RZA-NLMF algorithm-based adaptive sparse sensing for realizing compressive sensing

Gui, Guan; Xu, Li; Adachi, Fumiyuki

doi:10.1186/1687-6180-2014-125

Cited by 13 publications

(12 citation statements)

References 12 publications

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“…For channel sparsity to be exploited, various sparse LMStype algorithms [10][11][12] have been proposed. Also, sparse LMF algorithms 7,[13][14][15][16] and sparse LMS/F algorithms 14,17,18 have been developed in recent years. All of the mentioned sparse channel estimation algorithms based on error criterion function, hence, corresponding sparse channel estimation algorithms can keep the same convergence speed.…”

Section: Discussionmentioning

confidence: 99%

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Recursive least square–based fast sparse multipath channel estimation

Gui

2017

Int J Communication

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In the next-generation wireless communication systems, the broadband signal transmission over wireless channel often incurs the frequency-selective channel fading behavior and also results in the channel sparse structure, which is supported only by few large coefficients. For the stable wireless propagation to be ensured, linear adaptive channel estimation algorithms, eg, recursive least square and least mean square, have been developed. However, these traditional algorithms are unable to exploit the channel sparsity. Actually, channel estimation performance can be further improved by taking advantage of the sparsity. In this paper, 2 recursive least squarebased fast adaptive sparse channel estimation algorithm is proposed by introducing sparse constraints, L1-norm and L0-norm, respectively. To improve the flexibility of the proposed algorithms, this paper introduces a regularization parameter selection method to adaptively exploit the channel sparsity. Finally, Monte Carlo-based computer simulations are conducted to validate the effectiveness of the proposed algorithms.KEYWORDS adaptive filtering algorithm, recursive least square, sparse channel estimation, sparse constraintBroadband wireless transmission has been considered one of indispensable technologies for the next-generation wireless communications. 1-3 However, wireless signal transmission over the frequency-selective fading channel, accurate channel estimation is necessary to reconstruct finite impulse response (FIR). Currently, adaptive filtering algorithms can be acted as effective channel estimation because of their fast convergence and robust performance. Hence, various adaptive filtering algorithms have been developed during the last few years. On the basis of second-order statistical error criterion, least mean square (LMS) 4,5 and recursive least square (RLS) 5 have been developed.On the basis of fourth-order statistical error criterion, least mean fourth (LMF) 6,7 and (LMS/F) 8,9 have been developed as well. However, these kinds of linear channel estimation algorithms have not been considered to exploit channel sparsity in wireless channels. The channel estimation performance can be improved if we take the advantage of the prior sparsity information.For channel sparsity to be exploited, various sparse LMStype algorithms 10-12 have been proposed. Also, sparse LMF algorithms 7,13-16 and sparse LMS/F algorithms 14,17,18 have been developed in recent years. All of the mentioned sparse channel estimation algorithms based on error criterion function, hence, corresponding sparse channel estimation algorithms can keep the same convergence speed. It is well known that RLS algorithm can achieve faster convergence speed than LMS algorithm, 19 and it does not bring the eigenvalue spread problem. 5 The LMS algorithm aims to reduce the mean square error while the RLS algorithm finds the coefficients recursively to minimize a weighted linear least squares cost function relating to the input training signals. 5 In addition, in the derivation of the LMS and RLS,...

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Section: Discussionmentioning

confidence: 99%

“…where β denotes threshold parameter, which controls the approximation accuracy of ℓ 0 -norm. By adopting approximate ℓ 0 -norm in Equation 15, then RLS-L0 channel estimation algorithm can be derived as…”

Section: Sparse Rls Channel Estimation Algorithmsmentioning

confidence: 99%

Recursive least square–based fast sparse multipath channel estimation

Gui

2017

Int J Communication

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“…Motivated by reweighted L1-minimization sparse recovery algorithm [6] in CS [15], an improved ASCE method using RZA-NLMF algorithm was proposed in [11]. The cost function of RZA-NLMF is given as (9) where RZA     is a parameter which depends on step-size  , regularization parameter  RZA and reweighted factor  , respectively.…”

Section: Asce Using Rza-nlmfmentioning

confidence: 99%

“…The RL1-NLMF for sparse channel estimation has a better performance than the ZA-NLMF and RZA-NLMF which are usually employed in compressive sensing [11]. Because RL1-NLMF can exploit more channel sparsity information than ZA-NLMF as well as RZA-NLMF.…”

Section: Asce Using Rl1-nlmf (Proposed)mentioning

confidence: 99%

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Improved adaptive sparse channel estimation using re-weighted L1-norm normalized least mean fourth algorithm

Wang

Gui

et al. 2015

2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)

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Abstract:In next-generation wireless communications systems, accurate sparse channel estimation (SCE) is required for coherent detection. This paper studies SCE in terms of adaptive filtering theory, which is often termed as adaptive channel estimation (ACE). Theoretically, estimation accuracy could be improved by either exploiting sparsity or adopting suitable error criterion. It motivates us to develop effective adaptive sparse channel estimation (ASCE) methods to improve estimation performance. In our previous research, two ASCE methods have been proposed by combining forth-order error criterion based normalized least mean fourth (NLMF) and L1-norm penalized functions, i.e., zero-attracting NLMF (ZA-NLMF) algorithm and reweighted ZA-NLMF (RZA-NLMF) algorithm. Motivated by compressive sensing theory, an improved ASCE method is proposed by using reweighted L1-norm NLMF (RL1-NLMF) algorithm where RL1 can exploit more sparsity information than ZA and RZA. Specifically, we construct the cost function of RL1-NLMF and hereafter derive its update equation. In addition, intuitive figure is also given to verify that RL1 is more efficient than conventional two sparsity constraints. Finally, simulation results are provided to correlate this study.

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A Sparse Recovery Method for DOA Estimation Based on the Sample Covariance Vectors

Jing

Liu

2016

Circuits Syst Signal Process

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RZA-NLMF algorithm-based adaptive sparse sensing for realizing compressive sensing

Cited by 13 publications

References 12 publications

Recursive least square–based fast sparse multipath channel estimation

Recursive least square–based fast sparse multipath channel estimation

Improved adaptive sparse channel estimation using re-weighted L1-norm normalized least mean fourth algorithm

A Sparse Recovery Method for DOA Estimation Based on the Sample Covariance Vectors

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