Luliang Jia

Anti-Jamming Communications Using Spectrum Waterfall: A Deep Reinforcement Learning Approach

This letter investigates the problem of anti-jamming communications in a dynamic and intelligent jamming environment through machine learning. Different from existing studies which need to know (estimate) the jamming patterns and parameters, we use the temporal and spectral information, i.e., the spectrum waterfall, directly. First, to cope with the challenge of infinite state of spectrum waterfall, a recursive convolutional neural network is designed. Then, an anti-jamming deep reinforcement learning algorithm is proposed to obtain the optimal anti-jamming strategies. Finally, simulation results validate the proposed approach. The proposed algorithm does not need to model the jamming patterns, and naturally has the ability to explore the unknown environment, which implies that it can be widely used for combating dynamic and intelligent jamming.

A hierarchical learning approach to anti-jamming channel selection strategies

This paper investigates the channel selection problem for anti-jamming defense in an adversarial environment. In our work, we simultaneously consider malicious jamming and co-channel interference among users, and formulate this anti-jamming defense problem as a Stackelberg game with one leader and multiple followers. Specifically, the users and jammer independently and selfishly select their respective optimal strategies and obtain the optimal channels based on their own utilities. To derive the Stackelberg Equilibrium, a hierarchical learning framework is formulated, and a hierarchical learning algorithm (HLA) is proposed. In addition, the convergence performance of the proposed HLA algorithm is analyzed. Finally, we present simulation results to validate the effectiveness of the proposed algorithm.