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Online Energy-Efficient Power Control in Wireless Networks by Deep Neural Networks

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Abstract

The work describes how deep learning by artificial neural networks (ANNs) enables online power allocation for energy efficiency maximization in wireless interference networks. A deep ANN architecture is proposed and trained to take as input the network communication channels and to output suitable power allocations. It is shown that this approach requires a much lower computational complexity compared to traditional optimization-oriented approaches, dispensing with the need of solving the optimization problem anew in each channel coherence time. Despite the lower complexity, numerical results show that a properly trained ANN achieves similar performance as more traditional optimization-oriented methods.
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Dates and versions

hal-01962086 , version 1 (20-12-2018)

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Alessio Zappone, Merouane Debbah, Zwi Altman. Online Energy-Efficient Power Control in Wireless Networks by Deep Neural Networks. 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2018), Jun 2018, Kalamata, Greece. ⟨10.1109/SPAWC.2018.8445857⟩. ⟨hal-01962086⟩
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