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

arXiv: https://arxiv.org/abs/2505.04894 · HTML: https://mlsanity.com/paper/2505.04894/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 8 May 2025.

## Builds on (most cited first, 10 of 10)

- [T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction](https://mlsanity.com/paper/1811.05320/) (2018)
- [What Will 5G Be?](https://mlsanity.com/paper/1405.2957/) (2014)
- [Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communications](https://mlsanity.com/paper/2407.06518/) (2024)
- [Machine Learning for Reliable mmWave Systems: Blockage Prediction and Proactive Handoff](https://mlsanity.com/paper/1807.02723/) (2018)
- [Handover Control in Wireless Systems via Asynchronous Multi-User Deep Reinforcement Learning](https://mlsanity.com/paper/1801.02077/) (2018)
- [DetectorNet: Transformer-enhanced Spatial Temporal Graph Neural Network for Traffic Prediction](https://mlsanity.com/paper/2111.00869/) (2021)
- [Learning-based Handover in Mobile Millimeter-wave Networks](https://mlsanity.com/paper/2003.11009/) (2020)
- [Federated Learning Based Proactive Handover in Millimeter-wave Vehicular Networks](https://mlsanity.com/paper/2101.07032/) (2021)
- [Spatio-temporal Modeling for Large-scale Vehicular Networks Using Graph Convolutional Networks](https://mlsanity.com/paper/2103.07636/) (2021)
- [Neighbor Auto-Grouping Graph Neural Networks for Handover Parameter Configuration in Cellular Network](https://mlsanity.com/paper/2301.03412/) (2022)

## Abstract (arXiv)

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.