Machine Learning–Enhanced UWOC System for Robust Underwater Communication with FEC Integration
Appala Venkata Ramana Murthy
Underwater wireless optical communication (UWOC) often suffers from channel-induced bit errors that degrade adaptive threshold detection, particularly due to dynamic turbidity conditions. Standard approaches of applying mitigation techniques may improve the link performance to an extent. In this study, we have incorporated an integrated approach based on machine learning (ML) into the UWOC system, along with forward error correction (FEC), and tested it on various turbidity levels. This has sign