Self-Optimizing Energy Management in Heterogeneous Cellular Networks
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Bibliographic record
Abstract
In this paper, we develop and evaluate a distributed algorithm to efficiently balance the trade-off between network throughput and energy consumption in a heterogeneous cellular network. We formulate the problem as a joint optimization of base station activation, power control and user association. To solve the problem, which is a non-convex optimization problem, we design a self-optimizing algorithm based on Gibbs sampling in which each base station individually optimizes its configuration without the involvement of any central controller. In our algorithm, base stations only need to exchange information in a locally defined neighborhood, yet the network state eventually converges to the global optimal. Simulation results are also provided, which show that, i) the proposed algorithm indeed converges to a state that is close to optimal, and ii) by dynamically activating base stations, we see about 10% reduction in network energy consumption without penalizing the network throughput.
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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