Joint Intelligent Reflecting Surface-Aided Frequency-Hopping Anti-Jamming for Tactical Wireless Systems

The frequency hopping (FH) technique has always been crucial for anti-jamming tactical applications thanks to its advantages in avoiding the jammer's interception. However, modern tactical scenarios require FH systems to not only undertake defence missions but also meet increasingly high Quality of service (QoS) requirements. Unlike the prior works that mainly optimize FH systems by balancing anti-jamming capability and QoS performance, we propose a collaboration of FH and the intelligent reflecting surface (IRS) in an advanced anti-jamming scheme. Such approach shares the burden with the IRS and improves QoS. We formulate a joint IRS-aided FH anti-jamming problem as a Mixed Integer Programming (MIP) non-convex optimization. To address the intractability of traditional optimization methods in solving this problem in modern tactical scenarios, we design a solution based on deep reinforcement learning (DRL). The numerical results show that the performance of our solution is close to optimal, and it is scalable to be applicable in practical situations.