LORA: Loss Differentiation Rate Adaptation Scheme for Vehicle-to-Vehicle Safety Communications
Bibliographic record
Abstract
The existing study shows that safety applications supported by vehicle-to-vehicle (V2V) communications have the potential to address 80% of all road crash issues. IEEE 802.11p is a key enabling technology to support V2V safety applications. To meet the stringent delay and reliability requirements of these applications, rate adaptation (RA) approaches have been proposed to determine the optimal data transmission rate, according to the channel conditions such as packet losses. However, existing RA solutions cannot be directly applied to V2V safety communications in highway scenarios, which exhibit lots of dynamics and severe packet losses. Moreover, physical (PHY)-layer channel fading and medium-access-control (MAC)-layer interference contribute differently to the packet losses and, thus, should be treated separately. To address these issues, in this paper, we propose a LOss differentiation RA (LORA) scheme. LORA can estimate the average packet loss rate (PLR) for each sender and differentiate the fading losses from the interference losses. Extensive evaluation results demonstrate that LORA can provide reliability guarantees for V2V safety applications, as well as a response to environment changes in a real-time manner.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".