Narrowband Interference Mitigation in SC-FDMA Using Bayesian Sparse Recovery

Handle URI:
http://hdl.handle.net/10754/622571
Title:
Narrowband Interference Mitigation in SC-FDMA Using Bayesian Sparse Recovery
Authors:
Ali, Anum; Masood, Mudassir; Sohail, Muhammad; Al-Ghadhban, Samir; Al-Naffouri, Tareq Y.
Abstract:
This 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.
KAUST Department:
Electrical Engineering Program
Citation:
Ali 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.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Transactions on Signal Processing
KAUST Grant Number:
OSR 2016-KKI-2899
Issue Date:
29-Sep-2016
DOI:
10.1109/TSP.2016.2614484
Type:
Article
ISSN:
1053-587X; 1941-0476
Sponsors:
The 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].
Additional Links:
http://ieeexplore.ieee.org/document/7579608/
Appears in Collections:
Articles; Electrical Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.authorAli, Anumen
dc.contributor.authorMasood, Mudassiren
dc.contributor.authorSohail, Muhammaden
dc.contributor.authorAl-Ghadhban, Samiren
dc.contributor.authorAl-Naffouri, Tareq Y.en
dc.date.accessioned2017-01-02T09:55:31Z-
dc.date.available2017-01-02T09:55:31Z-
dc.date.issued2016-09-29en
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.en
dc.identifier.issn1053-587Xen
dc.identifier.issn1941-0476en
dc.identifier.doi10.1109/TSP.2016.2614484en
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.en
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].en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/document/7579608/en
dc.subjectNarrowband interference mitigationen
dc.subjectBayesian sparse signal recoveryen
dc.subjectSC-FDMAen
dc.subjectmultiple measurement vectorsen
dc.subjectdata-aided compressed sensingen
dc.titleNarrowband Interference Mitigation in SC-FDMA Using Bayesian Sparse Recoveryen
dc.typeArticleen
dc.contributor.departmentElectrical Engineering Programen
dc.identifier.journalIEEE Transactions on Signal Processingen
dc.contributor.institutionWireless Networking and Communications Group (WNCG), Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX, United Statesen
dc.contributor.institutionDepartment of Electrical Engineering, KFUPM, Dhahran, Saudi Arabiaen
dc.contributor.institutionDepartment of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kongen
kaust.authorAl-Naffouri, Tareq Y.en
kaust.grant.numberOSR 2016-KKI-2899en
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