Ultra-Density Aware Learning-Based Handover Management in High-Mobility 5G Vehicular Networks
Bibliographic record
Abstract
Ensuring connection stability is crucial for both vehicular safety and user experience. With the increasing amount of data sharing among connected vehicles, there is a need for more bandwidth, stability, and reliability. While 5G technology can offer these benefits with its small cellular range and densification, it also presents a challenge in frequent handovers (HOs). This issue can result in unnecessary HO, HO failures, and ping-pong effects, negatively impacting service delivery and compromising safety data sharing. To this end, we present High- mobility and Ultra-density Aware Handover decision-making (HMUD-H) approach using the SARSA Reinforcement Learning algorithm for connection management, which efficiently makes HO decisions to ensure stable connectivity. The HMUD-H algorithm is adaptable and can handle dynamic, highly mobile, and ultra-dense vehicular networks. Realistic simulated analyses have demonstrated that our algorithm significantly reduces the number of HOs, average cumulative HO time, HO failures, and ping-pong effects, thus improving overall connection stability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".