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What is Covariance-Based Sensing

Handbook of Research on Software-Defined and Cognitive Radio Technologies for Dynamic Spectrum Management
Spectrum sensing based on the calculation of the covariance matrix from the received signal samples.
Published in Chapter:
Spectrum Sensing Using Principal Components for Multiple Antenna Cognitive Radios
Farrukh A. Bhatti (Institute of Space Technology, Pakistan), Gerard B. Rowe (The University of Auckland, New Zealand), and Kevin W. Sowerby (The University of Auckland, New Zealand)
DOI: 10.4018/978-1-4666-6571-2.ch007
This chapter presents an experimental comparative analysis of the well-known Covariance-Based Detection (CBD) techniques, which include Covariance Absolute Value (CAV), Maximum-Minimum Eigenvalue (MME), Energy with Minimum Eigenvalue (EME), and Maximum Eigenvalue Detection (MED). CBD techniques overcome the noise uncertainty issue of the Energy Detector (ED) and can even outperform ED in the case of correlated signals. They can perform accurate blind detection given sufficient number of signal samples. This chapter also presents a novel CBD algorithm that is based on Principal Component (PC) analysis. A Software-Defined Radio (SDR)-based multiple antenna system is used to evaluate the detection performance of the considered algorithms. The PC algorithm significantly outperforms the MED and EME algorithms and it also outperforms MME and CAV algorithms in certain cases.
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