Robust Design of Reconfigurable Intelligent Surfaces for Parameter Estimation in MTC

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.