Underwater Optical Channel Generator: A Generative Adversarial Network Based Approach

Abstract

As a high-speed, large-capacity underwater communication technology, underwater wireless optical communication (UWOC) has broad development prospects. However, accurately characterizing the underwater optical channel for various underwater conditions is still a challenging task due to light absorption and scattering, which may limit further research on UWOC. To overcome these issues, this paper proposes a novel framework of the underwater optical channel impulse response (CIR) generator, named the UCIRG framework, based on the generative adversarial network. Unlike the classical Monte Carlo approach, which is a numerical simulation method taking a long time to calculate, the UCIRG framework can generate a large number of CIRs extremely close to the actual underwater optical CIRs in a short time based on limited underwater optical CIRs as training data. Besides, traditional approximation methods for underwater optical channels usually have limitations on underwater conditions, lacking generality and flexibility, hence a generalized algorithm for UCIRG is also investigated, which is valid for various underwater conditions and can further reduce training time. Moreover, the multi-carrier autoencoder based UWOC system is adopted to evaluate the performance of the trained UCIRG, and the simulation results prove that the CIRs generated by the UCIRG are extremely similar to the actual CIRs.