Multiple Correlated Jammers Suppression: A Deep Dueling Q-Learning Approach

For wireless networks under jamming attacks, suppressing the jammer is essential to guarantee a rehable communication link. However, it can be problematic to nullify the jamming signal when the correlations between transmitted jamming signals are deliberately varied over tone. Specifically recent studies reveal that the time-varying correlations create a "virtual change" m the jamming channel and thus their nullspace, even when the physical channels remain unchanged Unlike existing studies that only consider unchanged correlations or merely propose a heuristic solution to the "virtual change" problem by continuously monitoring the residual jamming signal then updating the beam-forming matrix, we develop a deep dueling Q-learning technique to minimize the magnitude of the "virtual change" by choosing a suitable allocated time for different phases of each communication frame. Extensive simulations show that the proposed techniques can suppress the jamming signal, even when the correlations vary over time, and the correlations’ trajectory is unrevealed. Moreover, our techniques do not require monitoring the residual jamming signals then updating the beam-forming matrix. Therefore, our technique can improve the system’s spectral efficiency and reduce the outage probability.