Content caching scheme for D2D communication underlaying cellular networks with capacoty restriction
Bibliographic record
Abstract
With the rapid growth of Internet services and the popularity of social media, mobile network operators are facing a serious challenge to delivery multimedia content to multiple users. In this paper, we consider device-to-device (D2D) communication supported mobile content delivery networks (mCDNs) which enables controllable and direct delivery of multimedia contents. With this network, we regard mobile device as caching server device (CSD) which can store popular multimedia contents and provide these for other devices in proximity to it via D2D link. Besides, we propose an optimization problem to determine the caching probability for the individual content in each CSD. In this problem, we intend to maximize system utility with the consideration of cache capacity restriction in each CSD. Further, we present a low-complexity search algorithm, namely discrete binary searching algorithm (DBSA), for solving the proposed optimization problem. Simulation results show that proposed optimal solution can obtain the best performance on system utility.
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".