In this paper, we propose a deep learning (DL)-based framework for optimizing three-dimensional (3D) trajectory planning and resource allocation in unmanned aerial vehicle (UAV)-enabled wireless networks composed of heterogeneous UAVs with varying capabilities. Unlike conventional approaches that assume uniform UAV cha...
Woongsup Lee, Kisong Lee· IEEE Transactions on Wireles...· 0 citations
This paper investigates a blockage-aware anti-jamming strategy for UAV-assisted data harvesting in dense urban environments. Ground jammers (GJs) not only emit interference but also attempt to detect UAVs via line-of-sight (LoS) surveillance. To counter this, the UAV leverages urban building layouts to remain hidden fr...
Conventional federated learning (FL) methods face critical challenges in realistic wireless edge networks, where training data are often limited and heterogeneous, which can lead to unstable training and poor generalization. To address these challenges in a principled manner, we propose a novel Bayesian wireless FL fra...
Jun-Pyo Hong, Hyowoon Seo, Kisong Lee· International Conference on...· 0 citations
Simulation results demonstrate that UAV placement should be designed not only for desired-link enhancement but also for interference mitigation through geometry-aware user separation through geometry-aware user separation.
This study investigates the optimization of three-dimensional (3D) trajectory planning and resource allocation in unmanned aerial vehicle (UAV)-enabled wireless networks with no-fly zones (NFZs) using a deep learning framework. The objective is to maximize the minimum average spectral efficiency (SE) among mobile users...
This paper addresses the self-sustainable operation of unmanned aerial vehicle (UAV) swarms in dense urban 6G networks, where both communication reliability and energy replenishment are strongly affected by building-induced blockage. Although solar harvesting and laser wireless power transfer (WPT) can extend UAV opera...
