Optimized Training Design for Wireless Energy Transfer (1403.7870v3)

Abstract: Radio-frequency (RF) enabled wireless energy transfer (WET), as a promising solution to provide cost-effective and reliable power supplies for energy-constrained wireless networks, has drawn growing interests recently. To overcome the significant propagation loss over distance, employing multi-antennas at the energy transmitter (ET) to more efficiently direct wireless energy to desired energy receivers (ERs), termed \emph{energy beamforming}, is an essential technique for enabling WET. However, the achievable gain of energy beamforming crucially depends on the available channel state information (CSI) at the ET, which needs to be acquired practically. In this paper, we study the design of an efficient channel acquisition method for a point-to-point multiple-input multiple-output (MIMO) WET system by exploiting the channel reciprocity, i.e., the ET estimates the CSI via dedicated reverse-link training from the ER. Considering the limited energy availability at the ER, the training strategy should be carefully designed so that the channel can be estimated with sufficient accuracy, and yet without consuming excessive energy at the ER. To this end, we propose to maximize the \emph{net} harvested energy at the ER, which is the average harvested energy offset by that used for channel training. An optimization problem is formulated for the training design over MIMO Rician fading channels, including the subset of ER antennas to be trained, as well as the training time and power allocated. Closed-form solutions are obtained for some special scenarios, based on which useful insights are drawn on when training should be employed to improve the net transferred energy in MIMO WET systems.

• The paper proposes an optimized training design for MIMO wireless energy transfer, balancing harvested energy gain and training energy cost to maximize net energy.
• Key findings show optimal channel training depends on channel conditions like coherence time and ESNR, and massive MIMO Rician fading achieves near-ideal scaling.
• This optimized training design enhances energy beamforming efficiency, supporting sustainable sensor networks and future multi-user WET systems and IoT applications.

Efficient Channel Acquisition for MIMO Wireless Energy Transfer

The paper "Optimized Training Design for Wireless Energy Transfer" by Yong Zeng and Rui Zhang presents a comprehensive paper on enhancing wireless energy transfer (WET) using advanced techniques in channel acquisition and energy beamforming within multiple-input multiple-output (MIMO) systems. With the growing demand for efficient power solutions in energy-constrained networks, this research targets the optimization of channel estimation methods essential for achieving reliable and effective WET.

In wireless systems, the challenge of propagation loss due to distance is addressed via energy beamforming—which necessitates precise channel state information (CSI) at the energy transmitter (ET). This paper focuses on a point-to-point MIMO system, leveraging channel reciprocity to estimate forward-link CSI using reverse-link training signals from energy receivers (ER). This approach contrasts traditional feedback-based methods, purportedly offering better scalability with the number of ET antennas and reducing complexity at the ER by negating the need for channel estimation and feedback processing.

A central contribution of this paper is the formulation of an optimization problem, which aims to maximize net harvested energy at the ER. This integrates the energy gained during transmission with the energy expended for training—a pivotal balance given the limited resources at ERs. The research explores optimizing parameters such as the subset of ER antennas involved in training, training duration, and power allocation. The authors derive closed-form solutions for specific scenarios, including MIMO Rayleigh fading channels and massive MIMO Rician fading channels, with particular attention to minimizing estimation errors and maximizing energy beamforming gains.

Key results indicate that for MIMO Rayleigh fading channels, channel training is advisable only under conditions of sufficient channel coherence time, large ET antenna numbers, and high effective signal-to-noise ratio (ESNR). In scenarios where large-scale MIMO systems are practical, channel reciprocity-based training is shown to achieve scaling results comparable to ideal systems, even if limited to a rank-1 deterministic Rician channel component. These findings substantiate the selection criteria for deploying ER antenna subset training and influence the design strategies for WET systems.

The implications of this paper span both theoretical advancements and practical applications. The reduction of energy consumption through improved beamforming directly impacts the design of sustainable sensor networks and RF energy harvesting systems. Moreover, the work opens pathways towards solutions for multiuser configurations and further applications in next-generation wireless communication domains, such as cognitive radios and relay networks.

Future research directions may include exploring correlated fading channels, assessing schemes for multiuser WET systems, and evaluating energy outage probabilities for a broader scope of IoT applications. The integration of the derived training schemes into existing and forthcoming network designs will likely catalyze innovations in efficient wireless power transmission, promoting self-sufficient energy networks on a larger scale.

This paper contributes significantly to energy transfer efficiency in wireless systems and establishes a fundamental groundwork for deploying multi-antenna systems with optimal training designs, thus enhancing the capabilities of MIMO technologies in practical RF applications.