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Underwater environments create a challenging channel for communications. Inthis paper, we design a novel receiver system by exploring the machine learningtechnique--Deep Belief Network (DBN)-- to combat the signal distortion causedby the Doppler effect and multi-path propagation. We evaluate the performanceof the proposed receiver system in both simulation experiments and sea trials.Our proposed receiver system comprises of DBN based de-noising andclassification of the received signal. First, the received signal is segmentedinto frames before the each of these frames is individually pre-processed usinga novel pixelization algorithm. Then, using the DBN based de-noising algorithm,features are extracted from these frames and used to reconstruct the receivedsignal. Finally, DBN based classification of the reconstructed signal occurs.Our proposed DBN based receiver system does show better performance in channelsinfluenced by the Doppler effect and multi-path propagation with a performanceimprovement of 13.2dB at $10^{-3}$ Bit Error Rate (BER).

Underwater Acoustic Communication Receiver Using Deep Belief Network