A set cover based algorithm for Cell Switch-Off with different cell sorting criteria
Bibliographic record
Abstract
The traffic distribution in cellular networks fluctuates in both time and space. This fluctuation results in some base stations (cells) being underutilized in light traffic conditions. Despite being underutilized, these cells still consume substantial amount of their energy. One possible technique to preserve this wasted energy is implementing the Cell Switch-Off (CSO) approach. In this approach, a common practice is to switch off cells based on their current loads. However, not only the cell load affects the switch-off procedure but also the order in which the cells are switched off (cell sorting). Hence, in this paper, we investigated different cell sorting criteria. The results illustrated that more energy can be preserved when sorting cells based on the number of users they can serve compared with the case of sorting cells based on their current load. To implement the CSO approach, we proposed a centralized greedy-add algorithm devised from the well known set cover problem. Simulation results showed that our algorithm outperformed the benchmark algorithm when the number of users per cell is large. The two algorithms were compared using the Urban-Micro (UMi) evaluation scenario.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".