Spectrum-efficient multi-channel design for coexisting IEEE 802.15.4 networks: A stochastic geometry approach

Hesham Elsawy, Ekram Hossain, Sergio Camorlinga

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


For networks with random topologies (e.g., wireless ad-hoc and sensor networks) and dynamically varying channel gains, choosing the long term operating parameters that optimize the network performance metrics is very challenging. In this paper, we use stochastic geometry analysis to develop a novel framework to design spectrum-efficient multi-channel random wireless networks based on the IEEE 802.15.4 standard. The proposed framework maximizes both spatial and time domain frequency utilization under channel gain uncertainties to minimize the number of frequency channels required to accommodate a certain population of coexisting IEEE 802.15.4 networks. The performance metrics are the outage probability and the self admission failure probability. We relax the single channel assumption that has been used traditionally in the stochastic geometry analysis. We show that the intensity of the admitted networks does not increase linearly with the number of channels and the rate of increase of the intensity of the admitted networks decreases with the number of channels. By using graph theory, we obtain the minimum required number of channels to accommodate a certain intensity of coexisting networks under a self admission failure probability constraint. To this end, we design a superframe structure for the coexisting IEEE 802.15.4 networks and a method for time-domain interference alignment. © 2002-2012 IEEE.
Original languageEnglish (US)
Pages (from-to)1611-1624
Number of pages14
JournalIEEE Transactions on Mobile Computing
Issue number7
StatePublished - Jul 2014

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


Dive into the research topics of 'Spectrum-efficient multi-channel design for coexisting IEEE 802.15.4 networks: A stochastic geometry approach'. Together they form a unique fingerprint.

Cite this