Enabling Cooperative Relaying VANET Clouds Over LTE-A Networks
Bibliographic record
Abstract
This paper addresses the area of heterogeneous wireless relaying vehicular clouds. We devise an advanced vehicular relaying technique for enhanced connectivity in densely populated urban areas. We investigate the performance of a transmission scheme over a Long-Term Evolution-Advanced (LTE-A) network where vehicles act as relaying cooperating terminals for a downlink session between a base station and an end-user. The abundance of moving vehicles, operating in an ad hoc fashion, can eliminate the need for establishing a dedicated relaying infrastructure. However, the associated wireless links in vehicular clouds are characterized by a doubly selective fading channel; this causes performance degradation in terms of increased error probability. Hence, we propose a precoded cooperative transmission technique to extract the underlying rich multipath-Doppler-spatial diversity, which is a relay selection scheme to take advantage of the potentially large number of available relaying vehicles. We further contribute by the derivation of a closed-form error rate expression, diversity gain, and outage expressions and introduce our derived performance unconditional expressions as a benchmark to assess our analysis and future research studies of such an approach. Our analytical and simulation results indicate that significant diversity gains and reduced error rates are achievable. In addition, there is a noticeable reduction in the required transmitting power compared with traditional transmission schemes, as well as an increase in distance coverage.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".