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 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 | 0.001 |
| 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".