Enhancing Sleeping Cell Detection Through Comparing Two Internal Mobile Network Analysing Tools

Abstract

Sleeping cells represent a challenging issue in mobile networks, where a cell remains undetected as faulty by standard monitoring systems but fails to provide service to end users. This thesis focuses on enhancing the logging of sleeping cells in 4G and 5G networks, with the goal of improving overall network software performance. While writing this thesis, I was employed in a Base Station software test team. The work is motivated by the limitations of the current test area, where available logs and performance counters have proven insufficient to reliably investigate such failures. The thesis proposes and implements a method that enables the detection and investigation of sleeping cells using available measurement data and test procedures. The method is first validated through manual testing to assess its effectiveness, after which a framework for automating the approach is designed and implemented. The study also examines throughput-related aspects and provides a comparison of two internal tools for network analysis. The thesis also includes a survey aimed at evaluating the tool and its automation. By establishing a systematic process for investigating hidden outages, this thesis not only improves the observability of the network and enables better debugging but also provides findings from the comparison of two options and the proposed automation plan. These results support more effective testing, leading to improved robustness and higher quality of the network software for the end user. In addition, the work offers immediate value in the test environment and lays a foundation for the future automation in our environment.