Abstract
The integration of dynamic Flying Ad hoc Networks (FANETs) and millimeter Wave (mmWave) technology can offer a promising solution for numerous data-intensive applications, as it enables the establishment of a robust flying infrastructure with significant data transmission capabilities. However, to enable effective mmWave communication within this dynamic network, it is essential to precisely align the steerable antennas mounted on Unmanned Aerial Vehicles (UAVs) with their corresponding peer units. Therefore, it is important to design a novel approach that can quickly determine an optimized alignment and network topology. In this paper, we propose a Generative Adversarial Network (GAN)-based approach, called WaveGAN, for FANET topology optimization aiming to maximize the network throughput by selecting the communication paths with the best channel conditions. The proposed approach consists of a WaveGAN model followed by a beam search. The former learns how to generate optimized network topologies from a supervised dataset, while the latter adjusts the generated topologies to meet the structure requirements of the mmWave-based FANET. The simulation results show that the proposed approach is able to quickly find FANET topologies with a very small optimality gap for different network sizes.
Recommended Citation
E. Odat et al., "A WaveGAN Approach For MmWave-Based FANET Topology Optimization," Sensors, vol. 24, no. 1, article no. 6, MDPI, Jan 2024.
The definitive version is available at https://doi.org/10.3390/s24010006
Department(s)
Electrical and Computer Engineering
Publication Status
Open Access
Keywords and Phrases
deep learning; FANET; Generative Adversarial Network (GAN); mmWave; UAV
International Standard Serial Number (ISSN)
1424-8220
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Jan 2024
PubMed ID
38202868
