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Presentation
Closed-Form Path-Loss Predictor for Gaussianly Distributed Nodes
Proc. of IEEE International Conference on Communications (ICC'10) (2010)
  • Dr. Mouhamed Abdulla, Concordia University, Montreal, Quebec, Canada
  • Prof. Yousef R. Shayan, Concordia University, Montreal, Quebec, Canada
Abstract
The emulation of wireless nodes spatial position is a practice used by deployment engineers and network planners to analyze the characteristics of a network. In particular, nodes geo-location will directly impact factors such as connectivity, signals fidelity, and service quality. In literature, in addition to typical homogeneous scattering, normal distribution is frequently used to model mobiles concentration in a cellular system. Moreover, Gaussian dropping is often considered as an effective placement method for airborne sensor deployment. Despite the practicality of this model, getting the network channel loss distribution still relies on exhaustive Monte Carlo simulation. In this paper, we argue the need for this inefficient approach and hence derived a generic and exact closed-form expression for the path-loss distribution density between a base-station and a network of nodes. Simulation was used to reaffirm the validity of the theoretical analysis using values from the new IEEE 802.20 standard.
Keywords
  • Stochastic Geometry,
  • Monte Carlo Simulation,
  • Spatial Distribution,
  • Cellular Communication,
  • Statistical Modeling,
  • Statistical Analysis,
  • Mobile Radio,
  • Network Geometry,
  • Cellular Radio,
  • Mobile Communication,
  • path loss,
  • Gaussian Distribution,
  • Network Deployment
Publication Date
Spring May 23, 2010
Location
Cape Town, South Africa
DOI
https://doi.org/10.1109/ICC.2010.5502200
Citation Information
M. Abdulla and Y. R. Shayan, “Closed-Form Path-Loss Predictor for Gaussianly Distributed Nodes,” In Proc. of IEEE International Conference on Communications (ICC'10), pp. 1-6, Cape Town, South Africa, May 23-27, 2010. Link: https://doi.org/10.1109/ICC.2010.5502200
Creative Commons License
Creative Commons License
This work is licensed under a Creative Commons CC_BY International License.