Mobility Management in 5G-enabled Vehicular Networks
Bibliographic record
Abstract
Over the past few years, the next generation of vehicular networks is envisioned to play an essential part in autonomous driving, traffic management, and infotainment applications. The next generation of intelligent vehicular networks enabled by 5G systems will integrate various heterogeneous wireless techniques to enable time-sensitive services with guaranteed quality of service and ultimate bandwidth usage. However, to allow the dense diversity of wireless technologies, seamless and reliable wireless communication protocols need to be thoroughly investigated in vehicular networks environment. Henceforth, efficient mobility management protocols that mitigate the challenges of vehicles’ mobility is essential to support massive data loads throughout various applications. In this article, we review different mobility management protocols and their ability to address issues related to 5G-enabled vehicular networks within the related works. First, we provide a broad view of existing models of vehicular networks and their applicability to the next generation of wireless networks. Next, we propose a classification of several vehicular network models that suit the 5G wireless network, followed by a thorough discussion of the mobility management challenges in each of these network models that need to be addressed and then discuss each of their benefits and drawbacks accordingly.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".