updated parameters is obtained, and the first intelligent model with updated parameters is applied to the next model training. Iterative training, when the output value of the loss function is greater than or equal to the second threshold, the terminal determines to complete the training of the first intelligent model, and obtains the trained first intelligent model.
- the network can inform the terminal of the training strategy of the intelligent model, and the terminal executes the training of the intelligent model based on the training strategy provided by the network, so that the model obtained by the terminal training can match the model adopted by the network and meet the expected performance requirements.
- the air interface resource overhead caused by the access network node sending the model parameters of the first intelligent model (such as the weight value, activation function and bias of each neuron, etc.) to the terminal is reduced.
- FIG. 5C is a schematic flowchart of a communication method 500 provided by the present disclosure. It should be noted that, for the same parts in the method shown in FIG. 5 as in the method shown in FIG. 4 , reference may be made to the description in the method shown in FIG. 4 , and details are not repeated here for brevity.
- the access network node sends first information to the terminal, where the first information is used to indicate a training policy of the first intelligent model.
- the terminal trains the first intelligent model according to the training policy.
- the terminal may obtain the structure of the first intelligent model to determine the first intelligent model, and use the training data set to train the first intelligent model by using the training policy.
- reference may be made to S402, which will not be repeated here.
- the access network node sends fourth information to the terminal, where the fourth information is used to indicate test information, and the test information is used to test performance of the first smart model.
- the terminal receives the fourth information from the access network node, and after receiving the fourth information, the terminal tests the trained first intelligent model according to the fourth information.
- test information includes one or more of the following:
- Test data information For evaluation methods or performance evaluation parameters.
- the measurement information includes one or two of test data information, performance evaluation methods, and performance evaluation parameters, such as including test data information and performance evaluation methods, other items not included, such as performance evaluation parameters, may be It is stipulated in the agreement or determined by other means, and shall not be restricted. Or in the following example, the terminal does not need to know the performance evaluation mode and/or performance evaluation parameters, and the access network node performs the performance evaluation.
- the test data information is used to indicate one or more of the test data, the type of the test data, the label data or the type of the label data.
- the performance evaluation method can be a method of calculating the loss value between the inference data and the label data, and the performance evaluation parameter can be parameters such as the threshold value of the evaluation loss value, but the disclosure is not limited thereto, and the performance evaluation method can also be an evaluation function, etc. .
- the terminal sends fifth information and/or sixth information to the access network node, where the fifth information is used to indicate a test result of the first intelligent model, and where the sixth information is used to indicate reasoning data.
- the reasoning data is obtained by reasoning the test data of the first intelligent model, and the test information indicated by the fourth information includes the test data.
- the terminal may send fifth information to the access network node, where the fifth information may indicate a test result of the first smart model.
- the test result may be that the performance of the first intelligent model meets or fails.
- the test information sent by the access network node to the terminal may include test data, verification data, and a verification threshold, and the terminal may use the test data as input to the first intelligent model after training, and obtain the first intelligent model based on the test data.
- Inference gets inference data.
- the terminal calculates a loss value between the inference data and the verification data.
- the terminal may use the loss function used when training the first intelligent model to calculate the loss value between the inference data and the verification data. But the present application is not limited thereto.
- the terminal compares the loss value with the verification threshold value.
- the terminal can determine that the trained first intelligent model meets the standard and can be applied to actual communication, and pass the fifth information Notify the access network node. Alternatively, if the loss value is greater than the verification threshold, the terminal may notify the access network node that the trained first intelligent model does not meet the standard through the fifth information.
- the test result may be the loss value between the inference data and the verification data.
- the access network node indicates the test data and the verification data through the fourth information, and the fourth information also indicates that the performance evaluation method is calculation reasoning The loss value between data and check data.
- the terminal uses the trained first intelligent model to infer to obtain the inference data, calculates the loss value between the inference data and the label data, and notifies the access network node of the loss value through the fifth information, and the network Based on the loss value, it is determined whether the performance of the first intelligent model trained by the terminal meets the standard.
- it may be an access network node or a third-party node in the network to judge whether the standard is met, and the third-party node can obtain the loss value through the forwarding of the access network node.
- the terminal may send sixth information to the access network node, where the sixth information is used to indicate the inference data of the first intelligent model.
