Two-Dimensional Set Membership Normalized Least Mean Square Adaptive Channel Estimation for OFDM Systems

2009 ◽  
Author(s):  
Tanvi Sharma ◽  
Akshay Soni ◽  
Vijaykumar Chakka
Keyword(s):  
Channel Estimation ◽  
Mean Square ◽  
Two Dimensional ◽  
Least Mean Square ◽  
Ofdm Systems ◽  
Set Membership ◽  
Adaptive Channel Estimation ◽  
Normalized Least Mean Square

scholarly journals Adaptive Channel Estimation Based on an Improved Norm-Constrained Set-Membership Normalized Least Mean Square Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Yingsong Li ◽  
Zhan Jin ◽  
Yanyan Wang
Keyword(s):  
Channel Estimation ◽  
Mean Square ◽  
Least Mean Square ◽  
Sparse Channel Estimation ◽  
Nlms Algorithm ◽  
Set Membership ◽  
Normalized Least Mean Square ◽  
The Cost ◽  
Norm Penalty ◽  
Zero Attractor

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 lp-norm penalty into the cost function of the traditional set-membership normalized least mean square (SM-NLMS) algorithm, which is also denoted as lp-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.

scholarly journals Set-Membership Quaternion Normalized LMS Algorithm

2018 ◽  
Author(s):  
Hamid Reza Moradi ◽  
Akram Zardadi
Keyword(s):  
Wind Profile ◽  
Adaptive Beamforming ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Step Size ◽  
Set Membership ◽  
Normalized Least Mean Square ◽  
Update Rate ◽  
Normalized Lms

In this paper, we propose the set-membership quaternion normalized least-mean-square (SM-QNLMS) algorithm. For this purpose, first, we review the quaternion least-mean-square (QLMS) algorithm, then go into the quaternion normalized least-mean-square (QNLMS) algorithm. By having the QNLMS algorithm, we propose the SM-QNLMS algorithm in order to reduce the update rate of the QNLMS algorithm and avoid updating the system parameters when there is not enough innovation in upcoming data. Moreover, the SM-QNLMS algorithm, thanks to the time-varying step-size, has higher convergence rate as compared to the QNLMS algorithm. Finally, the proposed algorithm is utilized in wind profile prediction and quaternionic adaptive beamforming. The simulation results demonstrate that the SM-QNLMS algorithm outperforms the QNLMS algorithm and it has higher convergence speed and lower update rate.

scholarly journals An Improved Proportionate Normalized Least-Mean-Square Algorithm for Broadband Multipath Channel Estimation

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yingsong Li ◽  
Masanori Hamamura
Keyword(s):  
Channel Estimation ◽  
Estimation Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Sparse Channel Estimation ◽  
Normalized Least Mean Square ◽  
Least Mean Square Algorithm ◽  
The Cost ◽  
Sparse Channel ◽  
Steady State Performance

To make use of the sparsity property of broadband multipath wireless communication channels, we mathematically propose anlp-norm-constrained proportionate normalized least-mean-square (LP-PNLMS) sparse channel estimation algorithm. A generallp-norm is weighted by the gain matrix and is incorporated into the cost function of the proportionate normalized least-mean-square (PNLMS) algorithm. This integration is equivalent to adding a zero attractor to the iterations, by which the convergence speed and steady-state performance of the inactive taps are significantly improved. Our simulation results demonstrate that the proposed algorithm can effectively improve the estimation performance of the PNLMS-based algorithm for sparse channel estimation applications.

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