On a Stochastic Delay Bound for Disrupted Vehicle-to-Infrastructure Communication with Random Traffic
Bibliographic record
Abstract
This paper studies the multihop packet delivery delay in a disrupted vehicle-to-infrastructure communication scenario, where an end-to-end connected path is not likely to exist between a vehicle and the nearest road side unit (RSU) due to the intermittent connectivity between adjacent vehicles. We present an analytical framework that takes into account the randomness of vehicle traffic and the statistical variation of the disrupted communication channel. Our framework employs the effective bandwidth theory and its dual, the effective capacity concept, in order to obtain the maximum distance between adjacent RSUs that stochastically limits the worst case packet delivery delay to a certain maximum value (i.e., allows only an arbitrarily small fraction of packets received by the RSU from the farthest vehicle to exceed a required delay bound). Simulation results demonstrate that our analytical framework is accurate in determining the separation distance between RSUs that probabilistically limit the worst case delay bound.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".