Distributed management over Unmanned Aerial Vehicle (UAV) based communication networks (UCNs) has attracted increasing research attention. In this work, we study a distributed user connectivity maximization problem in a UCN. The work features a horizontal study over different levels of information exchange during the distributed iteration and a consideration of dynamics in UAV set and user distribution, which are not well addressed in the existing works. Specifically, the studied problem is first formulated into a time-coupled mixed-integer non-convex optimization problem. A heuristic two-stage UAV-user association policy is proposed to faster determine the user connectivity. To tackle the NP-hard problem in scalable manner, the distributed user connectivity maximization algorithm 1 (DUCM-1) is proposed under the multi-agent deep Q learning (MA-DQL) framework. DUCM-1 emphasizes on designing different information exchange levels and evaluating how they impact the learning convergence with stationary and dynamic user distribution. To comply with the UAV dynamics, DUCM-2 algorithm is developed which is devoted to autonomously handling arbitrary quit's and join-in's of UAVs in a considered time horizon. Extensive simulations are conducted i) to conclude that exchanging state information with a deliberated task-specific reward function design yields the best convergence performance, and ii) to show the efficacy and robustness of DUCM-2 against the dynamics.
When UAV Swarm Meets IRS: Collaborative Secure Communications in Low-altitude Wireless Networks
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；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, Auburn University（阿伯茨罕大学电气与计算机工程系）；School of Electrical and Computer Engineering, The University of Sydney（悉尼大学电气与计算机工程学院）
URLLC-Aware Proactive UAV Placement in Internet of Vehicles
New Jersey Institute of Technology（新泽西理工学院）；Maynooth University（梅努斯大学）；University of Peradeniya（佩拉德尼亚大学）；University of Oulu（奥卢大学）；Khalifa University of Science and Technology（哈利法科学技术大学）；CentraleSupélec, Université Paris-Saclay（巴黎萨克雷大学中央理工-高等电力学院）
LLM-Aided Joint Secrecy Precoding and Trajectory for RSMA-Based Heterogeneous UAV Networks
School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）；Department of Applied Data Science, San Jose State University（圣何塞州立大学应用数据科学系）
