Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 7, Issue 3, Pages 1773-1787Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2019.2962699
Keywords
Energy beamforming; feedback; magnetic resonance; MIMO; wireless power transfer (WPT)
Categories
Funding
- National Nature Science Foundation of China [61801306]
- Shenzhen Fundamental Research [JCYJ20180302145755311]
- Shenzhen Overseas High-Caliber Personnel Innovation Funds [KQJSCX20170331161854780]
- Guangdong Special Fund for Science and Technology Development [2019A050503001]
- Shenzhen Discipline Construction Project for Urban Computing and Data Intelligence, Open Fund of State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications [IPOC2018B002]
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Magnetic MIMO is a wireless power transfer (WPT) system that employs multiple magnetic resonance coils to provide high efficient wireless power in the near field. Magnetic energy beamforming is a typical scheme to control the currents or voltages of the transmitter coils in order to achieve some objectives. Thus, the magnetic channel information is essential to magnetic beamforming (MagBF), and it needs complicated circuits and communication protocols to feedback such information. Such information may be not available due to the circuit limits or privacy concerns. In addition, the performance will be degraded with imperfect channel estimation in the noisy and mobile dynamic environment. In this case, only some limited feedback information is available, e.g., received power. In this article, we propose a random MagBF method to achieve maximum received power efficiency and simplify the system architecture. This scheme employs iterative Monte Carlo sampling and resampling to search an optimal beamforming solution based on the received power feedbacks. We design an online training protocol to implement the proposed scheme. It is computationally light and requires only limited feedback information, which avoids complex channel estimation or AC measurements. The simulation and real experimental results indicate that our algorithm can effectively increase the received power and approach the optimal performance with a fast convergent rate.
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