  This paper studies the optimal training design for a multiple-input
single-output (MISO) wireless energy transfer (WET) system in
frequency-selective channels, where the frequency-diversity and
energy-beamforming gains can be both achieved by properly learning the channel
state information (CSI) at the energy transmitter (ET). By exploiting channel
reciprocity, a two-phase channel training scheme is proposed to achieve the
diversity and beamforming gains, respectively. In the first phase, pilot
signals are sent from the energy receiver (ER) over a selected subset of the
available frequency sub-bands, through which the sub-band that exhibits the
largest sum-power over all the antennas at the ET is determined and its index
is sent back to the ER. In the second phase, the selected sub-band is further
trained for the ET to estimate the multi-antenna channel and implement energy
beamforming. We propose to maximize the net energy harvested at the ER, which
is the total harvested energy offset by that used for the two-phase channel
training. The optimal training design, including the number of sub-bands
trained and the energy allocated for each of the two phases, is derived.
