Reinforcement Learning Based Full-Duplex Cognitive Anti-jamming Using Improved Energy Detector

In this paper, reinforcement learning (RL) based cognitive anti-jamming system employing full duplex tactical radio is investigated under electromagnetic spectrum warfare scenario. Firstly, the analytical expressions of jamming sensing based on improved energy detection are derived to calculate the reward metric. Then, we propose the multidomain anti-jamming strategies based on different learning algorithm and the accurate reward. Simulation results indicate that learning-based cognitive anti-jamming strategies may increase about 25% of the throughput of tactical radio. Moreover, the upper confidence bound and Thompson sampling strategies almost have the same performance and they are superior to other RL anti-jamming schemes.