Joint Jamming Alleviation for Mixed RF/FSO Relay Networks: Optimization and Learning Approaches

Joint Jamming Alleviation for Mixed RF/FSO Relay Networks: Optimization and Learning Approaches

V. H. Le, T. T. Nguyen, K. K. Nguyen, V. Assoume and S. Singh, “Joint Jamming Alleviation for Mixed RF/FSO Relay Networks: Optimization and Learning Approaches,” MILCOM 2023 – 2023 IEEE Military Communications Conference (MILCOM), Boston, MA, USA, 2023, pp. 450-455, doi: 10.1109/MILCOM58377.2023.10356329.

Abstract or Summary

In the mixed radio frequency/free-space optical (RF/FSO) relay networks, the effective coordination between the two subsystems is crucial for maintaining system throughput. However, the challenge intensifies when both subsystems are simultaneously intercepted by enemy forces. In this paper, we propose a strategy for alleviating jamming to safeguard the RF/FSO relay system against active jammers. Specifically, we focus on the joint optimization of RF user transmit power and FSO Field-of-View (FoV) angle to maximize system throughput while adhering to jamming alleviation constraints. The strategy is formulated as an optimization problem, which is subsequently solved using an iterative method. Due to the intractable form, the solution for FoV is obtained through a closed-form expression derivation, while the transmit power solution is achieved by using an advanced technique known as the first-order Taylor approximation with the difference of convex functions (D.C). To address the computational complexity associated with the optimization method, we design a Multi-Agent Deep Reinforcement Learning (MADRL) based jamming alleviation algorithm to solve the problem. The numerical results show that the performance of the MADRL-based algorithm is comparable to that of the optimization method, while also being suitable for real-time deployment.

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