发表机构
School of Information and Communications Engineering, Xi’an Jiaotong University; School of Computer Science & Technology, Xi’an University of Posts & Telecommunications（西安交通大学信息与通信工程学院; 西安邮电大学计算机科学与技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对RIMSA大规模MIMO系统，提出基于压缩感知的两阶段细化信道估计方法，通过分阶段降低测量矩阵相干性并消除DoA模糊，提升估计精度。
AI 中文摘要
为满足对高数据速率和大容量的日益增长的需求，下一代无线通信系统需要配备大量天线的收发器。具有超表面天线的大规模多输入多输出（MIMO）已成为一种有前景的解决方案。本文研究了新兴的可重构智能超表面天线（RIMSA）阵列系统的信道估计问题。具体而言，我们基于压缩感知（CS）原理开发了一种两阶段细化（TSR）信道估计方法。在第一阶段，我们利用RIMSA的天线结构，通过在所有RIMSA上设置相同的相位响应向量来接收导频。这样，CS框架下测量矩阵的相干性得以降低，信道估计性能得到提升。然而，这种特殊设计引入了信道到达方向（DoA）估计模糊性，并产生了一个模糊的候选DoA集合。在第二阶段，我们优化超材料元素的相位响应以消除模糊性，并准确估计DoA和信道系数。总体而言，第一阶段的估计精度提升以模糊性为代价，而该模糊性在第二阶段被消除。我们通过呈现数值结果并将其与现有方法进行比较，展示了TSR方法的性能优势。
英文摘要
To meet the increasing demands for high data rates and large capacity, next generation wireless communication systems require transceivers equipped with a large number of antennas. Massive multiple-input multiple-output (MIMO) with metasurface antennas has emerged as a promising solution. In this paper, we investigate the channel estimation problem for the emerging reconfigurable intelligent metasurface antenna (RIMSA) array systems. Specifically, we develop a two-stage refinement (TSR) channel estimation method based on the compressed sensing (CS) principle. In the first stage, we exploit the antenna structure of RIMSA to receive pilots by setting identical phase response vectors across all RIMSAs. In this manner, the coherence of the measurement matrix under the CS framework is reduced and the channel estimation performance is improved. However, this special design introduces channel direction-of-arrival (DoA) estimation ambiguity and yields an ambiguous candidate DoA set. In the second stage, we optimize the phase responses of the metamaterial elements to resolve the ambiguity and accurately estimate the DoAs and channel coefficients. Overall, the estimation accuracy is improved in the first stage at the cost of ambiguity, and this ambiguity is eliminated in the second stage. We illustrate the performance advantages of the TSR method by presenting the numerical results and comparing it with the existing methods.
