Distributed Largest Eigenvalue-Based Spectrum Sensing Using Diffusion LMS

• Published in: IEEE transactions on signal and information processing over networks Vol. 4; no. 2; pp. 362 - 377
• Main Authors: Ainomae, Ahti, Bengtsson, Mats, Trump, Tonu
• Format: Journal Article
• Language: English
• Published: IEEE 01-06-2018
• Subjects: Cognitive radio; Covariance matrices; Detectors; diffusion LMS; diffusion networks; distributed detection; distributed estimation; Eigenvalues and eigenfunctions; Estimation; Information processing; random matrix; spectrum sensing
• ISSN: 2373-776X; 2373-776X; 2373-7778
• DOI: 10.1109/TSIPN.2017.2705483

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, thereby 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.