Spectrum sensing for a subdivided band in cognitive radio networks

Prosanta Paul, Chunsheng Xin, Min Song, Yanxiao Zhao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Spectrum sensing plays a critical role in cognitive radio networks. Most of existing works on spectrum sensing adopted energy detection which takes samples on a band and then compares the summation with a threshold to determine the state of the band. However, if a licensed band is subdivided by the primary users, such as in the unlicensed WiFi band, the energy detection faces a challenge. The threshold used to decide if there is a PU signal on the band now depends on the number of sub-bands that are being used by primary users, since the received signal power on the band is now dependent on the number of used sub-bands. In this work, we propose a wavelet based spectrum sensing approach that does not depend on the number of used sub-bands and adaptively detects PU signals on a licensed band. We use the measured real world signals to test the approach. The simulation results indicate that the proposed approach can effectively detect the PU signal on a licensed band without needing the knowledge of band subdivision. In addition, the comparative study with the existing techniques is performed to evaluate two performance metrics, true detection and false alarm, for primary users signal detection.

Original languageEnglish
Title of host publication24th International Conference on Computer Communications and Networks, ICCCN 2015
ISBN (Electronic)9781479999644
DOIs
StatePublished - 2 Oct 2015
Event24th International Conference on Computer Communications and Networks, ICCCN 2015 - Las Vegas, United States
Duration: 3 Aug 20156 Aug 2015

Publication series

NameProceedings - International Conference on Computer Communications and Networks, ICCCN
Volume2015-October
ISSN (Print)1095-2055

Conference

Conference24th International Conference on Computer Communications and Networks, ICCCN 2015
Country/TerritoryUnited States
CityLas Vegas
Period3/08/156/08/15

Keywords

  • Edge detection
  • Multi-scale summation
  • Spectrum sensing
  • Wavelet transform

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