Statistics of Co-Channel Interference in a Field of Poisson and Poisson-Poisson Clustered Interferers

• Published in: IEEE transactions on signal processing 2010-12, Vol.58 (12), p.6207-6222
• Main Authors: Gulati, Kapil, Evans, Brian L, Andrews, Jeffrey G, Tinsley, Keith R
• Format: Article
• Language: English
• Subjects: Analytical models; Annular; Applied sciences; Cellular communication; Clustering; Clusters; Co-channel interference; Exact sciences and technology; impulsive noise; Information, signal and communications theory; Interchannel interference; Interference; Mathematical model; Miscellaneous; Networks; Poisson equations; Poisson processes; probability; Receivers; Signal and communications theory; Signal processing; Signal representation. Spectral analysis; Signal, noise; Statistics; stochastic approximation; Telecommunications and information theory; Wireless communication; Wireless communications; Wireless networks
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2010.2072922

With increasing spatial reuse of radio spectrum, co-channel interference is becoming a dominant noise source and may severely degrade the communication performance of wireless transceivers. In this paper, we consider the problem of statistical-physical modeling of co-channel interference from an annular field of Poisson or Poisson-Poisson cluster distributed interferers. Poisson and Poisson-Poisson cluster processes are commonly used to model interferer distributions in large wireless networks without and with interferer clustering, respectively. Further, by considering the interferers distributed over a parametric annular region, we derive interference statistics for finite- and infinite- area interference region with and without a guard zone around the receiver. Statistical modeling of interference is a useful tool to analyze outage probabilities in wireless networks and design interference-aware transceivers. Our contributions include: 1) developing a unified framework for deriving interference models for various wireless network environments; 2) demonstrating the applicability of the symmetric alpha stable and Gaussian mixture (with Middleton Class A as a particular form) distributions in modeling co-channel interference; and 3) deriving analytical conditions on the system model parameters for which these distributions accurately model the statistical properties of the interference. Applications include co-channel interference modeling for various wireless networks, including wireless ad hoc, cellular, local area, and femtocell networks.