Software defined demodulation of multiple frequency shift keying with dense neural network for weak signal communications

TL;DR

This paper explores a neural network-based demodulation method for weak signal communications using multiple frequency shift keying, demonstrating improved interference immunity over noisy channels.

Contribution

It introduces a supervised dense neural network approach for demodulating MFSK signals in weak signal environments, enhancing interference robustness.

Findings

Achieved low bit error rates at -20 dB SNR

Demonstrated neural network effectiveness over traditional methods

Improved interference immunity in noisy channels

Abstract

In this paper we present the symbol and bit error rate performance of the weak signal digital communications system. We investigate orthogonal multiple frequency shift keying modulation scheme with supervised machine learning demodulation approach using simple dense end-to-end artificial neural network. We focus on the interference immunity over an additive white Gaussian noise with average signal-to-noise ratios from -20 dB to 0 dB.