We consider the estimation of a random vector observed through a linear transform followed
by a componentwise probabilistic measurement channel. Although such linear mixing …

We consider a class of approximated message passing (AMP) algorithms and characterize
their high-dimensional behavior in terms of a suitable state evolution recursion. Our proof …

We study the compressed sensing reconstruction problem for a broad class of random, band-
diagonal sensing matrices. This construction is inspired by the idea of spatial coupling in …

Compressed sensing posits that, within limits, one can undersample a sparse signal and yet
reconstruct it accurately. Knowing the precise limits to such undersampling is important both …

Compressive sensing (CS) has been recently and enthusiastically promoted as a joint
sampling and compression approach. The advantages of CS over conventional signal …

We consider the optimal quantization of compressive sensing measurements along with
estimation from quantized samples using generalized approximate message passing …

Compressive sensing is a recent technique for estimating a sparse vector from a reduced
number of observations. Several algorithms have been developed and studied in this …

The compressive sensing (CS) framework aims to ease the burden on analog-to-digital
converters (ADCs) by exploiting inherent structure in natural and man-made signals. It has …

Modern technologies generate vast amounts of fine-grained data at an unprecedented
speed. Nowadays, high-dimensional data, where the number of variables is much larger …

We consider a resource-constrained scenario where a compressed sensing-(CS) based
sensor has a low number of measurements which are quantized at a low rate followed by …