Cost-Effective Power Management for Green Mobile Base Stations
Bibliographic record
Abstract
Power consumption in mobile communication networks constitutes 20-40% of the operating expenditure. The energy footprint is especially high at the radio access network (RAN), where Base stations (BSs) account for 60-80% of network power usage. In this context, we propose in this paper a novel power coordination framework that efficiently utilizes multiple power sources including conventional grid power, renewable energy, and battery storage systems. The proposed model incorporates dynamic pricing schemes and considers environmental impact while maintaining operational cost efficiency. Using Mixed Integer Linear Programming (MILP), we develop a scheduling framework that optimizes when to charge batteries and utilize renewable energy sources for either BS operation or battery charging, aiming to reduce energy consumption costs. Our simulation results demonstrate that the proposed solution achieves significant cost reduction compared to traditional greedy power consumption methods while maintaining service quality. Moreover, it outperforms purely renewable energy-based solutions which, while cost-effective, suffer from service interruptions with a Mean Time Between Failures (MTBF) of 10 minutes. Also, our proposed solution achieves superior performance than heuristic methods, by obtaining optimal results in 2 seconds compared to 5 minutes for heuristic approaches. This work contributes to the development of more sustainable and economically viable mobile network operations without penalizing service quality.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".