Abstract
In this article, we study wireless power transfer (WPT) beam scheduling for a system which consists of IoT devices and a power beacon (PB) using switched beamforming. In such a system, the IoT devices have a non-static behavior (e.g., their location and power requests keep changing) in general, which conventional WPT beam scheduling algorithms are not capable of adaptively dealing with. To address the non-static behavior, we propose a procedure of deep neural network (DNN)-based WPT beam scheduling. In the procedure, the power-deficient IoT devices transmit a common pilot signal simultaneously. Then, the PB effectively provides power to them with a DNN-based WPT beam scheduling policy. In the DNN-based policy, an estimation of the non-static behavior from the received pilot signals and an adaptive beam generation considering the estimated non-static behavior are integrated thanks to the powerful representational capability of DNNs. To allow the DNN-based policy to learn the optimal policy, we propose a Deep WPT Beam scheduling policy Gradient (DWBG) algorithm using deep reinforcement learning. Through the simulation, we show that DWBG achieves a close performance to the optimal policy. This demonstrates that our algorithm can be applied for practical WPT IoT systems with non-static IoT devices.
Highlights
R ECENTLY, wireless power transfer using radio frequency signals (RF-WPT)1 has been widely considered to provide power to low-power Internet of Things (IoT) devices [1]–[9]
In this paper, we proposed a procedure of deep neural network (DNN)-based WPT beam scheduling for IoT systems with non-static devices
A DNN-based WPT beam scheduling policy adaptively schedules the WPT beam pattern according to the common pilot signals transmitted from the power requesting devices
Summary
R ECENTLY, wireless power transfer using radio frequency signals (RF-WPT) has been widely considered to provide power to low-power Internet of Things (IoT) devices [1]–[9]. We can integrate both estimation and generation processes into a single policy based on DNN thanks to the large representational capability of DNNs. In this paper, we study a WPT beam scheduling problem for IoT systems with non-static IoT devices and a PB using switched beamforming. The IoT devices transmit a common pilot signal to the PB simultaneously when they need to request power, and the PB receives the sum of the pilot signals from the power requesting IoT devices This allows the PB to estimate the channel gains and to schedule beam patterns taking account of the context information. The DNN-based policy directly learns the optimal WPT beam scheduling policy that chooses the optimal beam pattern for the given sum of received pilot signals in an online manner This enables the DNNbased policy to adaptively provide power to the IoT devices even with the non-static behavior of the IoT devices. The larger sum of the PTIs implies the more effective power provisioning
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