Information transmission method and apparatus

Abstract

Embodiments of the present application provide an information transmission method and apparatus. The method comprises: a first device determines first adjustment information on the basis of a first neural network, the first adjustment information being used for indicating adjustment information of at least one neural network layer in a second neural network, and the second neural network being a neural network in a second device. The first device sends the first adjustment information to the second device. The second device adjusts the at least one neural network layer in the second neural network on the basis of the first adjustment information. Hence, the first device can use the first neural network to assist the second device to adjust the second neural network, so that the second neural network adjusted by the second device can adapt to a communication scenario, thereby facilitating improvement of the generalization ability of the second neural network. For a scenario in which the second neural network is used by the second device to process transmitted data, the method is conducive to improving the generalization ability of the neural network used for processing the transmitted data in a communication system.

Description

Information transmission method and device Technical Field

[0001] The present application relates to the field of communication technology, and in particular to an information transmission method and device. Background Art

[0002] Machine learning is a technical means of achieving artificial intelligence. Neural networks are a type of machine learning. Neural networks have the ability to infinitely approximate any continuous function, as given by the universal approximation theorem. They can accurately abstractly model complex high-dimensional problems.

[0003] Neural networks can be applied to both the transmitter and receiver sides of a communication system to process transmitted data. The transmitter can use a neural network to process the data to be transmitted and send the processed data to the receiver; the receiver can also use a neural network to process the data received from the transmitter. Improving the generalization capabilities of the neural networks used to process transmitted data in communication systems is a technical challenge that needs to be addressed.

[0004] Summary of the Invention

[0005] The embodiments of the present application provide an information transmission method and apparatus, which are beneficial for improving the generalization capability of a neural network used to process transmitted data in a communication system.

[0006] In a first aspect, the present application provides an information transmission method, which can be applied to a first device, a chip in the first device, or a logic module or software that can implement all or part of the functions of the first device. The method is described below using the first device as an example. The method includes: the first device determines first adjustment information based on a first neural network, the first adjustment information is used to indicate adjustment information of at least one neural network layer in a second neural network, and the second neural network is a neural network in the second device. The first device sends the first adjustment information to the second device, and the first adjustment information is used by the second device to adjust at least one neural network layer in the second neural network.

[0007] It can be seen that the first device can use the first neural network to assist the second device in adjusting the second neural network. Based on the first neural network, first adjustment information adapted to the current communication scenario can be determined, which is conducive to the second neural network adjusted by the second device being able to adapt to the current communication scenario. The first neural network can be used to determine first adjustment information for different scenarios, which is conducive to the second device being able to adaptively adjust the second neural network for different scenarios, improving the universality of the second neural network, and making the second neural network have the ability to generalize in different scenarios. This information transmission method can be applied to the scenario where the second neural network is used by the second device to process transmitted data, which is conducive to improving the generalization ability of the neural network used to process transmitted data in the communication system.

[0008] In addition, compared to the data expansion method of improving the generalization ability of the second neural network by expanding the training data set of the second neural network, the information transmission method provided in the embodiment of the present application uses the first neural network to assist the second device in adjusting the second neural network, which can reduce the performance degradation of the second neural network caused by insufficient feature capture due to the complexity and diversity of wireless scenarios in the data expansion method. Moreover, the information transmission method provided in the embodiment of the present application enables efficient online adjustment of the neural network layers in the second neural network without the need for offline retraining of the second neural network, which can simultaneously ensure network performance and real-time requirements.

[0009] In an optional embodiment, the first adjustment information is specifically used to indicate one or more of the following: weight adjustment information, bias adjustment information, activation function, and structure adjustment information of at least one neural network layer in the second neural network. Thus, the first device can assist the second device in adjusting the weights, bias, activation function, and / or structure of the second neural network by utilizing the first neural network.

[0010] In an optional embodiment, the first adjustment information is obtained by inputting information characterizing the distribution of channel characteristics into the first neural network and outputting the information. Alternatively, the first adjustment information is obtained by inputting information characterizing the distribution of channel characteristics and structural information of at least one neural network layer in the second neural network into the first neural network and outputting the information. As can be seen, the input of the first neural network includes information characterizing the distribution of channel characteristics, which helps the second neural network, adjusted by the second device based on the first adjustment information, better adapt to the channel, thereby improving communication quality.

[0011] It can be understood that in this information transmission method, for multiple scenarios with different channel feature distributions, the first neural network can be used to determine the first adjustment information for adjusting the second neural network in each scenario based on the channel feature distribution in each scenario, which is conducive to improving the universality of the second neural network, so that the second neural network has generalization characteristics in scenarios with different channel feature distributions, so that the adjusted second neural network can quickly adapt to the distribution of its input information, and improve the adaptability of data and scenarios.

[0012] Optionally, the information used to characterize the channel characteristic distribution includes one or more of the following: channel state information, noise distribution information, signal-to-noise ratio, delay distribution information, power distribution information, Doppler spread information, and reference signal received power (RSRP).

[0013] In an optional embodiment, before the first device determines the first adjustment information based on the first neural network, the method further includes: the first device receiving first indication information from the second device, where the first indication information is used to request adjustment of the second neural network, or the first indication information is used to request activation of the first neural network. Thus, the second device can trigger adjustment of the second neural network or activation of the first neural network.

[0014] In another optional embodiment, before the first device determines the first adjustment information based on the first neural network, the method further includes: the first device sending first indication information to the second device, where the first indication information is used to request adjustment of the second neural network, or the first indication information is used to request activation of the first neural network. Thus, the first device can trigger adjustment of the second neural network or activation of the first neural network.

[0015] In an optional embodiment, the method further includes: the first device sending second indication information to the second device, where the second indication information is used to indicate at least one neural network layer to be adjusted in the second neural network. Thus, the first device can negotiate with the second device to determine the neural network layer to be adjusted in the second neural network.

[0016] In another optional embodiment, the method further includes: the first device receiving second indication information from the second device, where the second indication information is used to indicate at least one neural network layer to be adjusted in the second neural network. Thus, the first device can negotiate with the second device to determine the neural network layer to be adjusted in the second neural network.

[0017] In an optional embodiment, the method further includes: the first device determining second adjustment information based on the first neural network, where the second adjustment information is used to indicate adjustment information for at least one neural network layer in the third neural network. The first device adjusts at least one neural network layer in the third neural network based on the second adjustment information. It can be seen that the first device can also use the first neural network to assist in adjusting the third neural network it includes, which is beneficial to improving the universality of the third neural network and enabling the third neural network to have generalization capabilities in different scenarios. This information transmission method can be applied to the scenario where the third neural network is used by the first device to process transmitted data, which is beneficial to improving the generalization capabilities of the neural network used to process transmitted data in the communication system.

[0018] Optionally, the second adjustment information is specifically used to indicate one or more of the following: weight adjustment information, bias adjustment information, activation function, and structure adjustment information of at least one neural network layer in the third neural network.

[0019] In a second aspect, the present application provides an information transmission method that can be applied to a second device, a chip in the second device, or a logic module or software that can implement all or part of the functions of the second device. The method is described below using the second device as an example. The method includes: the second device receives first adjustment information from the first device, the first adjustment information is used to indicate adjustment information of at least one neural network layer in the second neural network, and the first adjustment information is determined by the first device based on the first neural network. The second device adjusts at least one neural network layer in the second neural network based on the first adjustment information.

[0020] It can be seen that the first device can use the first neural network to assist the second device in adjusting the second neural network. Based on the first neural network, first adjustment information adapted to the current communication scenario can be determined, which helps the second neural network adjusted by the second device to adapt to the current communication scenario. The first neural network can be used to determine first adjustment information for different scenarios, which helps the second device to adaptively adjust the second neural network for different scenarios, improves the universality of the second neural network, and enables the second neural network to have generalization capabilities in different scenarios. This information transmission method can be applied to the scenario where the second neural network is used by the second device to process transmitted data, which helps to improve the generalization capabilities of the neural network used to process transmitted data in the communication system.

[0021] In addition, compared to the data expansion method of improving the generalization ability of the second neural network by expanding the training data set of the second neural network, the information transmission method provided in the embodiment of the present application uses the first adjustment information output by the first neural network to adjust the second neural network, which can reduce the performance degradation of the second neural network caused by insufficient feature capture due to the complexity and diversity of wireless scenarios in the data expansion method. Moreover, the information transmission method provided in the embodiment of the present application enables efficient online adjustment of the neural network layers in the second neural network without the need for offline retraining of the second neural network, which can simultaneously ensure network performance and real-time requirements.

[0022] In an optional embodiment, the first adjustment information is specifically used to indicate one or more of the following: weight adjustment information, bias adjustment information, activation function, and structure adjustment information of at least one neural network layer in the second neural network. Thus, the first device can utilize the first neural network to assist the second device in adjusting the weights, biases, activation functions, and / or structure of the second neural network.

[0023] In an optional embodiment, the first adjustment information is obtained by the first device inputting information used to characterize the distribution of channel characteristics into the first neural network and outputting it. Alternatively, the first adjustment information is obtained by the first device inputting information used to characterize the distribution of channel characteristics and structural information of at least one neural network layer in the second neural network into the first neural network and outputting it. It can be seen that the input of the first neural network includes information used to characterize the distribution of channel characteristics, which helps the second neural network, adjusted by the second device based on the first adjustment information, better adapt to the channel, thereby improving communication quality.

[0024] It can be understood that in this information transmission method, for multiple scenarios with different channel feature distributions, the first neural network can be used to determine the first adjustment information for adjusting the second neural network in each scenario based on the channel feature distribution in each scenario, which is conducive to improving the universality of the second neural network, so that the second neural network has generalization characteristics in scenarios with different channel feature distributions, so that the adjusted second neural network can quickly adapt to the distribution of its input information, and improve the adaptability of data and scenarios.

[0025] Optionally, the information used to characterize the channel characteristic distribution includes one or more of the following: channel state information, noise distribution information, signal-to-noise ratio, delay distribution information, power distribution information, Doppler spread information, and RSRP.

[0026] In an optional embodiment, before the second device receives the first adjustment information from the first device, the method further includes: the second device sending first indication information to the first device, where the first indication information is used to request adjustment of the second neural network, or the first indication information is used to request activation of the first neural network. Thus, the second device can trigger adjustment of the second neural network or activation of the first neural network.

[0027] In another optional embodiment, before the second device receives the first adjustment information from the first device, the method further includes: the second device receiving first indication information from the first device, where the first indication information is used to request adjustment of the second neural network, or the first indication information is used to request activation of the first neural network. Thus, the first device can trigger adjustment of the second neural network or activation of the first neural network.

