Intelligent Anti-jamming Communication Based on the Modified Q-Learning
Chen Han, Yingtao Niu, Tianyang Pang, Zhi Xiang Xia · Procedia Computer Science · 2018
Based on the probability of communication in different channels, the jammer launches intelligent jamming attack, which poses a serious challenge to the reliable transmission capability of wireless communication system. In this paper, the channel selection problem for anti-jamming defense is formulated as a Markov decision process (MDP). Then, based on modified Q-Learning, an intelligent anti-jamming learning algorithm (IALA), is proposed to quickly learn the jamming rule, and it can effectively predict the jamming behavior and obtain the optimal anti-jamming policy. The simulation results are conducted to show that the performance of the proposed IALA algorithm.