To address this, we propose a novel deep learning model for 5G base station energy consumption estimation based on a real-world dataset. Unlike existing methods, our approach integrates the Base
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To understand this, we need to look closer at the base station power consumption characteristics (Figure 3). The model shows that there is significant energy consumption in the base
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More than 70 percent of this energy is estimated to be consumed by the radio access network (RAN), particularly by the base stations (BSs).
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An energy consumption optimization strategy of 5G base stations (BSs) considering variable threshold sleep mechanism (ECOS-BS) is proposed, which includes the initial matching
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This paper demonstrates the energy consumption modeling of a BS considering its energy-saving sleep modes. We design a Deep Neural Network (DNN) based energy consumption
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In order to quantify and optimize the energy consumption of mobile networks, theoretical models are required to estimate the effect of relevant parameters on the total energy consumption.
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These 5G base stations consume about three times the power of the 4G stations. The main reason for this spike in power consumption is the addition of massive MIMO and beamforming,
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An energy consumption optimization strategy of 5G base stations (BSs) considering variable threshold sleep mechanism (ECOS-BS) is proposed, which includes the initial matching
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This paper proposes a power control algorithm based on energy efficiency, which combines cell breathing technology and base station sleep technology to reduce base station energy consumption
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We demonstrate that this model achieves good estimation performance, and it is able to capture the benefits of energy saving when dealing with the complexity of multi-carrier base stations architectures.
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Base stations represent the main contributor to the energy consumption of a mobile cellular network. Since traffic load in mobile networks significantly varies during a working or weekend day, it is
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