Intelligent Caching Algorithms in Heterogeneous Wireless Networks with Uncertainty
Why this work is in the frame
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Bibliographic record
Abstract
A burgeoning number of wireless devices connecting to the Internet tend to impose a heavy traffic load on the network backbone. Caching the most popular content at the heterogeneous wireless network edge is a promising way to alleviate the network overload. However, to cache the diverse content effectively, a file popularity profile that may not be known in advance to network operators has to be utilized. To tackle the challenge caused by this uncertainty, online learning techniques can be considered. Additionally, in practice, dense small-cell networks are often deployed to maximize spectral efficiency, which will naturally bring overlapping coverage areas among individual small cells. In this paper, we propose to address the content caching problem in a scenario of overlapping coverage areas among small cells while further allowing users distributed in the overlapping area to stochastically choose to connect to the small-cell base station they can reach. We propose two effective and efficient online learning algorithms to address the aforementioned problem and also provide theoretical guarantees. Finally, experiments are conducted to verify the performance of the proposed algorithms practically.
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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 | 0.001 | 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 it