Final draft of deliverable D.WG3-02-Smart Energy Saving of
Smart Energy Saving of 5G Base Station: Based on AI and other emerging technologies to forecast and optimize the management of 5G wireless network energy consumption
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Smart Energy Saving of 5G Base Station: Based on AI and other emerging technologies to forecast and optimize the management of 5G wireless network energy consumption
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
This project aims to predict and analyze the energy consumption associated with 5G networks, leveraging machine learning techniques to forecast power usage based on various network
This project explores the application of machine learning and deep learning techniques to develop a predictive framework for forecasting power consumption, aiming to support energy providers
To further explore the energy-saving potential of 5 G base stations, this paper proposes an energy-saving operation model for 5 G base stations that incorporates
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
The fifth generation of the Radio Access Network (RAN) has brought new services, technologies, and paradigms with the corresponding societal benefits. However,
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
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
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
A new power model structure is proposed in order to assess the power consumption of traditional base stations, their extensions, and alternative architectures such as large-scale
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