Narrowband Interference Mitigation in SC-FDMA Using Bayesian Sparse Recovery
KAUST DepartmentElectrical Engineering Program
KAUST Grant NumberOSR 2016-KKI-2899
Permanent link to this recordhttp://hdl.handle.net/10754/622571
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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.
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.
SponsorsThe 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 .