A Clustered Energy Harvesting Framework for Autonomous RIS in Internet-of-Surfaces Network

Abstract

Combining insertion losses (CIL) and the non-linear (NL) behavior of rectification circuitry pose significant challenges to element splitting-based self-sustainable Reconfigurable Intelligent Surfaces (ESS-RIS) that utilize an RF-combining architecture for their energy harvesting (EH) elements. These factors not only degrade the end-to-end communication performance but also hinder the ability to meet ESS-RIS's operational energy requirements. This work, therefore, proposes a novel clustering-based energy harvesting architecture for EH elements in an ESS-RIS that optimizes element allocation, enhancing both self-sustainability and overall communication efficiency compared to the state-of-the-art. Our findings demonstrate that the proposed architecture maintains a significantly higher signal-to-noise ratio (SNR) by reducing the fraction of RIS elements required for EH by a large percentage. Following this, a statistical analysis using the Marcum-Q function and the central limit theorem approximation is also performed for the proposed architecture to compare the results to the one obtained from exact simulations in MATLAB. To address the increased hardware demands in the proposed architecture, an optimization problem is formulated and tackled using three approaches, viz, Joint Parameter Optimization, Alternating Optimization, and the Genetic Algorithm. These methods aim to balance communication performance with hardware complexity effectively. Finally, a time complexity analysis is conducted to evaluate the asymptotic worst-case and best-case bounds of the proposed approach. © 2025 Elsevier B.V., All rights reserved.