Deep learning has gained great popularity due to its widespread success on many inference
problems. We consider the application of deep learning to the sparse linear inverse …

We develop a broadband channel estimation algorithm for millimeter wave (mmWave)
multiple input multiple output (MIMO) systems with few-bit analog-to-digital converters …

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

Hybrid analog/digital architectures and receivers with low-resolution analog-to-digital
converters (ADCs) are two low power solutions for wireless systems with large antenna …

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 …

Channel estimation is one of the main challenges for implementing reconfigurable intelligent
surface (RIS)-aided communication system with a large number of antennas at the base …

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 …

In this paper, we research the multiuser detection (MUD) in a new Spatial Modulation
Multiple-Input-Multiple-Output (SM-MIMO) system, whose Base Station (BS) is equipped with …

The generalized linear model (GLM), where a random vector x is observed through a noisy,
possibly nonlinear, function of a linear transform output z= Ax, arises in a range of …

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 …