Towards a Better Understanding of Large-Scale Network Models

• Published in: IEEE/ACM transactions on networking Vol. 20; no. 2; pp. 408 - 421
• Main Authors: Guoqiang Mao, Anderson, B. D. O.
• Format: Journal Article
• Language: English
• Published: IEEE 01.04.2012
• Subjects: Analytical models; Australia; Connectivity; continuum percolation; dense network model; Euclidean distance; extended network model; infinite network model; Integral equations; Interference; random connection model; Signal to noise ratio; Spread spectrum communication
• DOI: 10.1109/TNET.2011.2160650
• ISSN: 1063-6692

Connectivity and capacity are two fundamental properties of wireless multihop networks. The scalability of these properties has been a primary concern for which asymptotic analysis is a useful tool. Three related but logically distinct network models are often considered in asymptotic analyses, viz. the dense network model, the extended network model, and the infinite network model, which consider respectively a network deployed in a fixed finite area with a sufficiently large node density, a network deployed in a sufficiently large area with a fixed node density, and a network deployed in with a sufficiently large node density. The infinite network model originated from continuum percolation theory and asymptotic results obtained from the infinite network model have often been applied to the dense and extended networks. In this paper, through two case studies related to network connectivity on the expected number of isolated nodes and on the vanishing of components of finite order respectively, we demonstrate some subtle but important differences between the infinite network model and the dense and extended network models. Therefore, extra scrutiny has to be used in order for the results obtained from the infinite network model to be applicable to the dense and extended network models. Asymptotic results are also obtained on the expected number of isolated nodes, the vanishingly small impact of the boundary effect on the number of isolated nodes, and the vanishing of components of finite order in the dense and extended network models using a generic random connection model.