Modulation identification using neural networks and wavelet domain based approaches

Digital communication signals are processed to identify the type of modulation. This paper presents several techniques for the classification of digital modulations. One of the techniques is based on training and testing feedforward neural networks using time or frequency-domain information, or the Shannon entropy. The second technique is based on estimation of higher order moments obtained in the wavelet domain. The classification techniques are tested and compared using a set of simulated signals at different SNR. In particular, ASK, FSK, BPSK, QPSK, and 16PSK modulation schemes are investigated.