Name
Title | ||
|---|---|---|
Network Slicing with Centralized and Distributed Reinforcement Learning for Combined Satellite/Ground Networks in a 6G Environment |
Abstract | ||
|---|---|---|
For the goals of beyond 5G and 6G networks, it is essential to maintain access everywhere and offer low latency with high reliability. To achieve such goals, satellite networks are an instrumental technology that grants network coverage even in remote areas without ground infrastructure and provides an offloading option when ground networks are too crowded with workload. However, for an efficient ... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/MWC.001.2100287 | IEEE Wireless Communications |
Keywords | DocType | Volume |
6G mobile communication, Satellites, Costs, Machine learning algorithms, Network slicing, 5G mobile communication, Reinforcement learning | Journal | 29 |
Issue | ISSN | Citations |
1 | 1536-1284 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tiago Koketsu Rodrigues | 1 | 0 | 0.34 |
| Nei Kato | 2 | 3982 | 263.66 |