Existing Tower Infrastructure Classification and Analysis for 5G Implementation in Jakarta Area Using Machine Learning Models

Tri Kushartadi, Arwidya Tantri Agtusia, Herry Tony Andhyka, Catur Apriono

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

Abstract

The Ministry of Communication and Informatics plans to construct infrastructure for fifth-generation network deployment in Indonesia. However, their readiness to support 5G systems should be evaluated. There is non-standalone architecture for 5G network related to the use of existing towers. The NSA architecture enables the use of towers that meet the 5G specification. Machine learning can be used to classify these towers quickly. Based on working frequency on the existing tower data, this research conduct tower classification using machine learning models to get the tower's quantity that are ready for the tower's non-standalone architecture and visualize the data on the open street map to evaluate the network needed in the area. The considered area for the tower data is Jakarta Area. The data shows 839 towers need to be upgraded to the fifth-generation frequency. The data also shows 1152 towers that are ready for the fifth-generation network. This result can benefit the operator in estimating the investment of the tower to be ready for the new technology and impact the CAPEX view to construct the new tower.

Original languageEnglish
Title of host publicationProceeding of 2023 9th International Conference on Wireless and Telematics, ICWT 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350305029
DOIs
Publication statusPublished - 2023
Event9th International Conference on Wireless and Telematics, ICWT 2023 - Solo, Indonesia
Duration: 6 Jul 20237 Jul 2023

Publication series

NameProceeding of 2023 9th International Conference on Wireless and Telematics, ICWT 2023

Conference

Conference9th International Conference on Wireless and Telematics, ICWT 2023
Country/TerritoryIndonesia
CitySolo
Period6/07/237/07/23

Keywords

  • 5G Tower
  • Classification
  • Machine Learning
  • Telecommunication Infrastructure
  • Tower Data

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