Massive MIMO With Radio Stripes for Indoor Wireless Energy Transfer

Abstract

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.

Optimal Traffic Load Allocation for Aloha-Based IoT LEO Constellations

“…The optimization problem in (9) can be solved by the SCA method [44], which has been used for solving nonconvex optimization problems, where in each iteration the nonconvex feasible set is approximated by an inner convex approximation [51]. The SCA approach has been used in several optimization problems related to wireless communications in [52]- [56], as well as in IoT networks assisted by satellites [57]- [60].…”

Statistical Tools and Methodologies for URLLC- A Tutorial

“…In short-range scenarios, precise (highly focused) spatial charging is needed to avoid increased EMF exposure levels in the surroundings of the ERs. This may be only achieved by operating at relatively large frequencies and in the massive MIMO regime [4], but also in the near-field region of the ETs [5].…”

“…However, the inherently low endto-end power transfer efficiency (PTE) of RF wireless charging and safety-related apprehensions are critical obstacles, slowing down standardization activities. These have motivated mostly customized low-power IoT charging applications while relying on i) energy beamforming (EBF) and waveform optimization [2]- [6]; ii) distributed and massive antenna systems [4]; iii) smart reflect arrays and reconfigurable metasurfaces [6], [7]; and iv) flexible energy transmitters (ETs) [1] (i.e., moving ETs and ETs equipped with rotary antennas); while in many cases explicitly incorporating electromagnetic field (EMF) radiation exposure control [2], [4]. We believe that a comprehensive combination and holistic optimization of these promising technologies (and others) are needed to make the PTE of RF-WPT systems competitive, expanding the market to relatively high-power charging applications.…”

Dynamic RF Combining for Multi-Antenna Ambient Energy Harvesting

“…There are many works either focusing on EB optimization, e.g., [9]- [11], or waveform optimization, e.g., [12]- [14], while much less on their joint optimization, e.g., [15], [16]. Moreover, the practical non-linearities of a WPT system are not considered in [9], [11], while the frameworks in [10], [12], [13], [15], [16] considered the rectifier non-linearity, but not the transmit HPA non-linearity. Interestingly, the authors in [14] consider both rectifier and HPA non-linearities.…”