Holistic Connectivity-Oriented GCN-LSTM for Pro-Active Handover in 5G Vehicular Networks
Bibliographic record
Abstract
Connected vehicles play a crucial role in enhancing safety and comfort within Intelligent Transportation Systems. The 5G technology offers high bandwidth, ultra-low latency, and high reliability with small cell densification, which is essential for efficient data sharing among connected vehicles. However, the limited range of 5G cells presents challenges, including frequent handovers (HO), which can lead to unnecessary HOs, HO failures, ping-pong effects, and packet loss, ultimately degrading user experience. To address these challenges, a proactive handover decision strategy based on a comprehensive analysis of network conditions is necessary to ensure stable connections. We introduce a holistic connectivity-oriented hybrid spatio-temporal graph-oriented approach, HCO-PHD, which leverages Graph Convolutional Networks and Long Short-Term Memory to capture spatial dependencies and temporal patterns across the network, proactively enabling HO decision-making. HCO-PHD enhances connection stability by dynamically assessing real-time network changes and making informed HO decisions in highly mobile and ultra-dense environments. Simulation results demonstrate that HCO-PHD significantly improves the efficiency of HO decision-making, ensuring stable connectivity and minimizing disruptions caused by HOs, outperforming existing approaches.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".