Name
Title | ||
|---|---|---|
QoE-Driven Adaptive Deployment Strategy of Multi-UAV Networks Based on Hybrid Deep Reinforcement Learning |
Abstract | ||
|---|---|---|
Unmanned aerial vehicles (UAVs) serve as aerial base stations to provide controlled wireless connections for ground users. Due to their constraints on both mobility and energy consumption, a key problem is how to deploy UAVs adaptively in a geographic area with changing traffic demand of mobile users, while meeting the aforementioned constraints. In this article, we propose a Quality of Experience... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/JIOT.2021.3066368 | IEEE Internet of Things Journal |
Keywords | DocType | Volume |
Games,Reinforcement learning,Unmanned aerial vehicles,Optimization,Adaptive systems,Throughput,Quality of experience | Journal | 9 |
Issue | ISSN | Citations |
8 | 2327-4662 | 0 |
PageRank | References | Authors |
0.34 | 28 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Zhou | 1 | 51 | 5.38 |
| Xiaoyong Ma | 2 | 0 | 1.69 |
| Shuting Hu | 3 | 0 | 1.01 |
| Danyang Zhou | 4 | 0 | 1.35 |
| Nan Cheng | 5 | 970 | 81.34 |
| Ning Lu | 6 | 727 | 37.36 |