Deep Learning Based Online Power Control for Large Energy Harvesting Networks
Mohit K. Sharma, Alessio Zappone, Mérouane Debbah, Mohamad Assaad
Abstract
In this paper, we propose a deep learning based approach to design online power control policies for large EH networks, which are often intractable stochastic control problems. In the proposed approach, for a given EH network, the optimal on-line power control rule is learned by training a deep neural network (DNN), using the solution of offline policy design problem. Under the proposed scheme, in a given time slot, the transmit power is obtained by feeding the current system state to the trained DNN. Our results illustrate that the DNN based online power control scheme outperforms a Markov decision process based policy. In general, the proposed deep learning based approach can be used to find solutions to large intractable stochastic control problems.
BibTeX
@inproceedings{icassp2019_deeplearningbase,
title = {Deep Learning Based Online Power Control for Large Energy Harvesting Networks},
author = {Mohit K. Sharma and Alessio Zappone and Mérouane Debbah and Mohamad Assaad},
booktitle = {ICASSP 2019},
year = {2019}
}