Optimizing UAV Network Routing with GNNs and Transfer Learning for Low Latency and High Throughput 


Vol. 50,  No. 9, pp. 1417-1423, Sep.  2025
10.7840/kics.2025.50.9.1417


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  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.

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[IEEE Style]

S. Lee, C. Park, H. Kim, "Optimizing UAV Network Routing with GNNs and Transfer Learning for Low Latency and High Throughput," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 9, pp. 1417-1423, 2025. DOI: 10.7840/kics.2025.50.9.1417.

[ACM Style]

Seunghyeon Lee, Changmin Park, and Hwangnam Kim. 2025. Optimizing UAV Network Routing with GNNs and Transfer Learning for Low Latency and High Throughput. The Journal of Korean Institute of Communications and Information Sciences, 50, 9, (2025), 1417-1423. DOI: 10.7840/kics.2025.50.9.1417.

[KICS Style]

Seunghyeon Lee, Changmin Park, Hwangnam Kim, "Optimizing UAV Network Routing with GNNs and Transfer Learning for Low Latency and High Throughput," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 9, pp. 1417-1423, 9. 2025. (https://doi.org/10.7840/kics.2025.50.9.1417)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880