Content Caching for Heterogeneous Small-Cell Networks with Intelligent Content Access
Bibliographic record
Abstract
To realize content caching at base stations (BSs), a caching system fetches contents to the appropriate base stations in advance then it uses the fetched contents to serve end users in the serving phase. This paper studies a caching problem for heterogeneous small-cell networks with QoS-aware and adaptive BS association where end users can be associated with either small-cell or macro-cell BSs. Toward this end, we derive the cache miss ratio for general caching strategy based on which we formulate a caching problem which aims at minimizing the cache miss ratio. To solve this problem, we propose two algorithms, namely Sparse Network Caching (SNC) and Two-Stage Caching (TSC) algorithms. We prove that the SNC algorithm can obtain the optimal caching solution as the request rate to each BS is much smaller than its serving capability. Numerical results demonstrate that the SNC algorithm performs well in the sparse network scenario while the TSC algorithm operates efficiently in all studied scenarios. Moreover, the proposed algorithms significantly outperform the random caching (RDC) and most popular caching (MPC) algorithms.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".