2026-09-21T10:29:08Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/124602 2026-01-14 col_2142_5131 col_2142_14847 com_2142_5130 com_2142_683 com_2142_397

Caesar, Matthew Chapman 2024-05 application/pdf en text Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01 The student, Shilan He, accepted the attached license on 2024-04-30 at 15:29. The student, Shilan He, submitted this Thesis for approval on 2024-04-30 at 15:58. This Thesis was approved for publication on 2024-05-01 at 09:36. DSpace SAF Submission Ingestion Package generated from Vireo submission #20724 on 2024-09-16 at 00:44:54 Establishing temporary networks for disaster relief using UAV swarms He, Shilan 2024-05-01 Unmanned Aerial Vehicles Deep Reinforcement Learning Communication Networks Multi-agent Systems Natural disasters can destroy communications infrastructure, introducing challenges for timely rescue. Unmanned Aerial Vehicles (UAVs) can act as aerial base stations to provide temporary communication services for ground users. In complex environments, obstacles such as trees and buildings can impede signal propagation, thus reducing communication quality. This thesis introduces an innovative approach using UAVs' observations of the surrounding obstacles to make informed decisions on the movement for improved user coverage. We use Deep Reinforcement Learning (DRL) within a multi-agent setting to optimize UAV swarm movements for establishing reliable communication networks in disaster-affected urban environments. This approach allows UAVs to dynamically adjust their positions for near-optimal user coverage, representing a significant advancement in disaster response technologies. By integrating real-time observations of obstacles and leveraging cooperative strategies among UAVs, the proposed method enhances Line-of-Sight (LoS) connections essential for effective communication coverage. Simulation results demonstrate that UAVs equipped with the proposed DRL-based decision-making framework achieve significantly improved communication coverage in urban scenarios characterized by diverse obstacles and user distributions. Additionally, our strategy enables UAVs to achieve coverage with shorter travel distances, enhancing operational efficiency. Text eng https://hdl.handle.net/2142/124602 Copyright 2024 Shilan He M.S. Thesis Electrical & Computer Engr University of Illinois at Urbana-Champaign Electrical & Computer Eng