基于改进 Richardson 的大规模 MIMO 信号检测算法

在大规模多输入多输出(multiple-input multiple-output, MIMO)系统信号检测中，最小均方误差(minimum mean square error, MMSE)算法可以得到近似最优检测性能，然而该算法需要高维矩阵求逆，其复杂度很高，无法保证信号的实时检测。因此提出一种改进Richardson信号检测方法，利用最速下降法和整体修正法改进Richardson算法性能，最速下降法可以提供更有效地搜索路径，得到不同近似解，并且为了提高求解精度，利用整体修正法对不同近似解进行修正，使算法收敛速度更快，同时将算法复杂度数量级由O(K³)降低到O(K²)。仿真结果表明，该算法只需3次迭代就可接近MMSE,在降低复杂度的同时提高了误码率性能。

Massive MIMO signal detection based on improved Richardson method

In the detection of massive multiple-input multiple-output systems, the minimum mean square error algorithm can obtain approximately optimal detection performance, its complexity is very high and cannot guarantee the real-time detection of the signal. An improved Richardson signal detection method is proposed, which uses the steepest descent and the whole-correction method to improve the performance of the Richardson algorithm. The steepest descent can provide more efficient search paths and obtain different approximate solutions, in order to improve the accuracy of the algorithm, the whole-correction method is used to modify the different approximate solutions, so that the convergence speed is faster, and the complexity of the algorithm is reduced from the order of magnitude O(K 3 ) to O(K 2 ) . Simulation results show that the proposed algorithm can approach MMSE with only 3 iterations, which reduces the complexity and improves the BER performance.