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  • Review Article
  • Open Access

Comparison among Cognitive Radio Architectures for Spectrum Sensing

  • 1Email author,
  • 1,
  • 1 and
  • 1
EURASIP Journal on Wireless Communications and Networking20112011:749891

https://doi.org/10.1155/2011/749891

  • Received: 28 July 2010
  • Accepted: 7 February 2011
  • Published:

Abstract

Recently, the growing success of new wireless applications and services has led to overcrowded licensed bands, inducing the governmental regulatory agencies to consider more flexible strategies to improve the utilization of the radio spectrum. To this end, cognitive radio represents a promising technology since it allows to exploit the unused radio resources. In this context, the spectrum sensing task is one of the most challenging issues faced by a cognitive radio. It consists of an analysis of the radio environment to detect unused resources which can be exploited by cognitive radios. In this paper, three different cognitive radio architectures, namely, stand-alone single antenna, cooperative and multiple antennas, are proposed for spectrum sensing purposes. These architectures implement a relatively fast and reliable signal processing algorithm, based on a feature detection technique and support vector machines, for identifying the transmissions in a given environment. Such architectures are compared in terms of detection and classification performances for two transmission standards, IEEE 802.11a and IEEE 802.16e. A set of numerical simulations have been carried out in a challenging scenario, and the advantages and disadvantages of the proposed architectures are discussed.

Keywords

  • Support Vector Machine
  • Cognitive Radio
  • Radio Resource
  • Radio Spectrum
  • Multiple Antenna

Publisher note

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Authors’ Affiliations

(1)
Department of Biophysical and Electronic Engineering, University of Genoa, Via Opera Pia 11a, 16145 Genova, Italy

Copyright

© Luca Bixio et al. 2011

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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