Multichannel group sparsity methods for compressive channel estimation in doubly selective multicarrier MIMO systems (extended version)

TL;DR

This paper introduces a novel multichannel group sparse compressed sensing method for channel estimation in doubly selective MIMO multicarrier systems, leveraging joint group sparsity in the delay-Doppler domain for improved accuracy.

Contribution

It develops a new compressive channel estimator exploiting joint group sparsity and basis optimization, with theoretical error bounds and complexity analysis.

Findings

Enhanced channel estimation accuracy demonstrated in simulations.

Performance gains from basis optimization and prior statistical information.

The method outperforms traditional estimators in doubly selective MIMO channels.

Abstract

We consider channel estimation within pulse-shaping multicarrier multiple-input multiple-output (MIMO) systems transmitting over doubly selective MIMO channels. This setup includes MIMO orthogonal frequency-division multiplexing (MIMO-OFDM) systems as a special case. We show that the component channels tend to exhibit an approximate joint group sparsity structure in the delay-Doppler domain. We then develop a compressive channel estimator that exploits this structure for improved performance. The proposed channel estimator uses the methodology of multichannel group sparse compressed sensing, which combines the methodologies of group sparse compressed sensing and multichannel compressed sensing. We derive an upper bound on the channel estimation error and analyze the estimator's computational complexity. The performance of the estimator is further improved by introducing a basis expansion yielding enhanced joint group sparsity, along with a basis optimization algorithm that is able to utilize prior statistical information if available. Simulations using a geometry-based channel simulator demonstrate the performance gains due to leveraging the joint group sparsity and optimizing the basis.