Energy savings under performance constraints via carrier shutdown with Bayesian learning

Lorenzo Maggi; Claudiu Mihailescu; Qike Cao; Alan Tetich; Saad Khan; Simo Aaltonen; Ryo Koblitz; Maunu Holma; Samuele Macchi; Maria Elena Ruggieri; Igor Korenev; Bjarne Klausen

arXiv · 2302.01093

Energy savings under performance constraints via carrier shutdown with Bayesian learning

Lorenzo Maggi·Claudiu Mihailescu·Qike Cao·Alan Tetich·Saad Khan·Simo Aaltonen·Ryo Koblitz·Maunu Holma·Samuele Macchi·Maria Elena Ruggieri·Igor Korenev·Bjarne Klausen

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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.

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Lorenzo Maggi, Claudiu Mihailescu, Qike Cao, Alan Tetich, Saad Khan, Simo Aaltonen, Ryo Koblitz, Maunu Holma, Samuele Macchi, Maria Elena Ruggieri, Igor Korenev, Bjarne Klausen. 2023-02-02. Energy savings under performance constraints via carrier shutdown with Bayesian learning. https://arxiv.org/abs/2302.01093

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