As the array dimension of massive MIMO systems increases to unprecedented levels, two
problems occur. First, the spatial stationarity assumption along the antenna elements is no …

One-bit quantization with time-varying sampling thresholds (also known as random
dithering) has recently found significant utilization potential in statistical signal processing …

We consider covariance estimation of any subgaussian distribution from finitely many iid
samples that are quantized to one bit of information per entry. Recent work has shown that a …

Covariance matrix recovery is a topic of great significance in the field of one-bit signal
processing and has numerous practical applications. Despite its importance, the …

We explore the impact of coarse quantization on matrix completion in the extreme scenario
of dithered one-bit sensing, where the matrix entries are compared with random dither …

This work introduces a novel class of channel estimators tailored for coarse quantization
systems. The proposed estimators are founded on conditionally Gaussian latent generative …

One-bit compressed sensing (1-bit CS) inherits the merits of traditional CS and further
reduces the cost and burden on the hardware device via employing the 1-bit analog-to …

In this paper, we propose a uniformly dithered 1-bit quantization scheme for high-
dimensional statistical estimation. The scheme contains truncation, dithering, and …

Modern datasets often exhibit heavy-tailed behavior, while quantization is inevitable in
digital signal processing and many machine learning problems. This paper studies the …

A covariance matrix estimator using two bits per entry was recently developed by Dirksen,
Maly and Rauhut [Annals of Statistics, 50 (6), pp. 3538-3562]. The estimator achieves near …