On Developing A New Method of Spectrum Demand Calculation for Mobile Broadband: Early Results from Auto Machine Learning Library

Wijanarko Joko Hastyo, Muhammad Suryanegara*, Dadang Gunawan, Filbert H. Juwono

*Corresponding author for this work

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

Abstract

This study aims to report the development of a new method for spectrum demand calculation for mobile broadband by using machine learning. The new method is built based on 2 underlying concepts: the use of throughput data and the use of machine learning methods. In the current paper, we present the early results of the use of the Auto Machine Learning library PyCaret and Prophet. By comparing with statistical methods, it is found that the machine learning method has better performance. The current analyses show that both auto machine learning PyCaret and Prophet can handle data trends. Also, it is found that the monthly downlink throughput data, which is obtained from crowdsourcing, can be used as an alternative to traffic data.

Original languageEnglish
Title of host publicationCOMNETSAT 2024 - IEEE International Conference on Communication, Networks and Satellite
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages191-196
Number of pages6
ISBN (Electronic)9798350368086
DOIs
Publication statusPublished - 2024
Event13th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2024 - Hybrid, Mataram, Indonesia
Duration: 28 Nov 202430 Nov 2024

Publication series

NameCOMNETSAT 2024 - IEEE International Conference on Communication, Networks and Satellite

Conference

Conference13th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2024
Country/TerritoryIndonesia
CityHybrid, Mataram
Period28/11/2430/11/24

Keywords

  • machine learning
  • mobile broadband
  • spectrum demand

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