Opponent-Awareness-Based Anti-Intelligent Jamming Channel Access Scheme: A Deep Reinforcement Learning Perspective

Abstract

As a fundamental requirement for IoT communication systems the importance of highly reliable anti-jamming communication methods in safety use cases is growing. In this article, we propose a novel anti-intelligent jamming scheme called opponent awareness-based anti-jamming algorithm (OA3). The user-jammer-environment interaction is formulated as a two-player simultaneous action stochastic game where participators have the ability to update their strategies. The decision-making process of each agent is modeled as a Markov decision process (MDP). Begin with the intuition "learn how the jammer learns," the opponent awareness-based iterative learning objective (OAL) of the user is presented by considering the learning awareness of the jammer to defeat the intelligent jamming. Finally, we introduce the framework, including offline policy learning and online policy exploiting to implement OAL and accelerate the learning. Simulations show that the OA3 outperforms the benchmark anti-jamming strategy in terms of packet success rate.