Understanding the feasibility of machine learning algorithms in a game theoretic environment for dynamic spectrum access

Abstract

The key enabling technology in dynamic spectrum access is Cognitive Radio that allows unlicensed secondary users to access the licensed bands without causing any interference to the primary users. In any situation where there are a certain number of secondary networks trying to get an available channel, there arises a game theoretic competition where they want to get the channel for themselves by incurring as minimum cost as possible. The increase in cost is equivalent to the increase in time caused by the need of a search for an available channel. This process could be sped up if the networks had a predictive mechanism to determine the optimal strategy. In this paper, we investigate various predictive algorithms: Linear regression, Support Vector Regression and Elastic Net and compare them with other traditional non-predictive game theoretic mechanisms. We measure the accuracy of these algorithms in terms of time taken to reach the system convergence. We also observe how a self-learning approach can be helpful in maximizing utilities of the players in comparison to traditional game theoretic approaches.