Augmenting Drive-Thru Internet via Reinforcement Learning-Based Rate Adaptation
Bibliographic record
Abstract
Drive-thru Internet has been considered as an effective Internet access method for Internet of Vehicles (IoV). Through the opportunistic vehicle-to-roadside WiFi connection, it can provide high throughput performance with low communication cost for IoV applications, such as intelligent transportation system, automotive infotainment, etc. However, its usability is highly affected by a fundamental issue called rate adaptation (RA), which is to adjust the modulation and coding rate to adapt to the dynamic wireless channel between the vehicle and the roadside access point (AP). Conventional WiFi RA schemes are designed for indoor or quasistatic scenarios and do not account for the channel variations in drive-thru Internet. In this article, we study the limitation of applying existing RA schemes in drive-thru Internet and propose a reinforcement learning (RL)-based RA scheme to capture the potential channel variation patterns and efficiently select the rate for every vehicle's egress frame. Simulation results demonstrate that the proposed RA scheme outperforms the existing schemes in network throughput and that the efficiency of the learning model can be generalized under various conditions. The proposed RA method can provide useful inspirations for designing robust and scalable link adaptation protocols in IoV.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".