Inter street interference cancelation in urban vehicular networks using network coding
Bibliographic record
Abstract
An urban scenario is the center stage for vehicles to roam around the concrete jungle. Any sorts of wireless communication would be affected by hidden terminal problems, fading and interferences. Unintended nodes are unnecessarily bothered by such huge volume of microwave communications. The most common forms of communication are beaconing messages, which let the vehicles know about its neighboring vehicles and possibly choose an appropriate forwarder for safety and non-safety messages. However, such influx of broadcast messages may lead to beacon overhead and congestion resulting in low message reception as well as excessive delay. Interferences due to inter-street beacon messages may affect emergency messages, channel arbitration messages and other control messages which share a common channel as specified by DRSC/WAVE. This paper proposes a scheme to cancel interferences due to inter-street beacon communications by adaptive transmission control, while maintaining application layer transmission range, through multi-hop beacon forwarding and network coding. The simulations show that our scheme has higher packet delivery ratio and higher successful channel utilization compared to CSMA/CA protocols.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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 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".