Name
Abstract | ||
|---|---|---|
As a promising solution for massive machine-type communication, grant-free non-orthogonal multiple access (GF-NOMA) has received considerable attention in recent years. However, the multidimensional constellation design (MCD) and multiuser detection (MUD) in GF-NOMA are usually optimized in a divide and conquer way, leading to local optima and performance degradation. To address this issue,... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TWC.2021.3108666 | IEEE Transactions on Wireless Communications |
Keywords | DocType | Volume |
Multiuser detection,Optimization,Wireless communication,Task analysis,Training,NOMA,Deep learning | Journal | 21 |
Issue | ISSN | Citations |
3 | 1536-1276 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhe Ma | 1 | 0 | 0.68 |
| wen wu | 2 | 117 | 15.85 |
| Mengnan Jian | 3 | 13 | 1.60 |
| Feifei Gao | 4 | 3093 | 212.03 |
| Xuemin Shen | 5 | 15389 | 928.67 |