Modulation Type Recognition Algorithm Based on Modulation Instantaneous Structure Difference and Deep Learning

Author:

Wang Zhongfang,Zhai Liuqun,Fu Jingwen

Abstract

Abstract In order to solve the problems of signal modulation recognition in non-cooperative communication, this paper proposes a modulation type recognition algorithm based on instantaneous difference by neural network. Firstly, the method uses the structural difference of modulation parameters in time domain of modulation signal, and displays the difference in the form of image, so as to transform the modulation recognition problem into image recognition problem; secondly, it uses the advantage of convolution neural network to automatically extract features, and it classify different modulation signals; finally, a hierarchical neural network structure is formed to identify the unknown modulation signals.

Publisher

IOP Publishing

Subject

General Engineering

Reference8 articles.

1. Over-the-Air Deep Learning Based Radio Signal Classification;O’Shea;IEEE Journal of Selected Topics in Signal Processing,2018

2. A new maximum-likelihood method for modulation classification;Wen;Conference Record of The Twenty-Ninth Asilomar Conference on Signals, Systems and Computers,1995

3. A general maximum likelihood classifier for modulation classification;Martret,1998

4. BPSK and QPSK modulation classification with unknown signal level;Hong;MILCOM 2000 Proceedings. 21st Century Military Communications. Architectures and Technologies for Information Superiority (Cat. No.00CH37155),2000

5. Digital modulation classification: the BPSK versus QPSK case. In: MILCOM 88, 21st Century Military Communications - What’s Possible?;Kim;Conference record. Military Communications Conference,1988

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