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Drones
Special Issues
Advances in UAV Networks Towards 6G
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Advances in UAV Networks Towards 6G

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drone Communications".

Deadline for manuscript submissions: closed (31 December 2025) | Viewed by 8375

Special Issue Editors

Prof. Dr. Changchuan Yin

E-Mail Website
Guest Editor
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: wireless communications and networking technology
Dr. Zhaohui Yang

E-Mail Website
Guest Editor
School of Information and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
Interests: intelligent communication and computing fusion system; wireless large model; edge learning; holographic communication
Special Issues, Collections and Topics in MDPI journals
Dr. Mingzhe Chen

E-Mail Website
Guest Editor
Electrical and Computer Engineering Department, University of Miami, Coral Gables, FL 33146, USA
Interests: machine learning; digital network twins; unmanned aerial vehicles; semantic communications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Unmanned Aerial Vehicles (UAVs) have been widely accepted as one of the potential technologies for future six-generation (6G) networks. On the one hand, UAVs can serve as aerial relays and access points (APs) to extend the coverage range of networks and provide communication connectivity. On the other hand, UAVs can also be flexibly deployed as aerial servers to provide computing ability, such as mobile edge computing-enabled UAV networks. Different from ground base stations in cellular networks, UAVs are characterized by the high mobility, which brings new optimization perspectives, such as cooperative control, positioning, and trajectory design. Furthermore, UAV networks are characterized by unique channel attributes of UAV–ground links, asymmetric quality of service (QoS) demands for downlink commands and uplink data transmission, and energy limitations. These distinctive features make the deployment of UAVs in future 6G networks a challenging issue that requires innovative strategies to ensure effective integration and operation.

The aim of this Special Issue is to spotlight the latest advancements in UAV network technologies toward 6G integration and emphasize their real-world applications within communication systems. This Special Issue seeks to attract academics, researchers, professionals, and engineers working on, but not limited to, the following key areas within UAV networks and 6G communication for future sustainable and efficient connectivity solutions.

This Special Issue aims to gather cutting-edge research, innovative methods, and practical applications of UAV networks towards 6G. The scope of topics includes, but is not limited to, the following:

  • Joint trajectory design and resource allocation for UAV-assisted wireless communications;
  • UAV swarm in 6G wireless applications;
  • Joint control and optimization in satellite-UAV-ground networks;
  • Cooperative positioning and navigation in UAV networks;
  • Data collection in UAV networks;
  • Energy-efficient UAV communications;
  • Positioning and localization in UAV networks;
  • Channel modeling for UAV-ground and UAV-UAV communications;
  • Three-dimensional beamforming for cellular-connected UAVs;
  • Massive MIMO/millimeter wave communications for UAVs;
  • Security in UAV-aided wireless networks.

Prof. Dr. Changchuan Yin
Dr. Zhaohui Yang
Dr. Mingzhe Chen
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 250 words) can be sent to the Editorial Office for assessment.

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. Drones is an international peer-reviewed open access monthly 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 2600 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

  • UAV swarm
  • integrated space–air–ground networks
  • positioning
  • trajectory design
  • data collection

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Published Papers (4 papers)

