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.
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As evidence that this will be a likely outcome, a European Union project dubbed the MAMMOET project has predicted that future massive MIMO base stations will consume less energy
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An energy consumption optimization strategy of 5G base stations (BSs) considering variable threshold sleep mechanism (ECOS-BS) is proposed, which includes the initial matching
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Sweden aims for complete 5G coverage by 2025 with a projected 30,000+ base stations. The harsh Nordic climate necessitates high-efficiency, cold-resilient battery systems for consistent...
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These 5G base stations consume about three times the power of the 4G stations. The main reason for this spike in power consumption is the addition of massive MIMO and beamforming,
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“Despite 5G consuming less power than 4G per unit of traffic, the overall energy consumption is still much higher, driven by more power-thirsty radios and network densification.
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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
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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
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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
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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
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