Interpretable and Secure Trajectory Optimization for UAV-Assisted Communication

Quan, Yunhao; Cheng, Nan; Wang, Xiucheng; Shen, Jinglong; Ma, Longfei; Yin, Zhisheng

doi:10.1109/iccc57788.2023.10233369

2023 IEEE/CIC International Conference on Communications in China (ICCC) 2023

DOI: 10.1109/iccc57788.2023.10233369

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Interpretable and Secure Trajectory Optimization for UAV-Assisted Communication

Yunhao Quan,

Nan Cheng,

Xiucheng Wang

et al.

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2024

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Cited by 2 publications

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Advancing UAV Communications: A Comprehensive Survey of Cutting-Edge Machine Learning Techniques

Sun,

Fontanesi,

Canberk

et al. 2024

IEEE Open J. Veh. Technol.

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This paper provides a comprehensive overview of the evolution of Machine Learning (ML), from traditional to advanced, in its application and integration into unmanned aerial vehicle (UAV) communication frameworks and practical applications. The manuscript starts with an overview of the existing research on UAV communication and introduces the most traditional ML techniques. It then discusses UAVs as versatile actors in mobile networks, assuming different roles from airborne user equipment (UE) to base stations (BS). UAV have demonstrated considerable potential in addressing the evolving challenges of next-generation mobile networks, such as enhancing coverage and facilitating temporary hotspots but pose new hurdles including optimal positioning, trajectory optimization, and energy efficiency. We therefore conduct a comprehensive review of advanced ML strategies, ranging from federated learning, transfer and meta-learning to explainable AI, to address those challenges. Finally, the use of stateof-the-art ML algorithms in these capabilities is explored and their potential extension to cloud and/or edge computing based network architectures is highlighted.

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Advancing UAV Communications: A Comprehensive Survey of Cutting-Edge Machine Learning Techniques

Sun,

Fontanesi,

Canberk

et al. 2024

IEEE Open J. Veh. Technol.

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Integrating Unmanned Aerial Vehicles in Airspace: A Systematic Review

Tuncal,

Erol

2024

Journal of Aviation Research

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In this article, a comprehensive review of the integration of Unmanned Aerial Vehicles (UAVs) into shared airspace is presented. The primary objective is to underscore and gain a full understanding of the challenges, issues, and potential solutions related to regulatory frameworks, safety, and coordination. The methodology employed is a systematic review approach, which ensures the reliability and efficacy of the selected literature. The findings underscore the significance of various components, including multi-layered airspace models, path planning, secure communication networks, Conflict Detection and Resolution (CDR), and regulatory measures, in augmenting the safety of UAV integration. Additionally, the article explores the utilization of Reinforcement Learning (RL) and Human-in-the-loop Reinforcement Learning (HRL) algorithms as promising techniques for instructing UAVs to navigate complex environments and adapt to changing circumstances. The study emphasizes the importance of collaboration among all stakeholders, the support of technology development, and the need for continuous research.

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Interpretable and Secure Trajectory Optimization for UAV-Assisted Communication

Cited by 2 publications

References 14 publications

Advancing UAV Communications: A Comprehensive Survey of Cutting-Edge Machine Learning Techniques

Advancing UAV Communications: A Comprehensive Survey of Cutting-Edge Machine Learning Techniques

Integrating Unmanned Aerial Vehicles in Airspace: A Systematic Review

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