Cooperative vehicular ad-hoc transmission for LTE-A MIMO-downlink using Amplify-and-Forward relaying
Bibliographic record
Abstract
Cooperative communication has been recently applied to vehicular networks to enable coverage extension and enhance link reliability through distributed spatial diversity. In this paper, we investigate the performance of cooperative vehicular relaying over a doubly-selective (i.e., frequency-selective and time-selective) fading channel for an LTE-Advanced downlink session. Using Amplify-and-Forward (AF) relaying with orthogonal cooperation protocol and Multiple-Input Multiple-Output (MIMO) deployment at the source and destination, we derive a pairwise error probability (PEP) expression and demonstrate the achievable diversity gains. Space-Time Block Coding (STBC) is used to ensure the orthogonality of the transmitted-received signals. Our results demonstrate that, via proper linear precoding constellation, the proposed cooperative-MIMO vehicular relaying is capable of extracting the maximum available diversity in frequency (through multipath diversity), time (through Doppler diversity) and space (through cooperative diversity as well as the MIMO deployment) dimensions. We further conduct numerical simulations to confirm the analytical derivations and present the error rate performance of the cooperative relaying vehicular scheme under consideration.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".