Robust Design of Reconfigurable Intelligent Surfaces for Parameter Estimation in MTC

TL;DR

This paper proposes a robust RIS-assisted parameter estimation scheme for MTC networks, integrating MMSE, SIC, and CSI acquisition methods, accounting for practical FBL and imperfect CSI effects, with simulations showing performance improvements.

Contribution

It introduces a novel RIS configuration and SIC decoding strategy tailored for MTC, considering practical constraints like FBL and imperfect CSI, which enhances estimation accuracy.

Findings

Larger RIS surfaces reduce mean square error in estimation.

Proper SIC decoding order significantly improves performance.

Accounting for FBL and CSI errors is crucial for realistic system design.

Abstract

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.