A game-theoretical anti-jamming scheme for cognitive radio networks

Abstract

Cognitive radio networks are a promising solution to the spectrum scarcity issue. However, cognitive radio networks are vulnerable to various kinds of security attacks, among which the jamming attack has attracted great attention as it can significantly degrade spectrum utilization. In this article we model the jamming and anti-jamming process as a Markov decision process. With this approach, secondary users are able to avoid the jamming attack launched by external attackers and therefore maximize the payoff function. We first use a policy iteration method to solve the problem. However, this approach is computationally intensive. To decrease the computation complexity, Q-function is used as an alternate method. Furthermore, we propose an algorithm to solve the Q-function. The simulation results indicate that our approach can achieve better performance than existing approaches to defend against the jamming attack.