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Bandwidth prediction for volatile networks with informer
Citation Link: https://doi.org/10.15480/882.16513
Publikationstyp
Conference Paper
Date Issued
2023-12-01
Sprache
English
Author(s)
TORE-DOI
Citation
2nd Workshop on Machine Learning & Netwoking, MaLeNe 2023
Contribution to Conference
5G networks provide high throughput and low latency connections, crucial for remote monitoring and control of mission-critical operations. Managing buffer levels and accurate bandwidth estimations are essential for low-latency applications. However, wireless networks are susceptible to fluctuations in quality metrics due to mobility and interference, impacting link utilization. Sudden quality deterioration can lead to lower Quality-of-Experience (QoE). To address this, we propose a neural network-based bandwidth prediction system. Our system utilizes historical data for time-series forecasting using the Informer model. It achieves 10% lower errors on a publicly available LTE dataset and 51% lower errors on a publicly available 5G dataset. Future work includes multivariate predictions and the creation of a new 5G dataset.
Subjects
Bandwidth prediction
LTE
5G
Informer
MLE@TUHH
DDC Class
621.3: Electrical Engineering, Electronic Engineering
004: Computer Sciences
Publication version
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2023_Denizer_BandwidthPredictionForVolatileNetworksWithInformer.pdf
Type
Main Article
Size
830.3 KB
Format
Adobe PDF