Online Energy-Efficient Power Control in Wireless Networks by Deep Neural Networks - CentraleSupélec Access content directly
Conference Papers Year : 2018

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

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.
Fichier principal
Vignette du fichier
PID5385981.pdf (259.2 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

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

Identifiers

Cite

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⟩
57 View
322 Download

Altmetric

Share

Gmail Facebook X LinkedIn More