Modulation classification using convolutional Neural Network based deep learning model

Shengliang Peng, Hanyu Jiang, Huaxia Wang, Hathal Alwageed, Yu Dong Yao

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

175 Scopus citations

Abstract

Deep learning (DL) is a powerful classification technique that has great success in many application domains. However, its usage in communication systems has not been well explored. In this paper, we address the issue of using DL in communication systems, especially for modulation classification. Convolutional neural network (CNN) is utilized to complete the classification task. We convert the raw modulated signals into images that have a grid-like topology and feed them to CNN for network training. Two existing approaches, including cumulant and support vector machine (SVM) based classification algorithms, are involved for performance comparison. Simulation results indicate that the proposed CNN based modulation classification approach achieves comparable classification accuracy without the necessity of manual feature selection.

Original languageEnglish
Title of host publication2017 26th Wireless and Optical Communication Conference, WOCC 2017
ISBN (Electronic)9781509049097
DOIs
StatePublished - 15 May 2017
Event26th Wireless and Optical Communication Conference, WOCC 2017 - Newark, United States
Duration: 7 Apr 20178 Apr 2017

Publication series

Name2017 26th Wireless and Optical Communication Conference, WOCC 2017

Conference

Conference26th Wireless and Optical Communication Conference, WOCC 2017
Country/TerritoryUnited States
CityNewark
Period7/04/178/04/17

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