Massive MIMO 3D空间相关信道超松弛检测算法

Successive overrelaxation iterative detection algorithm for 3D spatially correlated massive MIMO channel

为降低Massive multiple-input multiple-output(MIMO)信号检测算法的计算复杂度,采用迭代方法进行信号检测.在采用矩阵分解的迭代方法基础上,逐步推导引入超松弛迭代检测算法,利用行列式计算推导出松弛因子范围,同时采用几何方法,在二维空间相关信道模型基础上,构建三维空间相关信道模型并给出相应三维空间几何模型,同时忽略高阶项,推导出相应的空间相关信道相关性近似解析形式解,给出相关性近似解析表达式.仿真表明,三维空间相关信道模型会加剧信道的相关性,降低检测算法的误比特率检测性能.当迭代算法的迭代次数N=8,在一定误比特率条件下,采用优化松弛因子的超松弛迭代算法所需的信噪比有所下降.在一定信噪比下,误比特率能下降约两个数量级,接近迭代次数N=16的误比特率,同时分集增益有所提升,计算复杂度也有所下降.通过权衡分析信噪比和计算复杂度,选用优化松弛因子迭代检测算法能在较少的迭代次数下实现较低的误比特率检测性能,超松弛迭代检测算法能获得较优的算法检测性能.

To reduce the complexity of Massive multiple-input multiple-output （MIMO） signal detection, the iterative method is used for signal detection. Based on the implementation of the iterative method with matrix decomposition, the successive over relaxation algorithm is introduced and the range of relaxation factor is deduced by determinant calculation. Simultaneously, a three-dimensional spatially correlated channel model is built based on two-dimension with geometric method and the analytic form solution of spatially correlated channel is introduced by neglecting the high order. The simulation results show the three-dimensional spatially correlated channel model aggravates channel correlation and decreases detection performance. Under 8 times iteration and certain bit error rate, the signal-to-noise ratio of successive over relaxation iteration decreases with the optimal relaxation factor. In a definite signal-to-noise ratio, bit error rate decreases approximate two orders of magnitude, diversity gain promotes and detection algorithm performs equally 16 times iteration, which can also decrease computational complexity. The successive over relaxation detection algorithm can perform better in less iteration and achieve better detection performance with optimal relaxation factor by considering the signal-to-noise ratio and the complexity.