Structured Distributed Compressive Channel Estimation over Doubly Selective Channels

TL;DR

This paper introduces a structured distributed compressive sensing approach for joint multi-symbol channel estimation in OFDM systems over doubly selective channels, leveraging sparsity and temporal correlation to improve efficiency.

Contribution

It proposes a novel SDCS-based joint estimation scheme using CE-BEM, a block-based OMP algorithm, and smoothing techniques to enhance accuracy with fewer pilots.

Findings

Higher estimation accuracy than conventional schemes

Fewer pilot subcarriers needed for effective estimation

Improved spectral efficiency in OFDM systems

Abstract

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.