[0028] In an optional embodiment, the method further includes: the second device receiving second indication information from the first device, where the second indication information is used to indicate at least one neural network layer to be adjusted in the second neural network. Thus, the second device and the first device can negotiate to determine the neural network layer to be adjusted in the second neural network.

[0029] In another optional embodiment, the method further includes: the second device sending second indication information to the first device, where the second indication information is used to indicate at least one neural network layer to be adjusted in the second neural network. Thus, the second device can negotiate with the first device to determine the neural network layer to be adjusted in the second neural network.

[0030] In a third aspect, the present application further provides a communication device. The communication device has the function of implementing some or all of the embodiments described in the first aspect above, or has the function of implementing some or all of the functional implementations described in the second aspect above. The functions can be implemented by hardware, or by hardware executing corresponding software. The hardware or software includes one or more units or modules corresponding to the above functions.

[0031] In one possible design, the communication device may include a processing unit and a communication unit. The processing unit is configured to support the communication device in performing the corresponding functions in the above method. The communication unit is configured to support communication between the communication device and other communication devices. The communication device may also include a storage unit, which is coupled to the processing unit and the communication unit and stores program instructions and data necessary for the communication device.

[0032] In one embodiment, the communication device includes: a processing unit and a communication unit, and the processing unit is used to control the communication unit to send and receive data / signaling.

[0033] A processing unit is configured to determine first adjustment information based on a first neural network, where the first adjustment information is used to indicate adjustment information of at least one neural network layer in a second neural network, where the second neural network is a neural network in a second device.

[0034] A communication unit is used to send first adjustment information to the second device, where the first adjustment information is used by the second device to adjust at least one neural network layer in the second neural network.

[0035] In addition, in this aspect, other optional implementations of the communication device can refer to the relevant content of the first aspect above and will not be described in detail here.

[0036] In another embodiment, the communication device includes: a processing unit and a communication unit, and the processing unit is used to control the communication unit to send and receive data / signaling.

[0037] A communication unit is used to receive first adjustment information from a first device, where the first adjustment information is used to indicate adjustment information of at least one neural network layer in a second neural network, and the first adjustment information is determined by the first device based on the first neural network.

[0038] A processing unit is configured to adjust at least one neural network layer in the second neural network based on the first adjustment information.

[0039] In addition, in this aspect, other optional implementations of the communication device can refer to the relevant content of the second aspect above and will not be described in detail here.

[0040] As an example, the communication unit may be a transceiver or a communication interface, the storage unit may be a memory, and the processing unit may be a processor.

[0041] In one embodiment, the communication apparatus includes a processor and a transceiver. The processor is configured to determine first adjustment information based on a first neural network, the first adjustment information being used to indicate adjustment information for at least one neural network layer in a second neural network, where the second neural network is a neural network in a second device. The transceiver is configured to transmit the first adjustment information to the second device, the first adjustment information being used by the second device to adjust at least one neural network layer in the second neural network.

[0042] In addition, in this aspect, other optional implementations of the communication device can refer to the relevant content of the first aspect above and will not be described in detail here.

[0043] In another embodiment, the communication device includes a processor and a transceiver. The transceiver is configured to receive first adjustment information from a first device, the first adjustment information indicating adjustment information for at least one neural network layer in a second neural network, the first adjustment information being determined by the first device based on the first neural network. The processor is configured to adjust at least one neural network layer in the second neural network based on the first adjustment information.

[0044] In addition, in this aspect, other optional implementations of the communication device can refer to the relevant content of the first aspect above and will not be described in detail here.

[0045] In another embodiment, the communication device is a chip or a chip system. The processing unit may also be embodied as a processing circuit or a logic circuit; and the transceiver unit may be an input / output interface, an interface circuit, an output circuit, an input circuit, a pin, or a related circuit on the chip or chip system.

[0046] During implementation, the processor can be used to perform, for example, but not limited to, baseband-related processing, and the transceiver or communication interface can be used to perform, for example, but not limited to, radio frequency transceiver processing. The aforementioned components can be provided on separate chips, or at least partially or entirely on the same chip. For example, the processor can be further divided into an analog baseband processor and a digital baseband processor. The analog baseband processor can be integrated with the transceiver (or communication interface) on the same chip, while the digital baseband processor can be provided on a separate chip. With the continuous development of integrated circuit technology, more and more components can be integrated on the same chip. For example, a digital baseband processor can be integrated with multiple application processors (such as, but not limited to, a graphics processor, a multimedia processor, etc.) on the same chip. Such a chip can be called a system on a chip (SoC). Whether each component is provided independently on different chips or integrated on one or more chips often depends on the needs of the product design. The embodiments of the present application do not limit the implementation form of the aforementioned components.

[0047] In a fourth aspect, the present application also provides a processor for executing the various methods described above. In the process of executing these methods, the process of sending the above-mentioned signal and receiving the above-mentioned signal in the above-mentioned method can be understood as the process of outputting the above-mentioned signal by the processor, and the process of inputting the above-mentioned signal by the processor. When outputting the above-mentioned signal, the processor outputs the above-mentioned signal to the transceiver so that it is transmitted by the transceiver (or communication interface). After being output by the processor, the above-mentioned signal may also need to undergo other processing before reaching the transceiver (or communication interface). Similarly, when the processor receives the above-mentioned input signal, the transceiver (or communication interface) receives the above-mentioned signal and inputs it into the processor. Furthermore, after the transceiver (or communication interface) receives the above-mentioned signal, the above-mentioned signal may need to undergo other processing before being input into the processor.

[0048] For the sending and receiving operations involved in the processor, unless otherwise specified, or unless they conflict with their actual functions or internal logic in the relevant descriptions, they can be more generally understood as processor output, reception, input and other operations, rather than sending and receiving operations directly performed by the RF circuit and antenna.

[0049] During implementation, the processor may be a processor specifically configured to execute these methods, or may be a processor that executes computer instructions in a memory to execute these methods, such as a general-purpose processor. The memory may be a non-transitory memory, such as a read-only memory (ROM), which may be integrated with the processor on the same chip or disposed on separate chips. The embodiments of the present application do not limit the type of memory or the configuration of the memory and the processor.

[0050] In a fifth aspect, the present application further provides a communication system, which includes at least one first device and at least one second device according to the above aspects. In another possible design, the system may also include other devices that interact with the first device and / or the second device in the solution provided by the present application.

[0051] In a sixth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is run, the method described in the first or second aspect above is executed.

[0052] In a seventh aspect, the present application further provides a computer program product comprising instructions, the computer program product comprising: computer program code, which, when the computer program code is run, enables the method described in the first aspect or the second aspect above to be executed.

[0053] In an eighth aspect, the present application provides a chip system, which includes a processor and an interface, wherein the interface is used to obtain a program or instruction, and the processor is used to call the program or instruction to implement the functions involved in the first aspect, or to call the program or instruction to implement the functions involved in the second aspect. In one possible design, the chip system also includes a memory, which is used to store program instructions and data necessary for the terminal. The chip system can be composed of a chip, or it can include a chip and other discrete devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] FIG1 is a schematic diagram of a communication system provided in an embodiment of the present application;

[0055] FIG2 is a schematic diagram of a feedforward neural network provided in an embodiment of the present application;

[0056] FIG3 is a schematic diagram of a recursive neural network provided in an embodiment of the present application;

[0057] FIG4 is a schematic diagram of a hidden layer of a convolutional neural network provided in an embodiment of the present application;

[0058] FIG5 is a schematic diagram of an end-to-end network signal processing provided by an embodiment of the present application;

[0059] FIG6 is a schematic diagram of a physical layer architecture provided in an embodiment of the present application;

[0060] FIG7 is a schematic diagram of another physical layer architecture provided in an embodiment of the present application;

[0061] FIG8 is a flow chart of an information transmission method provided in an embodiment of the present application;

[0062] FIG9 is a schematic diagram of an adaptive intelligent architecture network provided in an embodiment of the present application;

[0063] FIG10 is a schematic diagram of another adaptive intelligent architecture network provided in an embodiment of the present application;

[0064] FIG11 is a schematic diagram of another information transmission method provided in an embodiment of the present application;

[0065] FIG12 is a schematic diagram of another information transmission method provided in an embodiment of the present application;

[0066] FIG13 is a schematic diagram of another information transmission method provided in an embodiment of the present application;

[0067] FIG14 is a schematic diagram of another information transmission method provided in an embodiment of the present application;

[0068] FIG15 is a schematic diagram of simulation results of a test using Gaussian noise provided in an embodiment of the present application;

[0069] FIG16 is a schematic diagram of simulation results of a test using mixed Gaussian noise according to an embodiment of the present application;

[0070] FIG17 is a schematic structural diagram of a communication device provided in an embodiment of the present application;

[0071] FIG18 is a schematic structural diagram of another communication device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0072] The technical solutions in the embodiments of the present application are described clearly and completely below in conjunction with the drawings in the embodiments of the present application.

[0073] In order to better understand the information transmission method disclosed in the embodiment of the present application, a communication system applicable to the embodiment of the present application is described.

[0074] The technical solutions of the embodiments of the present application can be applied to various communication systems. For example, global mobile communication systems, long term evolution (LTE) systems, universal mobile communication systems, fourth generation mobile communication technology (4G) systems, next generation radio access networks (NG-RAN), new radio (NR) systems, fifth generation mobile communication technology (5G) systems, and with the continuous development of communication technology, the technical solutions of the embodiments of the present application can also be used for subsequent evolved communication systems, such as sixth generation mobile communication technology (6G) systems, seventh generation mobile communication technology (7G) systems, and so on. The technical solutions of the embodiments of the present application are applicable to new air interface transmission scenarios of future communication systems and support adaptive intelligent architecture networks.

[0075] Please refer to Figure 1, which is a schematic diagram of the structure of a communication system provided in an embodiment of the present application. The communication system includes but is not limited to a first device and a second device. The number and configuration of the devices shown in Figure 1 are for illustrative purposes only and do not constitute a limitation on the embodiment of the present application. In actual applications, two or more first devices and two or more second devices may be included. The first device may be a network device or a terminal device, and the second device may be a network device or a terminal device.

