Weighted network traffic offloading in cache-enabled heterogeneous networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Due to explosive demands of multimedia services from mobile users, the growing network traffic load becomes a severe challenge for mobile network operators (MNOs). To address this problem, content caching is regarded as an effective emerging technique to reduce the duplicated transmissions of the content downloads demanded by mobile users, while heterogeneous networks (HetNets) are regarded as an effective technique to increase the network throughput. Thus, this paper focuses on content caching in HetNets to offload the weighted network traffic, in which we consider the problem of minimizing the weighted expected sum of traffic load of accessing the requested contents. By transforming the irregular problem into a binary integer linear programming problem, we propose a novel suboptimal heuristic algorithm with polynomial-time complexity to solve the problem, instead of using the existing optimal branch-and-bound method with exponential-time complexity. Numerical results demonstrate that our proposed content caching framework can reduce the weighted expected sum of traffic load significantly.
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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 | 0.000 |
| Open science | 0.001 | 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 it