Distributed base station activation for energy-efficient operation of cellular networks
Bibliographic record
Abstract
Dynamic base station activation (DBA) has recently emerged as a viable solution for reducing energy consumption in cellular networks. While most of the works on this topic focused on centralized decision making algorithms, in this paper we investigate distributive solutions. These solutions are particularly desirable due to importance of self-organization and self-optimization in future cellular networks. The goal of DBA is to achieve an optimal trade-off between network operator's revenue and operational cost while guaranteeing coverage for network users. The problem is posed as a network utility maximization aiming to find the optimal activation schedule of each base station. Using Lagrangian duality, the problem is decomposed into smaller subproblems, where each subproblem is solved locally at its associated base station. Controlled message passing among base stations ensures convergence to the global optimal solution. Moreover, this general solution is further extended to capture the combinatorial nature of DBA. Finally, numerical results are provided to demonstrate the behavior of our solution in terms of utility and cost trade-off and convergence in some example network scenarios.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".