CRB: Cooperative Relay Broadcasting for Safety Applications in Vehicular Networks
Bibliographic record
Abstract
Vehicular networks require a reliable and efficient one-hop broadcast service to disseminate delay sensitive messages for high-priority safety applications. However, frequent link breakage in the dynamic networking conditions, in the presence of high relative mobility and channel fading, poses technical challenges in satisfying the strict service requirements. Makeup transmissions can improve the performance through repetitive broadcasting of packets before detecting a transmission failure. In this paper, we introduce a node cooperation based makeup strategy for vehicular networks, referred to as cooperative relay broadcasting (CRB), such that neighboring nodes rebroadcast the packet from a source node, increasing the reliability of the broadcast service. The decision to perform CRB is taken proactively and based on the channel conditions between the relaying nodes and the target one-hop neighbors. We propose an optimization framework that provides an upper bound on the CRB performance with accurate channel information. Further, we propose a channel prediction scheme based on a two-state first-order Markov chain to select the best relaying node for CRB. We study the reliability of the broadcast service in terms of packet received rate and packet delivery rate. Through extensive simulations, we demonstrate that the proposed CRB scheme provides a more reliable broadcast service, as compared with existing approaches.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".