The advancement of 5G technology is initiating a transformation era for Vehicular Networks (VN), enabling seamless communication among vehicles and other entities. Connected vehicles hold significant potential for improving traffic safety, and enhancing in-vehicle entertainment. With the increasing of vehicular applications, the necessity for reliable, high-bandwidth, and low-latency connections has become increasingly paramount. Ensuring consistent connections in dynamic vehicular settings remains an ongoing challenge, especially given the necessity for smooth Handovers (HO) between transmission points as vehicles move rapidly. Frequent handovers, due to the limitations of communication range, can impact user experiences, especially in safety-critical situations. One potential solution involves transitioning to network virtualization to address the challenges posed by ultra-dense networks and the limited communication range in 5G. To tackle these challenges, we present an approach based on mobility prediction for selecting virtual cells using Support Vector Regression (SVR) and making Handover (HO) decisions using Long Short-Term Memory (LSTM). Our method, named M-LSVR, focuses on forming user-centric virtual cells based on network attributes and real-time data. The dynamic adjustment of virtual cell size using predictive mobility ensures stability and reduces unnecessary handovers. Integrating mobility prediction with HO decision-making aims to establish a more stable connection, enhancing the quality of virtual cells in high-mobility vehicular environments. This approach aims to optimize the user experience by minimizing unnecessary tower switches and creating efficient, high-quality virtual cells in the 5G vehicular network.
LSTM-oriented Handover Decision-making with SVR-based Mobility Prediction in 5G Vehicular Networks
14.02.2024
Hochschulschrift
Elektronische Ressource
Englisch
DDC: | 629 |