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On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks

1
Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80142 Naples, Italy
2
Department of Electrical Engineering, Yazd University, Yazd 89195-741, Iran
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(4), 689; https://doi.org/10.3390/electronics9040689
Received: 13 March 2020 / Revised: 16 April 2020 / Accepted: 20 April 2020 / Published: 23 April 2020
(This article belongs to the Section Networks)
A groundbreaking design of radio access networks (RANs) is needed to fulfill 5G traffic requirements. To this aim, a cost-effective and flexible strategy consists of complementing terrestrial RANs with unmanned aerial vehicles (UAVs). However, several problems must be solved in order to effectively deploy such UAV-based RANs (U-RANs). Indeed, due to the high complexity and heterogeneity of these networks, model-based design approaches, often relying on restrictive assumptions and constraints, exhibit severe limitation in real-world scenarios. Moreover, design of a set of appropriate protocols for such U-RANs is a highly sophisticated task. In this context, machine learning (ML) emerges as a useful tool to obtain practical and effective solutions. In this paper, we discuss why, how, and which types of ML methods are useful for designing U-RANs, by focusing in particular on supervised and reinforcement learning strategies. View Full-Text
Keywords: 5G and beyond systems; machine learning; radio access networks; reinforcement learning; supervised learning; unmanned aerial vehicles (UAVs) 5G and beyond systems; machine learning; radio access networks; reinforcement learning; supervised learning; unmanned aerial vehicles (UAVs)
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Kouhdaragh, V.; Verde, F.; Gelli, G.; Abouei, J. On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks. Electronics 2020, 9, 689.

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