🤖 AI Summary
This study addresses the eavesdropping and interference risks arising from signal leakage in aerial base stations by proposing a radio frequency containment framework that operates without prior knowledge of eavesdropper locations. The proposed approach jointly optimizes base station and jammer parameters, leveraging differentiable 3D ray tracing to enable end-to-end gradient-based optimization that precisely confines signals within arbitrary target regions. Digital twin-based simulations demonstrate that the signal-to-interference-plus-noise ratio (SINR) reaches 10 dB inside the designated area while dropping below zero outside it. Furthermore, the signal leakage rate is significantly reduced from 93% to under 46%. These results confirm that the framework effectively enhances the physical layer security of air-to-ground communications.
📝 Abstract
Aerial base stations (ABSs) can rapidly establish connectivity in ad hoc, infrastructure-deprived environments, but their broadcast, line-of-sight transmissions leak far beyond the intended service area, exposing communications to passive eavesdropping and interference. Prior physical layer defenses based on cooperative jamming typically assume known eavesdropper locations, simplified statistical channels, or continuously repositioned jammers. We instead pose the problem as a radio frequency (RF) containment: confining usable signal to a user-defined, arbitrarily-shaped target zone while denying it elsewhere independent of eavesdropper location. We present ARCTAN, a gradient-based optimization framework that jointly optimizes the position, orientation, and transmit power of stationary ABSs and cooperative jammers (CJs) by backpropagating through site-specific, differentiable 3D ray traced channels. Evaluated in a high-fidelity digital twin across three target zone geometries, ARCTAN achieves a mean in-zone SINR of approximately 10 dB while reducing mean out-of-zone SINR from 13-16 dB to -3-5 dB, and suppressing signal-leakage ratios from over 93% to below 46% requiring at most 10 of 12 candidate CJs.