Wireless Energy Harvesting For Autonomous Reconfigurable Intelligent Surfaces
View/ Open
Author
Ntontin, Konstantinos
Boulogeorgos, Alexandros Apostolos A.
Bjornson, Emil
Martins, Wallace Alves
Kisseleff, Steven
Abadal, Sergi
Alarcon, Eduard
Papazafeiropoulos, Anastasios
Lazarakis, Fotis
Chatzinotas, Symeon
Attention
2299/27027
Abstract
In the current contribution, we examine the feasibility of fully-energy-autonomous operation of reconfigurable intelligent surfaces (RIS) through wireless energy harvesting (EH) from incident information signals. Towards this, we first identify the main RIS energy-consuming components and present a suitable and accurate energy-consumption model that is based on the recently proposed integrated controller architecture and includes the energy consumption needed for channel estimation. Building on this model, we introduce a novel RIS architecture that enables EH through RIS unit-cell (UC) splitting. Subsequently, we introduce an EH policy, where a subset of the UCs is used for beamsteering, while the remaining UCs absorb energy. In particular, we formulate a subset al.ocation optimization problem that aims at maximizing the signal-to-noise ratio (SNR) at the receiver without violating the RIS’s energy consumption demands. As a problem solution, we present low-complexity heuristic algorithms. The presented numerical results reveal the feasibility of the proposed architecture and the efficiency of the presented algorithms with respect to both the optimal and very high-complexity brute-force approach and the one corresponding to random subset selection. Furthermore, the results reveal how important the placement of the RIS as close to the transmitter as possible is, for increasing the harvesting effectiveness.
Publication date
2023-03-30Published in
IEEE Transactions on Green Communications and NetworkingPublished version
https://doi.org/10.1109/TGCN.2022.3201190Other links
http://hdl.handle.net/2299/27027Metadata
Show full item recordRelated items
Showing items related by title, author, creator and subject.
-
Data-Driven Tools for Building Energy Consumption Prediction: A Review
Olu-Ajayi, Razak; Alaka, Hafiz; Owolabi, Hakeem; Akanbi, Lukman; Ganiyu, Sikiru (2023-03-09)The development of data-driven building energy consumption prediction models has gained more attention in research due to its relevance for energy planning and conservation. However, many studies have conducted the ... -
Application of Multilayer Extreme Learning Machine for Efficient Building Energy Prediction
Adegoke, Muideen; Hafiz, Alaka; Ajayi, Saheed; Olu-Ajayi, Razak (2022-12)Building energy efficiency is vital, due to the substantial amount of energy consumed in buildings and the associated adverse effects. A high-accuracy energy prediction model is considered as one of the most effective ways ... -
Optimising Flywheel Energy Storage Systems: The Critical Role of Taylor-Couette Flow in Reducing Windage Losses and Enhancing Heat Transfer
Eltaweel, Mahmoud; Herfatmanesh, Mohammad Reza (2024-09-05)Amidst the growing demand for efficient and sustainable energy storage solutions, Flywheel Energy Storage Systems (FESSs) have garnered attention for their potential to meet modern energy needs. This study uses Computational ...