Deep Learning Based Online Power Control for Large Energy Harvesting Networks
This addresses power management challenges in energy harvesting networks, but it is incremental as it applies existing deep learning methods to a specific domain problem.
The paper tackles the problem of online power control in large energy harvesting networks, which are intractable stochastic control problems, by proposing a deep learning approach that trains a DNN using offline policy solutions; results show it outperforms a Markov decision process-based policy.
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.