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We study wireless power transmission by an energy source to multiple energyharvesting nodes with the aim to maximize the energy efficiency. The sourcetransmits energy to the nodes using one of the available power levels in eachtime slot and the nodes transmit information back to the energy source usingthe harvested energy. The source does not have any channel state informationand it only knows whether a received codeword from a given node wassuccessfully decoded or not. With this limited information, the source has tolearn the optimal power level that maximizes the energy efficiency of thenetwork. We model the problem as a stochastic Multi-Armed Bandits problem anddevelop an Upper Confidence Bound based algorithm, which learns the optimaltransmit power of the energy source that maximizes the energy efficiency.Numerical results validate the performance guarantees of the proposed algorithmand show significant gains compared to the benchmark schemes.

Learning to Optimize Energy Efficiency in Energy Harvesting Wireless Sensor Networks