Coverage Optimization in RIS-Enabled Satellite-Ground Networks: A Digital Twin-Based Spatial-Temporal Approach
Bibliographic record
Abstract
In this paper, we propose a novel coverage optimization scheme for RIS-enabled satellite-ground networks, called digital twin-based spatial-temporal approach (DTST), to maximize time-averaged coverage probability under strict constraint satisfaction. Specifically, we design spatio-temporal coverage grain dynamics to model the coverage-power trade-off, with particular emphasis on orbital mechanics, RIS beamforming, and stochastic geometry. Then we develop distributed digital twin (DT) synchronization to generate synthetic experiences and maintain model-reality alignment. With the obtained synthetic experiences and calibrated models, conservative value calibration is explored to address sim-to-real value bias. The coverage optimization under spatio-temporal constraints can be tamed by minimizing time-averaged coverage loss and constraint violation rate in consideration of communication quality requirements and stochastic channel variations. Simulation results demonstrate that, in dynamic LEO networks with urban-rural transitions, the DTST approach achieves substantially enhanced coverage reliability and significantly reduced service disruptions under severe rain attenuation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".