M-ary Phase Position Shift Keying Demodulation Using Stacked Denoising Sparse Autoencoders

A deep-learning based detector for M-ary phase position shift keying (MPPSK) systems is proposed in this paper. The major components of this detector include a special impact filter, a stacked denoising sparse autoencoder (DSAE), which was trained in unsupervised learning to extract features from th...

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Bibliographic Details
Published in:Electronics (Basel) Vol. 11; no. 8; p. 1233
Main Authors: Lu, Conghui, Chen, Peng, Zhong, Hua, Wang, Mengyuan
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.04.2022
Subjects:
Bandwidths
Classifiers
Communication
Deep learning
Demodulation
Feature extraction
Keying
Neural networks
Noise
Noise reduction
Phase shift
Phase transitions
Random noise
Wireless networks
ISSN:2079-9292, 2079-9292
Online Access:Get full text
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Summary:A deep-learning based detector for M-ary phase position shift keying (MPPSK) systems is proposed in this paper. The major components of this detector include a special impact filter, a stacked denoising sparse autoencoder (DSAE), which was trained in unsupervised learning to extract features from the modulation signals, and a softmax classifier. The features learned by the stacked DSAE were then used to train the softmax classifier to demodulate the received signals into M classes. The architecture presented herein was trained and tested on a simple dataset extended by adding Gaussian noise only. The results from the theoretical analysis and simulation show that the detection performance of the proposed scheme is superior to that of existing detectors.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11081233