End-to-End Joint Waveform and Beamforming Optimization for RF Wireless Power Transfer with Hybrid Transmit Architecture and Non-Linear Energy Harvesters

Radio frequency (RF) wireless power transfer (WPT) is an appealing technology to provide sustainable and cost-efficent power supply to low-power devices in future wireless systems. However, the inherently low end-to-end power transfer efficiency (PTE) is a serious challenge for practical applications. The key contributing factors include channel losses, transceivers’ power consumption, and losses from components such as the digital-to-analog converter (DAC), high-power amplifier (HPA), and rectenna. Careful consideration of these factors is essential for optimizing PTE, which is the focus of this research. Herein, we consider a fully connected hybrid multi-antenna transmit architecture that aims to charge non-linear energy harvesters. First, we present a mathematical framework to determine the harvested power from multi-tone signal transmissions and the system’s power consumption. Then, we formulate a joint waveform and analog beamforming design problem to minimize system’s power consumption and fulfill user’s charging needs. With this in place, and due to the problem high-complexity, we propose a particle swarm optimization (PSO)-based solution. Moreover, we also model the problem as a Markov decision process and propose a solution based on deep deterministic policy gradient (DDPG). Numerical results demonstrate that the proposed algorithms converge to suboptimal solutions. Moreover, simulation results show that system power consumption reduces with lower DAC and phase shifter resolution, as well as increased antenna length. Conversely, power consumption rises with the number of users and RF chains. Notably, across all these scenarios, PSO-JWB outperforms DDPG-JWB, requiring lower overall system power consumption.