Deep Learning Based Online Power Control for Large Energy Harvesting Networks

TL;DR

This paper introduces a deep learning approach to design online power control policies for large energy harvesting networks, addressing complex stochastic control challenges more effectively than traditional methods.

Contribution

It presents a novel deep neural network-based method to learn optimal online power control policies from offline solutions, improving scalability for large energy harvesting networks.

Findings

DNN-based control outperforms MDP-based policies.

The approach effectively handles large, complex networks.

Demonstrates applicability to intractable stochastic control problems.

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 online 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.