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Title: Deep Learning-Based Autonomous UAV-BSs for NGWNs: Overview and a Novel Architecture
Authors: Demirtaş, Ali Murat
Seyfioğlu, Mehmet Saygın
Bor-Yalınız, İrem
Tavlı, Bülent
Yanikomeroğlu, Halim
Keywords: Quality of service
Convolutional neural networks
Trajectory optimization
Consumer electronics
Memory management
Energy dissipation
Wireless Networks
Issue Date: 2023
Publisher: Ieee-Inst Electrical Electronics Engineers Inc
Abstract: To address the ever-growing connectivity demand in communications, the adoption of ingenious solutions, such as utilization of unmanned aerial vehicles (UAVs) as mobile base stations, is imperative. In general, the location of a UAV base station (UAV-BS) is determined by optimization algorithms, which have high computationally complexities and are hard to run on UAVs due to energy consumption and time constraints. In this article, we overview the UAV-BS trajectory optimization problem for next generation wireless networks and show that a convolutional neural network (CNN) model can be trained to infer the location of a UAV-BS in real time. To this end, we create a framework to determine the UAV-BS locations considering the deployment of mobile users (MUs) to generate labels by using the data obtained from an optimization algorithm. Performance evaluations reveal that once the CNN model is trained with the given labels and locations of MUs, the proposed approach is, indeed, capable of approximating the results given by the adopted optimization algorithm with high fidelity, outperforming reinforcement learning-based approaches in resource-constrained settings. We also explore future research challenges and highlight key issues.
ISSN: 2162-2248
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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