- the fourth information indicates test data
- the terminal uses the trained first intelligent model to infer the test data to obtain inference data output by the first intelligent model, and the terminal sends the obtained inference data to the access network node.
- a network node (such as an access network node or a third-party node in the network) judges whether the performance of the trained first intelligent model meets the standard.
- the terminal may send the fifth information and the sixth information to the access network node.
- the terminal not only sends the test result to the access network node, but also sends inference data to the access network node. smart model. But the present disclosure is not limited thereto.
- the access network node sends seventh information to the terminal, where the seventh information is used to indicate the updated training policy of the first intelligent model, and/or is used to indicate the updated training strategy of the first intelligent model. structure.
- the terminal receives the seventh information from the access network node.
- the terminal trains the first smart model again based on the seventh information.
- the access network node after receiving the fifth information and/or sixth information from the terminal, the access network node determines that the performance of the first intelligent model trained by the terminal is not up to standard, and the access network node may send the second intelligent model to the terminal. Seventh, the information notifies the first intelligent model of the updated training strategy and/or the updated structure of the first intelligent model.
- the access network node determines that the first intelligent model needs to be updated based on the communication performance, and the access network node may send the first intelligent model to the terminal. Seven information.
- the access network node can send the seventh information to the terminal, so that the terminal can perform model training on the first intelligent model again , making the retrained first intelligent model adapt to the changed environment.
- the manner in which the seventh information indicates the training strategy may be the same as the manner in which the first information indicates the training strategy, and the manner in which the seventh information indicates the model structure may be the same as the manner in which the second information indicates the model structure.
- the seventh information may be specifically used to indicate at least one change amount of the training strategy, and/or at least one change amount of the structure of the first intelligent model.
- the seventh information indicates at least one variation of the training strategy, and after receiving the seventh information, the terminal determines an updated training strategy based on the variation indicated by the seventh information. For example, if the access network node adjusts the parameters of the optimization algorithm (for example, the learning rate is changed from 0.1 indicated by the first information to 0.01), only the parameters of the optimization algorithm in the training strategy change, and the other training strategies do not change, then the seventh information can indicate The seventh information indicates that the updated learning rate is 0.01.
- the first information indicates that the amount of batch-processed data is N, that is, each time N training data is taken for gradient descent update, and the access network node adjusts the batch-processed data amount to M, then the access network can be grounded through
- the present disclosure is not limited thereto.
- the seventh information may also indicate at least one variation of the model structure, and the specific manner is similar to that of indicating at least one variation of the training strategy, which may be implemented with reference to the above description, and will not be repeated here.
- the access network node can send test information to the terminal, and the terminal tests the trained first intelligent model based on the test information, and the terminal or the access network
- the node can judge whether the performance of the first intelligent model after training is up to standard based on the test results and/or inference data, and can apply the first intelligent model in actual communication when the performance is up to standard, so as to improve wireless communication performance (such as communication Reliability and other performance) purposes.
- Fig. 6 is another schematic flowchart of the communication method provided by the present disclosure. It should be noted that the same parts in the method shown in FIG. 6 and other methods may be mutually referenced or implemented in combination, and for the sake of brevity, details are not described here.
- the access network node sends second information to the terminal, the second information is used to indicate the structure of the first intelligent model, and the second information includes the network layer structure information of the first intelligent model, the dimension of input data or the dimension of output data One or more structural information in a dimension.
- the terminal receives the second information from the access network node.
- the network layer structure information of the first intelligent model may include but not limited to one or more of the following:
- the number of neural network layers contained in the first intelligent model the type of neural network layers, the use of neural network layers, the cascading relationship between neural network layers, the dimension of input data of neural network layers or the output of neural network layers Dimensions of the data.
- the terminal determines a first smart model according to the second information.
- the terminal may generate the first smart model with the structure based on the structure of the first smart model indicated by the second information.
- the terminal performs model training on the first smart model.
- the terminal After determining the first smart model in S602, the terminal trains the first smart model.
- the access network node notifies the terminal of the structure of the first intelligent model, and the terminal generates the first intelligent model with the structure based on the instruction of the access network node, so that the structure of the first intelligent model used by the terminal can meet the requirements of the access network. Node requirements, so that the first intelligent model can be used to improve the performance of wireless communication.
