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引用本文：

叶倩倩,张治中,闵小芳,等.大规模MIMO系统中块高斯-赛德尔检测算法[J].电讯技术，2021，61（7）： - . [点击复制]
YE Qianqian,ZHANG Zhizhong,MIN Xiaofang,et al.Block Gauss-Seidel detection algorithm for massive MIMO systems[J].，2021，61（7）： - . [点击复制]

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大规模MIMO系统中块高斯-赛德尔检测算法
叶倩倩,张治中,闵小芳,胡昊南
0 字体:加大+\|默认\|缩小-
(重庆邮电大学通信与信息工程学院，重庆 400065)

摘要:

最小均方误差(Minimum Mean Square Error,MMSE)检测算法，虽然能在大规模多输入多输出系统中获得接近最优的线性检测性能，但是涉及高维矩阵求逆运算，难以在实际应用中快速有效地实现。提出了块高斯-赛德尔(Block GaussSeidel,BGS)低复杂度信号检测算法，将MMSE检测器的滤波矩阵先进行分块预处理，构造分裂矩阵，再通过迭代求解发送信号向量估计值，以提高算法检测性能。仿真结果表明，BGS迭代算法在调制方式为64QAM、用户侧的天线数量设置为16、基站侧的天线数量设置为256时，迭代2次后就能快速接近MMSE检测性能。在设置近似初始值后，BGS算法的性能得到了进一步的改善。当调制方式为256QAM时，设置近似初始值的BGS算法在迭代2次后就能逼近MMSE算法的误码率(Bit Error Ratio,BER)性能曲线，此时算法的复杂度仍然保持在O(K2)。

关键词: 大规模多输入多输出最小均方误差检测高斯-赛德尔算法分块矩阵

DOI：

基金项目:国家自然科学基金资助项目（61901075）；重庆市重点产业共性关键技术创新专项（cstc2017zdcy-zdzxX0004）

Block Gauss-Seidel detection algorithm for massive MIMO systems

YE Qianqian,ZHANG Zhizhong,MIN Xiaofang,Hu Haonan

(School of Communications and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

Abstract:

Minimum mean square error(MMSE) detection algorithm is nearoptimal for massive multipleinput multipleoutput(MIMO) systems,but it involves matrix inversion with high complexity.Thus,it is difficult to apply quickly and effectively in practice.In this paper,a low complexity Block GaussSeidel(BGS) algorithm based on block matrix is proposed for improving the traditional GaussSeidel(GS) algorithm.The simulation results show that the proposed BGS iterative algorithm can approach the MMSE detection performance with 2 iterations,when the modulation mode is 64QAM,and the number of antennas on the user terminal and on the base station is set to 16 and 256,respectively.When the initial value is set,the performance of the BGS algorithm is further improved.When the modulation mode is 256QAM,the BGS algorithm can approximate the bit error rate(BER) performance curve of the MMSE algorithm with 2 iterations,and the complexity of the algorithm remains at O(K2).

Key words: massive MIMO MMSE detection Gauss Seidel algorithm block matrix