Reliable Network Coded MAC in Vehicular Ad-Hoc Networks
Bibliographic record
Abstract
We consider the problem of designing cooperative driver assistance and collision warning systems in vehicular networks. In this problem each car has a small size state information message that should be received by its neighborhood within a short lifetime of L timeslots. Because of the safety nature of the application, communication reliability (success probability) and delay are of critical importance. In a repetition-based MAC scheme, each car retransmits its safety message w times in a time frame of L timeslots. Based on the proposed opportunistic network coding in this paper, given the local feedback information and already heard messages, each node tries to find the best message combining strategy such that the number of nodes that can instantly decode an uncoded packet is maximized. Simulation results show that the proposed scheme outperforms the random linear network coding as well as the uncoded case in terms of message loss probability. Also it results in lower average message reception delay compared to random linear network coding.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".