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.
As more radios and base stations are installed for 5G, the models can be improved by re-running the data collection and training the same models on this new data. This is believed to improve performance since it enables more data, both in terms of bulk and in terms of newly deployed features and measurements such as MicroSleepTime for NR, etc.
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.
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In today’s 5G era, the energy efficiency (EE) of cellular base stations is crucial for sustainable communication. Recognizing this, Mobile Network Operators are actively prioritizing EE for …
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 …
For energy efficiency in 5G cellular networks, researchers have been studying at the sleeping strategy of base stations. In this regard, this study models a 5G BS as an (M^ { …
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 …
In recent years, with the widespread deployment of 5 G technology, global communication data traffic has experienced rapid growth, leading to an increase in the …
Base station operators deploy a large number of distributed photovoltaics to solve the problems of high energy consumption and high electricity costs of 5G base stations. In this …
Accurate energy consumption modeling is essential for developing energy-efficient strategies, enabling operators to optimize resource uti-lization while maintaining network …
In particular, this research took the UK as an example to investigate the spatiotemporal dynamic characteristics of 5G evolution, and further analysed the energy …
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), …
With the rapid development of 5G communication technology, global telecom operators are actively advancing 5G network construction. As a core component supporting …
Accurate power consumption forecasting plays a pivotal role in energy management, influencing both utility operations and customer experience. With increasing …
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 …
Change Log This document contains Version 1.0 of the ITU-T Technical Report on “Smart Energy Saving of 5G Base Station: Based on AI and other emerging technologies to …
At present, 5G mobile traffic base stations in energy consumption accounted for 60% ~ 80%, compared with 4G energy consumption increased three times. In the future, high …
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 …
Why is 5G Power Consumption Higher? 1. Increased Data Processing and Complexity These 5G base stations consume about three times the power of the 4G stations. …
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