Emerging Unmanned Aerial Vehicle Communication Techniques for the Next Generation of Wireless Networks

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: 15 June 2024 | Viewed by 1899

Special Issue Editors


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Guest Editor
Department of Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Interests: UAV-assisted communications; optimization of wireless communications; opportunistic spectrum access; learning theory; game theory

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Guest Editor
School of Electronic and Information, Northwestern Polytechnical University, Xi’an 710072, China
Interests: high-speed communication; AI enabled communication; MIMO communication; array signal processing
Special Issues, Collections and Topics in MDPI journals
Department of Computer Science, Norwegian University of Science and Technology, N-7491 Trondheim, Norway
Interests: Internet of Things; edge and fog computing; networking; distributed systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Informatics and Communication Engineering, Xiamen University, Fujian 361005, China
Interests: network security; wireless communications; smart grids

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Guest Editor
National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: wireless AI; wireless resource management

Special Issue Information

Dear Colleagues,

For several years, the academic and industrial communities have been sustainably elaborating on in-depth explorations and comprehensive innovations of B5/6G technology. Briefly, B5/6G aims to achieve seamless coverage, ultra-reliability, and ultra-low latency. At the same time, B5/6G is challenged by the explosive growth in the number of communication devices, increasingly severe communication security issues, and the challenge of communication reliability caused by interference. UAVs have been widely used in assisting wireless networks due to their advantages of low cost, flexible deployment, and high mobility, especially in communication scenarios in hard-to-reach areas (e.g., fire and earthquake-affected areas). In particular, with the advancement of artificial intelligence, movement and communication strategies can be guided and flexibly adjusted using learning-based approaches.

This Special Issue aims to outline the recent progress made on the latest techniques in enhancing the performance of UAV-assisted next-generation wireless networks. Specifically, the performance includes, but is not limited to, energy efficiency, high reliability, high security, and low latency:

  • AI for energy/spectrum efficient resource management in UAV networks;
  • Cooperative mobility edge computing in UAV networks;
  • Relaying techniques for UAV networks;
  • Interference mitigation in UAV networks;
  • Intelligent trajectory planning for UAV networks;
  • AI for anti-jamming in UAV networks;
  • Anti-eavesdropping techniques in UAV networks;
  • Beamforming and waveform design for UAV communications nodes;
  • Low-latency UAV swarm networks;
  • Routing protocol for UAV swarm networks;
  • UAV semantic communication;
  • Integrated sensing and communication applications in UAV-assisted networks;
  • Reconfigurable intelligent surface-assisted UAV communications.

Submissions focused on the various advanced analytical algorithms and technological innovations used to enhance the performance of UAV networks are welcome.

Dr. Nan Qi
Prof. Dr. Rugui Yao
Dr. Xiang Su
Prof. Dr. Liang Xiao
Dr. Lin Zhang
Guest Editors

Manuscript Submission Information

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Keywords

  • artificial intelligence
  • unmanned aerial vehicle communications
  • energy efficiency
  • high reliability
  • security and safety
  • low-latency routing protocol
  • reconfigurable intelligent surface

Published Papers (2 papers)

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16 pages, 6318 KiB  
Article
Trajectory Planning for UAV-Assisted Data Collection in IoT Network: A Double Deep Q Network Approach
by Shuqi Wang, Nan Qi, Hua Jiang, Ming Xiao, Haoxuan Liu, Luliang Jia and Dan Zhao
Electronics 2024, 13(8), 1592; https://doi.org/10.3390/electronics13081592 - 22 Apr 2024
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Abstract
Unmanned aerial vehicles (UAVs) are becoming increasingly valuable as a new type of mobile communication device and autonomous decision-making device in many application areas, including the Internet of Things (IoT). UAVs have advantages over other stationary devices in terms of high flexibility. However, [...] Read more.
Unmanned aerial vehicles (UAVs) are becoming increasingly valuable as a new type of mobile communication device and autonomous decision-making device in many application areas, including the Internet of Things (IoT). UAVs have advantages over other stationary devices in terms of high flexibility. However, a UAV, as a mobile device, still faces some challenges in optimizing its trajectory for data collection. Firstly, the high complexity of the movement action and state space of the UAV’s 3D trajectory is not negligible. Secondly, in unknown urban environments, a UAV must avoid obstacles accurately in order to ensure a safe flight. Furthermore, without a priori wireless channel characterization and ground device locations, a UAV must reliably and safely complete the data collection from the ground devices under the threat of unknown interference. All of these require the proposing of intelligent and automatic onboard trajectory optimization techniques. This paper transforms the trajectory optimization problem into a Markov decision process (MDP), and deep reinforcement learning (DRL) is applied to the data collection scenario. Specifically, the double deep Q-network (DDQN) algorithm is designed to address intelligent UAV trajectory planning that enables energy-efficient and safe data collection. Compared with the traditional algorithm, the DDQN algorithm is much better than the traditional Q-Learning algorithm, and the training time of the network is shorter than that of the deep Q-network (DQN) algorithm. Full article
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Review

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31 pages, 968 KiB  
Review
A Comprehensive Survey of Distributed Denial of Service Detection and Mitigation Technologies in Software-Defined Network
by Yinghao Su, Dapeng Xiong, Kechang Qian and Yu Wang
Electronics 2024, 13(4), 807; https://doi.org/10.3390/electronics13040807 - 19 Feb 2024
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Abstract
The widespread adoption of software-defined networking (SDN) technology has brought revolutionary changes to network control and management. Compared to traditional networks, SDN enhances security by separating the control plane from the data plane and replacing the traditional network architecture with a more flexible [...] Read more.
The widespread adoption of software-defined networking (SDN) technology has brought revolutionary changes to network control and management. Compared to traditional networks, SDN enhances security by separating the control plane from the data plane and replacing the traditional network architecture with a more flexible one. However, due to its inherent architectural flaws, SDN still faces new security threats. This paper expounds on the architecture and security of SDN, analyzes the vulnerabilities of SDN architecture, and introduces common distributed denial of service (DDoS) attacks within the SDN architecture. This article also provides a review of the relevant literature on DDoS attack detection and mitigation in the current SDN environment based on the technologies used, including statistical analysis, machine learning, policy-based, and moving target defense techniques. The advantages and disadvantages of these technologies, in terms of deployment difficulty, accuracy, and other factors, are analyzed. Finally, this study summarizes the SDN experimental environment and DDoS attack traffic generators and datasets of the reviewed literature and the limitations of current defense methods and suggests potential future research directions. Full article
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