GNN-Based Proportional Fair Dynamic Bandwidth Allocation in Wireless Vehicular Networks
Bibliographic record
Abstract
In wireless vehicular networks, dynamic bandwidth allocation (DBA) faces two main challenges: mobility and heterogeneity. Rapidly changing vehicle locations make it difficult to predict and allocate bandwidth efficiently, and the diverse communication capabilities and requirements of vehicles can make fair allocation a challenge. The solution proposed in this paper involves two algorithms. The first algorithm uses Graph Neural Networks (GNNs) to predict the connection topology of the vehicular network based on historical data. This topology can be used to prioritize vehicles for bandwidth allocation based on their Quality of Service (QoS) requirements and proximity to other vehicles. The second algorithm dynamically allocates available network resources based on demand, ensuring flexible, efficient, and reliable communication services to all vehicles. These algorithms work together to address the mobility and heterogeneity challenges while providing a fair distribution of network resources to all users. A novel approach is used to optimize resource allocation in a volatile network, ensuring that users with higher QoS requirements receive a larger share of bandwidth while maintaining fair distribution.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".