This project aims to predict energy consumption in 5G base stations using Supervised Learning Regression techniques. The goal is to model and estimate the energy consumed by different
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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
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Accurate energy consumption modeling is essential for developing energy-efficient strategies, enabling operators to optimize resource uti-lization while maintaining network
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To further develop energy modelling methodology and attempt to answer the questions presented in the previous section, different machine learning algorithm''s ability to predict energy
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The power consumption of the 5G base station mainly comes from the AU module processing and conversion and high power-consuming high radio frequency signals, the
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Accurate power consumption forecasting plays a pivotal role in energy management, influencing both utility operations and customer experience. With increasing
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This paper conducts a literature survey of relevant power consumption models for 5G cellular network base stations and provides a comparison of the models. It highlights
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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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This paper explores these novel architectures from the energy consumption and network power efficiency perspective considering the varying high volume traffic load, the
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The first step when modeling the energy consumption of wireless communication systems is to derive models of the power consumption for the main system components, which
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The power consumption of the 5G base station mainly comes from the AU module processing and conversion and high power
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