Energy cost reduction in cellular networks through dynamic base station activation
Bibliographic record
Abstract
In this paper, we investigate dynamic base station activation with the aim of reducing energy consumption in cellular networks. Using the two-timescale Lyapunov optimization approach, we develop an online control algorithm to choose active set of base stations so as to satisfy users' demands while incurring minimum energy consumption. The algorithm selects the minimum cardinality subset of base stations that ensures stabilization of user queues. Our algorithm achieves stabilization without relying on instantaneous feedback about the network conditions, instead it only requires information about the average load and demand over a coarse time scale. The formulated problem which consists of joint base station activation and user association is generally intractable. However, we show that it features submodularity, and consequently present a near-optimal solution for certain instances of the problem. We further develop a greedy algorithm to solve general cases of the problem. We supplement our theoretical analysis with numerical results to demonstrate the behavior of our algorithm in terms of energy and delay 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".