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Technology of Network & Communication
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 7
2171-2174

Anti-interference channel coding algorithm based on dynamic learning rate deep neural network

Xu Jianyea
Yang Xiaopengb
Li Weib
Wang Honglina
a. Graduate College, b. College of Information & Navigation, Air Force Engineering University, Xi'an 710038, China
DOI: 10.19734/j.issn.1001-3695.2018.12.0948

Abstract

Aiming at the situation that the communication signal is vulnerable to hostile suppression under the condition of electronic warfare, this paper proposed a new channel coding scheme based on DLr-DAE. It could improve the performance of the communication system against suppression interference. First, it preprocessed and converted the original input signal into a one-hot vector. Then it used the training data sample set to train the deep AutoEncoder in an unsupervised learning method, and updated the network parameters according to the stochastic gradient descent(SGD) method. In this process, it used the exponential decay function to continuously fine-tune the learning rate according to the number of iterations and the value of the network loss function. By this method, it reduced the network optimization epoch and avoided the convergence result to the suboptimal point. Thus, it obtained a channel coding deep learning network for the electronic warfare environment. The simulation results show that the proposed algorithm can improve the anti-suppression noise interference performance by 0.74 dB, compared with the traditional deep learning coding algorithm when obtained the same bit error rate.

Foundation Support

国家自然科学基金资助项目
航空科学基金资助项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.12.0948
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 7
Section: Technology of Network & Communication
Pages: 2171-2174
Serial Number: 1001-3695(2020)07-052-2171-04

Publish History

[2020-07-05] Printed Article

Cite This Article

徐建业, 杨霄鹏, 李伟, 等. 基于动态学习率深度神经网络的抗干扰信道编码算法 [J]. 计算机应用研究, 2020, 37 (7): 2171-2174. (Xu Jianye, Yang Xiaopeng, Li Wei, et al. Anti-interference channel coding algorithm based on dynamic learning rate deep neural network [J]. Application Research of Computers, 2020, 37 (7): 2171-2174. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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