A Graph-Based Spatial-Temporal Deep Reinforcement Learning Model for Edge Caching
Bibliographic record
Abstract
With increased Internet users, backhaul links are experiencing unprecedented traffic burdens. Meanwhile, edge caching is emerging to reduce the burden. In order to optimize edge caching efficiency, this paper proposes an intelligent caching strategy called “spatial-temporal graph attention network-soft actor-critic” (STGAN-SAC). STGAN-SAC is fully decentralized and makes caching decisions without prior knowledge of the content popularity. In addition, it takes user mobility into account and enables cooperative caching between neighbouring base stations (BSs). Our paper is the first to apply spatial-temporal models to the caching problem, and experimental results have demonstrated their importance in solving this problem. STGAN-SAC achieves at least a 22.4% higher cache hit ratio, 2.1% lower latency and 2.5% lower backhaul link load compared to the state-of-the-art caching policy DDRQN. Moreover, STGAN-SAC achieves at least a 51.4% higher cache hit ratio, 3.7% lower latency and 2.1% lower backhaul link load than the state-of-the-art caching strategy DDGARQN.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".