Dynamic Caching in a Hybrid Millimeter-wave/Microwave C-RAN
Bibliographic record
Abstract
Placing popular content at the edge of the network close to users, known as caching, is a promising approach in the 5th generation (5G) of wireless communications in order to lower latency and congestion of fronthaul links. In this paper, we investigate the optimization of caching and fetching decisions to minimize a long-term network cost in a cloud radio access network. Importantly, in our model, the popularities of files vary over time and user requests are considered as arising from hidden-mode Markov decision processes. The primary fronthaul link is modeled as a millimeter-wave (mmWave) link; recognizing that mmWave links may be blocked, we allow for switching to a microwave link. The cache policy at each decision time can influence the network cost in the future, leading to coupled decision variables in time. To deal with the complexity of the resulting optimization problem, we introduce a dynamic programming approach to approximate the future cost of each cache state. The total network cost is minimized at each time-slot while also accounting for the future effects of the decisions taken. To reduce the complexity of calculating the future cost, an approximation approach is introduced and its accuracy is validated numerically. Simulation results confirm the effectiveness of our proposed algorithm to lower the total network cost while dealing with variable popularities.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".