Dimensionality Reduction for Optimization of Radio Base Station Transmission Based on Energy Efficiency

Joss Armstrong, Enda Fallon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication2023 IEEE 64th International Scientific Conference on Information Technology and Management Science of Riga Technical University, ITMS 2023 - Proceedings
EditorsJanis Grabis, Andrejs Romanovs, Galina Kulesova
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350370294
ISBN (Print)9798350370294
DOIs
Publication statusPublished - 2023
Event2023 IEEE 64th International Scientific Conference on Information Technology and Management Science of Riga Technical University, ITMS 2023 - Riga, Latvia
Duration: 5 Oct 20236 Oct 2023

Publication series

Name2023 IEEE 64th International Scientific Conference on Information Technology and Management Science of Riga Technical University, ITMS 2023 - Proceedings

Conference

Conference2023 IEEE 64th International Scientific Conference on Information Technology and Management Science of Riga Technical University, ITMS 2023
Country/TerritoryLatvia
CityRiga
Period5/10/236/10/23

Keywords

  • 5G
  • artificial intelligence
  • energy efficiency
  • machine learning
  • telecommunications

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