UAV Communication

UAV: Unmanned Aerial Vehicle

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Latest papers

8 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

LiLib: Lifelong Air-to-Ground Path-Loss Prediction on UAVs via a Drift-Triggered Model Library
Oct 5, 2026Minh TranConcept Drift Wireless Communications
Hanoi University of Science and Technology (HUST), Hanoi, Vietnam
Threat-Aware Energy-Efficient Deployment for Dynamic UAV Networks: A Multi-Agent RL Approach
Sep 25, 2026Faisal Al-Kamali, Hussein A. Ammar, Francois Chan +3UAV Communication UAV Trajectory Optimization
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada · Department of Electrical Engineering, Ibb University, Ibb, Yemen · Department of Electrical and Computer Engineering, Royal Military College, Kingston, ON, K7K 7B4, Canada +1
Geometry-Aware Cross-Height Channel Knowledge Map Prediction for UAV-Assisted Communications With Uncertainty-Guided 3D Sensing
Jul 1, 2026Zhihan Zeng, Amir Hussain, Yue Xiu +4Active Sensing Wireless Communications
National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China · SDAIA-KFUPM Joint Research Centre of AI, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia · School of Science, Technology and Engineering, University of the Sunshine Coast, Moreton Bay Campus, Petrie, QLD 4502, Australia +3
Diffusion Offline Reinforcement Learning for Fair and Energy-Efficient UAV-Assisted Wireless Networks
Jun 15, 2026Eslam Eldeeb, Hirley AlvesOffline RL Wireless Communications
Centre for Wireless Communications (CWC), University of Oulu, Finland
X-Band UAV-enabled Integrated Sensing and Communications for Vehicular Networks
Jun 3, 2026Remon Polus, Soumaya CherkaouiIntegrated Sensing and Communication Wireless Communications
Department of Computer and Software Engineering Polytechnique Montréal Montréal, Canada
On Privacy-Preserving Image Transmission in Low-Altitude Networks: A Swin Transformer-Based Framework with Federated Learning
May 12, 2026Kexin Zhang, Lixin Li, Yuna Yan +5Semantic Communication Wireless Communications
School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, China · DecoreX Intelligent Technologies Co., Ltd., Xi’an 710075, China · Samsung AI Center, Cambridge CB1 2JH, UK +2
Plasticity-Enhanced Multi-Agent Mixture of Experts for Dynamic Objective Adaptation in UAVs-Assisted Emergency Communication Networks
Apr 10, 2026Wen Qiu, Zhiqiang He, Wei Zhao +1UAV Communication Reinforcement Learning
Department of Information and Communication Engineering, Kitami Institute of Technology, Japan. · Graduate School of Informatics and Engineering, the University of Electro-Communications, Japan. · School of Computer Science and Technology, Anhui University of Technology, China. +1
Hybrid Belief Reinforcement Learning for Efficient Coordinated Spatial Exploration
Mar 4, 2026Danish Rizvi, David BoyleUAV Communication Reinforcement Learning
Imperial College London, UK

Oct 5, 2026J/K move · Enter open · S save

LiLib: Lifelong Air-to-Ground Path-Loss Prediction on UAVs via a Drift-Triggered Model Library

Minh Tran

Hanoi University of Science and Technology (HUST), Hanoi, Vietnam

Concept Drift Wireless Communications Radio Propagation Modeling Online Continual Learning UAV Communication

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UAVs that act as relays or base stations need accurate air-to-ground path-loss predictions for rate adaptation and placement, but propagation conditions change as a UAV moves between suburban, urban and high-rise areas, and the same areas are often revisited. Online regressors that adapt by forgetting must relearn each environment from scratch, whereas a single model trained on all data averages incompatible regimes. We propose LiLib, a lightweight continual-learning scheme in which a UAV maintains a small library of recursive-least-squares experts. A windowed residual test detects drift; a short probe phase then either reuses the best stored expert or creates a new one. In simulations based on four standard urbanization profiles, LiLib reduces prediction RMSE from 5.89 dB (best sliding-window baseline) to 4.03 dB (p < 0.001), lowers the error shortly after a return to a known environment from 12.3 dB to 5.7 dB, and recovers 99% of the throughput of a regime-aware oracle in rate adaptation. The library stores four experts in under 0.5 KB, and identifies regimes with 92% purity without labels. When a second UAV is initialized with the library of a peer, its error after environment changes halves. LiLib does not reach the oracle, and similar regimes may be merged when shadowing is strong. The results indicate that, for recurring drift, remembering is more effective than re-adapting.