Name
Title | ||
|---|---|---|
Multi-Agent Deep Reinforcement Learning for Massive Access in 5G and Beyond Ultra-Dense NOMA System |
Abstract | ||
|---|---|---|
With the rapid development of machine-type communications (MTC), the future communication architecture needs to provide services for both human-type communications (HTC) and MTC with unique characteristics. The huge connections from MTC bring serious challenges to the existing wireless network. Ultra-dense network (UDN), a promising candidate technology, can support massive device access through d... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TWC.2021.3117859 | IEEE Transactions on Wireless Communications |
Keywords | DocType | Volume |
Resource management,NOMA,Uplink,Throughput,Wireless networks,Base stations,5G mobile communication | Journal | 21 |
Issue | ISSN | Citations |
5 | 1536-1276 | 4 |
PageRank | References | Authors |
0.39 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenjiang Shi | 1 | 4 | 0.39 |
| Jiajia Liu | 2 | 1372 | 94.60 |
| Shangwei Zhang | 3 | 4 | 0.39 |
| Nei Kato | 4 | 3982 | 263.66 |