GNN-based Handover Management in 5G Vehicular Networks
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".