The capacity of random wireless networks

Young Hwang, Seong Lyun Kim

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


We analyze how the capacity of the random wireless network scales with node density for stationary nodes, taking jointly into account link adaptation, media access control (MAC), routing and retransmission for error recovery (ARQ). For the purpose, we propose a generic per-hop-based routing scheme, in which relay probability is a key parameter, and use the routing scheme as a basis for our analysis. By jointly optimizing the above factors, we derive the capacity of the random network. Our analysis shows that the per-node throughput of a static random wireless network composed of n source-destination pairs is. This capacity estimation is similar to that of Gupta and Kumar, even if the assumptions are quite different.We also use simulations to investigate how node mobility affects the capacity of a random network. We have found that mobility can increase network capacity, by giving the nodes more chances to be close to each other. Our simulation results empirically show that the capacity of the random network under a mobility condition declines in the order of.

Original languageEnglish
Article number4712715
Pages (from-to)4968-4975
Number of pages8
JournalIEEE Transactions on Wireless Communications
Issue number12
Publication statusPublished - 2008 Dec

Bibliographical note

Funding Information:
This work was supported by BrOMA-ITRC (IITA-2007-C1090-0701-0037), MIC, Korea. Digital Object Identifier 10.1109/T-WC.2008.070723 1Throughout this paper, we use asymptotic notations to represent the capacity bounds: upper O, lower Ω, and tight bound Θ. Formally, f(x) = O(g(x)) if and only if there exist a positive real number M and a real number x0 such that for all x > x0, |f(x)| ≤ M|g(x)|. Similarly, f(x) = Ω(g(x)) if and only if there exist a positive real number M and a real number x0 such that for all x > x0, |f(x)| ≥ M|g(x)|. When f(x) = O(g(x)) and f(x) = Ω(g(x)), we can represent f(x) = Θ(g(x)).

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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