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dc.contributor.authorAli, Anum
dc.contributor.authorMasood, Mudassir
dc.contributor.authorSohail, Muhammad
dc.contributor.authorAl-Ghadhban, Samir
dc.contributor.authorAl-Naffouri, Tareq Y.
dc.date.accessioned2017-01-02T09:55:31Z
dc.date.available2017-01-02T09:55:31Z
dc.date.issued2016-09-29
dc.identifier.citationAli A, Masood M, Sohail MS, Al-Ghadhban SN, Al-Naffouri TY (2016) Narrowband Interference Mitigation in SC-FDMA Using Bayesian Sparse Recovery. IEEE Transactions on Signal Processing 64: 6471–6484. Available: http://dx.doi.org/10.1109/TSP.2016.2614484.
dc.identifier.issn1053-587X
dc.identifier.issn1941-0476
dc.identifier.doi10.1109/TSP.2016.2614484
dc.identifier.urihttp://hdl.handle.net/10754/622571
dc.description.abstractThis paper presents a novel narrowband interference (NBI) mitigation scheme for single carrier-frequency division multiple access systems. The proposed NBI cancellation scheme exploits the frequency-domain sparsity of the unknown signal and adopts a low complexity Bayesian sparse recovery procedure. At the transmitter, a few randomly chosen data locations are kept data free to sense the NBI signal at the receiver. Furthermore, it is noted that in practice, the sparsity of the NBI signal is destroyed by a grid mismatch between the NBI sources and the system under consideration. Toward this end, first, an accurate grid mismatch model is presented that is capable of assuming independent offsets for multiple NBI sources, and second, the sparsity of the unknown signal is restored prior to reconstruction using a sparsifying transform. To improve the spectral efficiency of the proposed scheme, a data-aided NBI recovery procedure is outlined that relies on adaptively selecting a subset of data-points and using them as additional measurements. Numerical results demonstrate the effectiveness of the proposed scheme for NBI mitigation.
dc.description.sponsorshipThe associate editor coordinating the review of this manuscript and approving it for publication was Prof. Sergios Theodoridis. This publication is based upon work supported in part by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR 2016-KKI-2899. The work of T. Y. Al-Naffouri was supported by KAUST, Office of Competitive Research through the CRG3 under Grant#2221. The work of M. Masood and S. Al-Ghadhban was supported by the Deanship of Scientific Research (DSR) at King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia, through project number KAUST-002. Part of this paper was presented at the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brisbane, Australia, 2015 [1].
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/document/7579608/
dc.subjectNarrowband interference mitigation
dc.subjectBayesian sparse signal recovery
dc.subjectSC-FDMA
dc.subjectmultiple measurement vectors
dc.subjectdata-aided compressed sensing
dc.titleNarrowband Interference Mitigation in SC-FDMA Using Bayesian Sparse Recovery
dc.typeArticle
dc.contributor.departmentElectrical Engineering Program
dc.identifier.journalIEEE Transactions on Signal Processing
dc.contributor.institutionWireless Networking and Communications Group (WNCG), Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX, United States
dc.contributor.institutionDepartment of Electrical Engineering, KFUPM, Dhahran, Saudi Arabia
dc.contributor.institutionDepartment of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong
kaust.personAl-Naffouri, Tareq Y.
kaust.grant.numberOSR 2016-KKI-2899


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