A neural demodulator for quadrature amplitude modulation signals

A neural demodulator is proposed for quadrature amplitude modulation (QAM) signals. It has several important features compared with conventional linear methods. First, necessary functions for the QAM demodulation, including wide-band noise rejection, pulse waveform shaping, and decoding, can be embedded in a single neural network. This means that these functions are not separately designed but are unified in a learning process. Second, these functions can be self-organized through the learning. Supervised learning algorithms, such as the back-propagation algorithm, can be applied for this purpose. Finally, both wide-band noise rejection and a very sharp waveform response can be simultaneously achieved. It is very difficult to be done by linear filtering. Computer simulation demonstrates efficiency of the proposed method.