Energy-based Bayesian spectrum sensing over α-κ-μ fading channels
Sanjeev Gurugopinath, S Shobitha · 2017
In this paper, we consider the problem of energy detection for spectrum sensing over the α-κ-μ fading channel, in a Bayesian framework. The α-κ-μ fading distribution includes popular models such as Rayleigh, Rice, Nakagami-m, Weibull, one-sided Gaussian, α-μ, κ-μ and κ-μ extreme distributions as special cases. We present a fast-converging infinite series expression for the probability of overall error, i.e., the convex combination of probability of false-alarm and probability of signal detection. We also present an analysis on optimal detection threshold that minimizes the probability of error. We discuss the performance of our detector for various values of the fading parameters through numerical techniques and validate our analysis through Monte Carlo simulations.