Semi-supervised learning based coverage hole detection in cellular networks

Shahriar Abdullah Al-Ahmed*, Muhammad Zeeshan Shakir

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

For any time mobile network dependent application and services, coverage is one of the prominent factors to provide the best Quality of Service (QoS) and Quality of Experience (QoE). A simple Coverage Hole (CH) may degrade the performance and the reputation of any operator by reducing the Key Performance Indicators (KPIs). This is one of the important aspects which need to be planned from the phase of network deployment throughout the whole operational stage. Many factors can cause CH such as attenuation, obstacles, and improper network planning. Traditionally, Drive Test (DT) used to be carried out in order to assess the quality of the mobile network signal. With the technological advancement, DT was replaced by the Minimization of Drive Test (MDT) and included as a part of Self‑Organizing Networks (SON). The MDT process is applicable to networks that operate in 3G, 4G and 5G technologies. With this method, operators are able to measure network performance with the help of end users’ device. Thus, the network can be managed more conveniently, performance is improved, quality is increased, and maintenance costs are reduced for the network. However, the processing of MDT at the operators’ side remains time‑consuming and complex specially for CH analysis and detection from mobile network data. Therefore, we present a method by utilising Semi‑supervised Learning (SSL) in this paper so that this task becomes uncomplicated with improved accuracy. Our results show that the proposed method achieves better accuracy than usual classification algorithm.
Original languageEnglish
Pages (from-to)623-631
Number of pages9
JournalITU Journal on Future and Evolving Technologies
Volume3
Issue number3
DOIs
Publication statusPublished - 2 Dec 2022

Keywords

  • coverage hole detection
  • machine learning
  • semi-supervised learning
  • 5G and beyond

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