Design of Ambient Backscatter Training for Retrodirective Wireless Power Transfer

Wireless power transfer (WPT) using energy beamforming is a promising solution for low power devices in the future Internet of Things (IoT). In this work, we propose a WPT scenario with a retrodirective antenna at the energy transmitter (ET) and ambient backscatter at the energy receiver (ER). The retrodirective WPT at the ET eliminates the requirement of knowing the channel from the ET to ER, and the use of ambient backscattering (as opposed to active transmission) minimizes the energy consumption at the ER. We propose a training sequence design, i.e., the pattern of varying the reflection coefficient at the ER, to eliminate the direct-link interference from the ambient source. We show that when the ambient symbol duration is known, the ambient interference is fully cancelled by using the proposed design. We analytically model the system and derive a closed-form expression for the average harvested power at the ER, assuming that the retrodirective array size is large. Our results show that with practical parameter values, the proposed solution is robust to a small timing offset mismatch at the correlator and allows the ER to successfully harvest tens of $\mu$W of power, which is an important improvement for low-power IoT devices.