Cell-Free Massive MIMO with Radio Stripes for Indoor Wireless Energy Transfer.

Radio frequency wireless energy transfer (WET) is a promising solution for powering autonomous IoT deployments. Recent works on WET have mainly focused on extremely low-power/cost IoT applications. However, trending technologies such as energy beamforming and waveform optimization, distributed and massive antenna systems, smart reflect arrays and reconfigurable metasurfaces, flexible energy transmitters, and mobile edge computing, may broaden WET applicability, and turn it plausible for powering more energy-hungry IoT devices. In this work, we specifically leverage energy beamforming for powering multiple user equipments (UEs) with stringent energy harvesting (EH) demands in an indoor cell-free massive MIMO. 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, specially 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.