Ensemble Machine Learning Empowered Energy Optimized Non-Line-Of-Sight Underwater SONAR And LIDAR Communication

Abstract

This paper introduces an innovative approach to improving underwater communication by merging ensemble machine learning techniques with energy-efficient non-line-of- sight (NLOS) SONAR and LIDAR systems. Through MATLAB simulation and Python for machine learning (ML), the proposed method addresses challenges such as signal attenuation and scattering prevalent in underwater environments. By optimizing energy consumption and enhancing NLOS signal propagation, the system demonstrates promising advancements in data transmission rates and reliability. This interdisciplinary collaboration between signal processing, underwater acoustics, and machine learning holds potential for significant progress in underwater communication capabilities.