Multiple Jammer Attacks on Cognitive Radio Networks over Noisy Channels: A Strategic Game with Incomplete Information

Abstract

We model the anti-jamming system for cognitive radio networks under multiple jammer attacks using game theory as incomplete information games. The game model has three players: nature (representing channel impairment), jammer, and cognitive radio with sequential moves. Typically, sequential games with incomplete information use the Perfect Bayesian equilibrium solution concept, where beliefs are determined by Bayes’ rule to identify the player type. In the proposed Perfect Hidden Markov - Viterbi equilibrium solution concept, beliefs are determined by the Hidden Markov Model - Viterbi decoding instead of Bayes’ rule to identify the jammer type. Based on the jammer’s ability to jam the current cognitive radio node’s transmission channel, the jammer type is classified as either strong or weak. A general form of expression is derived to compute players’ payoff for probability-based belief systems in the proposed game modelling. Over time with a gradual reduction in the bit error rate at the receiver without any channel coding for Binary Phase Shift Keying (BPSK) modulation in the noisy Rayleigh fading environment, the simulation results show that the Perfect Hidden Markov - Viterbi equilibrium solution concept performs better than traditional Perfect Bayesian equilibrium and the pseudorandom-based frequency hopping technique.