Analysis of Scaled Largest Eigenvalue Based Detection for Spectrum Sensing

Scaled largest eigenvalue based detection is an ideal solution to spectrum sensing problem in cognitive radio networks. However, results on the sensing performance are very limited. In this paper, we analytically investigate the detection performance by deriving simple and accurate test statistics distributions. These results are obtained by taking advantage of properties of the Mellin transform for products of independent random variables. The derived results yield a useful analytical tool in realistic sensing scenarios.