[0076] In the embodiments of the present application, the network device has a wireless transceiver function, and the network device includes but is not limited to: a base station (BS), a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a home network device (e.g., a home evolved Node B, or home Node B, HNB), a baseband unit (BBU), a wireless relay node, a wireless backhaul node, a transmission and reception point (TRP; or, a transmission point, TP), a satellite, etc. Among them, a base station is a device deployed in a wireless access network that can provide wireless communication functions, which can also be called a base station device, such as an evolved Node B (eNB or e-NodeB) in a long term evolution (LTE) system, a Node B (Node B), a base station (gNodeB or gNB) in a 5G system, a base station in a 6G system, etc. The base station may include a BBU and a remote radio unit (RRU). The BBU and RRU can be placed in different locations. For example, the RRU can be remotely located in a high-traffic area, while the BBU can be placed in a central equipment room. The BBU and RRU can also be placed in the same equipment room. The BBU and RRU can also be separate components within the same rack. Base stations can take the following forms: macro base stations, micro base stations (also known as small cells), pico base stations, relay stations, access points, balloon stations, and so on.

[0077] The terminal device may also be referred to as user equipment (UE), terminal, access terminal, subscriber unit, user station, mobile station, mobile station (MS), remote station, remote terminal, mobile device, user terminal, user agent or user device, and may be applied to 4G, 5G or even 6G systems. The terminal device in the embodiment of the present application may be a handheld device, a vehicle-mounted device, a wearable device, a computing device or other processing device connected to a wireless modem with wireless communication capabilities; the terminal device may be a terminal with a function of connecting to a cellular base station. For example, the terminal device may be a cellular phone, a smart phone, a tablet computer, a wireless data card, a personal digital assistant (PDA) computer, a tablet computer, a wireless modem, a handheld device, a laptop computer, a machine type communication (MTC) terminal, etc. The terminal device can also be a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal in industrial control, a wireless terminal in self-driving, a wireless terminal in remote medical, a wireless terminal in a smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city, a wireless terminal in a smart home, a vehicle-mounted terminal, a wireless communication device in a smart factory, and the like.

[0078] The following is a brief introduction to the relevant concepts involved in the embodiments of this application.

[0079] 1. Explainable artificial intelligence (XAI)

[0080] XAI's research objectives include understanding the decision-making process of artificial intelligence (AI), fully understanding AI models, and developing translatable and explainable AI models. XAI's research areas include making deep neural network components transparent, learning the semantic web from deep neural networks, and generating explanations (generating human-understandable explanations). XAI's research dimensions include rationality, understandability, and traceability. Rationality can be expressed as the need for the AI ​​model's reasoning and predictive capabilities to be reasonable. Understandability can be expressed as the ability for humans to partially or completely understand the model underlying AI decisions. Traceability can be expressed as the ability to track the reasoning or prediction process based on the nature of the data and the logic of the mathematical algorithm.

[0081] 2. Deep neural network (DNN)

[0082] Neural network algorithms are a type of machine learning technology, a means of achieving artificial intelligence. They possess the ability, enabled by universal approximation theorems, to infinitely approximate any continuous function. They can accurately abstractly model complex, high-dimensional problems. Deep neural networks and deep learning, implemented using neural network algorithms, can be applied to image processing, speech processing, natural language processing, disease diagnosis, and more.

[0083] Deep neural networks generally have a multi-layer structure. Increasing the depth and width of a neural network can improve its expressive power, thereby providing more powerful information extraction and abstract modeling capabilities for complex systems. For example, a feedforward neural network (FNN) is a deep neural network. Figure 2 is a schematic diagram of a feedforward neural network provided in an embodiment of the present application. In conjunction with Figure 2, the neural network layers in the FNN include an input layer, a hidden layer, and an output layer. The input layer can be used to input information to be processed. The hidden layer can be used to extract information features. A DNN generally contains at least two hidden layers, and these multiple hidden layers can be used to extract information features to varying degrees. Figure 2 uses one hidden layer as an example. The output layer can be used to map the required output information from the extracted feature information. The circular patterns in Figure 2 represent neurons in each layer, where white circular patterns represent neurons in the input layer, gray circular patterns represent neurons in the hidden layer, and black circular patterns represent neurons in the output layer. The connection method between neurons in each layer and the activation function used determine the expression function of the neural network.

[0084] Taking into account different needs, DNN can have a variety of construction methods. For example, recurrent neural networks (RNN), convolutional neural networks (CNN) and fully connected neural networks respectively represent a construction method of DNN. Figure 3 is a schematic diagram of a recursive neural network provided in an embodiment of the present application. In conjunction with Figure 3, the difference between RNN and the FNN shown in Figure 2 is that the output of the neurons in the RNN can directly act on itself at the next moment. For example, the input of the i-th layer neurons at the nth moment includes not only the output of the i-1th layer neurons at the nth moment, but also the output of the i-th layer neurons at the n-1th moment, as shown by the semicircular arrow in Figure 3.

[0085] Figure 4 is a schematic diagram of a hidden layer of a convolutional neural network provided by an embodiment of the present application. The large black boxes in Figure 4 are channels or feature maps of each layer, and the matrices represented by small boxes of different background colors are convolution kernels corresponding to different channels. For CNN, not all upper and lower layer neurons can be directly connected, but are connected through "convolution kernels" as intermediaries. In addition, the information to be processed in CNN still retains its original positional relationship after the convolution operation.

[0086] 3. End to End Learning (E2E Learning)

[0087] End-to-end learning involves directly optimizing the overall objective of the task without performing module-based or phased training during deep learning. End-to-end learning can be applied to information processing scenarios at both the sender and receiver ends of a communication system. In this scenario, an end-to-end joint optimization approach can be used to design the overall system, significantly improving transmission performance. As shown in Figure 5, the sender and receiver can construct problem models using a joint optimization approach when processing information, or they can utilize some of the other's auxiliary information (for example, the sender can forward-transmit auxiliary information to the receiver, and the receiver can reverse-transmit auxiliary information to the sender) to improve overall performance.

[0088] When neural networks (such as deep neural networks) are used for joint optimization of transmitters and receivers, the two neural networks must be designed together to ensure convergence during training and excellent performance during inference. Specifically, these neural networks can be constructed by jointly constructing a cost function, or by utilizing an autoencoder structure or various variations of the autoencoder structure, or by combining other neural network structures to form the final neural network. There are no restrictions.

[0089] For example, a physical layer architecture is shown in Figure 6. The transmitter processes the transmitted signal through multiple modules, including channel coding, interleaving, scrambling, pilot insertion, OFDM modulation, CP insertion, layer mapping, precoding, and BF weighting. The processed signal is then added with AWGN and transmitted to the receiver via a wireless channel. The receiver processes the received signal through multiple modules, including frequency offset estimation and compensation, channel estimation, MIMO detection, equalizer, whitening filter, deinterleaving, and channel decoding. As can be seen, the physical layer architecture shown in Figure 6 is complex and cumbersome. An end-to-end intelligent physical layer architecture can be shown in Figure 7. The transmitter selects one set of parameters from multiple sets as parameters for the modulation network, inputs the bits to be transmitted into the modulation network, and the modulation network processes the input bits. The transmitter then transmits the transmit symbols output by the modulation network through the channel to the receiver. The receiver can process the received symbols using a channel estimation network and a signal detection network, with the channel estimation network and the signal detection network alternating during the processing process. The modulation network, channel estimation network, and signal detection network are all neural networks. It can be seen that processing the transmitted data based on end-to-end learning neural networks at both the sending and receiving ends can simplify the processing process and improve overall performance.

[0090] The embodiments of the present application are described below with reference to the accompanying drawings.

[0091] Please refer to Figure 8, which is a flow chart of an information transmission method provided in an embodiment of the present application. The diagram uses the first device and the second device as examples to illustrate the corresponding method, but the present application does not limit the execution subjects of the interaction diagram. For example, the first device in the figure can also be a chip or chip system or processor that supports the first device to implement the corresponding method, or a logic module or software that can implement all or part of the functions of the first device. The second device in the figure can also be a chip or chip system or processor that supports the second device to implement the corresponding method, or a logic module or software that can implement all or part of the functions of the second device. The information transmission method includes the following steps.

[0092] S101. A first device determines first adjustment information based on a first neural network, where the first adjustment information is used to indicate adjustment information of at least one neural network layer in a second neural network.

[0093] Among them, at least one neural network layer in the second neural network is a partial neural network layer or all neural network layers in the second neural network. It is understandable that the first adjustment information can be used to indicate the adjustment information of a partial neural network layer or all neural network layers in the second neural network. In addition, the second neural network is a neural network in the second device, and the communication function carried by the second neural network is different from the communication function carried by the first neural network (the communication function carried by the first neural network includes the function for determining the first adjustment information). The embodiment of the present application does not limit the specific function of the second neural network. For example, the second neural network is used by the second device to process the data / signal to be sent. In this case, the second neural network can also be called a modulation network. For another example, the second neural network is used by the second device to process the received data / signal. In this case, the second neural network can also be called a detection network. For another example, the second neural network is used by the second device to compress channel state information. For another example, the second neural network is used by the second device to perform channel estimation. In addition, in the embodiment of the present application, the first neural network can also be called a dynamic adjustment network.

[0094] In an optional embodiment, the first adjustment information is obtained by the first device inputting information used to characterize the distribution of channel characteristics into the first neural network and outputting it. Alternatively, the first adjustment information is obtained by the first device inputting information used to characterize the distribution of channel characteristics and structural information of at least one neural network layer in the second neural network into the first neural network and outputting it. It can be seen that the input of the first neural network includes information used to characterize the distribution of channel characteristics, which helps the second neural network, adjusted by the second device based on the first adjustment information, better adapt to the channel, thereby improving communication quality.

[0095] It can be understood that in this information transmission method, for multiple scenarios with different channel feature distributions, the first neural network can be used to determine the first adjustment information for adjusting the second neural network in each scenario based on the channel feature distribution in each scenario, which is conducive to improving the universality of the second neural network, so that the second neural network has generalization characteristics in scenarios with different channel feature distributions, so that the adjusted second neural network can quickly adapt to the distribution of its input information, and improve the adaptability of data and scenarios.

[0096] Optionally, the information used to characterize the distribution of channel characteristics includes one or more of the following: channel state information (CSI), noise distribution information, signal-to-noise ratio, delay distribution information, power distribution information, Doppler spread information, reference signal receiving power (RSRP). Optionally, the channel state information may be obtained by the first device performing channel estimation based on a reference signal from the second device. For example, in a scenario where the first device is a terminal device and the second device is a network device, the first device may perform channel estimation based on a channel state information reference signal (CSIreference signal, CSI-RS) from the second device to obtain channel state information. For another example, in a scenario where the first device is a network device and the second device is a terminal device, the first device may perform channel estimation based on a demodulation reference signal (SRS) from the second device to obtain channel state information.

