GNN-based Handover Management in 5G Vehicular Networks

Abstract

The rapid advancement of 5G technology has transformed vehicular networks, delivering high bandwidth, low latency, and faster data rates for real-time applications in smart vehicles and smart cities. This enhances traffic safety and the quality of entertainment services. However, challenges remain, such as 5G's limited coverage range, which requires the installation of additional base stations, and frequent handovers, known as the "ping-pong effect," that can cause network instability, especially in high-mobility environments. Traditional reactive methods struggle to manage these issues effectively. In this study, we propose TH-GCN (Throughput-oriented Graph Convolutional Network), which optimizes handover management in dense 5G environments using graph neural networks (GNNs). TH-GCN predicts optimal connections and the best handover choices by modeling vehicles and towers as nodes in a dynamic graph, with connections depicted as edges and incorporating features like signal quality, vehicle mobility, throughput, and tower load. By shifting from a purely user-centric to a combined user equipment and base station-centric approach, our method provides a comprehensive view of the network and enhances adaptability in real-time handover decisions. After conducting several batch tests in our Simu5G simulator, the results showed significant improvements, including up to a 78% reduction in handovers and a 10% improvement in signal quality compared to state-of-the-art methods. TH-GCN effectively reduced handovers while maintaining optimal levels of throughput and latency, particularly in high-density, high-mobility scenarios.