Underwater Wireless Optical Communication With One-Bit Quantization: A Hybrid Autoencoder and Generative Adversarial Network Approach

Compared with underwater acoustic communication, underwater wireless optical communication (UWOC) has the advantages of wide communication bandwidth, high transmission speed and strong directional transmission, which make it have broad application prospects. However, the absorption and scattering effects in underwater environments cause a UWOC system to suffer from severe channel fading. To reduce the system complexity and power loss, the on- -off-keying (OOK) modulation scheme and one-bit analog-to-digital converter are considered in this work. Besides, to deal with the complex underwater optical channels and the nonlinearity of the one-bit quantization, a novel deep learning based architecture integrating the autoencoder (AE) and generative adversarial network (GAN), named hybrid AE-GAN, is developed. In the proposed hybrid AE-GAN, the generator in GAN is responsible for a generalized channel equalization, which aims to equalize both the impairments from underwater optical channels and the distortion from one-bit quantization. While the decoder in AE and the discriminator in GAN share the same network to distinguish true and fake as well as reconstruct the sent messages simultaneously. Meanwhile, the one-bit quantized signal is regarded as conditional information for GAN to assist in targeted channel equalization for various signals. Moreover, a specialized loss function and training strategy for the hybrid AE-GAN are also presented, which can optimize AE and GAN at the same time, and deal with the problem that the binarization of OOK stymies the gradient backpropagation. The simulation results indicate that the proposed hybrid AE-GAN can achieve superior performance.