Power control with reinforcement learning in cooperative cognitive radio networks against jamming

In this paper, we study the anti-jamming power control problem of secondary users (SUs) in a large-scale cooperative cognitive radio network attacked by a smart jammer with the capability to sense the ongoing transmission power. The interactions between cooperative SUs and a jammer are investigated with game theory. We derive the Stackelberg equilibrium of the anti-jamming power control game consisting of a source node, a relay node and a jammer and compare it with the Nash equilibrium of the game. Power control strategies with reinforcement learning methods such as Q-learning and WoLF-PHC are proposed for SUs without knowing network parameters (i.e., the channel gains and transmission costs of others and so on) to achieve the optimal powers against jamming in this cooperative anti-jamming game. Simulation results indicate that the proposed power control strategies can efficiently improve the anti-jamming performance of SUs.