@inbook{1dd491e22ba34c308a7269877e540840,
title = "Quantifying the Impact of Base Station Metrics on LTE Resource Block Prediction Accuracy",
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.",
keywords = "4G, cellular network, LTE, machine learning, physical resource blocks",
author = "Darijo Raca and Jason Quinlan and Ahmed Zahran and Cormac Sreenan and Riten Gupta and Abhishek Tiwari",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2023 ; Conference date: 20-05-2023 Through 20-05-2023",
year = "2023",
doi = "10.1109/INFOCOMWKSHPS57453.2023.10226095",
language = "English",
series = "IEEE INFOCOM 2023 - Conference on Computer Communications Workshops, INFOCOM WKSHPS 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "IEEE INFOCOM 2023 - Conference on Computer Communications Workshops, INFOCOM WKSHPS 2023",
address = "United States",
}