@inproceedings{8225d92ba6644019870c56ccbf12aea1,
title = "A sparsity-aware approach for NBI estimation and mitigation in large cognitive radio networks",
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.",
keywords = "Cognitive network, Compressive sensing, Interference cost constraint, Narrow-band interference, OFDM, Sparsity",
author = "A. Gouissem and R. Hamila and N. Al-Dhahir and S. Foufou",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 84th IEEE Vehicular Technology Conference, VTC Fall 2016 ; Conference date: 18-09-2016 Through 21-09-2016",
year = "2016",
month = jul,
day = "2",
doi = "10.1109/VTCFall.2016.7880883",
language = "English (US)",
series = "IEEE Vehicular Technology Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2016 IEEE 84th Vehicular Technology Conference, VTC Fall 2016 - Proceedings",
}