Near-Field Channel Estimation for mmWave/THz Communications with Extremely Large-Scale UPAs

TL;DR

This paper introduces a novel channel estimation method for extremely large-scale UPAs in mmWave/THz communications, leveraging a 2D block-sparse structure and a modified 2D-DFT dictionary to improve accuracy over existing techniques.

Contribution

It reformulates UPA near-field channels as an outer product of ULA channels and develops a 2D pattern-coupled sparse Bayesian learning approach for enhanced estimation.

Findings

Outperforms existing methods in accuracy

Maintains comparable computational complexity

Validates effectiveness through simulations

Abstract

Extremely large antenna arrays (ELAAs) are widely adopted in mmWave/THz communications to compensate for the severe path loss, wherein the channel estimation remains a significant challenge since the Rayleigh distance of ELAAs stretches to tens or even hundreds of meters and the near-field channel model should be considered. Existing polar-domain based methods and block-sparse based methods are originally devised for Uniform Linear Arrays (ULAs) near-field channel estimation. The polar-domain based method can be applied to Uniform Planar Arrays (UPAs), but it behaves plain since it ignores the specific sparsity structure of the UPA near-field channels. Meanwhile, the block-sparse based method cannot be extended to the UPA scenarios directly. To address these issues, we first reformulate the original UPA near-field channel as an outer product of two ULA near-field channels and we construct a modified two-dimensional DFT (2D-DFT) dictionary for it. With the proposed dictionary, we further prove that the UPA near-field channel admits a 2D block-sparse structure. Leveraging this specific sparse structure, we solve the channel estimation problem with the 2D Pattern-Coupled Sparse Bayesian Learning (2D-PCSBL) algorithm. Simulation results show that the proposed approach outperforms conventional existing methods while maintaining a comparable computational complexity.