Channel Estimation for Extremely Large-Scale Massive MIMO: Far-Field, Near-Field, or Hybrid-Field?

TL;DR

This paper introduces a hybrid-field channel estimation scheme for XL-MIMO systems that accurately models the combined far-field and near-field features, outperforming existing methods.

Contribution

It proposes a novel hybrid-field channel model and an estimation scheme tailored for XL-MIMO's unique channel characteristics.

Findings

The scheme outperforms existing methods in simulations.

The hybrid-field model captures both far-field and near-field components.

Accurate channel estimation improves XL-MIMO system performance.

Abstract

Extremely large-scale massive MIMO (XL-MIMO) is a promising technique for future 6G communications.However, existing far-field or near-field channel model mismatches the hybrid-field channel feature in the practical XL-MIMO system.Thus,existing far-field and near-field channel estimation schemes cannot be directly used to accurately estimate the hybrid-field XL-MIMO channel. To solve this problem, we propose an efficient hybrid-field channel estimation scheme by accurately modeling the XL-MIMO channel.Specifically,we firstly reveal the hybrid-field channel feature of the XL-MIMO channel, where different scatters may be in far-field or near-field region.Then, we propose a hybrid-field channel model to capture this feature, which contains both the far-field and near-field path components. Finally, we propose a hybrid-field channel estimation scheme, where the far-field and near-field path components are respectively estimated. Simulation results show that the proposed scheme performs better than existing schemes.