Aykut Hocanin

Recursive inverse adaptive filtering algorithm

In this paper, a new FIR adaptive filtering algorithm is introduced. This algorithm is based on the Quasi-Newton (QN) optimization algorithm. The approach uses a variable step-size in the coefficient update equation that leads to an improved performance. The simulation results show that the algorithm has very similar performance to the Robust Recursive Least Squares Algorithm (RRLS) while performing better than the Transform Domain LMS with Variable Step-Size (TDVSS) in stationary environments. The algorithm is tested in Additive White Gaussian Noise (AWGN) and Correlated Noise environments.

Robust detection in DS-CDMA

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.

Convergence analysis of the 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

Frequency-response-shaped LMS adaptive filter

{\ell _1}

An FIR notch filter for adaptive filtering of a sinusoid in correlated noise

ℓ1-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.

Two-dimensional CRC for efficient transmission of ATM cells over CDMA

Recursive Inverse (RI) adaptive filtering algorithm which uses a variable step-size and the instantaneous value of the autocorrelation matrix in the coefficient update equation was proposed in [1]. The algorithm was shown to have a higher performance compared with the RLS and RRLS algorithms. In this paper, a more efficient version with lower computational complexity is presented. The performance of the algorithm has been tested in a channel equalization setting and compared with those of the Recursive Least Squares (RLS) and Stabilized Fast Transversal Recursive Least Squares (SFTRLS) algorithms in Additive White Gaussian Noise (AWGN), Additive Correlated Gaussian Noise (ACGN), Additive White Impulsive Noise (AWIN) and Additive Correlated Impulsive Noise (ACIN) environments. Simulation results show that the Fast RI algorithm performs better than RLS and requires less computations. Additionally, the performance of the Fast RI algorithm is shown to be superior to that of the SFTRLS algorithm under the same conditions.

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

A new LMS algorithm is introduced for improved performance when a sinusoidal input signal is corrupted by correlated noise. The algorithm is based on shaping the frequency response of the transversal filter. This shaping is performed on-line by the inclusion of an additional term similar to the leakage factor in the adaptation equation of leaky LMS. This new term, which involves the multiplication of the filter coefficient vector by a matrix, is calculated in an efficient manner using the FFT. The proposed adaptive filter is shown analytically to converge in the mean and mean-square sense. The filter is also analyzed in the steady state in order to show the frequency-response-shaping capability. Simulation results illustrate that the performance of the frequency-response-shaped LMS (FRS-LMS) algorithm is very effective even for highly correlated noise.

Robust Recursive Inverse Adaptive Algorithm in Impulsive Noise

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.