• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.1
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• pp.343-347
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• 2012

Non-linear equalization techniques using decision feedback structure are highly demanded for cancellation of intersymbol interferences occurred in severe channel environments. In this paper decision feedback structure is applied to the linear blind equalizer algorithm that is based on information theoretic learning and a randomly generated symbol set. At the decision feedback equalizer (DFE) the random symbols are generated to have the same probability density function (PDF) as that of the transmitted symbols. By minimizing difference between the PDF of blind DFE output and that of randomly generated symbols, the proposed DFE algorithm produces equalized output signal. From the simulation results, the proposed method has shown enhanced convergence and error performance compared to its linear counterpart.

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.49-55
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• 2020

Information theoretic learning (ITL) methods based on random symbols (RS) use a set of random symbols generated according to a target distribution and are designed nonparametrically to minimize the cost function of the Euclidian distance between the target distribution and the input distribution. One drawback of the learning method is that it can not utilize the input power statistics by employing a constant stepsize for updating the algorithm. In this paper, it is revealed that firstly, information potential input (IPI) plays a role of input in the cost function-derivative related with information potential output (IPO) and secondly, input itself does in the derivative related with information potential error (IPE). Based on these observations, it is proposed to normalize the step-size with the statistically varying power of the two different inputs, IPI and input itself. The proposed algorithm in an communication environment of impulsive noise and multipath fading shows that the performance of mean squared error (MSE) is lower by 4dB, and convergence speed is 2 times faster than the conventional methods without step-size normalization.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.4
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• pp.1951-1956
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• 2013

Distribution-matching type algorithms based on a set of symbols generated in random order provide a limited performance under biased impulsive noise since the performance criterion for the algorithms has no variables for biased signal. For the immunity against biased impulsive noise, we propose, in this paper, a modified performance criterion and derived related blind algorithms based on augmented filter structures and the distribution-matching method using a set of random symbols. From the simulation results, the proposed algorithm based on the proposed criterion yielded superior convergence performance undisturbed by the strong biased impulsive noise.

• Journal of Internet Computing and Services
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• v.11 no.5
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• pp.19-26
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• 2010

The criterionmaximizing cross-correntropy (MCC) of two different random variables has yielded superior performance comparing to mean squared error criterion. In this paper we present a complex-valued blind equalizer algorithm for QAM and complex channel environments based on cross-correntropy criterion which uses, as two variables, equalizer output PDF and Parzen PDF estimate of a self-generated symbol set. Simulation results show significantly enhanced performance of symbol-point concentration with no phase rotation in complex-channel communication.

• Journal of Internet Computing and Services
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• v.15 no.4
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• pp.119-124
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• 2014

Blind adaptive systems based on the Euclidean distance (ED) between the distribution function of the output samples and that of a set of random symbols generated at the receiver matching with the distribution function of the transmitted symbol points estimate the ED at each iteration time to examine its convergence state or its minimum ED value. The problem is that this ED estimation obtained by block?data processing requires a heavy calculation burden. In this paper, a recursive ED estimation method is proposed that reduces the computational complexity by way of utilizing the relationship between the current and previous states of the datablock. The relationship provides a ground that the currently estimated ED value can be used for the estimation of the next ED without the need for processing the whole new data block. From the simulation results the proposed recursive ED estimation shows the same estimation values as that of the conventional method, and in the aspect of computational burden, the proposed method requires only O(N) at each iteration time while the conventional block?processing method does $O(N^2)$.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.1
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• pp.26-32
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• 2013

Blind algorithms based on Information potential of output samples and a set of symbols generated in random order at the receiver go through performance degradation when biased impulsive noise is added to the channel since the cost function composed of information potentials has no variable to deal with biased signal. Aiming at the robustness against biased impulsive noise, we propose, in this paper, a modified information potential, and derived related blind algorithms based on augmented filter structures and a set of random-order symbols. From the simulation results of blind equalization for multipath channels, the blind algorithm based on the proposed information potential produced superior convergence performance in the environments of strong biased impulsive noise.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.3
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• pp.149-154
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• 2014

The cross-correntropy concept can be expressed with inner products of two different probability density functions constructed by Gaussian-kernel density estimation methods. Blind algorithms based on the maximization of the cross-correntropy (MCC) and a symbol set of randomly generated N samples yield superior learning performance, but have a huge computational complexity in the update process at the aim of weight adjustment based on the MCC. In this paper, a method of reducing the computational complexity of the MCC algorithm that calculates recursively the gradient of the cross-correntropy is proposed. The proposed method has only O(N) operations per iteration while the conventional MCC algorithms that calculate its gradients by a block processing method has $O(N^2)$. In the simulation results, the proposed method shows the same learning performance while reducing its heavy calculation burden significantly.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.1A
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• pp.35-40
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• 2011

The complex-valued blind algorithm based on a set of randomly generated symbols and Euclidean distance can take advantage of information theoretic learning and cope with the channel phase rotation problems. On the algorithm, in this paper, the effect of kernel size has been studied and a kernel-modified version of the algorithm that rearranges the forces between the information potentials by kernel-modification has been proposed. In simulation results for 16 QAM and complex-channel models, the proposed algorithm show significantly enhanced performance of symbol-point concentration and no phase rotation problems caused by the complex channel models.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.12
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• pp.5036-5041
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• 2010

This paper introduces the complex-version of an Euclidean distance minimization algorithm based on a set of delta functions. The algorithm is analyzed to be able to compensate inherently the channel phase distortion caused by inferior complex channels. Also this algorithm has a relatively small size of Gaussian kernel compared to the conventional method of using a randomly generated symbol set. This characteristic implies that the information potential between desired symbol and output is higher so that the algorithm forces output more strongly to gather close to the desired symbol. Based on 16 QAM system and phase distorted complex-channel models, mean squared error (MSE) performance and concentration performance of output symbol points are evaluated. Simulation results show that the algorithm compensates channel phase distortion effectively in constellation performance and about 5 dB enhancement in steady state MSE performance.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.9
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• pp.10-16
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• 2011

Interleaving is applied to prevent from exceeding the error-correction capability of channel code. At the receiver, burst errors are converted into random errors after deinterleaving, so the error-correction capability of channel code is not exceeded. However, when a receiver does not have any information on parameters used at an interleaver, interleaving can be seen as an encryption with some pattern. In this case, deinterleaving becomes complicated. In the field of blind deinterleaving, there have recently been a number of researches using linearity of linear block code. In spite of those researches, since the linearity is not applicable to a convolutional code, it is difficult to estimate parameters as in a linear block code. In this paper, we propose a method of blind block deinterleaving using convolutional code reconstruction method.