Covert Surveillance via Proactive Eavesdropping Under Channel Uncertainty

Surveillance performance is studied for a wireless eavesdropping system, where a full-duplex legitimate monitor eavesdrops a suspicious user's link with artificial noise (AN) assistance. Different from the existing works, the suspicious receiver is assumed to be capable of detecting the presence of AN. Once such receiver detects the AN, the suspicious user will stop transmission, which can therefore degrade the surveillance performance. Hence, to improve the surveillance performance, AN should be transmitted covertly with a low detection probability. Under these assumptions, an optimization problem is formulated to maximize the surveillance performance under a covert constraint. Then, based on the detection ability at the suspicious receiver, a novel scheme is proposed to solve the optimization problem using an iterative search. Moreover, we investigate the impact of both the suspicious-transmitter-to-suspicious-receiver and the monitor-to-suspicious-receiver links uncertainties on the covert surveillance performance. Simulations are performed to verify the analyses. We show that the uncertainty in the suspicious user's link can enhance the surveillance performance, while the uncertainty in the monitor-to-suspicious-receiver link can degrade the surveillance performance.