Norm-adaption penalized least mean square/fourth algorithm for sparse channel estimation

2016 ◽  
Vol 128 ◽  
pp. 243-251 ◽  
Author(s):  
Yingsong Li ◽  
Yanyan Wang ◽  
Tao Jiang
Keyword(s):  
Channel Estimation ◽  
Mean Square ◽  
Least Mean Square ◽  
Sparse Channel Estimation ◽  
Sparse Channel

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.

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.

A Low-Complexity Sparse Channel Estimation Method for OFDM Systems

2010 ◽  
Vol E93-B (8) ◽  
pp. 2211-2214
Author(s):  
Bin SHENG ◽  
Pengcheng ZHU ◽  
Xiaohu YOU ◽  
Lan CHEN
Keyword(s):  
Channel Estimation ◽  
Estimation Method ◽  
Low Complexity ◽  
Ofdm Systems ◽  
Sparse Channel Estimation ◽  
Sparse Channel
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