基于WSSOR迭代的大规模MIMO系统软输出信号检测

SOFT OUTPUT SIGNAL DETECTION FOR MASSIVE MIMO SYSTEMS BASED ON WSSOR ITERATION

在大规模多输入多输出(Multiple Input Multiple Output,MIMO)系统中,最小均方误差(Minimum Mean Square Error,MMSE)检测算法能获得最优线性检测性能,但涉及复杂的高维矩阵求逆运算。基于加权-对称连续超松弛(Weighted Symmetric Successive Over Relaxation,WSSOR)迭代提出一种高性能低复杂度的软输出检测算法,避免复杂的矩阵求逆运算,降低算法的复杂度。采用一种简单的量化方法来求解松弛参数和加权因子,应用在软判决中,进一步提升算法性能。定量地分析了不同算法的复杂度,并通过仿真对不同检测算法的误码率性能和收敛速度进行研究,结果表明该算法在降低复杂度的情况下,能以较快的收敛速度接近最优的线性检测性能。

In massive multiple input multiple output(MIMO)system,the minimum mean square error(MMSE)detection algorithm can achieve optimal linear detection performance,but it involves complex high-dimensional matrix inversion operations.Therefore,we propose a high performance and low complexity soft-output signal detection algorithm based on weighted symmetric successive over relaxation(WSSOR)iteration,which avoids complex matrix inversion operations and reduces the complexity of the algorithm.we used a simple quantization method to solve the relaxation parameters and weighting factors.Then,it was applied in the soft decision to further improve the performance of the algorithm.Through theoretical research,the complexity of different algorithms was quantitatively analyzed,and the bit error rate performance and convergence speed of different detection algorithms were studied through simulation.The results show that our algorithm can approach the optimal linear detection performance with faster convergence speed under the condition of reducing the complexity.