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 …

Magnetic resonance imaging (MRI) is an inherently slow imaging modality, since it acquires
multi-dimensional k-space data through 1-D free induction decay or echo signals. This often …

Indoor positioning is one of the core technologies of Internet of Things (IoT) and artificial
intelligence (AI) and is expected to play a significant role in the upcoming era of AI …

Location-based service in the indoor environment is playing a crucial role in different
application scenarios. The introduction of technologies, such as ultradense network and …

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 …

Orthogonal frequency division multiplexing (OFDM) has been widely adopted in modern
wireless communication systems due to its robustness against the frequency selectivity of …

Sparse Bayesian learning (SBL) and specifically relevance vector machines have received
much attention in the machine learning literature as a means of achieving parsimonious …

Overcomplete representations are attracting interest in signal processing theory, particularly
due to their potential to generate sparse representations of signals. However, in general, the …

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 recent years, compressed sensing (CS) has attracted considerable attention in areas of
applied mathematics, computer science, and electrical engineering by suggesting that it may …