Skip to Main content Skip to Navigation
Conference papers

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.
Document type :
Conference papers
Complete list of metadatas

Cited literature [22 references]  Display  Hide  Download

https://hal-centralesupelec.archives-ouvertes.fr/hal-01962086
Contributor : Delphine Le Piolet <>
Submitted on : Thursday, December 20, 2018 - 1:33:18 PM
Last modification on : Wednesday, September 16, 2020 - 4:49:05 PM
Long-term archiving on: : Friday, March 22, 2019 - 10:42:34 AM

File

PID5385981.pdf
Files produced by the author(s)

Identifiers

Citation

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⟩

Share

Metrics

Record views

61

Files downloads

260