Mobility-Oriented Virtual Cell Handover Management in 5G Vehicular Networks
Bibliographic record
Abstract
Connected vehicles offer substantial potential for improving traffic safety and enhancing comfort services. However, maintaining consistent connections in dynamic vehicular environments remains a persistent challenge, especially due to the need for seamless handovers (HO) between cellular towers as vehicles travel at high speeds. The limited communication range often leads to frequent HOs and connection drops, which can degrade the reliability of services and resources. The virtual cell (VC) paradigm can help mitigate the challenges of the limited communication range in 5G networks for high-mobility, ultra-dense scenarios. To address these challenges, we propose a mobility-oriented approach using a multi-output regression model named MSVR to manage VCs. Our proposed approach ensures stable HO decision-making by dynamically managing VCs based on predictive mobility, considering network attributes and real-time data: speed, signal strength, and network quality. Realistic simulations and extensive result analyses have been conducted to demonstrate the effectiveness of the proposed MSVR approach over existing works in terms of throughput, frame loss ratio, number of HO, and size of VC.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".