Underwater Acoustic Noise Modeling Based on Generative-Adversarial-Network

This chapter introduces underwater acoustic noise modeling based on generative-adversarial-network (GAN). In underwater acoustic communications, accurately fitting the impulsive noise is crucial. Traditional models with fixed parameters can only approximate the global heavy-tail distribution of the impulsive noise, failing to capture local distributions of varying lengths. To address this limitation, a GAN-based underwater noise simulator (GANUNS) has been presented. The GANUNS consists of a deep-neural-network-based generator and a convolutional-neural-network-based discriminator that learn the heavy-tail distribution of the impulsive noise. By utilizing real noise data collected in Xiamen, the simulated underwater acoustic noise generated by the GANUNS exhibits significantly lower Kullback–Leibler divergence, Jensen-Shannon divergence, and mean square error compared to traditional approximate models. This demonstrates the effectiveness of the suggested GANUNS in accurately modeling the impulsive noise for underwater acoustic communications.