Deep Learning Aided Robust Joint Channel Classification, Channel Estimation, and Signal Detection for Underwater Optical Communication
Huaiyin Lu, Ming Jiang, Julian Cheng
Abstract
Underwater wireless optical communication (UWOC) has been recently proposed for high-rate data services. In underwater scenarios, the channel conditions are complicated due to various effects of the water waves, such as absorption, scattering and turbulence, which exhibit different statistical properties with diverse water types. Such capricious phenomena make channel estimation (CE) and signal detection (SD) become challenging issues for high-rate and reliable transmissions. To address these challenges, we devise a deep learning (DL) based joint channel classification (CC), CE and SD scheme for UWOC systems. Different from existing systems, where CE is typically an independent and fundamental module, we combine CE with SD by utilizing a new deep neural network (DNN). Furthermore, the proposed scheme exploits the channel characteristics extracted through an offline training powered by a new robust DNN channel classifier (CC), which can identify and classify the water types online to produce optimized estimated combinational weights (ECW) for improving the CE/SD performances under time-varying UWOC channels. Simulation and experimental results demonstrate the superiority of the proposed system in terms of link performances in various UWOC channel environments.