Compressed sensing (CS) is an emerging field that has attracted considerable research
interest over the past few years. Previous review articles in CS limit their scope to standard …

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 this article, we review sampling strategies that target reduction of the ADC rate below
Nyquist. Our survey covers classic works from the early 1950s through recent publications …

We consider the application of compressed sensing (CS) to the estimation of doubly
selective channels within pulse-shaping multicarrier systems (which include orthogonal …

Compressive sensing is a new signal processing paradigm that aims to encode sparse
signals by using far lower sampling rates than those in the traditional Nyquist approach. It …

Identification of time-varying linear systems, which introduce both time-shifts (delays) and
frequency-shifts (Doppler-shifts), is a central task in many engineering applications. This …

Channel estimation for an orthogonal frequency-division multiplexing (OFDM) broadband
system over a doubly selective channel is very challenging. This is mainly due to the …

In this paper, we study the identification of a time-varying linear system from its response to a
known input signal. More specifically, we consider systems whose response to the input …