发表机构
Duke University（杜克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ARCTAN通过可微分射线追踪联合优化空中基站与协作干扰器的位置、朝向和功率，实现任意形状区域的射频约束，显著降低信号泄漏。
AI 中文摘要
空中基站（ABSs）能够在临时、缺乏基础设施的环境中快速建立连接，但其广播式视距传输会泄漏到预期服务区域之外，使通信暴露于被动窃听和干扰之下。先前的基于协作干扰的物理层防御通常假设已知窃听者位置、简化统计信道或持续重新定位的干扰器。我们则将问题表述为射频（RF）约束：将可用信号限制在用户定义的任意形状目标区域内，同时在其他区域拒绝信号，且不依赖窃听者位置。我们提出ARCTAN，一种基于梯度的优化框架，通过反向传播经过站点特定的可微分三维射线追踪信道，联合优化静止ABS和协作干扰器（CJs）的位置、朝向和发射功率。在三个目标区域几何形状的高保真数字孪生中评估，ARCTAN实现了约10 dB的平均区域内信干噪比（SINR），同时将平均区域外SINR从13-16 dB降至-3-5 dB，并将信号泄漏率从超过93%抑制到低于46%，所需协作干扰器最多为12个候选中的10个。
英文摘要
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.
CommentsTo appear in the Proceedings of the 2026 IEEE Military Communications Conference (MILCOM)
