Data-Driven and Machine Learning-Based Analysis of Handover Behavior and Network Stability in Mobile Networks

Handover management is a fundamental process in modern mobile networks, ensuring service continuity under user mobility. However, the relationship between network conditions and handover behavior remains insufficiently understood under real-world measurement conditions. This study presents a data-driven analysis of handover behavior based on drive-test measurements collected in an urban environment. A formal definition of handover events is proposed and implemented for automatic detection using changes in the serving cell identifier. The dataset is further analyzed to assess the influence of radio signal indicators, QoS metrics, and mobility-related variables on handover occurrence. Logistic Regression is used as an interpretable baseline, while Random Forest is applied to capture nonlinear feature interactions. The results show that individual QoS indicators demonstrate limited direct explanatory capability when considered independently. Random Forest achieved higher predictive performance than Logistic Regression, with AUC = 0.902 compared to 0.787, indicating the importance of nonlinear relationships in handover behavior. Degradation events are additionally identified using a threshold-based proxy, showing that latency is a more sensitive indicator of degraded conditions than throughput. Overall, the findings suggest that handover behavior depends on multiple interacting network conditions rather than a single dominant predictor, highlighting the importance of QoS-aware and data-driven mobility analysis in 5G networks and beyond.