Deep Learning-Based Demodulation of Radio Signal

Abstract

M-ary quadrature amplitude modulation (M-QAM) modulated signal is commonly used in digital telecommunication systems for its arbitrarily high spectral efficiencies limited only by the noise level and linearity of the communications channel. Typical demodulation techniques for M-QAM signal utilize variants of coherent demodulation. This paper aims to exploit the robustness of deep learning, specifically by using neural networks to demodulate M-QAM symbols. This is achieved with simulated time-domain baseband M-QAM signals across a range of channel impairments namely additive white Gaussian noise, DC offset and I/Q imbalance. The presented results show an improvement when utilizing deep learning over optimal receiver.