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.

中文翻译：

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用于磁性 MIMO 无线功率传输系统的随机能量波束成形


磁MIMO是一种无线功率传输（WPT）系统，采用多个磁共振线圈在近场提供高效无线功率。磁能波束形成是控制发射器线圈的电流或电压以实现某些目标的典型方案。因此，磁通道信息对于磁波束形成（MagBF）至关重要，并且需要复杂的电路和通信协议来反馈这些信息。由于电路限制或隐私问题，此类信息可能无法获得。此外，在噪声和移动动态环境中，由于信道估计不完善，性能也会下降。在这种情况下，只有一些有限的反馈信息可用，例如接收功率。在本文中，我们提出了一种随机 MagBF 方法来实现最大接收功率效率并简化系统架构。该方案采用迭代蒙特卡洛采样和重采样来根据接收到的功率反馈来搜索最佳波束形成解决方案。我们设计了一个在线培训协议来实施所提出的方案。它计算量小，仅需要有限的反馈信息，从而避免了复杂的信道估计或AC测量。仿真和实际实验结果表明，该算法能够有效提高接收功率，并以较快的收敛速度接近最优性能。