A Novel Demodulation Scheme of MSK Signals Based on One-Dimensional Convolutional Neural Network under Impulsive Noise

Abstract

The atmospheric noise widely present in very-low-frequency and low-frequency (VLF/LF) communication systems is usually considered as a type of impulsive noise, which can degrade the performance of signal processing methods based on Gaussian noise. To solve the problems of difficult model training and complex noise parameter estimation under impulsive noise in other demodulation schemes of minimum shift keying (MSK) signals, we use one-dimensional convolutional neural networks (1D-CNNs) to replace the integrator and decision module in coherent demodulation instead of the entire demodulation process. Our scheme preserves the operations that are beneficial for neural network training in the conventional coherent demodulation process, and the use of neural networks avoids the estimation of noise parameters. To further improve the symbol error rate (SER) performance, we remove the symbol conversion before output generation and add the symbol conversion before MSK modulation. The simulation results show that our scheme with fewer parameters and less calculation has better SER performance than other neural network demodulators. Our scheme’s SER performance exceeds the performance of the demodulation algorithm based on a myriad branch metric, whose performance is very close to the maximum likelihood (ML) performance. And, our scheme does not require complex noise parameter estimation.