CAMS transmission rate adaptation for vehicular safety application in LTE
Bibliographic record
Abstract
Researchers have started to investigate the possibility of using cellular networks for vehicular applications. Among cellular networks, LTE is the most promising technology that supports high data rate, low latency and wide coverage. Vehicular safety applications are based on broadcast of vehicle's information to it's neighbours periodically. As LTE is an infrastructure-based network, all communications should pass through the infrastructure. Due to the expense of using LTE bandwidth and the fact that most of the bandwidth is used by other applications, limited amount of bandwidth can be allocated for safety applications. As vehicles do not know number of other vehicles and their transmission rate, they might send more than available bandwidth and make the infrastructure congested which leads to high latency. In this paper we propose an algorithm to control transmission rate of vehicles by a remote host as a central node which knows the available bandwidth and total data rate of vehicles at each time. Our proposed algorithm also considers the vehicles speed for rate adaptation. Simulation results show that applying our algorithm can control the latency. It also improves the position accuracy of faster vehicles by considering the vehicles speed in adaptation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".