[0097] In addition, the embodiments of the present application do not limit the content included in the information used to characterize the distribution of channel characteristics. For example, in addition to the content listed above, the information used to characterize the distribution of channel characteristics may also include input information of the second neural network. Alternatively, the information used to characterize the distribution of channel characteristics may also include signals of other modes, for example, it may include relevant information of the previous several moments for characterizing time correlation, other frequency band information for characterizing frequency domain correlation, other port information for characterizing spatial correlation, and so on.

[0098] In an optional embodiment, the first adjustment information is specifically used to indicate one or more of the following: weight adjustment information, bias adjustment information, activation function, and structure adjustment information of at least one neural network layer in the second neural network. The weight adjustment information indicated by the first adjustment information is used to adjust the weight of the neural network layer in the second neural network, the bias adjustment information indicated by the first adjustment information is used to adjust the bias of the neural network layer in the second neural network, the activation function indicated by the first adjustment information is used to adjust the activation function of the neural network layer in the second neural network, and the structure adjustment information indicated by the first adjustment information is used to adjust the structure of the neural network layer in the second neural network. In addition, when the first adjustment information indicates multiple items of weight adjustment information, bias adjustment information, activation function, and structure adjustment information, the multiple items indicated by the first adjustment information can be used to adjust the same neural network layer in the second neural network, or can be used to adjust different neural network layers in the second neural network, without limitation.

[0099] For example, the second neural network includes neural network layers #1 to #3. The first adjustment information is specifically used to indicate weight adjustment information #1 of neural network layer #1 and bias adjustment information #1 of neural network layer #1 in the second neural network; wherein weight adjustment information #1 is used to adjust the weight of neural network layer #1, and bias adjustment information #1 is used to adjust the bias of neural network layer #1.

[0100] For another example, the second neural network includes neural network layers #1 to #3. The first adjustment information is specifically used to indicate weight adjustment information #1 of neural network layer #1, bias adjustment information #1 of neural network layer #1, and bias adjustment information #1 of neural network layer #2 in the second neural network; wherein weight adjustment information #1 is used to adjust the weight of neural network layer #1, bias adjustment information #1 is used to adjust the bias of neural network layer #1, and bias adjustment information #2 is used to adjust the bias of neural network layer #2.

[0101] In an optional embodiment, before the first device determines the first adjustment information based on the first neural network, the method may further include: transmitting first indication information between the first device and the second device, thereby triggering adjustment of the second neural network or triggering activation of the first neural network. The first indication information is used to request adjustment of the second neural network, or the first indication information is used to request activation of the first neural network. Triggering adjustment of the second neural network or triggering activation of the first neural network can be manifested as: triggering the first device to perform model training to obtain the first neural network. The details can be as described in the following optional embodiments 1.1 and 1.2.

[0102] In implementation 1.1, the second device sends a first instruction to the first device. In response, the first device receives the first instruction from the second device and performs model training to obtain a first neural network. This indicates that the second device can trigger adjustments to the second neural network or activate the first neural network.

[0103] Optionally, in embodiment 1.1, after the first device receives the first indication information from the second device, the method may further include: the first device sends third indication information to the second device; accordingly, the first device receives the third indication information from the second device. The third indication information is used to indicate confirmation information for the first indication information. Specifically, if the first indication information is used to apply for adjustment of the second neural network, the third indication information can be used to indicate confirmation information for adjusting the second neural network; if the first indication information is used to request activation of the first neural network, the third indication information can be used to indicate confirmation information for activating the first neural network. In addition, the embodiment of the present application does not restrict the order between the operation of the first device sending the third indication information to the second device and the operation of the first device performing model training to obtain the first neural network. For example, after the first device receives the first indication information from the second device, it can first send the third indication information to the second device, and then perform the operation of performing model training to obtain the first neural network. For another example, in a scenario where the first device is a network device (e.g., a base station), after the first device receives the first indication information from the second device, it can first perform the operation of performing model training to obtain the first neural network, and then send the third indication information to the second device.

[0104] Optionally, in implementation 1.1, the operation of the second device sending the first indication information to the first device may be performed periodically, or on-demand, or non-periodically, or semi-permanently (also referred to as semi-persistently). In an optional manner, steps S101 to S103 may be performed each time the operation of the second device sending the first indication information to the first device is performed. Optionally, steps S101 to S103 may be performed periodically each time the operation of the second device sending the first indication information to the first device is performed, until the second device sends a termination indication to the first device, and stops repeatedly performing steps S101 to S103.

[0105] In implementation 1.2, the first device sends a first instruction to the second device; in response, the second device receives the first instruction from the first device. The first device performs model training to obtain a first neural network. This indicates that the first device can trigger adjustments to the second neural network or activate the first neural network.

[0106] Optionally, in embodiment 1.2, after the second device receives the first indication information from the first device, the method may further include: the second device sends third indication information to the first device; accordingly, the first device receives the third indication information from the second device. The third indication information is used to indicate confirmation information for the first indication information. Specifically, if the first indication information is used to apply for adjustment of the second neural network, the third indication information may be used to indicate confirmation information for adjusting the second neural network; if the first indication information is used to request activation of the first neural network, the third indication information may be used to indicate confirmation information for activating the first neural network. In addition, the embodiment of the present application does not restrict the order between the operation of the first device receiving the third indication information from the second device and the operation of the first device performing model training to obtain the first neural network. For example, after the first device sends the first indication information to the second device, it may perform the operation of performing model training to obtain the first neural network when / after receiving the third indication information from the second device. For another example, in a scenario where the first device is a network device (e.g., a base station), after the first device receives the first indication information from the second device, it may first perform model training to obtain the first neural network and then receive the third indication information from the second device.

[0107] Optionally, in implementation 1.2, the operation of the first device sending the first indication information to the second device may be performed periodically, or on-demand, or non-periodically, or semi-permanently (also referred to as semi-persistently). In an optional manner, steps S101 to S103 may be performed each time the operation of the first device sending the first indication information to the second device is performed. Optionally, steps S101 to S103 may be performed periodically each time the operation of the first device sending the first indication information to the second device is performed, until the first device sends a termination indication to the second device, and stops repeatedly performing steps S101 to S103.

[0108] In an optional embodiment, the method may further include: the first device sends a second indication message to the second device, the second indication message being used to indicate at least one neural network layer to be adjusted in the second neural network; correspondingly, the second device receives the second indication message from the first device. Through this embodiment, the first device can negotiate with the second device to determine the neural network layer to be adjusted in the second neural network. Exemplarily, this embodiment can be applied to the scenario described in embodiment 1.1 where the second device sends the first indication message to the first device. After receiving the first indication message from the second device, the first device can perform the operation of sending the second indication message to the second device. In addition, the embodiment of the present application does not limit the order of the operation of the first device sending the second indication message to the second device and the operation of performing model training with the first device to obtain the first neural network.

[0109] In another optional embodiment, the method may also include: the second device sends second indication information to the first device, the second indication information is used to indicate at least one neural network layer to be adjusted in the second neural network; accordingly, the first device receives the second indication information from the second device. Through this embodiment, the second device can negotiate with the first device to determine the neural network layer to be adjusted in the second neural network. Exemplarily, this embodiment can be applied to the scenario described in embodiment 1.2 where the first device sends the first indication information to the second device. After receiving the first indication information from the first device, the second device can perform the operation of sending the second indication information to the first device. In addition, the embodiment of the present application does not limit the order of the operation of the first device receiving the second indication information from the second device and the operation of performing model training with the first device to obtain the first neural network.

[0110] Optionally, the aforementioned second indication information may include an identifier (e.g., an index) of at least one neural network layer to be adjusted in the second neural network. Alternatively, the second indication information may include other information capable of identifying at least one neural network layer to be adjusted in the second neural network, without limitation.

[0111] S102. The first device sends first adjustment information to the second device; correspondingly, the second device receives the first adjustment information from the first device.

[0112] S103. The second device adjusts at least one neural network layer in the second neural network based on the first adjustment information.

[0113] For example, the first adjustment information is specifically used to indicate weight adjustment information #1 of neural network layer #1, bias adjustment information #1 of neural network layer #1, and bias adjustment information #2 of neural network layer #2 in the second neural network. Then, the second device can adjust the weight of neural network layer #1 based on weight adjustment information #1, adjust the bias of neural network layer #2 based on bias adjustment information #1, and adjust the bias of neural network layer #2 based on bias adjustment information #2.

[0114] As another example, consider the case where the first device shown in FIG9 is a terminal device and the second device is a network device. Referring to FIG9 , the second neural network in the network device is used by the network device to process data to be sent to the terminal device, and the third neural network in the terminal device is used by the terminal device to process data received from the network device. The second and third neural networks both include a convolution (Conv) layer, a rectified linear unit (ReLU) layer, and a linear layer, and the third neural network also includes a Sigmoid function layer. The input to the first neural network in the terminal device may include some / all input information of the third neural network. The terminal device determines first adjustment information based on the first neural network, where the first adjustment information specifically indicates weight adjustment information #1 and bias adjustment information #1 for the linear layer in the second neural network. The terminal device sends the first adjustment information to the network device. The network device can then adjust the weights of the linear layer in the second neural network layer based on the weight adjustment information #1 and adjust the bias of the linear layer in the second neural network layer based on the bias adjustment information #1.

[0115] In addition, the embodiment of the present application does not limit the specific adjustment method of the second device adjusting at least one neural network layer in the second neural network based on the first adjustment information. It is understandable that the embodiment of the present application does not limit the mathematical operations used in the process of the second device adjusting at least one neural network layer in the second neural network based on the first adjustment information. For example, the mathematical operations used may include one or more of the following: addition, multiplication, convolution, and direct substitution. The following is explained by taking the example of the second device adjusting the weights of at least one neural network layer in the second neural network based on the first adjustment information. The specific adjustment method of the second device adjusting the bias and structure of at least one neural network layer in the second neural network based on the first adjustment information is similar and will not be repeated.

[0116] For example, the first adjustment information is specifically used to indicate weight adjustment information #1 (weight adjustment information #1 includes weight #1) of neural network layer #1 in the second neural network. The weight of neural network layer #1 in the second neural network before adjustment is weight #2. The second device may use the result obtained by multiplying weight #1 and weight #2 as the adjusted weight of neural network layer #1. Alternatively, the second device may use the result obtained by adding weight #1 and weight #2 as the adjusted weight of neural network layer #1. Alternatively, the second device may use the result obtained by convolving weight #1 and weight #2 as the adjusted weight of neural network layer #1. Alternatively, the second device may use the result obtained by performing one or more other mathematical operations on weight #1 and weight #2 as the adjusted weight of neural network layer #1 in the second neural network. Alternatively, the second device may directly use weight #1 as the adjusted weight of neural network layer #1. Alternatively, the second device may use the result obtained by performing one or more mathematical operations on weight #1 as the adjusted weight of neural network layer #1. There is no limitation.

