Joint beamforming and admission control for cache‐enabled Cloud‐RAN with limited fronthaul capacity
Bibliographic record
Abstract
Caching is a promising solution for the cloud radio access network (Cloud‐RAN) to mitigate the traffic load problem in the fronthaul links. Multiuser downlink beamforming plays an important role in efficient utilisation of spectrum and transmission power while satisfying the user's quality of service requirements. When the number of users exceeds the serving capacity of the network, certain users will have to be dropped or rescheduled. This is normally achieved by appropriate admission control mechanisms. Introducing local storage or cache at the remote radio heads where some popular contents are cached, the authors propose beamforming and admission control techniques for cache‐enabled Cloud‐RAN in the downlink. This minimises the total network cost including power and fronthaul cost while admitting as many users as possible. They formulate this multi‐objective optimisation problem as a single objective optimisation problem. The original problem, which is a mixed‐integer non‐linear programme, is first converted to the mixed‐integer second‐order cone programming form. The branch and bound algorithm is then used to determine the optimal and suboptimal solutions. A simulation study has been conducted to assess the performance of both methods.
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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 | 0.001 |
| 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".