GCN-Based Throughput-Oriented Handover Management in Dense 5G Vehicular Networks

TL;DR

This paper introduces TH-GCN, a graph neural network-based method for optimizing handover management in dense 5G vehicular networks, significantly reducing handovers and enhancing signal quality in high-mobility scenarios.

Contribution

The paper proposes a novel GNN-based approach, TH-GCN, for real-time, throughput-oriented handover management in dense 5G vehicular networks, addressing network instability issues.

Findings

Reduces handovers by up to 78%

Improves signal quality by 10%

Outperforms existing handover management methods

Abstract

The rapid advancement of 5G has transformed vehicular networks, offering high bandwidth, low latency, and fast data rates essential for real-time applications in smart cities and vehicles. These improvements enhance traffic safety and entertainment services. However, the limited coverage and frequent handovers in 5G networks cause network instability, especially in high-mobility environments due to the ping-pong effect. This paper presents TH-GCN (Throughput-oriented Graph Convolutional Network), a novel approach for optimizing handover management in dense 5G networks. Using graph neural networks (GNNs), TH-GCN models vehicles and base stations as nodes in a dynamic graph enriched with features such as signal quality, throughput, vehicle speed, and base station load. By integrating both user equipment and base station perspectives, this dual-centric approach enables adaptive, real-time handover decisions that improve network stability. Simulation results show that TH-GCN reduces handovers by up to 78 percent and improves signal quality by 10 percent, outperforming existing methods.