Optimizing Caching in a C-RAN With a Hybrid Millimeter-Wave/Microwave Fronthaul Link via Dynamic Programming
Bibliographic record
Abstract
Placing selected content at the edge of the network close to the users, known as caching, can lower network latency and congestion in the fronthaul link. Unlike most works that assume a fixed or limited variation in file popularities, to better address user requests, we consider a time-varying popularity resulting in hidden-mode Markov decision processes. In fact, each mode captures environmental changes, and we optimize the fetching and dropping (of files) decisions to minimize a long-term network cost in a cloud radio access network. Importantly, the primary fronthaul link is a millimeter (mmWave) link with large capacity supported by a microwave backup link in case of blockage. Since caching decisions are coupled over time and can affect the future, we introduce a dynamic programming approach to solve the caching problem. We approximate the future cost of each cache state in each mode. To reduce the complexity of calculating the future cost, we introduce two approximation approaches and illustrate the accuracy of the approximations under different environmental conditions. Finally, our simulation results confirm the effectiveness of our proposed algorithm in finding effective caching and fetching decisions to lower the total network cost while dealing with time-varying popularities.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".