This paper introduces a reconfigurable intelligent surface (RIS) to support parameter estimation in machine-type communications (MTC). We focus on a network where single-antenna sensors transmit spatially correlated measurements to a multiple-antenna collector node (CN) via non-orthogonal multiple access. We propose an estimation scheme based on the minimum mean square error (MMSE) criterion. We also integrate successive interference cancelation (SIC) at the receiver to mitigate communication failures in noisy and interference-prone channels under the finite blocklength (FBL) regime. Moreover, recognizing the importance of channel state information (CSI), we explore various methodologies for its acquisition at the CN. We statistically design the RIS configuration and SIC decoding order to minimize estimation error while accounting for channel temporal variations and short packet lengths. To mirror practical systems, we incorporate the detrimental effects of FBL communication and imperfect CSI errors in our analysis. Simulations demonstrate that larger reflecting surfaces lead to smaller MSEs and underscore the importance of selecting an appropriate decoding order for accuracy and ultimate performance.

翻译：本文引入可重构智能表面（RIS）以支持机器类通信（MTC）中的参数估计。我们研究一种网络架构，其中单天线传感器通过非正交多址接入方式，将空间相关测量值传输至多天线收集节点（CN）。我们提出一种基于最小均方误差（MMSE）准则的估计方案。为缓解有限块长（FBL）机制下噪声与干扰信道中的通信失效问题，我们在接收端集成了连续干扰消除（SIC）技术。此外，考虑到信道状态信息（CSI）的重要性，我们探索了在CN端获取CSI的多种方法。通过统计方法设计RIS配置与SIC解码顺序，在考虑信道时变特性与短包长度的同时最小化估计误差。为贴近实际系统，我们在分析中纳入了FBL通信与不完美CSI误差的不利影响。仿真结果表明，更大的反射表面能带来更小的均方误差，并凸显了选择合适解码顺序对精度与最终性能的重要性。