Reconfigurable intelligent surface and UAV-assisted communications: a deep reinforcement learning approach
type: CreativeWork
description: This thesis proposes novel methods based on the deep reinforcement learning algorithms (DRL) for maximising the energy efficiency (EE), sum-rate in reconfigurable intelligent surface (RIS) and unmanned aerieal vehicles (UAV)-aided wireless communications. The thesis carries out comprehensive optimization and evaluation of various DRL algorithms for several real-life applications including UAV's trajectory design, power allocation, data collection, wireless power transfer and RIS's phase shift matrix adjustment. The thesis presents three major contributions. Firstly, we design a new UAV-assisted Internet-of -things (IoT) system relying on the shortest flight path of the UAVs while maximising the amount of data collected from IoT devices. then, a DRL-based technique is conceived for finding
abstract: This thesis proposes novel methods based on the deep reinforcement learning algorithms (DRL) for maximising the energy efficiency (EE), sum-rate in reconfigurable intelligent surface (RIS) and unmanned aerieal vehicles (UAV)-aided wireless communications. The thesis carries out comprehensive optimization and evaluation of various DRL algorithms for several real-life applications including UAV's trajectory design, power allocation, data collection, wireless power transfer and RIS's phase shift matrix adjustment. The thesis presents three major contributions. Firstly, we design a new UAV-assisted Internet-of -things (IoT) system relying on the shortest flight path of the UAVs while maximising the amount of data collected from IoT devices. then, a DRL-based technique is conceived for finding
author: Nguyen, Khoi Khac
language: eng
date published: 2022-7
date modified: 2026-07-24T03:56:18Z
provider: Queen's University Belfast
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Queen's University Belfast