Demodulation of faded wireless signals using deep convolutional neural networks

Abstract

This paper demonstrates exceptional performance of approximately 10.0 dB learning-based gain using the Deep Convolutional Neural Network (DCNN) for demodulation of a Rayleigh-faded wireless data signal. We simulate FSK demodulation over an AWGN Rayleigh fading channel with average signal to noise ratios (SNR) from 10 dB to 20 dB. The most recent and accurate classifier is the Deep Convolutional Neural Network (DCNN) which resulted in the lowest error bit probabilities between 0.00128 to 0.00019 for the range of SNRs. A comparative study has been applied between DCNN and other machine learning classifiers such as Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Multi-Layer Perceptron (MLP) which give bit error probabilities between 0.021 to 0.002, and Quadratic Discriminant Analysis (QDA) which gives bit error probabilities between 0.027 to 0.003. Frequency-shift keying (FSK) demodulation using matched filtering showed bit error probabilities between 0.025 to 0.0025. We also discuss the complexity issues with the DCNN regarding decoding rates and training set sizes. This work shows how much the DCNN would provide substantial benefit as the demodulator.