Norm Penalized Joint-Optimization NLMS Algorithms for Broadband Sparse Adaptive Channel Estimation

Abstract

Abstract:A joint-optimization method is proposed for enhancing the behavior of the l 1 -norm-and sum-log norm-penalized NLMS algorithms to meet the requirements of sparse adaptive channel estimations. The improved channel estimation algorithms are realized by using a state stable model to implement a joint-optimization problem to give a proper trade-off between the convergence and the channel estimation behavior. The joint-optimization problem is to optimize the step size and regularization parameters for minimizing the estimation bias of the channel. Numerical results achieved from a broadband sparse channel estimation are given to indicate the good behavior of the developed joint-optimized NLMS algorithms by comparison with the previously proposed l 1 -normand sum-log norm-penalized NLMS and least mean square (LMS) algorithms.

Analysis of convergence of adaptive single­step algorithms for the identification of non­stationary objects

“…When satisfying (28), the region of convergence of the algorithm (22) The known ratios for the Nagumo-Noda algorithm follow from the obtained expressions at 0. δ =…”

“…The algorithm by Kaczmarz, better known as NLMSnormalized least-mean-square algorithm, is widely used not only in the systems of identification of stationary [23] and non-stationary [24][25][26] system. In [27][28][29], its application to solving problems of filtration was described. It should be noted that in papers [19,[24][25][26], to describe the non-stationary parameters, the first-order Markovian model was used, while papers [30,31] used the modified first-order Markovian model (this model received fairly wide use in training artificial neural networks [31]).…”

A Denoising Method of Micro-Turbine Acoustic Pressure Signal Based on CEEMDAN and Improved Variable Step-Size NLMS Algorithm

“…NLMS provides faster adaptive calculation and ensures an increasingly stable combination based on various input signal powers [31]. However, although many scholars have improved the LMS algorithm [32][33][34], for wideband signals, the noise reduction effect of using only the LMS algorithm is uneven, and the performance is unstable.…”

Modified Combined-Step-Size Affine Projection Sign Algorithms for Robust Adaptive Filtering in Impulsive Interference Environments

“…System identifications, including channel estimation, seismic system identification, noise or echo cancellation, and image recovery have been successfully realized by adaptive filters (AFs) [1]. Among conventional AF algorithms, the least-mean-square (LMS) and normalized LMS (NLMS) algorithms play important roles due to their low computational complexity and mathematical tractability [2,3]. However, such LMS-type algorithms suffer from filtering performance degradation under the heavy-tailed impulsive interference and heavy-colored input signal [4].…”