Convolutional neural network-based modulation recognition  technique for communication signals

Yunchao Zou

doi:10.54254/2755-2721/12/20230308

Research Article

Open access

Published on 25 September 2023

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Zou,Y. (2023). Convolutional neural network-based modulation recognition technique for communication signals. Applied and Computational Engineering,12,122-134.

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Convolutional neural network-based modulation recognition technique for communication signals

Yunchao Zou *^,1,

¹ Beihang University

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/12/20230308

Abstract

The last decade has seen breakthroughs in communication technology. The increasingly complex signal transmission environment has placed higher demands on signal modulation recognition. Traditional modulation recognition approaches cannot guarantee satisfactory recognition accuracy. Fortunately, with the continuous advancement of deep learning algorithms, convolutional neural network-based communication signal modulation recognition techniques have become the mainstream of current research. Therefore, this paper first reviews the development history of signal modulation recognition techniques and introduces the concepts of signal modulation theory. It includes ASK, PSK and FSK modulation methods, which are common today. Subsequently, I analyze the principles of signal modulation recognition and the implementation method of CNN in modulation recognition. To further explore the shortcomings of CNNs, I propose two optimized models, the residual network model and the CLDNN model. After comparing the performance, the former has higher performance, but its computational complexity is higher while the latter takes into account the high recognition accuracy while still reducing the network parameters as much as possible to keep the complexity at a low level.

Keywords

signal modulation, deep earning, CNN, residual network, CLDNN.

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Cite this article

Zou,Y. (2023). Convolutional neural network-based modulation recognition technique for communication signals. Applied and Computational Engineering,12,122-134.

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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Mechatronics and Smart Systems

Conference website: https://2023.confmss.org/

ISBN：978-1-83558-013-4(Print) / 978-1-83558-014-1(Online)

Conference date: 24 June 2023

Editor：Seyed Ghaffar, Alan Wang

Series: Applied and Computational Engineering

Volume number: Vol.12

ISSN：2755-2721(Print) / 2755-273X(Online)

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