Name
Title | ||
|---|---|---|
A Low Complexity High Performance Weighted Neumann Series-based Massive MIMO Detection |
Abstract | ||
|---|---|---|
In massive multiple-input multiple-output (MIMO) system, Neumann series (NS) expansion-based linear minimum mean square error (LMMSE) detection has been proposed due to its simple and efficient multi-stage pipeline hardware implementation. However, it suffers from poor performance and slow convergence as the number of the users grows. To address this issue, we proposed a novel weighted Neumann series (WNS)-based LMMSE detection to minimize the error between the exact matrix inversion and the WNS-based matrix inversion. Moreover, the optimal weights are obtained according to on-line learning basis. Numerical results indicate that the learning-based WNS detection outperforms the conventional NS-based detection and achieves near-LMMSE performance with a significantly lower computational complexity. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/WOCC.2019.8770550 | 2019 28th Wireless and Optical Communications Conference (WOCC) |
Keywords | Field | DocType |
Massive multiple-input multiple-output (MI-MO),linear minimum mean square error (LMMSE),weighted Neumann series (WNS),matrix inversion,off-line | Convergence (routing),Neumann series,Wireless,Inversion (meteorology),Matrix (mathematics),Computer science,MIMO,Algorithm,Minimum mean square error,Electronic engineering,Computational complexity theory | Conference |
ISSN | ISBN | Citations |
2379-1268 | 978-1-7281-0661-8 | 0 |
PageRank | References | Authors |
0.34 | 17 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaofei Liu | 1 | 0 | 0.68 |
| Zhenyu Zhang | 2 | 17 | 6.80 |
| Xiyuan Wang | 3 | 119 | 15.30 |
| Jing Lian | 4 | 30 | 10.81 |
| Xiaoming Dai | 5 | 100 | 21.23 |