A sparsity-aware approach for NBI estimation and mitigation in large cognitive radio networks

A. Gouissem, R. Hamila, N. Al-Dhahir, Sebti Foufou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Underlay cognitive networks should follow strict interference thresholds to operate in parallel with primary networks. This constraint limits their transmission power and eventually the coverage area. Therefore, in this paper, we first design a new approach for asynchronous narrow-band interference (NBI) estimation and mitigation in orthogonal frequency-division multiplexing cognitive radio networks that does not require prior knowledge of the NBI characteristics. Our proposed approach allows the primary user to exploit the sparsity of the secondary users' interference signal to recover it and cancel it based on sparse signal recovery theory. We also propose two subcarrier selection schemes that allow the primary user to further reduce the effect of the secondary users' interference based on sparse signal recovery algorithms. We show that although the primary and secondary transmissions are performed at the same time, the performance of our proposed techniques approach the interference-free limit over practical ranges of NBI power levels.

Original languageEnglish (US)
Title of host publication2016 IEEE 84th Vehicular Technology Conference, VTC Fall 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509017010
DOIs
StatePublished - Mar 17 2017
Event84th IEEE Vehicular Technology Conference, VTC Fall 2016 - Montreal, Canada
Duration: Sep 18 2016Sep 21 2016

Other

Other84th IEEE Vehicular Technology Conference, VTC Fall 2016
CountryCanada
CityMontreal
Period9/18/169/21/16

Fingerprint

Cognitive Radio Networks
Cognitive radio
Sparsity
Interference
Recovery
Signal interference
Power transmission
Orthogonal frequency division multiplexing
Cancel
Orthogonal Frequency Division multiplexing (OFDM)
Prior Knowledge
Coverage
Range of data

Keywords

  • Cognitive network
  • Compressive sensing
  • Interference cost constraint
  • Narrow-band interference
  • OFDM
  • Sparsity

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

Cite this

Gouissem, A., Hamila, R., Al-Dhahir, N., & Foufou, S. (2017). A sparsity-aware approach for NBI estimation and mitigation in large cognitive radio networks. In 2016 IEEE 84th Vehicular Technology Conference, VTC Fall 2016 - Proceedings [7880883] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VTCFall.2016.7880883

A sparsity-aware approach for NBI estimation and mitigation in large cognitive radio networks. / Gouissem, A.; Hamila, R.; Al-Dhahir, N.; Foufou, Sebti.

2016 IEEE 84th Vehicular Technology Conference, VTC Fall 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. 7880883.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Gouissem, A, Hamila, R, Al-Dhahir, N & Foufou, S 2017, A sparsity-aware approach for NBI estimation and mitigation in large cognitive radio networks. in 2016 IEEE 84th Vehicular Technology Conference, VTC Fall 2016 - Proceedings., 7880883, Institute of Electrical and Electronics Engineers Inc., 84th IEEE Vehicular Technology Conference, VTC Fall 2016, Montreal, Canada, 9/18/16. https://doi.org/10.1109/VTCFall.2016.7880883
Gouissem A, Hamila R, Al-Dhahir N, Foufou S. A sparsity-aware approach for NBI estimation and mitigation in large cognitive radio networks. In 2016 IEEE 84th Vehicular Technology Conference, VTC Fall 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. 7880883 https://doi.org/10.1109/VTCFall.2016.7880883
Gouissem, A. ; Hamila, R. ; Al-Dhahir, N. ; Foufou, Sebti. / A sparsity-aware approach for NBI estimation and mitigation in large cognitive radio networks. 2016 IEEE 84th Vehicular Technology Conference, VTC Fall 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017.
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