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Gradient Compared Lp-LMS Algorithms for Sparse System Identification [article]

Yong Feng, Jiasong Wu, Rui Zeng, Limin Luo, Huazhong Shu
2015 arXiv   pre-print
in sparse system identification settings.  ...  In this paper, we propose two novel p-norm penalty least mean square (Lp-LMS) algorithms as supplements of the conventional Lp-LMS algorithm established for sparse adaptive filtering recently.  ...  Then the numerical simulations are given in Section III to investigate Gradient Compared ℓ p -LMS Algorithms for Sparse System Identification 2 PROPOSED ALGORITHMS Throughout this paper, matrices and  ... 
arXiv:1503.01185v3 fatcat:ruyncbvmjrgehh45rd3q2ay5ie

Error Gradient-based Variable-Lp Norm Constraint LMS Algorithm for Sparse System Identification [article]

Yong Feng, Fei Chen, Rui Zeng, Jiasong Wu, Huazhong Shu
2015 arXiv   pre-print
To address this problem, we propose a novel variable p-norm constraint least mean square (LMS) algorithm, which serves as a variant of the conventional Lp-LMS algorithm established for sparse system identification  ...  However, when applied for system identification, most priori work in sparse norm constraint adaptive filtering suffers from the difficulty of adaptability to the sparsity of the systems to be identified  ...  k   w w ) and convergence rate, and the results are compared with those from standard LMS, the Lp-LMS and Lvp(GSD)-LMS algorithms in sparse system identification with different sparsity levels.  ... 
arXiv:1509.07951v1 fatcat:3rvoarhci5gdrbay3nx3p7dxuq

Variable p norm constrained LMS algorithm based on gradient of root relative deviation.pdf [article]

Yong Feng, Fei Chen, Jiasong Wu
2016 arXiv   pre-print
of sparse system identification in the presence of noise.  ...  A new Lp-norm constraint least mean square (Lp-LMS) algorithm with new strategy of varying p is presented, which is applied to system identification in this letter.  ...  Set in the application of sparse system identification, its performances are compared with those of the classical LMS and Lp-LMS algorithms in different sparsity levels.  ... 
arXiv:1603.09022v1 fatcat:wqq3gnnukvabzgfttnxnuot5ju

p Norm Constraint Leaky LMS Algorithm for Sparse System Identification [article]

Yong Feng, Rui Zeng, Jiasong Wu
2015 arXiv   pre-print
This paper proposes a new leaky least mean square (leaky LMS, LLMS) algorithm in which a norm penalty is introduced to force the solution to be sparse in the application of system identification.  ...  then enhances the performance of the filter in system identification settings, especially when the impulse response is sparse.  ...  Our future work will focus on the choices of the parameters of the ℓp-LLMS and ℓp-LMS algorithm for sparse system identification, for we found that the parameters leaky factor γ, p-norm trade-off ρ and  ... 
arXiv:1503.01337v1 fatcat:o2ssqccoozdvlgy3cwa5ggyqpu

Improved adaptive sparse channel estimation based on the least mean square algorithm

Guan Gui, Wei Peng, Fumiyuki Adachi
2013 2013 IEEE Wireless Communications and Networking Conference (WCNC)  
To exploit the channel sparsity, LMS-based adaptive sparse channel estimation methods, e.g., zero-attracting LMS (ZA-LMS), reweighted zero-attracting LMS (RZA-LMS) andnorm sparse LMS (LP-LMS), have also  ...  To take full advantage of channel sparsity, in this paper, we propose several improved adaptive sparse channel estimation methods using -norm normalized LMS (LP-NLMS) and -norm normalized LMS (L0-NLMS)  ...  Traditional least mean square (LMS) algorithm is one of the most popular methods for adaptive system identification [13] , e.g. channel estimation.  ... 
doi:10.1109/wcnc.2013.6555058 dblp:conf/wcnc/GuiPA13 fatcat:6yfmd765vvb2lnyjyus4x2kaum

Sparse least mean fourth algorithm for adaptive channel estimation in low signal-to-noise ratio region

Guan Gui, Fumiyuki Adachi
2013 International Journal of Communication Systems  
Because the wireless channel vector is often sparse, sparse LMS-based approaches have been proposed with different sparse penalties, for example, zero-attracting LMS and L p -norm LMS.  ...  Comparatively, LMF can achieve better solution in low SNR region.  ...  This work was supported by the Japan Society for the Promotion of Science (JSPS) postdoctoral fellowship.  ... 
doi:10.1002/dac.2531 fatcat:svijhxavfvazxin7loil7msrpy

Sparse LMS/F algorithms with application to adaptive system identification

Guan Gui, Abolfazl Mehbodniya, Fumiyuki Adachi
2013 Wireless Communications and Mobile Computing  
To take advantage of system sparsity, different sparse LMS algorithms with l p -LMS and l 0 -LMS have been proposed to improve adaptive identification performance.  ...  We propose two sparse LMS/F algorithms using two sparse constraints to improve adaptive identification performance.  ...  Koichi Adachi at Institute for Infocomm Research for his valuable comments and suggestions, as well as for improving the English language  ... 
doi:10.1002/wcm.2453 fatcat:5gb5ruc7d5a6rpk6mz7tkfiw7u

p-norm-like Constraint Leaky LMS Algorithm for Sparse System Identification [article]

