@inproceedings{f96ec5e104a54050992000188ed96ab9,
title = "Dimensionality Reduction for Optimization of Radio Base Station Transmission Based on Energy Efficiency",
abstract = "Traffic volumes in radio networks have increased exponentially over the last decade. While the efficiency of these networks has also improved minimizing energy use is an important challenge in the telecommunications industry. To ensure maximum energy efficiency it is necessary for the configuration of each element in the network is optimal at all times. Minimizing the amount of data that needs to be processed in order to identify entities in the network that are operating with low energy efficiency can provide increases in the speed of detection and reductions in the processing power and data storage required for this task. This work demonstrates how techniques for calculating the marginal contribution of individual features in a dataset can be effectively used to significantly reduce the amount of data that need to be processed to detect sub-optimally configured elements in the network that may need reconfiguration.",
keywords = "5G, artificial intelligence, energy efficiency, machine learning, telecommunications",
author = "Joss Armstrong and Enda Fallon",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE 64th International Scientific Conference on Information Technology and Management Science of Riga Technical University, ITMS 2023 ; Conference date: 05-10-2023 Through 06-10-2023",
year = "2023",
doi = "10.1109/ITMS59786.2023.10317744",
language = "English",
isbn = "9798350370294",
series = "2023 IEEE 64th International Scientific Conference on Information Technology and Management Science of Riga Technical University, ITMS 2023 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Janis Grabis and Andrejs Romanovs and Galina Kulesova",
booktitle = "2023 IEEE 64th International Scientific Conference on Information Technology and Management Science of Riga Technical University, ITMS 2023 - Proceedings",
address = "United States",
}