Spectrum-efficient multi-channel design for coexisting IEEE 802.15.4 networks: A stochastic geometry approach
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Permanent link to this recordhttp://hdl.handle.net/10754/563617
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AbstractFor 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.
SponsorsThis work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) through the Industrial Postgraduate Scholarships (IPS) Program and the Discovery Grants (DG) Program, and in part by a scholarship from TRTech, Winnipeg, Manitoba, Canada. The work was done during H. ElSawy's Ph.D. studies at the University of Manitoba, Canada.