Online algorithms for energy cost minimization in cellular networks
Bibliographic record
Abstract
Dynamic base station activation and transmission power control are the key mechanisms to reduce energy consumption in cellular networks. In this work, we consider employing these methods for the purpose of minimizing long-term energy cost in cellular networks. Based on the two-timescale Lyapunov optimization technique, we formulate an online control problem to ensure achieving minimal energy cost while stabilizing use queues. While the control problem can be solved in a centralized manner, we limit our attention to distributed solutions which are highly attractive in the design of next generation mobile networks. Due to the combinatorial nature of the problem and the complex relation of achievable rates to interfering signals, the problem is non-convex. Consequently, conventional duality methods cannot be employed to achieve the distributed solution. Thus, we design a distributed solution for the problem based on Gibbs sampling method. The proposed algorithm can be implemented in a fully distributed manner, does not depend on the convexity or continuity of the energy cost functions, and guarantees solution optimality. Numerical results are provided to demonstrate the behavior of the solution in some example network scenarios.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".