Bibliographic record
Abstract
Vehicular safety applications are based on broadcasting of safety messages to the neighboring vehicles. As LTE is an infrastructure-based network, vehicles cannot broadcast their safety messages directly to their neighbors; thus, all messages should pass through the infrastructure. However, these messages may congest the network and lead to high delays for some vehicles. Furthermore, in vehicular safety applications, faster vehicles are more sensitive to delay, since their positions change more frequently than slower vehicles within the same time frame. Therefore faster vehicles should be assigned a higher priority to send messages in order to have low delay. Existing LTE schedulers do not consider this factor when allocating resources. This leads to high delays and unacceptable position errors for faster vehicles. In this paper we propose a Speed and Location Aware (SLA) scheduler for LTE which is suitable for vehicular applications. SLA scheduler considers the speed and location of vehicles in order to assign priorities for resource allocation. Faster vehicles receive priority for the allocation of Physical Resource Blocks (PRB). Simulation results show that SLA scheduler outperforms existing algorithms by preventing high delays and large position errors for faster vehicles.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.018 |
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".