[0117] In an optional embodiment, the number of first neural networks may be one or more. If there is only one first neural network, the first device may determine a piece of first adjustment information based on the first neural network, and the second device may adjust the second neural network based on the first adjustment information. If there are multiple first neural networks, the first device may determine a piece of first adjustment information based on each of the multiple first neural networks, and the second device may adjust the second neural network based on the multiple first adjustment information.

[0118] For example, the first device determines first adjustment information #1 based on the first neural network #1, where the first adjustment information #1 is used to indicate weight adjustment information #1 of neural network layer #1 and bias adjustment information #1 of neural network layer #1 in the second neural network. The first device also determines first adjustment information #2 based on the first neural network #2, where the first adjustment information #2 is used to indicate weight adjustment information #2 of neural network layer #1 and bias adjustment information #2 of neural network layer #1 in the second neural network. The first device may send the first adjustment information #1 and the first adjustment information #2 to the second device. The second device may adjust the weights of neural network layer #1 based on the weight adjustment information #1 and the weight adjustment information #2, and adjust the bias of neural network layer #1 based on the bias adjustment information #1 and the bias adjustment information #2.

[0119] In an optional embodiment, the method may further include: the first device determines second adjustment information based on the first neural network, the second adjustment information is used to indicate adjustment information of at least one neural network layer in the third neural network; the first device adjusts at least one neural network layer in the third neural network based on the second adjustment information.

[0120] In this case, at least one neural network layer in the third neural network is part of or all of the neural network layers in the third neural network. It is understandable that the second adjustment information can be used to indicate adjustment information for part of or all of the neural network layers in the third neural network. Furthermore, the third neural network is a neural network in the first device, and the communication function carried by the third neural network is different from the communication function carried by the first neural network. The embodiments of the present application do not limit the specific functions of the third neural network. For example, the third neural network is used by the first device to process data / signals to be transmitted. In this case, the third neural network can also be referred to as a modulation network. For another example, the third neural network is used by the first device to process received data / signals. In this case, the third neural network can also be referred to as a detection network. For another example, the third neural network is used by the first device to compress channel state information. For another example, the third neural network is used by the first device to perform channel estimation.

[0121] Optionally, the third neural network can be a neural network adapted to the second neural network. For example, the second neural network is used by the second device to process data / signals to be sent to the first device, and the third neural network is used by the first device to process data / signals received by the second device after being processed by the second neural network. For another example, the third neural network is used by the first device to process data / signals to be sent to the second device, and the second neural network is used by the second device to process data / signals received by the first device after being processed by the third neural network.

[0122] Optionally, the second adjustment information is specifically used to indicate one or more of the following: weight adjustment information, bias adjustment information, activation function, and structural adjustment information of at least one neural network layer in the third neural network. Optionally, the second adjustment information is obtained by inputting information used to characterize the distribution of channel characteristics into the first neural network; or, the second adjustment information is obtained by inputting information used to characterize the distribution of channel characteristics and structural information of at least one neural network layer in the third neural network into the first neural network. For the scenario in which the first device simultaneously determines the first adjustment information and the second adjustment information based on the first neural network, the first device inputs the information used to characterize the distribution of channel characteristics into the first neural network, or the first device inputs the information used to characterize the distribution of channel characteristics and structural information of at least one neural network layer in the second neural network and / or structural information of at least one neural network layer in the third neural network into the first neural network, and the first neural network can output the first adjustment information and the second adjustment information.

[0123] In addition, the second adjustment information is similar to the aforementioned first adjustment information. The first device adjusts at least one neural network layer in the third neural network based on the second adjustment information, similar to the aforementioned second device adjusting at least one neural network layer in the second neural network based on the first adjustment information. For a detailed description, please refer to the aforementioned related description and will not be repeated here. In addition, with respect to the embodiment in which the first device determines the second adjustment information based on the first neural network, the first indication information mentioned in the aforementioned embodiments 1.1 and 1.2 may also be used to request adjustment of the third neural network.

[0124] In an optional manner, the method may further include: the first device sends fourth indication information to the second device, the fourth indication information being used to indicate at least one neural network layer to be adjusted in the third neural network; accordingly, the second device receives the fourth indication information from the first device. In another optional manner, the method may further include: the second device sends fourth indication information to the first device, the fourth indication information being used to indicate at least one neural network layer to be adjusted in the third neural network; accordingly, the first device receives the fourth indication information from the second device. It is understandable that the first device and the second device may negotiate to determine the neural network layer to be adjusted in the third neural network. Optionally, the fourth indication information may include an identifier (e.g., an index) of at least one neural network layer to be adjusted in the third neural network. Alternatively, the fourth indication information may include other information capable of identifying at least one neural network layer to be adjusted in the third neural network, without limitation.

[0125] It is understandable that in the embodiment of the present application, the first device can determine the first adjustment information based on the first neural network, but not the second adjustment information. Alternatively, the first device can simultaneously determine the first adjustment information and the second adjustment information based on the first neural network. Alternatively, the first device can determine the second adjustment information based on the first neural network, but not the first adjustment information. The first adjustment information is used by the second device to adjust the second neural network, and the second adjustment information is used by the first device to adjust the third neural network. For a detailed description of the first adjustment information and the second adjustment information, please refer to the aforementioned related description, which will not be repeated here. In addition, in the case where the first device determines multiple first neural networks, the multiple first neural networks are all used to determine the first adjustment information but not the second adjustment information, or the multiple first neural networks are all used to determine the second adjustment information but not the first adjustment information, or some or all of the multiple first neural networks are used to determine the first adjustment information and the second adjustment information. The following is an example of the first device performing model training to obtain the first neural network #1 and the first neural network #2.

[0126] For example, the first device determines first adjustment information #1 based on the first neural network #1 and determines first adjustment information #2 based on the first neural network #2. Both the first adjustment information #1 and the first adjustment information #2 are used by the second device to adjust the second neural network.

[0127] For another example, the first device determines second adjustment information #1 based on the first neural network #1 and determines second adjustment information #2 based on the first neural network #2. The second adjustment information #1 and the second adjustment information #2 are both used by the first device to adjust the third neural network.

[0128] For another example, the first device determines first adjustment information #1 based on the first neural network #1 and determines second adjustment information #2 based on the first neural network #2. The first adjustment information #1 is used by the second device to adjust the second neural network, and the second adjustment information #2 is used by the first device to adjust the third neural network.

[0129] For another example, the first device determines first adjustment information #1 and second adjustment information #1 based on the first neural network #1, and determines first adjustment information #2 based on the first neural network #2. Both first adjustment information #1 and first adjustment information #2 are used by the second device to adjust the second neural network, and second adjustment information #1 is used by the first device to adjust the third neural network.

[0130] In an optional embodiment, the method may further include: the second device performs model training to obtain a fourth neural network, and the second device determines third adjustment information and / or fourth adjustment information based on the fourth neural network. The third adjustment information is used to indicate adjustment information for at least one neural network in the second neural network, and the third adjustment information is used by the second device to adjust at least one neural network layer in the second neural network. The fourth adjustment information is used to indicate adjustment information for at least one neural network in the third neural network, and the fourth adjustment information is used by the first device to adjust at least one neural network layer in the third neural network. It can be seen that the second device can also train the fourth neural network itself to determine adjustment information that can be used to adjust other neural networks.

[0131] In the event that the second device determines third adjustment information based on the fourth neural network, the second device may adjust at least one neural network layer in the second neural network based on the third adjustment information. Optionally, if the second device also receives first adjustment information from the first device, the second device may adjust at least one neural network layer in the second neural network based on the third adjustment information and the first adjustment information.

[0132] In the event that the second device determines fourth adjustment information based on the fourth neural network, the second device further sends the fourth adjustment information to the first device. The first device may then adjust at least one neural network layer in the third neural network based on the fourth adjustment information. Alternatively, if the first device also determines second adjustment information based on the first device, the first device may adjust at least one neural network layer in the third neural network based on the fourth adjustment information and the second adjustment information.

[0133] In addition, the number of fourth neural networks can be one or more, and any one of the one or more fourth neural networks can be used to determine the third adjustment information and / or the fourth adjustment information. The fourth neural network is similar to the first neural network, and the difference between the two is that the first neural network is a neural network trained by the first device, and the fourth neural network is a neural network trained by the second device. For the specific description of the fourth neural network, please refer to the aforementioned description of the first neural network, and will not be repeated here. In addition, in the embodiment of the present application, the fourth neural network can also be referred to as a dynamic adjustment network, the first neural network is the dynamic adjustment network in the first device, and the fourth neural network is the dynamic adjustment network in the second device.

[0134] Optionally, the third adjustment information is specifically used to indicate one or more of the following: weight adjustment information, bias adjustment information, activation function, and structural adjustment information of at least one neural network layer in the second neural network. The fourth adjustment information is specifically used to indicate one or more of the following: weight adjustment information, bias adjustment information, activation function, and structural adjustment information of at least one neural network layer in the third neural network. Optionally, the third adjustment information and / or the fourth adjustment information is output by the second device inputting information used to characterize the channel characteristic distribution and structural information of at least one neural network layer in the second neural network into the fourth neural network. Alternatively, the third adjustment information is output by the second device inputting information used to characterize the channel characteristic distribution and structural information of at least one neural network layer in the third neural network into the fourth neural network. Alternatively, the fourth adjustment information is output by the second device inputting information used to characterize the channel characteristic distribution and structural information of at least one neural network layer in the third neural network into the fourth neural network. The third adjustment information is similar to the aforementioned second adjustment information, and the fourth adjustment information is similar to the aforementioned first adjustment information. For details, please refer to the aforementioned related explanations and will not be repeated here.

