This paper presents a measurement-based framework for characterizing altitude-dependent spectral behavior of signals received by a tethered Helikite unmanned aerial vehicle (UAV). Using a multi-year spectrum measurement campaign in an outdoor urban environment, power spectral density snapshots are collected over the 89 MHz--6 GHz range. Three altitude-dependent spectral metrics are extracted: band-average power, spectral entropy, and spectral sparsity. We introduce the Altitude-Dependent Spectral Structure Model (ADSSM) to characterize the spectral power and entropy using first-order altitude-domain differential equations, and spectral sparsity using a logistic function, yielding closed-form expressions with physically consistent asymptotic behavior. The model is fitted to altitude-binned measurements from three annual campaigns at the AERPAW testbed across six licensed and unlicensed sub-6 GHz bands. Across all bands and years, the ADSSM achieves low root-mean-square error and high coefficients of determination. Results indicate that power transitions occur over narrow low-altitude regions, while entropy and sparsity evolve over broader, band-dependent altitude ranges, demonstrating that altitude-dependent spectrum behavior is inherently multidimensional. By explicitly modeling altitude-dependent transitions in spectral structure beyond received power, the proposed framework enables spectrum-aware UAV sensing and band selection decisions that are not achievable with conventional power- or threshold-based occupancy models.
Movable Antenna-Enabled Integrated Sensing and Communication in Low-Altitude UAV Networks
Nanjing University of Information Science and Technology（南京信息工程大学）；Anhui University of Finance & Economics（安徽财经大学）；Beijing Institute of Technology（北京理工大学）
Integrating Low-Altitude SAR Imaging into UAV Data Backhaul
Nanjing University of Information Science and Technology（南京信息工程大学）；National Mobile Communications Research Laboratory, Southeast University（东南大学移动通信国家重点实验室）；Southeast University（东南大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Southern University of Science and Technology (SUSTech)（南方科技大学）；Shanghai Jiao Tong University（上海交通大学）
Intelligent Sky Mirrors: SAC-Driven MF-RIS Optimization for Secure NOMA in Low-Altitude Economy
Guangzhou University（广州大学）；City University of Hong Kong（香港城市大学）；China Telecom Research Institute（中国电信研究院）；University College London（伦敦大学学院）；Yonsei University（延世大学）；Hong Kong Metropolitan University（香港都会大学）
Wireless Communication for Low-Altitude Economy with UAV Swarm Enabled Two-Level Movable Antenna System
Nanjing University of Science and Technology（南京理工大学）；National Mobile Communications Research Laboratory, Southeast University（东南大学）；Purple Mountain Laboratories（紫金山实验室）；Southeast University（东南大学）
Low-altitude UAV Friendly-Jamming for Satellite-Maritime Communications via Generative AI-enabled Deep Reinforcement Learning
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University（教育部符号计算与知识工程重点实验室）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Department of Electrical and Computer Engineering, The University of British Columbia（不列颠哥伦比亚大学电气与计算机工程系）
Integrated Sensing, Communication, Computing, and Control Meets UAV Swarms in 6G
School of Electronic and Information Engineering, Beijing Jiaotong University（北京交通大学电子与信息工程学院）；College of Electronic Science and Technology, National University of Defense Technology（国防科技大学电子科学学院）
Trajectory Design for UAV-Based Low-Altitude Wireless Networks in Unknown Environments: A Digital Twin-Assisted TD3 Approach
School of Information and Electronics, Beijing Institute of Technology（信息与电子学院，北京理工大学）；School of Information and Communication Engineering, Beijing University of Posts and Telecommunications（信息与通信工程学院，北京邮电大学）；Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong (Shenzhen)（大数据深圳研究院，香港中文大学（深圳））；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）
