Dual Anti-Jamming Alleviation for Radio Frequency/Free-Space Optical (RF/FSO) Tactical Systems

In this paper, we design a jamming alleviation plan to protect a mixed radio frequency/free-space optical (RF/FSO) relay tactical network in the context that both RF and FSO systems are simultaneously attacked by enemy jammers. Unlike prior works that focused mainly on a single type of jamming attack (e.g. RF jamming), our proposed plan can protect the entire network against multiple types of jamming at the same time. We formulate a joint optimization problem of power allocation (PA) and Field-of-View (FoV) tuning strategy to maximize the RF uplink sum rate, subject to capacity and security constraints for both FSO and RF systems. To address this non-convex optimization problem, at first, we derive a closed-form expression of the optimal FoV angle. Then, the optimal FoV angle solution is computed to solve the PA problem. Since the PA problem has a non-convex form, we use an advanced technique of first-order Taylor approximation with the difference of convex functions method to solve it. The obtained solution of the optimization problem is then used for training a machine learning model that optimizes the system in real-time. Based on the Multi-Agent Deep Reinforcement Learning (MADRL) method, we develop a MADRL-based jamming alleviation algorithm to obtain the optimized solution of PA in near real-time. The numerical results show that the performance of the proposed MADRL-based jamming alleviation algorithm with low computational complexity is close to that of the optimization method.