This paper gives an overview of the majorization-minimization (MM) algorithmic framework,
which can provide guidance in deriving problem-driven algorithms with low computational …

Large-scale distributed antenna systems with many access points (APs) that serve the users
by coherent joint transmission is being considered for 5G-and-beyond networks. The …

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

Multiframe super-resolution algorithms reconstruct high-resolution images by exploiting
complementary information in multiple low-resolution frames. However, despite their …

We advance both the theory and practice of robust $\ell_p $-quasinorm regression for $ p\in
(0, 1] $ by using novel variants of iteratively reweighted least-squares (IRLS) to solve the …

The underlying mechanism of how the human brain solves the cocktail party problem is
largely unknown. Recent neuroimaging studies, however, suggest salient temporal …

In this paper, we present a novel hybrid framework combining compressive spectrum
sensing with geo-location database to find spectrum holes in a decentralized cognitive …

In this contribution, we explore further possibilities for statistical robustification of the
traditional-space based time-of-arrival location estimator under impulsive noise conditions …

Many algorithms for high-dimensional regression problems require the calibration of
regularization hyperparameters. This, in turn, often requires the knowledge of the unknown …

Seeking sparse solutions of underdetermined linear systems is required in many areas of
engineering and science such as signal and image processing. The efficient sparse …