Mobile UAV Eavesdropper Position Tracking for Improving Physical Layer Security in 6G Networks

This paper investigates the secrecy performance of recon-figurable intelligent surfaces (RISs)-unmanned aerial vehicles (UAVs)within a 6G communication system. Specifically, a base station employ-ing rate-splitting multiple access (RSMA) transmits both common andprivate messages to Internet of Things (IoT) devices (IDs) via a UAV re-lay (UR). Additionally, a mobile UAV eavesdropper (UE) is considered,capable of moving to intercept signals relayed by the UR. Therefore, weaddress the challenge of mobile UE position tracking to enhance physicallayer security (PLS). We propose a framework that integrates real-timeUE tracking using a particle filter (PF), a sequential Monte Carlo, forestimating the UE’s state. By continuously updating the UE location es-timates, the considered system enables a proactive security measure, suchas friendly jammers, to minimize the risk of intercepted communications.Simulation results demonstrate that accurate UE tracking significantlyimproves secrecy capacity and reduces the likelihood of successful eaves-dropping.