Statistical Modeling of Spectrum Sensing Energy in Multi-Hop Cognitive Radio Networks

Abstract

The aim of this letter is to address the statistical modeling of the spectrum sensing energy consumption in cognitive radio networks. A Poisson point process has been shown to yield tractable and accurate results for the modeling of the interference in cognitive radio networks. We adopt this homogeneous stochastic process to develop an unified framework for deriving the energy consumption of the spectrum sensing in clustered cognitive radio networks. Furthermore, we extend the framework to multi-hop networks. The letter demonstrates that the spectrum sensing energy can be modeled as a Gamma-truncated distribution, as a function of the number of secondary users, their spatial density, and the number of hops of the cognitive radio network.