A Survey on Machine-Learning Techniques for UAV-Based Communications
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2019-11-26
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UAV-based communicationschannel modelingmachine learningresource managementwireless communication networks
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Abstract (AI)
Unmanned aerial vehicles (UAVs) will be an integral part of the next generation wireless communication networks. Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions. However, this new degree of freedom that will be included in the network will also add new challenges. In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes. In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security.
Key Findings
1
Integrating UAVs into next-generation networks introduces additional communication-design and operational challenges.
2
Machine-learning methods are identified as a framework for addressing diverse challenges in UAV-based communications.
3
The paper provides a comprehensive review of research using ML to improve design and functional aspects of UAV-based communication systems.
4
The survey organizes relevant ML applications around channel modeling, resource management, positioning, and security.
5
UAVs are expected to enhance wireless-network coverage and spectral efficiency compared with traditional ground-based communication solutions.
Research Object
UAV-based wireless communications in next-generation networks
Research Subject
Applications of machine-learning techniques to improve channel modeling, resource management, positioning, and security
Publication Details
Publication Date
2019-11-26
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