Cooperative Diversity for Intervehicular Communication: Performance Analysis and Optimization
Bibliographic record
Abstract
Although there has been a growing literature on cooperative diversity, the current literature is mainly limited to the Rayleigh fading channel model, which typically assumes a wireless communication scenario with a stationary base station antenna above rooftop level and a mobile station at street level. In this paper, we investigate cooperative diversity for intervehicular communication based on cascaded Nakagami fading. This channel model provides a realistic description of an intervehicular channel where two or more independent Nakagami fading processes are assumed to be generated by independent groups of scatterers around the two mobile terminals. We investigate the performance of amplify-and-forward relaying for an intervehicular cooperative scheme assisted by either a roadside access point or another vehicle that acts as a relay. Our diversity analysis reveals that the cooperative scheme is able to extract the full distributed spatial diversity. We further formulate a power-allocation problem for the considered scheme to optimize the power allocated to the broadcasting and relaying phases. Performance gains up to 3 dB are obtained through optimum power allocation, depending on the relay location.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".