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
The fifth generation of the Radio Access Network (RAN) has brought new services, technologies, and paradigms with the corresponding societal benefits. However,
To improve the energy efficiency of 5G networks, it is imperative to develop sophisticated models that accurately reflect the influence of base station (BS) attributes and operational conditions on energy usage.
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...
Abstract: Accurate power consumption forecasting plays a pivotal role in energy management, influencing both utility operations and customer experience. With increasing emphasis on sustainable energy use and
An energy consumption optimization strategy of 5G base stations (BSs) considering variable threshold sleep mechanism (ECOS-BS) is proposed, which includes the initial matching association process
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 Station Identifier
The CM data contains all parameters that are used to configure each radio base station in the network, including configured power, bandwidth, frequency, number of antennas, position, activated energy saving features, etc.
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 models is provided hereafter.
· This paper conducts a literature survey of relevant power consumption models for 5G cellular network base stations and provides a comparison of the models.
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