Reinforcement Learning-Based Energy-Efficient Data Access for Airborne Users in Civil Aircrafts-Enabled SAGIN
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Airborne users are always dreaming of enjoying a good Internet access experience while in the air. However, due to long propagation delay and limited network coverage, the existing data communication methods utilized in space and ground communications not only fail to ensure the quality-of-service (QoS) of airborne users, but also incur significant energy consumption to process content requests. In this paper, we introduce the aeronautical ad hoc network (AANET) as a new method of network access and design an energy-efficient data access scheme in civil aircrafts-enabled space-air-ground integrated networks (CAE-SAGIN). In order to minimize the energy consumption, we propose a service selection scheme based on reinforcement learning and formulate a joint optimization problem of resource allocation and request distribution. Leveraged by the Lyapunov optimization method, the optimization problem can be solved by the proposed joint optimization algorithm. Extensive simulations are conducted to confirm the stability of the CAE-SAGIN, and demonstrate that the proposed data access scheme can effectively reduce both the energy consumption and the processing delay. Moreover, the advantages of using AANET are becoming more obvious when higher data rate is required.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it