[0135] For example, as shown in FIG10 , a first device determines first adjustment information #1 and second adjustment information #1 based on a first neural network #1. The first adjustment information #1 specifically indicates weight adjustment information #1 and bias adjustment information #1 for neural network layer #1 in a second neural network, and the second adjustment information #1 specifically indicates weight adjustment information #2 and bias adjustment information #2 for neural network layer #2 in a third neural network. The first device sends the first adjustment information #1 to a second device. The second device determines third adjustment information #1 based on a fourth neural network #1. The third adjustment information #1 specifically indicates weight adjustment information #3 and bias adjustment information #3 for neural network layer #1 in the second neural network. The second device can then adjust the weights of neural network layer #1 in the second neural network based on the weight adjustment information #1 and weight adjustment information #3, and adjust the bias of neural network layer #1 in the second neural network based on the bias adjustment information #1 and bias adjustment information #3. The first device can adjust the weights of neural network layer #2 in the third neural network based on the weight adjustment information #2, and adjust the bias of neural network layer #2 in the third neural network based on the bias adjustment information #2.

[0136] In an optional embodiment, after the second device adjusts the second neural network and / or the first device adjusts the third neural network, it may also be performed that: the second device trains the adjusted second neural network and / or the first device trains the adjusted third neural network.

[0137] In addition, the embodiments of the present application do not impose any restrictions on the signaling used to carry any information (such as the first adjustment information, the fourth adjustment information, the first indication information, the second indication information, the third indication information, and the fourth indication information) transmitted between the first device and the second device mentioned above. The embodiments of the present application do not impose any restrictions on the transmission channel, transmission period, data quantization, and index format for transmitting any information between the first device and the second device mentioned above.

[0138] In summary, in this information transmission method, a first device may determine first adjustment information based on a first neural network, where the first adjustment information indicates adjustment information for at least one neural network layer in a second neural network, where the second neural network is a neural network in the second device. The first device transmits the first adjustment information to the second device. The second device adjusts at least one neural network layer in the second neural network based on the first adjustment information.

[0139] It can be seen that the first device can use the first neural network to assist the second device in adjusting the second neural network. Based on the first neural network, first adjustment information adapted to the current communication scenario can be determined, which is conducive to the second neural network adjusted by the second device being able to adapt to the current communication scenario. The first neural network can be used to determine first adjustment information for different scenarios, which is conducive to the second device being able to adaptively adjust the second neural network for different scenarios, improving the universality of the second neural network, and making the second neural network have the ability to generalize in different scenarios. This information transmission method can be applied to the scenario where the second neural network is used by the second device to process transmitted data, which is conducive to improving the generalization ability of the neural network used to process transmitted data in the communication system.

[0140] In addition, compared to the data expansion method of improving the generalization ability of the second neural network by expanding the training data set of the second neural network, the information transmission method provided in the embodiment of the present application uses the first neural network to assist the second device in adjusting the second neural network, which can reduce the performance degradation of the second neural network caused by insufficient feature capture due to the complexity and diversity of wireless scenarios in the data expansion method. Moreover, the information transmission method provided in the embodiment of the present application enables efficient online adjustment of the neural network layers in the second neural network without the need for offline retraining of the second neural network, which can simultaneously ensure network performance and real-time requirements.

[0141] The information transmission method provided in the embodiment of the present application is exemplarily described below in conjunction with specific scenarios.

[0142] The following example illustrates a method in which the first device is a terminal device, the second device is a network device, the second neural network is used by the network device to process data to be sent to the terminal device, and the third neural network is used by the terminal device to process data received from the network device. The information transmission method provided in the embodiments of the present application can be as described in Examples 1 and 2 below.

[0143] Example 1: The terminal device triggers the adjustment of the second neural network and the third neural network or triggers the activation of the first neural network. As shown in FIG11 , the exemplary information transmission method includes the following steps S201 to S211 .

[0144] S201: A terminal device sends first instruction information to a network device, where the first instruction information is used to request adjustment of a second neural network and a third neural network, or the first instruction information is used to request activation of a first neural network. Accordingly, the network device receives the first instruction information from the terminal device.

[0145] S202. The network device sends to the terminal device: third indication information, and / or second indication information and fourth indication information. The third indication information indicates confirmation information for the first indication information, the second indication information indicates at least one neural network layer to be adjusted in the second neural network, and the fourth indication information indicates at least one neural network layer to be adjusted in the third neural network. Accordingly, the terminal device receives the third indication information, and / or the second indication information and fourth indication information from the network device.

[0146] S203: The terminal device performs model training to obtain a first neural network.

[0147] S204. The terminal device determines first adjustment information and second adjustment information based on the first neural network, wherein the first adjustment information is used to indicate adjustment information of at least one neural network layer in the second neural network, and the second adjustment information is used to indicate adjustment information of at least one neural network layer in the third neural network.

[0148] S205. The terminal device sends first adjustment information to the network device; correspondingly, the network device receives the first adjustment information from the terminal device.

[0149] S206. The network device adjusts at least one neural network layer in the second neural network based on the first adjustment information.

[0150] S207. The terminal device adjusts at least one neural network layer in the third neural network based on the second adjustment information.

[0151] In an optional embodiment, steps S202 to S207 may be executed periodically until step S208 is executed, at which point steps S202 to S206 may be stopped. In step S208, the terminal device sends a termination instruction to the network device; in response, the network device receives the termination instruction from the terminal device.

[0152] S209: The network device processes the data to be sent to the terminal device based on the adjusted second neural network.

[0153] S210: The network device sends data processed by the adjusted second neural network to the terminal device. Correspondingly, the terminal device receives data processed by the adjusted second neural network from the network device.

[0154] S211. The terminal device processes data from the network device based on the adjusted third neural network.

[0155] The embodiment of the present application does not limit the order of step S205 and step S207. The detailed description of steps S201-S211 can be found in the related description of the aforementioned information transmission method, which will not be repeated here.

[0156] Example 2: The network device triggers the adjustment of the second and third neural networks or the activation of the first neural network. The information transmission method in this case differs from the information transmission method described in Example 1 in that step S201 is replaced by step S301, step S202 is replaced by step S302, and step S208 is replaced by step S303, as shown in the dashed box in Figure 12.

[0157] S301: A network device sends first indication information to a terminal device. Correspondingly, the terminal device receives the first indication information from the network device.

[0158] S302: The terminal device sends the third indication information and / or the second indication information and the fourth indication information to the network device. Correspondingly, the network device receives the third indication information and / or the second indication information and the fourth indication information from the terminal device.

[0159] S303: The network device sends a termination instruction to the terminal device. Correspondingly, the terminal device receives the termination instruction from the network device.

[0160] The following example illustrates a situation in which the first device is a network device, the second device is a terminal device, the third neural network is used by the network device to process data to be sent to the terminal device, and the second neural network is used by the terminal device to process data received from the network device. The information transmission method provided in the embodiments of the present application can be as described in Examples 3 and 4 below.

[0161] Example 3: The network device triggers the adjustment of the second neural network and the third neural network or triggers the activation of the first neural network. As shown in FIG13 , the exemplary information transmission method includes the following steps S401 to S411 .

[0162] S401: A network device sends first instruction information to a terminal device, where the first instruction information is used to request adjustment of a second neural network and a third neural network, or the first instruction information is used to request activation of a first neural network. Accordingly, the terminal device receives the first instruction information from the network device.

[0163] S402. The terminal device sends the following information to the network device: third indication information, and / or second indication information and fourth indication information. The third indication information indicates confirmation information for the first indication information, the second indication information indicates at least one neural network layer to be adjusted in the second neural network, and the fourth indication information indicates at least one neural network layer to be adjusted in the third neural network. Accordingly, the network device receives the following information from the terminal device: third indication information, and / or second indication information and fourth indication information.

[0164] S403: The network device performs model training to obtain a first neural network.

[0165] S404: The network device determines first adjustment information and second adjustment information based on the first neural network, wherein the first adjustment information is used to indicate adjustment information of at least one neural network layer in the second neural network, and the second adjustment information is used to indicate adjustment information of at least one neural network layer in the third neural network.

[0166] S405. The network device sends first adjustment information to the terminal device; correspondingly, the terminal device receives the first adjustment information from the network device.

[0167] S406. The terminal device adjusts at least one neural network layer in the second neural network based on the first adjustment information.

[0168] S407. The network device adjusts at least one neural network layer in the third neural network based on the second adjustment information.

[0169] In an optional embodiment, steps S402 to S407 may be executed periodically until step S408 is executed, at which point the repetition of steps S402 to S407 may be stopped. In step S408, the network device sends a termination instruction to the terminal device; in response, the terminal device receives the termination instruction from the network device.

[0170] S409: The network device processes the data to be sent to the terminal device based on the adjusted third neural network.

[0171] S410: The network device sends data processed by the adjusted third neural network to the terminal device. Correspondingly, the terminal device receives data processed by the adjusted third neural network from the network device.

[0172] S411. The terminal device processes data from the network device based on the adjusted second neural network.

[0173] The embodiment of the present application does not limit the order of step S405 and step S407. The detailed description of steps S401-S411 can be found in the related description of the aforementioned information transmission method, which will not be repeated here.

[0174] Example 4: The terminal device triggers the adjustment of the second and third neural networks or the activation of the first neural network. The information transmission method in this case differs from the information transmission method described in Example 3 in that step S401 is replaced by step S501, step S402 is replaced by step S502, and step S408 is replaced by step S503. In addition, after the network device receives the first indication information from the terminal device in step S501, it may first execute steps S403 and S404 before executing step S502. This is shown in the dotted box in Figure 14.

[0175] S501: A terminal device sends first indication information to a network device. Correspondingly, the network device receives the first indication information from the terminal device.

[0176] S502: The network device sends the third indication information and / or the second indication information and the fourth indication information to the terminal device. Correspondingly, the terminal device receives the third indication information and / or the second indication information and the fourth indication information from the network device.

[0177] S503: The terminal device sends a termination instruction to the network device. Correspondingly, the terminal device receives the termination instruction from the network device.

[0178] In addition, for the scenario in which one of the second neural network and the third neural network is a modulation network (the modulation network is used to process the data to be sent at the transmitting end) and the other neural network is a detection network (the detection network is used to process the received data at the receiving end), based on the information transmission method provided in the embodiment of the present application (the information transmission method as shown in Figure 8), the following configuration is used for simulation: the simulation channel is a Rayleigh flat fading channel, the modulation order is 4 bits / symbol (bits / symbol), the number of training samples is 100,000, and the performance indicator is the bit error rate (Ber). When the noise distribution is Gaussian (GAU) noise, the simulation results of the information transmission method provided in the embodiment of the present application can be shown in Figure 15. When the noise distribution is a mixture of Gaussian (MoG) noise, the simulation results of the information transmission method provided in the embodiment of the present application can be shown in Figure 16, wherein the mixed Gaussian noise includes two Gaussian components, the means of the two Gaussian components are -3 and 3 respectively, and the ratio of the two Gaussian components is 0.3:0.7.

