Design Optimization of Wireless Access Virtualization Based on Cost & QoS Trade-Off Utility Maximization
Bibliographic record
Abstract
Wireless access virtualization is an emerging avenue for research for future 5G networks. For its ability to augment network sharing and its subsequent impact on reducing network setup and operational costs, network virtualization is greatly sought after by telecommunications operators all over the world. This paper classifies virtual wireless access into three possible PHY-MAC models that differ in terms of the degree of segregation of baseband signal processing and radio access units. One of the models uses special purpose hardware while another leverages software defined networking (SDN) and cloud computing technologies for implementing virtual wireless access using general purpose hardware. The proposed models differ in terms of their associated network operational cost as well as in terms of the level of QoS they can provide. A new multi-criteria utility function is hence proposed in order to assess the tradeoffs between network cost & QoS of these models from a PHY-MAC layer perspective. The new utility function provides guidelines for a network designer to choose the optimal virtualization model that best fits an operator's budget constraint as well as the QoS requirement of the intended service. Analytical results show that a novel hybrid model that properly combines both special purpose and SDN & cloud computing technologies maximizes the newly introduced utility function by attaining the best balance between overall network cost and QoS. This occurs in most expected practical cases where precisely, one of the two does not relatively outweigh the other and viceversa.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".