5G network mobility analysis for user equipment

The rapid proliferation of 5G networks necessitates advanced techniques for analyzing and managing user equipment (UE) mobility. Efficient mobility estimation is critical for optimizing network performance, enhancing user experience, and supporting seamless connectivity. This study focuses on 5G network mobility analysis for user equipment, aiming to develop robust methodologies for predicting and understanding UE movement patterns. Hence, leveraging network-based data, including signal strength, handover events, and location information, our approach provides a comprehensive framework for mobility estimation. One important aspect of 5G and the next networks is the coexistence of small and mega cells. Due to this heterogeneity and the greater portability of user devices, there may be a high handover frequency, which may result in an unreasonable call drop probability or an unsatisfactory user experience. The network can ensure smooth and seamless cell transitions by proactively adapting to the user through smart mobility management. In this research, we establish an algorithm that estimates the user’s mobility level with minimal computational overhead and without requiring any changes to the consumer device/equipment (UE) side, using sounded reference signal (SRS) evaluations that are readily accessible from the base location (eNodeB in 4G systems). We utilize a combination of statistical models, machine learning algorithms, and real-time network data to predict UE movement with high accuracy. Key metrics such as handover frequency, dwell time, and path prediction are analyzed to understand mobility patterns. The integration of these metrics into the network management system allows for proactive resource allocation and improved quality of service (QoS). The efficacy of the method is demonstrated with real-world information including mobility patterns. Our findings indicate that network-based mobility analysis can significantly enhance the performance of 5G networks. By accurately estimating UE mobility, network operators can optimize handovers, reduce latency, and ensure stable connections even at high speeds. Furthermore, this approach aids in the efficient management of network resources, reducing congestion and enhancing overall network efficiency. According to the results, it is possible to classify UE speed into three mobility groups with a success rate of 90 % for minimal mobility, 89 % for moderate mobility, while 98 % for extreme mobility.