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

TLDR

XL‑MIMO promises 6G performance, yet existing far‑field or near‑field models miss the hybrid‑field reality of practical XL‑MIMO systems. We propose an efficient hybrid‑field channel estimation scheme that accurately models the XL‑MIMO channel. The scheme first characterizes the hybrid‑field nature of XL‑MIMO by identifying far‑ and near‑field scatterers, then introduces a hybrid‑field channel model incorporating both path components, and finally estimates each component separately. Simulations demonstrate that the proposed scheme outperforms existing far‑ or near‑field estimation methods.

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.