PowerNetMax: A DRL-GNN framework for IRS-Assisted IOT network optimization

IF 4.6 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Networks Pub Date : 2025-10-09 DOI:10.1016/j.comnet.2025.111760

Muhammad Farhan , Lei Wang , Nadir Shah , Gabriel-Miro Muntean , Awais Bin Asif , Houbing Herbert Song

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引用次数: 0

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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PowerNetMax：用于irs辅助物联网网络优化的DRL-GNN框架

智能反射面（IRS）最近成为6G物联网（IoT）通信中的一项前沿技术，提供了大量的连接性增强，特别是在远程、高移动性或易受障碍物影响的环境中。本文提出了PowerNetMax，这是一个创新的框架，旨在提高irs辅助物联网通信系统的整体网络连接、可靠性和能源效率。PowerNetMax利用了一套全面的网络参数，并集成了深度强化学习（DRL）和图神经网络（GNN）的优势，以实现智能和自适应优化。通过广泛的实验，与最先进的基于gnn和启发式解决方案相比，PowerNetMax在移动性条件下的接收功率提高了5 - 20%，收敛速度提高了50%，吞吐量提高了20%。广泛的仿真结果证实，PowerNetMax具有卓越的适应性和鲁棒性，突出了其在未来irs辅助物联网网络中的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computer Networks 工程技术-电信学

CiteScore

10.80

自引率

3.60%

发文量

434

审稿时长

8.6 months

期刊介绍： Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.