Adaptive Service Rate and Vacation Length for Energy-Efficient HeNB Based on Queueing Analysis
Bibliographic record
Abstract
To improve the energy efficiency of small-cell networks, in this paper, we analyze the operating procedure of home base stations, i.e., Home evolved Node B (HeNB), in femtocells, by introducing an MAP/PH/1/k queueing model. In this analytical model, the HeNB's power on/off is represented as the alternative service and vacation periods. In addition, the hybrid-access mode defined in Third-Generation Partnership Project (3GPP) Release 9 is considered, which involves both high-priority and low-priority users. Based on the analytical results, an adaptive service rate and vacation length (ASV) method is proposed to maximize the HeNB's energy efficiency while satisfying its quality-of-service (QoS) requirements, such as blocking probability and user waiting time. Simulation results demonstrate that the proposed ASV method is more effective in improving energy efficiency compared with its counterpart.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".