When machine learning meets compressive sampling for wideband spectrum sensing

Bassem Khalfi, Adem Zaid, Bechir Hamdaoui

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

39 Citations (Scopus)

Abstract

This paper proposes a novel technique that exploits spectrum occupancy behaviors inherent to wideband spectrum access to enable efficient cooperative wideband spectrum sensing. Our technique requires lesser number of sensing measurements while still recovering spectrum occupancy information accurately. It does so by leveraging compressive sampling theory to exploit the block-like occupancy structure of wideband spectrum access. Our technique is also adaptive in that it accounts for the variability of spectrum occupancy over time. It exploits supervised learning to provide and use accurate realtime estimates of the spectrum occupancy. Using simulations, we show that our proposed technique outperforms existing approaches by making accurate spectrum occupancy decisions with lesser sensing communication and energy overheads.

Original languageEnglish
Title of host publication2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1120-1125
Number of pages6
ISBN (Electronic)9781509043729
DOIs
Publication statusPublished - 19 Jul 2017
Externally publishedYes
Event13th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2017 - Valencia, Spain
Duration: 26 Jun 201730 Jun 2017

Publication series

Name2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC 2017

Conference

Conference13th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2017
Country/TerritorySpain
CityValencia
Period26/06/1730/06/17

Keywords

  • Compressive sampling
  • Cooperative wideband spectrum sensing
  • Supervised learning

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