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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".