Popularity and Size-Aware Caching With Cooperative Transmission in Hybrid Microwave/ Millimeter Wave Heterogeneous Networks
Bibliographic record
Abstract
In this paper, a file popularity and size-aware (PSA) caching scheme is proposed for a hybrid heterogeneous network (HetNet) comprising a file server, macro base stations (BSs) operating in the microwave frequency bands, and pico BSs operating in the millimeter wave frequency wave bands. Different than the state-of-the-art size-weighted popularity (SWP) and popularity-based caching schemes that assume equal file size, the file PSA caching scheme utilizes knowledge of the file popularity and size distributions to maximize the network average cache hit probability and the network average success probability subject to the available cache capacity. Moreover, the maximization of the network average success probability is shown to be non-convex and dynamic programming and branch and bound algorithms are employed to circumvent its convexity. New expressions for the optimal file caching probabilities that maximize the network average success probability in noise-limited and interference-limited HetNet scenarios are provided which are evaluated using numerical techniques. The results demonstrate that the proposed file PSA caching scheme provides up to 7% gain in the network average success probability compared to the SWP-based scheme. Lastly, the combination of coded caching with cooperative transmission achieves up to 40% gain compared to a non-cooperative transmission scheme without coded caching.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".