2019/03/08 by Mohit Kumar Sharma, Sharma, Mohit K, Alessio Zappone +5 · 1 citation
Computer Science · Engineering · #Advanced MIMO Systems Optimization #Age of Information Optimization #Energy Harvesting in Wireless Networks #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Machine Learning (cs.LG) #Optimization and Control (math.OC) #Signal Processing (eess.SP) #Smart Grid Energy Management #Smart Grid Security and Resilience #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1903.03652
openalex publication_date 2019/03/08 · openalex created_date 2022/07/24 · openalex updated_date 2026/07/28
In this paper, we propose a deep learning based approach to design online\npower control policies for large EH networks, which are often intractable\nstochastic control problems. In the proposed approach, for a given EH network,\nthe optimal online power control rule is learned by training a deep neural\nnetwork (DNN), using the solution of offline policy design problem. Under the\nproposed scheme, in a given time slot, the transmit power is obtained by\nfeeding the current system state to the trained DNN. Our results illustrate\nthat the DNN based online power control scheme outperforms a Markov decision\nprocess based policy. In general, the proposed deep learning based approach can\nbe used to find solutions to large intractable stochastic control problems.\n