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WO2023283785A1 - Procédé de traitement de signal, et récepteur - Google Patents

Procédé de traitement de signal, et récepteur Download PDF

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Publication number
WO2023283785A1
WO2023283785A1 PCT/CN2021/105842 CN2021105842W WO2023283785A1 WO 2023283785 A1 WO2023283785 A1 WO 2023283785A1 CN 2021105842 W CN2021105842 W CN 2021105842W WO 2023283785 A1 WO2023283785 A1 WO 2023283785A1
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WO
WIPO (PCT)
Prior art keywords
signal
receiver
decoder
online training
received signal
Prior art date
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Ceased
Application number
PCT/CN2021/105842
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English (en)
Chinese (zh)
Inventor
肖寒
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangdong Oppo Mobile Telecommunications Corp Ltd
Original Assignee
Guangdong Oppo Mobile Telecommunications Corp Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangdong Oppo Mobile Telecommunications Corp Ltd filed Critical Guangdong Oppo Mobile Telecommunications Corp Ltd
Priority to CN202180095411.1A priority Critical patent/CN116982300A/zh
Priority to PCT/CN2021/105842 priority patent/WO2023283785A1/fr
Publication of WO2023283785A1 publication Critical patent/WO2023283785A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/16—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence

Definitions

  • the AI decoder In order to improve the decoding accuracy of the AI decoder, it is necessary to pre-train the AI decoder based on the preset training set before deploying the AI decoder to the receiver (that is, go online to the receiver), also known as "offline train".
  • the actual communication system is more complicated, and the training data in the training set cannot cover all the situations, and there will be a big difference with the received signal actually received by the receiver. If the AI decoder is trained offline only based on the training set, it will As a result, the generalization ability of the trained AI decoder is poor.
  • Fig. 6 is a schematic diagram of an autoencoder-based CSI feedback system.
  • the pilot symbols are inserted into the modulation symbols to form a signal to be transmitted, wherein the pilot symbols can be used for channel estimation and symbol detection by the receiver.
  • the above-mentioned signal is carried on a channel and transmitted to a receiver. Wherein, during the transmission process of the signal through the channel, noise is usually superimposed.
  • Pooling layer 430 because it is often necessary to reduce the number of training parameters, it is often necessary to periodically introduce a pooling layer after the convolutional layer, for example, it can be a layer of convolutional layer followed by a layer of pooling layer as shown in Figure 4 , can also be a multi-layer convolutional layer followed by one or more pooling layers. In signal processing, the sole purpose of pooling layers is to reduce the spatial size of the extracted information.
  • the CNN model In order to minimize the loss function, the CNN model needs to be trained.
  • the CNN model may be trained using a backpropagation algorithm (BP).
  • the training process of BP consists of forward propagation process and back propagation process.
  • the input data In the process of forward propagation (the propagation from 410 to 450 in Fig. 4 is forward propagation), the input data is input into the above layers of the CNN model, processed layer by layer and transmitted to the output layer. If the result output at the output layer is quite different from the ideal result, the above loss function is minimized as the optimization goal, and transferred to backpropagation (as shown in Fig.
  • the partial derivative of the optimization target to the weight of each neuron constitutes the gradient of the optimization target to the weight vector, which is used as the basis for modifying the model weight.
  • the training process of CNN is completed in the weight modification process. When the above error reaches the expected value, the training process of CNN ends.
  • the CNN shown in Figure 4 is only an example of a convolutional neural network.
  • the convolutional neural network can also exist in the form of other network models, which are not discussed in this embodiment of the present application. limited.
  • Network equipment and terminal equipment can be deployed on land, including indoors or outdoors, hand-held or vehicle-mounted; they can also be deployed on water; they can also be deployed on aircraft, balloons and satellites in the air. In this embodiment of the application, there is no limitation on the scenarios where network devices and terminal devices are located.
  • the AI decoder after online training will be able to use the received signal When decoding, it can also have a high accuracy rate, which is conducive to improving the generalization ability of the AI decoder.
  • the indication information of the above online training may indicate the time of the online training in various ways.
  • the indication information of online training may indicate the online training period to instruct the receiver to periodically perform online training for the AI decoder.
  • the above online training indication information may also directly indicate the start time and/or end time of the online training.
  • the indication information of the online training may indicate the start time of the online training in a display or implicit manner.
  • the instruction information of the online training may directly carry the start time.
  • the receiver can start from the time-domain unit where the indication information for online training is transmitted, and offset the preset time-domain unit to obtain the shifted time-domain unit, which is the starting point start time, where the time domain unit may be, for example, a time slot, a subframe, and the like.
  • the online training start time and the online training end time may be carried in the indication information of the online training, and correspondingly, the receiver performs the online training within the time period indicated by the start time and the end time.
  • only the online training start time may be carried in the online training indication information, correspondingly, after receiving the online training indication information, the receiver performs an online training at the start time.
  • the receiver directly uses the aforementioned online trained AI decoder to decode the received signal received in the current transmission period.
  • the foregoing first random noise may be pre-stored by the receiver, or may be pre-generated by the receiver, which is not limited in this embodiment of the present application.
  • the indication information of the online training further includes second indication information, where the second indication information is used to indicate the size of the training data used in the first type of transmission period.
  • the received signal is a received signal of a pilot signal transmitted by the transmitter
  • the processing unit 1720 may also be configured to input the received signal into the AI decoder for decoding, perform channel estimation, and obtain the The decoded signal, the decoded signal includes estimated first channel information; and according to the first channel information and first random noise, the pilot signal stored in the receiver is processed to obtain the restored signal , wherein the pilot signal transmitted by the transmitter is the same as the pilot signal stored by the receiver.
  • all or part of them may be implemented by software, hardware, firmware or any combination thereof.
  • software When implemented using software, it may be implemented in whole or in part in the form of a computer program product.
  • the computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the processes or functions according to the embodiments of the present application will be generated in whole or in part.
  • the computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable devices.

