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BandSeer: bandwidth prediction for cellular networks
Publikationstyp
Conference Paper
Date Issued
2024-09-09
Sprache
English
Author(s)
Citation
49th IEEE Conference on Local Computer Networks, LCN 2024
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
IEEE
ISBN of container
979-8-3503-8800-8
In the context of cellular networks, such as with 5G and upcoming 6G networks, the available bandwidth of a connection is inherently dynamic. Accurate prediction of future bandwidth availability within a link is essential for latencysensitive and mission-critical applications such as video streaming or remote driving. Bandwidth prediction ensures efficient utilization of a link and thus prevents delays. This paper introduces BandSeer, a stacked Bi-LSTM-based approach for bandwidth prediction in LTE and 5G cellular networks. BandSeer captures complex correlations in historical metrics better than prior work and outperforms SotA baselines. It achieves reductions of up to 18.32% in RMSE and 26.87% in MAE on the Berlin V2X dataset, and reductions of up to 12.43% in RMSE and 28.45% in MAE on the Beyond 5G dataset compared to the SotA Informer baseline. Furthermore, we argue that any bandwidth algorithm must be resource efficient to enable for development on various devices. Our evaluations show that BandSeer consumes one order of magnitude fewer resources and needs roughly a quarter to half the inference time of its closest competitor, the Informer model.
Subjects
5G
Bandwidth Prediction
Bi-LSTM
Efficiency
MLE@TUHH
DDC Class
600: Technology