On the Beneficial Roles of Fading and Transmit Diversity in Wireless Power Transfer With Nonlinear Energy Harvesting

Abstract

We study the effect of channel fading in Wireless Power Transfer (WPT) and show that fading enhances the RFto-DC conversion efficiency of nonlinear RF energy harvesters. We then develop a new form of signal design for WPT, denoted as Transmit Diversity, that relies on multiple dumb antennas at the transmitter to induce fast fluctuations of the wireless channel. Those fluctuations boost the RF-to-DC conversion efficiency thanks to the energy harvester nonlinearity. In contrast with (energy) beamforming, Transmit Diversity does not rely on Channel State Information at the Transmitter (CSIT) and does not increase the average power at the energy harvester input, though it still enhances the overall end-to-end power transfer efficiency. Transmit Diversity is also combined with recently developed (energy) waveform and modulation to provide further enhancements. The efficacy of the scheme is analyzed using physics-based and curve fitting-based nonlinear models of the energy harvester and demonstrated using circuit simulations, prototyping and experimentation. Measurements with two transmit antennas reveal gains of 50% in harvested DC power over a single transmit antenna setup. The work (again) highlights the crucial role played by the harvester nonlinearity and demonstrates that multiple transmit antennas can be beneficial to WPT even in the absence of CSIT.

Strategies and Techniques for Powering Wireless Sensor Nodes through Energy Harvesting and Wireless Power Transfer

“…It is worth noting that to increase the maximum distance from which an off device can be woken up, for given transmitted frequency and antenna gain, the main parameter to optimize is the power sensitivity of the RF to DC converter. Very important is also the way the power is transmitted while performing WPT as channel fading and proper signal design enhances the RF to DC conversion efficiency of non-linear RF energy harvesters [52,53].…”

Statistical Analysis of Multiple Antenna Strategies for Wireless Energy Transfer

“…7 Although in this work we just state the harvested energy as a function of the incident RF power, we would like to highlight that in practice g also depends on the modulation and incoming waveform [26], and some approaches have been considered in the literature for exploiting this. For instance [27]: energy waveform, which relies on deterministic multisine at the transmitter so as to excite the rectifier in a periodic manner by sending high energy pulses; and energy modulation, which relies on single-carrier transmission with inputs following a probability distribution that boosts the fourth order term in the Taylor expansion of the diode characteristic function. improved by considering three main factors that limit strongly the performance of a WET receiver [3], [14], [28], [29]: (i) its sensitivity ̟ 1 , which is the minimum RF input power required for energy harvesting; (ii) its saturation level ̟ 2 , which is the RF input power for which the diode starts working in the breakdown region, and from that point onwards the output DC power keeps practically constant; and (iii) the energy efficiency η ∈ [0, 1] in the interval ̟ 1 ≤ ξ rf j ≤ ̟ 2 , which we assume as constant.…”

Nonlinear Channel Estimation for OFDM System by Wavelet Transform Based Weighted TSVR

“…In selective multipath fading channel, the channel response presents complicated nonlinearities caused by some reasons, such as the saturation of components and the dispersion of optical fiber [16]- [18], which may lower the estimation precision if using linear methods [19]. So it is necessary to use the nonlinear method for channel estimation.…”