Abstract
The complex and variable oceanic environment challenges channel modeling of Underwater Wireless Optical Communication (UWOC) systems. Most of the classical modeling methods focus mainly on the water environment and ignore the effect of communication equipment on signal transmission, thus making it difficult to model the UWOC channel’s complicated characteristics comprehensively. In this work, a UWOC channel emulator based on Deep Convolutional Conditional Generative Adversarial Networks is established and verified to address the challenge, which can effectively learn the characteristics of channel response and generate emulated signals with randomness like a real UWOC system in a practical application environment. Compared with the approaches based on multi-layer perceptron and convolutional neural network, the experimental results of the proposed method indicate outstanding performances in time domain, frequency domain and universality with different turbidity levels, respectively. This approach provides a new idea for applying deep learning techniques to the field of UWOC channel modeling.