PowerNetMax
A DRL-GNN framework for IRS-Assisted IOT network optimization
- verfasst von
- Muhammad Farhan, Lei Wang, Nadir Shah, Gabriel Miro Muntean, Awais Bin Asif, Houbing Herbert Song
- 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.
- Organisationseinheit(en)
-
Fachgebiet Nachrichtenübertragungssysteme
- Externe Organisation(en)
-
Dalian University of Technology
COMSATS Institute of Information Technology
Dublin City University
University of Maryland Baltimore County
- Typ
- Artikel
- Journal
- Computer networks
- Band
- 273
- ISSN
- 1389-1286
- Publikationsdatum
- 12.2025
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Computernetzwerke und -kommunikation
- Ziele für nachhaltige Entwicklung
- SDG 7 – Erschwingliche und saubere Energie
- Elektronische Version(en)
-
https://doi.org/10.1016/j.comnet.2025.111760 (Zugang:
Geschlossen)
