TY - JOUR
T1 - Reinforcement Learning for a Cellular Internet of UAVs
T2 - Protocol Design, Trajectory Control, and Resource Management
AU - Hu, Jingzhi
AU - Zhang, Hongliang
AU - Song, Lingyang
AU - Han, Zhu
AU - Poor, H. Vincent
N1 - Funding Information:
Acknowledgments This work was supported in part by the National Natural Science Foundation of China under grant number 61625101 and in part by U.S. AFOSR MURI 18RT0073 and MURI FA9550-18-1-0502, NSF EARS-1839818, CNS1717454, CNS-1731424, CCF-1908308, and CNS-1646607.
PY - 2020/2/1
Y1 - 2020/2/1
N2 - Unmanned aerial vehicles (UAVs) can be powerful Internet of Things components to execute sensing tasks over the next-generation cellular networks, which are generally referred to as the cellular Internet of UAVs. However, due to the high mobility of UAVs and shadowing in airto-ground channels, UAVs operate in a dynamic and uncertain environment. Therefore, UAVs need to improve the quality of service of sensing and communication without complete information, which makes reinforcement learning suitable for use in the cellular Internet of UAVs. In this article, we propose a distributed sense-and-send protocol to coordinate UAVs for sensing and transmission. Then we apply reinforcement learning in the cellular Internet of UAVs to solve key problems such as trajectory control and resource management. Finally, we point out several potential future research directions.
AB - Unmanned aerial vehicles (UAVs) can be powerful Internet of Things components to execute sensing tasks over the next-generation cellular networks, which are generally referred to as the cellular Internet of UAVs. However, due to the high mobility of UAVs and shadowing in airto-ground channels, UAVs operate in a dynamic and uncertain environment. Therefore, UAVs need to improve the quality of service of sensing and communication without complete information, which makes reinforcement learning suitable for use in the cellular Internet of UAVs. In this article, we propose a distributed sense-and-send protocol to coordinate UAVs for sensing and transmission. Then we apply reinforcement learning in the cellular Internet of UAVs to solve key problems such as trajectory control and resource management. Finally, we point out several potential future research directions.
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U2 - 10.1109/MWC.001.1900262
DO - 10.1109/MWC.001.1900262
M3 - Article
AN - SCOPUS:85081675531
VL - 27
SP - 116
EP - 123
JO - IEEE Wireless Communications
JF - IEEE Wireless Communications
SN - 1536-1284
IS - 1
M1 - 9023933
ER -