Magnetic resonant coupling (MRC) enabled wireless power transfer (WPT) systems have shown significant promise in efficiently charging multiple devices simultaneously through beamforming technology. The existing works propose various mechanisms for achieving better charging performance, but they still lack exploration of transmitter (TX) coil orientation adjustment and rarely consider the dynamic deployment of TXs. In this work, we propose the D istributed A daptive E xploration and E xploitation ( DAEE ) algorithm for orientation adjustment in MRC-WPT systems, which includes both hardware and software innovations. The hardware component features a servo motor-based mechanical device that adjusts the TX coil orientation. In the software aspect, we decompose the charging performance optimization problem and devise a distributed orientation control algorithm combining exploration and exploitation mechanisms. We develop a system prototype for the DAEE algorithm and conduct extensive experiments to validate its performance. Specifically, the TX orientation adjustment significantly enhances performance, achieving an average 103% improvement in power-delivered-to-load (PDL) compared to state-of-the-art frequency adjustment-based solutions that do not adjust orientation. Additionally, the combination of exploration and exploitation strategies in the DAEE algorithm proves effective, delivering a 24% performance improvement over the random beamforming (RB)-based exploration method.

Wireless power transfer (WPT) systems using magnetic resonant coupling (MRC) have made significant progress recently, leading to various optimization methods in scenarios involving multiple-input multiple-output (MIMO) to improve charging performance. Adjusting the coil orientation of the power transmitter (TX) is a simple but effective method due to the directional nature of magnetic field distribution, but existing approaches often require unnecessary coil rotations. In this study, we introduce a Location-Aware Orientation Adjustment algorithm, known as LAORA, to address these inefficiencies. LAORA focuses on solving the problems of charging devices (RX) localization and location-based optimization. We begin by introducing the concept of Equivalent Impedance Distribution Image (EIDI) and transform the RX localization problem into a combined matching process involving EIDI. In addition, we establish a dynamic simulation framework to predict charging performance using RX-related knowledge, enabling us to obtain optimal TX orientations through reinforcement learning without needing to rotate on mechanical devices physically. We then implement the prototype and conduct extensive experiments. The results show that, compared to other existing orientation adjustment methods, LAORA achieves an average improvement of 186 % while reducing the mechanical rotations by 83.3 %.

Recently, multiple-input multiple-output (MIMO) technology has been introduced into magnetic resonant coupling (MRC) enabled wireless power transfer (WPT) systems for concurrent charging of multiple devices. However, impedance mismatching phenomena caused by strong TX-RX, TX-TX, or RX-RX coupling greatly affect the power delivered to load (PDL) in practical charging systems. To solve this issue, we propose an effective scheduling algorithm for Impedance Matching–enhanced PDL optimization in MIMO MRC-WPT systems (called IMeP ), which integrates the transmitter scheduling together with the impedance matching techniques, i.e., adjusting TX coils for tuning TX-RX/TX-TX coupling and grouping RXs to separate strongly coupled RX pairs. We formulate this as a joint optimization problem and decouple it into three sub-problems, i.e., current scheduling, coil adjustment, and RX grouping. We then solve them through alternating direction method of multipliers–based, randomized beamforming–based, and graph clique cover–based algorithms, respectively. Extensive experiments are performed on a prototype testbed, and the results demonstrate the effectiveness of our solution. Compared with the state-of-the-art power transfer efficiency maximization solution, the proposed algorithm IMeP achieves a 74.7× performance improvement of PDL on average.

WiFi-based gait recognition is an attractive method for device-free user identification, but path-sensitive Channel State Information (CSI) hinders its application in multi-path environments, which exacerbates sampling and deployment costs (i.e., large number of samples and multiple specially placed devices). On the other hand, although video-based ideal CSI generation is promising for dramatically reducing samples, the missing environment-related information in the ideal CSI makes it unsuitable for general indoor scenarios with multiple walking paths.In this paper, we propose WiVi, a WiFi-video cross-modal fusion based multi-path gait recognition system which needs fewer samples and fewer devices simultaneously. When the subject walks naturally in the room, we determine whether he/she is walking on the predefined judgment paths with a K-Nearest Neighbors (KNN) classifier working on the WiFi-based human localization results. For each judgment path, we generate the ideal CSI through video-based simulation to decrease the number of needed samples, and adopt two separated neural networks (NNs) to fulfill environment-aware comparison among the ideal and measured CSIs. The first network is supervised by measured CSI samples, and learns to obtain the semi-ideal CSI features which contain the room-specific ‘accent’, i.e., the long-term environment influence normally caused by room layout. The second network is trained for similarity evaluation between the semi-ideal and measured features, with the existence of short-term environment influence such as channel variation or noises.We implement the prototype system and conduct extensive experiments to evaluate the performance. Experimental results show that WiVi’s recognition accuracy ranges from 85.4% for a 6-person group to 98.0% for a 3-person group. As compared with single-path gait recognition systems, we achieve average 113.8% performance improvement. As compared with the other multi-path gait recognition systems, we achieve similar or even better performance with needed samples being reduced by 57.1-93.7%

Recently, multiple-input multiple-output (MIMO) technology has been introduced into magnetic resonant coupling (MRC) enabled wireless power transfer (WPT) systems for concurrent charging of multiple devices. However, impedance mismatching phenomena caused by strong TX-RX or RX-RX coupling greatly affect the power delivered to load (PDL) in practical charging systems. To solve this issue, we propose an effective scheduling algorithm for Impedance Matching enhanced PDL optimization in MIMO MRC-WPT systems (called IMP), which integrates the transmitter scheduling together with the impedance matching techniques, i.e., adjusting TX coils for tuning TX-RX coupling and grouping RXs to separate strongly coupled RX pairs. We formulate this as a joint optimization problem and decouple it into three sub-problems, i.e., current scheduling, coil adjustment, and RX grouping, and solve them through alternating direction method of multipliers (ADMM) based, tabu search (TS) based, and graph clique cover based algorithms, respectively. Extensive experiments are performed on a prototype testbed, and the results demonstrate the effectiveness of our solution. Compared with the state-of-the-art power transfer efficiency (PTE) maximization solution, the proposed algorithm IMP achieves a 74.7X performance improvement of PDL on average.

Backscatter communication integrated with RF energy harvesting provides a promising solution to prolong the lifetime of wireless sensor networks (WSNs). However, the existing centralized or flooding-based routing protocols can not be applied directly to large-scale backscatter-enabled WSN due to fussy implementation or excessive messages. In this paper, we investigate the routing protocol for such networks to maximize the throughput by arranging the uploading path of each sensor. We first propose a centralized solution by converting the original problem into a maximum flow problem. Then, after inspecting the characteristics of the backscatter-enabled sensors, we propose a flow balancing-based push-relabel algorithm. We conduct extensive experiments to evaluate the proposed algorithms. The results demonstrate the effectiveness of our distributed protocol, which outperforms the other baseline solutions and keeps close approximation to the centralized solution.