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Efficient utilization of licensed spectrum in the cognitive radio network ischallenging due to lack of coordination among the Secondary Users (SUs).Distributed algorithms proposed in the literature aim to maximize the networkthroughput by ensuring orthogonal channel allocation for the SUs. However,these algorithms work under the assumption that all the SUs faithfully followthe algorithms which may not always hold due to the decentralized nature of thenetwork. In this paper, we study distributed algorithms that are robust againstmalicious behavior (jamming attack). We consider both the cases of jammerslaunching coordinated and uncoordinated attacks. In the coordinated attack, thejammers select non-overlapping channels to attack in each time slot and cansignificantly increase the number of collisions for SUs. We setup the problemin each scenario as a multi-player bandit and develop algorithms. The analysisshows that when the SUs faithfully implement proposed algorithms, the regret isconstant with high probability. We validate our claims through exhaustivesynthetic experiments and also through a realistic USRP based experiments.

Learning to Coordinate in a Decentralized Cognitive Radio Network in Presence of Jammers