Deep Learning-Based Energy Harvesting with Intelligent Deployment of Ris-Assisted Uav-Cfmmimos
18 Pages Posted: 11 Feb 2023
Abstract
The ever-evolving internet of things (IoT) has spawned hundreds of wireless sensors that communicate via the internet infrastructure. The lifetime and self-sustainability of these sensors are pivotal factors dictating the performance of respective application infrastructure. Radio frequency energy harvesting (RFEH) technology has exhibited the capability of effectively augmenting the battery lifetime of these sensors. In this work, we present CURe, a novel framework for RFEH that successfully utilizes the combined benefits of cell-free massive multiple-input multiple-output (CFmMIMO) and reconfigurable intelligent surfaces (RISs) to deliver seamless energy harvesting to IoT devices. CFmMIMO integrates the advantages of distributed systems and massive MIMO, while RIS improves the signal strength of the information transfer and RFEH via its passive reflection capabilities. Moreover, we consider unmanned aerial vehicles (UAVs) equipped with CFmMIMO as mobile access points (APs) to better serve the moving sensory devices. To further improve the RFEH, we propose DeNCE, deep learning (DL)-based channel estimation technique, which eliminates the conventional closed-form equation-based channel estimation. Through evaluation, we first validate the performance of CURe by comparing it with the max-min fairness (MMF) algorithm and later corroborate that DeNCE significantly improves the performances of both models. Finally, to optimize the UAV deployment and ensure continuous RFEH coverage, we propose dARL, a deep reinforcement learning (DRL)-based scheduling framework that enables UAV-CFmMIMO swarms to perform continuous energy harvesting in the coverage area collaboratively.
Keywords: Energy harvesting, Deep Learning, reconfigurable intelligent surfaces
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