A set cover based algorithm for Cell Switch-Off with different cell sorting criteria
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it