Intelligent Reflecting Surface Aided Wireless Power Transfer With a DC-Combining Based Energy Receiver and Practical Waveforms

Abstract

This paper studies intelligent reflecting surface (IRS) aided wireless power transfer (WPT) to batteryless Internet of Everything (IoE) devices. A practical energy receiver (ER) with multiple antennas is investigated. Multiple RF energy flows gleaned by all the receive antennas are input multiple energy harvesters, which are further rectified to direct-current (DC) energy. The resultant multiple DC energy flows are then combined in the DC domain for energy storage. Three classic waveforms, namely deterministic waveform, M-QAM waveform, and Gaussian waveform, are considered for WPT. We maximize the output DC power by jointly designing the active transmit beamformer of the transmitter and the passive reflecting beamformer of the IRS with the above-mentioned waveforms, respectively, subject to the transmit power constraint at the transmitter and to the limited resolution constraints on the phase-shifters of the IRS. A low complexity alternating optimization (AO) algorithm is proposed, which converges to a Karush-Kuhn-Tucker (KKT) point and thus results in a locally optimal solution. The numerical results demonstrate that the Gaussian waveform has the best energy performance with a low input RF power to the energy harvesters. By contrast, the deterministic waveform becomes superior with a high input RF power to the energy harvesters.