Wireless Energy Beamforming Using Signal Strength Feedback

Abstract

Abstract-Multiple antenna techniques, that allow energy beamforming, have been looked upon as a possible candidate for increasing the efficiency of the transfer process between the energy transmitter (ET) and the energy receiver (ER) in wireless energy transfer. This paper introduces a novel scheme that facilitates energy beamforming by utilizing Received Signal Strength Indicator (RSSI) values to estimate the channel. Firstly, in the training stage, the ET will transmit sequentially using each beamforming vector in a codebook, which is pre-defined using a Cramer-Rao lower bound analysis. The RSSI value corresponding to each beamforming vector is fed back to the ET, and these values are used to estimate the channel through a maximum likelihood analysis. The results that are obtained are remarkably simple, requires minimal processing, and can be easily implemented. Also, the results are general and hold for all well known fading models. The paper also validates the analytical results numerically, as well as experimentally, and it is shown that the proposed method achieves impressive results in wireless energy transfer. I. INTRODUCTIONWireless energy transfer (WET) focuses on delivering energy to charge freely located devices, over the air interface, using Electromagnetic radiation in the radio frequency (RF) bands [2]. When it comes to RF signal enabled WET, increasing the efficiency of the energy transfer between the energy transmitter (ET) and the energy receiver (ER) is of paramount importance. Multiple antenna techniques that also enhance the range between the ET and the ERs have been looked upon as a possible solution to address this concern [3]. This paper proposes a novel approach that increases the efficiency of a WET system that utilizes multiple antennas to facilitate the energy transfer.To this end, multiple antennas at the ET enable focusing the transmitted energy to the ERs via beamforming. However, the coherent addition of the signals transmitted from the ET at the ER depends on the availability of channel state information (CSI), which necessitates channel estimation at the ER in most cases. The estimation process involves analog to digital conversion and baseline processing which require significant energy [4,5]. Under tight energy constraints and hardware limitations, such an estimation process may become infeasible at the ER. In this paper, we propose a more energy efficient

Cluster-based wireless energy transfer for low complex energy receivers

“…We can see that the loss is rather acceptable given the practicality of the proposed method compared to having perfect CSI at the ET. We can also see that there is a significant improvement of going for optimal beamforming using the channel estimation techniques in this paper, compared to the EGT beamforming used in [7]. For the selected parameters in this simulation, the improvement is approximately 20%.…”

“…III. TRAINING STAGE AND CHANNEL ESTIMATION In [7], K and N are assumed to be 2 and 1, respectively, and a method of utilizing RSSI feedback values to estimate the phase difference of the two MISO channels between the ET antennas and the ER antenna has been proposed. This method can be directly extended to estimate…”

“…respectively, where θ l = 2(l−1)π L . Proof: Differentiating E in (7) with respect to φ i,v , and setting it equal to zero gives us…”

“…Also, using channel reciprocity for channel estimation leads to many practical difficulties, due to the non-symmetric characteristics of the RF front-end circuitry at the receiver and the transmitter [5]. Prior works [6][7][8] propose energy efficient channel estimation methods based on RSSI values that are fed back from the receiver to the transmitter, and among them, [7,8] can be considered to be the most related to our work. Specifically, [7,8] propose an approach of estimating the phase values of the channels between a single ER and each antenna of the ET, and these estimates are utilized to employ equal gain transmit (EGT) beamforming for WET.…”

“…Prior works [6][7][8] propose energy efficient channel estimation methods based on RSSI values that are fed back from the receiver to the transmitter, and among them, [7,8] can be considered to be the most related to our work. Specifically, [7,8] propose an approach of estimating the phase values of the channels between a single ER and each antenna of the ET, and these estimates are utilized to employ equal gain transmit (EGT) beamforming for WET. In this paper, we focus on utilizing RSSI values to estimate both channel phase and channel magnitude information using a maximum likelihood analysis, in order to perform more superior maximum ratio transmit (MRT) beamforming for the energy transfer in single-ER case.…”