An Analog Beamforming Strategy based on Statistical Channel Information for Recharging Wireless Powered Sensor Networks

Wireless energy transfer (WET) technology is key for future wireless networks as it can enable uninterrupted and maintenance-free operation of Internet of Things (IoT) networks, which will be composed of billions of wireless sensors. Although, perfect channel state information (PCSI) is appealing for beamforming design at the power beacon (PB). Such knowledge is unavailable in most practical WET systems and is mostly of theoretical interest. In this paper, we propose a novel WET approach to minimize the total power recharging time of the devices within a Wireless Powered Sensor Network (WPSN) by designing the analog beamforming at the PB based only on statistical CSI (SCSI) knowledge. The devices efficiently recharge also by harvesting energy from the WET signals targeting nearby devices. Finally, we verified that opportunistically exploiting the recharging times of neighboring devices reduces the total recharging time by up to 70% than when each device is recharged independently. We also demonstrate that the proposed strategy based on SCSI considerably reduces the performance difference with a WET system based on PCSI, achieving significantly low recharged times by accessible information.