We address the problem of channel estimation in massive multiple-input multiple-output
(Massive MIMO) systems where both hybrid analog-digital processing and low-resolution …

Massive multiuser multiple-input multiple-output (MU-MIMO) systems are expected to be the
core technology in fifth-generation wireless systems because they significantly improve …

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 …

In this paper we consider the generalized approximate message passing (GAMP) algorithm
for recovering a sparse signal from modulo samples of randomized projections of the …

This paper addresses lossy distributed source coding for acquiring correlated sparse
sources via compressed sensing (CS) in wireless sensor networks. Noisy CS measurements …

This paper proposes an efficient channel estimation algorithm for millimeter wave
(mmWave) systems with a hybrid analog-digital multiple-input multiple-output (MIMO) …

In this paper we study the quantization stage that is implicit in any compressed sensing
signal acquisition paradigm. We propose using Sigma–Delta (ΣΔ) quantization and a …

Large multiple-input multiple-output (MIMO) appears in massive multi-user MIMO and
randomly-spread code-division multiple access (CDMA)-based wireless systems. In order to …

We investigate lossy compressed sensing (CS) of a hidden, or remote, source, where a
sensor observes a sparse information source indirectly. The compressed noisy …

In this study, the problem of recovering structured sparse signals with a priori distribution
whose structure patterns are unknown is studied from one‐bit adaptive (AD) quantised …