“Advancing Secretly by an Unknown Path”: A Reinforcement Learning-Based Hidden Strategy for Combating Intelligent Reactive Jammer

This letter investigates the problem of combating the intelligent reactive jammer through a deep reinforcement learning (DRL) based hidden strategy. Existing anti-jamming researches always assume a capacity-limited adversary which is non-reactive or only launches attacks while detecting activities of the transmitter. In this letter, we consider an intelligent reactive jammer that releases track jamming once activities of the transmitter are detected, otherwise it launches indiscriminate jamming (e.g., sweep or comb jamming). A DRL-based hidden strategy is proposed to resist this powerful jamming via adjusting power and accessing idle channels simultaneously. Moreover, the parallel policy network and individual reward function are introduced to enhance the anti-jamming performance. Simulation results confirm that the proposed algorithm can significantly elude the jammer’s detection and achieve dynamic spectrum access in a dynamic and unknown environment.