Compressive sensing (CS) has received much attention in several fields such as digital
image processing, wireless channel estimation, radar imaging, and cognitive radio (CR) …

Machine learning (ML) has attracted a great research interest for physical layer design
problems, such as channel estimation, thanks to its low complexity and robustness. Channel …

The theory of compressed sensing suggests that successful inversion of an image of the
physical world (broadly defined to include speech signals, radar/sonar returns, vibration …

High-rate data communication over a multipath wireless channel often requires that the
channel response be known at the receiver. Training-based methods, which probe the …

In this paper, we investigate various channel estimators that exploit channel sparsity in the
time and/or Doppler domain for a multicarrier underwater acoustic system. We use a path …

In millimeter-wave communications, multiple-input-multiple-output (MIMO) systems use large
antenna arrays to achieve high gain and spectral efficiency. These massive MIMO systems …

Compressed sensing (CS) has recently emerged as a powerful signal acquisition paradigm.
In essence, CS enables the recovery of high-dimensional sparse signals from relatively few …

This paper is concerned with estimation of two-dimensional (2-D) frequencies from partial
time samples, which arises in many applications such as radar, inverse scattering, and …

A type of norm-adaption penalized least mean square/fourth (NA-LMS/F) algorithm is
proposed for sparse channel estimation applications. The proposed NA-LMS/F algorithm is …

This paper studies the problem of recovering a sparse signal x∈ ℝ n from highly corrupted
linear measurements y= Ax+ e∈ ℝ m, where e is an unknown error vector whose nonzero …