Improving ANN BFSK Demodulator Performance with Training Data Sequence Sent by Transmitter

Abstract

In this paper the effect of training neural network BFSK demodulator with noisy data (sent by transmitter and affected by channel) is discussed and the results is compared with predefined noiseless data bits. Distributed time-delay neural network is selected and get trained by both noisy and noiseless data bits. Simulations show that training a neural network demodulator by predetermined data bits sent by transmitter (noisy data) helps demodulator detect data bits with less error. That is because noisy data can give the neural network demodulator some information about channel behavior and environmental noise and consequently it can help receiver to detect data bits intelligently. Matlab simulations in an AWGN channel prove the idea.