Modulation Classification of Underwater Acoustic Communication Signals Based on Deep Learning
Ding Li-Da, Shilian Wang, Wei Zhang · 2018
The Automatic Modulation Classification (AMC) of the underwater acoustic communication signals is still difficult via traditional methods in the case of poor underwater acoustic channels condition and impulse noise. In this paper, we propose a novel deep neural network model for AMC of underwater acoustic communication combining the convolutional neural network (CNN) and the long short-term memory network (LSTM). The CNN learns from time domain IQ data and LSTM learns from amplitude and phase. Multipath fading underwater acoustic channels with alpha-stable impulse noise and doppler frequency shift are modeled for signal dataset generation based on the real marine environment data. Experimental results validate this approach has a high recognition rate with the burst low SNR signal and has a performance stability under the Alpha-stable impulse noise, which is better compared to other existing schemes.