The generalized approximate message passing (GAMP) algorithm is an efficient method of
MAP or approximate-MMSE estimation of x observed from a noisy version of the transform …

Quantization is an essential step in digitizing signals, and, therefore, an indispensable
component of any modern acquisition system. This chapter explores the interaction of …

This letter focuses on the estimation of low-complexity signals when they are observed
through M uniformly quantized compressive observations. Among such signals, we consider …

Abstract Approximate Message Passing (AMP) has been shown to be a superior method for
inference problems, such as the recovery of signals from sets of noisy, lower-dimensionality …

In cosparse analysis compressive sensing (CS), one seeks to estimate a non-sparse signal
vector from noisy sub-Nyquist linear measurements by exploiting the knowledge that a given …

Due to the high bandwidths and carrier frequencies to be used at millimeter wave
(mmWave), high resolution and high sampling rate analog-to-digital converters (ADCs) with …

For the problem of binary linear classification and feature selection, we propose algorithmic
approaches to classifier design based on the generalized approximate message passing …

Communication systems with low-resolution analog-to-digital-converters (ADCs) can exploit
channel state information at the transmitter (CSIT) and receiver. This paper presents initial …

In this paper, we research the multiuser detection (MUD) in a new massive MIMO with
quantized phase-only (PO) measurements. Because the phase quantization can be …

This paper addresses channel-robust compressed sensing (CS) acquisition of sparse
sources under complexity-constrained encoding over noisy channels in wireless sensor …