A back-to-back coordination-based learning scheme for deceiving reactive jammers in distributed networks

Reactive jammers select jamming strategies according to the users’ responses; thus, conventional anti-jamming methods such as frequency hopping are inadequate to defeat the jamming attack. In this article, the authors propose a novel uncoupled deception scheme to trap the reactive jammer into attacking a decoy channel in distributed networks. Specifically, the authors design a multi-functional network utility for every user to mislead the jammer with a minimum energy consumption while achieving the highest network throughput. Based on the network utility, the anti-jamming problem is formulated as an exact potential game such that the existence of Nash equilibrium can be guaranteed theoretically. The authors further propose a back-to-back coordination-based learning algorithm to reach the optimal channel selection and power adaption in a non-cooperative way. To alleviate the lack of mutual information exchange, the back-to-back coordination mechanism derives all users to deceive the jammer by inferring others’ strategies based on a shared belief. Simulation results show that the proposed algorithm yields higher network throughput and efficiency-cost ratio compared to the state-of-the-art cooperative schemes.