Transmission Capacity Analysis for Vehicular Ad Hoc Networks
Bibliographic record
Abstract
Traditional studies focused on the transmission capacity of vehicular ad hoc network (VANET) contains two deficiencies: the lack of a realistic model mimicking the behaviors of vehicles and the failure to consider the impacts from enhanced distributed channel access (EDCA) mechanisms applied by IEEE 802.11p. In this paper, the car-following model is introduced to describe the distribution of vehicles, and an EDCA-based linear VANET model is analyzed. Compared to previous works, a tighter transmission capacity upper bound in a large-scale fading environment is calculated. Furthermore, under Rayleigh fading channels, an elementary expression of transmission capacity fitting a sparse vehicles scenario and an upper bound of transmission capacity applicable to a dense scenario are obtained. In conclusion, the transmission capacity of a linear VANET is illustrated by the elementary expression in a sparse vehicles scenario and the upper bound in a dense vehicles scenario. The simulation results are well constrained by the proposed theoretical expression and upper bound.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".