Special Issues
Table of Content

Aerial Innovation Spectrum: All-Domain Research in UAV Communication, Navigation, and Autonomy

Submission Deadline: 31 August 2026 View: 643 Submit to Special Issue

Guest Editor(s)

Dr. Kaleem Arshid

Email: kaleem.arshid@edu.unige.it

Affiliation: University of Genoa, Genoa, 16100, Italy

Homepage:

Research Interests: UAV communication systems (A2G, A2A, 5G/6G-enabled networks) Wireless networking and flying ad hoc networks (FANETs) UAV navigation, guidance, and autonomous flight control AI- and ML-driven UAV decision-making and perception Multi-UAV coordination, swarming, and collaborative missions Sensor fusion, localization, and UAV-based remote sensing Security, privacy, and reliability in UAV systems UAV applications in monitoring, smart cities, agriculture, and disaster management


Summary

Aerial Innovation Spectrum: All-Domain Research in UAV Communication, Navigation, and Autonomy.
1) Issue Introduction
Unmanned Aerial Vehicles (UAVs) have rapidly evolved into essential components of modern communication, sensing, and autonomous systems. With breakthroughs in wireless technologies, AI-driven control, and multi-domain applications, comprehensive research in UAV communication, navigation, and autonomy has become increasingly important for future intelligent and connected systems.


2) Aim and Scope
This Special Issue aims to bring together cutting-edge research innovations that advance the development, operation, and integration of UAV systems across communication, navigation, and autonomous intelligence. It welcomes original contributions addressing foundational technologies, analytical models, algorithms, system prototypes, and emerging applications of UAVs. Topics include communication frameworks, autonomous decision-making, swarm coordination, sensing technologies, AI-driven control, and security challenges. The Special Issue particularly encourages interdisciplinary studies that bridge theory and real-world deployment, enabling UAVs to play a transformative role in smart cities, environmental monitoring, industrial inspection, logistics, and future intelligent networks.


3) Suggested Themes
1. UAV Communication and Network Architectures
2. Autonomous Navigation, Guidance, and Control Algorithms
3. AI-Driven UAV Intelligence and Multi-UAV Coordination
4. Sensing, Perception, and UAV-Based Monitoring Systems
5. Security, Privacy, and Trust in UAV Operations
6. UAV Applications in Industry, Environment, and Smart Cities
7. Emerging Cross-Domain UAV Technologies and System Integration


Graphic Abstract

Aerial Innovation Spectrum: All-Domain Research in UAV Communication, Navigation, and Autonomy

Keywords

UAV Communication, Autonomous Navigation, UAV Autonomy, Flying Ad Hoc Networks (FANETs), Multi-UAV Coordination, UAV Sensing and Perception, AI-Driven Control, 5G/6G-Enabled UAV Systems, UAV Security and Privacy, Intelligent Aerial Systems

Published Papers


  • Open Access

    ARTICLE

    LRT-BF: A Lightweight and Robust Blind Beamforming Method for High-Dynamic UAV Communications

    Zheng Xu, Zihao Pan, Ning Yang, Daoxing Guo
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.080559
    (This article belongs to the Special Issue: Aerial Innovation Spectrum: All-Domain Research in UAV Communication, Navigation, and Autonomy)
    Abstract Unmanned Aerial Vehicle (UAV) communications in complex electromagnetic environments face challenges such as strong interference, high dynamic Doppler shifts, and limited onboard computing power. In these scenarios, traditional blind beamforming algorithms suffer from slow convergence and difficulty in handling Gaussian-like signals (e.g., Orthogonal Frequency Division Multiplexing (OFDM)). To address these issues, this paper proposes a Lightweight Robust Transfer learning-based Blind Beam Forming method (LRT-BF). This method constructs a self-supervised optimization framework centered on a pre-trained signal classifier and innovatively introduces a joint loss function combining classification confidence guidance with output power minimization, achieving fully blind… More >

  • Open Access

    ARTICLE

    IRL-TP: Deep Inverse Reinforcement Learning-Based Trajectory Planning for UAVs in Complex and Interference-Constrained Environments

    Xuan-Thuc Nguyen, Le-Minh Nguyen, Ngoc-Quynh Nguyen, Nhu-Nghia Bui, Dinh-Quy Vu, Thai-Viet Dang
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.080008
    (This article belongs to the Special Issue: Aerial Innovation Spectrum: All-Domain Research in UAV Communication, Navigation, and Autonomy)
    Abstract The development of unmanned automated vehicles (UAVs) has become a key focus in aerial robotics, fueling the need for navigation systems capable of performing complex and delicate tasks with speed and precision. However, the end-to-end path tracking process often encounters challenges in learning efficiency, generalization, and varying environmental conditions. In this paper, we propose the novel IRL-TP framework for learning-based UAVs’ trajectory planning that employs a deep inverse reinforcement learning (IRL) approach. Firstly, the RL-based path planner must develop a reward function that effectively captures flight safety, collision avoidance, trajectory smoothness, and navigation efficiency within… More >

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