Protecting Tactical Ground Combat Vehicle Networks Against Dual Wireless Interceptions

We investigate the problem of dual protection for Warfighter Information Network-Tactical (WIN-T) of high-mobility ground combat vehicles (GCVs) against simultaneous energy-based and correlation-based interceptions. We design a joint resource optimization strategy in which the power allocation (PA) scheme controls transmit power, avoiding energy interception, and at the same time, the spreading factor assignment (SA) scheme manages correlation signal peaks to protect the network against the correlation analysis. We mathematically formulate this dual anti-interception resource allocation problem as a non-convex optimization model. We decompose this intractable optimization problem into two sub-problems, then solve the first sub-problem using an iterative method. To handle the non-convex form of the second sub-problem, we combine first-order Taylor approximation with the difference of convex functions (D.C) method. To obtain the optimized solution in near real-time, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) approach. The numerical results show that the performance of the low computational complexity MADRL is close to that of the optimization method. Thus, the MADRL method has the potential to be applicable in high-complexity military scenarios.