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Electronics
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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 2025 | Viewed by 7457

Special Issue Editors

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
Prof. Dr. Su Hu

E-Mail Website
Guest Editor
National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: wireless communication; 5G\6G; satellite communication; information and communication engineering; signal and information processing
Special Issues, Collections and Topics in MDPI journals

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.

Technical Program Committee Member:

Dr. Ping Wang
Academy of Military Science, Beijing 100091, China

Dr. Yuan Gao
Prof. Dr. Su Hu
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. 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

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Published Papers (5 papers)

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Research

17 pages, 2499 KiB  
Article
Intelligent Path Planning for UAV Patrolling in Dynamic Environments Based on the Transformer Architecture
by Ching-Hao Yu, Jichiang Tsai and Yuan-Tsun Chang
Electronics 2024, 13(23), 4716; https://doi.org/10.3390/electronics13234716 - 28 Nov 2024
Cited by 1 | Viewed by 1168
Abstract
Due to its NP-Hard property, the Travelling Salesman Problem (TSP) has long been a prominent research topic in path planning. The goal is to design the algorithm with the fastest execution speed in order to find the path with the lowest travelling cost. [...] Read more.
Due to its NP-Hard property, the Travelling Salesman Problem (TSP) has long been a prominent research topic in path planning. The goal is to design the algorithm with the fastest execution speed in order to find the path with the lowest travelling cost. In particular, new generative AI technology is continually emerging. The question of how to exploit algorithms from this realm to perform TSP path planning, especially in dynamic environments, is an important and interesting problem. The TSP application scenario investigated by this paper is that of an Unmanned Aerial Vehicle (UAV) that needs to patrol all specific ship-targets on the sea surface before returning to its origin. Hence, during the flight, we must consider real-time changes in wind velocity and direction, as well as the dynamic addition or removal of ship targets due to mission requirements. Specifically, we implement a Deep Reinforcement Learning (DRL) model based on the Transformer architecture, which is widely used in Generative AI, to solve the TSP path-planning problem in dynamic environments. Finally, we conduct numerous simulation experiments to compare the performance of our DRL model and the traditional heuristic algorithm, the Simulated Annealing (SA) method, in terms of operation time and path distance in solving the ordinary TSP, to verify the advantages of our model. Notably, traditional heuristic algorithms cannot be applied to dynamic environments, in which wind velocity and direction can change at any time. Full article
(This article belongs to the Special Issue Parallel, Distributed, Edge Computing in UAV Communication)
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15 pages, 779 KiB  
Article
BWSAR: A Single-Drone Search-and-Rescue Methodology Leveraging 5G-NR Beam Sweeping Technologies for Victim Localization
by Ming He, Keliang Du, Haoran Huang, Qi Song and Xinyu Liu
Electronics 2024, 13(21), 4317; https://doi.org/10.3390/electronics13214317 - 2 Nov 2024
Viewed by 1373
Abstract
Drones integrated with 5G New Radio (NR) base stations have emerged as a promising solution for efficient victim search and localization in emergency zones where cellular networks are disrupted by natural disasters. Traditional approaches relying solely on uplink Sounding Reference Signal (SRS) for [...] Read more.
Drones integrated with 5G New Radio (NR) base stations have emerged as a promising solution for efficient victim search and localization in emergency zones where cellular networks are disrupted by natural disasters. Traditional approaches relying solely on uplink Sounding Reference Signal (SRS) for localization face limitations due to User Equipment (UE) power constraints. To overcome this, our paper introduces BWSAR, a novel three-stage Search-and-Rescue (SAR) methodology leveraging 5G-NR beam sweeping technologies. BWSAR utilizes downlink Synchronization Signal Block (SSB) for coarse-grained direction estimation, guiding the drone towards potential victim locations. Subsequently, finer-grained beam sweeping with Positioning Reference Signal (PRS) is employed within the identified direction, enabling precise three-dimensional UE coordinate estimation. Furthermore, we propose a trajectory optimization algorithm to expedite the drone’s navigation to emergency areas. Simulation results underscore BWSAR’s efficacy in reducing positioning errors and completing SAR missions swiftly, within minutes. Full article
(This article belongs to the Special Issue Parallel, Distributed, Edge Computing in UAV Communication)
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20 pages, 3488 KiB  
Article
Sea-Based UAV Network Resource Allocation Method Based on an Attention Mechanism
by Zhongyang Mao, Zhilin Zhang, Faping Lu, Yaozong Pan, Tianqi Zhang, Jiafang Kang, Zhiyong Zhao and Yang You
Electronics 2024, 13(18), 3686; https://doi.org/10.3390/electronics13183686 - 17 Sep 2024
Viewed by 931
Abstract
As humans continue to exploit the ocean, the number of UAV nodes at sea and the demand for their services are increasing. Given the dynamic nature of marine environments, traditional resource allocation methods lead to inefficient service transmission and ping-pong effects. This study [...] Read more.
As humans continue to exploit the ocean, the number of UAV nodes at sea and the demand for their services are increasing. Given the dynamic nature of marine environments, traditional resource allocation methods lead to inefficient service transmission and ping-pong effects. This study enhances the alignment between network resources and node services by introducing an attention mechanism and double deep Q-learning (DDQN) algorithm that optimizes the service-access strategy, curbs action outputs, and improves service-node compatibility, thereby constituting a novel method for UAV network resource allocation in marine environments. A selective suppression module minimizes the variability in action outputs, effectively mitigating the ping-pong effect, and an attention-aware module is designed to strengthen node-service compatibility, thereby significantly enhancing service transmission efficiency. Simulation results indicate that the proposed method boosts the number of completed services compared with the DDQN, soft actor–critic (SAC), and deep deterministic policy gradient (DDPG) algorithms and increases the total value of completed services. Full article
(This article belongs to the Special Issue Parallel, Distributed, Edge Computing in UAV Communication)
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16 pages, 6936 KiB  
Article
Collaborative Channel Perception of UAV Data Link Network Based on Data Fusion
by Zhiyong Zhao, Zhongyang Mao, Zhilin Zhang, Yaozong Pan and Jianwu Xu
Electronics 2024, 13(18), 3643; https://doi.org/10.3390/electronics13183643 - 13 Sep 2024
Cited by 1 | Viewed by 710
Abstract
The existing collaborative channel perception suffers from unreasonable data fusion weight allocation, which mismatches the channel perception capability of the node devices. This often leads to significant deviations between the channel perception results and the actual channel state. To solve this issue, this [...] Read more.
The existing collaborative channel perception suffers from unreasonable data fusion weight allocation, which mismatches the channel perception capability of the node devices. This often leads to significant deviations between the channel perception results and the actual channel state. To solve this issue, this paper integrates the data fusion algorithm from evidence fusion theory with data link channel state perception. It applies the data fusion advantages of evidence fusion theory to evaluate the traffic pulse statistical capability of network node devices. Specifically, the typical characteristic parameters describing the channel perception capability of node devices are regarded as evidence parameter sets under the recognition framework. By calculating the credibility and falsity of the characteristic parameters, the differences and conflicts between nodes are measured to achieve a comprehensive evaluation of the traffic pulse statistical capabilities of node devices. Based on this evaluation, the geometric mean method is adopted to calculate channel state perception weights for each node within a single-hop range, and a weight allocation strategy is formulated to improve the accuracy of channel state perception. Full article
(This article belongs to the Special Issue Parallel, Distributed, Edge Computing in UAV Communication)
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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
Cited by 2 | Viewed by 2197
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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