Progressive hybrid greyfield wireless access virtualization: Graph-optimized dynamic utility tradeoffs between cloud, fog, and legacy RANs
Bibliographic record
Abstract
In this paper, we develop a new dynamic utility for wireless access virtualization (WAV) optimization embodying highly-dimensional time-varying multi-criteria metrics (i.e., CAPEX and OPEX costs, QoS or QoE, multi-tier and/or multi-RAT HetNets, etc.) that gauge the best deployment and viability scenarios of cloud (C)- and fog (F)-RANs within legacy networks. Exploiting the powerful tool of graph theory, we devise a progressive greyfield WAV strategy that optimizes our dynamic utility through an efficient combination of C- and F-RANs. This strategy is able to readjust very quickly to any changes in existing or new constraints as they evolve or occur in time, respectively. The resulting optimized hybrid RAN deployment outperforms both the greenfield and the pre-planed greyfield “turnkey” WAV strategies.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".