Journal ArticleDOI
Greener RAN Operation Through Machine Learning
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TLDR
Numerical results show that the considered ML algorithms succeed in achieving effective trade-offs between energy consumption and QoS, and show that energy savings strongly depend on traffic patterns that are typical of the considered area.Abstract:
The use of base station (BS) sleep modes is one of the most studied approaches for the reduction of the energy consumption of radio access networks (RANs). Many papers have shown that the potential energy saving of sleep modes is huge, provided the future behavior of the RAN traffic load is known. This paper investigates the effectiveness of sleep modes combined with machine learning (ML) approaches for traffic forecast. A portion of an RAN is considered, comprising one macro BS and a few small cell BSs. Each BS is powered by a photovoltaic (PV) panel, equipped with energy storage units, and a connection to the power grid. The PV panel and battery provide green energy, while the power grid provides brown energy. This paper examines the impacts of different prediction models on the consumed energy mix and on QoS. Numerical results show that the considered ML algorithms succeed in achieving effective trade-offs between energy consumption and QoS. Results also show that energy savings strongly depend on traffic patterns that are typical of the considered area. This implies that a widespread implementation of these energy saving strategies without the support of ML would require a careful tuning that cannot be performed autonomously and that needs continuous updates to follow traffic pattern variations. On the contrary, ML approaches provide a versatile framework for the implementation of the desired trade-off that naturally adapts the network operation to the traffic characteristics typical of each area and to its evolution.read more
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References
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Proceedings ArticleDOI
Greening of the internet
Maruti Gupta,Suresh Singh +1 more
TL;DR: This paper examines the somewhat controversial subject of energy consumption of networking devices in the Internet, motivated by data collected by the U.S. Department of Commerce, and discusses the impact on network protocols of saving energy by putting network interfaces and other router & switch components to sleep.
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A Survey of Energy-Efficient Techniques for 5G Networks and Challenges Ahead
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Journal ArticleDOI
Challenges and enabling technologies for energy aware mobile radio networks
Luis M. Correia,Dietrich Zeller,Oliver Blume,Dieter Ferling,Ylva Jading,Gunther Auer,Liesbet Van der Perre +6 more
TL;DR: In this article, a holistic approach for energy efficient mobile radio networks is presented and the matter of having appropriate metrics and evaluation methods that allow assessing the energy efficiency of the entire system is discussed.
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Short-term load forecasting using an artificial neural network
TL;DR: In this paper, an artificial neural network (ANN) method is applied to forecast the short-term load for a large power system, where the load has two distinct patterns: weekday and weekend-day patterns.
ReportDOI
PVWatts Version 5 Manual
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