Spectrum Sensing using Variant Eigenvalue methods for Cognitive Radio

Abstract

Spectrum sensing is a fundamental to cognitive radio (CR) and quickness, as well as complexity, are the basic metrics in spectrum sensing. In real world environments, CR does not have any information about the primary user (PU) hence it has to follow blind detection method. Eigenvalue based detection methods are blind detection methods and the detection performance is well under low SNR environment. Eigenvalue moment ratio (EMR) detection method has better detection performance, less computational complexity and computational time compared to other eigenvalue base detection methods. According to the IEEE 802.22 standards for CR, a novel two stage spectrum sensing is proposed in which energy detection as coarse sensing and eigenvalue moment ratio detection technique (EMR) as a fine sensing second stage. EMR method is used for the estimation of noise and feedbacks it to ED. The proposed algorithm is designed to improve the detection in less computational complexity. Second stage with EMR is used to detect and estimate the noise power, that convert ED into fully blind technique by feed backing the estimated noise power. EMR that works with small data size enhances quickness of detection compared to previously proposed methods. Comparative analysis with previous methods done in terms of detection using Receiver operating characteristics and complexity analysis for proposed method. Fading and shadowing are common in the urban environment and which degrade the performance of CR. The efficiency of EMR detector under fading channels are examined.