تقرير
Energy savings under performance constraints via carrier shutdown with Bayesian learning
| العنوان: | Energy savings under performance constraints via carrier shutdown with Bayesian learning |
|---|---|
| المؤلفون: | Maggi, Lorenzo, Mihailescu, Claudiu, Cao, Qike, Tetich, Alan, Khan, Saad, Aaltonen, Simo, Koblitz, Ryo, Holma, Maunu, Macchi, Samuele, Ruggieri, Maria Elena, Korenev, Igor, Klausen, Bjarne |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Mathematics |
| مصطلحات موضوعية: | Computer Science - Information Theory |
| الوصف: | By shutting down frequency carriers, the power consumed by a base station can be considerably reduced. However, this typically comes with traffic performance degradation, as the congestion on the remaining active carriers is increased. We leverage a hysteresis carrier shutdown policy that attempts to keep the average traffic load on each sector within a certain min/max threshold pair. We propose a closed-loop Bayesian method optimizing such thresholds on a sector basis and aiming at minimizing the power consumed by the power amplifiers while maintaining the probability that KPI's are acceptable above a certain value. We tested our approach in a live customer 4G network. The power consumption at the base station was reduced by 11% and the selected KPI's met the predefined targets. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2302.01093 |
| رقم الأكسشن: | edsarx.2302.01093 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |