Quantifying the impact of base station metrics on LTE resource block prediction accuracy

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Raca, Darijo
Quinlan, Jason
Zahran, Ahmed
Sreenan, Cormac J.
Gupta, Riten
Tiwari, Abhishek
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Accurate prediction of cellular link performance represents a corner stone for many adaptive applications, such as video streaming. State-of-the-art solutions focus on distributed device-based methods relying on historic throughput and PHY metrics obtained through device APIs. In this paper, we study the impact of centralised solutions that integrate information collected from other network nodes. Specifically, we develop and compare machine learning inference engines for both distributed and centralised approaches to predict the LTE physical resource blocks using ns3-simulation. Our results illustrate that network load represents the most important feature in the centralised approaches resulting in halving the RB prediction error to 14% in comparison to 28% for the distributed case.
Cellular network , Machine learning , Physical resource blocks , LTE , 4G
Raca, D., Quinlan, J. J., Zahran, A. H., Sreenan, C. J., Gupta, R. and Tiwari, A. (2023) 'Quantifying the impact of base station metrics on LTE resource block prediction accuracy', IEEE International Conference on Computer Communications (INFOCOM 2023), New York, USA, 17-20 May, pp. 1-2. doi: 10.1109/INFOCOMWKSHPS57453.2023.10226095
© 2023, the Authors.