隐蔽多跳多模态网络的分布式跨层优化：指数级快速收敛与鲁棒跟踪
Distributed Cross-Layer Optimization for Covert Multi-Hop, Multi-Modal Networks: Exponentially Fast Convergence and Robust Tracking
- Auburn University（奥本大学）
- DEVCOM Army Research Laboratory（DEVCOM陆军研究实验室）
- The Ohio State University（俄亥俄州立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文针对隐蔽多跳多模态无线网络，提出基于PP-ADMM的分布式跨层优化算法，实现指数级快速收敛，可在信道衰落与监测者移动下鲁棒跟踪，解决了基于DEP的隐蔽网络优化难题。
AI中文摘要：
本文针对隐蔽多跳多模态无线网络开发了首个分布式跨层算法，用于联合拥塞控制、路由、调度与功率控制，其中对抗型监测者（Willies）通过能量检测对无线电模态进行监测。检测错误概率（DEP，即Willie无法可靠检测正在进行的传输的概率）通常在发射功率上非凹，这使得基于DEP的隐蔽网络优化颇具挑战性。我们通过构造对数DEP的最紧凹下界解决了该问题，得到一个保守凸问题，其可保证满足原始DEP约束，并将严格隐蔽性约束与隐蔽性-效用最大化统一到单个问题中。针对所得跨层问题，我们开发了并行近端交替方向乘子法（PP-ADMM）算法，且在标准正则条件下证明其全局Q-线性收敛，即指数级快速收敛至最优解集合。数值结果证实了线性收敛性，并展示了其在信道衰落与Willie移动性下的鲁棒跟踪性能。
英文摘要：
This paper develops the first distributed cross-layer algorithm for joint congestion control, routing, scheduling, and power control in covert multi-hop, multi-modal wireless networks, where adversarial wardens (Willies) monitor radio modalities via energy detection. The Detection Error Probability (DEP), the probability that a Willie fails to reliably detect ongoing transmissions, is generally non-concave in the transmit powers, making DEP-based covert network optimization challenging. We resolve this by constructing the tightest concave lower bound on the log-DEP, yielding a conservative convex problem that guarantees satisfaction of the original DEP constraints and unifies hard covertness constraints and covertness-utility maximization in a single problem. We develop a Parallel Proximal Alternating Direction Method of Multipliers (PP-ADMM) algorithm for the resulting cross-layer problem and prove global Q-linear convergence, i.e., exponentially fast convergence, to the set of optimal solutions under standard regularity conditions. Numerical results confirm linear convergence and demonstrate robust tracking performance under channel fading and Willie mobility.
