A Distributed Channel Access Scheme for Vehicles in Multi-Agent V2I Systems
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.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".