What should we do to raise the quality of signal processing publications to an even higher level? We believe it to be crucial to maintain the precision in describing our work in publications, ensured through a high-quality reviewing process. We also believe that if the experiments are performed on a large data set, the algorithm is compared to the state-of-the-art methods, the code and/or data are well documented and available online, we will all benefit and make it easier to build upon each other's work. It is a clear win-win situation for our community: we will have access to more and more algorithms and can spend time inventing new things rather than recreating existing ones.

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Reproducible research in signal processing

Sparse adaptive channel estimation problem is one of the most important topics in broadband wireless communications systems due to its simplicity and robustness. So far many sparsity-aware channel estimation algorithms have been developed based on the well-known minimum mean square error (MMSE) criterion, such as the zero-attracting least mean square (ZALMS),which are robust under Gaussian assumption. In non-Gaussian environments, however, these methods are often no longer robust especially when systems are disturbed by random impulsive noises. To address this problem, we propose in this work a robust sparse adaptive filtering algorithm using correntropy induced metric (CIM) penalized maximum correntropy criterion (MCC) rather than conventional MMSE criterion for robust channel estimation. Specifically, MCC is utilized to mitigate the impulsive noise while CIM is adopted to exploit the channel sparsity efficiently. Both theoretical analysis and computer simulations are provided to corroborate the proposed methods.

Maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non-Gaussian environments

Norm-adaption penalized least mean square/fourth algorithm for sparse channel estimation

Sparse adaptive channel estimation problem is one of the most important topics in broadband wireless communications systems due to its simplicity and robustness. So far many sparsity-aware channel estimation algorithms have been developed based on the well-known minimum mean square error (MMSE) criterion, such as the zero-attracting least mean square (ZALMS), which are robust under Gaussian assumption. In non-Gaussian environments, however, these methods are often no longer robust especially when systems are disturbed by random impulsive noises. To address this problem, we propose in this work a robust sparse adaptive filtering algorithm using correntropy induced metric (CIM) penalized maximum correntropy criterion (MCC) rather than conventional MMSE criterion for robust channel estimation. Specifically, MCC is utilized to mitigate the impulsive noise while CIM is adopted to exploit the channel sparsity efficiently. Both theoretical analysis and computer simulations are provided to corroborate the proposed methods.

In wireless system where multiuser detection techniques may be applied, the ambient channel noise is known through experimental measurements to be decidedly non Gaussian This paper consider the problem of adaptive robust detection of direct sequence spread spectrum(DS SS) signal in the presence of unknown correlated SS interference and additive non Gaussian noise Many efforts have been made and most of them have two major problems:the first, explicit a priori knowledge of the underlying noise statistics is generally not available in a realistic scenario; second, computational complexity must be taken into consideration From traditional subspace tracking techniques, we derive a robust method with low computational complexity Analytical and simulation results show that the proposed robust techniques offer significant performance better than the normal subspace tracking methods in impulsive noise

Robust Multiuser Detection in Non Gaussian Channels

Recursive inverse adaptive filtering algorithm

Robust single-user detection is employed in a direct sequence code-division multiple-access (DS-CDMA) system in which the noise process contains impulsive components. The breakdown point is computed for a mixture noise model. The bit error probability expressions are derived under a Gaussian mixture. The performance is also evaluated in the presence of power imbalance and asynchronous reception. Noise, rather than interference, is shown to be the primary obstacle in achieving good performance for certain practical signal power and user load levels. It is concluded that DS-CDMA employing a robust correlator receiver performs better than the conventional matched filter in an impulsive noise environment.

http://wcl.boun.edu.tr/publications/pdfs/Robust%20Detection%20in%20DS-CDMA.pdf

Robust detection in DS-CDMA

In this paper, new adaptive algorithms are proposed to improve the performance of the variable step-size LMS (VSSLMS) algorithm when the system is sparse. The first proposed algorithm is the zero-attracting (ZA) VSSLMS. This algorithm outperforms the standard VSSLMS if the system is highly sparse. However, the performance of the ZA-VSSLMS algorithm deteriorates when the sparsity of the system decreases. To further improve the performance of the ZA-VSSLMS filter, the weighted zero-attracting (WZA)-VSSLMS algorithm is introduced. The algorithm performs the same or better than the ZA-VSSLMS if the system is highly sparse. On the other hand, when the sparsity of the system decreases, it performs better than the ZA-VSSLMS and better or the same as the standard VSSLMS algorithm. Also, both proposed algorithms have the same order of computational complexity as that of the VSSLMS algorithm (O(N)). For a system identification setting, the results indicate the high performance of the proposed algorithms in convergence speed and/or steady-state error under sparsity condition compared with the standard VSSLMS algorithm.

/pdf/a-zero-attracting-variable-step-size-lms-algorithm-for-3e6avslizp.pdf

A zero-attracting variable step-size LMS algorithm for sparse system identification

The variable step-size least-mean-square algorithm (VSSLMS) is an enhanced version of the least-mean-square algorithm (LMS) that aims at improving both convergence rate and mean-square error. The VSSLMS algorithm, just like other popular adaptive methods such as recursive least squares and Kalman filter, is not able to exploit the system sparsity. The zero-attracting variable step-size LMS (ZA-VSSLMS) algorithm was proposed to improve the performance of the variable step-size LMS (VSSLMS) algorithm for system identification when the system is sparse. It combines the $${\ell _1}$$
 -norm penalty function with the original cost function of the VSSLMS to exploit the sparsity of the system. In this paper, we present the convergence and stability analysis of the ZA-VSSLMS algorithm. The performance of the ZA-VSSLMS is compared to those of the standard LMS, VSSLMS, and ZA-LMS algorithms in a sparse system identification setting.

Convergence analysis of the zero-attracting variable step-size LMS algorithm for sparse system identification

In this paper, a 2-D form of the recently proposed recursive inverse (RI) adaptive algorithm is introduced The filter coefficients can be updated along both the horizontal and vertical directions on a 2-D plane The proposed approach uses a variable step size and avoids the use of the inverse autocorrelation matrix in the coefficient update equation, which leads to an improved and more stable performance Performance of the 2-D RI algorithm is compared to that of the 2-D RLS algorithm in an image deconvolution and an adaptive line enhancer problem settings The simulation results show that the proposed 2-D RI algorithm leads to an improved performance compared to that of the 2-D RLS algorithm

Aykut Hocanin

Papers

Recursive inverse adaptive filtering algorithm

Robust detection in DS-CDMA

A zero-attracting variable step-size LMS algorithm for sparse system identification

Convergence analysis of the zero-attracting variable step-size LMS algorithm for sparse system identification

A 2-D recursive inverse adaptive algorithm