Distributed largest eigenvalue detection

• Published in: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 3519 - 3523
• Main Authors: Ainomae, Ahti, Trump, Tonu, Bengtsson, Mats
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-03-2017
• Subjects: Adaptation models; Algorithm design and analysis; Cognitive radio; Covariance matrices; diffusion LMS; distributed detection; distributed estimation; Eigenvalues and eigenfunctions; Estimation; Performance analysis; Sensors; Spectrum Sensing
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP.2017.7952811

Cognitive radio (CR) systems need to detect the presence of a primary user (PU) signal by continuously sensing the spectrum area of interest. Radiowave propagation effects like fading and shadowing often complicate sensing of spectrum holes because the PU signal can be weak in a particular area. Cooperative spectrum sensing is seen as a prospective solution to enhance the detection of PU signals. In this paper we study distributed spectrum sensing, based on the largest eigenvalue of adaptively estimated correlation matrices (CMs) of received signals. The PU signal is assumed to be temporally correlated. In this paper an Combine and Adapt (CTA) least mean square (LMS) diffusion based mean vector estimation scheme is proposed. No fusion center (FC) for estimation or detection is used. We analyse the resulting detection performance and verify the theoretical findings through simulations.