Robust Design of Reconfigurable Intelligent Surfaces for Parameter Estimation in MTC

TL;DR

This work tackles parameter estimation in RIS-aided MTC under NOMA, finite blocklength, and imperfect CSI. It develops an MMSE-based estimator that accounts for FBL and I-CSI, and introduces binary and non-binary RIS training protocols with grouping to estimate cascaded channels efficiently. An SIC decoding framework under short packets is combined with an AO-based robust RIS design, optimizing RIS training, data-phase RIS, group size, and decoding order to minimize the average MSE. Results show that larger RIS surfaces substantially improve estimation accuracy, while the decoding order and training strategy critically influence performance, especially under rapid channel variations. The approach offers a scalable, low-overhead path to reliable parameter estimation in dense MTC networks with RIS assistance.

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.

Figures (9)

• Figure 1: Summary of main contributions.
• Figure 2: Illustrative example of a setup with $M = 4$, $L = 9$, and $K = 3$. Solid and dotted lines indicate strong and weak paths, respectively (cf. Lie23).
• Figure 3: Training phase of the channel estimation (different colors represent different pilot sequences).
• Figure 4: NMSE vs. $L$. Solid and dashed lines indicate the proposals and benchmarks, respectively.
• Figure 5: NMSE vs. $L$ for different SINR bounds.