Context-Aware Proactive Caching for Heterogeneous Networks with Energy Harvesting: An Online Learning Approach
Bibliographic record
Abstract
In this paper, we investigate a context-aware proactive caching problem in a heterogeneous network consisting of a single macro-cell base station (MBS) with grid power supply and multiple small-cells with energy harvesting, aiming to maximize the service ratio at the small-cell base stations (SBSs) by designing an effective context-aware proactive caching policy. We first formulate this problem as a Markov Decision Process (MDP) framework. Then, to address the incomplete stochastic information about the system dynamics and the “curse of dimensionality” issue of the formulated MDP, we propose a Post-Decision State based Approximate Reinforcement Learning (PDS-ARL) algorithm, which learns on-the-fly the optimal proactive caching policy with a high learning efficiency. The simulation results validate the efficacy of our algorithm by comparing it with baselines in terms of both the learning rate and the service ratio performance at the SBSs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".