Abstract
The minimum error entropy (MEE) algorithm is known to be superior in signal processing applications under impulsive noise. In this paper, based on the analysis of behavior of the optimum weight and the properties of robustness against impulsive noise, a normalized version of the MEE algorithm is proposed. The step size of the MEE algorithm is normalized with the power of input entropy that is estimated recursively for reducing its computational complexity. The proposed algorithm yields lower minimum MSE (mean squared error) and faster convergence speed simultaneously than the original MEE algorithm does in the equalization simulation. On the condition of the same convergence speed, its performance enhancement in steady state MSE is above 3 dB.
Highlights
Wired or wireless communication channels are under multipath fading as well as impulsive noise from various sources [1,2]
Most algorithms are designed based on the mean squared error (MSE) criterion, but it often fails in impulsive noise environments [3].One of the cost functions based on information theoretic learning (ITL), minimum error entropy (MEE) has been developed by Erdogmus [4]
In this paper, based on the analysis of behavior of optimum weight and some factors on mitigation of influence from large errors due to impulsive noise, we propose to employ a time-varying step size through normalization by the input power that is recursively estimated for effectiveness in computational complexity
Summary
Wired or wireless communication channels are under multipath fading as well as impulsive noise from various sources [1,2]. As a nonlinear version of MEE, the decision feedback MEE (DF-MEE) algorithm has been known to yield superior performance under severe channel distortions and impulsive noise environments [5]. In the work conducted by [7], a computation reducing method by the recursive gradient estimation of the DF-MEE has been proposed for practical implementation Though those practical difficulties have been removed through the recursive method, theoretic analysis in depth on its optimum solutions and their behavior has not been carried out yet for further enhancement of the algorithm. In this paper, based on the analysis of behavior of optimum weight and some factors on mitigation of influence from large errors due to impulsive noise, we propose to employ a time-varying step size through normalization by the input power that is recursively estimated for effectiveness in computational complexity. Entropy 2016, 18, 239 problems with impulsive noise that can be encountered in experiments investigating physical phenomenon [8]
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