Adaptive FSK decoding with an artificial neural network

We describe an empirical study of the capability of an artificial neural network (ANN) to decode a frequency shift key (FSK) signal. An algorithm for generating a minimal, yet comprehensive ANN training data set is discussed. The FSK signal is over sampled. The samples are presented to the ANN as a window in time. The window is one symbol wide. After initial training, white Gaussian noise is added to the samples and the ANN´s ability to generalize is tested. We then conduct additional training, using the noisy data, to test the ANN´s ability to adaptively recover. Simulation results are reported