T
Tareq Y. Al-Naffouri
Researcher at King Abdullah University of Science and Technology
Publications - 484
Citations - 9062
Tareq Y. Al-Naffouri is an academic researcher from King Abdullah University of Science and Technology. The author has contributed to research in topics: Communication channel & Orthogonal frequency-division multiplexing. The author has an hindex of 39, co-authored 452 publications receiving 6480 citations. Previous affiliations of Tareq Y. Al-Naffouri include Delft University of Technology & Georgia Institute of Technology.
Papers
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Underwater Optical Wireless Communications, Networking, and Localization: A Survey
TL;DR: This work provides a comprehensive survey on the challenges, advances, and prospects of underwater optical wireless networks (UOWNs) from a layer by layer perspective which includes physical layer issues including propagation characteristics, channel modeling, and modulation techniques.
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Next Generation Terahertz Communications: A Rendezvous of Sensing, Imaging, and Localization
TL;DR: It is posited that the breakthrough the THz band will introduce will not be solely driven by achievable high data rates, but more profoundly by the interaction between THz sensing, imaging, and localization applications.
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Efficient Coordinated Recovery of Sparse Channels in Massive MIMO
TL;DR: This paper presents a novel channel estimation approach which utilizes the sparsity and common support properties to estimate sparse channels and requires a small number of pilots and two algorithms based on this approach have been developed that perform Bayesian estimates of sparse channels even when the prior is non-Gaussian or unknown.
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Transient analysis of data-normalized adaptive filters
TL;DR: This paper develops an approach to the transient analysis of adaptive filters with data normalization that characterizes the transient behavior of such filters in terms of a linear time-invariant state-space model based on energy-conservation arguments.
Proceedings ArticleDOI
Impulse noise cancellation in OFDM: an application of compressed sensing
TL;DR: Deterministic construction of projection matrices that provably guarantee reconstruction with high probability are developed and compared with some simple capacity lower and upper bounds and with the recently obtained capacity of the Gaussian erasure channel.