Name
Title | ||
|---|---|---|
Low-Complexity Joint Weighted Neumann Series And Gauss-Seidel Soft-Output Detection For Massive Mimo Systems |
Abstract | ||
|---|---|---|
We introduce a joint weighted Neumann series (WNS) and Gauss-Seidel (GS) approach to implement an approximated linear minimum mean-squared error (LMMSE) detector for uplink massive multiple-input multiple-output (M-MIMO) systems. We first propose to initialize the GS iteration by a WNS method, which produces a closer-to-LMMSE initial solution than the conventional zero vector and diagonal-matrix based scheme. Then the GS algorithm is applied to implement an approximated LMMSE detection iteratively. Furthermore, based on the WNS, we devise a low-complexity approximate log-likelihood ratios (LLRs) computation method whose performance loss is negligible compared with the exact method. Numerical results illustrate that the proposed joint WNS-GS approach outperforms the conventional method and achieves near-LMMSE performance with significantly lower computational complexity. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s11277-021-08557-2 | WIRELESS PERSONAL COMMUNICATIONS |
Keywords | DocType | Volume |
Linear minimum mean-squared error (LMMSE), Gauss–, Seidel (GS), weighted Neumann series (WNS), massive multiple-input multiple-output (M-MIMO) | Journal | 120 |
Issue | ISSN | Citations |
4 | 0929-6212 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoming Dai | 1 | 100 | 21.23 |
| Tiantian Yan | 2 | 0 | 0.34 |
| Yuanyuan Dong | 3 | 0 | 0.34 |
| Yuquan Luo | 4 | 0 | 0.34 |
| Hua Li | 5 | 0 | 0.34 |