| Title | AoI Minimization for UAV-to-Device Underlay Communication by Multi-agent Deep Reinforcement Learning |
| Authors | Wu, Fanyi Zhang, Hongliang Wu, Jianjun Song, Lingyang Han, Zhu Poor, H. Vincent |
| Affiliation | Peking Univ, Dept Elect, Beijing, Peoples R China Princeton Univ, Dept Elect Engn, Princeton, NJ 08544 USA Univ Houston, Dept Elect & Comp Engn, Houston, TX USA Kyung Hee Univ, Dept Comp Sci & Engn, Seoul, South Korea |
| Keywords | DESIGN OPTIMIZATION |
| Issue Date | 2020 |
| Publisher | 2020 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) |
| Abstract | In this paper, we consider a cellular Internet of UAVs, where the sensory data can be transmitted either to the base station via cellular links, or to the mobile devices by underlay UAV-to-Device communications. To evaluate the freshness of the sensory data, the age of information (AoI) is adopted, in which a lower AoI implies fresher data. Since UAVs' AoIs are determined by their trajectories during sensing and transmission, we aim to minimize the AoIs of UAVs by designing their trajectories. This problem is a Markov decision problem with an infinite state-action space, and thus, we propose a multi-UAV trajectory design algorithm by leveraging multi-agent deep reinforcement learning to solve it. Simulation results show that our proposed algorithm outperforms both a greedy algorithm and a policy gradient algorithm. |
| URI | http://hdl.handle.net/20.500.11897/625817 |
| ISBN | 978-1-7281-8298-8 |
| ISSN | 2334-0983 |
| DOI | 10.1109/GLOBECOM42002.2020.9322539 |
| Indexed | CPCI-S(ISTP) |
| Appears in Collections: | 信息科学技术学院 |
