A Reinforcement Learning Technique for Optimizing Downlink Scheduling in an Energy-Limited Vehicular Network
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Bibliographic record
Abstract
In a vehicular network where roadside units (RSUs) are deprived from a permanent grid-power connection, vehicle-to-infrastructure (V2I) communications are disrupted once the RSU's battery is completely drained. These batteries are recharged regularly either by human intervention or using energy harvesting techniques, such as solar or wind energy. As such, it becomes particularly crucial to conserve battery power until the next recharge cycle in order to maintain network operation and connectivity. This paper examines a vehicular network whose RSU dispossesses a permanent power source but is instead equipped with a large battery, which is periodically recharged. In what follows, a reinforcement learning technique, i.e., protocol for energy-efficient adaptive scheduling using reinforcement learning (PEARL), is proposed for the purpose of optimizing the RSU's downlink traffic scheduling during a discharge period. PEARL's objective is to equip the RSU with the required artificial intelligence to realize and, hence, exploit an optimal scheduling policy that will guarantee the operation of the vehicular network during the discharge cycle while fulfilling the largest number of service requests. The simulation input parameters were chosen in a way that guarantees the convergence of PEARL, whose exploitation showed better results when compared with three heuristic benchmark scheduling algorithms in terms of a vehicle's quality of experience and the RSU's throughput. For instance, the deployment of the well-trained PEARL agent resulted in at least 50% improved performance over the best heuristic algorithm in terms of the percentage of vehicles departing with incomplete service requests.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it