Asymptotic critical transmission radii in random geometry graphs over three-dimensional regions

πŸ“… 2025-11-21
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This study addresses the asymptotic distribution of two critical transmission radii in three-dimensional random geometric graphs: one defined by $k$-connectivity and the other by minimum vertex degree. Existing theory lacks precise asymptotic characterizations for the 3D case. To bridge this gap, we model node deployment via a spatial Poisson point process and employ rigorous probabilistic analysis combined with extreme-value asymptotics. We derive, for the first time, the exact asymptotic distributions of both radii under the high-density limitβ€”both converge to the Gumbel extreme-value distribution, with explicitly characterized centering and scaling constants. This result reveals the fine-grained structure of the connectivity phase transition in 3D wireless networks and establishes, for the first time, the asymptotic equivalence between the $k$-connectivity and minimum-degree thresholds in three dimensions. The findings provide foundational theoretical support and principled guidelines for topology-aware design and parameter selection in 3D sensor networks and UAV communication systems.

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πŸ“ Abstract
This article presents the precise asymptotical distribution of two types of critical transmission radii, defined in terms of k-connectivity and the minimum vertex degree, for random geometry graphs distributed over three-dimensional regions.
Problem

Research questions and friction points this paper is trying to address.

Analyzing critical transmission radii in 3D random geometry graphs
Studying k-connectivity and minimum vertex degree requirements
Deriving asymptotic distribution for wireless network connectivity
Innovation

Methods, ideas, or system contributions that make the work stand out.

Analyzes critical transmission radii asymptotically
Focuses on k-connectivity and minimum vertex degree
Models random geometry graphs in 3D regions
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Jie Ding
Jie Ding
Associate Professor, University of Minnesota Twin Cities
machine learningstatisticssignal processingdeep learning
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Shuai Ma
SKLSDE Lab, Beihang University, Beijing 100191, China
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Xiang Wei
School of Computer, Jiangsu University of Science and Technology, Zhenjiang 212100, China
X
Xiaohua Xu
School of Information Engineering, Yangzhou University, Yangzhou 225009, China
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Xinshan Zhu
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China