Joint user association and content placement for Cache-enabled wireless access networks
Bibliographic record
Abstract
This paper considers the optimal placement of content in cache-enabled base-stations (BSs) for reducing backhaul traffic in a densely deployed wireless access network. By caching popular files, users requesting these files can be served directly by their associated BSs without needing to fetch content from the core network. This paper makes an observation that a real network consists of distinct classes of users with different file preferences, so jointly optimizing cache placement and user-BS association can result in significant benefit. This paper considers such a joint optimization problem for achieving an optimized tradeoff between load balancing and backhaul saving, while accounting for both the physical layer wireless propagation characteristics and the finite cache size at the BSs. By proposing a numerical algorithm that iteratively optimizes the content placement policy for fixed user-association and optimizes the user association policy for fixed content placement, with a goal of maximizing a backhaul-aware proportional fairness network utility, this paper shows that placing similar content at strategically located BSs can result in significant backhaul saving without sacrificing as much in user access rates.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".