Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or
compressible signals. Sparse coding represents a signal as a sparse linear combination of …

In the past few decades, with the growing popularity of compressed sensing (CS) in the
signal processing field, the quantization step in CS has received significant attention …

Consider the recovery of an unknown signal x from quantized linear measurements. In the
one-bit compressive sensing setting, one typically assumes that x is sparse, and that the …

The design of conventional sensors is based primarily on the Shannon? Nyquist sampling
theorem, which states that a signal of bandwidth W Hz is fully determined by its discrete time …

Recent results in one-bit sampling provide a framework for a relatively low-cost, low-power
sampling, at a high rate by employing time-varying sampling threshold sequences. Another …

Low-rank tensor recovery (LRTR) is a natural extension of low-rank matrix recovery (LRMR)
to high-dimensional arrays, which aims to reconstruct an underlying tensor from incomplete …

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 …

Quantized compressive sensing deals with the problem of coding compressive
measurements of low-complexity signals with quantized, finite precision representations, ie …