Improved energy-efficient cooperative spectrum sensing schemes based on noise variance estimation for Cognitive Radio network

Cognitive Radios (CR) are regarded as a viable solution to enabling flexible use of the frequency spectrum in future generations of wireless networks. An important aspect of spectrum management in CR systems is adaptation of the spectrum sensing methods employed by CRs in order to accurately detect the changing patterns of spectrum use and to update the spectrum and interference constraints under which CR terminals operate. The cognitive radio paradigm is based on the ability to detect the presence of primary users in a given frequency band. In this scenario a spectrum monitor may estimate the signal power levels of all frequency channels in the band of interest, together with the background noise level. We address Maximum Likelihood estimation for this problem, exploiting a priori knowledge about the primary network, summarized in the spectral shape of primary transmissions. An iterative asymptotic ML estimate is proposed, which can be further simplified in order obtain a computationally more an efficient Least Squares estimator with performance very close to the Cramer-Rao lower bound in several cases of interest.