WO2022151069A1 - Procédé et appareil de traitement d'informations reçues, dispositif informatique et support de stockage - Google Patents

Procédé et appareil de traitement d'informations reçues, dispositif informatique et support de stockage Download PDF

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Publication number
WO2022151069A1
WO2022151069A1 PCT/CN2021/071547 CN2021071547W WO2022151069A1 WO 2022151069 A1 WO2022151069 A1 WO 2022151069A1 CN 2021071547 W CN2021071547 W CN 2021071547W WO 2022151069 A1 WO2022151069 A1 WO 2022151069A1
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Prior art keywords
information
transmission mode
received information
receiver
received
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PCT/CN2021/071547
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English (en)
Chinese (zh)
Inventor
田文强
肖寒
刘文东
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Oppo广东移动通信有限公司
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Application filed by Oppo广东移动通信有限公司 filed Critical Oppo广东移动通信有限公司
Priority to PCT/CN2021/071547 priority Critical patent/WO2022151069A1/fr
Priority to CN202180075039.8A priority patent/CN116458094A/zh
Publication of WO2022151069A1 publication Critical patent/WO2022151069A1/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B10/00—Transmission systems employing electromagnetic waves other than radio-waves, e.g. infrared, visible or ultraviolet light, or employing corpuscular radiation, e.g. quantum communication
    • H04B10/50—Transmitters
    • H04B10/516—Details of coding or modulation

Definitions

  • the present application relates to the field of wireless communication technologies, and in particular, to a method, an apparatus, a computer device, and a storage medium for processing received information.
  • the transmitting end In order to realize the normal transmission of data in free space, the transmitting end needs to encode the data to be sent through the transmitter, and send it to the space through the antenna. Correspondingly, the electromagnetic signal received by the antenna of the receiving end is It is sent to the receiver for decoding to realize the normal transmission of data.
  • the workflow is that the transmitter performs operations such as encoding, modulation, and encryption on the information source at the transmitting end to form the transmission information to be transmitted.
  • the transmitted information is transmitted to the receiving end through the wireless space, and the receiving end decodes, decrypts and demodulates the received received information, and finally restores the source information. That is, there are separate decoding, demodulation, decryption and other modules in the receiver, which correspond to the transmitter's coding, modulation, encryption and other modules, so as to realize the normal processing of data.
  • each module of the receiver is designed corresponding to each module of the transmitter, and the bit error rate during data processing of the receiver is relatively high.
  • Embodiments of the present application provide a received information processing method, apparatus, computer equipment, and storage medium.
  • the technical solution is as follows:
  • an embodiment of the present application provides a method for processing received information, the method is used for a receiving end device, and the method includes:
  • the received information is processed by the receiver model to obtain the first information; the receiver model is a machine learning model obtained after training according to the received information samples and the information processing samples.
  • an embodiment of the present application provides an apparatus for processing received information, the apparatus is used for a receiving end device, and the apparatus includes:
  • the wireless signal receiving module is used to perform wireless signal receiving and obtain receiving information
  • a transmission mode information acquisition module configured to acquire the transmission mode information corresponding to the received information
  • a model determination module configured to determine a receiver model according to the transmission mode information
  • the first information acquisition module is configured to process the received information through the receiver model to obtain the first information;
  • the receiver model is a machine learning model obtained after training according to the received information samples and the information processing samples.
  • an embodiment of the present application provides a computer device, the computer device is a receiver device, the receiver device includes a processor, a memory, and a transceiver, the memory stores a computer program, and the computer program for being executed by the processor to realize the above-mentioned method for processing received information.
  • an embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored in the storage medium, and the computer program is loaded and executed by a processor to implement the above method for processing received information.
  • the embodiments of the present application also provide a computer program product or computer program, where the computer program product or computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium.
  • the processor of the terminal reads the computer instruction from the computer-readable storage medium, and the processor executes the computer instruction, so that the terminal executes the above-mentioned method for processing received information.
  • the receiver device uses the machine learning model trained by the training samples as the receiver model, and processes the received received information according to the receiver model to obtain the first information.
  • the neural network model is used in the processing method of received information, and according to the different transmission methods, the receiver model used to process the received information is also different, that is, for different received information, according to different received information
  • the transmission method uses different receiver models for data processing, which improves the accuracy of data processing.
  • FIG. 1 is a schematic diagram of a network architecture of a communication system provided by an embodiment of the present application.
  • FIG. 2 shows a flow chart of signal transmission in a wireless communication system.
  • FIG. 3 shows a flowchart of a method for processing received information provided by an embodiment of the present application.
  • FIG. 4 shows a flowchart of a method for processing received information provided by an embodiment of the present application.
  • FIG. 5 shows a schematic diagram of a fully connected network involved in the embodiment shown in FIG. 4 .
  • FIG. 6 shows a schematic diagram of a convolutional neural network involved in the embodiment shown in FIG. 4 .
  • FIG. 7 shows a receiver neural network model involved in the embodiment shown in FIG. 4 .
  • FIG. 8 shows a receiver neural network model involved in the embodiment shown in FIG. 4 .
  • FIG. 9 is a schematic flowchart of a receiving end device implementing a method for processing received information according to an exemplary embodiment of the present application.
