A novel neuro-optimization method for multi-operator scheduling in cloud-RANs
Bibliographic record
Abstract
The software-defined approach of cloud radio access networks (C-RANs) enables supporting multiple virtual operators (VOs) on the same physical infrastructure. In this shared environment, a coordinator is needed to manage the sharing of resources between the VOs. Designing a coordinator is about striking a good balance between the flexibility given to the VOs, and the efficiency of the resource utilization. In this paper, we study the problem of coordinated scheduling in the multi-operator cloud-RAN environment. We formulate the problem as a distributed scheduling performed by the VOs, after which they submit their resource requests to a centralized coordinator. The coordinator selects a subset of non-conflicting requests for transmission. We show that the problem in the general case is NP-hard. We then discuss two special cases and relate them to the existing communication protocols. By gaining insights from these two special cases, we propose a general heuristic, which works on any formulation of the problem, and is still able to provide close-to-optimum performance in the special cases we considered. The heuristic is shown to have some similarities to the neuro-computation techniques such as Hopfield-network. Finally, simulation results are provided to show the efficiency of the proposed algorithms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".