Adaptive Multi-Dimensional Resource Slicing in Cognitive Satellite-Terrestrial Vehicular Networks
Bibliographic record
Abstract
Satellite-terrestrial vehicular networks (STVN) are envisioned as a promising architecture to provide ubiquitous connectivity for network-reliant vehicular services. In this paper, we investigate resource slicing in cognitive STVN by adaptively managing both communication and caching resources in low Earth orbit (LEO) satellites and terrestrial base stations to support High-Definition (HD) map distribution. Leveraging distinct multicast and unicast features of satellite and terrestrial networks, a novel resource slicing architecture is proposed for cognitive STVN. Two kinds of slices, one for multicast and another for unicast transmissions, are created to tailor the different resource usage and transmission characteristics. To address the challenges posed by spatiotemporal dynamics in service demands and satellite availability, we formulate a long-term resource slicing optimization problem. A two-layer resource slicing (TLRS) scheme is proposed for adaptive multi-dimensional resource management employing a hybrid data-model co-driven approach. In the inner layer, a swap-based matching algorithm is developed to determine the multicast and caching decisions within each slicing window. In the outer layer, a hybrid proximal policy optimization (HPPO)-based reinforcement learning algorithm is designed to adaptively adjust the slicing window length and communication resources in each slice. Simulation results demonstrate that the proposed TLRS scheme in cognitive STVN can effectively guarantee service requirements with efficient resource usage and lower delay performance.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".