用图神经网络优化5G车联网切换，减少78%频繁切换
GCN-Based Throughput-Oriented Handover Management in Dense 5G Vehicular Networks
- 构建车与基站的动态图，融合信号质量等多维特征
- 切换次数减少78%，信号质量提升10%
- 适合高移动性场景下的5G车联网系统设计
5G的快速发展推动了车联网演进，提供高带宽、低延迟和高速率，满足智慧城市与车辆中实时应用需求，提升交通安全与娱乐服务。然而，5G覆盖范围有限且频繁切换导致网络不稳定，尤其在高移动性环境下因乒乓效应加剧。本文提出一种新型的吞吐量导向图卷积网络（TH-GCN）方法，用于优化密集5G车联网中的切换管理。通过图神经网络（GNN）将车辆与基站建模为动态图节点，并融合信号质量、吞吐量、车速及基站负载等特征。该双中心方法结合用户设备与基站视角，实现自适应、实时的切换决策，增强网络稳定性。仿真结果表明，相较于现有方法，TH-GCN可降低高达78%的切换次数，同时提升信号质量10%。
原文摘要 · Abstract (English)
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.
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