Fast channel impulse response estimation scheme for adaptive maximum likelihood sequence estimation equalizer—proposal of variable-gain least mean square algorithm

1996 ◽  
Vol 79 (7) ◽  
pp. 104-115
Author(s):  
Satoshi Denno ◽  
Yoichi Saito
Keyword(s):  
Maximum Likelihood ◽  
Impulse Response ◽  
Channel Impulse Response ◽  
Mean Square ◽  
Least Mean Square ◽  
Variable Gain ◽  
Sequence Estimation ◽  
Maximum Likelihood Sequence Estimation ◽  
Estimation Scheme ◽  
Least Mean Square Algorithm

An Improved ZA-LMS Algorithm for Sparse System Identification with Variable Sparsity

2014 ◽  
Vol 602-605 ◽  
pp. 2415-2419 ◽  
Author(s):  
Hui Luo ◽  
Yun Lin ◽  
Qing Xia
Keyword(s):  
System Identification ◽  
Impulse Response ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Sparse System ◽  
Sparse System Identification ◽  
Least Mean Square Algorithm ◽  
Improved Algorithm

The standard least mean square algorithm does not consider the sparsity of the impulse response,and the performs of the ZA-LMS algorithm deteriorates ,as the degree of system sparsity reduces or non-sparse . Concerning this issue ,the ZA-LMS algorithm is studied and modified in this paper to improve the performance of sparse system identification .The improved algorithm by modify the zero attraction term, which attracts the coefficients only in a certain range (the “inactive” taps), thus have a good performance when the sparsity decreases. The simulations demonstrate that the proposed algorithm significantly outperforms then the ZA-LMS with variable sparisity.

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