Efficient Resource Allocation for Multi-Beam Satellite-Terrestrial Vehicular Networks: A Multi-Agent Actor-Critic Method With Attention Mechanism
Bibliographic record
Abstract
With the rapid development of intelligent transportation systems, there is an increasing demand for a variety of vehicular services, such as automated driving assistance, emergency alert, infotainment, etc. However, in some situations (e.g., remote areas or maritime scenarios), the terrestrial networks alone cannot serve the vehicular applications very well due to the infrastructure deployment and maintenance issues. Satellite networks have become an effective supplement to terrestrial networks, which complement well in terms of coverage, flexibility, reliability, and availability. In this paper, we consider the low orbit multi-beam satellite-terrestrial networks to serve for vehicles. We model this problem as a cooperative multi-agent reinforcement learning process, where each beam acts as an agent, and the global bandwidth is cooperatively shared among all the agents. A multi-agent actor-critic method with attention mechanism is proposed to allocate resources for vehicles with strict delay requirements and minimum bandwidth consumption. When allocating bandwidth, the channel efficiency, the angle of the beams and the priorities of requests in different regions are also considered. Centralized training and distributed execution is performed in the training of the agents. Extensive simulation results verify the effectiveness of our proposed method, where all the agents can well cooperative to achieve efficient resource allocation on-demand for the vehicles under strictly limited bandwidth resources.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".