[0179] As can be seen from Figures 15 and 16, compared with methods (1) to (4), the information transmission method provided by the embodiment of the present application has a lower bit error rate, thereby improving the signal decoding performance and improving the communication quality. Among them, method (1): traditional link (Trad_16QAM), the transmitting end uses 16 orthogonal amplitude modulation (quadrature amplitude modulation, QAM) to modulate the signal to be transmitted, and the receiving end uses least squares (LS) for channel estimation and linear minimum mean square error (LMMSE) for signal detection. Method (2): intelligent link with distribution matching (AI_16QAM), the modulation network and detection network are trained on a data set with a matching distribution. Method (3): intelligent link with mismatched distribution (AI_XX_16QAM, specifically AI_GAU_16QAM when the noise distribution is mixed Gaussian noise, and specifically AI_MoG_16QAM when the noise distribution is mixed Gaussian noise), the modulation network and detection network are trained on a data set with a mismatched distribution. Method (4): Hybrid trained intelligent link (AImix_16QAM), which trains the modulation network and detection network on a mixed dataset of two distributions.

[0180] To implement the various functions of the methods provided in the embodiments of the present application, the first device or the second device may include hardware structures and / or software modules, and implement the aforementioned functions in the form of hardware structures, software modules, or a combination of hardware structures and software modules. Whether a particular one of the aforementioned functions is implemented in the form of hardware structures, software modules, or a combination of hardware structures and software modules depends on the specific application and design constraints of the technical solution.

[0181] As shown in Figure 17, an embodiment of the present application provides a communication device 1700. The communication device 1700 can be a first device or a second device, or can also be a component of the first device (for example, an integrated circuit, a chip, etc.), or can also be a component of the second device (for example, an integrated circuit, a chip, etc.). The communication device 1700 can also be other communication units for implementing the method in the method embodiment of the present application. The communication device 1700 may include a processing unit 1701. Optionally, the communication device 1700 may also include a communication unit 1702, and the processing unit 1701 is used to control the communication unit 1702 to send and receive data / signaling. The communication unit 1702 may also be referred to as a transceiver unit. Optionally, the communication unit 1702 may include a sending unit and a receiving unit. The sending unit can be used to send data / signaling, and the receiving unit can be used to receive data / signaling. Optionally, the communication device 1700 may further include a storage unit 1703 , which may be used to store information and / or data and / or instructions, etc. The storage unit 1703 may interact with the processing unit 1701 and may also interact with the communication unit 1702 .

[0182] In one possible design, for a case where the communication apparatus 1700 is used to implement the function of the first device in the above method embodiment:

[0183] Processing unit 1701 is used to determine first adjustment information based on the first neural network, where the first adjustment information is used to indicate adjustment information of at least one neural network layer in the second neural network, and the second neural network is a neural network in the second device.

[0184] Communication unit 1702 is used to send first adjustment information to the second device, where the first adjustment information is used by the second device to adjust at least one neural network layer in the second neural network.

[0185] In an optional embodiment, the first adjustment information is specifically used to indicate one or more of the following: weight adjustment information, bias adjustment information, activation function, and structure adjustment information of at least one neural network layer in the second neural network.

[0186] In an optional embodiment, the first adjustment information is obtained by inputting information used to characterize the distribution of channel characteristics into the first neural network and outputting the information. Alternatively, the first adjustment information is obtained by inputting information used to characterize the distribution of channel characteristics and structural information of at least one neural network layer in the second neural network into the first neural network and outputting the information.

[0187] Optionally, the information used to characterize the channel characteristic distribution includes one or more of the following: channel state information, noise distribution information, signal-to-noise ratio, delay distribution information, power distribution information, Doppler spread information, and RSRP.

[0188] In an optional embodiment, the communication unit 1702 is further used to receive first indication information from the second device, where the first indication information is used to apply for adjustment of the second neural network, or the first indication information is used to request activation of the first neural network.

[0189] In another optional embodiment, the communication unit 1702 is further used to send first indication information to the second device, where the first indication information is used to apply for adjustment of the second neural network, or the first indication information is used to request activation of the first neural network.

[0190] In an optional embodiment, the communication unit 1702 is further used to send second indication information to the second device, where the second indication information is used to indicate at least one neural network layer to be adjusted in the second neural network.

[0191] In another optional embodiment, the communication unit 1702 is further used to receive second indication information from the second device, where the second indication information is used to indicate at least one neural network layer to be adjusted in the second neural network.

[0192] In an optional embodiment, processing unit 1701 is further used to determine second adjustment information based on the first neural network, where the second adjustment information is used to indicate adjustment information of at least one neural network layer in the third neural network; processing unit 1701 is further used to adjust at least one neural network layer in the third neural network based on the second adjustment information.

[0193] Optionally, the second adjustment information is specifically used to indicate one or more of the following: weight adjustment information, bias adjustment information, activation function, and structure adjustment information of at least one neural network layer in the third neural network.

[0194] In another possible design, for a case where the communication apparatus 1700 is used to implement the function of the second device in the above method embodiment:

[0195] Communication unit 1702 is used to receive first adjustment information from the first device, where the first adjustment information is used to indicate adjustment information of at least one neural network layer in the second neural network, and the first adjustment information is determined by the first device based on the first neural network.

[0196] The processing unit 1701 is configured to adjust at least one neural network layer in the second neural network based on the first adjustment information.

[0197] In an optional embodiment, the first adjustment information is specifically used to indicate one or more of the following: weight adjustment information, bias adjustment information, activation function, and structure adjustment information of at least one neural network layer in the second neural network.

[0198] In an optional embodiment, the first adjustment information is obtained by the first device inputting information used to characterize the channel characteristic distribution into the first neural network and outputting the information. Alternatively, the first adjustment information is obtained by the first device inputting information used to characterize the channel characteristic distribution and structural information of at least one neural network layer in the second neural network into the first neural network and outputting the information.

[0199] Optionally, the information used to characterize the channel characteristic distribution includes one or more of the following: channel state information, noise distribution information, signal-to-noise ratio, delay distribution information, power distribution information, Doppler spread information, and RSRP.

[0200] In an optional embodiment, the communication unit 1702 is further used to send first indication information to the first device, where the first indication information is used to apply for adjustment of the second neural network, or the first indication information is used to request activation of the first neural network.

[0201] In another optional embodiment, the communication unit 1702 is further used to receive first indication information from the first device, where the first indication information is used to apply for adjusting the second neural network, or the first indication information is used to request activation of the first neural network.

[0202] In an optional embodiment, the communication unit 1702 is used to receive second indication information from the first device, where the second indication information is used to indicate at least one neural network layer to be adjusted in the second neural network.

[0203] In another optional embodiment, the communication unit 1702 is further used to send second indication information to the first device, where the second indication information is used to indicate at least one neural network layer to be adjusted in the second neural network.

[0204] The application embodiment and the method embodiment shown above are based on the same concept, and the technical effects they bring are also the same. For the specific principles, please refer to the description of the embodiment shown above, and no further details will be given.

[0205] The present application also provides a communication device 1800, as shown in Figure 18. Communication device 1800 can be a first device or a second device, or a chip, chip system, or processor that supports the first device in implementing the above method, or a chip, chip system, or processor that supports the second device in implementing the above method. This device can be used to implement the method described in the above method embodiment. For details, please refer to the description of the above method embodiment.

[0206] The communication device 1800 may include one or more processors 1801. The processor 1801 may be used to implement some or all of the functions of the first device or the second device through logic circuits or running computer programs. The processor 1801 may be a general-purpose processor or a dedicated processor, etc. For example, it may be a baseband 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, a discrete hardware component or a central processing unit (CPU). The baseband processor may be used to process communication protocols and communication data, and the central processing unit may be used to control the communication device, execute software programs, and process data of the software programs, wherein the communication device is, for example, a base station, a baseband chip, a terminal, a terminal chip, a distributed unit (DU) or a distributed unit (CU), etc.

[0207] Optionally, the communication device 1800 may include one or more memories 1802, on which instructions 1804 may be stored. These instructions may be executed on the processor 1801, causing the communication device 1800 to perform the method described in the above method embodiment. Optionally, the memory 1802 may also store data. The processor 1801 and the memory 1802 may be provided separately or integrated together.

[0208] The memory 1802 may include, but is not limited to, non-volatile memory such as a hard disk drive (HDD) or a solid-state drive (SSD), random access memory (RAM), erasable programmable ROM (EPROM), ROM or compact disc read-only memory (CD-ROM), etc.

[0209] Optionally, the communication device 1800 may further include a transceiver 1805 and an antenna 1806. The transceiver 1805 may be referred to as a transceiver unit, a transceiver, or a transceiver circuit, etc., and is configured to implement transceiver functions. The transceiver 1805 may include a receiver and a transmitter. The receiver may be referred to as a receiver or a receiving circuit, etc., and is configured to implement a receiving function; the transmitter may be referred to as a transmitter or a transmitting circuit, etc., and is configured to implement a transmitting function.

[0210] In one possible design, for a case where the communication apparatus 1800 is used to implement the function of the first device in the above method embodiment:

[0211] Processor 1801 is used to determine first adjustment information based on a first neural network, where the first adjustment information is used to indicate adjustment information of at least one neural network layer in a second neural network, where the second neural network is a neural network in a second device.

[0212] Transceiver 1805 is used to send first adjustment information to the second device, where the first adjustment information is used by the second device to adjust at least one neural network layer in the second neural network.

[0213] In an optional embodiment, the first adjustment information is specifically used to indicate one or more of the following: weight adjustment information, bias adjustment information, activation function, and structure adjustment information of at least one neural network layer in the second neural network.

[0214] In an optional embodiment, the first adjustment information is obtained by inputting information used to characterize the distribution of channel characteristics into the first neural network and outputting the information. Alternatively, the first adjustment information is obtained by inputting information used to characterize the distribution of channel characteristics and structural information of at least one neural network layer in the second neural network into the first neural network and outputting the information.

[0215] Optionally, the information used to characterize the channel characteristic distribution includes one or more of the following: channel state information, noise distribution information, signal-to-noise ratio, delay distribution information, power distribution information, Doppler spread information, and RSRP.

[0216] In an optional embodiment, the transceiver 1805 is further used to receive first indication information from the second device, where the first indication information is used to apply for adjustment of the second neural network, or the first indication information is used to request activation of the first neural network.

[0217] In another optional embodiment, the transceiver 1805 is further used to send first indication information to the second device, where the first indication information is used to apply for adjustment of the second neural network, or the first indication information is used to request activation of the first neural network.

