  Radio frequency wireless energy transfer (WET) is a promising solution for
powering autonomous Internet of Things (IoT) deployments. In this work, we
leverage energy beamforming for powering multiple user equipments (UEs) with
stringent energy harvesting (EH) demands in an indoor distributed massive
multiple-input multiple-output system. Based on semi-definite programming,
successive convex approximation (SCA), and maximum ratio transmission (MRT)
techniques, we derive optimal and sub-optimal precoders aimed at minimizing the
radio stripes' transmit power while exploiting information of the power
transfer efficiency of the EH circuits at the UEs. Moreover, we propose an
analytical framework to assess and control the electromagnetic field (EMF)
radiation exposure in the considered indoor scenario. Numerical results show
that i) the EMF radiation exposure can be more easily controlled at higher
frequencies at the cost of a higher transmit power consumption, ii) training is
not a very critical factor for the considered indoor system, iii) MRT/SCA-based
precoders are particularly appealing when serving a small number of UEs, thus,
especially suitable for implementation in a time domain multiple access (TDMA)
scheduling framework, and iv) TDMA is more efficient than spatial domain
multiple access (SDMA) when serving a relatively small number of UEs. Results
suggest that additional boosting performance strategies are needed to increase
the overall system efficiency, thus making the technology viable in practice.
