Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 111760 |
| Fachzeitschrift | Computer networks |
| Jahrgang | 273 |
| Frühes Online-Datum | 9 Okt. 2025 |
| Publikationsstatus | Veröffentlicht - Dez. 2025 |
Abstract
Intelligent Reflecting Surfaces (IRS) have recently emerged as a cutting-edge technology in 6G Internet of Things (IoT) communications, offering substantial connectivity enhancements, particularly in remote, high-mobility, or obstacle-prone environments. This paper proposes PowerNetMax, an innovative framework designed to improve overall network connectivity, reliability, and energy efficiency in IRS-assisted IoT communication systems. PowerNetMax leverages a comprehensive set of network parameters and integrates the strengths of Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNN) to enable intelligent and adaptive optimization. Through extensive experimentation, PowerNetMax demonstrates up to 5–20 % higher received power, 50 % faster convergence, and 20 % higher throughput under mobility conditions compared to state-of-the-art GNN-based and heuristic solutions. Extensive simulation results confirm that PowerNetMax achieves superior adaptability and robustness, highlighting its effectiveness for future IRS-assisted IoT networks.
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- Computernetzwerke und -kommunikation
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in: Computer networks, Jahrgang 273, 111760, 12.2025.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - PowerNetMax
T2 - A DRL-GNN framework for IRS-Assisted IOT network optimization
AU - Farhan, Muhammad
AU - Wang, Lei
AU - Shah, Nadir
AU - Muntean, Gabriel Miro
AU - Asif, Awais Bin
AU - Song, Houbing Herbert
N1 - Publisher Copyright: © 2025 Elsevier B.V.
PY - 2025/12
Y1 - 2025/12
N2 - Intelligent Reflecting Surfaces (IRS) have recently emerged as a cutting-edge technology in 6G Internet of Things (IoT) communications, offering substantial connectivity enhancements, particularly in remote, high-mobility, or obstacle-prone environments. This paper proposes PowerNetMax, an innovative framework designed to improve overall network connectivity, reliability, and energy efficiency in IRS-assisted IoT communication systems. PowerNetMax leverages a comprehensive set of network parameters and integrates the strengths of Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNN) to enable intelligent and adaptive optimization. Through extensive experimentation, PowerNetMax demonstrates up to 5–20 % higher received power, 50 % faster convergence, and 20 % higher throughput under mobility conditions compared to state-of-the-art GNN-based and heuristic solutions. Extensive simulation results confirm that PowerNetMax achieves superior adaptability and robustness, highlighting its effectiveness for future IRS-assisted IoT networks.
AB - Intelligent Reflecting Surfaces (IRS) have recently emerged as a cutting-edge technology in 6G Internet of Things (IoT) communications, offering substantial connectivity enhancements, particularly in remote, high-mobility, or obstacle-prone environments. This paper proposes PowerNetMax, an innovative framework designed to improve overall network connectivity, reliability, and energy efficiency in IRS-assisted IoT communication systems. PowerNetMax leverages a comprehensive set of network parameters and integrates the strengths of Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNN) to enable intelligent and adaptive optimization. Through extensive experimentation, PowerNetMax demonstrates up to 5–20 % higher received power, 50 % faster convergence, and 20 % higher throughput under mobility conditions compared to state-of-the-art GNN-based and heuristic solutions. Extensive simulation results confirm that PowerNetMax achieves superior adaptability and robustness, highlighting its effectiveness for future IRS-assisted IoT networks.
KW - Deep reinforcement learning
KW - Graph neural network
KW - Intelligent reflecting surface
KW - Non-terrestrial network
KW - Unmanned aerial vehicles
UR - http://www.scopus.com/inward/record.url?scp=105019064361&partnerID=8YFLogxK
U2 - 10.1016/j.comnet.2025.111760
DO - 10.1016/j.comnet.2025.111760
M3 - Article
AN - SCOPUS:105019064361
VL - 273
JO - Computer networks
JF - Computer networks
SN - 1389-1286
M1 - 111760
ER -