Yong Feng, Rui Zeng, Jiasong Wu
2015 arXiv   pre-print
enhances the performance in sparse system identification settings.  ...  In this paper, we propose a novel leaky least mean square (leaky LMS, LLMS) algorithm which employs a p-norm-like constraint to force the solution to be sparse in the application of system identification  ...  In Section 3, simulation results are shown which compare the performance of the proposed algorithm with those of the standard LMS, standard LLMS and ℓplike-LMS algorithm in sparse system identification  ... 
arXiv:1503.01484v1 fatcat:5fwdtz3vvrdmjac67lppu7ms6a

A Sparsity-Aware Variable Kernel Width Proportionate Affine Projection Algorithm for Identifying Sparse Systems

Zhengxiong Jiang, Yingsong Li, Xinqi Huang, Zhan Jin
2019 Symmetry  
Compared with the LMS and NLMS, the behavior of the AP algorithms is more outstanding, especially for the colored inputs [12] .  ...  Then, the identification behavior of the LP-VPAP is compared with AP, ZA-AP, RZA-AP, PAP, MCC, VKW-MCC, and PAPMCC algorithms with WGN and CN input.  ... 
doi:10.3390/sym11101218 fatcat:vbw5wqqpf5exnbriynsqdnauqa

A Sparsity-Aware Proportionate Normalized Maximum Correntropy Criterion Algorithm For Sparse System Identification In Non-Gaussian Environment

Yingsong Li, Yanyan Wang, Rui Yang
2018 Zenodo  
ACKNOWLEDGMENT This paper is funded by the International Exchange Program of Harbin Engineering University for Innovation-oriented Talents Cultivation. This work was also partially support-  ...  However, most of the adaptive filter algorithms are mainly presented for non-sparse systems and Gaussian noise environment.  ...  Moreover, channel estimations and system identifications have attracted a great concern in recent decades.  ... 
doi:10.5281/zenodo.1159346 fatcat:cz3v5rzzdzeulp5jawg6nqamfe

A Class of Diffusion Zero Attracting Stochastic Gradient Algorithms with Exponentiated Error Cost Functions

Zhengyan Luo, Haiquan Zhao, Xiangping Zeng
2019 IEEE Access  
In this paper, a class of diffusion zero-attracting stochastic gradient algorithms with exponentiated error cost functions is put forward due to its good performance for sparse system identification.  ...  Distributed estimation algorithms based on the popular mean-square error criterion have poor behavior for sparse system identification with color noise.  ...  Simulations are implemented to verify the performances of the LP-DLE2 and PZA-VSIDLSE algorithms for the sparse system identification.  ... 
doi:10.1109/access.2019.2961162 fatcat:dzruc2mnavcvji6c2bfs2tnpga

Improved least mean square algorithm with application to adaptive sparse channel estimation

Guan Gui, Fumiyuki Adachi
2013 EURASIP Journal on Wireless Communications and Networking  
To fully take advantage of channel sparsity, in this paper, an improved sparse channel estimation method using ℓ 0 -norm LMS algorithm is proposed.  ...  To solve this problem, we propose several improved adaptive sparse channel estimation methods using normalized LMS algorithm with different sparse penalties, which normalizes the power of input signal.  ...  Koichi Adachi of the Institute for Infocomm Research for his valuable comments and suggestions as well as for the improvement of the English expression of this paper.  ... 
doi:10.1186/1687-1499-2013-204 fatcat:ye6ogtjacfgzrnqfxoqmqpygn4

Two Are Better Than One: Adaptive Sparse System Identification Using Affine Combination of Two Sparse Adaptive Filters

Guan Gui, Shinya Kumagai, Abolfazl Mehbodniya, Fumiyuki Adachi
2014 2014 IEEE 79th Vehicular Technology Conference (VTC Spring)  
One of popular adaptive sparse system identification (ASSI) methods is adopting only one sparse least mean square (LMS) filter.  ...  Sparse system identification problems often exist in many applications, such as echo interference cancellation, sparse channel estimation, and adaptive beamforming.  ...  Koichi Adachi of Institute for Infocomm Research for his valuable comments and suggestions.  ... 
doi:10.1109/vtcspring.2014.7023132 dblp:conf/vtc/GuiKMA14 fatcat:qdh5kzlx6zdntid7jyrhutco3y

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

Yingsong Li, Masanori Hamamura
2014 The Scientific World Journal  
Our simulation results demonstrate that the proposed algorithm can effectively improve the estimation performance of the PNLMS-based algorithm for sparse channel estimation applications.  ...  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  ...  echo cancellation and system identification, which are known as the zero-attracting LMS (ZA-LMS) and reweighted ZA-LMS (RZA-LMS) algorithms, respectively [15] .  ... 
doi:10.1155/2014/572969 pmid:24782663 pmcid:PMC3981014 fatcat:zc3udzyljfe33la6zp6fri7s24

Two are Better Than One: Adaptive Sparse System Identification using Affine Combination of Two Sparse Adaptive Filters [article]

Guan Gui, Shinya Kumagai, Abolfazl Mehbodniya, Fumiyuki Adachi
2013 arXiv   pre-print
One of popular adaptive sparse system identification (ASSI) methods is adopting only one sparse least mean square (LMS) filter.  ...  Sparse system identification problems often exist in many applications, such as echo interference cancellation, sparse channel estimation, and adaptive beamforming.  ...  ACKNOWLEDGMENT This work was supported by grant-in-aid for the Japan Society for the Pro motion of Science (JSPS) fellows grant number 24 02366.  ... 
arXiv:1311.1312v1 fatcat:ferj4ofzxzhuxn3f7s3luapulq
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