Robust design of reconfigurable intelligent surfaces for parameter estimation in MTC

(2025) 2025:17 Liesegang et al. J Wireless Com Network https://doi.org/10.1186/s13638-025-02445-0 EURASIP Journal on Wireless Communications and Networking Open Access RESEARCH Robust design of reconfigurable intelligent surfaces for parameter estimation in MTC Sergi Liesegang1,2* , Antonio Pascual‑Iserte3 and Olga Muñoz3 *Correspondence: 1 Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, 03043 Cassino, Italy 2 Consorzio Nazionale Interuniversitario per le Telecomunicazioni, 43124 Parma, Italy 3 Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain 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. Keywords: Machine-type communications, Reconfigurable intelligent surfaces, Parameter estimation, Imperfect channel knowledge, Successive interference cancelation, Finite blocklength 1 Introduction Machine-type communications (MTC) have become pivotal for the advancement of mobile generations [1]. They represent systems where groups of simple devices nonorthogonally transmit information to a base station (BS) or collector node (CN) with minimal to no human oversight [2]. Applications of MTC include health monitoring, location tracking, and smart metering, among others. In scenarios involving sensors measuring specific parameters (e.g., temperature), the CN estimates sensed data based on potentially noisy observations from these devices. Due to the spatial density of terminals, data correlation is significant [3], allowing for improved accuracy through the use of appropriate estimators [4]. In instances where the channel quality between sensors and the CN is poor, transmitting measurements from MTC devices can pose challenges. Examples include setups with (i) strong Rayleigh or Rician fading with a weak line of sight (LoS) [5], as well as (ii) © The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Liesegang et al. J Wireless Com Network (2025) 2025:17 significant propagation losses in millimeter wave (mmWave) [6] or terahertz (THz) [7] bands. Such conditions result in excessively high decoding error probabilities, rendering reliable communication uncertain. Moreover, the need for retransmissions in case of transmission failure could exacerbate complexity, latency, and power consumption, which are the most critical factors in the majority of MTC networks [2]. Finally, given that MTC data packets are typically short, classical Shannon metrics overestimate performance, and finite blocklength (FBL) analysis must be used instead [8]. This is especially true in applications such as narrowband Internet of Things (NB-IoT) [9], where the number of time-frequency resources is quite limited. Additionally, the lack of decoding error of the infinite packets assumption is unrealistic and must be disregarded (there is always a nonzero probability of communication failure). This paper delves into leveraging reconfigurable intelligent surfaces (RIS) to enhance system performance [10, 11]. RISs are expansive passive surface structures capable of adapting to the wireless environment. Functioning as reflectors, they can redirect signals toward target destinations to amplify signal strength. With attributes such as received power gain, high scalability, low cost, and ease of deployment, RISs emerge as promising technologies for future cellular systems [12]. In this context, channel state information (CSI) becomes indispensable for achieving substantial beamforming gains. However, due to the passive nature of RISs and their numerous elements, channel estimation poses a formidable challenge [13]. Consequently, we will explore various strategies for acquiring this crucial knowledge feasibly. Accordingly, the RIS will be configured to minimize parameter estimation errors while explicitly considering the impact of FBL communication and imperfect CSI (I-CSI) errors. The design will heavily rely on statistical information, ensuring robustness against the aforementioned uncertainties over the long term. Given the extensive connectivity in MTC networks, the available resources are insufficient for orthogonal transmission. In other words, the scarcity of electromagnetic spectrum forces devices to share all resources. This situation aggravates even more when the number of sensors increases, i.e., massive MTC (mMTC). Consequently, to keep the analysis general (schemes with such reuse and interference), resorting to non-orthogonal multiple access (NOMA) becomes imperative [14–16]. As a result, the signals received from different sensors are also susceptible to interference. We will explore successive interference cancelation (SIC) as a decoding procedure to address this issue and examine various proposals for selecting the decoding order [17–19]. In this context, the RI (...truncated)