Measurements and Modelling of Base Station Power Consumption under Real
The real data in terms of the power consumption and traffic load have been obtained from continuous measurements performed on a fully operated base station site.
The real data in terms of the power consumption and traffic load have been obtained from continuous measurements performed on a fully operated base station site.
BSs are one of the most power consuming elements of a 5G network. It is important to model their energy consumption for analyzing overall energy efficiency of a
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
Thus, the objective is to develop a machine learning model to estimate the energy consumption of 5G base stations, taking into account different engineering configurations, traffic conditions,
This study introduces a predictive modeling approach for base station energy consumption by combining Seasonal and Trend decomposition using Loess (STL) and Long Short-Term
The real data in terms of the power consumption and traffic load have been obtained from continuous measurements performed on a fully operated base station site.
The real data in terms of the power consumption and traffic load have been obtained from continuous measurements performed on a
BSs are one of the most power consuming elements of a 5G network. It is important to model their energy consumption for analyzing overall energy efficiency of a
Published in: 2019 IEEE International Conference on Big Data (Big Data) Article #: Date of Conference: 09-12 December 2019 Date Added to IEEE Xplore: 24 February 2020
Published in: 2019 IEEE International Conference on Big Data (Big Data) Article #: Date of Conference: 09-12 December 2019 Date Added to IEEE Xplore: 24 February 2020
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 real data in terms of the power consumption and traffic load have been obtained from continuous measurements performed on a fully operated base station site.
When symbol shutdown is activated, the AAU switches off the MCPAs, and its power consumption is reduced to the sum of the baseline power consumption, P0, the baseband
An energy consumption optimization strategy of 5G base stations (BSs) considering variable threshold sleep mechanism (ECOS-BS) is proposed, which includes the initial
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The largest energy consumer in the BS is the power amplifier, which has a share of around 65% of the total energy consumption . Of the other base station elements, significant energy consumers are: air conditioning (17.5%), digital signal processing (10%) and AC/DC conversion elements (7.5%) .
The real data in terms of the power consumption and traffic load have been obtained from continuous measurements performed on a fully operated base station site. Measurements show the existence of a direct relationship between base station traffic load and power consumption.
Figure 1.1(c) then shows that of the energy consumption of the network, 70%-90% comes from the RAN (Radio Access Network) of which 70% of the energy consumption comes from the Radio Base Stations, see Figure 1.1(d).
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 network to see how well they are able to predict energy consumption from field data of 5G radio base stations.