Cloud-Based Spectrum Sharing in Virtual Wireless Networks
Bibliographic record
Abstract
This paper studies the network-wide spectrum sharing problem in virtual cellular networks applicable to Cloud Radio Access Network architecture. Virtual wireless networks share network resources such as time-frequency resources on the same physical infrastructure. A critical problem is then the allocation of shared physical resources to the virtual networks so that the utilization of the resources is maximized. We formulate the problem as a linear integer maximization problem, which is shown to be NP-hard. We then develop a polynomial time heuristic algorithm called Non-balancing Spectrum Sharing (NSS), which is guaranteed to achieve a solution whose resource utilization approaches half of that of the optimal solution in the worst-case. Two additional heuristic algorithms are also proposed to improve the worst-case performance of NSS by enabling load balancing among adjacent base stations. We have simulated the proposed algorithms and the optimal algorithm under different network configurations. The simulation results confirm that i) NSS performs remarkably close to the optimal algorithm, and ii) the two other heuristic algorithms outperform NSS, and consequently are even closer to the optimal algorithm in the simulated 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".