Data-driven network performance prediction for B5G networks: a graph neural network approach

Yaqoob, Mahnoor, Trestian, Ramona ORCID logoORCID: https://orcid.org/0000-0003-3315-3081 and Nguyen, Huan X. ORCID logoORCID: https://orcid.org/0000-0002-4105-2558 (2022) Data-driven network performance prediction for B5G networks: a graph neural network approach. 2022 IEEE Ninth International Conference on Communications and Electronics (ICCE). In: IEEE 9th International Conference on Communications and Electronics, 27- 29 Jul 2022, Nha Trang City, Vietnam. e-ISBN 9781665497459, e-ISBN 9781665497442, pbk-ISBN 9781665497466. [Conference or Workshop Item] (doi:10.1109/ICCE55644.2022.9852048)

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Abstract

Extreme connectivity, dynamic resource provision-ing and demand of quality assurance in 5G and Beyond 5G (B5G) networks calls for advance network modeling solutions. We need functional network models that are able to produce accurate prediction of Key Performance Indicators (KPI) such as latency, overall delay, jitter or packet loss at low cost. Graph Neural Networks (GNN) have already shown great potential for network performance prediction, because of their ability to understand the network configurations. In this paper, we focus on improving the generalization capabilities of GNN in relatively complex IP transport network scenarios of future generation networks. We take RouteNet GNN as a reference model and present an alternative GNN. We train both models with relatively smaller network scenarios while for evaluation we use complex and large network configurations. After hyper-parameter tuning for RouteNet and proposed GNN, the results show that our model outperforms baseline architecture in evaluation phase. The validation losses for scenarios not seen during training phase, are significantly lower than the RouteNet.

Item Type: Conference or Workshop Item (Paper)
Sustainable Development Goals:
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 35317
Notes on copyright: © 2022 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.
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Depositing User: Ramona Trestian
Date Deposited: 30 Jun 2022 11:15
Last Modified: 20 Oct 2022 04:41
URI: https://eprints.mdx.ac.uk/id/eprint/35317

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