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  • Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Databases & Information Systems (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

Sont divulgués un procédé de traitement d'un signal et un récepteur. Le procédé comprend les étapes suivantes : un récepteur reçoit un signal sans fil émis par un émetteur, et obtient un signal reçu ; le récepteur entre le signal reçu dans un décodeur d'intelligence artificielle (AI) pour décodage, et obtient un signal décodé ; le récepteur, selon le signal décodé, génère un signal de récupération du signal reçu ; le récepteur, selon la différence entre le signal de récupération et le signal reçu, entraîne en ligne le décodeur AI. Étant donné que le signal reçu et le signal de récupération peuvent être acquis par le récepteur, le procédé peut mettre en œuvre un processus d'apprentissage en ligne du décodeur AI afin d'améliorer l'aptitude à la généralisation du décodeur AI.
PCT/CN2021/105842 2021-07-12 2021-07-12 Procédé de traitement de signal, et récepteur Ceased WO2023283785A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
CN202180095411.1A CN116982300A (zh) 2021-07-12 2021-07-12 信号处理的方法及接收机
PCT/CN2021/105842 WO2023283785A1 (fr) 2021-07-12 2021-07-12 Procédé de traitement de signal, et récepteur

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2021/105842 WO2023283785A1 (fr) 2021-07-12 2021-07-12 Procédé de traitement de signal, et récepteur

Publications (1)

Publication Number Publication Date
WO2023283785A1 true WO2023283785A1 (fr) 2023-01-19

Family

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Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/105842 Ceased WO2023283785A1 (fr) 2021-07-12 2021-07-12 Procédé de traitement de signal, et récepteur

Country Status (2)

Country Link
CN (1) CN116982300A (fr)
WO (1) WO2023283785A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024164946A1 (fr) * 2023-02-06 2024-08-15 华为技术有限公司 Procédé et appareil d'interaction d'informations, et support de stockage lisible

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108566257A (zh) * 2018-04-27 2018-09-21 电子科技大学 一种基于反向传播神经网络的信号恢复方法
CN109743268A (zh) * 2018-12-06 2019-05-10 东南大学 基于深度神经网络的毫米波信道估计和压缩方法
WO2020035684A1 (fr) * 2018-08-15 2020-02-20 Imperial College Of Science, Technology And Medicine Codage de canal source commun de sources d'informations à l'aide de réseaux neuronaux
CN112183736A (zh) * 2019-07-05 2021-01-05 三星电子株式会社 人工智能处理器及其执行神经网络运算的方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108566257A (zh) * 2018-04-27 2018-09-21 电子科技大学 一种基于反向传播神经网络的信号恢复方法
WO2020035684A1 (fr) * 2018-08-15 2020-02-20 Imperial College Of Science, Technology And Medicine Codage de canal source commun de sources d'informations à l'aide de réseaux neuronaux
CN109743268A (zh) * 2018-12-06 2019-05-10 东南大学 基于深度神经网络的毫米波信道估计和压缩方法
CN112183736A (zh) * 2019-07-05 2021-01-05 三星电子株式会社 人工智能处理器及其执行神经网络运算的方法

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2024164946A1 (fr) * 2023-02-06 2024-08-15 华为技术有限公司 Procédé et appareil d'interaction d'informations, et support de stockage lisible

Also Published As

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