ML-based Joint Doppler Estimation and Compensation in Underwater Acoustic Communications

With the rapid growth of Machine Learning (ML) in recent years, more and more technical issues, which were usually solved by model-based solutions, have an opportunity to be solved with data driven solutions. Underwater Doppler effect was tackled with model-based solutions in tracking the motion and compensating the interference caused by multipath Doppler effect in communications. However, a too complex model for the harsh underwater conditions leads to massive computation and becomes an obstacle for the real-time Doppler compensation. In this research, we adopt ML techniques to solve underwater Doppler issues. We propose ML-based tracking and a tracking-aid ML-based compensation. The results show that joint tracking and compensation method have tap choosing accuracy , , and in different power ratios of the two-dominant path condition with fine tree, linear Support Vector Machine (SVM), quadratic SVM and cubic SVM.