Recently, various iterative methods are investigated to achieve linear minimum mean square error (MMSE) detection accuracy for uplink massive multiple-input multiple-output (MIMO) systems. This letter introduces the non-stationary Richardson (NSR) iteration to achieve fast convergence rate, and reduces its complexity with approximate eigenvalues in massive MIMO system. However, when the system scale grows and channel correlation is considered, the performance of NSR method decays obviously. To improve the robustness, this letter further proposes a deeply fused SDNSR algorithm, which effectively overcomes the weakness of NSR method by fully utilizing the information obtained through the steepest descent (SD) method and NSR method. Moreover, the complexity is significantly reduced by adopting matrix-vector multiplication and reusing intermediate results. Simulation results and complexity analysis exhibit that the SDNSR method achieves superior performance with lower complexity compared to the recently reported works.

中文翻译：

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基于最速下降和非平稳Richardson迭代的大规模MIMO系统深度融合检测算法

最近，研究了各种迭代方法，以实现上行链路大规模多输入多输出 (MIMO) 系统的线性最小均方误差 (MMSE) 检测精度。这封信介绍了非平稳理查森（NSR）迭代以实现快速收敛速度，并在大规模 MIMO 系统中通过近似特征值降低其复杂度。但是，当系统规模增大并考虑信道相关性时，NSR 方法的性能明显下降。为了提高鲁棒性，本文进一步提出了深度融合SDNSR算法，充分利用最速下降法和NSR法获得的信息，有效克服了NSR法的弱点。而且，采用矩阵-向量乘法和重用中间结果显着降低了复杂度。仿真结果和复杂性分析表明，与最近报道的工作相比，SDNSR 方法以较低的复杂性实现了卓越的性能。