Worst-Case Access Delay of HomePlug Green PHY (HPGP) for Delay-Critical In-Vehicle Applications
Bibliographic record
Abstract
The increasing complexity of automotive electronics has put considerable pressure on automotive communication networking to accommodate in-vehicle information flows. The use of power lines has been a promising alternative to in- vehicle communications because of elimination of extra data cables. In this paper, we focus on the latest HomePlug Green PHY (HPGP) which has been promoted by major automotive manufacturers for green communications with electric vehicles, and study its worst-case access delay performance in supporting Delay-critical in-vehicle applications using both theoretical analysis and the simulation. Specifically, we apply Network Calculas as a deterministic modeling approach to evaluate the worst delay and further verify its performance using the OMNeT++ simulation. Evaluation results are also supplemented to compare with legacy methods and provide useful guidelines for developing HPGP based vehicular power line communication systems.
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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 | 0.000 |
| 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".