Joint Frequency Reuse and Cache Optimization in Backhaul-Limited Small-Cell Wireless Networks
Bibliographic record
Abstract
Caching at base stations (BSs) is a promising approach for supporting the tremendous traffic growth of content delivery over future small-cell wireless networks with limited backhaul. This paper considers exploiting spatial caching diversity (i.e., caching different subsets of popular content files at neighboring BSs) that can greatly improve the cache hit probability, thereby leading to better overall system performance. A key issue in exploiting spatial caching diversity is that the cached content may not be located at the nearest BS, which means that to access such content, a user needs to overcome strong interference from the nearby BSs; this significantly limits the gain of spatial caching diversity. In this paper, we consider a joint design of frequency reuse and caching, such that the benefit of an improved cache hit probability induced by spatial caching diversity and the benefit of interference coordination induced by frequency reuse can be achieved simultaneously. We obtain a closed-form characterization of the approximate successful transmission probability for the proposed scheme and analyze the impact of key operating parameters on the performance. We design a low-complexity algorithm to optimize the frequency reuse factor and the cache storage allocation. Simulations show that the proposed scheme achieves a higher successful transmission probability than existing caching schemes.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".