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Sensors
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Challenges and Future Trends of UAV Communications
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Challenges and Future Trends of UAV Communications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: 27 August 2026 | Viewed by 1530

Special Issue Editors

Prof. Dr. Wei Wu

E-Mail Website
Guest Editor
College of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Interests: UAV swarm intelligence; mobile edge computing and edge intelligence; machine learning; wireless 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
Prof. Dr. Lisheng Fan

E-Mail Website
Guest Editor
School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 511370, China
Interests: IoT networks; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Tianwen Guo

E-Mail Website
Guest Editor
College of Computer and Information Engineering, Nanjing Tech University, Nanjing 211816, China
Interests: wireless mobile communications; wireless signal processing; physical layer security; semantic communications

Special Issue Information

Dear Colleagues,

The advances in networks to 6G and beyond promise to revolutionize unmanned aerial vehicle (UAV) communications, which transform UAVs from mere flying devices into intelligent, autonomous nodes within a seamlessly integrated air–ground–space network. This paradigm shift is driven by the convergence of cutting-edge technologies, such as artificial intelligence, semantic and goal-oriented communications, integrated sensing and communication (ISAC), and non-terrestrial networks (NTNs). These advancements will enable unprecedented applications, from large-scale IoT connectivity and smart grid inspection to ultra-reliable emergency response and real-time semantic-aware decision-making.

This Special Issue aims to collate the latest research and innovative solutions to the challenges of embedding UAVs into the 6G ecosystem. We invite contributions that explore novel architectures, algorithms, and protocols to unlock the full potential of UAVs as aerial base stations, relays, and data mules, while ensuring security, energy efficiency, and seamless integration with existing infrastructures such as satellite and terrestrial networks.

Potential topics include but are not limited to the following:

  • AI-Driven UAV Trajectory Optimization and Resource Management for 6G Networks;
  • Semantic and Goal-Oriented Communication for Autonomous UAV Swarms;
  • UAV-Assisted Integrated Sensing and Communication (ISAC);
  • Network Digital Twin for UAV Performance Prediction and Management in B5G/6G;
  • UAV-Terrestrial-Satellite Integrated Networks: Architecture, Protocols, and Routing;
  • Energy-Efficient UAV Communication and Charging for Sustainable IoT Services;
  • Security, Privacy, and Trustworthy AI for UAV Networks;
  • UAV-Assisted Edge Computing and Caching for Delay-Sensitive Applications;
  • Channel Modeling and Waveform Design for UAV Communications in Terahertz and mmWave Bands;
  • UAV Applications in Critical Infrastructures: Smart Grid Inspection, Precision Agriculture, and Delivery Logistics;
  • Reconfigurable Intelligent Surface (RIS)-Empowered UAV Communications for Enhanced Coverage.

Prof. Dr. Wei Wu
Dr. Mingzhe Chen
Prof. Dr. Lisheng Fan
Dr. Tianwen Guo
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. Sensors 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 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

  • unmanned aerial vehicle
  • 6G mobile communication
  • semantic communication
  • embodied artificial intelligence
  • edge computing
  • Internet of Things
  • integrated sensing and communication
  • non-terrestrial networks

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Published Papers (2 papers)

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Research

26 pages, 10447 KB  
Article
Resource-Adaptive Semantic Transmission and Client Scheduling for OFDM-Based V2X Communications
by Jiahao Liu, Yuanle Chen, Wei Wu and Feng Tian
Sensors 2026, 26(9), 2615; https://doi.org/10.3390/s26092615 - 23 Apr 2026
Viewed by 628
Abstract
Proportional, fair scheduling in OFDM-based vehicle-to-everything (V2X) uplink causes the resource-block allocation of each vehicle to vary from slot to slot, yet conventional semantic encoders produce a fixed number of output tokens regardless of the instantaneous channel capacity. When the encoder output exceeds [...] Read more.
Proportional, fair scheduling in OFDM-based vehicle-to-everything (V2X) uplink causes the resource-block allocation of each vehicle to vary from slot to slot, yet conventional semantic encoders produce a fixed number of output tokens regardless of the instantaneous channel capacity. When the encoder output exceeds the slot budget, transmitted features are truncated and the resulting federated learning gradient is corrupted—a problem that affected 23% of training rounds for non-line-of-sight vehicles in our experiments. The difficulty is worsened by a spatial pattern common in urban deployments: vehicles at congested intersections suffer the poorest propagation conditions while carrying the training data most relevant to safety, and throughput-driven client selection excludes them in favor of vehicles with strong channels but uninformative scenes. We address both issues within a single framework for OFDM-based V2X federated learning. On the transmission side, a Sensing-Guided Adaptive Modulation (SGAM) module derives a per-slot token budget from the current resource-block allocation and selects tokens through differentiable Gumbel-TopK pruning with a hard capacity clip, so the transmitted token count stays within the slot budget. On the scheduling side, a Channel-Decoupled Federated Learning (CDFL) module partitions clients independently by channel quality and data complexity, selects diverse representatives per partition via facility location optimization, and corrects for partition-size imbalance through inverse propensity weighting during model aggregation. Experiments on NuScenes with 20 non-IID vehicular clients under realistic OFDM channel simulation demonstrate a Macro-F1 of 0.710 (+8.7 points over the Oort-adapted baseline), zero budget violations throughout training, and a 75% reduction in training variance; the worst-class F1 more than doubles relative to FedAvg. Full article
(This article belongs to the Special Issue Challenges and Future Trends of UAV Communications)
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20 pages, 1481 KB  
Article
Reinforcement Learning for Secure Semantic LEO Satellite Networks: Joint Fidelity-Secrecy Power Allocation
by Feifei Zhou and Xiaorong Zhu
Sensors 2026, 26(8), 2546; https://doi.org/10.3390/s26082546 - 21 Apr 2026
Viewed by 497
Abstract
Semantic communications have emerged as a key paradigm for intelligent sixth-generation (6G) wireless networks, which aim to convey the meaning of information rather than accurate bit sequences. However, in open-space low Earth orbit (LEO) satellite links, the broadcast nature and wide beam coverage [...] Read more.
Semantic communications have emerged as a key paradigm for intelligent sixth-generation (6G) wireless networks, which aim to convey the meaning of information rather than accurate bit sequences. However, in open-space low Earth orbit (LEO) satellite links, the broadcast nature and wide beam coverage expose semantic transmissions to severe eavesdropping risks. This paper establishes a unified theoretical and algorithmic framework for secure semantic downlink transmission in satellite networks. In particular, we first develop an integrated mathematical model that couples the semantic representation process, physical-layer satellite propagation characteristics, and information-theoretic secrecy into a single analytical formulation. By defining a joint semantic security cost function, the antagonistic trade-off between semantic fidelity and secrecy capacity is quantitatively characterized under realistic power, beamforming, and propagation constraints. To balance semantic fidelity and information secrecy, a reinforcement-learning-based optimization framework is proposed, wherein an actor–critic agent learns optimal power allocation and semantic weighting strategies through continuous interaction with the environment. This learning-based optimization approach enables autonomous control without requiring explicit channel distribution knowledge or offline parameter tuning. Extended simulation results show that the proposed approach consistently enhances both semantic fidelity and secrecy performance compared with conventional power-control schemes and demonstrate its potential as a foundational architecture for secure and intelligent semantic communications in next-generation satellite networks. Full article
(This article belongs to the Special Issue Challenges and Future Trends of UAV Communications)
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