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For an orthogonal frequency-division multiplexing (OFDM) system over a doublyselective (DS) channel, a large number of pilot subcarriers are needed toestimate the numerous channel parameters, resulting in low spectral efficiency.In this paper, by exploiting temporal correlation of practical wirelesschannels, we propose a highly efficient structured distributed compressivesensing (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 thannumerous channel taps. Then a sparse pilot pattern within multiple OFDM symbolsis designed to obtain an ICI-free structure and transform the channelestimation problem into a joint-block-sparse model. Next, a novel block-basedsimultaneous orthogonal matching pursuit (BSOMP) algorithm is proposed tojointly recover coefficient vectors accurately. Finally, to reduce the CE-BEMmodeling error, we carry out smoothing treatments of already estimated channeltaps via piecewise linear approximation.Simulation results demonstrate that theproposed channel estimation scheme can achieve higher estimation accuracy thanconventional schemes, although with a smaller number of pilot subcarriers.

Structured Distributed Compressive Channel Estimation over Doubly Selective Channels