Adapting underwater physical link parameters using data driven algorithms

Abstract

This thesis addresses the question of transferring a file in minimum possible time using an underwater acoustic link that can be tuned by changing physical link parameters. Assuming we have no prior knowledge about the average data rate resulting from any of the parameter choices, we have to decide between exploring for new parameter values versus exploiting the best from the known parameter values. Hence our objective is to devise a strategy to balance this exploration and exploitation in order to transfer a file in minimum time. In the process of finding an optimal solution, several data driven algorithms such as Brute Force, First Hit, Random Selection and epsilon-Greedy were studied primarily and the effect of performance for varying search space, burst size and file size for each algorithm were investigated. When they did not produce promising results, we moved on to exploring our own strategies and enhancing the available strategies with the contextual information available. Enhanced epsilon-Greedy is one such example. Learning from widely accepted theories of optimization, such as Simulated Annealing and Rank-based assignments, the proposed Ranked Exploration Strategy was formulated. It does not have a fixed epsilon probability to explore, but rather it has a distribution from which it decides whether to explore or to exploit. And this distribution is not fixed either. The more confident we become about the observations made, the more biased the distribution becomes towards exploitation. This was also analysed on its performance with respect to the various parameters. Simulations were performed on channel data matrices which effectively model the underwater acoustic environment. Simulation results showed that the Ranked Exploration performed well while providing a computationally efficient solution.