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

Loading...
Thumbnail Image
Date
2023-05
Authors
Raca, Darijo
Quinlan, Jason
Zahran, Ahmed
Sreenan, Cormac J.
Gupta, Riten
Tiwari, Abhishek
Journal Title
Journal ISSN
Volume Title
Publisher
Research Projects
Organizational Units
Journal Issue
Abstract
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.
Description
Keywords
Cellular network , Machine learning , Physical resource blocks , LTE , 4G
Citation
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
Copyright
© 2023, the Authors.