Wireless resource virtualization: opportunities, challenges, and solutions
Bibliographic record
Abstract
Abstract Wireless resource virtualization (WRV) is currently emerging as a key technology to overcome the major challenges facing the mobile network operators (MNOs) such as reducing the capital, minimizing the operating expenses, improving the quality of service, and satisfying the growing demand for mobile services. Achieving such conflicting objectives simultaneously requires a highly efficient utilization of the available resources including the network infrastructure and the reserved spectrum. In this paper, the most dominant WRV frameworks are discussed where different levels of network infrastructure and spectrum resources are shared between multiple MNOs. Moreover, we summarize the major benefits and most pressing business challenges of deploying WRV. We further highlight the technical challenges and requirements for abstraction and sharing of spectrum resources in next generation networks. In addition, we provide guidelines for implementing comprehensive solutions that are able to abstract and share the spectrum resources in next generation network. The paper also presents an efficient algorithm for base station virtualization in long‐term evolution (LTE) networks to share the wireless resources between MNOs who apply different scheduling polices. The proposed algorithm maintains a high‐level of isolation and offers throughput performance gain. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".