Spectrum sensing using sub-nyquist rate sampling

Zahid Saleem, Samir Al-Ghadhban

Research output: Contribution to journalConference articlepeer-review


Spectrum sensing in wideband regime requires huge amount of samples. The observed frequency spectrum is usually sparse. Compressed sensing technique provides a viable solution to reconstruct the sparse signals. The observed wideband spectrum can be reconstructed using compressive sensing technique. Inherent constraints of the compressed sensing algorithms hinder the flexible implementation of spectrum sensing process. The structure-based Bayesian sparse recovery algorithm is used in this paper to implement spectrum sensing process. Spectrum sensing performed using the Bayesian estimation approach resulted in better performance compared to the results based on compressed sensing technique. Various cases have been discussed considering the amount of information available for the observed frequency band. Spectrum sensing performed using the Bayesian algorithm showed improvement of more than 5 dB in all cases.

Original languageEnglish
Pages (from-to)91-94
Number of pages4
JournalAdvanced International Conference on Telecommunications, AICT
Issue numberJanuary
StatePublished - 2013

Bibliographical note

Publisher Copyright:
Copyright © 2013 IARIA.


  • Cognitive radio
  • Compressive sensing
  • Spectrum sensing
  • Structure-based Bayesian sparse recovery algorithm

ASJC Scopus subject areas

  • Computer Networks and Communications


Dive into the research topics of 'Spectrum sensing using sub-nyquist rate sampling'. Together they form a unique fingerprint.

Cite this