Massive MIMO With Radio Stripes for Indoor Wireless Energy Transfer

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