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Electronics
Special Issues
Parallel, Distributed, Edge Computing in UAV Communication
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Parallel, Distributed, Edge Computing in UAV Communication

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 July 2024 | Viewed by 933

Special Issue Editor

Dr. Yuan Gao

E-Mail Website
Guest Editor
Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing 100084, China
Interests: 5G/6G wireless communication; NTN network; resource allocation; intelligent computing; AI driven network

Special Issue Information

Dear Colleagues,

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

The UAV  network is an important part of the sixth generation (6G) wireless communication system in the future. Compared with the traditional communication network based on ground infrastructure, the drone network has many unique attributes, such as low-cost, high mobility, easily deployment, widely coverage, strong viewing links, controllable mobility, etc., these features integrate communication, perception, computing, intelligence, and security. It provides new opportunities in enhancing coverage, improving spectrum efficiency, and user service quality. The UAV network is expected to provide communication, perception, computing, cache and other services for various application scenarios. However, the high mobility of drones has also brought great challenges to many aspects of the drone network application, including the intelligent network, channel modeling, flight deployment, mobility control, trajectory optimization, and optimization of drone networks, this has also become a bottleneck restricting such drone network development. At present, the research and development of UAV networks is not yet mature, and researchers need to study the key theory and technologies of the UAV network in depth to promote the development of 6G wireless communication systems.

The contents of this album include but are not limited to the following directions:

  • Intelligent coverage of drone wireless networks
  • Drone wireless transmission channel measurement and modeling
  • New network architecture based on drones
  • Spectral management and network planning of drone networks
  • Flight trajectory optimization in the drone network
  • Collaborative communication of drones
  • Collaborative perception of drones
  • Federal Learning of Drone Group
  • Drone communication perception integration
  • Drone communication calculation integration
  • High -energy -efficient drone network access control
  • MMO and beam -shaped of the drone network
  • Interference control of the drone network
  • AI -based drone network control
  • Physical layer security technology of drone network
  • The application of drone network in rail transit
  • The application of drone network in intelligent transportation
  • The application of the drone network in other fields.

I look forward to receiving your contributions.

Dr. Yuan Gao
Guest Editor

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. Electronics 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 2400 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

  • wireless network
  • 5G/6G
  • resource allocation
  • air to ground network
  • NTN
  • remote sensing
  • drone network
  • satellite network
  • emergency communication
  • SON
  • AI driven network
  • edge computing

Published Papers (1 paper)

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Research

38 pages, 985 KiB  
Article
How to Design Reinforcement Learning Methods for the Edge: An Integrated Approach toward Intelligent Decision Making
by Guanlin Wu, Dayu Zhang, Zhengyuan Miao, Weidong Bao and Jiang Cao
Electronics 2024, 13(7), 1281; https://doi.org/10.3390/electronics13071281 - 29 Mar 2024
Viewed by 398
Abstract
Extensive research has been carried out on reinforcement learning methods. The core idea of reinforcement learning is to learn methods by means of trial and error, and it has been successfully applied to robotics, autonomous driving, gaming, healthcare, resource management, and other fields. [...] Read more.
Extensive research has been carried out on reinforcement learning methods. The core idea of reinforcement learning is to learn methods by means of trial and error, and it has been successfully applied to robotics, autonomous driving, gaming, healthcare, resource management, and other fields. However, when building reinforcement learning solutions at the edge, not only are there the challenges of data-hungry and insufficient computational resources but also there is the difficulty of a single reinforcement learning method to meet the requirements of the model in terms of efficiency, generalization, robustness, and so on. These solutions rely on expert knowledge for the design of edge-side integrated reinforcement learning methods, and they lack high-level system architecture design to support their wider generalization and application. Therefore, in this paper, instead of surveying reinforcement learning systems, we survey the most commonly used options for each part of the architecture from the point of view of integrated application. We present the characteristics of traditional reinforcement learning in several aspects and design a corresponding integration framework based on them. In this process, we show a complete primer on the design of reinforcement learning architectures while also demonstrating the flexibility of the various parts of the architecture to be adapted to the characteristics of different edge tasks. Overall, reinforcement learning has become an important tool in intelligent decision making, but it still faces many challenges in the practical application in edge computing. The aim of this paper is to provide researchers and practitioners with a new, integrated perspective to better understand and apply reinforcement learning in edge decision-making tasks. Full article
(This article belongs to the Special Issue Parallel, Distributed, Edge Computing in UAV Communication)
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