Enhancing Underwater Visible Light Communication with End-to-End Learning Techniques

This paper investigates the potential of end-to-end learning as a means to improve the performance and reliability of wireless communication systems. Unlike traditional approaches that rely on manual feature extraction and engineering, a process that is time consuming and requires specialized expertise, end-to-end learning promises to streamline the design of communication systems. The aim is to reduce the complexity of signal processing algorithms, bolster system robustness against environmental conditions, and enable more efficient bandwidth utilization. Specifically, this study focuses on leveraging end-to-end learning to im-prove underwater visible light communication (VLC) systems. Facilitates the automatic learning of complex mappings be-tween input signals and output symbols, eliminating the need for manually crafted features or prior channel knowledge. This method is expected to overcome the challenges inherent in traditional signal processing techniques, such as sensitivity to channel variations and environmental disturbances, paving the way for the development of more efficient and resilient underwater communication systems. Importantly, the model's capability to be trained on large datasets is critical in underwater environments, where data availability is often scarce.