Performance analysis of convolutional codes in dynamic underwater visible light communication systems

In this paper, we investigate the impact of both static and dynamic effects of motion on Underwater visible light communication (UVLC) channels. While previous studies have focused on static channel modeling, we argue that dynamic channel modeling is crucial for accurate UVLC analysis. To model static and dynamic UVLC channels, we use the Monte Carlo ray tracing (MCRT) method in Zemax Optics Studio; a reliable and practical setup simulator that produces results similar to experimental data. We evaluate the effect of channel coding on system performance by comparing nine different combinations and using Goodness-of-Fit (GoF) tests to determine the best-fit distribution. Our results reveal that the static case outperforms the dynamic case in terms of raw performance without any coding. However, we also show that the bit error rate (BER) is severely impacted in various realistic scenarios for a dynamic environment. To improve the BER and achieve adequate received signal quality, we recommend using convolutional codes. This paper emphasizes the importance of dynamic channel modeling in UVLC and provides practical solutions to enhance the UVLC performance in dynamic environments. The findings of our study provide valuable insights into the performance of UVLC systems and emphasize the need for researchers and designers to account for the dynamic realistic behavior in the underwater channel to achieve optimal performance in UVLC systems for underwater communication applications.