Low-Complexity Soft-Output Signal Detection Based on Gauss-Seidel Method for Uplink Multiuser Large-Scale MIMO Systems

被引:215
作者
Dai, Linglong [1 ]
Gao, Xinyu [1 ]
Su, Xin [1 ]
Han, Shuangfeng [2 ]
I, Chih-Lin [2 ]
Wang, Zhaocheng [1 ]
机构
[1] Tsinghua Natl Lab Informat Sci & Technol TNList, Dept Elect Engn, Beijing 100084, Peoples R China
[2] China Mobile Res Inst, Green Commun Res Ctr, Beijing 100053, Peoples R China
基金
中国国家自然科学基金;
关键词
Gauss-Seidel (GS) method; large-scale multiple-input-multiple-output (MIMO); low complexity; minimum mean square error (MMSE); signal detection; DESIGN;
D O I
10.1109/TVT.2014.2370106
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
For uplink large-scale multiple-input-multiple-output (MIMO) systems, the minimum mean square error (MMSE) algorithm is near optimal but involves matrix inversion with high complexity. In this paper, we propose to exploit the Gauss-Seidel (GS) method to iteratively realize the MMSE algorithm without the complicated matrix inversion. To further accelerate the convergence rate and reduce the complexity, we propose a diagonal-approximate initial solution to the GS method, which is much closer to the final solution than the traditional zero-vector initial solution. We also propose an approximated method to compute log-likelihood ratios for soft channel decoding with a negligible performance loss. The analysis shows that the proposed GS-based algorithm can reduce the computational complexity from (sic)(K-3) to (sic)(K-2), where K is the number of users. Simulation results verify that the proposed algorithm outperforms the recently proposed Neumann series approximation algorithm and achieves the near-optimal performance of the classical MMSE algorithm with a small number of iterations.
引用
收藏
页码:4839 / 4845
页数:8
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