Maximizing Group-Based Vehicle Communications and Fairness: A Reinforcement Learning Approach
Bibliographic record
Abstract
Vehicle-to-vehicle (V2V) communications retain immense potential in elevating network throughput for next-generation vehicular applications. This study investigates the problem of maximizing the total number of communications while ensuring fairness among V2V communication pairs (MVGCF). In the experiments conducted, each vehicle has a dedicated data stream to be shared among others in the same group. However, not all pairs can directly communicate due to communication range limitations. Hence, the current study focuses on relaying data packets in networks through multi-hop vehicles sharing resource blocks within a time frame while adhering to signal-to-interference-plus-noise ratio (SINR) and half-duplex constraints. To accomplish the research objectives mentioned above, two reinforcement learning (RL) algorithms, namely Q-Learning and Double Deep Q-Networks (DDQN), are proposed. However, to overcome the scalability and computational limitations of RL methods, we devise hybrid heuristic-based reinforcement learning methods, MVGCF_QLearning and MVGCF_DDQN. The numerical results demonstrate the hybrid approaches' effectiveness in terms of the number of successful communications and max-min fairness when compared to a random_agent, and the conventional RL methods for small and large networks.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".