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首页> 外文期刊>Wireless communications & mobile computing >Adaptive Channel Estimation Based on an Improved Norm-Constrained Set-Membership Normalized Least Mean Square Algorithm
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Adaptive Channel Estimation Based on an Improved Norm-Constrained Set-Membership Normalized Least Mean Square Algorithm

机译：基于改进的范数约束集成员归一化最小二乘算法的自适应信道估计

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摘要

An improved norm-constrained set-membership normalized least mean square (INCSM-NLMS) algorithm is proposed for adaptive sparse channel estimation (ASCE). The proposed INCSM-NLMS algorithm is implemented by incorporating an -norm penalty into the cost function of the traditional set-membership normalized least mean square (SM-NLMS) algorithm, which is also denoted as -norm penalized SM-NLMS (LPSM-NLMS) algorithm. The derivation of the proposed LPSM-NLMS algorithm is given theoretically, resulting in a zero attractor in its iteration. By using this proposed zero attractor, the convergence speed is effectively accelerated and the channel estimation steady-state error is also observably reduced in comparison with the existing popular SM-NLMS algorithms for estimating exact sparse multipath channels. The estimation behaviors are investigated via a typical sparse wireless multipath channel, a typical network echo channel, and an acoustic channel. The computer simulation results show that the proposed LPSM-NLMS algorithm is better than those corresponding sparse SM-NLMS and traditional SM-NLMS algorithms when the channels are exactly sparse.
机译：提出了一种改进的范数约束集成员归一化最小均方（INCSM-NLMS）算法，用于自适应稀疏信道估计（ASCE）。拟议的INCSM-NLMS算法是通过将-norm罚分合并到传统集成员资格归一化最小均方（SM-NLMS）算法的成本函数中来实现的，该算法也称为-norm罚分SM-NLMS（LPSM-NLMS ）算法。理论上给出了所提出的LPSM-NLMS算法的推导，其迭代过程中的吸引子为零。与现有的流行的精确估计稀疏多径信道的SM-NLMS算法相比，通过使用该提出的零吸引子，有效地加快了收敛速度，并显着降低了信道估计稳态误差。通过典型的稀疏无线多径信道，典型的网络回声信道和声学信道来研究估计行为。计算机仿真结果表明，在信道较为稀疏的情况下，所提出的LPSM-NLMS算法要优于相应的稀疏SM-NLMS算法和传统的SM-NLMS算法。

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