  • FIG. 10 is a schematic flowchart of a receiving end device implementing a method for processing received information according to an exemplary embodiment of the present application.
  • FIG. 11 shows a block diagram of an apparatus for processing received information provided by an embodiment of the present application.
  • FIG. 12 shows a schematic structural diagram of a computer device provided by an embodiment of the present application.
  • the network architecture and service scenarios described in the embodiments of the present application are for the purpose of illustrating the technical solutions of the embodiments of the present application more clearly, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application.
  • the evolution of new business scenarios and the emergence of new business scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.
  • FIG. 1 shows a schematic diagram of a network architecture of a communication system provided by an embodiment of the present application.
  • the network architecture may include: a terminal 10 and a network-side device 20 .
  • the number of terminals 10 is usually multiple, and one or more terminals 10 may be distributed in a cell managed by each network-side device (base station) 20 .
  • the terminal 10 may include various handheld devices with wireless communication functions, vehicle-mounted devices, wearable devices, computing devices or other processing devices connected to the wireless modem, as well as various forms of user equipment (User Equipment, UE), mobile stations ( Mobile Station, MS), terminal device, etc.
  • UE User Equipment
  • MS Mobile Station
  • the network-side device 20 is a device deployed in an access network to provide a wireless communication function for the terminal 10 .
  • the network-side device 20 may include various forms of macro-network-side devices, micro-network-side devices, relay stations, access points, and the like.
  • the names of devices with network-side device functions may be different.
  • the network-side device ie, base station
  • gNodeB gNodeB
  • base station As communication technology evolves, the name "base station” may change.
  • the above-mentioned apparatuses for providing a wireless communication function for the terminal 10 are collectively referred to as network-side devices.
  • the above-mentioned network architecture also includes other network devices, such as: a central control node (Central network control, CNC), a session management function (Session management function, SMF), a user plane function ( User Plane Function, UPF) equipment or AMF (Access and Mobility Management Function, access and mobility management function) equipment and so on.
  • a central control node Central network control, CNC
  • Session management function Session management function, SMF
  • UPF User Plane Function
  • AMF Access and Mobility Management Function, access and mobility management function
  • the "5G NR system" in the embodiments of the present disclosure may also be referred to as a 5G system or an NR system, but those skilled in the art can understand its meaning.
  • the technical solutions described in the embodiments of the present disclosure may be applicable to the 5G NR system, and may also be applicable to the subsequent evolution system of the 5G NR system.
  • FIG. 2 shows a flow chart of signal transmission in a wireless communication system.
  • the transmitter device 201 transmits the source information 200 through processing methods such as coding, modulation, encryption, etc., and then transmits the transmission information 202 corresponding to the source information 200 to the outside through the transmission antenna.
  • processing methods such as coding, modulation, encryption, etc.
  • the transmission information 202 corresponding to the source information 200 to the outside through the transmission antenna.
  • the transmitted information propagates in free space, it is affected by the channel environment and interference noise in free space, received by the receiving antenna, and converted into a received signal 203 that can be recognized by the receiver and transmitted to the receiver device 204.
  • the receiver device 204 After the receiver device 204 performs data processing methods such as decoding, demodulation, and decryption on the received signal 203, it is restored to the source information or the restored information 205, and the restored information can be restored to the source information through further data processing.
  • FIG. 3 shows a flowchart of a method for processing received information provided by an embodiment of the present application.
  • the method may be executed by a receiving end device, wherein the above receiving end device may be in the network architecture shown in FIG. 1 .
  • the method may include the following steps:
  • Step 310 performing wireless signal reception to obtain reception information.
  • the receiving end device is a device used to implement all or part of the functions of the receiver in the wireless communication system, that is, the receiving end device can be used to implement part or all of the functions of decoding, demodulation, and decryption.
  • the received information is digital signal information formed by digital-to-analog conversion of an analog signal formed after a receiving antenna receives an electromagnetic wave signal in a free space.
  • the received information is a quasi-modulation symbol.
  • the transmitter modulates and encodes the source information to form modulation symbols, and the modulation symbols are sent to the space channel in the free space through the antenna, and then transmitted to the receiving end device.
  • the received signal received by the receiving end device is the quasi-modulation symbol after the modulation symbol is transmitted through the spatial channel and subjected to noise interference, and the receiving end device receives through the antenna and undergoes analog-to-digital conversion.
  • Step 320 Obtain transmission mode information corresponding to the received information.
  • the transmission mode information is used to indicate a transmission scheme corresponding to the received information.
  • the transmission scheme is a configuration scheme used by the sender device to transmit the received information.
  • Step 330 Determine a receiver model according to the transmission mode information.
  • the receiving end device may have different receiver models, so as to realize the processing of the received information of the different transmission mode information according to the different receiver models.
  • Step 340 Process the received information through the receiver model to obtain the first information; the receiver model is a machine learning model obtained after training according to the received information samples and the information processing samples.
  • the receiver model may be obtained by training the received information samples as input and the information processing samples as labels.
  • the received information sample is the same type of information as the received information, and the information processing sample is the same type of information as the first information.
  • the receiving end device uses the machine learning model trained by the training samples as the receiver model, and processes the received information according to the receiver model, and obtains the first information.
