A hierarchical learning approach to anti-jamming channel selection strategies
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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.
KeywordsAnti-jamming Stackelberg game Channel selection Stochastic learning
This work was supported in part by the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province under Grant BK20160034, in part by the National Science Foundation of China under Grant 61631020, Grant 61671473, Grant 61401508, and Grant 61401505, in part by Jiangsu Provincial Natural Science Foundation of China Grant BK20130069, and Grant BK20151450, and in part by the Open Research Foundation of Science and Technology in Communication Networks Laboratory.
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