Large-Scale Multiantenna Multisine Wireless Power Transfer

Abstract

Abstract-Wireless Power Transfer (WPT) is expected to be a technology reshaping the landscape of low-power applications such as the Internet of Things, RF identification (RFID) networks, etc. To that end, multi-antenna multi-sine waveforms adaptive to the Channel State Information (CSI) have been shown to be a promising building block of WPT. However, the current design is computationally too complex to be applied to large-scale WPT, where the transmit signal is sent across a large number (tens) of antennas and frequencies. In this paper, we derive efficient singleuser and multi-user algorithms based on a generalizable optimization framework, in order to design transmit waveforms that maximize the weighted-sum/minimum rectenna DC output voltage. The study highlights the significant effect of the nonlinearity introduced by the rectification process on the design of waveforms in single/multi-user systems. Interestingly, in the single-user case, the optimal spatial domain beamforming, obtained prior to the frequency domain power allocation optimization, turns out to be Maximum Ratio Transmission (MRT). On the contrary, in the general multi-user weighted sum criterion maximization problem, the spatial domain beamforming optimization and the frequency domain power allocation optimization are coupled. Assuming channel hardening, low-complexity algorithms are proposed based on asymptotic analysis, to maximize the two criteria. The structure of the asymptotically optimal spatial domain precoder can be found prior to the optimization. The performance of the proposed algorithms is evaluated. Numerical results confirm the inefficiency of the linear model-based design for the single and multi-user scenarios. It is also shown that as nonlinear modelbased designs, the proposed algorithms can benefit from an increasing number of sinewaves at a computational cost much lower than the existing method. Simulation results highlight the significant benefits of the large-scale WPT architecture to boost the end-to-end power transfer efficiency and the transmission range.

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

“…It ranges from 1 for M = 1 (i.e. no gain) to 2 in the limit of large M (which corresponds to the gain offered by CSCG fading in Section III) 9 , as illustrated in Fig. 3.…”

“…In frequency-selective channels, a channel-adaptive waveform exploits jointly the channel frequency selectivity and the energy harvester nonlinearity so as to maximize e rf−rf ×e rf−dc . Waveforms can also be designed for a multi-antenna transmitter so as to additionally exploit a beamforming gain [8], [9]. Channel-adaptive waveforms rely on CSIT and therefore require appropriate channel acquisition strategies, suited for WPT with nonlinear energy harvesting [10].…”

“…The derivations are pretty straightforward and similar to the ones used to compute the scaling laws in[8]. They are therefore not provided here 9. The diversity gain is larger when the channels on each antenna have similar strengths (e.g.…”

Robust Energy Efficient Beamforming in MISOME-SWIPT Systems With Proportional Secrecy Rate

“…These works mainly focused on the usage of harvested energy to optimize certain performance metrics, such as throughput [9], [10], system secrecy [11], [12], outage probability [13]- [15], power consumption [16], and energy efficiency (EE) [17], [18]. Another branch of research on RF-EH technology is named as simultaneous wireless information and power transferring (SWIPT) systems, where the energy and information signals are broadcast simultaneously [19]- [22]. The concept of SWIPT systems was originally proposed by Varshney in [19], where he studied the fundamental tradeoff within the rate-energy region in point-to-point (PtP) systems.…”

Cognitive Wireless Power Transfer in the Presence of Reactive Primary Communication User

“…Under given transmit waveforms, the authors in [16] and [17] proposed wideband WPT (or SWIPT) designs, in which the ET can adaptively adjust its power allocation over sub-carriers (SCs) to exploit the frequency diversity for efficient energy transfer. Furthermore, multi-antenna energy beamforming is another promising technique, which enables the ET to adjust the transmit energy beamforming directions towards the intended ERs, thus significantly improving the energy transfer efficiency [4], [6], [18]. To fully reap the benefit of adaptive power allocation and energy beamforming, it is crucial for the ET to acquire the channel state information (CSI) with the ERs.…”