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Distributed Largest Eigenvalue-Based Spectrum Sensing Using Diffusion LMS
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DOI：10.1109/TSIPN.2017.2705483.png)
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Abstract
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In this paper, we propose a distributed detection scheme for cognitive radio (CR) networks, based on the largest eigenvalues (LEs) of adaptively estimated correlation matrices (CMs), assuming that the primary user signal is temporally correlated. The proposed algorithm is fully distributed, there by avoiding the potential single point of failure that a fusion center would imply. Different forms of diffusion least mean square algorithms are used for estimating and averaging the CMs over the CR network for the LE detection and the resulting estimation performance is analyzed using a common framework. In order to obtain analytic results on the detection performance, the exact distribution of the CM estimates are approximated by a Wishart distribution, by matching the moments. The theoretical findings are verified through simulations.
Keywords:
Cognitive radio
distributed estimation
diffusion LMS
diffusion networks
distributed detection
spectrum sensing
random matrix
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