A Distributed Channel Access Scheme for Vehicles in Multi-Agent V2I Systems
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Bibliographic record
Abstract
Due to the limited bandwidth of Roadside Units (RSUs) deployed in drive-thru networks, vehicles entering the network coverage with data requests have to contend for the access to the data service provided by RSUs. In order to maximize the vehicle utility, efficient access schemes are indispensable at the vehicles' side. This paper studies the optimal access control of vehicles in multi-agent drive-thru systems. In such networks, each vehicle, acting as an independent agent, can take an access decision that could potentially maximize the individual utility based on its own observations of the instantaneous environment states. Consequently, the decision of one vehicle will influence those of others, making environment states only partially observable at the vehicles' side and complicating the optimal access design. To tackle this coupling decision issue, we first formulate the optimization problem as a finite Markov Decision Process (MDP). Then, we propose a distributed access algorithm that combines the statistic learning method and the dynamic programming technique. With the proposed algorithm, missing vehicle states and related transition probabilities will be estimated by vehicles. The optimization problem is recursively solved by applying the dynamic programming technique. Simulation results are provided to show the significant improvement achieved by the proposed algorithm on multiple performance metrics. The convergence of the algorithm is numerically confirmed, verifying the stability of our approach.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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