- the terminal executes the training of the first intelligent model, it can be applied in communication.
- the air interface resource overhead caused by the model parameters (such as the weight value, activation function and bias of each neuron) notified by the access network node to the first intelligent model can be reduced.
- the terminal may train the first intelligent model by using a predefined training policy agreed upon by the terminal and the access network node, or the access network node may notify the terminal of the training policy of the first intelligent model through the first information.
- the training data set used by the terminal to train the first intelligent model may be pre-stored by the terminal or received from an access network node.
- each network element may include a hardware structure and/or a software module, and realize the above functions in the form of a hardware structure, a software module, or a hardware structure plus a software module. Whether one of the above-mentioned functions is executed in the form of a hardware structure, a software module, or a hardware structure plus a software module depends on the specific application and design constraints of the technical solution.
- Fig. 7 is a schematic block diagram of a communication device provided by the present disclosure.
- the communication device 700 may include a transceiver unit 720 .
- the communication device 700 may correspond to the terminal device in the above method, or a chip configured in (or used in) the terminal device, or other devices, modules, circuit or unit etc.
- the communications apparatus 700 may include a unit for executing the method performed by the terminal device in the methods shown in FIG. 4 , FIG. 5C , and FIG. 6 .
- each unit and the above-mentioned other operations and/or functions in the communication device 700 are for realizing the corresponding processes of the methods shown in FIG. 4 , FIG. 5C , and FIG. 6 .
- the communication device 700 may further include a processing unit 710, and the processing unit 710 may be configured to process instructions or data to implement corresponding operations.
- the transceiver unit 720 in the communication device 700 may be an input/output interface or circuit of the chip, and the processing in the communication device 700 Unit 710 may be a processor in a chip.
- the communication device 700 may further include a storage unit 730, which may be used to store instructions or data, and the processing unit 710 may execute the instructions or data stored in the storage unit, so that the communication device realizes corresponding operations .
- a storage unit 730 which may be used to store instructions or data
- the processing unit 710 may execute the instructions or data stored in the storage unit, so that the communication device realizes corresponding operations .
- the transceiver unit 720 in the communication device 700 can be implemented through a communication interface (such as a transceiver or an input/output interface), for example, it can correspond to the transceiver 810 in the terminal device 800 shown in FIG. 8 .
- the processing unit 710 in the communication apparatus 700 may be implemented by at least one processor, for example, may correspond to the processor 820 in the terminal device 800 shown in FIG. 8 .
- the processing unit 710 in the communication device 700 may also be implemented by at least one logic circuit.
- the storage unit 730 in the communication device 700 may correspond to the memory in the terminal device 800 shown in FIG. 8 .
- the communication device 700 may correspond to the access network node in the above method, for example, a chip configured (or used) in the access network node, or other devices capable of implementing access A device, module, circuit or unit, etc. of a method of a network node.
- the communication device 700 may include a unit for performing the method performed by the access network node in the methods shown in FIG. 4 , FIG. 5C , and FIG. 6 .
- each unit and the above-mentioned other operations and/or functions in the communication device 700 are for realizing the corresponding processes of the methods shown in FIG. 4 , FIG. 5C , and FIG. 6 .
- the communication device 700 may further include a processing unit 710, and the processing unit 710 may be configured to process instructions or data to implement corresponding operations.
- the transceiver unit 720 in the communication device 700 may be an input/output interface or circuit of the chip, and the communication device 700
- the processing unit 710 may be a processor in a chip.
- the communication device 700 may further include a storage unit 730, which may be used to store instructions or data, and the processing unit 710 may execute the instructions or data stored in the storage unit, so that the communication device realizes corresponding operations .
- a storage unit 730 which may be used to store instructions or data
- the processing unit 710 may execute the instructions or data stored in the storage unit, so that the communication device realizes corresponding operations .
- the transceiver unit 720 in the communication device 700 can be implemented through a communication interface (such as a transceiver or an input/output interface), for example, it can correspond to the Transceiver 910 in network device 900 .
- the processing unit 910 in the communication device 900 can be implemented by at least one processor, for example, it can correspond to the processor 920 in the network device 900 shown in FIG. circuit implementation.
