ONLINE ENERGY-EFFICIENT POWER CONTROL IN WIRELESS NETWORKS BY DEEP NEURAL NETWORKS

Alessio Zappone 1 Merouane Debbah 2 Zwi Altman 3
1 Institute for Cartography - Instituts für Kartographie [Dresden]
2 L2S - Laboratoire des signaux et systèmes
3 Orange Labs [Issy les Moulineaux]
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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Submitted on : Thursday, December 20, 2018 - 1:33:18 PM
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  • HAL Id : hal-01962086, version 1

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CENTRALESUPELEC | CNRS | SUP_LSS | L2S | UNIV-PARIS-SACLAY | SUP_SIGNAUX

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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. ⟨hal-01962086⟩

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