Mobile Service Amount Based Link Scheduling for High-Mobility Cooperative Vehicular Networks
Bibliographic record
Abstract
This paper investigates the link scheduling for relay-aided high-mobility vehicular networks, where the vehicles with good vehicle-to-infrastructure (V2I) links are employed as cooperative relay nodes to help forward information to the ones with poor V2I links over vehicle-to-vehicle (V2V) links. To overcome the inefficiency of current instantaneous information rate based link scheduling (IIR-LS) method, especially in high-mobility scenarios, we propose a mobile service amount based link scheduling (MSA-LS) for high-mobility vehicular networks. We formulate an optimization problem to maximize the MSA of MSA-LS by jointly scheduling the V2I and V2V links. Since the resulted combinational optimization problem is too complex to solve, we design an efficient low-complexity algorithm, where Sort-then-Select, Hungarian algorithm, and Bisection search are employed. Simulation results demonstrate that our proposed MSA-LS is able to achieve new optimal performance. It is also shown that our proposed MSA-LS is much more efficient for high-mobility vehicular systems, which can improve the system average throughput with increment of about 13% compared with existing IIR-LS and with about 22% increment compared with traditional non-cooperation scheduling.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".