Learning-based Cache Placement and Content Delivery for Satellite-Terrestrial Integrated Networks
Bibliographic record
Abstract
To support the explosive content demands from multifarious services and applications, cache-enabled satellite-terrestrial integrated networks (STINs) are envisioned as a key enabler to reduce the content delivery delay and alleviate the backhaul pressure. In this paper, we investigate the joint optimization of cache placement and content delivery in the STIN to minimize the long-term overall content delivery delay. Considering that cache placement and content delivery are interrelated and affected by network dynamics in terms of satellite movement and random content requests, the joint optimization problem is formulated as a sequential decision making problem by leveraging a Markov decision process. We propose a hierarchical deep Q learning (HDQL) algorithm by leveraging two independent deep neural networks to learn the cache placement and content delivery policies with small action space and low time complexity. Simulation results demonstrate that the proposed HDQL algorithm outperforms the benchmark algorithms in terms of content delivery delay in the STINs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".