Near-Field Beam Training Through Beam Diverging

2025-09-19

AI Summary

Conventional focused beam training for extremely large-scale antenna arrays (ELAAs) in the near-field regime suffers from high scanning overhead, sensitivity to misalignment, and limited angular resolution. To address these challenges, this paper proposes a novel divergent-beam training paradigm. It innovatively exploits the natural beam divergence effect for coarse user localization, and introduces a polar-coordinate hierarchical codebook (DPC) with divergent codewords. Integrated with angular-range compression and pilot-set expansion, the scheme achieves high-accuracy angle-domain localization using only $2log_2(N)$ pilots under a single-RF-chain architecture. Theoretical analysis and numerical results demonstrate that the proposed method attains pilot overhead approaching the information-theoretic lower bound and localization accuracy nearly matching optimal exhaustive search—significantly outperforming state-of-the-art near-field beam training schemes. This work establishes a low-overhead, highly robust beam alignment framework for near-field communications.

Technology Category

• Search and Optimization: Local Search
• Machine Learning: Distributed Machine Learning & Federated Learning
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

This paper investigates beam training techniques for near-field (NF) extremely large-scale antenna arrays (ELAAs). Existing NF beam training methods predominantly rely on beam focusing, where the base station (BS) transmits highly spatially selective beams to locate the user equipment (UE). However, these beam-focusing-based schemes suffer from both high beam sweeping overhead and limited accuracy in the NF, primarily due to the narrow beams' high susceptibility to misalignment. To address this, we propose a novel NF beam training paradigm using diverging beams. Specifically, we introduce the beam diverging effect and exploit it for low-overhead, high-accuracy beam training. First, we design a diverging codeword to induce the beam diverging effect with a single radio frequency (RF) chain. Next, we develop a diverging polar-domain codebook (DPC) along with a hierarchical method that enables angular-domain localization of the UE with only 2 log_2(N) pilots, where N denotes the number of antennas. Finally, we enhance beam training performance through two additional techniques: a DPC angular range reduction strategy to improve the effectiveness of beam diverging, and a pilot set expansion method to increase overall beam training accuracy. Numerical results show that our algorithm achieves near-optimal accuracy with a small pilot overhead, outperforming existing methods.

Problem

• Reducing beam training overhead in near-field large arrays
• Improving accuracy of user localization with diverging beams
• Minimizing pilot usage while maintaining optimal performance

Innovation

• Diverging beams for low-overhead training
• Single RF chain diverging codeword design
• Hierarchical polar-domain codebook with logarithmic pilots