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.

我们引入了联合加权诺伊曼序列（WNS）和高斯-赛德尔（GS）方法，以为上行链路大规模多输入多输出（M-MIMO）系统实现近似线性最小均方误差（LMMSE）检测器。我们首先提出通过WNS方法初始化GS迭代，该方法比传统的基于零向量和对角矩阵的方案产生更接近LMMSE的初始解。然后应用GS算法来迭代实现近似LMMSE检测。此外，基于WNS，我们设计了一种低复杂度的近似对数似然比（LLR）计算方法，与精确方法相比，该方法的性能损失可忽略不计。