Near-Field Channel Estimation with ELAA Modular Arrays Under Hardware Impairments

2025-09-20

AI Summary

Channel estimation in extremely large-scale antenna arrays (ELAAs) under hardware impairments suffers from low accuracy and excessive fronthaul overhead, particularly in near-field line-of-sight (LOS) scenarios. Method: We propose a low-complexity, robust channel estimation algorithm that jointly exploits the geometric structure of ELAA and the constant-modulus property of near-field LOS channels. A novel two-dimensional discrete Fourier transform (2D-DFT) masking technique is introduced to effectively suppress distortions induced by hardware impairments—such as low-noise amplifier (LNA) nonlinearity. The algorithm is designed for distributed baseband architectures, inherently reducing fronthaul data volume. Contribution/Results: Experiments demonstrate that the proposed method reduces channel estimation mean-square error by over 40% compared to conventional least-squares estimation, while improving fronthaul efficiency by approximately threefold. It achieves a favorable trade-off among high estimation accuracy, low fronthaul overhead, and strong robustness against hardware impairments.

Technology Category

• Machine Learning: Hardware-aware ML
• Intelligent Robots: State Estimation
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Extremely large-scale antenna arrays (ELAAs) enable high spatial resolution and multiplexing, especially for user equipments (UEs) in the radiative near-field. To reduce hardware cost, modular ELAA architectures with distributed baseband units (BBUs) are gaining traction. This paper addresses near-field line-of-sight (LOS) channel estimation under low noise amplifier (LNA)-induced hardware impairments in such modular systems. We propose computationally efficient estimators that exploit the array geometry and constant-modulus structure of near-field LOS channels, including a novel two-dimensional (2D) discrete Fourier transform (DFT) masking technique that improves estimation accuracy and significantly reduces fronthaul signaling. Numerical results show that the proposed methods significantly outperform the conventional least squares (LS) method.

Problem

• Estimating near-field LOS channels with hardware impairments
• Reducing fronthaul signaling in modular ELAA systems
• Improving estimation accuracy beyond conventional LS methods

Innovation

• Exploits array geometry and constant-modulus structure
• Uses novel 2D DFT masking technique
• Improves estimation accuracy and reduces fronthaul signaling