Deep Learning based Automatic Modulation Classification for Varying SNR Environment

Automatic modulation classification (AMC) is a crucial task for various communications applications. Deep learning (DL) based classifier is emerging as a prevalent choice for AMC. Previous research on DL based AMC usually assumes an environment of fixed signal to noise ratio (SNR). This paper consid...

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Bibliographic Details
Published in2019 28th Wireless and Optical Communications Conference (WOCC) pp. 1 - 5
Main Authors Xie, Xiaojuan, Ni, Yanqin, Peng, Shengliang, Yao, Yu-Dong
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2019
Subjects
Binary phase shift keying
Constellation diagram
Signal to noise ratio
Training
Wireless communication
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Summary:Automatic modulation classification (AMC) is a crucial task for various communications applications. Deep learning (DL) based classifier is emerging as a prevalent choice for AMC. Previous research on DL based AMC usually assumes an environment of fixed signal to noise ratio (SNR). This paper considers DL based AMC for varying SNR environment. Two algorithms, including M2M4-aided algorithm and multi-label DL based algorithm, are proposed to combat the varying SNR. The former utilizes an M2M4 estimator to estimate SNR, according to which a proper trained DL model can be selected for AMC. The latter exploits multi-label DL to train a model, with which SNR scenario and modulation type can be inferred simultaneously. Experiment results show that the performance of both algorithms is fairly close to that of DL based AMC under fixed SNR environment.
ISSN:2379-1276
DOI:10.1109/WOCC.2019.8770611