The linear inverse problem is fundamental to the development of various scientific areas.
Innumerable attempts have been carried out to solve different variants of the linear inverse …

With the development of intelligent networks such as the Internet of Things, network scales
are becoming increasingly larger, and network environments increasingly complex, which …

In this paper, a novel system architecture including a massive multi-input multi-output
(MIMO) or a reconfigurable intelligent surface (RIS) and multiple autonomous vehicles is …

Electrical impedance tomography (EIT) is developed to investigate the internal conductivity
changes of an object through a series of boundary electrodes, and has become increasingly …

Conventional compressed sensing theory assumes signals have sparse representations in
a known dictionary. Nevertheless, in many practical applications such as line spectral …

This paper considers the joint channel estimation and device activity detection in the grant-
free random access systems, where a large number of Internet-of-Things devices intend to …

We present a Xampling-based technology enabling interference-free operation of radar and
communication systems over a common spectrum. Our system uses a recently developed …

As we aim at alleviating the curse of high-dimensionality, subspace learning is becoming
more popular. Existing approaches use either information about global or local structure of …

We propose a pattern-coupled sparse Bayesian learning method for inverse synthetic
aperture radar (ISAR) imaging by exploiting a block-sparse structure inherent in ISAR target …

Grant-Free Non-Orthogonal Multiple Access (GF-NOMA) is considered as a promising
technology to support the massive connectivity of Machine-Type Communications (MTC) …