Real-Time Decision Optimization in Satellite-Ground Links using Edge Computing and Artificial Intelligence
Bibliographic record
Abstract
Satellite-ground communication systems demand real-time optimization to maintain reliability, low latency, and high throughput under dynamic conditions. Conventional cloud-based decision systems often suffer from delay and limited adaptability due to centralized processing. This paper proposes an edge-computing framework enhanced with artificial intelligence for real-time decision optimization in satellite communication. The system integrates long short-term memory (LSTM) networks for predictive link forecasting and a deep Q-network (DQN) reinforcement learning model for adaptive link control. Simulation results in NS-3 show that the proposed framework reduces decision latency by 37.5%, increases throughput by up to 17%, and improves packet delivery ratio under interference and congestion scenarios compared to cloud-based baselines. The design achieves low inference times and operates effectively on compact edge devices, demonstrating its potential for resilient and autonomous satellite-ground communication.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".