5G DL Transmit Power Design
Cell Reference Power determines the baseline power for a 5G cell and depends on bandwidth, RBs, and hardware capabilities. SS-PBCH-BlockPower is a crucial parameter for
Cell Reference Power determines the baseline power for a 5G cell and depends on bandwidth, RBs, and hardware capabilities. SS-PBCH-BlockPower is a crucial parameter for
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
Parameters used for the evaluations with this cellular base station power model. The 5G NR standard has been designed based on the knowledge of the typical traffic activity
I. Reference Signal Power This is the power value measured and reported by the terminal (UE) and the total transmit power of the cell can be calculated by the following formula
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
Importantly, this study item indicates that new 5G power consumption models are needed to accurately develop and optimize new energy saving solutions, while also considering the
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
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
In this article, we propose a novel model for a realistic characterization of the power consumption of 5G multi-carrier BSs, which builds on a large data collection campaign.
In this article, we propose a novel model for a realistic characterization of the power consumption of 5G multi-carrier BSs, which builds on a large data collection campaign.
I. Reference Signal Power This is the power value measured and reported by the terminal (UE) and the total transmit power of the cell
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
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 energy
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Cell Reference Power determines the baseline power for a 5G cell and depends on bandwidth, RBs, and hardware capabilities. SS-PBCH-BlockPower is a crucial parameter for enhancing signal strength, calculated by adding a boost power value to the cell reference power.
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.
To improve the energy eficiency of 5G networks, it is imperative to develop sophisticated models that accurately reflect the influence of base station (BS) attributes and operational conditions on energy usage.
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.