A data-driven framework has been developed to assess the carbon emissions of mobile networks in China, revealing that the launch of 5G networks leads to a decline in carbon efficiency. A deep reinforcement learning algorithm, DeepEnergy, is proposed to increase the carbon efficiency of mobile networks and reduce carbon emissions.
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References
I, C.-L., Han, S. & Bian, S. Energy-efficient 5G for a greener future. Nat. Electron. 3, 182–184 (2020). This paper raises concerns about the energy consumption of 5G networks.
Ding, Y. et al. Carbon emissions and mitigation potentials of 5G base station in China. Resour. Conserv. Recycl. 182, 106339 (2022). This paper discusses the carbon emissions of a single 5G base station from a whole life-cycle perspective.
Yang, Y. et al. Mean field multi-agent reinforcement learning. In Proc. 35th International Conference on Machine Learning (eds Dy, J. & Krause, A.) 5571–5580 (PMLR, 2018). This paper proposes a mean field multi-agent reinforcement learning algorithm.
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This is a summary of: Li, T. et al. Carbon emissions of 5G mobile networks in China. Nat. Sustain. https://doi.org/10.1038/s41893-023-01206-5 (2023).
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Artificial intelligence for reducing the carbon emissions of 5G networks in China. Nat Sustain 6, 1522–1523 (2023). https://doi.org/10.1038/s41893-023-01208-3
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DOI: https://doi.org/10.1038/s41893-023-01208-3
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