Energy Savings under Performance Constraints via Carrier Shutdown with Bayesian Learning
Lorenzo Maggi, Claudiu Mihailescu, Qike Cao, Alan Tetich, Saad Ahmad Khan, Simo Aaltonen, Ryo Koblitz, Maunu Holma, Samuele Macchi, Maria Elena Ruggieri, Igor Korenev, Bjarne Klausen
Abstract
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.