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

TL;DR

This paper develops robust distributed algorithms for cognitive radio networks that maintain high performance despite malicious jamming attacks, validated through synthetic and real-world experiments.

Contribution

It introduces multi-player bandit algorithms that are resilient to coordinated and uncoordinated jamming, ensuring low regret in decentralized spectrum sharing.

Findings

Regret remains constant with high probability under proposed algorithms.

Algorithms are effective against both coordinated and uncoordinated jamming.

Experimental validation confirms theoretical robustness.

Abstract

Efficient utilization of licensed spectrum in the cognitive radio network is challenging due to lack of coordination among the Secondary Users (SUs). Distributed algorithms proposed in the literature aim to maximize the network throughput by ensuring orthogonal channel allocation for the SUs. However, these algorithms work under the assumption that all the SUs faithfully follow the algorithms which may not always hold due to the decentralized nature of the network. In this paper, we study distributed algorithms that are robust against malicious behavior (jamming attack). We consider both the cases of jammers launching coordinated and uncoordinated attacks. In the coordinated attack, the jammers select non-overlapping channels to attack in each time slot and can significantly increase the number of collisions for SUs. We setup the problem in each scenario as a multi-player bandit and develop algorithms. The analysis shows that when the SUs faithfully implement proposed algorithms, the regret is constant with high probability. We validate our claims through exhaustive synthetic experiments and also through a realistic USRP based experiments.