In this paper, we study wireless energy transfer-enabled passive communication networks, where passive nodes (PNs) harvest radio frequency (RF) energy emitted by an active node (AN) and/or scatter the RF wave to a receiver for data transfer. The performance of such networks highly depends on channel state information (CSI), but its acquisition is quite challenging since energy-and-hardware constrained PNs are generally unable to estimate or feedback CSI. We propose a harvest-while-scatter protocol, where every PN uses the time when other PNs scatter to harvest RF energy, while only introducing minimum interference. Furthermore, we develop a channel training approach for this protocol to estimate means and (co)variances of channel gains via collecting and utilizing historical data and energy transmissions. To minimize the energy consumed at the AN with limited statistical CSI, we formulate a distributionally robust energy minimization problem involving a non-convex objective function and a quality-of-service chance constraint. In addition, we develop an iterative algorithm to optimally solve it with low complexity. Simulation results show the effectiveness of our proposed protocol and algorithm, and reveal the effect of relative node locations on energy consumption in terms of energy harvesting and data transfer.
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