- the storage unit 730 in the communication device 700 may correspond to the memory in the network device 900 shown in FIG. 9 .
- FIG. 8 is a schematic structural diagram of a terminal device 800 provided in the present disclosure.
- the terminal device 800 can be applied to the system shown in FIG. 1 to perform the functions of the terminal device in the above method.
- the terminal device 800 includes a processor 820 and a transceiver 810 .
- the terminal device 800 further includes a memory.
- the processor 820, the transceiver 810, and the memory may communicate with each other through an internal connection path, and transmit control signals and/or data signals.
- the memory is used to store computer programs, and the processor 820 is used to execute the computer programs in the memory to control the transceiver 810 to send and receive signals.
- the above-mentioned processor 820 can be used to execute the actions described in the previous method implemented inside the terminal device, and the transceiver 810 can be used to execute the actions described in the previous method that the terminal device sends to or receives from the network device.
- the transceiver 810 can be used to execute the actions described in the previous method that the terminal device sends to or receives from the network device.
- the terminal device 800 may further include a power supply, configured to provide power to various devices or circuits in the terminal device.
- FIG. 9 is a schematic structural diagram of a network device 900 provided in the present disclosure.
- the network device 900 may be applied to the system shown in FIG. 1 to perform the functions of the network device in the above method.
- the network device 900 includes a processor 920 and a transceiver 910 .
- the network device 900 further includes a memory.
- the processor 920, the transceiver 910, and the memory may communicate with each other through an internal connection path, and transmit control and/or data signals.
- the memory is used to store computer programs, and the processor 920 is used to execute the computer programs in the memory to control the transceiver 910 to send and receive signals.
- the above-mentioned processor 920 can be used to execute the actions described in the previous method implemented by the network device, and the transceiver 910 can be used to perform the actions described in the previous method sent by the network device to the network device or received from the network device.
- the transceiver 910 can be used to perform the actions described in the previous method sent by the network device to the network device or received from the network device.
- the foregoing network device 900 may further include a power supply, configured to provide power to various devices or circuits in the network device.
- the processor and the memory may be combined into a processing device, and the processor is used to execute the program code stored in the memory to realize the above functions.
- the memory may also be integrated in the processor, or be independent of the processor.
- the processor may correspond to the processing unit in FIG. 7 .
- the transceiver may correspond to the transceiver unit in FIG. 7 .
- the transceiver 810 may include a receiver (or called a receiver, a receiving circuit) and a transmitter (or called a transmitter, a transmitting circuit). Among them, the receiver is used to receive signals, and the transmitter is used to transmit signals.
- a processor may be a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, and may realize or execute the present disclosure
- a general purpose processor may be a microprocessor or any conventional processor or the like. The steps combined with the method of the present disclosure may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor.
- the memory may be a non-volatile memory, such as a hard disk (hard disk drive, HDD) or a solid-state drive (solid-state drive, SSD), etc., or a volatile memory (volatile memory), such as random access Memory (random-access memory, RAM).
- a memory is, but is not limited to, any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
- the memory in the present application may also be a circuit or any other device capable of implementing a storage function for storing program instructions and/or data.
- the present disclosure also provides a processing device, including a processor and a (communication) interface; the processor is configured to perform any of the above methods.
- the above processing device may be one or more chips.
- the processing device may be a field programmable gate array (field programmable gate array, FPGA), an application specific integrated circuit (ASIC), or a system chip (system on chip, SoC). It can be a central processor unit (CPU), a network processor (network processor, NP), a digital signal processing circuit (digital signal processor, DSP), or a microcontroller (micro controller unit) , MCU), can also be a programmable controller (programmable logic device, PLD) or other integrated chips.
- CPU central processor unit
- NP network processor
- DSP digital signal processor
- microcontroller micro controller unit
- PLD programmable logic device
- the present disclosure also provides a computer program product, the computer program product comprising: computer program code, when the computer program code is executed by one or more processors, causing an apparatus including the processor to execute The method shown in Fig. 4, Fig. 5C and Fig. 6 .
- the technical solution provided by the present disclosure may be fully or partially realized by software, hardware, firmware or any combination thereof.
