Stochastic-Geometry Based Characterization of Aggregate Interference in TVWS Cognitive Radio Networks

TL;DR

This paper uses stochastic geometry to analytically characterize the worst-case aggregate interference in TV white space cognitive radio networks, providing formulas that improve spectrum access and detection performance.

Contribution

It introduces a novel stochastic geometry-based method to derive closed-form interference distributions for TVWS networks, aiding spectrum management.

Findings

Derived closed-form PDF of aggregate interference

Showed improved spectrum detection using the new interference model

Quantified worst-case interference scenarios

Abstract

In this paper, we characterize the worst-case interference for a finite-area TV white space heterogeneous network using the tools of stochastic geometry. We derive closed-form expressions on the probability distribution function (PDF) and an average value of the aggregate interference for various values of path loss exponent. The proposed characterization of the interference is simple and can be used in improving the spectrum access techniques. Using the derived PDF, we demonstrate the performance gain in the spectrum detection of an eigenvalue-based detector for cognitive radio networks.