Mean-square performance of the modified frequency-domain block LMS algorithm

Mean-square performance of the modified frequency-domain block LMS algorithm
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改进的频域块 LMS 算法的均方性能

DOI:
10.1016/j.sigpro.2019.04.030
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发表时间:
2019-10
期刊:
影响因子:
4.4
通讯作者:
Yang Jun
Yang Jun
中科院分区:
工程技术2区
文献类型:
--
作者:
Yang Feiran;Yang Jun

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在非因果和建模不足的情况下,归一化频域块最小均方误差(NFBLMS)算法的平均权向量不能收敛到均方误差意义下的最优解。针对这一问题,提出了一种改进的频域块最小均方(MFBLMS)算法,该算法被认为具有最优的稳态性能。在本文中,我们对MFBLMS算法在完全建模和未建模条件下进行了全面的统计分析。我们首先给出了MFBLMS的更新方程和误差向量在时间域上的等价表达式,这使得我们可以在时间域内进行完全的性能分析。在不假定特定输入分布的情况下,给出了MFBLMS平均性能和均方性能的解析模型,并给出了步长上界的闭合解。研究发现,对于相关输入,MFBLMS算法的步长上限比NFBLMS算法小得多,而且MFBLMS算法并不总是比NFBLMS算法获得更好的稳态性能。仿真结果与理论分析吻合较好。
The mean weight vector of the normalized frequency-domain block least-mean-square (NFBLMS) algorithm cannot converge to the optimal solution in the mean-square error sense for the non-causal and under-modeling cases. A modified frequency-domain block least-mean-square (MFBLMS) algorithm was proposed to resolve this problem, which was claimed to have optimal steady-state performance. In this paper, we present a comprehensive statistical analysis of the MFBLMS algorithm in both the full- and under-modeling conditions. We first present the equivalent time-domain expressions for the update equation and the error vector of the MFBLMS, which allows us to carry out the performance analysis completely in the time domain. The analytical model for both the mean and mean-square performance of the MFBLMS are provided without assuming a specific input distribution, and the closed-form solution of the step-size bound is given. It is found that the upper step-size bound of the MFBLMS algorithm is much smaller than that of the NFBLMS algorithm for the correlated inputs, and the MFBLMS algorithm does not always achieve a better steady-state performance than the NFBLMS algorithm. Simulation results agree with our theoretical analysis quite well.
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