On the Effect of Coverage Range Extent on Next-Cell Prediction Error for Vehicular Mobility in 5G/6G Networks: A Novel Theoretic Model

Abstract

Over the last decade, 5G and forthcoming 6G architectures have undergone extensive standardization and preparations for the future. The literature in this field is saturated with studies on predicting mobile trajectories in cellular systems and guaranteeing quality of service and an adequate user experience in these environments. The current study aims to bridge mobility prediction and 5G/6G predictive approaches and demonstrate that the intrinsic paradigm of femto-cell and nano-cell deployment (based on very small radio coverage radii) for 5G provides the means to obtain more accurate time series data on user mobility and thus enable predictive models (e.g., machine learning) as suitable technologies for integration with 6G standards. This field is therefore an important avenue of research.