Message-passing estimation from quantized samples
Estimation of a vector from quantized linear measurements is a common problem for which
simple linear techniques are suboptimal--sometimes greatly so. This paper develops …
simple linear techniques are suboptimal--sometimes greatly so. This paper develops …
Message-passing de-quantization with applications to compressed sensing
Estimation of a vector from quantized linear measurements is a common problem for which
simple linear techniques are suboptimal-sometimes greatly so. This paper develops …
simple linear techniques are suboptimal-sometimes greatly so. This paper develops …
Approximate message passing with parameter estimation for heavily quantized measurements
Designing efficient sparse recovery algorithms that could handle noisy quantized
measurements is important in a variety of applications–from radar to source localization …
measurements is important in a variety of applications–from radar to source localization …
An expectation-maximization approach to tuning generalized vector approximate message passing
Abstract Generalized Vector Approximate Message Passing (GVAMP) is an efficient iterative
algorithm for approximately minimum-mean-squared-error estimation of a random vector x …
algorithm for approximately minimum-mean-squared-error estimation of a random vector x …
Covariance Estimation under One‐bit Quantization
We consider the classical problem of estimating the covariance matrix of a subgaussian
distribution from iid samples in the novel context of coarse quantization, ie, instead of having …
distribution from iid samples in the novel context of coarse quantization, ie, instead of having …
Memoryless scalar quantization for random frames
K Melnykova, Ö Yilmaz - Sampling Theory, Signal Processing, and Data …, 2021 - Springer
Memoryless scalar quantization (MSQ) is a common technique to quantize generalized
linear samples of signals. The non-linear nature of quantization makes the analysis of the …
linear samples of signals. The non-linear nature of quantization makes the analysis of the …
Optimal quantization for compressive sensing under message passing reconstruction
We consider the optimal quantization of compressive sensing measurements along with
estimation from quantized samples using generalized approximate message passing …
estimation from quantized samples using generalized approximate message passing …
A unified Bayesian inference framework for generalized linear models
In this letter, we present a unified Bayesian inference framework for generalized linear
models (GLM), which iteratively reduces the GLM problem to a sequence of standard linear …
models (GLM), which iteratively reduces the GLM problem to a sequence of standard linear …
On the Trade-Off Between Bit Depth and Number of Samples for a Basic Approach to Structured Signal Recovery From -Bit Quantized Linear Measurements
M Slawski, P Li - IEEE Transactions on Information Theory, 2018 - ieeexplore.ieee.org
We consider the problem of recovering a high-dimensional structured signal from
independent Gaussian linear measurements each of which is quantized to bits. The focus is …
independent Gaussian linear measurements each of which is quantized to bits. The focus is …
Non-Bayesian estimation with partially quantized observations
N Harel, T Routtenberg - 2017 22nd International Conference …, 2017 - ieeexplore.ieee.org
In this paper, we consider non-Bayesian parameter estimation in wireless sensor networks
(WSNs) with multiple sensors that have different quantization resolutions. Quantized …
(WSNs) with multiple sensors that have different quantization resolutions. Quantized …