1-Bit compressive sensing

By Petros T. Boufounos, Richard Baraniuk. Annual Conference on Information Sciences and Systems, 2008

754 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/ff37286eeddad768239de87567c35e082eca62b7

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In Biomedical Engineering

In Computational Mechanics

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A Survey on Learning to Hash (2016, background): “1-bit compressive sensing [8]: Its goal is to design a measurement matrix A and a recovery algorithm such that a ksparse unit vector x can be efficiently recovered from the sign of its linear measurements, i.”
Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors (2011, background): “Third, fine quantization is more susceptible to non-linear distortion in the ADC electronics, requiring explicit treatment in the reconstruction [29].”
Optimal errors and phase transitions in high-dimensional generalized linear models (2017, methodology): “The free entropy for this model, again given by a normalization factor of the posterior of w given ˜ Y 0 and V , is exactly [6] .”
1-Bit Matrix Completion (2012, background): “As noted above, matrix completion is closely related to the ﬁeld of compressed sensing, where a theory to deal with single-bit quantization has recently been developed [10,32,33,43,51,52].”
One‐Bit Compressed Sensing by Linear Programming (2011, positive): “Ax / D y and k x k 2 D 1: This optimization program was ﬁrst proposed in [6].”
A feasible method for optimization with orthogonality constraints (2012, background): “Compressive sensing (CS) [8] acquires a sparse signal of interest, not by taking many uniform samples, but rather by measuring a few incoherent linear projections followed by an optimization-based reconstruction.”
Channel Estimation in Broadband Millimeter Wave MIMO Systems With Few-Bit ADCs (2016, methodology): “B N r 2C N r, and the noisy quantized received signal y 2C N r p 1. B. Channel Estimation Algorithm The channel estimation problem in (14) is similar to the noisy quantized compressed sensing problem [43]–[47]. Essentially, the goal is to estimate x given the measurement matrix A , VeT NB N r 2C r N”
Capacity Analysis of One-Bit Quantized MIMO Systems With Transmitter Channel State Information (2014, methodology): “Initial work on channel estimation in the onebit compressive sensing framework has been reported in [51], [5 2] and our work in [53].”
Robust 1-bit Compressed Sensing and Sparse Logistic Regression: A Convex Programming Approach (2012, background): “ical challenges. We also demonstrated the connection to sparse binomial regression, and derived novel results for this problem as well. The problem setup in 1-bit compressed sensing (as rst dened in [3]) is quite elegant, allowing for a theoretical approach. On the other hand, there are many compres”