Q-Learning Based Edge Caching Optimization for D2D Enabled Hierarchical Wireless Networks
Bibliographic record
Abstract
Caching at the edge of mobile networks can significantly offload network traffic while satisfying content requests from mobile users locally. The contents can be requested from the proximity users via Device-to-device (D2D) communications while proactive caching the popular content to local users. However, the assumptions that content popularity is equal to user preference in several existing studies, which are invalid and not rigorous due to the fact that content popularity is calculated by the statistic of user requests within a certain period while user preference reflects the probability of a content requested by the individual user. Motivated by this, in this paper, we study the edge caching optimization of hierarchical wireless networks. Our aiming is to maximize the size of content offload by D2D communications. In particular, the edge caching policy with D2D sharing model based on the analysis of user mobility and social relationship is derived. We first prove the problem is NP-hard and then formulate it as a Markov Decision Process (MDP) problem, finally a Q-learning based distributed content replacement strategy is proposed. The large-scale real trace based experiment results show the effectiveness of our proposed framework.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".