Multilayer satellite network collaborative mobile edge caching: A GCN-based multi-agent approach
Bibliographic record
Abstract
With the explosive growth of high definition video streaming data, a substantial increase in network traffic has ensued. The emergency of mobile edge caching (MEC) can not only alleviate the burden on core network, but also significantly improve user experience. Integrating with the MEC and satellite networks, the network is empowered popular content ubiquitously and seamlessly. Addressing the research gap between multilayer satellite networks and MEC, we study the caching placement problem in this paper. Initially, we introduce a three-layer distributed network caching management architecture designed for efficient and flexible handling of large-scale networks. Considering the constraint on satellite capacity and content propagation delay, the cache placement problem is then formulated and transformed into a markov decision process (MDP), where the content coded caching mechanism is utilized to promote the efficiency of content delivery. Furthermore, a new generic metric, content delivery cost, is proposed to elaborate the performance of caching decision in large-scale networks. Then, we introduce a graph convolutional network (GCN)-based multi-agent advantage actor-critic (A2C) algorithm to optimize the caching decision. Finally, extensive simulations are conducted to evaluate the proposed algorithm in terms of content delivery cost and transferability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".