  • the neural network model is used in the processing method of received information, and according to the different transmission methods, the receiver model used to process the received information is also different, that is, for different received information, according to different received information
  • the transmission method uses different receiver models for data processing, which improves the accuracy of data processing.
  • FIG. 4 shows a flowchart of a method for processing received information provided by an embodiment of the present application.
  • the method may be executed by a receiving end device, wherein the above receiving end device may be in the network architecture shown in FIG. 1 .
  • the method may include the following steps:
  • Step 401 performing wireless signal reception to obtain reception information.
  • the received information may be information formed by channel interference and device noise of the receiving end device after information delivered by the network side device.
  • the received information may be information formed by the information uploaded by the terminal after channel interference and device noise of the network side receiving end device.
  • Step 402 Obtain transmission mode information corresponding to the received information.
  • the transmission mode information includes at least one of a modulation mode and multiple-in-multiple-out MIMO (Multiple-In Multiple Out, multiple-in-multiple-out) configuration information.
  • MIMO Multiple-In Multiple Out, multiple-in-multiple-out
  • the modulation mode is the modulation mode corresponding to the received information, that is, after the transmitting end device corresponding to the received information modulates the source information to be sent according to the modulation mode, and then passes through the channel interference and the device noise of the receiving end device. , the received information can be formed.
  • the MIMO configuration information is the antenna port and precoding scheme information used by the transmitting end device corresponding to the received information when sending the information corresponding to the received information.
  • the transmission mode corresponding to the received information is determined; the transmission mode is the transmission mode used by the terminal to transmit the information corresponding to the received information; The transmission mode; according to the transmission mode, obtain the transmission mode information corresponding to the received information.
  • the network side device instructs the communication configuration between the terminal and the network side device in advance, that is, first The transmission mode between the network side device and the terminal is determined, and then the transmission mode is delivered to the terminal, so as to instruct the terminal to transmit the transmission information obtained by encoding the source information according to the transmission mode to the spatial channel.
  • the network side device can receive the received information formed after the transmitted information is interfered by the spatial channel, etc., and at the same time determine the transmission mode information corresponding to the received information according to the transmission mode corresponding to the received information.
  • the above-mentioned interaction process between the network-side device and the terminal may be performed in the following manner: the network-side device first specifies the transmission mode for sending information corresponding to the terminal, and sends it to the terminal through downlink signaling, such as through broadcast messages or RRC ( Radio Resource Control) message is sent to the terminal. After receiving the downlink signaling, the terminal configures the transmission mode with the network side device according to the configuration information in the downlink signaling, and uses the transmission mode to communicate with the device.
  • downlink signaling such as through broadcast messages or RRC ( Radio Resource Control) message is sent to the terminal.
  • RRC Radio Resource Control
  • the source information is processed, and the transmitted information is sent to the free space; the network side device receives the received information formed by the interference of the transmitted information, and determines the transmission mode information corresponding to the transmission mode according to the transmission mode of the received information (for example, the identification information corresponding to the transmission mode).
  • the receiving end device when the receiving end device is a terminal, the transmission mode information sent by the network side device is received.
  • Step 403 Determine a receiver model according to the transmission mode information.
  • the receiver model is stored in the receiver device, and after the transmission mode information is determined, the receiver model is directly selected in the receiver device according to the transmission mode information.
  • the receiver model is stored in the corresponding sender device. After the transmission mode information is determined, the receiver device can send a model acquisition request to the sender device, so that the sender device can download the model. Post this model.
  • the model structure and/or model parameters of the receiver model are determined according to the transmission mode information.
  • the model structure of the receiver model can be determined according to the transmission mode information; or the model parameters of the receiver model can be determined according to the transmission mode; or the receiver model can be determined according to the transmission mode The model structure and model parameters of the machine model.
  • the transmission characteristic information corresponding to the received information is acquired; the receiver model is determined according to the transmission mode information and the transmission characteristic information.
  • the transmission characteristic information includes at least one of data characteristic information of the received information and channel information corresponding to the received information; the data characteristic information is used to indicate the data type of the received information; the The channel information is used to indicate the channel condition of the channel used to transmit the received information.
  • the transmission characteristic information is determined by the terminal according to the source information corresponding to the received information and the transmission channel corresponding to the received information, for example, the data characteristic information It can be determined according to the source information corresponding to the received information, and the channel information can be determined according to the transmission channel status corresponding to the received information detected by the terminal.
  • the channel condition may be used to indicate the attenuation condition of the signal in the transmission channel corresponding to the received information.
  • the channel information may be channel state information CSI (Channel State Information), that is, the channel information may be a channel attribute of a communication link, at this time, the channel state of the channel may indicate the attenuation of the signal on each transmission path of the channel factors (i.e. signal scattering, environmental attenuation, distance attenuation, etc.).
  • CSI Channel State Information
  • the data feature information may be used to indicate at least one of the data size, data priority, and data type of the source information corresponding to the received information.
  • the priority of the data is used to indicate the importance of the source information corresponding to the received information; the data type is used to indicate the type information corresponding to the source information, for example, the data type is used to indicate the source information corresponding to the received information. Is configuration information or request information.
  • the transmission feature information corresponding to the received information may be issued through downlink signaling.
