Enhancing QoE-Aware Wireless Edge Caching With Software-Defined Wireless Networks
Bibliographic record
Abstract
Software-defined networking and in-network caching are promising technologies in the next generation wireless networks. In this paper, we propose enhancing the quality of experience (QoE)-aware wireless edge caching with bandwidth provisioning in software-defined wireless networks (SDWNs). Specifically, we design a novel mechanism to jointly provide proactive caching, bandwidth provisioning, and adaptive video streaming. The caches are requested to retrieve data in advance dynamically according to the behaviors of users, the current traffic, and the resource status. Then, we formulate a novel optimization problem regarding the QoE-aware bandwidth provisioning in SDWNs with jointly considering in-network caching strategy. The caching problem is decoupled from the bandwidth provisioning problem by deploying the dual-decomposition method. Additionally, we relax the binary variables to real numbers so that those two problems are formulated as a linear problem and a convex problem, respectively, which can be solved efficiently. Simulation results are presented to show that the latency is decreased and the utilization of caches is improved in the proposed scheme.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".