Energy efficient strategies with BS sleep mode in green small cell networks
Bibliographic record
Abstract
The traditional mobile cellular networks are often designed so that a base station (BS) is always under uninterrupted working condition without considering the dynamic nature of user traffic, which results in an inefficient usage of energy. How to improve the system energy efficiency in order to achieve green networking is our major concern in this chapter. Beginning with a comprehensive review of the related works in literature, we introduce a self-organized BS virtual small networking (VSN) protocol so as to adaptively manage BSs' working states based on heterogeneity of user traffic changing in space and time. Motivated by the fact that low-traffic areas can apply a more aggressive BS-off strategy than hotspots, the proposed method is targeted at dividing BSs into groups with some similarity measurements so that the BS-off strategy can be performed more efficiently. Numerical results show that our proposals can save energy consumption on the entire cellular network to a great extent.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".