[0218] In an optional embodiment, the transceiver 1805 is further used to send second indication information to the second device, where the second indication information is used to indicate at least one neural network layer to be adjusted in the second neural network.

[0219] In another optional embodiment, the transceiver 1805 is further used to receive second indication information from the second device, where the second indication information is used to indicate at least one neural network layer to be adjusted in the second neural network.

[0220] In an optional embodiment, processor 1801 is further used to determine second adjustment information based on the first neural network, where the second adjustment information is used to indicate adjustment information of at least one neural network layer in the third neural network; processor 1801 is further used to adjust at least one neural network layer in the third neural network based on the second adjustment information.

[0221] Optionally, the second adjustment information is specifically used to indicate one or more of the following: weight adjustment information, bias adjustment information, activation function, and structure adjustment information of at least one neural network layer in the third neural network.

[0222] In another possible design, for a case where the communication apparatus 1800 is used to implement the function of the second device in the above method embodiment:

[0223] Transceiver 1805 is used to receive first adjustment information from the first device, where the first adjustment information is used to indicate adjustment information of at least one neural network layer in the second neural network, and the first adjustment information is determined by the first device based on the first neural network.

[0224] Processor 1801 is used to adjust at least one neural network layer in the second neural network based on the first adjustment information.

[0225] In an optional embodiment, the first adjustment information is specifically used to indicate one or more of the following: weight adjustment information, bias adjustment information, activation function, and structure adjustment information of at least one neural network layer in the second neural network.

[0226] In an optional embodiment, the first adjustment information is obtained by the first device inputting information used to characterize the channel characteristic distribution into the first neural network and outputting the information. Alternatively, the first adjustment information is obtained by the first device inputting information used to characterize the channel characteristic distribution and structural information of at least one neural network layer in the second neural network into the first neural network and outputting the information.

[0227] Optionally, the information used to characterize the channel characteristic distribution includes one or more of the following: channel state information, noise distribution information, signal-to-noise ratio, delay distribution information, power distribution information, Doppler spread information, and RSRP.

[0228] In an optional embodiment, the transceiver 1805 is further used to send first indication information to the first device, where the first indication information is used to apply for adjustment of the second neural network, or the first indication information is used to request activation of the first neural network.

[0229] In another optional embodiment, the transceiver 1805 is further used to receive first indication information from the first device, where the first indication information is used to apply for adjustment of the second neural network, or the first indication information is used to request activation of the first neural network.

[0230] In an optional embodiment, the transceiver 1805 is used to receive second indication information from the first device, where the second indication information is used to indicate at least one neural network layer to be adjusted in the second neural network.

[0231] In another optional embodiment, the transceiver 1805 is further used to send second indication information to the first device, where the second indication information is used to indicate at least one neural network layer to be adjusted in the second neural network.

[0232] In another possible design, processor 1801 may include a transceiver for implementing receiving and transmitting functions. For example, the transceiver may be a transceiver circuit, an interface, or an interface circuit. The transceiver circuit, interface, or interface circuit for implementing the receiving and transmitting functions may be separate or integrated. The transceiver circuit, interface, or interface circuit may be used for reading and writing code / data, or the transceiver circuit, interface, or interface circuit may be used for transmitting or delivering signals.

[0233] In another possible design, processor 1801 may optionally store instructions 1803. Instructions 1803, when executed on processor 1801, may cause communication device 1800 to perform the method described in the above method embodiment. Instructions 1803 may be fixed in processor 1801. In this case, processor 1801 may be implemented by hardware.

[0234] In another possible design, the communication device 1800 may include a circuit that can implement the functions of sending, receiving, or communicating in the aforementioned method embodiments. The processor and transceiver described in the embodiments of the present application can be implemented in an integrated circuit (IC), an analog IC, a radio frequency integrated circuit (RFIC), a mixed signal IC, an application specific integrated circuit (ASIC), a printed circuit board (PCB), an electronic device, etc. The processor and transceiver can also be manufactured using various IC process technologies, such as complementary metal oxide semiconductor (CMOS), N-type metal oxide semiconductor (nMetal-oxide-semiconductor, NMOS), P-type metal oxide semiconductor (positive channel metal oxide semiconductor, PMOS), bipolar junction transistor (bipolar junction transistor, BJT), bipolar CMOS (BiCMOS), silicon germanium (SiGe), gallium arsenide (GaAs), etc.

[0235] Those skilled in the art will also appreciate that the various illustrative logical blocks and steps listed in the embodiments of the present application can be implemented by electronic hardware, computer software, or a combination of both. Whether such functions are implemented by hardware or software depends on the specific application and the design requirements of the entire system. Those skilled in the art may use various methods to implement the described functions for each specific application, but such implementation should not be construed as exceeding the scope of protection of the embodiments of the present application.

[0236] The embodiments of the present application and the above-mentioned method embodiments are based on the same concept, and the technical effects they bring are also the same. For the specific principles, please refer to the description in the above-mentioned method embodiments, and no further details will be given.

[0237] The present application also provides a computer-readable storage medium for storing computer software instructions, which, when executed by a communication device, implements the functions of any of the above method embodiments.

[0238] The present application also provides a computer program product for storing computer software instructions, which, when executed by a communication device, implements the functions of any of the above method embodiments.

[0239] The present application also provides a computer program that, when executed on a computer, implements the functions of any of the above method embodiments.

[0240] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The 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. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a high-density digital video disc (DVD)), or a semiconductor medium (eg, an SSD).

[0241] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. An information transmission method, characterized in that: The method comprises: Based on the first neural network, determining first adjustment information, the first adjustment information is used to indicate adjustment information of at least one neural network layer in a second neural network, the second neural network is a neural network in a second device; The first adjustment information is sent to the second device, where the first adjustment information is used by the second device to adjust the at least one neural network layer in the second neural network.

2. The method according to claim 1, characterized in that The first adjustment information is specifically used to indicate one or more of the following: weight adjustment information, bias adjustment information, activation function, and structure adjustment information of at least one neural network layer in the second neural network.

3. The method according to claim 1 or 2, characterized in that: The first adjustment information is obtained by inputting information used to characterize channel feature distribution into the first neural network and outputting the information; or, The first adjustment information is obtained by inputting information used to characterize channel feature distribution and structural information of at least one neural network layer in the second neural network into the first neural network and outputting the information.

4. The method according to claim 3, characterized in that The information used to characterize the channel characteristic distribution includes one or more of the following: channel state information, noise distribution information, signal-to-noise ratio, delay distribution information, power distribution information, Doppler spread information, and reference signal received power RSRP.

5. The method according to any one of claims 1 to 4, characterized in that: Before determining the first adjustment information based on the first neural network, the method further includes: Receive first indication information from the second device, where the first indication information is used to apply for adjusting the second neural network, or the first indication information is used to request activating the first neural network.

6. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: Sending second indication information to the second device, where the second indication information is used to indicate the at least one neural network layer to be adjusted in the second neural network.

7. The method according to any one of claims 1 to 4, characterized in that: Before determining the first adjustment information based on the first neural network, the method further includes: Sending first indication information to the second device, where the first indication information is used to apply for adjusting the second neural network, or the first indication information is used to request activating the first neural network.

8. The method according to any one of claims 1 to 4 and 7, characterized in that: The method further comprises: Receive second indication information from the second device, where the second indication information is used to indicate the at least one neural network layer to be adjusted in the second neural network.

9. The method according to any one of claims 1 to 8, characterized in that: The method further comprises: Based on the first neural network, determining second adjustment information, wherein the second adjustment information is used to indicate adjustment information of at least one neural network layer in the third neural network; Based on the second adjustment information, at least one neural network layer in the third neural network is adjusted.

10. The method according to claim 9, characterized in that The second adjustment information is specifically used to indicate one or more of the following: weight adjustment information, bias adjustment information, activation function, and structure adjustment information of at least one neural network layer in the third neural network.

11. An information transmission method, characterized in that: The method comprises: Receive first adjustment information from a first device, wherein the first adjustment information is used to indicate at least one neural network layer in a second neural network adjustment information, wherein the first adjustment information is determined by the first device based on the first neural network; Based on the first adjustment information, adjust the at least one neural network layer in the second neural network.

12. The method according to claim 11, characterized in that The first adjustment information is specifically used to indicate one or more of the following: weight adjustment information, bias adjustment information, activation function, and structure adjustment information of at least one neural network layer in the second neural network.

13. The method according to claim 11 or 12, characterized in that: The first adjustment information is obtained by the first device inputting information used to characterize channel feature distribution into the first neural network and outputting it; or, The first adjustment information is obtained by the first device inputting information used to characterize channel feature distribution and structural information of at least one neural network layer in the second neural network into the first neural network and outputting it.

14. The method according to claim 13, characterized in that The information used to characterize the channel characteristic distribution includes one or more of the following: channel state information, noise distribution information, signal-to-noise ratio, delay distribution information, power distribution information, Doppler spread information, and reference signal received power RSRP.

15. The method according to any one of claims 11 to 14, characterized in that Before receiving the first adjustment information from the first device, the method further includes: Sending first indication information to the first device, where the first indication information is used to apply for adjusting the second neural network, or the first indication information is used to request activating the first neural network.

16. The method according to any one of claims 11 to 15, characterized in that The method further comprises: Receive second indication information from the first device, where the second indication information is used to indicate the at least one neural network layer to be adjusted in the second neural network.

17. The method according to any one of claims 11 to 14, characterized in that Before receiving the first adjustment information from the first device, the method further includes: Receive first indication information from the first device, where the first indication information is used to apply for adjusting the second neural network, or the first indication information is used to request activating the first neural network.

18. The method according to any one of claims 11 to 14 and 17, characterized in that: The method further comprises: Sending second indication information to the first device, where the second indication information is used to indicate the at least one neural network layer to be adjusted in the second neural network.

19. A communication device, characterized in that: The device includes a module or unit for implementing the method according to any one of claims 1 to 10, or the device includes a module or unit for implementing the method according to any one of claims 11 to 18.

20. A communication device, characterized in that: Including processors; The processor is used to execute a computer program or instruction in a memory so that the communication device executes the method according to any one of claims 1 to 10, or so that the communication device executes the method according to any one of claims 11 to 18.

21. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 10 is implemented, or the method according to any one of claims 11 to 18 is implemented.

22. A computer program product, characterized in that The computer program product comprises: a computer program code, and when the computer program code is run, it implements the method according to any one of claims 1 to 10, or implements the method according to any one of claims 11 to 18.

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