A genetic algorithm based cell switch-off scheme for energy saving in dense cell deployments
Bibliographic record
Abstract
The energy consumption of mobile networks is rapidly growing. Operators have both economic and environmental incentives to increase the energy efficiency of their networks. One way of saving energy is to switch off cells during periods of light traffic. However, cell switch-off is a difficult problem to solve through conventional optimization; existing research makes various assumptions to simplify the problem and offers some heuristics to solve it. The problem can be constructed in different ways depending on the system model that is chosen. We examine the cell switch-off problem with the assumption that each user terminal (UT) has a minimum rate requirement, and show that it can be formulated and solved as a binary integer linear programming (BILP) problem when interference is considered to be constant. This formulation is equivalent to the bin-packing problem, which is NP-hard, if the spectral efficiency of each UT to all cells is fixed to a constant. Allowing the interference to be a function of the UT assignment, which allows for a more realistic construction of the problem, increases the complexity even further and thereby necessitates a heuristic method. For this case, we present a genetic algorithm based cell switch-off scheme which offers good results with linear complexity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".