- software When implemented using software, it may be implemented in whole or in part in the form of a computer program product.
- the computer program product includes one or more computer instructions.
- the processes or functions according to the present invention will be generated in whole or in part.
- the above computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, and the computer-readable storage medium may be any available medium that can be accessed by a computer or contain One or more data storage devices such as servers and data centers that can be integrated with media.
- the available medium may be a magnetic medium (for example, a floppy disk, a hard disk, or a magnetic tape), an optical medium (for example, a digital video disc (digital video disc, DVD)), or a semiconductor medium.
- the present disclosure also provides a computer-readable storage medium, the computer-readable storage medium stores program code, and when the program code is executed by one or more processors, the The device executes the methods shown in FIG. 4 , FIG. 5C , and FIG. 6 .
- the present disclosure also provides a system, which includes the aforementioned one or more first devices.
- the system can also further include the aforementioned one or more third devices.
- the disclosed systems, devices and methods may be implemented in other ways.
- the devices described above are only schematic.
- the division of the units is only a logical function division.
- there may be other division methods for example, multiple units or components can be combined or integrated.
- to another system or some features may be ignored, or not implemented.
- the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in electrical, mechanical or other forms.
- the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of this solution.
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Abstract
Description
Claims (63)
- 一种通信方法,其特征在于,包括:接收第一信息,所述第一信息用于指示第一智能模型的训练策略;根据所述训练策略,对所述第一智能模型进行模型训练。
- 根据权利要求1所述的方法,其特征在于,所述训练策略包括以下一项或多项:模型训练方式、损失函数信息、模型初始化方式、模型优化算法的类型、或优化算法的参数。
- 根据权利要求2所述的方法,其特征在于,所述模型优化算法为自适应动量估计算法、随机梯度下降算法或批量梯度下降算法;和/或,所述优化算法的参数包括学习率、迭代次数、或批量处理的数据量中的一项或多项。
