Machine learning analysis of multi-radio access technology selection in 5G NSA Network

Nurudeen Oladehinbo Salau, Muhammad Zeeshan Shakir

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

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Abstract

The exponential growth of traffic across the mobile networks called for exploitation of new spectrum bands of 5G networks; whose deployment still rely on support from underlying 4G long term evolution (LTE) networks in both stand-alone (SA) and non-stand-alone (NSA) architectures. This scenario poses challenges on the choice of Radio Access Technology (RAT) selection between 4G LTE and 5G new radio (NR) networks to these ever increasing mobile users with respect to their geographical location, mobility and network coverage. Hence, this study investigates joint user requirements and network constraints for appropriate RAT selection between 4G LTE and 5G NR by recording live radio measurements over a distance of 300 meters between a pedestrian user and 5G NSA base-station. The problem (RAT selection) was formulated as a classification process, hence implemented with classification machine learning (ML) algorithms: Decision Tree (DT), Extra Tree (XTREE), Random Forest (RF), Gradient Boosting (GB), and eXtreme Gradient Boosting (XGBoost) to select an appropriate RAT. Evaluation of results with standard classification metrics, show measure of accuracy of algorithms: DT at 91.82%, RF at 87.64%, XTREE at 86.75%, GB at 91.86%, and XGBoost at 93.86%, where XGBoost showed highest performance value, therefore proposed as ML model for RAT selection to achieve effective and efficient resource allocation.
Original languageEnglish
Title of host publication2023 IEEE Wireless Communications and Networking Conference, WCNC 2023 - Proceedings
Place of PublicationPiscataway, NJ
PublisherIEEE
Number of pages6
Edition2023
ISBN (Electronic)9781665491228
ISBN (Print)9781665491235
DOIs
Publication statusPublished - 12 May 2023

Publication series

NameIEEE Conference proceedings
PublisherIEEE
ISSN (Print)1525-3511
ISSN (Electronic)1558-2612

Keywords

  • 5G
  • multi-RAT
  • SA
  • NSA
  • machine learning

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