Robust and Real-Time Automatic Modulation Classification System Based on Deep Learning

Ruichen Yuan, Jian Xie

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

1 Scopus citations

Abstract

Automatic modulation classification (AMC), whose main purpose is to recognize the received signal modulation mode under multi-signal environment and noise interference, provides the foundation for subsequent signal processing. In our paper, we propose a novel robust and real-time AMC implementation method based on deep learning (DL) and cognitive radio (CR), which can get faster recognition and higher accuracy. In this paper, it only needs 79.76us to recognize a set of data, and the accuracy is great than 90% at low signal-to-noise ratio.

Original languageEnglish
Title of host publicationAdvances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020
EditorsLiang Yan, Haibin Duan, Xiang Yu
PublisherSpringer Science and Business Media Deutschland GmbH
Pages4189-4199
Number of pages11
ISBN (Print)9789811581540
DOIs
StatePublished - 2022
EventInternational Conference on Guidance, Navigation and Control, ICGNC 2020 - Tianjin, China
Duration: 23 Oct 202025 Oct 2020

Publication series

NameLecture Notes in Electrical Engineering
Volume644 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

ConferenceInternational Conference on Guidance, Navigation and Control, ICGNC 2020
Country/TerritoryChina
CityTianjin
Period23/10/2025/10/20

Keywords

  • Automatic modulation classification
  • Deep learning
  • Software defined radio

Fingerprint

Dive into the research topics of 'Robust and Real-Time Automatic Modulation Classification System Based on Deep Learning'. Together they form a unique fingerprint.

Cite this