Energy Consumption Modelling for 5G Radio Base Stations
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
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
Based on this method, a software platform for power estimation is developed. The proposed method models power consumption on different abstraction levels by splitting a
Measurements show the existence of a direct relationship between base station traffic load and power consumption. According to
Based on this utility function, an aggregated control method is proposed, including real-time available power estimation and model predictive control (MPC) for the gNBs-cluster,
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
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
Measurements show the existence of a direct relationship between base station traffic load and power consumption. According to this relationship, we develop a linear power
These insights highlight the need for ongoing research into better methods for accurately measuring and optimizing power consumption in base stations. This research is crucial for
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
We demonstrate that this model achieves good estimation performance, and it is able to capture the benefits of energy saving when dealing with the complexity of multi-carrier base stations
A significant portion of this energy is consumed by the Radio Access Network (RAN), particularly by base stations (BSs). The goal is to build a machine learning model that can estimate
In this paper, we present a regression-based power consumption estimation method based on voice and data traffic provided by base stations with 2G and 3G capabilities.
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