A BFSK Neural Network Demodulator with Fast Training Hints

Abstract

In this paper an artificial neural network demodulator to demodulate binary frequency shift keying signal is proposed. This demodulator has some important features compared with conventional method such as coherent and non-coherent demodulator and also other proposed neural network demodulators. In contrast with conventional demodulator, this demodulator (which uses a tapped delayed line in its two layers) does not need any band pass filter (to select the desired frequency band), any pulse shaping filter (to worry about its output sharpness) and any synchronous local oscillator and the other usual demodulator components. it is just a neural network implementation demodulator, that should be called soft demodulator, because once it is trained properly for a special kind of modulation, it works well for that kind of modulation and it is easy to train it for another modulation scheme without changing hardware, i.e., train it and then use it ! Compared with the other ANN demodulators proposed before it can be trained faster (or with less training data bits), it has more efficient BER curve and also has a better performance (MSE or SSE).