Spectrum sensing algorithms via finite random matrices

Wensheng Zhang, Giuseppe Abreu, Mamiko Inamori, Yukitoshi Sanada

Research output: Contribution to journalArticle

41 Citations (Scopus)

Abstract

We address the Primary User (PU) detection (spectrum sensing) problem, relevant to cognitive radio, from a finite random matrix theoretical (RMT) perspective. Specifically, we employ recently-derived closed-form and exact expressions for the distribution of the standard condition number (SCN) of uncorrelated and semi-correlated random dual central Wishart matrices of finite sizes in the design Hypothesis-Testing algorithms to detect the presence of PU signals. In particular, two algorithms are designed, with basis on the SCN distribution in the absence (H 0) and in the presence (H 1) of PU signals, respectively. Due to an inherent property of the SCN's, the H 0 test requires no estimation of SNR or any other information on the PU signal, while the H 1 test requires SNR only. Further attractive advantages of the new techniques are: a) due to the accuracy of the finite SCN distributions, superior performance is achieved under a finite number of samples, compared to asymptotic RMT-based alternatives; b) since expressions to model the SCN statistics both in the absence and presence of PU signal are used, the statistics of the spectrum sensing problem in question is completely characterized; and c) as a consequence of a) and b), accurate and simple analytical expressions for the receiver operating characteristic (ROC) - both in terms of the probability of detection as a function of the probability of false alarm (P D versus P F) and in terms of the probability of acquisition as a function of the probability of miss detection (P A versus P M) -are yielded. It is also shown that the proposed finite RMT-based algorithms outperform all similar alternatives currently known in the literature, at a substantially lower complexity. In the process, several new results on the distributions of eigenvalues and SCNs of random Wishart Matrices are offered, including a closed-form of the Marchenko-Pastur's Cumulative Density Function (CDF) and extensions of the latter, as well as variations of asymptotic the distributions of extreme eigenvalues (Tracy-Widom) and their ratio (Tracy-Widom-Curtiss), which are simpler than those obtained with the "spiked population model".

Original languageEnglish
Article number6094130
Pages (from-to)164-175
Number of pages12
JournalIEEE Transactions on Communications
Volume60
Issue number1
DOIs
Publication statusPublished - 2012 Jan

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Statistics
Cognitive radio
Probability density function
Testing

Keywords

  • Cognitive radio
  • Hypothesis test
  • Random matrix
  • Spectrum sensing
  • Standard condition number

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Spectrum sensing algorithms via finite random matrices. / Zhang, Wensheng; Abreu, Giuseppe; Inamori, Mamiko; Sanada, Yukitoshi.

In: IEEE Transactions on Communications, Vol. 60, No. 1, 6094130, 01.2012, p. 164-175.

Research output: Contribution to journalArticle

Zhang, Wensheng ; Abreu, Giuseppe ; Inamori, Mamiko ; Sanada, Yukitoshi. / Spectrum sensing algorithms via finite random matrices. In: IEEE Transactions on Communications. 2012 ; Vol. 60, No. 1. pp. 164-175.
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