Low complexity norm-adaption least mean square/fourth algorithm and its applications for sparse channel estimation
Low complexity norm-adaption least mean square/fourth algorithm and its applications for sparse channel estimation
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DOI：
10.1109/wcnc.2016.7564732
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发表时间：
2016-04
期刊：
影响因子：
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通讯作者：
Yingsong Li;Yanyan Wang;Tao Jiang
中科院分区：
文献类型：
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作者：
Yingsong Li;Yanyan Wang;Tao Jiang
针对无线多径信道的稀疏特性，提出了一种低复杂度的范数自适应最小均方/四阶（LCNA-LMS/F）算法。提出的LCNA-LMS/F算法通过使用分段函数代替重加权范数自适应最小均方/四分之一（RNA-LMF）算法中的重加权因子来实现，去除了除法运算，降低了计算复杂度。在稀疏信道下研究了LCNA-LMS/F算法的信道估计性能，计算机仿真结果表明，与传统的最小均方/四分之一（LMS/F）算法及其稀疏形式相比，LCNA-LMS/F算法在收敛速度和稳态误差平层方面具有上级性能.
A low-complexity norm-adaption least-mean-square/fourth (LCNA-LMS/F) algorithm is proposed to exploit the sparse properties of the wireless multi-path channel in this paper. The proposed LCNA-LMS/F algorithm is realized by using a segment function instead of the reweighting factor in the reweighted norm-adaption least-mean-square/fourth (RNA-LMF) algorithm to remove the division operation, which can reduce the computational complexity. The channel estimation behaviors of the proposed LCNA-LMS/F algorithm are investigated over a sparse channel and the computer simulation results show that the proposed LCNA-LMS/F algorithm achieves superior performance with respect to the convergence speed and the steady-state error floor compared with the conventional least-mean-square/fourth (LMS/F) and its sparsity forms.