TY - GEN
T1 - Delay Minimization in Multi-UAV Assisted Wireless Networks
T2 - 2nd EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2020
AU - Wu, Chenyu
AU - Gu, Xuemai
AU - Shi, Shuo
N1 - Publisher Copyright:
© 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
PY - 2021
Y1 - 2021
N2 - Unmanned Aerial Vehicles (UAVs) assisted communications are promising technology for meeting the demand of unprecedented demands for wireless services. In this paper, we propose a novel framework for delay minimization driven deployment of multiple UAVs. The problem of joint non-convex three dimensional (3D) deployment for minimizing average delay is formulated and solved by Deep Q network (DQN), which is a reinforcement learning based algorithm. Firstly, we obtain the cell partition by K-means algorithm. Then, we find the optimal 3D position for each UAV in each cluster to provide low delay service. Finally, when users are roaming, the UAVs are still able to track the real-time users. Numerical results show that the proposed DQN-based delay algorithm shows a fast convergence after a small number of iterations. Additionally, the proposed deployment algorithm outperforms several benchmarks in terms of average delay.
AB - Unmanned Aerial Vehicles (UAVs) assisted communications are promising technology for meeting the demand of unprecedented demands for wireless services. In this paper, we propose a novel framework for delay minimization driven deployment of multiple UAVs. The problem of joint non-convex three dimensional (3D) deployment for minimizing average delay is formulated and solved by Deep Q network (DQN), which is a reinforcement learning based algorithm. Firstly, we obtain the cell partition by K-means algorithm. Then, we find the optimal 3D position for each UAV in each cluster to provide low delay service. Finally, when users are roaming, the UAVs are still able to track the real-time users. Numerical results show that the proposed DQN-based delay algorithm shows a fast convergence after a small number of iterations. Additionally, the proposed deployment algorithm outperforms several benchmarks in terms of average delay.
KW - Delay minimization
KW - Deployment
KW - Reinforcement learning
KW - Unmanned Aerial Vehicles
UR - https://www.scopus.com/pages/publications/85104492091
U2 - 10.1007/978-3-030-69066-3_22
DO - 10.1007/978-3-030-69066-3_22
M3 - 会议稿件
AN - SCOPUS:85104492091
SN - 9783030690656
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 249
EP - 259
BT - Artificial Intelligence for Communications and Networks - 2nd EAI International Conference, AICON 2020, Proceedings
A2 - Shi, Shuo
A2 - Ye, Liang
A2 - Zhang, Yu
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 19 December 2020 through 20 December 2020
ER -