Toward neighborhood prediction using Physical-Layer Network Coding
Bibliographic record
Abstract
In this paper we investigate the improvements in the capability of neighborhood prediction when the Physical-Layer Network Coding (PLNC) is used for relaying messages in Vehiculars Ad Hoc Networks (VANETs). We compute the probability that a link between two nodes is available at a given time in a three-node cooperative network, and we demonstrate that the use of PLNC, compared to the use of Network Coding (NC)-based or traditional routing (TR)-based relaying techniques, leads to a better accuracy of the neighborhood prediction. We also demonstrate that the accuracy of the prediction is tightly related to the mobility model used and that this tight relationship can be relaxed by using PLNC-based relaying neighborhood prediction (PRNP). The results demonstrate that PRNP can improve the accuracy of neighborhood prediction due to the high network capacity of PLNC-based networks.
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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 | 0.001 |
| 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".