Reconfigurable intelligent surface (RIS) is a promising technology for radio-frequency wireless power transfer (WPT) as it is capable of beamforming and beam focusing without using active and power-hungry components. In this article, we propose a multitile RIS beam scanning (MTBS) algorithm for powering up Internet of Things (IoT) devices. Considering the hardware limitations of the IoT devices, the proposed algorithm requires only power information to enable the beam focusing capability of the RIS. Specifically, we first divide the RIS into smaller RIS tiles. Then, all RIS tiles and the phased array transmitter are iteratively scanned and optimized to maximize the receive power. We elaborately analyze the proposed algorithm and build a simulator to verify it. Furthermore, we have built a real-life testbed of RIS-aided WPT systems to validate the algorithm. The experimental results show that the proposed MTBS algorithm can properly control the transmission phase of the transmitter and the reflection phase of the RIS to focus the power at the receiver. Consequently, after executing the algorithm, about 20-dB improvement of the receive power is achieved compared to the case that all unit cells of the RIS are in OFF state. By experiments, we confirm that the RIS with the MTBS algorithm can greatly enhance the power transfer efficiency.

We propose and implement a novel 1-bit coding metasurface that is capable of focusing and steering beam for enhancing power transfer efficiency of the electromagnetic (EM) wave-based wireless power transfer systems. The proposed metasurface comprises 16 × 16 unit cells which are designed with a fractal structure and the operating frequency of 5.8 GHz. One PIN diode is incorporated within each unit cell and enables two states with ${180}^{°}$ phase change of the reflected signal at the unit cell. The two states of the unit cell correspond to the ON and OFF states of the PIN diode or “0” and “1” coding in the metasurface. By appropriately handling the ON/OFF states of the coding metasurface, we can control the reflected EM wave impinged on the metasurface. To verify the working ability of the coding metasurface, a prototype metasurface with a control board has been fabricated and measured. The results showed that the coding metasurface is capable of focusing beam to desired direction. For practical scenarios, we propose an adaptive optimal phase control scheme for focusing the beam to a mobile target. Furthermore, we prove that the proposed adaptive optimal phase control scheme outperforms the random phase control and beam synthesis schemes.

In this paper, we present a 5.8 GHz radio-frequency (RF) wireless power transfer (WPT) system that consists of 64 transmit antennas and 16 receive antennas. Unlike the inductive or resonant coupling-based near-field WPT, RF WPT has a great advantage in powering low-power internet of things (IoT) devices with its capability of long-range wireless power transfer. We also propose a beam scanning algorithm that can effectively transfer the power no matter whether the receiver is located in the radiative near-field zone or far-field zone. The proposed beam scanning algorithm is verified with a real-life WPT testbed implemented by ourselves. By experiments, we confirm that the implemented 5.8 GHz RF WPT system is able to transfer 3.67 mW at a distance of 25 meters with the proposed beam scanning algorithm. Moreover, with the proposed beam scanning algorithm, the power transfer efficiency reaches 20.32 % and 0.24 % at distances of 0.5 and 5 meters, respectively, whereas the far-field-only-scanning scheme achieved the transfer efficiencies of 13.45 % and 0.23 % at the same receiver positions. Since the proposed transmit antenna array has the maximum linear dimension of 299.12 mm, the approximate boundary between far-field and radiative near-field is 3.45 meters based on the Fraunhofer distance calculation. The results show that the proposed algorithm can effectively cover radiative near-field region differently from the conventional scanning schemes which are designed under the assumption of the far-field WPT.

Recently, reconfigurable intelligent surface (RIS) with passive beamforming capability is emerging as a potential technology for wireless power transfer (WPT) applications thanks to its cost-effective and energy-efficient features. This work studies multifocus techniques for RIS-aided WPT systems to simultaneously and adaptively charge multiple Internet of Things (IoT) devices in the Fresnel zone. In particular, we propose three multifocus methods, which are pattern addition (PA), random unit cell interleaving (RUI), and RIS tile division (RTD), for enabling multifocus RIS-aided WPT applications. We elaborate the algorithms for computing the RIS reflection phases for all methods. The proposed methods can balance the power levels of beams focused on multiple receivers by controlling weight factors. Furthermore, we have implemented a real-life RIS-aided WPT testbed to verify these proposed methods. The system consists of a phased antenna array transmitter, two receivers, and a 1-bit RIS with 512 unit cells. The WPT experiments have been performed in several test scenarios to show the effectiveness of the proposed techniques. The experiment results demonstrate that the proposed schemes effectively generate multiple focusing beams with adjustable power levels toward the desired receivers.

In this paper, we provide a tool for predicting the power transfer efficiency of radio-frequency (RF) wireless power transfer (WPT). With its capability of long-range wireless power transfer, RF WPT is considered as a very promising recharging technique for powering low-power internet of things (IoT) devices. The prediction of the power transfer efficiency is a prerequisite for setting up a proper design goal of RF WPT systems. We propose an analytic method that enables to calculate the efficiency in various multi-antenna-to-multi-antenna WPT scenarios with arbitrary positions and attitudes of antenna arrays. We have built a prototype RF WPT system with 64 transmit and 16 receive antennas, the operating frequency of which is 5.8 GHz, and verified the accuracy of the proposed analysis.

This article proposes a sparsity-aware channel estimation scheme for reconfigurable intelligent surface (RIS)-assisted wireless communications. We present an angular domain-channel sparsity model in a closed-form mathematical expression. A comprehensive formulation of the RIS channel estimation problem based on the sparsity analysis and RIS reflection model is also presented in this manuscript. This work aims to achieve a practical RIS beamforming algorithm without requiring excessive training overhead or any sensor deployment. We achieve the goal by proposing two sparsity-aware RIS channel estimation schemes based on the compressive sensing (CS) algorithms such as the Dantzig selector (DS) and orthogonal matching pursuit (OMP). Differently from the existing works on the CS algorithms for RIS, we consider a fully passive RIS without any active sensor. We validate the theory and algorithm through both simulations and experiments. We have experimented orthogonal frequency division multiplexing (OFDM) communications on our 5.8 GHz 1-bit RIS testbed with QPSK, 16QAM, 64QAM, and 256QAM modulation schemes. By experiments, it is shown that the proposed scheme is able to adaptively form a beam toward the receiver and improves the quality of the wireless communication to a notable level. Thanks to the properties of the proposed scheme, the wireless channel can be estimated without excessive training time and complexity.