Capacity and delay analysis for social-proximity urban vehicular networks
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, the asymptotic capacity and delay performance of social-proximity urban vehicular networks with inhomogeneous vehicle density are analyzed. Specifically, we investigate the case of N vehicles in a grid-like street layout while the number of road segments increases linearly with the population of vehicles. Each vehicle moves in a localized mobility region centered at a fixed social spot and communicates to a destination vehicle in the same mobility region via a unicast flow. With a variant of the two-hop relay scheme applied, we show that social-proximity urban networks are scalable: a constant average per-vehicle throughput can be achieved with high probability. Furthermore, although the throughput and delay of a unicast flow may degrade in a high density area, almost constant per-vehicle throughput Ω(1/log (N)) and almost constant delay O(log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (N)) (except for the polylogarithmic factor) are still achievable with high probability. By identifying the key impact factors of performance mathematically, our results should provide insight on the design and deployment of future vehicular networks.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it