Estimation of Rapidly Time-Varying Sparse Channels

By Weichang Li, James C. Preisig. IEEE Journal of Oceanic Engineering, 2007

462 citations recorded by the source corpus.

Subfields: Ocean Engineering, Oceanography, Signal Processing

View Paper: https://www.semanticscholar.org/paper/04745ad8ebbacf69611dc39f103b94ed393b6db1

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Citations in the corpus

Compressed Sensing for Wireless Communications: Useful Tips and Tricks (2015, background): “Typical examples include the ultra-wideband channel [24]– [26], underwater acoustic channel [27]–[29], and even some cellular channels (e.”
Underwater Optical Wireless Communication (2016, background): “Over a period of time, many researchers have developed sophisticated methods for channel estimation and designed various algorithms to provide more effective underwater acoustic communication [21]–[25].”
Compressed Channel Sensing: A New Approach to Estimating Sparse Multipath Channels (2010, methodology): “Here, on e of the main differences between the approaches of [21] and [27] is that [21] attempts to exploit the sparsity of the channel i the delay domain only.”
Comparing Measures of Sparsity (2008, background): “Whether with sparsity constraints or with sparsity assumptions, the concept of sparsity is readily used in diverse areas such as oceanic engineering [1], antennas and propagation [2], face recognition [3], image processing [4], [5] and medical imaging [6].”
Sparse Recovery of Streaming Signals Using $\ell_1$-Homotopy (2013, methodology): “Most of the existing methods for sparse signal recovery assume a static system: the unknown signal is a finite-length vector for which a fixed set of linear measurements and a sparse representation basis are available and an `1-norm minimization program is solved for the reconstruction.”
Statistical Characterization and Computationally Efficient Modeling of a Class of Underwater Acoustic Communication Channels (2013, methodology): “Once these steps were completed, the orthogonal matching pursuit (OMP) algorithm [24] was used for channel estimation.”
Channel estimation for OFDM systems (2017, methodology): “These two facts motivates us to explore two well-known sparse approximation algorithms; namely, Orthogonal Matching Pursuit (MP) [13, 14, 15] and Basis Pursuit (BP) [16] and apply them to 18”
A Two-Stage Approach for the Estimation of Doubly Spread Acoustic Channels (2015, methodology): “Li and Preisig [5] propose the delay-Doppler-spread function representation of”