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

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.

6 pages, 9 figures

References in corpus (4)

• Adam: A Method for Stochastic Optimization
• The NumPy array: a structure for efficient numerical computation
• TensorFlow: A system for large-scale machine learning
• Signal Demodulation with Machine Learning Methods for Physical Layer Visible Light Communications: Prototype Platform, Open Dataset and Algorithms

Cited by in corpus (2)

• Software demodulation of weak radio signals using convolutional neural network
• Software implemented fault diagnosis of natural gas pumping unit based on feedforward neural network