Power consumption based on 5G communication
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 consumption
Power Consumption Modeling of 5G Multi-Carrier Base Stations:
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 complexity emerging
Comparison of Power Consumption Models for 5G Cellular Network
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 antenna...
Final draft of deliverable D.WG3-02-Smart Energy Saving of 5G
It also analyses how enhanced technologies like deep sleep, symbol aggregation shutdown etc., have been developing in the 5G era. This report aims to detail these fundamentals. However, it is far away
Power Consumption Modeling of 5G Multi-Carrier Base Stations: A
The fifth generation of the Radio Access Network (RAN) has brought new services, technologies, and paradigms with the corresponding societal benefits. However,
Optimal energy-saving operation strategy of 5G base station with
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 communication caching and
Comparison of Power Consumption Models for 5G Cellular Network
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 analysis of access network in 5G mobile
Energy consumption growth of the fifth-generation (5G) mobile network infrastructure can be significant due to the increased traffic demand for a massive number of end-users with increasing
Modelling the 5G Energy Consumption using Real-world Data:
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 Base
Energy Consumption Modelling for 5G Radio Base Stations with
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 radio base stations.