TY - JOUR
T1 - Semi-supervised learning based coverage hole detection in cellular networks
AU - Al-Ahmed, Shahriar
AU - Shakir, Muhammad Zeeshan
PY - 2022/12/2
Y1 - 2022/12/2
N2 - 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.
AB - 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.
KW - coverage hole detection
KW - machine learning
KW - semi-supervised learning
KW - 5G and beyond
UR - https://www.itu.int/en/journal/j-fet/Pages/publication-rights-copyright.aspx
UR - https://www.itu.int/en/journal/j-fet/Pages/default.aspx
U2 - 10.52953/TLFD1744
DO - 10.52953/TLFD1744
M3 - Article
SN - 2616-8375
VL - 3
SP - 623
EP - 631
JO - ITU Journal on Future and Evolving Technologies
JF - ITU Journal on Future and Evolving Technologies
IS - 3
ER -