UAV Applications and Optimization

Últimos artículos

• Spatio-Temporal Attention Enhanced Multi-Agent DRL for UAV-Assisted Wireless Networks with Limited Communications

  Che Chen, Lanhua Li, Shimin Gong, Yu Zhao, Yuming Fang, Dusit Niyato · 24 de marzo de 2026

  In this paper, we employ multiple UAVs to accelerate data transmissions from ground users (GUs) to a remote base station (BS) via the UAVs' relay communications. The UAVs' intermittent information exchanges typically result in delays in acquiring the complete system state and hinder their effective …

• JCAS-MARL: Joint Communication and Sensing UAV Networks via Resource-Constrained Multi-Agent Reinforcement Learning

  Islam Guven, Mehmet Parlak · 24 de marzo de 2026

  Multi-UAV networks are increasingly deployed for large-scale inspection and monitoring missions, where operational performance depends on the coordination of sensing reliability, communication quality, and energy constraints. In particular, the rapid increase in overflowing waste bins and illegal du…

• Cyber Deception for Mission Surveillance via Hypergame-Theoretic Deep Reinforcement Learning

  Zelin Wan, Jin-Hee Cho, Mu Zhu, Ahmed H. Anwar, Charles Kamhoua, Munindar P. Singh · 24 de marzo de 2026

  Unmanned Aerial Vehicles (UAVs) are valuable for mission-critical systems like surveillance, rescue, or delivery. Not surprisingly, such systems attract cyberattacks, including Denial-of-Service (DoS) attacks to overwhelm the resources of mission drones (MDs). How can we defend UAV mission systems a…

• CageDroneRF: A Large-Scale RF Benchmark and Toolkit for Drone Perception

  Mohammad Rostami, Atik Faysal, Hongtao Xia, Hadi Kasasbeh, Ziang Gao, Huaxia Wang · 23 de marzo de 2026

  We present CageDroneRF (CDRF), a large-scale benchmark for Radio-Frequency (RF) drone detection and identification built from real-world captures and systematically generated synthetic variants. CDRF addresses the scarcity and limited diversity of existing RF datasets by coupling extensive raw recor…

• Learn for Variation: Variationally Guided AAV Trajectory Learning in Differentiable Environments

  Xiucheng Wang, Zhenye Chen, Nan Cheng · 20 de marzo de 2026

  Autonomous aerial vehicles (AAVs) empower sixth-generation (6G) Internet-of-Things (IoT) networks through mobility-driven data collection. However, conventional reward-driven reinforcement learning for AAV trajectory planning suffers from severe credit assignment issues and training instability, bec…

• Agentic AI for Embodied-enhanced Beam Prediction in Low-Altitude Economy Networks

  Min Hao, Zhizhuo Li, Zirui Zhang, Maoqiang Wu, Han Zhang, Rong Yu · 13 de marzo de 2026

  Millimeter-wave or terahertz communications can meet demands of low-altitude economy networks for high-throughput sensing and real-time decision making. However, high-frequency characteristics of wireless channels result in severe propagation loss and strong beam directivity, which make beam predict…

• UAV-MARL: Multi-Agent Reinforcement Learning for Time-Critical and Dynamic Medical Supply Delivery

  Islam Guven, Mehmet Parlak · 12 de marzo de 2026

  Unmanned aerial vehicles (UAVs) are increasingly used to support time-critical medical supply delivery, providing rapid and flexible logistics during emergencies and resource shortages. However, effective deployment of UAV fleets requires coordination mechanisms capable of prioritizing medical reque…

• Hybrid Belief Reinforcement Learning for Efficient Coordinated Spatial Exploration

  Danish Rizvi, David Boyle · 5 de marzo de 2026

  Coordinating multiple autonomous agents to explore and serve spatially heterogeneous demand requires jointly learning unknown spatial patterns and planning trajectories that maximize task performance. Pure model-based approaches provide structured uncertainty estimates but lack adaptive policy learn…

• Intent-Context Synergy Reinforcement Learning for Autonomous UAV Decision-Making in Air Combat

  Jiahao Fu, Feng Yang · 3 de marzo de 2026

  Autonomous UAV infiltration in dynamic contested environments remains a significant challenge due to the partially observable nature of threats and the conflicting objectives of mission efficiency versus survivability. Traditional Reinforcement Learning (RL) approaches often suffer from myopic decis…

