Adaptive random beamforming for MIMO wireless power transfer system

The radio-frequency (RF) enabled wireless power transfer (WPT) system can be benefit from the MIMO technique. However, due to the limited resource, internet of things (IoT) devices can only feedback partial information which is received signal strength (RSS) value instead of channel state information (CSI). Thus, channel estimation based beamforming scheme from receiver side is not applicable for real applications. In this paper, we propose an adaptive random beamforming algorithm based on Monte-Carlo method to supply multiple batteryless IoT devices with high received power efficiency. Our algorithm does not require the complex channel estimation and adapts the beamforming scheme only according to the partial feedback information. We employ Gibbs sampling and re-sampling methods to generate several random beamforming weight vectors, and choose the optimal one. A simulated annealing algorithm is employed to control the convergence rate. We use the proposed algorithm to supply power in two cases: the maximum power transmission and robust power transmission. The simulation results indicate that this algorithm can fast converge to an optimal value and provide far-field power to multiple IoT devices.