- 根据权利要求1至3中任一项所述的方法,其特征在于,所述方法还包括:获取第二信息,所述第二信息用于指示第一智能模型的结构。
- 根据权利要求4所述的方法,其特征在于,所述第二信息具体用于指示所述第一智能模型的以下一种或多种结构信息:网络层结构信息、输入数据的维度或输出数据的维度。
- 根据权利要求5所述的方法,其特征在于,所述网络层结构信息包括以下一项或多项:所述第一智能模型包含的神经网络层的层数、所述神经网络层的类型、所述神经网络层的使用方式、所述神经网络层之间的级联关系、所述神经网络层的输入数据的维度或神经网络层的输出数据的维度。
- 根据权利要求1至6中任一项所述的方法,其特征在于,所述方法还包括:接收第三信息,所述第三信息用于指示训练数据集合的信息,所述训练数据集合用于训练所述第一智能模型。
- 根据权利要求7所述的方法,其特征在于,所述训练数据集合包括训练样本,或者包括训练样本和标签。
- 根据权利要求1至8中任一项所述的方法,其特征在于,所述方法还包括:发送能力信息,所述能力信息用于指示运行智能模型的能力。
- 根据权利要求9所述的方法,其特征在于,所述能力信息具体用于指示以下一项或多项能力:是否支持运行智能模型、支持运行的智能模型的类型、数据处理能力或存储能力。
- 根据权利要求1至10中任一项所述的方法,其特征在于,所述方法还包括:接收第四信息,所述第四信息用于指示测试信息,所述测试信息用于测试所述第一智能模型的性能。
- 根据权利要求11所述的方法,其特征在于,所述测试信息包括以下一项或多项:测试数据信息、性能评估方式或性能评估参数。
- 根据权利要求11或12所述的方法,其特征在于,所述方法还包括:发送第五信息,所述第五信息用于指示所述第一智能模型的测试结果;和/或,发送第六信息,所述第六信息用于指示推理数据,所述推理数据是所述第一智能模型推理测试数据得到的,所述测试信息包括所述测试数据。
- 根据权利要求13所述的方法,其特征在于,所述方法还包括:接收第七信息,所述第七信息用于指示所述第一智能模型的更新后的训练策略,和/或,用于指示所述第一智能模型的更新后的结构。
- 根据权利要求14所述的方法,其特征在于,所述第七信息具体用于指示所述训练策略的至少一个变换量,和/或所述第一智能模型的结构的至少一个变化量。
- 一种通信方法,其特征在于,包括:发送第一信息,所述第一信息用于指示第一智能模型的训练策略。
- 根据权利要求16所述的方法,其特征在于,所述训练策略包括以下一项或多项:模型训练方式、损失函数信息、模型初始化方式、模型优化算法的类型、或优化算法的参数。
- 根据权利要求17所述的方法,其特征在于,所述模型优化算法的类型为自适应动量估计算法、随机梯度下降算法或批量梯度下降算法;和/或,所述优化算法的参数包括学习率、迭代次数、或批量处理的数据量中的一项或多项。
- 根据权利要求16至18中任一项所述的方法,其特征在于,所述方法还包括:发送第二信息,所述第二信息用于指示第一智能模型的结构。
- 根据权利要求19所述的方法,其特征在于,所述第二信息具体用于指示所述第一智能模型的以下一种或多种结构信息:网络层结构信息、输入数据的维度、或输出数据的维度。
- 根据权利要求20所述的方法,其特征在于,所述网络层结构信息包括以下一项或多项:所述第一智能模型包含的神经网络层的层数、所述神经网络层的类型、所述神经网络层的使用方式、所述神经网络层之间的级联关系、所述神经网络层的输入数据的维度或所述神经网络层的输出数据的维度。
- 根据权利要求16至21中任一项所述的方法,其特征在于,所述方法还包括:发送第三信息,所述第三信息用于指示用于训练数据集合的信息,所述训练数据集合用于训练所述第一智能模型。
- 根据权利要求22所述的方法,其特征在于,所述训练数据集合包括训练样本,或者包括训练样本和标签。
- 根据权利要求16至23中任一项所述的方法,其特征在于,所述方法还包括:接收能力信息,所述能力信息用于指示运行智能模型的能力。
- 根据权利要求24所述的方法,其特征在于,所述能力信息用于指示以下一项或多项能力:是否支持运行智能模型、支持运行的智能模型的类型、数据处理能力或存储能力。
- 根据权利要求16至25中任一项所述的方法,其特征在于,所述方法还包括:发送第四信息,所述第四信息用于指示测试所述第一智能模型性能的测试信息。
- 根据权利要求26所述的方法,其特征在于,所述测试信息包括以下一项或多项:测试数据信息、性能评估方式或性能评估参数。
- 根据权利要求26或27所述的方法,其特征在于,所述方法还包括:接收第五信息,所述第五信息用于指示所述第一智能模型的测试结果;和/或,接收第六信息,所述第六信息用于指示推理数据,所述推理数据是所述第一智能模型 推理测试数据得到的,所述测试信息包括所述测试数据。
- 根据权利要求28所述的方法,其特征在于,所述方法还包括:发送第七信息,所述第七信息用于指示所述第一智能模型的更新后的训练策略,和/或,用于指示所述第一智能模型的更新后的结构。
- 根据权利要求29所述的方法,其特征在于,所述第七信息具体用于指示所述训练策略的至少一个参数变换量,和/或所述第一智能模型的结构的至少一个参数变化量。
