@article{M5AE155D9,
title = "Optimizing UAV Network Routing with GNNs and Transfer Learning for Low Latency and High Throughput",
journal = "The Journal of Korean Institute of Communications and Information Sciences",
year = "2025",
issn = "1226-4717",
doi = "10.7840/kics.2025.50.9.1417",
author = "Seunghyeon Lee, Changmin Park, Hwangnam Kim",
keywords = "UAV networks, Graph Neural Network, Routing optimization, Transfer Learning, Low-latency, communication",
abstract = "Unmanned Aerial Vehicle (UAV) networks, while offering benefits like high mobility and line-of-sight communication, face significant challenges such as high latency and unreliable connectivity. To overcome these issues, this paper introduces a Graph Neural Network (GNN)-based routing approach leveraging transfer learning to optimize path prediction with a focus on both latency and throughput. Experimental results indicate that the proposed method outperforms Dijkstra-based routing in terms of inference speed and accuracy, especially in large-scale networks, highlighting its potential as an effective low-latency, high-throughput solution for UAV networks."
}