Competitive Wireless Access for Data Streaming over Vehicle-to-Roadside Communications
Bibliographic record
Abstract
This paper considers the problem of optimal and competitive wireless access for data streaming over vehicle-to-roadside (V2R) communication. In a service area, the onboard units (OBUs) in vehicles use wireless access to download streaming data from the roadside units (RSUs). The downloaded streaming data can be stored in proxy buffer for the application to playout. The wireless access can be in reservation or on-demand mode. While the price of wireless access in reservation mode is fixed, that of on-demand mode is determined from the total demand from all OBUs. The OBUs compete with each other for wireless access to a particular RSU. The objective of an OBU is to minimize the cost for wireless access while the quality-of-service (QoS) requirement (e.g., buffer underrun probability) of the streaming application is met. A stochastic game is formulated to model this competitive situation in which OBUs are the players of this game. The strategy of an OBU is the wireless access policy (i.e., the amount of bandwidth to be used for downloading streaming data). The constrained Nash equilibrium is considered to be the solution of this stochastic game. This solution ensures that the cost of each OBU is minimized given the wireless access policies of other OBUs and thus none of the OBUs would unilaterally change its policy for wireless access. In addition, the solution guarantees that the QoS requirement of streaming application is met.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".