Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8908
Title: Deep Learning Based Autonomous UAV-BSs for NGWNs: Overview and A Novel Architecture
Authors: Demirtas A.M.
Seyfioglu M.S.
Bor-Yaliniz I.
Tavli B.
Yanikomeroglu H.
Keywords: Consumer electronics
Convolutional neural networks
Energy dissipation
Memory management
Quality of service
Training
Trajectory optimization
Issue Date: 2022
Publisher: Institute of Electrical and 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 (BSs), 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 paper, we overview the UAV-BS trajectory optimization problem for next generation wireless networks (NGWNs) 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 (RL)-based approaches in resource-constrained settings. We also explore future research challenges and highlight key issues. IEEE
URI: https://doi.org/10.1109/MCE.2022.3201366
https://hdl.handle.net/20.500.11851/8908
ISSN: 2162-2248
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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