- 一种通信方法,其特征在于,包括:接收第二信息,所述第二信息用于指示第一智能模型的结构,所述第二信息包括所述第一智能模型的网络层结构信息、输入数据的维度或输出数据的维度中的一种或多种结构信息;根据所述第二信息,确定所述第一智能模型的结构;对所述第一智能模型进行模型训练。
- 根据权利要求31所述的方法,其特征在于,所述方法还包括:获取第一信息,所述第一信息用于指示第一智能模型的训练策略;以及,所述对所述第一智能模型进行模型训练,包括:根据所述训练策略,对所述第一智能模型进行模型训练。
- 根据权利要求32所述的方法,其特征在于,所述训练策略包括以下一项或多项:模型训练方式、损失函数信息、模型初始化方式、模型优化算法的类型、或优化算法的参数。
- 根据权利要求31至33中任一项所述的方法,其特征在于,所述方法还包括:接收第三信息,所述第三信息用于指示训练数据集合的信息,所述训练数据集合用于训练所述第一智能模型。
- 根据权利要求34所述的方法,其特征在于,所述训练数据集合包括训练样本,或者包括训练样本和标签。
- 根据权利要求31至35中任一项所述的方法,其特征在于,所述方法还包括:发送能力信息,所述能力信息用于指示运行智能模型的能力。
- 根据权利要求36所述的方法,其特征在于,所述能力信息用于指示以下一项或多项能力:是否支持运行智能模型、支持运行的智能模型的类型、数据处理能力或存储能力。
- 根据权利要求31至37中任一项所述的方法,其特征在于,所述方法还包括:接收第四信息,所述第四信息用于指示测试信息,所述测试信息用于测试所述第一智能模型的性能。
- 根据权利要求38所述的方法,其特征在于,所述测试信息包括以下一项或多项:测试数据信息、性能评估方式或性能评估参数。
- 根据权利要求38或39所述的方法,其特征在于,所述方法还包括:发送第五信息,所述第五信息用于指示所述第一智能模型的测试结果;和/或,发送第六信息,所述第六信息用于指示推理数据,所述推理数据是所述第一智能模型推理测试数据得到的,所述测试信息包括所述测试数据。
- 根据权利要求31至40中任一项所述的方法,其特征在于,所述方法还包括:接收第七信息,所述第七信息用于指示所述第一智能模型的更新后的训练策略,和/ 或,用于指示所述第一智能模型的更新后的结构。
- 根据权利要求41所述的方法,其特征在于,所述第七信息具体用于指示所述训练策略的至少一个变换量,和/或所述第一智能模型的结构的至少一个变化量。
- 一种通信方法,其特征在于,包括:发送第二信息,所述第二信息用于指示第一智能模型的结构,所述第二信息包括所述第一智能模型的网络层结构信息、输入数据的维度或输出数据的维度中的一种或多种结构信息。
- 根据权利要求43所述的方法,其特征在于,所述方法还包括:发送第一信息,所述第一信息用于指示第一智能模型的训练策略。
- 根据权利要求44所述的方法,其特征在于,所述训练策略包括以下一项或多项:模型训练方式、损失函数信息、模型初始化方式、模型优化算法的类型、或优化算法的参数。
- 根据权利要求43至45中任一项所述的方法,其特征在于,所述方法还包括:发送第三信息,所述第三信息用于指示训练数据集合的信息,所述训练数据集合用于训练所述第一智能模型。
- 根据权利要求46所述的方法,其特征在于,所述训练数据集合包括训练样本,或者包括训练样本和标签。
- 根据权利要求43至47中任一项所述的方法,其特征在于,所述方法还包括:接收能力信息,所述能力信息用于指示运行智能模型的能力。
- 根据权利要求43所述的方法,其特征在于,所述能力信息用于指示以下一项或多项能力:是否支持运行智能模型、支持运行的智能模型的类型、数据处理能力或存储能力。
- 根据权利要求43至49中任一项所述的方法,其特征在于,所述方法还包括:发送第四信息,所述第四信息用于指示测试信息,所述测试信息用于测试所述第一智能模型的性能。
- 根据权利要求50所述的方法,其特征在于,所述测试信息包括以下一项或多项:测试数据信息、性能评估方式或性能评估参数。
- 根据权利要求50或51所述的方法,其特征在于,所述方法还包括:接收第五信息,所述第五信息用于指示所述第一智能模型的测试结果;和/或,接收第六信息,所述第六信息用于指示推理数据,所述推理数据是所述第一智能模型推理测试数据得到的,所述测试信息包括所述测试数据。
- 根据权利要求43至52中任一项所述的方法,其特征在于,所述方法还包括:发送第七信息,所述第七信息用于指示所述第一智能模型的更新后的训练策略,和/或,用于指示所述第一智能模型的更新后的结构。
- 根据权利要求53所述的方法,其特征在于,所述第七信息具体用于指示所述训练策略的至少一个变换量,和/或所述第一智能模型的结构的至少一个变化量。
- 一种通信装置,其特征在于,用于实现如权利要求1至15、31至42中任一项所述的方法。
- 一种通信装置,其特征在于,包括处理器和存储器,所述存储器和所述处理器耦合,所述处理器用于执行权利要求1至15中、31至42中任一项所述的方法。
- 一种通信装置,其特征在于,包括处理器和通信接口,所述处理器利用所述通信接口,执行权利要求1至15中、31至42中任一项所述的方法。
