UAV-Assisted Edge Caching Under Uncertain Demand: A Data-Driven Distributionally Robust Joint Strategy
Bibliographic record
Abstract
Unmanned aerial vehicle (UAV) assisted edge caching has been emerged as a promising solution to alleviate network congestion, which can provide users with their desired contents with reduced latency. For achieving effective UAV-assisted edge caching, how to jointly design the trajectory and caching strategy is critical, which, however, is not straightforward due to the heterogeneous and uncertain demand in the network. In this paper, aiming at maximizing the reduced delay brought by the UAV-assisted caching, we propose a proactive joint strategy on trajectory and caching for the UAV, where the demand uncertainty is particularly studied. Specifically, by regarding the demand on each content as a random variable, we formulate the strategy design as a risk-averse stochastic optimization problem to make the network performance guaranteed under certain confidence level. Different from most existing works assuming the perfect distributional information is available to deal with the uncertainty, we develop a data-driven approach based on the first and second order statistics to achieve a distributionally robust (DR) solution, which can make the strategy trustworthy with guaranteed network performance even though the specific distributional information is unknown. Simulation results have demonstrated the effectiveness of the proposed DR strategy.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".