Distributed spectrum sensing for cognitive radio networks by exploiting sparsity

Juan Andrés Bazerque, Georgios B. Giannakis

Research output: Contribution to journalArticle

307 Scopus citations

Abstract

A cooperative approach to the sensing task of wireless cognitive radio (CR) networks is introduced based on a basis expansion model of the power spectral density (PSD) map in space and frequency. Joint estimation of the model parameters enables identification of the (un)used frequency bands at arbitrary locations, and thus facilitates spatial frequency reuse. The novel scheme capitalizes on two forms of sparsity: the first one introduced by the narrow-band nature of transmit-PSDs relative to the broad swaths of usable spectrum; and the second one emerging from sparsely located active radios in the operational space. An estimator of the model coefficients is developed based on the Lasso algorithm to exploit these forms of sparsity and reveal the unknown positions of transmitting CRs. The resultant scheme can be implemented via distributed online iterations, which solve quadratic programs locally (one per radio), and are adaptive to changes in the system. Simulations corroborate that exploiting sparsity in CR sensing reduces spatial and frequency spectrum leakage by 15 dB relative to least-squares (LS) alternatives.

Original languageEnglish (US)
Pages (from-to)1847-1862
Number of pages16
JournalIEEE Transactions on Signal Processing
Volume58
Issue number3 PART 2
DOIs
StatePublished - Mar 1 2010

Keywords

  • Cognitive radios
  • Compressive sampling
  • Cooperative systems
  • Distributed estimation
  • Parallel network processing
  • Sensing
  • Sparse models
  • Spectral analysis

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