Real Time Traffic Base Station Power Consumption Model
The rapid growth of mobile subscribers and number of base stations necessitate the need to study the relationship between traffic load and power consumption at a base station.
The rapid growth of mobile subscribers and number of base stations necessitate the need to study the relationship between traffic load and power consumption at a base station.
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
Simulations, utilizing actual device data, demonstrate the effectiveness of the proposed method in improving power system frequency performance while guaranteeing 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
This Technical Report explores how network energy saving technologies that have emerged since the 4th generation of wireless networks (4G) era, such as carrier shutdown, channel
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
Our measurement results show a linear relationship between cellular traffic load and BS power consumption. We then propose a real time traffic base station power consumption
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
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
With 5G projected to increase capacity up to approximately 1000-fold and high frequency millimeter wave (mmWave) transmission driving exponentially higher cell density, this
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