An Overview of the Different Principal Spectrum Sensing Techniques in Cognitive Radio Systems

Abstract

Cognitive radio is a technology proposed to increase the effective use of the spectrum. This can be done through the main function of cognitive radio technology, which is the spectrum sensing. In our work, we propose an analysis of the following spectrum sensing techniques: the matched filter detector, the cyclostationary feature detector, the energy detector and the maximum eigenvalue detector. More attention is given to blind sensing techniques that they do not need any knowledge of the primary user signal characteristics, namely the energy detection and maximum eigenvalue detection. These methods are evaluated in terms of Receiver Operational Characteristic curves and detection probability for various values of Signal to Noise Ratio based on Monte Carlo simulations, using MATLAB. As a result of this study, we found that the energy detection offers a good performance only for high SNR. Furthermore, with the maximum eigenvalue detector, the noise uncertainty problem encountered by the energy detection is solved when the value of the smoothing factor L ≥ 8 and. Finally, a summary of the comparative analysis is presented.