Time Series Analysis in Mobile Communications Systems

Abstract

The evolution of mobile communication systems, marked by the exponential growth of users and the demand for efficient network management, underscores the importance of optimization to mitigate congestion, increase transmission rates, and reduce packet loss. In this context, the Kalman-Takens Filter (KTF) stands out for its analytical capability in prediction and optimization, offering a dynamic approach to spectrum allocation in mobile networks. This study, grounded in a comprehensive literature review and time series analysis, investigates the application of the KTF, with its effectiveness quantified by the root mean square error (RMSE). Computational simulations have shown significant improvements in network performance, demonstrating that the KTF can operate efficiently in real-time, optimizing resource allocation. This approach aims to enhance user experience in high-data-demand environments, contributing to the development of advanced management strategies in 5G networks. The meticulous analysis of the RMS error, aiming at its minimization, proved effective, providing crucial insights for resource management, essential to addressing the increasing traffic volume and complexity in future mobile communications networks.