Optimized Amplify-and-Forward Relaying for Vehicular Ad-Hoc Networks
Bibliographic record
Abstract
Cooperative communication techniques promise the advantages of MIMO (multi-input multi-output) communications for wireless scenarios with single-antenna terminals. In this paper, we investigate the performance of a vehicle-to-vehicle cooperative scheme where another vehicle in the vicinity of the source vehicle acts as a relay. The underlying source-to-relay, relay-to-destination, and source-to-destination links are modeled as cascaded (double) Rayleigh fading. This statistical model provides a realistic description of inter-vehicular channel where two or more independent Rayleigh fading processes are assumed to be generated by independent groups of scatterers around the two mobile terminals. We derive a pairwise error probability (PEP) expression for the inter-vehicular cooperative scheme under consideration and show that the full distributed spatial diversity is extracted. Based on the derived PEP expressions, we obtain union bounds on the bit error rate performance which are then minimized to optimally allocate power between broadcasting and relaying phases. Optimum power allocation brings performance gains up to 3dB depending on the relay location and deployed modulation scheme.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".