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Sensing and Communication for Unmanned Aerial Vehicles Networks

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

Deadline for manuscript submissions: closed (15 May 2026) | Viewed by 2353

Special Issue Editors


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Guest Editor
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
Interests: federated learning; edge/fog computing; UAV/vehicular networking
Special Issues, Collections and Topics in MDPI journals

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Guest Editor Assistant
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
Interests: integrated sensing and communication; space–air–ground networks; edge computing

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicle (UAV) networks are fast becoming critical infrastructure for emergency response, environmental monitoring, smart agriculture, logistics, and beyond. Integrated sensing and communication (ISAC) on aerial platforms offer a compelling path to higher spectrum/energy efficiency and resilience by sharing hardware, spectrum, and control loops. However, due to the size, weight, and power constraints of UAVs, their controllable mobility, and the line-of-sight air–ground channels, ISAC-enabled UAV networks introduce new opportunities and challenges. This Special Issue seeks contributions that harness the duality of sensing-assisted UAV communication and communication-assisted sensing to deliver measurable gains in reliability, latency, coverage, and efficiency. We seek high-quality original research papers on relevant topics related to ISAC-enabled UAV networks including, but not limited to, the following: resource allocation/ waveform design for ISAC-enabled UAV networks, deployment/trajectory design for ISAC-enabled UAV networks, and AI strategies for UAV-enabled ISAC, sensing-assisted UAV communication, etc.

We especially welcome cross-layer designs that couple waveform/protocol co-design with joint trajectory–beamforming–scheduling optimization, cooperative multi-UAV sensing/relaying, and edge intelligence for real-time decision-making. In other words, this Special Issue aims to catalyze practical, energy-efficient, and scalable low-altitude networks for 6G and beyond.

Prof. Dr. Zheng Chang
Guest Editor

Dr. Mingan Luan
Guest Editor Assistant

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Keywords

  • integrated sensing and communication (ISAC)
  • sensing-assisted UAV communication
  • resource allocation for ISAC-enabled UAV networks
  • deployment/trajectory design for UAV networks
  • AI strategies for ISAC-enabled UAV networks

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Published Papers (1 paper)

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Research

11 pages, 1845 KB  
Article
Acoustic Source Drone Detection System Using Tetrahedral Microphone Array and Deep Neural Networks
by Marian Traian Ghenescu, Veta Ghenescu and Serban Vasile Carata
Sensors 2026, 26(6), 1778; https://doi.org/10.3390/s26061778 - 11 Mar 2026
Viewed by 1935
Abstract
The rapid integration of Unmanned Aerial Vehicles (UAVs) into civilian airspace has introduced complex security challenges, particularly regarding the protection of critical infrastructure and personal privacy. While conventional detection mechanisms such as radar and optical sensors are widely deployed, they are frequently limited [...] Read more.
The rapid integration of Unmanned Aerial Vehicles (UAVs) into civilian airspace has introduced complex security challenges, particularly regarding the protection of critical infrastructure and personal privacy. While conventional detection mechanisms such as radar and optical sensors are widely deployed, they are frequently limited by line-of-sight obstructions and the small radar cross-section of modern commercial drones. Acoustic analysis presents a viable passive alternative; however, accurate three-dimensional localization remains a computationally demanding task, further complicated by the use of directional sensors with non-uniform sensitivity patterns. In this paper, a deep learning framework is proposed to address these ambiguities. The method involves the fusion of raw acoustic data with explicit sensor geometry metadata within a neural network architecture. To enhance localization precision, a composite loss function is introduced, which independently optimizes planar and altitude coordinates while penalizing outlier predictions. Experimental validation was conducted using a custom dataset of real-world drone flights, utilizing a distributed array of directional microphones. The results demonstrate that the proposed system effectively mitigates the spatial irregularities of ad hoc sensor deployment, achieving robust localization performance in complex acoustic environments. Full article
(This article belongs to the Special Issue Sensing and Communication for Unmanned Aerial Vehicles Networks)
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