Connectivity Analysis for V2I Communications in Cognitive Vehicular Networks
Bibliographic record
Abstract
In cognitive vehicular networks (CVNs), the connectivity between any cognitive vehicle (CV) and roadside unit (RSU) relies not only on the communication links between them but also on the detection of the idle spectrum. Although cognitive radio (CR) technology has been widely studied in 5G communications, its influence on connectivity, particularly in CVNs, has been seldom discussed. In this paper, probability theory is adopted to deduce the connectivity probabilities of CVNs by analyzing the cognitive and communication characteristics of CVs, so as to unveil the correlations between network connectivity and the channel environment together with road traffic factors. In particular, to demonstrate the impact of cognitive and vehicular capacity on connectivity performance, two traffic scenarios with different vehicular transmission coverages and relay behaviors are discussed. Simulation results present the variation of connectivity probabilities with different signal-to-noise ratios (SNRs), coverage ranges of both RSUs and CVs, and the densities of CVs. It can be found that the improvement of channel environment leads to better network connectivity, thus demonstrating the significant impact of cognitive activities on the connectivity of CVNs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".