Adaptive spectrum sensing with noise variance estimation for dynamic cognitive radio systems

Abstract

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. In this paper we study adaptation of the spectrum sensing threshold in CR using discrete Fourier transform (DFT) filter bank (DFB) method in a dynamic scenario where the sensing threshold is adapted to minimize the spectrum sensing error in the presence of noise. We present an algorithm for spectrum sensing threshold adaptation using DFB with estimated noise variance which we illustrate with numerical examples obtained from simulations. These show the effectiveness of the proposed method in dynamic scenarios with varying noise variance.