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Research

31 pages, 1406 KB  
Article
Performance Analysis of Unmanned Aerial Vehicle-Assisted and Federated Learning-Based 6G Cellular Vehicle-to-Everything Communication Networks
by Abhishek Gupta and Xavier Fernando
Drones 2025, 9(11), 771; https://doi.org/10.3390/drones9110771 - 7 Nov 2025
Cited by 1 | Viewed by 1971
Abstract
The paradigm of cellular vehicle-to-everything (C-V2X) communications assisted by unmanned aerial vehicles (UAVs) is poised to revolutionize the future of sixth-generation (6G) intelligent transportation systems, as outlined by the international mobile telecommunication (IMT)-2030 vision. This integration of UAV-assisted C-V2X communications is set to [...] Read more.
The paradigm of cellular vehicle-to-everything (C-V2X) communications assisted by unmanned aerial vehicles (UAVs) is poised to revolutionize the future of sixth-generation (6G) intelligent transportation systems, as outlined by the international mobile telecommunication (IMT)-2030 vision. This integration of UAV-assisted C-V2X communications is set to enhance mobility and connectivity, creating a smarter and reliable autonomous transportation landscape. The UAV-assisted C-V2X networks enable hyper-reliable and low-latency vehicular communications for 6G applications including augmented reality, immersive reality and virtual reality, real-time holographic mapping support, and futuristic infotainment services. This paper presents a Markov chain model to study a third-generation partnership project (3GPP)-specified C-V2X network communicating with a flying UAV for task offloading in a Federated Learning (FL) environment. We evaluate the impact of various factors such as model update frequency, queue backlog, and UAV energy consumption on different types of communication latency. Additionally, we examine the end-to-end latency in the FL environment against the latency in conventional data offloading. This is achieved by considering cooperative perception messages (CPMs) that are triggered by random events and basic safety messages (BSMs) that are periodically transmitted. Simulation results demonstrate that optimizing the transmission intervals results in a lower average delay. Also, for both scenarios, the optimal policy aims to optimize the available UAV energy consumption, minimize the cumulative queuing backlog, and maximize the UAV’s available battery power utilization. We also find that the queuing delay can be controlled by adjusting the optimal policy and the value function in the relative value iteration (RVI). Moreover, the communication latency in an FL environment is comparable to that in the gross data offloading environment based on Kullback–Leibler (KL) divergence. Full article
(This article belongs to the Special Issue Advances in UAV Networks Towards 6G)
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38 pages, 1093 KB  
Article
Neural-Guided Adaptive Clustering for UAV-Based User Grouping in 5G/6G Post-Disaster Networks
by Mohammed Sani Adam, Nor Fadzilah Abdullah, Asma Abu-Samah, Oluwatosin Ahmed Amodu and Rosdiadee Nordin
Drones 2025, 9(11), 731; https://doi.org/10.3390/drones9110731 - 22 Oct 2025
Viewed by 1128
Abstract
In post-disaster scenarios, Unmanned Aerial Vehicles (UAVs) acting as Mobile Aerial Base Stations (MABSs) offer a flexible means of restoring communication for isolated user equipment (UE) when conventional infrastructure is unavailable. More broadly, clustering is a fundamental tool for organizing spatially distributed entities [...] Read more.
In post-disaster scenarios, Unmanned Aerial Vehicles (UAVs) acting as Mobile Aerial Base Stations (MABSs) offer a flexible means of restoring communication for isolated user equipment (UE) when conventional infrastructure is unavailable. More broadly, clustering is a fundamental tool for organizing spatially distributed entities in wireless, IoT, and sensor networks. However, static algorithms such as Affinity Propagation Clustering (APC) often fail to generalize across diverse environments and user densities. This study introduces a hybrid clustering framework that dynamically selects between APC and density-based clustering (DBSCAN), guided by a neural classifier trained on spatial distribution features. The chosen centroids then seed a Genetic Algorithm (GA) that evolves UAV trajectories under multiple performance indicators, including coverage, capacity, and path efficiency. Simulation results demonstrate that the hybrid clustering approach improves the adaptability and effectiveness of UAV deployments by learning context-aware clustering strategies. Beyond UAV-assisted disaster recovery, the proposed framework illustrates how intelligent clustering selection can enhance performance in heterogeneous, real-time applications such as IoT connectivity, smart city monitoring, and large-scale sensor coordination. Full article
(This article belongs to the Special Issue Advances in UAV Networks Towards 6G)
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24 pages, 2940 KB  
Communication
Secure Transmission for RIS-Assisted Downlink Hybrid FSO/RF SAGIN: Sum Secrecy Rate Maximization
by Jiawei Li, Weichao Yang, Tong Liu, Li Li, Yi Jin, Yixin He and Dawei Wang
Drones 2025, 9(3), 198; https://doi.org/10.3390/drones9030198 - 10 Mar 2025
Cited by 4 | Viewed by 1996
Abstract
This paper proposes a novel reconfigurable intelligent surface (RIS)-assisted downlink hybrid free-space optics (FSO)/radio frequency (RF) space–air–ground integrated network (SAGIN) architecture, where the high altitude platform (HAP) converts the optical signal sent by the satellite into an electrical signal through optoelectronic conversion. The [...] Read more.
This paper proposes a novel reconfigurable intelligent surface (RIS)-assisted downlink hybrid free-space optics (FSO)/radio frequency (RF) space–air–ground integrated network (SAGIN) architecture, where the high altitude platform (HAP) converts the optical signal sent by the satellite into an electrical signal through optoelectronic conversion. The drone equipped with RIS dynamically adjusts the signal path to serve ground users, thereby addressing communication challenges caused by RF link blockages from clouds or buildings. To improve the security performance of SAGIN, this paper maximizes the sum secrecy rate (SSR) by optimizing the power allocation, RIS phase shift, and drone trajectory. Then, an alternating iterative framework is proposed for a joint solution using the simulated annealing algorithm, semi-definite programming, and the designed deep deterministic policy gradient (DDPG) algorithm. The simulation results show that the proposed scheme can significantly enhance security performance. Specifically, compared with the NOMA and SDMA schemes, the SSR of the proposed scheme is increased by 39.7% and 286.7%, respectively. Full article
(This article belongs to the Special Issue Advances in UAV Networks Towards 6G)
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28 pages, 910 KB  
Article
Virtual Force-Based Swarm Trajectory Design for Unmanned Aerial Vehicle-Assisted Data Collection Internet of Things Networks
by Xuanlin Liu, Sihua Wang and Changchuan Yin
Drones 2025, 9(1), 28; https://doi.org/10.3390/drones9010028 - 3 Jan 2025
Cited by 4 | Viewed by 2295
Abstract
In this paper, the problem of trajectory design for unmanned aerial vehicle (UAV) swarms in data collection Internet of Things (IoT) networks is studied. In the considered model, the UAV swarm is deployed to patrol a designated area and collect status information from [...] Read more.
In this paper, the problem of trajectory design for unmanned aerial vehicle (UAV) swarms in data collection Internet of Things (IoT) networks is studied. In the considered model, the UAV swarm is deployed to patrol a designated area and collect status information from sensors monitoring physical processes. The sense-collect-interchange-explore (SCIE) protocol is proposed to regulate UAV actions, ensuring synchronization and adaptability in a distributed manner. To maintain real-time monitoring while reducing data transmission, we introduce status freshness, which is an extension of age of information (AoI) and allows negative values to reflect the swarm’s predictive capabilities. The trajectory design problem is then formulated as an optimization problem to minimize average status freshness. A virtual force-based algorithm is developed to solve this problem, where UAVs are influenced by attractive forces from sensors and repulsive forces from neighbors. These forces guide UAVs toward sensors requiring data transmission while reducing communication overlap. The proposed distributed algorithm allows each UAV to independently design its trajectory, reducing redundancy and enhancing scalability. Simulation results show that the proposed method can significantly reduce average status freshness under the same energy efficiency conditions compared to artificial potential field algorithm. The proposed method also achieves significantly reduction in terms of communication overhead, compared to fully connected strategies, ensuring scalability in large-scale UAV deployments. Full article
(This article belongs to the Special Issue Advances in UAV Networks Towards 6G)
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