  For an orthogonal frequency-division multiplexing (OFDM) system over a doubly
selective (DS) channel, a large number of pilot subcarriers are needed to
estimate the numerous channel parameters, resulting in low spectral efficiency.
In this paper, by exploiting temporal correlation of practical wireless
channels, we propose a highly efficient structured distributed compressive
sensing (SDCS) based joint multi-symbol channel estimation scheme.
Specifically, by using the complex exponential basis expansion model (CE-BEM)
and exploiting the sparsity in the delay domain within multiple OFDM symbols,
we turn to estimate jointly sparse CE-BEM coefficient vectors rather than
numerous channel taps. Then a sparse pilot pattern within multiple OFDM symbols
is designed to obtain an ICI-free structure and transform the channel
estimation problem into a joint-block-sparse model. Next, a novel block-based
simultaneous orthogonal matching pursuit (BSOMP) algorithm is proposed to
jointly recover coefficient vectors accurately. Finally, to reduce the CE-BEM
modeling error, we carry out smoothing treatments of already estimated channel
taps via piecewise linear approximation.Simulation results demonstrate that the
proposed channel estimation scheme can achieve higher estimation accuracy than
conventional schemes, although with a smaller number of pilot subcarriers.
