Joint UAV 3D Trajectory Design and Resource Scheduling for Space-Air-Ground Integrated Power IoRT: A Deep Reinforcement Learning Approach

• Published in: IEEE transactions on network science and engineering Vol. 11; no. 3; pp. 2632 - 2646
• Main Authors: Liu, Jiayan, Zhao, Xiongwen, Qin, Peng, Du, Fei, Chen, Zhiyu, Zhou, Hongxi, Li, Junqin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.05.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; association control; Autonomous aerial vehicles; Communication; Deep learning; Design optimization; Energy consumption; multi-agent deep deterministic policy gradient (MADDPG); Multiagent systems; power optimization; Remote sensors; Resource scheduling; Satellites; Sensors; Space-air-ground integrated power Internet of Remote Things (SAG-PIoRT); Three-dimensional displays; Throughput; Tradeoffs; Trajectory; UAV three-dimensional (3D) trajectory design; Unmanned aerial vehicles

The terrain-independent space-air-ground integrated power Internet of Remote Things (SAG-PIoRT) is able to bring efficient communication services with seamless coverage for sensors in remote areas without information and communication network coverage. In this paper, considering the fairness among s...