发表机构
School of Information and Electronics, Beijing Institute of Technology; School of Cyberspace Science and Technology, Beijing Institute of Technology; Terahertz Wireless Communications (TWC) Laboratory, Shanghai Jiao Tong University（北京理工大学信息与电子学院; 北京理工大学网络空间安全学院; 上海交通大学太赫兹无线通信实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对近场XL-MIMO的UCA系统，提出感知分辨率的分层码本与HDA-BAR两阶段波束训练方案，仅需384个探测时隙，较传统穷举搜索降低约99.66%训练开销。
AI 中文摘要
极大多输入多输出（XL-MIMO）技术支持第六代（6G）通信实现近场特定位置的波束聚焦。均匀圆形阵列（UCA）因具备旋转对称性和均匀方位覆盖，已成为近场XL-MIMO系统的关键支撑架构。本文针对近场UCA系统提出一种感知分辨率的分层码本，并结合高效的两阶段波束训练方案以大幅降低训练开销。具体而言，基于几何球面波传播模型，刻画了近场区域UCA系统的最小可分辨距离，揭示其在联合角度-距离域的空间分辨率能力。基于该结果，设计了UCA专用分层码本：其中高能效、抗距离鲁棒的波束成形（DRBF）码本用于粗方位定位，而根据最小可分辨距离采样的全精度（FP）码本则支持精细的角度-距离波束搜索。正则化模态补偿可抑制弱模态放大，并在单位范数传输下的绝对幅度增益与距离鲁棒性之间提供可控权衡。基于该码本，开发了分层解耦架构贝叶斯回归（HDA-BAR）方案以实现快速准确的近场波束训练。针对所考虑的阵列配置，所得HDA-BAR训练过程仅需384个探测时隙，相较于传统近场穷举搜索基准，开销降低约99.66%。
英文摘要
Extremely large-scale multiple-input multiple-output (XL-MIMO) enables near-field location-specific beam focusing for sixth-generation (6G) communications. Uniform circular arrays (UCAs), with rotational symmetry and uniform azimuth coverage, have emerged as a key enabling architecture for near-field XL-MIMO systems. In this paper, we propose a resolution-aware hierarchical codebook for near-field UCA systems, along with an efficient two-stage beam training scheme to significantly reduce the training overhead. Specifically, we characterize the minimum resolvable distance of UCA systems in the near-field region based on a geometric spherical-wave propagation model, revealing their spatial resolution capability in the joint angle--distance domain. Guided by this result, we design a UCA-specific hierarchical codebook, where a power-efficient distance-robust beamforming (DRBF) codebook provides coarse azimuth localization and a full-precision (FP) codebook sampled according to the minimum resolvable distance enables refined angle--distance beam search. The regularized modal compensation suppresses weak-mode amplification and provides a controllable tradeoff between absolute amplitude gain under unit-norm transmission and distance robustness. Based on this codebook, we develop a hierarchical decoupled-architecture Bayesian regression (HDA-BAR) scheme for fast and accurate near-field beam training. For the considered array configuration, the resulting HDA-BAR training procedure requires $384$ probing slots, corresponding to an approximately \(99.66\%\) overhead reduction relative to the conventional near-field exhaustive-search benchmark.