  • the transmission feature information may be uploaded to the network-side device after being determined by the terminal.
  • Step 404 Process the received information by using the receiver model to obtain first information.
  • the first information is source information corresponding to the received information; or, the first information is used to restore the source information corresponding to the received information.
  • the receiver model can be used to realize all or part of the functions of the receiver.
  • the receiver model can realize the decoding, decryption, and decoding of the received information.
  • the first information obtained by processing the received information through the receiver model is the source information corresponding to the received information.
  • the receiver model can be equivalently used as a receiver to perform processing on the received signal. deal with.
  • the receiver model When the receiver model is used to realize part of the functions of the receiver, the receiver model can realize part of the operations of decoding, decryption and demodulation of the received information, that is, after processing the received information through the receiver model The obtained first information needs to be further processed to restore the source information corresponding to the received information.
  • the receiver model can be equivalent to a part of the receiver module for processing the received signal.
  • the receiver model is a fully connected neural network model composed of N layers of fully connected layers, N ⁇ 1, and N is an integer.
  • the receiver model may use a fully connected network, please refer to FIG. 5 , which shows a schematic diagram of a fully connected network involved in the embodiment of the present application.
  • the fully connected network constitutes an AI (Artificial Intelligence, artificial intelligence) receiver.
  • the fully connected network here is composed of N fully connected layers 502, and the number of neurons in each fully connected layer can be Cn.
  • the AI receiver constituted by a fully connected network can be used to process the received information 501 as the first information 503 to realize all or part of the functions of the receiver.
  • the receiver model is a convolutional neural network model composed of M layers of convolutional layers, M ⁇ 1, and M is an integer.
  • the receiver model may also use a convolutional neural network
  • FIG. 6 shows a schematic diagram of a convolutional neural network involved in the embodiment of the present application.
  • the convolutional neural network constitutes the AI receiver.
  • the AI receiver formed by the convolutional neural network can also be used to process the received information 601 into the first information 603 to realize all or part of the functions of the receiver.
  • the above-mentioned fully-connected network-based and convolution-based neural network designs can be used alone or in combination, and separate network layers such as activation layer, normalization layer, and quantization layer can also be added.
  • the receiver model includes a first fully connected layer, a second fully connected layer, A common layers, a first convolutional layer, and a second convolutional layer; each common layer includes sequential The connected third convolutional layer, normalization layer and activation layer; the A common layers are connected in sequence; A ⁇ 1, and A is an integer;
  • the received information is processed according to the first fully connected layer to obtain the first feature information; the first feature information is processed according to the A common layers, the first convolutional layer and the second convolutional layer to obtain the second feature information. feature information; obtain third feature information according to the first feature information and the second feature information; and obtain the first information by processing the third feature information according to the second fully connected layer.
  • the above-mentioned first fully connected layer includes a single fully connected layer, or is formed by connecting at least two fully connected layers in sequence, that is to say, the number of layers of the first fully connected layer may be 1 layer, or may be 2 layers or 2 floors or more.
  • the above-mentioned first convolutional layer includes a single convolutional layer, or is formed by connecting at least two convolutional layers in sequence;
  • the second convolutional layer includes a single convolutional layer, or is sequentially connected by at least two convolutional layers
  • the third convolutional layer includes a single convolutional layer, or is formed by connecting at least two convolutional layers in sequence;
  • the second fully-connected layer includes a single fully-connected layer, or is formed by connecting at least one fully-connected layer in sequence.
  • the received information is input into the first fully connected layer to obtain first feature extraction information; the first feature extraction information is dimensionally adjusted to obtain the first feature information.
  • the dimension of the vector data can be adjusted as Matrix data, which is convenient for the neural network model to process the data.
  • dimension adjustment is performed on the third feature information to obtain dimension adjustment information; according to the second fully connected layer, the dimension adjustment information is processed to obtain the first information.
  • the obtained data is still two-dimensional matrix data, and the data is processed at the receiver.
  • the final need is to obtain continuous one-dimensional data in the time domain, so the third feature information in the form of a matrix needs to be dimensionally adjusted to obtain continuous one-dimensional vector data in the time domain.
  • the first feature information is sampled to obtain the first feature sampling information; and the third feature information is obtained according to the first feature sampling information and the second feature information.
  • the deep residual network ResNet is a residual block composed of a series of residual blocks, which can be expressed as:
  • x l+1 x l +f(x l , w l )
  • the residual block is divided into two parts, the direct mapping part and the residual part.
  • x l is the direct mapping part
  • f(x l , w l ) is the residual part, which is generally composed of two or three convolution operations.
  • the 1-layer network must contain more image information than the 1-layer, and solve the problem of gradient disappearance when the neural network level is large.
  • FIG. 7 it shows a receiver neural network model involved in the embodiment of the present application.
  • the received signal 701 first performs feature extraction through the fully connected layer 702, and then undergoes dimension adjustment through the dimension adjustment module 703.
  • the dimension adjusted features are sampled to obtain sampled features, and the dimension adjusted features enter the convolution synchronously.
  • Layer 704 performs feature extraction, and then enters A serially connected common layer module 705, and then passes through the convolution layer 706 and superimposes the sampled features obtained after sampling, and then passes through the dimension adjustment module 707 after dimension adjustment, through the fully connected layer. 708 is processed, and finally the first information 709 is obtained as output.
