Largest Eigenvalue-based Spectrum Sensing Over κ-μ Extreme Fading Channels

We consider the problem of largest eigenvalue-based spectrum sensing over kappa-mu extreme fading channels. This test, also known as the Roy's largest root test, is asymptotically optimal when the primary signal is not known apriori. The theoretical expression for average probability of detection is derived, and validated through Monte Carlo simulations. The kappa-mu extreme distribution, a special case of kappa-mu distribution, is known to be a sufficiently accurate and flexible model that follows several field data measurements, in contrast to, for e.g., a two-ray distribution fit. Therefore, this work characterizes the performance of largest eigenvalue-based detector for spectrum sensing in an enclosed, practically significant urban environment with harsh fading.