Wireless power and information transmission : nonlinear model and beamforming optimization

Abstract

In Internet-of-Things (IoT) paradigm, massive amount of low-power electronic devices need to be connected, but providing energy for these devices is a chal- lenging task. For tackling this issue, wireless power and information transmission (WPIT) is a promising technique as it enables data and energy to be transmitted from a distance via radio frequency signals. Currently, the major obstacle to implement WPIT is the low transmission ef- ficiency, and it is known that beamforming is a key technique to combat against the low efficiency. However, the effectiveness of beamforming design is depen- dent on the accuracy of energy harvesting model. While existing works mostly adopt a linear energy harvesting model due to its simplicity, a practical energy harvester contains nonlinear elements such as diodes. This leads to a mismatch between model and circuit, and consequently the degradation of the performance of WPIT. In order to address the above problem, a practical nonlinear energy harvesting model, which matches the experimental data of energy harvesting products very well, is proposed in this thesis. The impact of nonlinear model on beamforming design is illustrated in a point-to-point WPIT system. It is shown that the beamforming design based on the proposed model significantly improves the WPIT transmission efficiency compared to traditional designs based on linear model. With the nonlinear model, the beamforming designs in two other WPIT sys- tems are further investigated. In the first system, the beamforming design of a mobile charger is considered, with the aim of maximizing the data-rate subject to a given energy budget. Due to the additional degree of freedom brought by moving, the beamforming vector needs to be jointly designed with stopping times along the moving path. To this end, a convergence guaranteed iterative algorithm based on difference of convex (DC) programming is proposed, and it is shown that the corresponding beamforming solution achieves a data-rate close to the upper bound. Furthermore, it is found that with an appropriate moving path and the proposed beamforming design, the data-rate gain could be large compared to the fixed charger case. In the second system, the beamforming design under massive antenna arrays at the transmitter is considered. While existing WPIT beam- forming algorithms could achieve good performance, they involve the inverse of Hessian matrices. This leads to extremely time consuming computations and cannot be used in practice if the number of antennas is in the range of hundreds or more. To this end, an accelerated first-order algorithm, which only needs to compute the gradients, is proposed. Both theoretical and numerical results show that the proposed method reduces the computation time by orders of magni- tude while still guaranteeing the same performance compared to state-of-the-art methods.