- 一种通信装置,其特征在于,用于实现如权利要求16至30、43至54中任一项所述的方法。
- 一种通信装置,其特征在于,包括处理器和存储器,所述存储器和所述处理器耦合,所述处理器用于执行权利要求16至30、43至54中任一项所述的方法。
- 一种通信装置,其特征在于,包括处理器和通信接口,所述处理器利用所述通信接口,执行权利要求16至30、43至54中任一项所述的方法。
- 一种通信系统,其特征在于,包括权利要求55至57中任一项所述的通信装置,和权利要求58至60中任一项所述的通信装置。
- 一种计算机可读存储介质,其特征在于,存储有指令,当所述指令在计算机上运行时,使得所述计算机执行如权利要求1至54中任一项所述的方法。
- 一种计算机程序产品,其特征在于,包括指令,当所述指令在计算机上运行时,使得计算机执行如权利要求1至54中任一项所述的方法。
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Citations (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111612153A (zh) * | 2019-02-22 | 2020-09-01 | 华为技术有限公司 | 训练模型的方法和装置 |
CN112101551A (zh) * | 2020-09-25 | 2020-12-18 | 北京百度网讯科技有限公司 | 用于训练模型的方法、装置、设备以及存储介质 |
CN113128686A (zh) * | 2020-01-16 | 2021-07-16 | 华为技术有限公司 | 模型训练方法及装置 |
CN113412494A (zh) * | 2019-02-27 | 2021-09-17 | 华为技术有限公司 | 一种确定传输策略的方法及装置 |
CN113938232A (zh) * | 2020-07-13 | 2022-01-14 | 华为技术有限公司 | 通信的方法及通信装置 |
CN114036994A (zh) * | 2020-07-21 | 2022-02-11 | 中兴通讯股份有限公司 | 训练通信决策模型的方法、电子设备、计算机可读介质 |
CN114143799A (zh) * | 2020-09-03 | 2022-03-04 | 华为技术有限公司 | 通信方法及装置 |
CN114679355A (zh) * | 2020-12-24 | 2022-06-28 | 华为技术有限公司 | 通信方法和装置 |
CN114793453A (zh) * | 2020-11-23 | 2022-07-26 | 北京小米移动软件有限公司 | 一种训练方法、训练装置及存储介质 |
CN114936117A (zh) * | 2021-09-02 | 2022-08-23 | 华为技术有限公司 | 模型训练的方法、服务器、芯片以及系统 |
-
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Patent Citations (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111612153A (zh) * | 2019-02-22 | 2020-09-01 | 华为技术有限公司 | 训练模型的方法和装置 |
CN113412494A (zh) * | 2019-02-27 | 2021-09-17 | 华为技术有限公司 | 一种确定传输策略的方法及装置 |
CN113128686A (zh) * | 2020-01-16 | 2021-07-16 | 华为技术有限公司 | 模型训练方法及装置 |
CN113938232A (zh) * | 2020-07-13 | 2022-01-14 | 华为技术有限公司 | 通信的方法及通信装置 |
CN114036994A (zh) * | 2020-07-21 | 2022-02-11 | 中兴通讯股份有限公司 | 训练通信决策模型的方法、电子设备、计算机可读介质 |
CN114143799A (zh) * | 2020-09-03 | 2022-03-04 | 华为技术有限公司 | 通信方法及装置 |
CN112101551A (zh) * | 2020-09-25 | 2020-12-18 | 北京百度网讯科技有限公司 | 用于训练模型的方法、装置、设备以及存储介质 |
CN114793453A (zh) * | 2020-11-23 | 2022-07-26 | 北京小米移动软件有限公司 | 一种训练方法、训练装置及存储介质 |
CN114679355A (zh) * | 2020-12-24 | 2022-06-28 | 华为技术有限公司 | 通信方法和装置 |
CN114936117A (zh) * | 2021-09-02 | 2022-08-23 | 华为技术有限公司 | 模型训练的方法、服务器、芯片以及系统 |
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