It is recognized that wireless channels often exhibit a sparse structure, especially for
wideband and ultra-wideband systems. In order to exploit this sparse structure and make it …

In Orthogonal Frequency Division Multiplexing (OFDM) systems, the technique used to
estimate and track the time-varying multipath channel is critical to ensure reliable, high data …

It is well known that the impulse response of a wideband wireless channel is approximately
sparse, in the sense that it has a small number of significant components relative to the …

The impulse response of a typical wireless multipath channel can be modeled as a tapped
delay line filter whose non-zero components are sparse relative to the channel delay …

Compressed Sensing (CS)-based sparse channel estimation in a Bayesian framework for
Orthogonal Frequency Division Multiplexing (OFDM)-based communication systems is …

In recent years many sparse estimation methods, also known as compressed sensing, have
been developed for channel identification problems in digital communications. However, all …

Cluster-sparse channels often exist in frequency-selective fading broadband communication
systems. The main reason is received scattered waveform exhibits cluster structure which is …

Compressive sensing (CS)-based channel estimation considerably reduces pilot symbols
usage by exploiting the sparsity of the propagation channel in the delay-Doppler domain. In …

Existing methods for sparse channel estimation typically provide an estimate computed as
the solution maximizing an objective function defined as the sum of the log-likelihood …

In this paper, we propose a channel estimation approach based on Bayesian compressive
sensing that has the advantage of computation simplicity, noise robust to some extent and …