Sparse channel estimation with l&lt;inf&gt;p&lt;/inf&gt;-norm and reweighted l&lt;inf&gt;1&lt;/inf&gt;-norm penalized least mean squares

Taheri, Omid; Vorobyov, Sergiy A.

doi:10.1109/icassp.2011.5947082

Cited by 80 publications

(98 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The algorithms being considered here are the ZA-LMS and RZA-LMS algorithms of [7] as well as the proposed reweighted l 1 -norm penalized LMS algorithm and the l p -pseudo-norm penalized LMS algorithm [24]. The standard LMS algorithm is also included for comparison in our simulation figures.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Expanding the right hand side of (24) and then taking expectation of the both sides results in the following equation…”

Section: Excess Msementioning

confidence: 99%

“…It is due to the fact that a properly reweighted l 1 -norm approximates the l 0 -pseudo-norm, which actually needs to be minimized, better than the l 1 -norm. Therefore, one approach to enforce the sparsity of the solution for the sparsity-aware LMS-type algorithms is to introduce the reweighted l 1 -norm penalty term in the cost function [24]. 3 Our reweighted l 1 -norm penalized LMS algorithm considers a penalty term proportional to the reweighted l 1 -norm of the coefficient vector.…”

Section: Standard Lmsmentioning

confidence: 99%

“…Several sparsity-aware modifications of the standard LMS have been introduced in the literature [7,8,9,10,11,12,13,24].…”

Section: Standard Lmsmentioning

confidence: 99%

“…2 Some preliminary results (the method and some simulation results) have been reported in the conference contribution [24].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Reweighted l1-norm penalized LMS for sparse channel estimation and its analysis

Taheri

Vorobyov

2014

Signal Processing

Self Cite

View full text Add to dashboard Cite

A new reweighted l 1 -norm penalized least mean square (LMS) algorithm for sparse channel estimation is proposed and studied in this paper. Since standard LMS algorithm does not take into account the sparsity information about the channel impulse response (CIR), sparsity-aware modifications of the LMS algorithm aim at outperforming the standard LMS by introducing a penalty term to the standard LMS cost function which forces the solution to be sparse. Our reweighted l 1 -norm penalized LMS algorithm introduces in addition a reweighting of the CIR coefficient estimates to promote a sparse solution even more and approximate l 0 -pseudo-norm closer. We provide in depth quantitative analysis of the reweighted l 1 -norm penalized LMS algorithm. An expression for the excess mean square error (MSE) of the algorithm is also derived which suggests that under the right conditions, the reweighted l 1 -norm penalized LMS algorithm outperforms the standard LMS, which is expected. However, our quantitative analysis also answers the question of what is the maximum sparsity level in the channel for which the reweighted l 1 -norm penalized LMS algorithm is better than the standard LMS. Simulation results showing the better performance of the reweighted l 1 -norm penalized LMS algorithm compared to other existing LMS-type algorithms are given.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

“…Expanding the right hand side of (24) and then taking expectation of the both sides results in the following equation…”

Section: Excess Msementioning

confidence: 99%

Section: Standard Lmsmentioning

confidence: 99%

“…Several sparsity-aware modifications of the standard LMS have been introduced in the literature [7,8,9,10,11,12,13,24].…”

Section: Standard Lmsmentioning

confidence: 99%

“…2 Some preliminary results (the method and some simulation results) have been reported in the conference contribution [24].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Reweighted l1-norm penalized LMS for sparse channel estimation and its analysis

Taheri

Vorobyov

2014

Signal Processing

Self Cite

View full text Add to dashboard Cite

show abstract

Zero‐attracting variable‐step‐size least mean square algorithms for adaptive sparse channel estimation

Hamamura

2015

Adaptive Control & Signal

View full text Add to dashboard Cite

Recently, sparsity-aware least mean square (LMS) algorithms have been proposed to improve the performance of the standard LMS algorithm for various sparse signals, such as the well-known zero-attracting LMS (ZA-LMS) algorithm and its reweighted ZA-LMS (RZA-LMS) algorithm. To utilize the sparsity of the channels in wireless communication and one of the inherent advantages of the RZA-LMS algorithm, we propose an adaptive reweighted zero-attracting sigmoid functioned variable-step-size LMS (ARZA-SVSS-LMS) algorithm by the use of variable-step-size techniques and parameter adjustment method. As a result, the proposed ARZA-SVSS-LMS algorithm can achieve faster convergence speed and better steady-state performance, which are verified in a sparse channel and compared with those of other popular LMS algorithms. The simulation results show that the proposed ARZA-SVSS-LMS algorithm outperforms the standard LMS algorithm and the previously proposed sparsity-aware algorithms for dealing with sparse signals. Copyright

show abstract

Tracking of power quality disturbances using sparse model–based extended Kalman filters

Sahoo

Subudhi

Mishra

2018

Adaptive Control & Signal

View full text Add to dashboard Cite

Accurate estimation and tracking of power quality (PQ) disturbances requires efficient adaptive model-based techniques, which should have elegant structures to be applicable in practical systems. Though extended Kalman filter (EKF) has been used as a popular estimator to track the time-varying PQ events, the performance is limited due to higher-order nonlinearity exists in system dynamics.Moreover, the computational complexity and sensitivity to the measurement noise affect the estimation accuracy and error convergence properties. If the underlying sparse behavior of adaptive filters can be exploited through norm penalty, then significant improvement can be achieved in terms of convergence speed, stability, and steady state excess mean square error performance. In this paper, a modification to EKF is proposed by using sparse model to estimate PQ disturbances with better estimation accuracy and faster convergence speed.A thorough comparison is presented in terms of simulation results by using a family of existing algorithms like least mean square, recursive least square, and EKF. Adaptive estimation model incorporates real and complex state space representations to represent discrete time PQ disturbances. The convergence of the proposed sparse model-based filter is also mathematically proved considering the mean square error performance.

show abstract

Sparse channel estimation with l<inf>p</inf>-norm and reweighted l<inf>1</inf>-norm penalized least mean squares

Cited by 80 publications

References 11 publications

Reweighted l1-norm penalized LMS for sparse channel estimation and its analysis

Reweighted l1-norm penalized LMS for sparse channel estimation and its analysis

Zero‐attracting variable‐step‐size least mean square algorithms for adaptive sparse channel estimation

Tracking of power quality disturbances using sparse model–based extended Kalman filters

Contact Info

Product

Resources

About