Packet Loss Rate Prediction for Vehicular Networks with Regression Methods
Bibliographic record
Abstract
Vehicle communication aims to improve the reliability, security and latency performance of vehicle ad hoc network communication. In this study, an heuristic function that calculates the packet loss rate (PLR) in the vehicle network using regression methods is proposed, given different vehicle density, transmission speed, and transmission power values. In order to measure the performance of the regression methods, the transmissions of the connected vehicles under realistic traffic scenarios were obtained by simulations. Simulations are made for different urban and highway conditions by working together with Omnet++, Sumo and Veins environments. The results obtained from these simulations are translated into the PLR dataset. The data set is used as training and test data in various regression algorithms. Numerical results show that Catboost regression method gives the least error between predicted and actual results compared to other regression methods. Thanks to the heuristic we have obtained, given a set of transmission parameters, the PLR can be determined directly. In this way, the system designer chooses among the solutions that provide the given PLR according to the needs, or it becomes possible to reduce the PLR below the target level by re-adjusting the transmission parameters.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".