Performance Evaluation of Pisarenko Harmonic Decomposition and Music-Like Algorithms for Narrowband Spectrum Sensing in Cognitive Radio

Abstract

Spectrum sensing is one of the enabling functionalities for cognitive radio systems to operate in the spectrum white space. To protect the primary incumbent users from interference the Cognitive Radio (CR) is required to detect the incumbent signals at a very low SNR. In this paper spectrum sensing algorithms are proposed based on the sampled correlation matrix calculated from the received signal samples. The computed correlation matrix is used to estimate the frequency function of the Pisarenko Harmonic Decomposition (PHD) method and Music-like algorithms to detect the occupied and unoccupied channels. The salient feature of this approach is that, no prior knowledge of signal properties (which would lead to uncertainly problems) are necessary. Further, the efficiency of this method is assessed by calculating the detection probability of the occupied channel as a function of the signal to noise ratio of random input signals. The simulation results demonstrate a reliable detection, at a low SNR.