  Radio-frequency (RF) enabled wireless energy transfer (WET), as a promising
solution to provide cost-effective and reliable power supplies for
energy-constrained wireless networks, has drawn growing interests recently. To
overcome the significant propagation loss over distance, employing
multi-antennas at the energy transmitter (ET) to more efficiently direct
wireless energy to desired energy receivers (ERs), termed \emph{energy
beamforming}, is an essential technique for enabling WET. However, the
achievable gain of energy beamforming crucially depends on the available
channel state information (CSI) at the ET, which needs to be acquired
practically. In this paper, we study the design of an efficient channel
acquisition method for a point-to-point multiple-input multiple-output (MIMO)
WET system by exploiting the channel reciprocity, i.e., the ET estimates the
CSI via dedicated reverse-link training from the ER. Considering the limited
energy availability at the ER, the training strategy should be carefully
designed so that the channel can be estimated with sufficient accuracy, and yet
without consuming excessive energy at the ER. To this end, we propose to
maximize the \emph{net} harvested energy at the ER, which is the average
harvested energy offset by that used for channel training. An optimization
problem is formulated for the training design over MIMO Rician fading channels,
including the subset of ER antennas to be trained, as well as the training time
and power allocated. Closed-form solutions are obtained for some special
scenarios, based on which useful insights are drawn on when training should be
employed to improve the net transferred energy in MIMO WET systems.
