Edge Caching and Content Delivery with Minimized Delay for Both High-Speed Train and Local Users
Bibliographic record
Abstract
In this paper, we investigate the edge caching and content delivery problem for both high-speed train (HST) passengers and low-mobility cellular users. Under multi-dimensional resources constraints, we formulate an optimization problem to minimize the content retrieval delay of HST passengers and meanwhile guarantee the delay requirements of cellular users. As the formulated problem is a mixed-integer nonconvex optimization problem, which is intractable directly, we propose an efficient iterative algorithm that optimizes the three decision variables (i.e., content placement, subchannel allocation, and transmission power allocation) alternately. In specific, Lagrangian multiplier is introduced to convert the constrained optimization, which transforms the content caching problem into a Lagrangian relaxed knapsack problem. Afterwards, the subchannel assignment problem is solved by the Hungarian algorithm with polynomial time complexity, and the power allocation strategy is obtained by the bisection method. Extensive simulations are carried out and results demonstrate that our proposed caching strategy can reduce the content retrieval delay by up to 25% in comparison with the benchmark 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".