A Reinforcement Learning Approach for Dynamic Spectrum Anti-jamming in Fading Environment

In this article, we study the problem of anti-jamming through channel selection in fading environment. Different from the most existing works which ignored the fading characteristic of the channels, we use a Markov channel model to capture the influence of variable channel transmission rate in fading environment. Then, we model the anti-jamming channel selection problem as a Markov decision process. On this basis, an online reinforcement learning algorithm (Q-learning) is proposed to select the optimal channel intelligently. Simulation results show that the proposed algorithm outperforms the sensing algorithm. The reason is that it can learn both the pattern of jamming signal and the condition of channel, so as to select the channel with better condition for data transmission. Based on the proposed algorithm, we develop a real-life dynamic spectrum anti-jamming testbed based on the USRP platform in the indoor environment to demonstrate the effectiveness of the algorithm.