Distributed spectrum sensing for cognitive radio networks by exploiting sparsity

Juan Andrés Bazerque, Georgios B. Giannakis

Research output: Contribution to journalArticlepeer-review

392 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 2010

Bibliographical note

Funding Information:
Manuscript received January 12, 2009; accepted November 09, 2009. First published December 11, 2009; current version published February 10, 2010. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Daniel Palomar. Prepared through collaborative participation in the Communications and Networks Consortium sponsored by the U.S. Army Research Laboratory under the Collaborative Technology Alliance Program, Cooperative Agreement DAAD19-01-2-0011. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon. Results from this paper were presented in the Forty-Second Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, October 26–29, 2008,.

Keywords

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

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