Software defined demodulation of multiple frequency shift keying with dense neural network for weak signal communications
This addresses weak signal communication for applications like IoT or deep-space links, but appears incremental as it applies a standard neural network to a known modulation scheme.
The paper tackled demodulation of weak signals using orthogonal multiple frequency shift keying with a dense neural network, achieving improved interference immunity over additive white Gaussian noise at signal-to-noise ratios from -20 dB to 0 dB.
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.