发表机构
ASELSAN Inc.; School of EECS, KTH Royal Institute of Technology; Ericsson Research（ASELSAN公司; KTH皇家理工学院电气与计算机工程学院; 爱立信研究）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于部分松弛的框架，用于近场非视距多用户上行信道估计，通过多球面波建模、贪婪最大似然和秩自适应协方差拟合方法，实现强估计性能并优于现有方法。
AI 中文摘要
未来无线接入网络预计将依赖大孔径天线阵列，其中覆盖区域的越来越大部分可能落入辐射近场区域。在这种状态下，传统的远场模型变得不准确，接收空间特征同时依赖于传播距离和方位角，使得可靠的上行链路信道获取成为关键的物理层挑战。许多现有的近场估计工作依赖于简化的视距主导或单路径信道模型，这些模型无法捕捉实际非视距（NLoS）多径环境。与先前单路径近场部分松弛（PR）公式不同，本文通过将每个用户信道建模为多个由距离和方位角表征的球面波分量的叠加，开发了一种基于PR的近场NLoS上行链路信道估计框架。对于已知导频情况，我们开发了一种贪婪的基于PR的最大似然估计器，迭代提取主导传播路径，同时减轻多用户干扰。对于未知符号情况，我们提出了一种基于PR的秩自适应协方差拟合方法，以捕捉多径结构。我们进一步推导了这两种情况对应的克拉美-罗界。数值结果表明，所提出的多径PR方法实现了强大的估计性能，接近相应界限，并在所考虑的场景中优于近场二维多信号分类，支持它们对未来大孔径上行链路系统的相关性。
英文摘要
Future radio access networks are expected to rely on large-aperture antenna arrays, for which an increasing portion of the coverage region may fall within the radiative near-field. In this regime, conventional far-field models become inaccurate, and the received spatial signature depends jointly on the propagation range and azimuth, making reliable uplink channel acquisition a key physical-layer challenge. Many existing near-field estimation works rely on simplified line-of-sight-dominant or single-path channel models, which fail to capture practical non-line-of-sight (NLoS) multipath environments. In contrast to prior single-path near-field partial relaxation (PR) formulations, in this paper, we develop a PR-based framework for near-field NLoS uplink channel estimation by modeling each user channel as a superposition of multiple spherical-wave components characterized by their ranges and azimuths. For the known-pilot case, we develop a greedy PR-based maximum likelihood estimator that iteratively extracts dominant propagation paths while mitigating multi-user interference. For the unknown-symbol case, we propose a PR-based rank-adaptive covariance-fitting approach that captures the multipath structure. We further derive the corresponding Cramér-Rao bounds for both cases. Numerical results show that the proposed multipath PR-based methods achieve strong estimation performance, remain close to the corresponding bounds, and outperform near-field two-dimensional multiple signal classification across the considered scenarios, supporting their relevance for future large-aperture uplink systems.
Comments6 pages, 5 figures. Accepted to IEEE CSCN 2026
