A CNN-MPSK Demodulation Architecture with Ultra-Light Weight and Low-Complexity for Communications

Abstract

Modulation is an indispensable component in modern communication systems and multiple phase shift keying (MPSK) is widely studied to improve the spectral efficiency. It is of great significance to study the MPSK modulations of symmetric phases in practice. Based on convolutional neural networks (CNNs), we propose a generic architecture for MPSK demodulation, referred to as CNN-MPSK. The architecture utilizes a single-layer CNN and a pooling trick to crop network parameters. In comparison with conventional coherent demodulation, the CNN-MPSK eliminates three modules, i.e., carrier multiplication, bandpass filter and sampling decision. Thus, we can avoid π-inverted phenomenon from the multiplication of two carrier waves with different phases, as the carrier multiplication is not employed. In addition, we can reduce errors introduced by sampling decision. Furthermore, we conduct bit-error-rate tests for binary-PSK, 4PSK, 8PSK, and 16PSK demodulation. Experimental results reveal that the performance of CNN-MPSK is almost the same to that of conventional coherent demodulation. However, the CNN-MPSK demodulation reduces computational complexity from O(n2) to O(n) as compared to the latter one. Additionally, the proposed scheme can be readily applied for demodulation of non-symmetric MPSK constellations that maybe distorted by linear and nonlinear impairments in communication systems