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1st International ICST Workshop on Spatial Stochastic Models for Wireless Networks

Research Article

Analytical Lower Bounds on the Critical Density in Continuum Percolation

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BibTeX Plain Text
  • @INPROCEEDINGS{10.1109/WIOPT.2007.4480080,
        author={Zhenning Kong and Edmund M. Yeh},
        title={Analytical Lower Bounds on the Critical Density in Continuum Percolation},
        proceedings={1st International ICST Workshop on Spatial Stochastic Models for Wireless Networks},
        publisher={IEEE},
        proceedings_a={SPASWIN},
        year={2008},
        month={3},
        keywords={Analytical models  Bonding  Decoding  Large-scale systems  Lattices  Mathematical model  Probability density function  Random variables  Solid modeling  Wireless networks},
        doi={10.1109/WIOPT.2007.4480080}
    }
    
  • Zhenning Kong
    Edmund M. Yeh
    Year: 2008
    Analytical Lower Bounds on the Critical Density in Continuum Percolation
    SPASWIN
    IEEE
    DOI: 10.1109/WIOPT.2007.4480080
Zhenning Kong1,*, Edmund M. Yeh1,*
  • 1: Department of Electrical Engineering Yale University New Haven, CT 06520, USA
*Contact email: zhenning.kong@yale.edu, edmund.yeh@yale.edu

Abstract

Percolation theory has become a useful tool for the analysis of large-scale wireless networks. We investigate the fundamental problem of characterizing the critical density lambdac (d) for d-dimensional Poisson random geometric graphs in continuum percolation theory. By using a probabilistic analysis which incorporates the clustering effect in random geometric graphs, we develop a new class of analytical lower bounds for the critical density lambdac (d) in d-dimensional Poisson random geometric graphs. The lower bounds are the tightest known to date. In particular, for the two-dimensional case, the analytical lower bound is improved to lambdac (2) ges 0.7698. For the three-dimensional case, we obtain lambdac (3) ges 0.4494.

Keywords
Analytical models Bonding Decoding Large-scale systems Lattices Mathematical model Probability density function Random variables Solid modeling Wireless networks
Published
2008-03-31
Publisher
IEEE
Modified
2011-07-28
http://dx.doi.org/10.1109/WIOPT.2007.4480080
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