发表机构
National Mobile Communications Research Laboratory, Frontiers Science Center for Mobile Information Communication and Security, Southeast University; Purple Mountain Laboratories; Key Laboratory of Intelligent Support Technology for Complex Environments, Ministry of Education, Nanjing University of Information Science and Technology（东南大学移动通信国家重点实验室、移动信息与安全前沿科学中心; 紫金山实验室; 南京信息工程大学教育部复杂环境智能支持技术重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对流体天线系统信道重建，提出基于非负最小二乘的协方差域方法，利用非均匀端口扩大虚拟孔径，估计到达角，仿真显示优于ULA-MUSIC和ULA-ESPRIT，且视距估计显著改善。
AI 中文摘要
从少数激活端口重建流体天线系统（FAS）的全孔径信道，需要从有限的空间样本中推断未观测的响应。本文提出了一种基于非负最小二乘（NNLS）的协方差域FAS信道重建方法。非均匀端口排列构建了具有扩大孔径的差分共阵。将样本协方差向量化，将物理阵列映射到虚拟导向字典，并将所得的复协方差拟合模型重铸为严格等价的实值NNLS问题。从零初始化的投影梯度迭代估计非负角度出现系数，L_s个最强且分离良好的峰值产生到达角（AoAs）。仿真表明，所提出的FAS-NNLS估计器利用了扩大的虚拟孔径，在到达角均方误差方面优于均匀线性阵列（ULA）多重信号分类（ULA-MUSIC）和ULA旋转不变技术估计信号参数（ULA-ESPRIT）。其非视距（NLoS）路径精度与FAS协方差匹配追踪相当，而视距（LoS）估计显著改善。
英文摘要
Reconstructing the full-aperture channel of a fluid antenna system (FAS) from a few activated ports requires inferring unobserved responses from limited spatial samples. This paper proposes a covariance-domain FAS channel-reconstruction method based on nonnegative least squares (NNLS). A nonuniform port arrangement constructs a difference coarray with an enlarged aperture. Vectorizing the sample covariance maps the physical array to a virtual steering dictionary, and the resulting complex covariance-fitting model is recast as a strictly equivalent real-valued NNLS problem. A projected-gradient iteration initialized at zero estimates the nonnegative angular-occurrence coefficients, and the L_s strongest well-separated peaks yield the angles of arrival (AoAs). Simulations show that the proposed FAS-NNLS estimator exploits the enlarged virtual aperture and achieves lower AoA mean squared errors than uniform linear array (ULA) multiple signal classification (ULA-MUSIC) and ULA estimation of signal parameters via rotational invariance techniques (ULA-ESPRIT). Its non-line-of-sight (NLoS) path accuracy matches FAS covariance matching pursuit, while its line-of-sight (LoS) estimation is significantly improved.
