Performance Analysis and Enhancement of WAVE for V2V Non-Safety Applications
Bibliographic record
Abstract
The wireless access for vehicular environment (WAVE) mandates that data packets of non-safety applications are to be sent within WAVE basic service sets (WBSS). These WBSS are to be established on the least congested service channels. WAVE proposes a mechanism to select such channels; yet, owing to vehicles' high mobility, there is high chance of having overlapped WBSS, yielding unsatisfactory performance. Several approaches have been proposed to mitigate this problem. Nevertheless, they are either inefficient or cost-ineffective. In this paper, we propose a novel approach called altruistic service channel selection (ASSCH) that compels vehicles to cooperate in order to select the least congested service channels for vehicle-to-vehicle (V2V) non-safety applications. ASSCH has three phases: 1) identifying the channel's current state (i.e., free or occupied); 2) predicting channels that are likely to be free in the near future; and 3) selecting the least used channel among them. We then propose a stochastic analytical model for the throughput of V2V non-safety applications considering various factors, including the busy channel at zero, discarded by all existing IEEE 802.11p EDCA models. Simulation results demonstrate that ASSCH outperforms existing allocation-based schemes as it incurs low capture delay, low ratio of overlapping WBSS, and high throughput. Simulation results also show that our analytical model closely matches the throughput of EDCA access categories.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".