• Blockchain-Enabled Routing for Zero-Trust Low-Altitude Intelligent Networks

  Ziye Jia, Sijie He, Ligang Yuan, Fuhui Zhou, Qihui Wu, Zhu Han, Dusit Niyato · 2 de marzo de 2026

  Due to the scalability and portability, low-altitude intelligent networks (LAINs) are essential in various fields such as surveillance and disaster rescue. However, in LAINs, unmanned aerial vehicles (UAVs) are characterized by the distributed topology and high mobility, thus vulnerable to security …

• ASL360: AI-Enabled Adaptive Streaming of Layered 360$^\circ$ Video over UAV-assisted Wireless Networks

  Alireza Mohammadhosseini, Jacob Chakareski, Nicholas Mastronarde · 24 de febrero de 2026

  We propose ASL360, an adaptive deep reinforcement learning-based scheduler for on-demand 360$^\circ$ video streaming to mobile VR users in next generation wireless networks. We aim to maximize the overall Quality of Experience (QoE) of the users served over a UAV-assisted 5G wireless network. Our sy…

• Large Language Model-Assisted UAV Operations and Communications: A Multifaceted Survey and Tutorial

  Yousef Emami, Hao Zhou, Radha Reddy, Atefeh Hajijamali Arani, Biliang Wang, Kai Li, Luis Almeida, Zhu Han · 24 de febrero de 2026

  Uncrewed Aerial Vehicles (UAVs) are widely deployed across diverse applications due to their mobility and agility. Recent advances in Large Language Models (LLMs) offer a transformative opportunity to enhance UAV intelligence beyond conventional optimization-based and learning-based approaches. By i…

• Voice-Driven Semantic Perception for UAV-Assisted Emergency Networks

  Nuno Saavedra, Pedro Ribeiro, Andr\'e Coelho, Rui Campos · 20 de febrero de 2026

  Unmanned Aerial Vehicle (UAV)-assisted networks are increasingly foreseen as a promising approach for emergency response, providing rapid, flexible, and resilient communications in environments where terrestrial infrastructure is degraded or unavailable. In such scenarios, voice radio communications…

• A High-Level Survey of Optical Remote Sensing

  Panagiotis Koletsis, Vasilis Efthymiou, Maria Vakalopoulou, Nikos Komodakis, Anastasios Doulamis, Georgios Th. Papadopoulos · 20 de febrero de 2026

  In recent years, significant advances in computer vision have also propelled progress in remote sensing. Concurrently, the use of drones has expanded, with many organizations incorporating them into their operations. Most drones are equipped by default with RGB cameras, which are both robust and amo…

• A Disentangled Representation Learning Framework for Low-altitude Network Coverage Prediction

  Xiaojie Li, Zhijie Cai, Nan Qi, Chao Dong, Guangxu Zhu, Haixia Ma, Qihui Wu, Shi Jin · 17 de febrero de 2026

  The expansion of the low-altitude economy has underscored the significance of Low-Altitude Network Coverage (LANC) prediction for designing aerial corridors. While accurate LANC forecasting hinges on the antenna beam patterns of Base Stations (BSs), these patterns are typically proprietary and not r…

• Deep Reinforcement Learning based Autonomous Decision-Making for Cooperative UAVs: A Search and Rescue Real World Application

  Thomas Hickling, Maxwell Hogan, Abdulla Tammam, Nabil Aouf · 17 de febrero de 2026

  This paper presents the first end-to-end framework that combines guidance, navigation, and centralised task allocation for multiple UAVs performing autonomous search-and-rescue (SAR) in GNSS-denied indoor environments. A Twin Delayed Deep Deterministic Policy Gradient controller is trained with an A…

• Simulation-Based Study of AI-Assisted Channel Adaptation in UAV-Enabled Cellular Networks

  Andrii Grekhov, Volodymyr Kharchenko, Vasyl Kondratiuk · 17 de febrero de 2026

  This paper presents a simulation based study of Artificial Intelligence assisted communication channel adaptation in Unmanned Aerial Vehicle enabled cellular networks. The considered system model includes communication channel Ground Base Station Aerial Repeater UAV Base Station Cluster of Cellular …

