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In this paper, we investigate the anti-jamming channel selection problem for interference mitigation (IM) based dense wireless networks in dynamic environment, in which the active user set is variable due to their specific traffic demands. We jointly consider the mutual interference among users and external jamming in IM-based dense wireless networks, and propose a generalized maximum protocol interference and jamming model to accurately capture the mutual interference and external jamming. Then, the anti-jamming channel selection problem is formulated as an anti-jamming dynamic game, and subsequently it is proved to be an exact potential game, which has at least one pure strategy Nash equilibrium (NE). Based on the stochastic learning theory, a distributed anti-jamming channel selection algorithm (DACSA) is proposed to find the NE solution. Moreover, the simulation results are presented to demonstrate the effectiveness of the proposed DACSA algorithm. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]