Proactive Bandwidth Allocation for V2X Networks With Multi-Attentional Deep Graph Learning
Bibliographic record
Abstract
The increasing number of connected vehicles exacerbates the scarcity of spectrum resources in vehicle-to-everything (V2X) communication. To optimize the utilization of wireless resources, it is crucial to allocate the limited spectrum blocks to each roadside unit (RSU) based on the real-time bandwidth demand of vehicles within their coverage. However, the complex mobility patterns of vehicles and dynamic traffic conditions make it challenging to accurately and promptly estimate the bandwidth demand. To address this issue, a spatial-temporal multi-attentional network (STMA-net) is designed to predict the future bandwidth demand of RSUs. Based on the predicted bandwidth demand, a prediction error-compensable proactive bandwidth allocation algorithm is proposed to adaptively allocate spectrum resources and narrow the discrepancy between predicted and actual demand. Experimental results with realistic traffic in Bologna demonstrate that the proposed STMA-net achieves 11.25% higher prediction accuracy compared to state-of-the-art methods. Furthermore, the proposed proactive bandwidth allocation method outperforms existing methods, providing the highest throughput and serving 5% more vehicles while reducing the service drop rate by an order of magnitude.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".