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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".