  • Each common layer module 705 in FIG. 7 is formed by concatenating a convolution layer, a normalization layer, and an activation function in series.
  • the received signal 801 first performs feature extraction through a fully connected layer composed of 1024 neurons.
  • the received signal 801 may be 512 modulation symbols modulated by QPSK (Quadrature Phase Shift Keying, quadrature phase shift keying).
  • QPSK Quadrature Phase Shift Keying, quadrature phase shift keying
  • the 1024 neurons in the fully-connected layer 802 perform feature extraction on the 512 modulation symbols in a fully-connected manner, and the obtained feature has a dimension of 1024*1.
  • the feature with the dimension of 1024*1 is then input into the leakrelu layer 803, that is, the nonlinear transformation is realized through the leakrelu activation function, and then the dimension is adjusted (adjusted to 2-dimensional data of 32*32) for sampling, and then enters 256 convolution kernels
  • the convolution layer 804 is formed, and then enters 18 serially connected public layer modules 805 for feature extraction.
  • Each public module consists of a 256-core convolution layer, a normalization layer, and an activation function leakrelu layer.
  • the convolution layer 806 composed of a convolution kernel is superimposed with the original sample, and after dimension adjustment, a vector with a length of 1024*1 is obtained, and then a fully connected layer 807 composed of 1024 neurons and a sigmoid activation function layer 808 are used. , and finally obtain the first information 809 as output.
  • a training sample set is obtained; the training sample set includes the received information sample and the information processing sample; and the receiver model is trained according to the training sample set.
  • the received information sample is the same type of information as the received information, and the information processing sample is the same type of information as the first information.
  • the receiver model is trained according to the training sample set, so that the receiver model can obtain first information corresponding to the received information after processing the input received information.
  • the first information is source information corresponding to the received information
  • the information processing sample is source information corresponding to the received information sample.
  • the received information can be processed according to the received information sample and the receiver model trained by the information processing sample to obtain the first information.
  • One piece of information is source information corresponding to the received information. That is, when the information processing sample is the source information corresponding to the received information sample, the receiver model obtained according to the received information sample and the information processing sample can have all the functions of the receiver, so as to realize the direct processing of the received information. It is restored to the source information corresponding to the received information.
  • the first information is used to obtain the source information corresponding to the received information after the first processing
  • the information processing sample is the second processing of the source information corresponding to the received information sample. owned.
  • the first processing includes at least one of decryption operation, decoding operation, and demodulation operation; and the second processing includes at least one of encryption operation, encoding operation, and modulation operation.
  • the first processing is an inverse processing operation corresponding to the second processing.
  • the second processing is an inverse processing operation corresponding to the decryption operation, that is, an encryption operation; or, when the first processing includes a decoding operation and a demodulation operation, the second processing It is an inverse processing operation of the first processing, that is, the second processing includes an encoding operation and a modulation operation.
  • the information processing sample is an encrypted information processing sample obtained after the source information corresponding to the received information sample has undergone second processing (taking the second processing as an encryption operation as an example), the information processing sample also needs to be encoded, Modulation and other operations form the transmitted information corresponding to the information processing sample, and the transmitted information is subjected to channel interference to form a received information sample and received by the receiver.
  • the training goal of the receiver model is to process the received information sample into a corresponding information processing sample. Therefore, through the received information sample and the receiver model at the training location of the information processing sample, the received information is obtained after processing the received information.
  • the first information should also be encrypted information source information, so the first information needs to go through the first processing (ie, the decoding operation corresponding to the second processing) to obtain the information source information.
  • the receiver model when the receiver model performs processing according to the received information samples and the information processing samples (that is, the source information corresponding to the receiver samples), only the receiver model may be selected to be trained to implement the receiver model.
  • Partially functional model For example, when training the receiver model, a data processing model can be added after the receiver model (take the data processing model to implement the function of the decryption module as an example). At this time, the receiver model needs to input the data input after receiving the information sample. Then input the decryption module to perform decryption processing to obtain the prediction information corresponding to the information processing sample. At this time, the receiver model can update the receiver model through the back propagation algorithm according to the prediction information and the information processing sample. After processing the receiver model through a large number of received information samples and information processing samples, the receiver model can process the received information into encrypted source information, and the encrypted source information can be obtained after passing through the decryption module. Source information corresponding to the received information.
  • the decryption module in the above scheme can also be replaced with other functional modules, that is, through the received information sample and the information processing sample (the information source information corresponding to the received information sample), the receiver model can be trained to be able to realize the receiving information.
  • Information samples implement all or part of the functionality of a machine learning model for data processing.
  • the received information can be processed according to the received information sample and the receiver model trained by the information processing sample. processing, to obtain the first information for recovering the source information.
  • the training sample set further includes at least one of sample transmission mode information and sample transmission feature information; the sample transmission feature information includes at least one of data feature information and channel feature information.
  • the sample transmission mode information is information of the same type as the transmission mode information
  • the sample transmission characteristic information is information of the same type as the sample transmission characteristic information.
  • different receiver models can also be determined through the sample transmission mode information and the sample transmission feature information.
