Real-time Overshoot and Undershoot Detection in Cellular Networks

One of the most crucial aspects of cellular networks is coverage, as it determines the areas where users can connect to the network and utilize its services. In the past, the use of planning tools was common practice for the establishment of coverage areas and network capacity prior to the deployment of a network. However, issues with coverage, such as interference or coverage gaps, may arise due to equipment malfunctions, suboptimal configurations, or alterations in the propagation environment. In particular, an inadequate antenna tilt configuration can result in overshoot or undershoot situations on the network, which in turn can give rise to the aforementioned problems. This paper proposes a methodology for the real-time detection of overshoot and undershoot situations. To achieve this goal, KPI (Key Performance Indicators) are analyzed using machine learning techniques. Given the difficulty of detecting coverage problems in mobile networks, the results obtained suggest that the methodology provides a consistent knowledge base for optimizing the antenna tilt, thereby improving network performance.