Dual Wireless Anti-Interception for Ground Combat Vehicles
Abstract or Summary
Protecting a large number of wireless communication links against the interception of enemy in Warfighter Information Network-Tactical (WIN-T) systems is challenging. Due to the high mobility of ground combat vehicles (GCVs), the Low Probability of Intercept (LPI) capacity can easily be violated. Prior work focuses mainly on a single interception technique, which exposes vulnerabilities when multiple interception techniques are deployed simultaneously. We propose a strategy against both energy-based and correlation-based interception techniques by jointly optimizing power allocation (PA) and spreading factor assignment (SA) of the WIN-T. This non-convex problem is then solved by advanced optimization techniques such as decomposition and difference of convex functions (DC). We also propose a communication mode selection strategy to improve the throughput performance in the context of LPI conservation. To obtain the optimized solution in near real-time, we design a Multi-Agent Deep Reinforcement Learning (MADRL) algorithm. Our numerical results show the performance of the proposed MADRL algorithm is close to the optimal solution, making it applicable for practical systems.
