Packet relay‐assisted V2V communication with sectorised relay station employing payload combining scheme
Bibliographic record
Abstract
A reliable vehicle‐to‐vehicle communication (V2VC) is essential to enable safe and highly automated driving system. Addition of a relay station (RS) can improve packet delivery rate (PDR) of CSMA/CA‐based V2VC. However, the improvement is limited by packet collision at RS due to a hidden terminal (HT). This HT problem can be alleviated by employing sectorised receive antenna at RS, i.e. sectorised relay‐assisted (SR‐) V2VC. However, SR‐V2VC still faces another problem, i.e. packet congestion at RS under high traffic conditions due to the improved packet reception rate. This leads to packet drop at RS and hence limits the improvement brought by SR‐V2VC. Consequently, it is crucial to introduce a kind of traffic congestion avoidance methods to RS. In this article, the authors propose SR‐V2VC with a payload combining forwarding (PCF) method (SR‐V2VC/PCF) and theoretically evaluate the system performance using a CSMA/CA collision model. Large‐scale computer simulations are also conducted to confirm the performance improvement brought by SR‐V2VC/PCF. It is shown that the lowest broadcast PDR of 62% for the non‐relay system is improved to higher than 81% by SR‐V2VC/PCF and the directivity of the sector antenna has impact on the benefit of the proposed scheme.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".