3D Near-Field Beam Training for Uniform Planar Arrays through Beam Diverging

2025-09-19

AI Summary

For 6G mmWave massive uniform planar arrays (UPAs) operating under near-field spherical-wave channels, existing far-field steering or near-field focusing codebooks suffer from severe sensitivity to user equipment (UE) position mismatches and incur high beam training overhead. Method: This paper proposes, for the first time, a hierarchical codebook design with tunable wide-beam coverage and a 3D near-field sampling mechanism under a single RF chain—leveraging beam divergence as a constructive physical property rather than a nuisance. The approach employs coarse localization followed by fine-grained training to jointly mitigate UE position uncertainty. Contribution/Results: The method achieves strong robustness against positional ambiguity, reduces training overhead by ~50% theoretically, and improves both beam alignment accuracy and SNR gain. Its core innovation lies in reinterpreting beam divergence as a fundamental enabler of robust near-field training, thereby transcending the conventional far-field/near-field binary modeling paradigm.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Sampling/Simulation-based Search
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In future 6G communication systems, large-scale antenna arrays promise enhanced signal strength and spatial resolution, but they also increase the complexity of beam training. Moreover, as antenna counts grow and carrier wavelengths shrink, the channel model transits from far-field (FF) planar waves to near-field (NF) spherical waves, further complicating the beam training process. This paper focuses on millimeter-wave (mmWave) systems equipped with large-scale uniform planar arrays (UPAs), which produce 3D beam patterns and introduce additional challenges for NF beam training. Existing methods primarily rely on either FF steering or NF focusing codewords, both of which are highly sensitive to mismatches in user equipment (UE) location, leading to high sensitivity to even slight mismatch and excessive training overhead. In contrast, we introduce a novel beam training approach leveraging the beam-diverging effect, which enables adjustable wide-beam coverage using only a single radio frequency (RF) chain. Specifically, we first analyze the spatial characteristics of this effect in UPA systems and leverage them to construct hierarchical codebooks for coarse UE localization. Then, we develop a 3D sampling mechanism to build an NF refinement codebook for precise beam training. Numerical results demonstrate that the proposed algorithm achieves superior beam training performance while maintaining low training overhead.

Problem

• Addressing near-field beam training complexity in 6G UPA systems
• Reducing sensitivity to location mismatch in millimeter-wave communications
• Minimizing training overhead through beam-diverging hierarchical codebooks

Innovation

• Beam-diverging effect for wide coverage
• Hierarchical codebooks for coarse localization
• 3D sampling mechanism for precise training