Deep learning based automatic modulation classification for varying SNR environment

Xiaojuan Xie, Yanqin Ni, Shengliang Peng, Yu Dong Yao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

26 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings
ISBN (Electronic)9781728106601
DOIs
StatePublished - 1 May 2019
Event28th Wireless and Optical Communications Conference, WOCC 2019 - Beijing, China
Duration: 9 May 201910 May 2019

Publication series

Name2019 28th Wireless and Optical Communications Conference, WOCC 2019 - Proceedings

Conference

Conference28th Wireless and Optical Communications Conference, WOCC 2019
Country/TerritoryChina
CityBeijing
Period9/05/1910/05/19

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