A Lightweight CNN Architecture for Automatic Modulation Classification

Wang, Zhongyong; Sun, Dongzhe; Gong, Kexian; Wang, Wei; Sun, Peng

doi:10.3390/electronics10212679

Open AccessArticle

A Lightweight CNN Architecture for Automatic Modulation Classification

by

Zhongyong Wang

,

Dongzhe Sun

,

Kexian Gong

,

Wei Wang

and

Peng Sun

^*

School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China

^*

Author to whom correspondence should be addressed.

Electronics 2021, 10(21), 2679; https://doi.org/10.3390/electronics10212679

Submission received: 4 October 2021 / Revised: 29 October 2021 / Accepted: 30 October 2021 / Published: 2 November 2021

(This article belongs to the Special Issue Intelligent Signal Processing and Communication Systems)

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Abstract

Automatic modulation classification (AMC) algorithms based on deep learning (DL) have been widely studied in the past decade, showing significant performance advantage compared to traditional ones. However, the existing DL methods generally behave worse in computational complexity. For this, this paper proposes a lightweight convolutional neural network (CNN) for AMC task, where we design a depthwise separable convolution (DSC) residual architecture for feature extraction to prevent the vanishing gradient problem and lighten the computational burden. Besides that, in order to further reduce model complexity, global depthwise convolution (GDWConv) is adopted for feature reconstruction after the last (non-global) convolutional layer. Compared to recent works, the experimental results show that the proposed network can save approximately 70~98% model parameters and 30~99% inference time on two well-known benchmarks.

Keywords: automatic modulation classification; convolutional neural network; depthwise separable convolution; feature reconstruction; global depthwise convolution

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MDPI and ACS Style

Wang, Z.; Sun, D.; Gong, K.; Wang, W.; Sun, P. A Lightweight CNN Architecture for Automatic Modulation Classification. Electronics 2021, 10, 2679. https://doi.org/10.3390/electronics10212679

AMA Style

Wang Z, Sun D, Gong K, Wang W, Sun P. A Lightweight CNN Architecture for Automatic Modulation Classification. Electronics. 2021; 10(21):2679. https://doi.org/10.3390/electronics10212679

Chicago/Turabian Style

Wang, Zhongyong, Dongzhe Sun, Kexian Gong, Wei Wang, and Peng Sun. 2021. "A Lightweight CNN Architecture for Automatic Modulation Classification" Electronics 10, no. 21: 2679. https://doi.org/10.3390/electronics10212679

APA Style

Wang, Z., Sun, D., Gong, K., Wang, W., & Sun, P. (2021). A Lightweight CNN Architecture for Automatic Modulation Classification. Electronics, 10(21), 2679. https://doi.org/10.3390/electronics10212679

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A Lightweight CNN Architecture for Automatic Modulation Classification

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