In this thesis linear regression is compared with the gradient boosted trees method and a neural network to see how well they are able to predict energy consumption from field data of 5G radio base stations.
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Based on this method, a software platform for power estimation is developed. The proposed method models power consumption on different abstraction levels by splitting a typical
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The output power of a base station in GSM like systems depends on the cell tra c over the base station. In this paper, we derived the expression for cumulative distribution function calculation of output
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Power consumption models for base stations are briefly discussed as part of the development of a model for life cycle assessment. An overview of relevant base station power
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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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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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In this paper, we present a regression-based power consumption estimation method based on voice and data traffic provided by base stations with 2G and 3G capabilities.
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Measurements show the existence of a direct relationship between base station traffic load and power consumption. According to this relationship, we develop a linear power consumption
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Based on this utility function, an aggregated control method is proposed, including real-time available power estimation and model predictive control (MPC) for the gNBs-cluster, which
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A significant portion of this energy is consumed by the Radio Access Network (RAN), particularly by base stations (BSs). The goal is to build a machine learning model that can estimate energy
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