Deep Reinforcement Learning-Driven Reconfigurable Intelligent Surface-Assisted Radio Surveillance With a Fixed-Wing UAV

Unmanned aerial vehicles (UAVs) play a critical role in radio surveillance to decipher malicious messages, thanks to their flexibility, mobility, and likely line-of-sight (LoS) to ground targets. Reconfigurable intelligent surfaces (RISs) can potentially create radio surveillance channels towards the UAVs by passively configuring the radio environments without raising suspicion. This paper presents a new deep reinforcement learning (DRL)-driven framework for radio surveillance, where a fixed-wing UAV is employed to acquire the radio fingerprint of a suspicious transmitter (Tx) with the aid of a benign RIS. A new Twin Delayed Deep Deterministic policy gradient (TD3) model is designed to allow the UAV to learn its trajectory and the RIS configuration based on its observed transmit rate of the suspicious Tx, eliminating the need for channel state information to and from the RIS. The novel contributions include the consideration of the fixed-wing UAV, and the action and reward designed to capture the mobility constraint of the UAV. Simulations demonstrate that the new approach offers the UAV monitor an exceptional and reliable radio surveillance capability, while keeping a desired distance from the UAV to the Tx. The use of the RIS allows for significant improvements of over 37% and 59% in the eavesdropping success probability and average eavesdropping rate, respectively.