Problem
Research questions and friction points this paper is trying to address.
Distributed Cross-Layer Optimization
Covert Multi-Hop Networks
Detection Error Probability
Energy Detection
Wireless Networks
Distributed Cross-Layer Optimization for Covert Multi-Hop, Multi-Modal Networks: Exponentially Fast Convergence and Robust Tracking
🤖 AI Summary
本文提出了一种分布式跨层算法，用于在隐蔽多跳、多模态无线网络中解决拥塞控制、路由、调度和功率控制的联合优化问题，通过构建对数检测错误概率的最紧凸下界，并使用PP-ADMM算法实现了指数级快速收敛。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Distributed Cross-Layer Algorithm
Concave Lower Bound on log-DEP
Parallel Proximal Alternating Direction Method of Multipliers (PP-ADMM)
Exponentially Fast Convergence
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