  • the receiver model is trained according to the training sample set. That is, different receiver models can be determined according to the sample transmission mode information and sample transmission feature information, and the different receiver models are trained according to different training sample sets, and each receiver model after training corresponds to different transmission mode information.
  • selecting a trained receiver model according to the transmission mode information and the transmission feature information can improve the data processing effect of the receiver under the transmission mode corresponding to the transmission mode information and the transmission feature information.
  • the training process of the above receiver model is trained in the receiver device, and the trained receiver model is directly stored in the memory of the receiver device.
  • the receiver model delivered by the sender device is received.
  • the training process of the above receiver model can also be performed in the sending end device.
  • the receiver model Before the sending end device communicates with the receiving end device, the receiver model can be delivered to the sending end device. The sending end device Then, the received information corresponding to the transmitting end device is processed according to the receiver model. At this time, only the device that receives the receiver model can process the information normally, which improves the confidentiality of the information.
  • the training process of the receiver model is as follows:
  • the training device corresponding to the receiver model initializes the weight parameters corresponding to the receiver model according to the model structure of the set receiver model, so as to obtain an initial receiver model that has not been trained.
  • the initialization process may be random assignment of each weight parameter of the receiver model, or a preset initial value may be input into the receiver model.
  • the predicted sample value; then the predicted sample value and the information processing sample corresponding to the received information sample are input into a loss function, and the loss function value corresponding to the received information sample is obtained.
  • the receiver model can be gradient updated through the back propagation algorithm according to the loss function value.
  • the receiver model may be updated with gradients through a back-propagation algorithm according to one loss function value, or may be updated according to multiple loss function values (for example, through the sum of multiple loss function values or the mean of multiple loss function values),
  • the receiver model is updated by the back-propagation algorithm.
  • the loss function can be a suitable loss function according to the type of the signal and the structure of the model, such as a cross entropy loss function, etc., and there is no limit here.
  • the specified condition may be that the number of training times reaches a training threshold, or the specified condition may be that the accuracy of the receiver model being verified through the verification set is greater than the verification threshold.
  • the above-mentioned model training process can be applied to the above-mentioned models of different structures.
  • the above-mentioned fully-connected neural network model, convolutional neural network model, deep residual network, etc. can all train the network model weights through the above-mentioned model training process.
  • the receiving end device uses the machine learning model trained by the training samples as the receiver model, and processes the received received information according to the receiver model to obtain the first information.
  • the neural network model is used in the processing method of received information, and according to the different transmission methods, the receiver model used to process the received information is also different, that is, for different received information, the received information can be processed according to different received information.
  • the transmission method uses different receiver models for data processing, which improves the accuracy of data processing.
  • FIG. 9 is a schematic flowchart of a receiving end device implementing a method for processing received information according to an exemplary embodiment of the present application.
  • the receiving end device is a terminal, and the receiving information received by the terminal is sent from the network side device, as shown in Figure 9:
  • the terminal receives the transmission mode information delivered by the network side device.
  • the terminal when the terminal communicates with the network-side device by receiving information, it needs to receive the transmission mode information sent by the network-side device first, so as to determine the terminal's processing method for the information sent by the network-side device.
  • the terminal acquires reception information.
  • the received information is the transmitted information obtained by the network-side device after encoding, modulation, and encryption according to the source information, and is formed by the interference of the channel and the like in the free space.
  • the terminal determines the receiver model according to the transmission mode information delivered by the network side device.
  • the receiver model may be pre-trained by the terminal and stored in the memory of the terminal.
  • the receiver model may be delivered to the terminal after training by the network-side device, and stored in the terminal-side memory.
  • the receiver model may also be delivered by the network side device to the terminal according to downlink signaling in response to the sending information corresponding to the sending source information.
  • the terminal processes the received information according to the receiver model corresponding to the transmission mode information to obtain the first information.
  • the first information may be source information corresponding to the received information, or may be recovery information that needs to be processed to obtain the source information.
  • FIG. 10 is a schematic flowchart of a receiving end device implementing a method for processing received information according to an exemplary embodiment of the present application.
  • the receiving end device is a network side device, and the reception information received by the network side device is sent by the terminal. As shown in Figure 10:
  • a network side device determines a transmission mode.
  • the terminal when the terminal sends information corresponding to the source information to the free space, it needs to obtain the indication information of the network side device first, so as to determine the transmission mode of the source information.
  • the terminal receives the transmission mode delivered by the network side device.
  • the terminal sends the sending information corresponding to the source information to the space according to the transmission mode, and the network side device receives the receiving information corresponding to the source information.
  • the received information is obtained after the transmitted information undergoes interference such as channels.
  • the network side device determines the transmission mode information corresponding to the received information according to the transmission mode delivered to the terminal.
  • the transmission mode information may be an ID identifier corresponding to the transmission mode.
  • the network side device determines the receiver model according to the transmission mode information.
  • the receiver model is pre-trained by the network-side device, and the network-side device can determine the receiver model corresponding to the transmission mode information according to the transmission mode information (ID identifier).
  • the network-side device processes the received information by using the receiver model to obtain first information.
  • the first information may be source information corresponding to the received information, or may be recovery information that needs to be processed to obtain the source information.
  • FIG. 11 shows a block diagram of an apparatus for processing received information provided by an embodiment of the present application.
