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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