Best-Relay Selection for Multi-Hop Vehicular Communication in Highways
Bibliographic record
Abstract
Multi-hop relaying is an efficient transmission technique that splits the communication link between the source and the destination into several, possibly shorter, hops for extended coverage, improved reliability, and less power consumption. Previous works on multi-hop communications use the assumption of frequency-flat and quasi-static fading, which can be justified only for narrowband systems in stationary or quasi-static channels. In this paper, we extend multi-hop relaying into vehicular communication in highways.We propose a precoded transmission over multi-hop vehicle-to-vehicle links with time- and frequency-selective fading in highways. We investigate the performance gains where traveling vehicles are allowed to relay signals via neighboring vehicles to the final destination. With the aid of the precoded transmission and best-relaying vehicular selection, we succeeded to extract the rich diversity gains that are inherited in these types of doubly selective fading channels, through time, frequency, and space dimensions. We developed a mathematical model and derived a tight upper-bound expression for the pairwise error probability for future studies and analysis. Computer simulations are used to verify the correctness and accuracy of the derived analytical error.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".