Two-Tier Architecture for Spectrum Auction in SDN-Enabled Cloud Radio Access Network
Bibliographic record
Abstract
The demand for mobile services is growing aberrantly, which provides both challenges and opportunities for wireless networks. Wireless network virtualization is suggested as a key progression path for enhancing the capacity and resource utilization in the forthcoming fifth-generation mobile networks. In this paper, a software-defined network (SDN) enabled cloud radio access network (C-RAN) framework is proposed for enabling spectrum auction with a two-tier architecture support. In Tier-I, several remote radio heads (RRHs) are introduced to act as the secondary service providers to provide services to its small cell users (SUEs) by exploiting the purchased underutilized or idle resources from the primary service provider in Tier-II. Specifically, in order to maintain quality-of-service requirements of the SUEs, the revenue maximization problem for the RRHs is formulated by considering the user association, band assignment, interference management, and budget allowance. In Tier-II, an SDN-enabled spectrum auction mechanism is proposed for maximizing the social welfare based on the SUEs' service requirements of all participating RRHs from Tier-I. In this auction mechanism, the bipartite graph is utilized to determine the socially optimal winners and the price charging scheme is proposed inspired by the well-known Vickrey-Clarke-Groves auction method. Simulation results reveal the performance and the benefits of the proposed SDN-enabled spectrum auction mechanism under different scenarios.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".