Block Gauss-Seidel Method for Signal Detection in Uplink Massive MIMO Systems

Qianqian Ye, Zhizhong Zhang, Xiaofang Min, Bingguang Deng, Jinyan Li, Lei Zhang
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Abstract

Minimum mean square error (MMSE) detection algorithm is near-optimal for uplink massive MIMO systems, but it involves matrix inversion with high complexity. Thus, the conventional Gauss-Seidel (GS) method has been applied for obtain a low-complexity MMSE detector without employing the computationally intensive matrix inversion. In this paper, we propose an improving GS method for the conventional GS method based on block matrix in order to reduce complexity and accelerate the convergence rate. Simulation results show that the proposed algorithm can closely match the performance of the MMSE algorithm with few numbers of iterations. It also outperforms GS method in terms of bit error rate (BER) performance with same number of iterations.
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上行海量MIMO系统信号检测的块高斯-塞德尔方法
最小均方误差(MMSE)检测算法是上行海量MIMO系统的最优检测算法,但该算法涉及矩阵反演,复杂度较高。因此,采用传统的高斯-塞德尔(GS)方法获得低复杂度的MMSE检测器,而无需使用计算量大的矩阵反演。本文提出了一种改进的基于分块矩阵的遗传算法,以降低遗传算法的复杂度,加快遗传算法的收敛速度。仿真结果表明,该算法迭代次数少,性能接近MMSE算法。在相同的迭代次数下,它在误码率(BER)性能方面也优于GS方法。
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