Green Wireless Access Virtualization Implementation: Cost vs. QoS Trade-offs
Bibliographic record
Abstract
Wireless access virtualization is considered to be a major enabling concept of future 5G networks. It fosters network innovation, rapid time to market for emerging networking concepts and enables cohabitation of different virtual networks with customized network protocols on the same physical infrastructure. It can also alleviate the ossification problem of radio spectrum that has been a major concern for telecommunication operators. Virtualization is also a key enabler for green communications as it not only reduces energy consumption by ensuring efficient use of hardware resources through resource sharing but also facilitates use of renewable energy sources for the communications infrastructure. This paper presents two different types of frameworks to classify wireless network virtualization design alternatives. The benefits of virtual wireless networks are very often expected from a cost perspective. Yet provisioning of stringent quality of service (QoS) requirements calls for a thorough analysis especially from PHY a MAC layers perspectives. A method for selecting the most efficient network architecture has been proposed that takes into account both network operators' (and/or service providers') cost and QoS constraints. The analytical model considers both the capital expenditure (CAPEX) and operational expenditure (OPEX) for cost analysis, while the achievable data rate in different virtual frameworks has been considered for QoS modelling.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".