• Traffic Simulation in Ad Hoc Network of Flying UAVs with Generative AI Adaptation

  Andrii Grekhov, Volodymyr Kharchenko, Vasyl Kondratiuk · 17 de febrero de 2026

  The purpose of this paper is to model traffic in Ad Hoc network of Unmanned Aerial Vehicles and demonstrate a way for adapting communication channel using Artificial Intelligence. The modeling was based on the original model of Ad Hoc network including 20 Unmanned Aerial Vehicles. The dependences of…

• A Safety-Constrained Reinforcement Learning Framework for Reliable Wireless Autonomy

  Abdikarim Mohamed Ibrahim, Rosdiadee Nordin · 17 de febrero de 2026

  Artificial intelligence (AI) and reinforcement learning (RL) have shown significant promise in wireless systems, enabling dynamic spectrum allocation, traffic management, and large-scale Internet of Things (IoT) coordination. However, their deployment in mission-critical applications introduces the …

• Hierarchical Reinforcement Learning for Cooperative Air-Ground Delivery in Urban System

  Songxin Lei, Chunming Ma, Haomin Wen, Yexin Li, Lizhenghe Chen, Qianyu Yang, Fugee Tsung, Lei Chen, Sijie Ruan, Yuxuan Liang · 16 de febrero de 2026

  Cooperative air-ground delivery has emerged as a promising logistics paradigm by leveraging the complementary strengths of UAVs and ground carriers. However, effective dispatching in such heterogeneous systems faces two critical challenges: i) the heterogeneity between flight and road dynamics, ii) …

• Probability Hacking and the Design of Trustworthy ML for Signal Processing in C-UAS: A Scenario Based Method

  Liisa Janssens, Laura Middeldorp · 10 de febrero de 2026

  In order to counter the various threats manifested by Unmanned Aircraft Systems (UAS) adequately, specialized Counter Unmanned Aircraft Systems (C-UAS) are required. Enhancing C-UAS with Emerging and Disruptive Technologies (EDTs) such as Artificial Intelligence (AI) can lead to more effective count…

• UAV Trajectory Optimization via Improved Noisy Deep Q-Network

  Zhang Hengyu, Maryam Cheraghy, Liu Wei, Armin Farhadi, Meysam Soltanpour, Zhong Zhuoqing · 6 de febrero de 2026

  This paper proposes an Improved Noisy Deep Q-Network (Noisy DQN) to enhance the exploration and stability of Unmanned Aerial Vehicle (UAV) when applying deep reinforcement learning in simulated environments. This method enhances the exploration ability by combining the residual NoisyLinear layer wit…

• IMAGINE: Intelligent Multi-Agent Godot-based Indoor Networked Exploration

  Tiago Leite, Maria Concei\c{c}\~ao, Ant\'onio Grilo · 4 de febrero de 2026

  The exploration of unknown, Global Navigation Satellite System (GNSS) denied environments by an autonomous communication-aware and collaborative group of Unmanned Aerial Vehicles (UAVs) presents significant challenges in coordination, perception, and decentralized decision-making. This paper impleme…

• Lyapunov Stability-Aware Stackelberg Game for Low-Altitude Economy: A Control-Oriented Pruning-Based DRL Approach

  Yue Zhong, Jiawen Kang, Yongju Tong, Hong-Ning Dai, Dong In Kim, Abbas Jamalipour, Shengli Xie · 3 de febrero de 2026

  With the rapid expansion of the low-altitude economy, Unmanned Aerial Vehicles (UAVs) serve as pivotal aerial base stations supporting diverse services from users, ranging from latency-sensitive critical missions to bandwidth-intensive data streaming. However, the efficacy of such heterogeneous netw…

• Semantically Aware UAV Landing Site Assessment from Remote Sensing Imagery via Multimodal Large Language Models

  Chunliang Hua, Zeyuan Yang, Lei Zhang, Jiayang Sun, Fengwen Chen, Chunlan Zeng, Xiao Hu · 3 de febrero de 2026

  Safe UAV emergency landing requires more than just identifying flat terrain; it demands understanding complex semantic risks (e.g., crowds, temporary structures) invisible to traditional geometric sensors. In this paper, we propose a novel framework leveraging Remote Sensing (RS) imagery and Multimo…