5G Network Coverage Hole Prediction and Detection Using Machine Learning

Abstract

A signal-free area in a wireless network is called a coverage hole (CH). The signal at this location is either nonexistent or too weak to be detected or monitored. There may sometimes be coverage gaps or places with poor radio frequency (RF) performance due to wireless infrastructure components’ inability to adapt to changing RF dynamics and offer adequate coverage of the locations. Finding coverage gaps and RF problem spots needs a client-side approach rather than the traditional infrastructure-driven solution because of the importance of network intuition. This article’s goal is to locate coverage gaps or weak signal places in a variety of scenarios, including 5G KPIs and QoS parameters (QCI, or quality of service class identifier). The primary objective is to apply classification techniques to determine which use cases or network slices are impacted by the decreased signal strength. Training and test datasets for supervised machine learning techniques are pre-collected measured report data from a live 5G network monitoring counter and data system. Since most KPIs are numerical data, the study uses the classification methods ANN, RF, NB, and LR. This is not at all like the traditional methods—such as driving tests, etc.—for gathering data for coverage-hole detection. Orange Canvas and Microsoft Excel are two instances of data mining technologies that are used for both detection and prediction.