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Multi-antenna assisted spectrum sensing in spatially correlated noise environments
KTH, School of Electrical Engineering (EES), Communication Theory.ORCID iD: 0000-0002-7926-5081
2015 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 108, 69-76 p.Article in journal (Refereed) Published
Abstract [en]

A significant challenge in spectrum sensing is to lessen the signal to noise ratio needed to detect the presence of primary users while the noise level may also be unknown. To meet this challenge, multi-antenna based techniques possess a greater efficiency compared to other algorithms. In a typical compact multi-antenna system, due to small interelement spacing, mutual coupling between thermal noises of adjacent receivers is significant. In this paper, unlike most of the spectrum sensing algorithms which assume spatially uncorrelated noise, the noises on the adjacent antennas can have arbitrary correlations. Also, in contrast to some other algorithms, no prior assumption is made on the temporal properties of the signals. We exploit low-rank/sparse matrix decomposition algorithms to obtain an estimate of noise and received source covariance matrices. Given these estimates, a Semi-Constant False Alarm Rate (S-CFAR) detector, in which the probability of false alarm is constant over the scaling of the noise covariance matrix, to examine the presence of primary users is proposed. In order to analyze the efficiency of our algorithm, we derive approximate probability of detection. Numerical simulations show that the proposed algorithm consistently and considerably outperforms state-of-the-art multiantenna based spectrum sensing algorithms.

Place, publisher, year, edition, pages
2015. Vol. 108, 69-76 p.
Keyword [en]
Spectrum sensing, Semi-constant false alarm rate (S-CFAR), Cognitive radio (CR), Generalized likelihood ratio test (GLRT), Low-rank/sparse matrix decomposition, Spatial correlation
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
URN: urn:nbn:se:kth:diva-160043DOI: 10.1016/j.sigpro.2014.08.032ISI: 000346542300008ScopusID: 2-s2.0-84907777379OAI: diva2:793314

QC 20150306

Available from: 2015-03-06 Created: 2015-02-13 Last updated: 2015-03-06Bibliographically approved

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Skoglund, Mikael
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