Data-driven network performance prediction for B5G networks: a graph neural network approach
Conference paper
Yaqoob, M., Trestian, R. and Nguyen, H. 2022. Data-driven network performance prediction for B5G networks: a graph neural network approach. IEEE 9th International Conference on Communications and Electronics. Nha Trang City, Vietnam 27 - 29 Jul 2022 IEEE. pp. 55-60 https://doi.org/10.1109/ICCE55644.2022.9852048
Type | Conference paper |
---|---|
Title | Data-driven network performance prediction for B5G networks: a graph neural network approach |
Authors | Yaqoob, M., Trestian, R. and Nguyen, H. |
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. |
Sustainable Development Goals | 9 Industry, innovation and infrastructure |
Conference | IEEE 9th International Conference on Communications and Electronics |
Page range | 55-60 |
ISBN | |
Electronic | 9781665497459 |
Electronic | 9781665497442 |
Paperback | 9781665497466 |
Publisher | IEEE |
Publication dates | |
27 Jul 2022 | |
Online | 16 Aug 2022 |
Publication process dates | |
Deposited | 30 Jun 2022 |
Accepted | 09 Jun 2022 |
Output status | Published |
Accepted author manuscript | |
Copyright Statement | © 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. |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ICCE55644.2022.9852048 |
Language | English |
Book title | 2022 IEEE Ninth International Conference on Communications and Electronics (ICCE) |
https://repository.mdx.ac.uk/item/89x32
Download files
77
total views23
total downloads6
views this month2
downloads this month