Bayesian optimization enhanced deep reinforcement learning for trajectory planning and network formation in multi-UAV networks
In this paper, we employ multiple UAVs coordinated by a base station (BS) to help the ground users (GUs) to offload their sensing data. Different UAVs can adapt their trajectories and network formation to expedite data transmissions via multi-hop relaying. The trajectory planning aims to collect all...
Saved in:
Main Authors: | Gong, Shimin, Wang, Meng, Gu, Bo, Zhang, Wenjie, Hoang, Dinh Thai, Niyato, Dusit |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Article |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/170818 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
An Efficient Solution for Joint Power and Trajectory Optimization in UAV-Enabled Wireless Network
by: Tang, H., et al.
Published: (2021) -
Compressing Trajectory for Trajectory Indexing
by: Feng, Kaiyu, et al.
Published: (2018) -
Trajectory based optimal segment computation in road network databases
by: Li, X., et al.
Published: (2014) -
Extrapolative Bayesian optimization with Gaussian process and neural network ensemble surrogate models
by: Lim, Yee-Fun, et al.
Published: (2022) -
When UAV meets IRS: expanding air-ground networks via passive reflection
by: Pang, Xiaowei, et al.
Published: (2022)