Contextual-Learning-Based Waveform Scheduling for Wireless Power Transfer With Limited Feedback

In this article, we study the waveform scheduling problem for a wireless power transfer (WPT) system consisting of a power beacon (PB) and multiple energy-harvesting-empowered Internet of Things (EH-IoT) devices. In each time slot, each device requests power to the PB if it needs power, and the PB transmits a WPT signal for which the waveform is designed based on the harvested power satisfaction rate of the power-requesting devices. Under this setup, we formulate an optimization problem that maximizes the average number of EH-IoT devices whose power requests are satisfied. We first solve this problem, assuming that the perfect channel state information (CSI) of all devices is known at the PB. Since the problem is difficult to solve even with perfect CSI, we transform it into a more tractable problem via proper approximations and propose an efficient algorithm to solve it. Next, to tackle the issue that it is practically difficult for the PB to acquire the perfect CSI of each device, we propose a contextual learning-based WPT waveform scheduling algorithm, requiring only 1-bit feedback from each device at one time. Numerical results show that our proposed waveform scheduling algorithm provides a higher satisfaction rate than existing algorithms under perfect CSI, and that with limited CSI feedback achieves performance close to the case with perfect CSI.