Massive multiple-input multiple-output (MIMO) is a key technology to meet the user demands
in performance and quality of services (QoS) for next generation communication systems …

Nowadays, a large amount of information has to be transmitted or processed. This implies
high-power processing, large memory density, and increased energy consumption. In …

As a paradigm to recover unknown entries of a matrix from partial observations, low-rank
matrix completion (LRMC) has generated a great deal of interest. Over the years, there have …

As a paradigm to recover the sparse signal from a small set of linear measurements,
compressed sensing (CS) has stimulated a great deal of interest in recent years. In order to …

The fifth-generation (5G) of cellular networks and beyond requires massive connectivity,
high data rates, and low latency. Millimeter-wave (mmWave) communications is a key 5G …

Many algorithms for computer vision rely on locating interest points, or keypoints in each
image, and calculating a feature description from the pixel region surrounding the interest …

In frequency selective channel environment, channel estimation in hybrid precoding
millimeter-wave (mmWave) massive multiple input multiple output (MIMO) system is a …

Single-pixel imaging (SPI) uses a single-pixel detector instead of a detector array with a lot
of pixels in traditional imaging techniques to realize two-dimensional or even multi …

Recovering sparse signals from linear measurements has demonstrated outstanding utility
in a vast variety of real-world applications. Compressive sensing is the topic that studies the …

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 …