Deep Learning-Enabled Multi-Unmanned Aerial Vehicle Networks: Theories, Algorithms and Applications
A special issue of Journal of Sensor and Actuator Networks (ISSN 2224-2708). This special issue belongs to the section "Communications and Networking".
Deadline for manuscript submissions: closed (6 June 2023) | Viewed by 354
Special Issue Editors
Interests: vehicular networks; physical layer security; optimization techniques
Interests: network technology; virtual & augmented reality; computer networks (planning, design, architecture); cloud, fog, and edge computing; Internet of Things (IoT); software defined networks (SDN); Ad-hoc and wireless sensor networks; machine learning & artificial intelligence; connected/autonomous vehicles
Special Issues, Collections and Topics in MDPI journals
Interests: Internet of Things (IoT); wireless power transfer; intelligent reflecting surface; rate splitting multiple access; digital twin; semantic communications
Special Issues, Collections and Topics in MDPI journals
Interests: Internet of Things; smart grid; blockchain; resource allocation and network optimization
Special Issues, Collections and Topics in MDPI journals
Interests: edge/cloud computing and edge intelligence; green and cognitive communications; integrated sensing; communications; and computing; intelligent communications systems and networks; vehicular communications and networks; energy informatics
Special Issues, Collections and Topics in MDPI journals
Interests: radio resource management; heterogeneous wireless networks; game theory and machine learning in 5G networks and beyond
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the increasing maturity of Unmanned Aerial Vehicle (UAV) and wireless communication technologies, multi-UAV collaborative networking has been envisioned as a promising solution to coverage and deployment issues of 6G networks，due to the great advantages of flexible deployment, cost effectiveness, and comprehensive coverage.
With the diversifying and challenging service requirements (e.g., highly reliable connections, real-time controls, and cybersecurity) faced by inter-UAV communication, Deep Learning (DL) techniques offer a powerful architectural framework to solve a broad range of problems such as service optimization, dynamic resource allocation, and trajectory planning for moving multi-UAV communication systems. However, to deploy DL, several challenges should be addressed, such as more efficient DL solutions on their theories and algorithms for multi-UAV networks, the optimal system deployment of multi-UAV networks, and privacy and cyber security issues, etc. This Special Issue aims to use DL techniques to address the potential research areas in multi-UAV networks. We seek the submission of high-quality, original and unpublished manuscripts on topics including, but not limited to:
- DL solutions for network decision-making regarding multi-UAV networks, including network control, task scheduling, consensus path-planning of UAV swarms, etc.
- DL solutions for network resource management in multi-UAV networks, including network slicing, handoff management, user association, mobility management, etc.
- DL solutions for multi-UAV networking, including designs regarding clustering mechanism, designs of incentive mechanism, crowd behavior research, etc.
- DL solutions for multi-UAV communications in Space–Air–Ground Integrated Networks
- DL solutions for physical layer functionality of multi-UAV networks, including channel modeling analysis, interference mitigation, etc.
- DL solutions for privacy and cyber security issues in DL-enabled muti-UAV networks.
- Green and low-carbon technologies for DL-enabled multi-UAV networks, including energy-aware efficient DL algorithm designs, energy efficiency optimization issues of networks, etc.
- Performance evaluation and optimization for the convergence of DL and multi-UAV networks.
- Emerging technologies (e.g., digital twin, blockchain, federated learning, AR/VR, and metaverse) for DL-enabled multi-UAV networks.
Dr. Yuanai Xie
Dr. Jie Gao
Dr. Nguyen Cong Luong
Dr. Xumin Huang
Dr. Yuan Wu
Dr. Xianfu Chen
Dr. Saurav Sthapit
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Sensor and Actuator Networks is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- DL solutions
- multi-UAV networks
- privacy and cyber security
- performance evaluation and optimization
- resource management
- energy-aware
- space–air–ground integrated networks
- physical layer