  • the device is used for a receiving end device, and has the function of implementing the above-mentioned method for processing received information.
  • the apparatus may include:
  • a transmission mode information acquisition module 1102 configured to acquire transmission mode information corresponding to the received information
  • a model determination module 1103, configured to determine a receiver model according to the transmission mode information
  • the first information acquisition module 1104 is configured to process the received information through the receiver model to obtain the first information; the receiver model takes the received information sample as input, and uses the information processing sample as the label for training Get a machine learning model.
  • the first information is source information corresponding to the received information
  • the information processing sample is source information corresponding to the received information sample.
  • the first information is used to obtain the source information corresponding to the received information after the first processing;
  • the information processing sample is that the source information corresponding to the received information sample is processed through the first processing. obtained after the second treatment.
  • the first processing is an inverse processing operation corresponding to the second processing.
  • the apparatus when the receiving end device is a network side device, the apparatus further includes:
  • a transmission mode determination module configured to determine the transmission mode corresponding to the received information; the transmission mode is the transmission mode used by the terminal to transmit the sent information corresponding to the received information;
  • a transmission mode issuing module configured to issue the transmission mode to the terminal
  • the transmission mode information acquisition module 1102 is used to:
  • the transmission mode obtain the transmission mode information corresponding to the received information.
  • the transmission mode information acquisition module 1102 is configured to:
  • the transmission mode information includes at least one of a modulation mode and multiple-input multiple-output MIMO configuration information.
  • the module determines a module for
  • the apparatus further includes:
  • a transmission characteristic information acquisition module configured to acquire transmission characteristic information corresponding to the received information
  • the model determination module 1103 is further configured to:
  • the receiver model is determined according to the transmission mode information and the transmission characteristic information.
  • the transmission characteristic information includes at least one of data characteristic information of the received information and channel information corresponding to the received information;
  • the data characteristic information is used to indicate the data type of the received information; the channel information is used to indicate the channel status of the channel used to transmit the received information.
  • the receiver model is a fully connected neural network model composed of N layers of fully connected layers, N ⁇ 1, and N is an integer.
  • the receiver model is a convolutional neural network model composed of M layers of convolutional layers, M ⁇ 1, and M is an integer.
  • the receiver model includes a first fully connected layer, a second fully connected layer, A common layers, a first convolutional layer and a second convolutional layer; each common layer contains The third convolution layer, the normalization layer and the activation layer connected in sequence; the A common layers are connected in sequence; A ⁇ 1, and A is an integer;
  • the first information acquisition module 1104 includes:
  • a first feature information obtaining unit configured to process the received information according to the first fully connected layer to obtain first feature information
  • a second feature information obtaining unit configured to process the first feature information according to the A common layers, the first convolution layer and the second convolution layer to obtain second feature information
  • a third feature information obtaining unit configured to obtain third feature information according to the first feature information and the second feature information
  • the first information obtaining unit is configured to process the third feature information according to the second fully connected layer to obtain the first information.
  • the first feature information acquisition unit includes:
  • a first feature extraction subunit configured to input the received information into the first fully connected layer to obtain first feature extraction information
  • a first dimension adjustment unit configured to perform dimension adjustment on the first feature extraction information to obtain the first feature information
  • the first information acquisition unit includes:
  • a second dimension adjustment subunit configured to perform dimension adjustment on the third feature information to obtain dimension adjustment information
  • the first information obtaining subunit is configured to process the dimension adjustment information according to the second fully connected layer to obtain the first information.
  • the third feature information obtaining unit is further configured to:
  • the third feature information is obtained according to the first feature sampling information and the second feature information.
  • the apparatus further includes:
  • a training sample set acquisition module used for acquiring a training sample set;
  • the training sample set includes the received information samples and the information processing samples;
  • a receiver training module configured to train the receiver model according to the training sample set.
  • the training sample set further includes at least one of sample transmission mode information and sample transmission feature information;
  • the sample transmission feature information includes at least one of data feature information and channel feature information ;
  • the receiver training module includes:
  • a receiver model determining unit configured to determine a receiver model corresponding to the training sample set according to at least one of the sample transmission feature information and the sample transmission mode information;
  • a receiver training submodule configured to train the receiver model corresponding to the training sample set based on the training sample set.
  • the apparatus further includes:
  • the receiving module is used to receive the receiver model sent by the sender device.
  • the receiving end device uses the machine learning model trained by the training samples as the receiver model, and processes the received information according to the receiver model, and obtains the first information.
  • the neural network model is used in the processing method of received information, and according to the different transmission methods, the receiver model used to process the received information is also different, that is, for different received information, the received information can be processed according to different received information.
  • the transmission method uses different receiver models for data processing, which improves the accuracy of data processing.
  • the device provided in the above embodiment realizes its functions, only the division of the above functional modules is used as an example for illustration. In practical applications, the above functions can be allocated to different functional modules according to actual needs. That is, the content structure of the device is divided into different functional modules to complete all or part of the functions described above.
  • FIG. 12 shows a schematic structural diagram of a computer device 1200 provided by an embodiment of the present application.
  • the computer device 1200 may include: a processor 1201 , a receiver 1202 , a transmitter 1203 , a memory 1204 and a bus 1205 .
