Compressive estimation of doubly selective channels in multicarrier systems: Leakage effects and sparsity-enhancing processing

TL;DR

This paper applies compressed sensing to doubly selective channel estimation in multicarrier systems, introducing sparsity-enhancing basis techniques and methods to estimate interference channels, improving spectral efficiency and robustness.

Contribution

It proposes novel basis expansion and optimization methods to mitigate leakage effects and enhance sparsity in CS-based channel estimation for doubly selective channels.

Findings

Sparsity-enhancing basis improves channel estimation accuracy.

Optimized basis reduces leakage effects in delay-Doppler domain.

Proposed estimator effectively captures intersymbol and intercarrier interference.

Abstract

We consider the application of compressed sensing (CS) to the estimation of doubly selective channels within pulse-shaping multicarrier systems (which include OFDM systems as a special case). By exploiting sparsity in the delay-Doppler domain, CS-based channel estimation allows for an increase in spectral efficiency through a reduction of the number of pilot symbols. For combating leakage effects that limit the delay-Doppler sparsity, we propose a sparsity-enhancing basis expansion and a method for optimizing the basis with or without prior statistical information about the channel. We also present an alternative CS-based channel estimator for (potentially) strongly time-frequency dispersive channels, which is capable of estimating the "off-diagonal" channel coefficients characterizing intersymbol and intercarrier interference (ISI/ICI). For this estimator, we propose a basis construction combining Fourier (exponential) and prolate spheroidal sequences. Simulation results assess the performance gains achieved by the proposed sparsity-enhancing processing techniques and by explicit estimation of ISI/ICI channel coefficients.