This thesis presents a comprehensive analysis of power consumption models of base stations. The research delves into the distribution of power consumption across different types of base
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Accurate predictive modeling of base station energy consumption is imperative for optimizing energy efficiency and ensuring sustainable network management.
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The real data in terms of the power consumption and traffic load have been obtained from continuous measurements performed on a fully operated base station site.
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Mathematical optimization of energy consumption requires a model of the prob-lem at hand. In this thesis linear regression is compared with the gradient boosted trees method and a neural
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Abstract. Cellular base stations consume a lot of energy since it requires a 24-h continuous power supply which results in an increased operational expenditure (OPEX) and
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The increasing demand for wireless communication services has led to a significant growth in the number of base stations, resulting in a substantial increase in energy
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A mobile operator base station based VPP-only consumption-based approach is feasible since base stations cannot generate power. Reducing consumption is much simpler
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Uganda communication base station wind power hybrid power source Due to the widespread installation of Base Stations, the power consumption of cellular communication is increasing
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Aiming at minimizing the base station (BS) energy consumption under low and medium load scenarios, the 3GPP recently completed a Release 18 study on energy saving
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However, there is still a need to understand the power consumption behavior of state-of-the-art base station architectures, such as multi-carrier active antenna units (AAUs),
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The real data in terms of the power consumption and traffic load have been obtained from continuous measurements performed on a fully operated base station site.
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