Wireless transmission design with neural network for radio-frequency energy harvesting

Abstract

Devices with the capability of radio-frequency energy harvesting can collect the radiated energy from adjacent wireless energy transmitters. If the multi-antenna transmitter knows the vector channel to the energy harvester, it can design an optimal transmit covariance matrix that satisfies the energy harvesting requirement. However, it is impractical for the energy harvester to estimate the channel. In this paper, we propose a method to design the wireless transmission with a neural network. The transmitter uses a set of special beam patterns and the energy harvester measures the received power and feeds the power values back to the transmitters. The neural network then takes in the power values and outputs the transmit covariance matrix that can meet the energy harvesting requirement. The neural network is trained offline with a large number of simulated data. Simulation results validate the proposed method and show better performance than other wireless energy transmission methods.