Tuning an underwater communication link

Abstract

A family of machine learning algorithms to optimize an underwater communication link are developed in this thesis. We continuously adjust the physical layer parameters of a point-to-point communication link and aim to maximize the average data rate. The algorithms are statistical in nature and are driven by bit error rate information, hence they are independent of the actual physical layer implementation.