A_GNN_based_Supervised_Learning_Framework_for_Resource_Allocation_in_Wireless_IoT_Networks.pdf (9.26 MB)
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A GNN based supervised learning framework for resource allocation in wireless IoT networks

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journal contribution
posted on 15.11.2021, 10:04 by Tianrui ChenTianrui Chen, Xinruo Zhang, Minglei You, Gan ZhengGan Zheng, Sangarapillai LambotharanSangarapillai Lambotharan
The Internet of Things (IoT) allows physical devices to be connected over the wireless networks. Although device-to-device (D2D) communication has emerged as a promising technology for IoT, the conventional solutions for D2D resource allocation are usually computationally complex and time-consuming. The high complexity poses a significant challenge to the practical implementation of wireless IoT networks. A graph neural network (GNN) based framework is proposed to address this challenge in a supervised manner. Specifically, the wireless network is modeled as a directed graph, where the desirable communication links are modeled as nodes and the harmful interference links are modeled as edges. The effectiveness of the proposed framework is verified via two case studies, namely the link scheduling in D2D networks and the joint channel and power allocation in D2D underlaid cellular networks. Simulation results demonstrate that the proposed framework outperforms the benchmark schemes in terms of the average sum rate and the sample efficiency. In addition, the proposed GNN approach shows potential generalizability to different system settings and robustness to the corrupted input features. It also accelerates the D2D resource optimization by reducing the execution time to only a few milliseconds.

Funding

Unlocking Potentials of MIMO Full-duplex Radios for Heterogeneous Networks (UPFRONT)

Engineering and Physical Sciences Research Council

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Leverhulme Trust Research Project Grant under grant number RPG-2017-129

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE Internet of Things Journal

Volume

9

Issue

3

Pages

1712 - 1724

Publisher

IEEE

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Publication date

2021-06-22

Copyright date

2022

eISSN

2327-4662

Language

en

Depositor

Prof Gan Zheng. Deposit date: 12 November 2021