Energy-based Bayesian spectrum sensing over α-μ / stacy / generalized gamma fading channels

Abstract

In this paper, we study the performance of energy detection for spectrum sensing over α-μ (a rewritten form of the Stacy or the generalized gamma) fading channels, following a Bayesian approach. We derive an infinite series expression for the probability of error, which is a convex combination of probabilities of false-alarm and signal detection. Also, we present an analysis on the optimal detection threshold that minimizes the probability of error. The analysis is validated through Monte Carlo simulations, and the performance of the detector for different values of the parameters α and μ are discussed. The derived results are useful as the α-μ fading distribution includes several practical fading models such as Rayleigh, Nakagami-m, and Weibull distributions, as special cases.