Sparse channel estimation via matching pursuit with application to equalization

By Shane F. Cotter, Bhaskar D. Rao. IEEE Transactions on Communications, 2002

696 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/0943420905098805a703bad3b06fa4d0f200adca

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Citations in the corpus

Online Adaptive Estimation of Sparse Signals: Where RLS Meets the $\ell_1$ -Norm (2010, methodology): “Exploitation of sparsity is critical in applications as div erse as variable selection in linear regression models for diabe tes [24], image compression [5], distributed spectrum sensing for cognitive radios [3], estimation of wireless multipath cha nnels [9], [23], and signal decomposition using ov”
The null space property for sparse recovery from multiple measurement vectors (2011, neutral): “[12]), and equalization of sparse communication channels (cf.”
Structured Compressed Sensing: From Theory to Applications (2011, background): “Examples include DNA microarray analysis [140], [141], equalization of sparse communication channels [105], and source localization [104].”
Toeplitz Compressed Sensing Matrices With Applications to Sparse Channel Estimation (2010, background): “Then, for a specified , it is known that satisfies RIP with probability at least provided , where are functions of [10].”
Sparse Bayesian learning for basis selection (2004, background): “Example applications include biomagnetic imaging [4], channel equalization [5]–[7], bandlimited extrapolation and spectral estimation [8], [9], direction-of-arrival estimation [10], functional approximation [11]–[13], echo cancellation [14], [15], and image restoration [16].”
Channel Estimation for OFDM (2014, background): “spaced sparse channel is considered in [76]–[79].”
Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit (2006, background): “B. D. Rao and his colleagues have considered applications to magnetoencephalography [GGR95] and to the equalization of sparse communications channels [ CR02 ].”
Stable recovery of sparse overcomplete representations in the presence of noise (2006, background): “…in signal processing, it has been reported that decomposition into separate transforms gives improved compression [1], [8] and improved equalization [6]; and in image processing, it has been shown that one can separate images into disjoint signal types using such decompositions [31], [32], [22].”
Compressed Sensing for Wireless Communications: Useful Tips and Tricks (2015, result): “Note that this problem has the same form as (13) and CS techniques a re effective in recoveringh from the measurements y [45], [46].”
Off-Grid Direction of Arrival Estimation Using Sparse Bayesian Inference (2011, background): “The topic of sparse signal representation and reconstruction has recently been developed into the well-known area named compressed/compressive sensing (CS) [13–15], and a broad spectrum of different applications can be found in excellent references [16–23].”
Sparse solutions to linear inverse problems with multiple measurement vectors (2005, methodology): “Recently, forward sequential search-based methods have been applied to the equalization of sparse channels which are found in some communication environments [9], [42].”
A Novel Strategy for Radar Imaging Based on Compressive Sensing (2008, neutral): “hyperspectral imaging [9], communications [10], etc.”
Reduce and Boost: Recovering Arbitrary Sets of Jointly Sparse Vectors (2008, methodology): “Familiar formats like MP3 (for audio signals) and JPEG (for images) implement this approach.”
Estimation of Rapidly Time-Varying Sparse Channels (2007, background): “rithm or its orthogonalized variant have recently increased noticeably [16], [17], [18].”