Journal ArticleDOI
A Compressed Sensing Approach for Modeling the Super-Resolution Near-Field Structure Disc System With a Sparse Volterra Filter
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TLDR
The evaluation results show that the CS algorithms can efficiently construct a sparse Volterra model for the super-RENS read- out channel and that observable nonlinear interactions take place among restricted components in the read-out channel.Abstract:
In this paper, we investigate the compressed sensing (CS) algorithms for modeling a super-resolution near-field structure (super-RENS) disc system with a sparse Volterra filter. It is well known that the super-RENS disc system has severe nonlinear inter-symbol interference (ISI). A nonlinear system with memory can be well described with the Volterra series. Furthermore, CS can restore sparse or compressed signals from measurements. For these reasons, we employ the CS algorithms to estimate a sparse super-RENS read-out channel. The evaluation results show that the CS algorithms can efficiently construct a sparse Volterra model for the super-RENS read-out channel and that observable nonlinear interactions take place among restricted components in the read-out channel.read more
Citations
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Proceedings ArticleDOI
Performance evaluation of decision feedback equalizers in fiber communication links
TL;DR: The performance of the conventional Decision Feedback (DFE) equalizer as well as the Volterra DFE is investigated in the context of intensity modulated direct detection optical communications links, when non-return to zero on-off keyed and optical differential encoded phase shift keyed transmission is employed.
Proceedings ArticleDOI
Electronic dispersion compensation of fiber links using sparsity induced volterra equalizers
TL;DR: The Sparse Learning via Iterative Minimization (SLIM) algorithm is employed for the design of reduced size Volterra Decision Feedback (VDFE) equalizers in the context of optical communications, leading to both enhanced convergence speed and significant computational complexity savings.
Journal ArticleDOI
A hierarchical alternative updated adaptive Volterra filter with pipelined architecture
Yanjie Pang,Jiashu Zhang +1 more
TL;DR: Simulations of nonlinear system adaptive identification, nonlinear channel equalization, and speech prediction show that the proposed HPAVF with different independent weight vectors in nonlinear subsection has superior performance to conventional Volterra filters, diagonally truncated VolterRA filters, and PAVFs in terms of initial convergence, steady-state error, and computational complexity.
Journal ArticleDOI
Domain Bloom in Super-Resolution Near-Field Structure Read-Out Signals
TL;DR: In this article, the effect of the domain bloom on the super-RENS read-out signal was investigated, where the asymmetric symbol conversion scheme was employed to generate asymmetric symbols corresponding to a bit pattern.
Journal ArticleDOI
Sparse modeling of super-resolution near-field structure read-out signals
TL;DR: The results demonstrate that the sparse model of a simplified polynomial form achieves good modeling performance for the super-RENS disc read-out signals.
References
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Book
Compressed sensing
TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.
Journal ArticleDOI
Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
TL;DR: In this paper, the authors considered the model problem of reconstructing an object from incomplete frequency samples and showed that with probability at least 1-O(N/sup -M/), f can be reconstructed exactly as the solution to the lscr/sub 1/ minimization problem.
Journal ArticleDOI
An Introduction To Compressive Sampling
TL;DR: The theory of compressive sampling, also known as compressed sensing or CS, is surveyed, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition.
Journal ArticleDOI
Matching pursuits with time-frequency dictionaries
Stéphane Mallat,Zhifeng Zhang +1 more
TL;DR: The authors introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions, chosen in order to best match the signal structures.
Journal ArticleDOI
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
Joel A. Tropp,Anna C. Gilbert +1 more
TL;DR: It is demonstrated theoretically and empirically that a greedy algorithm called orthogonal matching pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal.