Do 5G base stations consume a lot of energy?The energy consumption of the fifth generation (5G) of mobile networks is one of the major concerns of the telecom industry. However, there is not currently an accurate and tractable approach to evaluate 5G base stations' (BSs') power consumption.
Should power consumption models be used in 5G networks?This restricts the potential use of the power models, as their validity and accuracy remain unclear. Future work includes the further development of the power consumption models to form a unified evaluation framework that enables the quantification and optimization of energy consumption and energy efficiency of 5G networks.
Does a balanced dataset improve energy prediction of 5G base stations?For energy prediction of 5G base stations, this thesis finds that using a more balanced dataset, in terms of the number of samples for each product, has a positive impact for the ANN and the Gradient Boosted Trees model while the linear regression performs worse.
How does mobile data traffic affect the energy consumption of 5G base stations?The explosive growth of mobile data traffic has resulted in a significant increase in the energy consumption of 5G base stations (BSs).
Can machine learning predict energy consumption for 5g/4g radio base stations?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 consumption is investigated for 5G/4G radio base stations.
What is a minimal 5G BS energy consumption optimization model?Therefore, the problem can be formulated as a minimal 5G BS energy consumption optimization model, i.e., the energy consumption reduced by reasonably switching off the idle or lightly loaded BSs and reasonably associate UEs with BSs (i.e., the BS switching state and BS-UE association state schem
This project involves working with the ''5G-Energy Consumption'' dataset provided by the International Telecommunication Union (ITU) in 2023 as part of a global challenge for data
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Download Citation | On Jul 1, 2024, Alexander M. Busch and others published Comparison of Power Consumption Models for 5G Cellular Network Base Stations | Find, read and cite all the
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Mar 17, 2022 · Abstract: The high-energy consumption and high construction density of 5G base stations have greatly increased the demand for backup energy storage batteries. To maximize
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Jul 1, 2024 · 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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Oct 25, 2022 · The energy consumption of the fifth generation (5G) of mobile networks is one of the major concerns of the telecom industry. However, there is not currently an accurate and
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Jun 26, 2024 · This paper proposes a novel 5G base stations energy con-sumption modelling method by learning from a real-world dataset used in the ITU 5G Base Station Energy
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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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Aug 1, 2023 · An energy consumption optimization strategy of 5G base stations (BSs) considering variable threshold sleep mechanism (ECOS-BS) is proposed, which includes the initial
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Jan 23, 2023 · 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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Apr 25, 2025 · Accurate power consumption forecasting plays a pivotal role in energy management, influencing both utility operations and customer experience. With increasing
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The energy consumption of the fifth generation (5G) of mobile networks is one of the major concerns of the telecom industry. However, there is not currently an accurate and tractable approach to evaluate 5G base stations' (BSs') power consumption.
This restricts the potential use of the power models, as their validity and accuracy remain unclear. Future work includes the further development of the power consumption models to form a unified evaluation framework that enables the quantification and optimization of energy consumption and energy efficiency of 5G networks.
For energy prediction of 5G base stations, this thesis finds that using a more balanced dataset, in terms of the number of samples for each product, has a positive impact for the ANN and the Gradient Boosted Trees model while the linear regression performs worse.
The explosive growth of mobile data traffic has resulted in a significant increase in the energy consumption of 5G base stations (BSs).
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 consumption is investigated for 5G/4G radio base stations.
Therefore, the problem can be formulated as a minimal 5G BS energy consumption optimization model, i.e., the energy consumption reduced by reasonably switching off the idle or lightly loaded BSs and reasonably associate UEs with BSs (i.e., the BS switching state and BS-UE association state scheme).
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