Super-Resolution Sparse MIMO-OFDM Channel Estimation Based on Spatial and Temporal Correlations

TL;DR

This paper introduces a super-resolution MIMO-OFDM channel estimation method leveraging spatial and temporal correlations, achieving high accuracy and reduced pilot overhead by exploiting channel sparsity and the finite rate of innovation theory.

Contribution

It presents a novel sparse MIMO-OFDM channel estimation scheme based on FRI theory that exploits spatial and temporal correlations for improved accuracy and efficiency.

Findings

Achieves super-resolution path delay estimation with arbitrary values.

Utilizes spatial and temporal correlations to enhance estimation accuracy.

Reduces pilot overhead through joint processing of signals from multiple antennas.

Abstract

This letter proposes a parametric sparse multiple input multiple output (MIMO)-OFDM channel estimation scheme based on the finite rate of innovation (FRI) theory, whereby super-resolution estimates of path delays with arbitrary values can be achieved. Meanwhile, both the spatial and temporal correlations of wireless MIMO channels are exploited to improve the accuracy of the channel estimation. For outdoor communication scenarios, where wireless channels are sparse in nature, path delays of different transmit-receive antenna pairs share a common sparse pattern due to the spatial correlation of MIMO channels. Meanwhile, the channel sparse pattern is nearly unchanged during several adjacent OFDM symbols due to the temporal correlation of MIMO channels. By simultaneously exploiting those MIMO channel characteristics, the proposed scheme performs better than existing state-of-the-art schemes. Furthermore, by joint processing of signals associated with different antennas, the pilot overhead can be reduced under the framework of the FRI theory.