Namyong Kim

Blind signal processing for impulsive noise channels

In this paper, a new blind signal processing scheme for equalization in fading and impulsive-noise channel environments is introduced based on probability density function matching method and a set of Dirac-delta functions. Gaussian kernel of the proposed blind algorithm has the effect of cutting out the outliers on the difference between the desired level values and impulse-infected outputs. And also the proposed algorithm has relatively less sensitivity to channel eigenvalue ratio and has reduced computational complexity compared to the recently introduced correntropy algorithm. According to these characteristics, simulation results show that the proposed blind algorithm produces superior performance in multi-path communication channels corrupted with impulsive noise.

Maximization of zero-error probability for adaptive channel equalization

A new blind equalization algorithm that is based on maximizing the probability that the constant modulus errors concentrate near zero is proposed. The cost function of the proposed algorithm is to maximize the probability that the equalizer output power is equal to the constant modulus of the transmitted symbols. Two blind information-theoretic learning (ITL) algorithms based on constant modulus error signals are also introduced: One for minimizing the Euclidean probability density function distance and the other for minimizing the constant modulus error entropy. The relations between the algorithms and their characteristics are investigated, and their performance is compared and analyzed through simulations in multi-path channel environments. The proposed algorithm has a lower computational complexity and a faster convergence speed than the other ITL algorithms that are based on a constant modulus error. The error samples of the proposed blind algorithm exhibit more concentrated density functions and superior error rate performance in severe multi-path channel environments when compared with the other algorithms.

Communication Equalizer Algorithms with Decision Feedback based on Error Probability

For intersymbol interference (ISI) compensation from communication channels with multi-path fading and impulsive noise, a decision feedback equalizer algorithm that minimizes Euclidean distance of error probability is proposed. The Euclidean distance of error probability is defined as the quadratic distance between the probability error signal and Dirac-delta function. By minimizing the distance with respect to equalizer weight based on decision feedback structures, the proposed decision feedback algorithm has shown to have significant effect of residual ISI cancellation on severe multipath channels as well as robustness against impulsive noise.

Euclidian distance minimization of probability density functions for blind equalization

Blind equalization techniques have been used in broadcast and multipoint communications. In this paper, two criteria of minimizing Euclidian distance between two probability density functions (PDFs) for adaptive blind equalizers are presented. For PDF calculation, Parzen window estimator is used. One criterion is to use a set of randomly generated desired symbols at the receiver so that PDF of the generated symbols matches that of the transmitted symbols. The second method is to use a set of Dirac delta functions in place of the PDF of the transmitted symbols. From the simulation results, the proposed methods significantly outperform the constant modulus algorithm in multipath channel environments.

Blind signal processing algorithms under DC biased Gaussian noise

Distortions caused by the DC-biased laser input can be modeled as DC biased Gaussian noise and removing DC bias is important in the demodulation process of the electrical signal in most optical communications. In this paper, a new performance criterion and a related algorithm for unsupervised equalization are proposed for communication systems in the environment of channel distortions and DC biased Gaussian noise. The proposed criterion utilizes the Euclidean distance between the Dirac-delta function located at zero on the error axis and a probability density function of biased constant modulus errors, where constant modulus error is defined by the difference between the system out and a constant modulus calculated from the transmitted symbol points. From the results obtained from the simulation under channel models with fading and DC bias noise abruptly added to background Gaussian noise, the proposed algorithm converges rapidly even after the interruption of DC bias proving that the proposed criterion can be effectively applied to optical communication systems corrupted by channel distortions and DC bias noise.

Blind Signal Processing for Wireless Sensor Networks

Abstract In indoor sensor networks equalization algorithms based on the minimization of Euclidean distance (MED) for the dis-tributions of constant modulus error (CME) have yielded superior performance in compensating for signal distortions inducedfrom optical fiber links, wireless-links and for impulsive noise problems. One main drawback of MED-CME algorithms is aheavy computational burden hindering its implementation. In this paper, a recursive gradient estimation for weight updates ofthe MED-CME algorithm is proposed for reducing the operations of the conventional MED-CME to at each iter-ation time for N data-block size. From the simulation results of the proposed recursive method producing exactly the sameresults as the conventional method, the proposed estimation method can be considered to be a reliable candidate for imple-mentation of efficient receivers in indoor sensor networks. Keywords: Complexity, CME, Euclidean distance, Error distribution, Impulsive noise, Sensor networks

Lagged cross-correlation of probability density functions and application to blind equalization

In this paper, the lagged cross-correlation of two probability density functions constructed by kernel density estimation is proposed, and by maximizing the proposed function, adaptive filtering algorithms for supervised and unsupervised training are also introduced. From the results of simulation for blind equalization applications in multipath channels with impulsive and slowly varying direct current (DC) bias noise, it is observed that Gaussian kernel of the proposed algorithm cuts out the large errors due to impulsive noise, and the output affected by the DC bias noise can be effectively controlled by the lag τ intrinsically embedded in the proposed function.

Step-size control for width adaptation in radial basis function networks for nonlinear channel equalization

A method of width adaptation in the radial basis function network (RBFN) using stochastic gradient (SG) algorithm is introduced. Using Taylors expansion of error signal and differentiating the error with respect to the step-size, the optimal time-varying step-size of the width in RBFN is derived. The proposed approach to adjusting widths in RBFN achieves superior learning speed and the steady-state mean square error (MSE) performance in nonlinear channel environment. The proposed method has shown enhanced steady-state MSE performance by more than 3 dB in both nonlinear channel environments. The results confirm that controlling over step-size of the width in RBFN by the proposed algorithm can be an effective approach to enhancement of convergence speed and the steady-state value of MSE.

On Training Neural Network Algorithms for Odor Identification for Future Multimedia Communication Systems

Future multimedia communication system can be developed to identify, transmit and provide odors besides voice and image. In this paper, an improved odor identification method is introduced. We present an analysis of center-gradient and a new method of using convergence parameters in training RBFN-SVD-SG (radial basis function network using Singular Value Decomposition combined with stochastic gradient) algorithm for odor identification. Through mathematical analysis, it was found that the steady-state weight fluctuation and large values of convergence parameter can lead to an increase of variance of center-gradient, which induces ill-behaving convergence. The proposed method of using raised-cosine functions for time-decaying convergence parameter shows faster convergence and better recognition performance

Function Analysis of the Euclidean Distance between Probability Distributions

Minimization of the Euclidean distance between output distribution and Dirac delta functions as a performance criterion is known to match the distribution of system output with delta functions. In the analysis of the algorithm developed based on that criterion and recursive gradient estimation, it is revealed in this paper that the minimization process of the cost function has two gradients with different functions; one that forces spreading of output samples and the other one that compels output samples to move close to symbol points. For investigation the two functions, each gradient is controlled separately through individual normalization of each gradient with their related input. From the analysis and experimental results, it is verified that one gradient is associated with the role of accelerating initial convergence speed by spreading output samples and the other gradient is related with lowering the minimum mean squared error (MSE) by pulling error samples close together.