  • the processor 1201 includes one or more processing cores, and the processor 1201 executes various functional applications and information processing by running software programs and modules.
  • the receiver 1202 and the transmitter 1203 may be implemented as a communication component, which may be a communication chip.
  • the communication chip may also be referred to as a transceiver.
  • the memory 1204 is connected to the processor 1201 through the bus 1205 .
  • the memory 1204 can be used to store a computer program, and the processor 1201 is used to execute the computer program to implement each step performed by the server device, configuration device, cloud platform or account server in the above method embodiments.
  • memory 1204 may be implemented by any type or combination of volatile or non-volatile storage devices including, but not limited to, magnetic or optical disks, electrically erasable programmable Read Only Memory, Erasable Programmable Read Only Memory, Static Anytime Access Memory, Read Only Memory, Magnetic Memory, Flash Memory, Programmable Read Only Memory.
  • the computer device includes a processor, a memory, and a transceiver (the transceiver may include a receiver for receiving information and a transmitter for transmitting information);
  • the terminal when the computer device is implemented as a receiver device, the terminal includes a processor, a memory and a transceiver;
  • the receiver configured to perform wireless signal reception and obtain reception information
  • the processor configured to obtain the transmission mode information corresponding to the received information
  • the processor configured to determine a receiver model according to the transmission mode information
  • the processor is configured to process the received information through the receiver model to obtain the first information;
  • the receiver model is a machine learning model obtained after training according to the received information samples and the information processing samples.
  • the processor and transceiver in the receiving end device involved in the embodiment of the present application may perform the steps performed by the receiving end device in the above-mentioned received information processing method shown in FIG. 3 or FIG. 4 , which will not be repeated here.
  • An embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored in the storage medium, and the computer program is loaded and executed by a processor to implement the received information shown in any of the above-mentioned FIG. 3 or FIG. 4 .
  • the various steps in the processing method are described in detail below.
  • the application also provides a computer program product or computer program, the computer program product or computer program comprising computer instructions stored in a computer-readable storage medium.
  • the processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes each step in the method for processing received information shown in FIG. 3 or FIG. 4 .
  • Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another.
  • a storage medium can be any available medium that can be accessed by a general purpose or special purpose computer.

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  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

La présente demande se rapporte au domaine technique des communications sans fil. Elle concerne un procédé et un appareil permettant de traiter des informations reçues, ainsi qu'un dispositif informatique et un support de stockage. Le procédé consiste à : effectuer une réception de signal sans fil afin d'obtenir des informations reçues ; acquérir des informations de mode de transmission correspondant aux informations reçues ; déterminer un modèle de récepteur en fonction des informations de mode de transmission ; et traiter les informations reçues au moyen du modèle de récepteur afin d'obtenir des premières informations, le modèle de récepteur étant un modèle d'apprentissage automatique obtenu par apprentissage en fonction d'un échantillon d'informations reçu et d'un échantillon de traitement d'informations. Au moyen de la solution, pour différentes informations reçues, en fonction des modes de transmission des différentes informations reçues, un dispositif d'extrémité de réception peut utiliser différents modèles de récepteur pour un traitement de données, ce qui permet d'améliorer la précision du traitement de données.
PCT/CN2021/071547 2021-01-13 2021-01-13 Procédé et appareil de traitement d'informations reçues, dispositif informatique et support de stockage WO2022151069A1 (fr)

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PCT/CN2021/071547 WO2022151069A1 (fr) 2021-01-13 2021-01-13 Procédé et appareil de traitement d'informations reçues, dispositif informatique et support de stockage
CN202180075039.8A CN116458094A (zh) 2021-01-13 2021-01-13 接收信息处理方法、装置、计算机设备及存储介质

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CN111181612A (zh) * 2019-12-31 2020-05-19 内蒙古大学 一种大规模mimo系统的协作波束赋型方法
CN111614398A (zh) * 2020-05-12 2020-09-01 北京邮电大学 基于异或神经网络的调制格式及信噪比识别方法及装置
CN111614587A (zh) * 2020-05-25 2020-09-01 齐鲁工业大学 一种基于自适应集成深度学习模型的sc-fde系统信号检测方法
US10797766B1 (en) * 2018-04-24 2020-10-06 Genghiscomm Holdings, LLC Distributed radio system
CN112118066A (zh) * 2020-11-23 2020-12-22 西南交通大学 一种基于改进卷积神经网络的fbmc-pon解调方法

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Publication number Priority date Publication date Assignee Title
US10797766B1 (en) * 2018-04-24 2020-10-06 Genghiscomm Holdings, LLC Distributed radio system
CN111181612A (zh) * 2019-12-31 2020-05-19 内蒙古大学 一种大规模mimo系统的协作波束赋型方法
CN111614398A (zh) * 2020-05-12 2020-09-01 北京邮电大学 基于异或神经网络的调制格式及信噪比识别方法及装置
CN111614587A (zh) * 2020-05-25 2020-09-01 齐鲁工业大学 一种基于自适应集成深度学习模型的sc-fde系统信号检测方法
CN112118066A (zh) * 2020-11-23 2020-12-22 西南交通大学 一种基于改进卷积神经网络的fbmc-pon解调方法

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