A variable step-size sign algorithm for channel estimation

Li, Yuan Ping; Lee, Ta-Sung; Wu, Bing-Fei

doi:10.1016/j.sigpro.2014.03.030

Cited by 36 publications

(21 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Regarding the Gaussian noise model assumption, indeed, similar performance analysis of the proposed method was investigated in [14]. Also, its performance was also derived under the generalized Gaussian noise environment [15]. Different from the previous research, our analysis is based on α-stable noise model, which is suitable for many application systems [13].…”

Section: Proposed Sig-lms Methodsmentioning

confidence: 99%

Stable adaptive channel estimation method under impulsive noise environments

Gui

Zhang

Dan

et al. 2015

Int J Communication

View full text Add to dashboard Cite

SUMMARYChannel estimation is one of the key technologies for ensuring reliable wireless communications under impulsive noise environments. This paper studies robust adaptive channel estimation methods for mitigating harmful impulsive noises, which are described as alpha-stable (α-stable) distribution models. Traditional adaptive channel estimation using the second-order statistics based least mean square (SOS-LMS) algorithm does not perform well under α-stable noise environments, even though it was considered one of attractive approaches for estimating channels in the case of Gaussian noises. Unlike the traditional SOS-LMS algorithm, in this research, we propose a stable sign-function-based LMS algorithm, which can mitigate the impulsive noises. Specifically, we first construct the cost function with minimum

show abstract

Section: Proposed Sig-lms Methodsmentioning

confidence: 99%

Stable adaptive channel estimation method under impulsive noise environments

Gui

Zhang

Dan

et al. 2015

Int J Communication

View full text Add to dashboard Cite

show abstract

“…Letting R = E[x(n)x T (n)] denotes the covariance matrix of input signal x(n) and λ max as its maximum eigenvalue. The well-known stable convergence condition of the SLMS is 0 < µ LMS < 1/λ max (13) In order to remove SαS noise, the traditional SLMS algorithm [14] was first proposed as…”

Section: Traditional Channel Estimation Techniquementioning

confidence: 99%

“…Based on the SαS noise model, several adaptive filtering based robust channel estimation techniques have been developed [14][15][16]. These techniques are based on the channel model 1 -norm (RL1) [19], p -norm (LP), and 0 -norm (L0) [20], to take advantage of sparsity and to mitigate non-Gaussian noise interference.…”

Section: Introductionmentioning

confidence: 99%

Sign Function Based Sparse Adaptive Filtering Algorithms for Robust Channel Estimation under Non-Gaussian Noise Environments

Zhang

Gui

2016

Algorithms

View full text Add to dashboard Cite

Robust channel estimation is required for coherent demodulation in multipath fading wireless communication systems which are often deteriorated by non-Gaussian noises. Our research is motivated by the fact that classical sparse least mean square error (LMS) algorithms are very sensitive to impulsive noise while standard SLMS algorithm does not take into account the inherent sparsity information of wireless channels. This paper proposes a sign function based sparse adaptive filtering algorithm for developing robust channel estimation techniques. Specifically, sign function based least mean square error (SLMS) algorithms to remove the non-Gaussian noise that is described by a symmetric α-stable noise model. By exploiting channel sparsity, sparse SLMS algorithms are proposed by introducing several effective sparse-promoting functions into the standard SLMS algorithm. The convergence analysis of the proposed sparse SLMS algorithms indicates that they outperform the standard SLMS algorithm for robust sparse channel estimation, which can be also verified by simulation results.

show abstract

“…4,6,11 However, the convergence performance of these algorithms is severely deteriorated when the input signal is correlated (also called the colored input signal). 6,7,[16][17][18][19] To efficiently suppress the impulsive noise, the sign algorithm (SA) 16 and its variants 6,17 were proposed by optimizing the l 1 -norm cost function, but their convergence rates are very slow for the correlated input. In the SAF, the correlated input signal is partitioned into almost mutually exclusive multiple subband signals by the analysis filters; then, the decimated subband signals close to white are used to update the filter's weight vector, thus improving the convergence rate.…”

Section: Introductionmentioning

confidence: 99%

Novel combination schemes of individual weighting factors sign subband adaptive filter algorithm

Zhao

2017

Adaptive Control & Signal

View full text Add to dashboard Cite

To improve the performance of the recently presented individual weighting factors sign subband adaptive filter (IWF-SSAF) algorithm, its 2 combination algorithms using different step sizes are proposed. The first algorithm is to convexly combine the weight vectors of a large step-size IWF-SSAF filter and a small step-size one; and the second algorithm is to obtain a time-varying step size for the IWF-SSAF by combining a large step size and a small one. The minimization of the sum of the l 1 -norm of subband errors is used to indirectly update the mixing parameters in these 2 algorithms through a modified sigmoidal function. Moreover, in the first algorithm, to implement a smooth transition from the large step-size IWF-SSAF filter to the small step-size one, the component filters receive a cyclic feedback of the combined weight vector. Both proposed algorithms have almost the same convergence performance, but the second algorithm saves computational cost. Simulation results in impulsive noise scenarios demonstrate the superiority of our proposed algorithms.

show abstract

A variable step-size sign algorithm for channel estimation

Cited by 36 publications

References 14 publications

Stable adaptive channel estimation method under impulsive noise environments

Stable adaptive channel estimation method under impulsive noise environments

Sign Function Based Sparse Adaptive Filtering Algorithms for Robust Channel Estimation under Non-Gaussian Noise Environments

Novel combination schemes of individual weighting factors sign subband adaptive filter algorithm

Contact Info

Product

Resources

About