AI & ML
Distributed Cross-Layer Optimization for Covert Multi-Hop, Multi-Modal Networks: Exponentially Fast Convergence and Robust Tracking
arXiv math.IT0 views
arXiv:2608.28469v5 Announce Type: replace 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.
Source:https://arxiv.org/abs/2608.28469PreviousInVideo Alternatives 2026: 5 Tools by Job, With Free Plans and PricesNextConfusion-Erasure Bounds of Error-Bounded Decoders under QAM
Related posts
Google Is Forcing AI Overviews in Classrooms. Schools Are Searching for a Way Out
0Wired
Hackers stole DreamHost customer files and inboxes during months-long attack
0TechCrunch
Does ChatGPT Recommend Your Business? How I Check Every Week
0dev.to
OpenAI to acquire Pierre Computer Company
0Hacker News
Qualcomm Cloud AI 100 Ultra – 150W AI Accelerator Card with 128GB at 548GB/s
0Hacker News

Apple is Putting Employees on an AI Budget
0MacRumors