Contextual Learning-Based Wireless Power Transfer Beam Scheduling for IoT Devices

Abstract

In this paper, we consider Internet of Things (IoT) systems in which IoT devices request power to a power beacon (PB) when their available power is deficient and the PB provides power to the IoT devices using switched beamforming. We study wireless power transfer (WPT) beam scheduling for the IoT systems under one-bit feedback which aims at maximizing the time-average number of the IoT devices whose power requests are satisfied. To achieve this, we propose a contextual learning-based WPT beam scheduling algorithm with one-bit feedback (CWBO) that learns the channel information using only one-bit feedback information and exploits it for the beam scheduling. Within CWBO, a beam pattern generation (BPG) problem should be solved in each time slot. To efficiently solve it, we develop a BPG algorithm based on monotonic optimization that can optimally solve the BPG problem. In addition, we also develop a heuristic BPG algorithm that has a lower computational complexity than the monotonic optimization-based BPG algorithm, while providing comparable performance. For CWBO in single-device WPT, we prove an analytical performance bound, which shows its optimality in terms of the long-term average performance even with one-bit feedback. In addition, through the simulation results, we show that our algorithms achieve performances close to that of the optimal beam scheduling policy in multidevice WPT as well. This demonstrates that our algorithms can be used for WPT IoT systems with IoT devices having only limited capabilities for feedback and estimation of the channel information due to their limited power.