• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.11
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• pp.2158-2165
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• 2007

In this paper, a Modified Fuzzy C-Means algorithm with Gaussian Weights(MFCM_GW) is presented for nonlinear blind channel equalization. The proposed algorithm searches the optimal channel output states of a nonlinear channel from the received symbols, based on the Bayesian likelihood fitness function and Gaussian weighted partition matrix instead of a conventional Euclidean distance measure. Next, the desired channel states of a nonlinear channel are constructed with the elements of estimated channel output states, and placed at the center of a Radial Basis Function(RBF) equalizer to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with those of a simplex genetic algorithm(GA), a hybrid genetic algorithm(GA merged with simulated annealing(SA): GASA), and a previously developed version of MFCM. It is shown that a relatively high accuracy and fast search speed has been achieved.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.9
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• pp.1957-1967
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• 1997

Adaptive channel equlization accomplished without resorting to a training sequence is known as blind equalization. In this paper, in order to reduce the speed of the convergence and the steady-state mean squared error simultaneously, we propose the multi-stage RD(radius-directed) algorithm derived from the combination of the constant modulus algorithm and the radius-directed algorithm. In the starting stage, multi-stage RD algorithm are identical to the constant modulus algorithm which guarantees the convergence of the equalizer. As the blind identical to the constant modulus algorithm which guarantees the convergence of the equalizer. As the blind equalizer converges, the number of the level of the quantizers is increased gradually, so that the proposed algorithm operate identical to the radius-directed algorithm which leads to the low error power after the covnergence. Therefore, the multi-stage RD algorithm obtains fast convergence rage and low steady stage mean square error.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.829-832
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• 2001

본 논문에서는 2차 통계치를 이용하여 패널추징 및 등화 기법을 제안하였다. 기존의 채널 추정 알고리듬은 잡음이 없는 환경에서 LS방법을 이용하기 때문에 잡음이 강한 패널에서는 원하는 성능을 얻을 수 없는 단점이 있다. 수신신호의 상관행렬의 최소 고유값에 대응하는 고유벡터는 채널의 임펄스 응답에 관한 정보를 포함하고 있다. 이러한 고유 벡터를 매시간마다 갱신시키면서 구하는 적응 알고리듬을 제안하고 이를 이용하여 블라인드 채널 추정 및 등화기 파라미터를 추정하였다. 제안한 알고리듬은 잡음에 강인한 특성을 보일 뿐 아니라 기존의 알고리듬들 보다 우수한 채널 추정 및 등화 성능을 모의 실험을 통하여 검증하였다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.57-62
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• 2014

This paper proposes the improving the equalization performance using the adaptive modulus concept to the MCMA blind equalizer in order to the reduction of intersymbol interference which occurs in the band limited and time dispersive communication channel. In MCMA blind algorithm, it is possible to reducing the amplitude and phase rotation of intersymbol interference without training sequence, the fixed constant modulus of transmission signal is used. But in proposed algorithm, the modulus are adaptively varies according to the equalizer output signal, then the improved equalization performance were obtained by the computer simulation. For this, the recovered signal constellation that is the output of the equalizer, the convergence performance by MSE, MD (maximum distortion) and residual isi characteristic learning curve were used. The propose algorithm has fairly good performance compared to the traditional MCMA algorithm in the same adaptive equalization algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.465-468
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• 2005

MMA(Multi-Modulus Algorithm) is inferior at a initial equalization in high ISI(intersymbol interference), because it is the inaccurate decision. To improve this probel SMMA(Sliced Multi-Modulus Algorithm) is based on using the MCMA(Modified Constant Modulus Algorithm). SMMA is a improved capability than MMA in high SNR but is inaccurate decision in low SNR. In this paper, We propose some multi-modulus blind equalization algorithm scheme. It is a method of operation in some multi-modulus algorithm which does no obstruct a convergence property at the initial equalization in the low SNR. Proposed algorithm improves the steady-state performance. And it uses residual ISI of the equalizer output in order to decide the optimum switching time between the single modulus and the multi-modulus algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.115-121
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• 2019

In this paper, we propose MSAG-SCS-MMA-I adaptive blind equalization with double step size with very small residual ISI and MSE at steady-state while significantly improving the convergence speed of the traditional SCS-MMA-I algorithm in 256-QAM system. And we evaluate the equalization performance for this algorithm. Different step sizes according to the absolute value of decision-directed error instead of a fixed step-size are applied to the tap update equation of MSAG-SCS-MMA-I, which is controlled by binary flags of '1' or '0' obtained from SCS-MMA-I and decision-directed algorithms. This makes for excellent equalization performance. As a result of computer simulation, we confirmed that the proposed algorithm has more better performance than the MMA, SCS-MMA-I, and MSAG-SCS-MMA-I algorithms in terms of the performance index such as residual ISI, MSE, and MD.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.869-872
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• 2001

Blind identification and equalization of communication channel is important because it does not need training sequence, nor does it require a priori channel information. So, we can increase the bandwidth efficiency. The linear prediction error method is perhaps the most attractive in practice due to the insensitive to blind channel estimator and equalizer length mismatch as well as for its simple adaptive algorithms. In this paper, we propose method for fractionally spaced blind equalizer with arbitrary delay using one-step forward prediction error filter from second-order statistics of the received signals for SIMO channel. Our algorithm utilizes the forward prediction error as training sequences for data estimation and desired signal for channel estimation.

• The Journal of the Acoustical Society of Korea
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• v.14 no.5
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• pp.20-28
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• 1995

This paper studied adaptive blind equalizer which belong to Bussgang type. It is well known that blind equalizer performs equalization without using a training sequence. Especially, this paper concentrated on real time processing of them. The channel characteristic was obtained from measurements taken in a real urban multipath environment. A T/2 fractionally-spaced equalizer was used at the receiving end. Our computer simulations demonstrated that Stop and Go, Benveniste-Goursat, and optimal Bussgang algorithms have relatively low MSE property. CMA shows faster convergence property than any other of Bussgang type algorithm.

• ETRI Journal
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• v.26 no.2
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• pp.101-111
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• 2004

This paper presents a near-optimum blind decision feedback equalizer (DFE) for the receivers of Advanced Television Systems Committee (ATSC) digital television. By adopting a modified trellis decoder (MTD) with a trace- back depth of 1 for the decision device in the DFE, we obtain a hardware-efficient, blind DFE approaching the performance of an optimum DFE which has no error propagation. In the MTD, the absolute distance is used rather than the squared Euclidean distance for the computation of the branch metrics. This results in a reduction of the computational complexity over the original trellis decoding scheme. Compared to the conventional slicer, the MTD shows an outstanding performance improvement in decision error probability and is comparable to the original trellis decoder using the Euclidean distance. Reducing error propagation by use of the MTD in the DFE leads to the improvement of convergence performance in terms of convergence speed and residual error. Simulation results show that the proposed blind DFE performs much better than the blind DFE with the slicer, and the difference is prominent at the trellis decoder following the blind DFE.

• Journal of Internet Computing and Services
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• v.11 no.5
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• pp.1-8
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• 2010

This paper presentsthe performance study of blind equalizer algorithms for impulsive-noise environments based on Gaussian kernel and constant modulus error(CME). Constant modulus algorithm(CMA) based on CME and mean squared error(MSE) criterion fails in impulsive noise environment. Correntropy blind method recently introduced for impulsive-noise resistance has shown in PAM system not very satisfying results. It is revealed in theoretical and simulation analysis that the maximization of zero-error probability based on CME(MZEP-CME) originally proposed for Gaussian noise environments produces superior performance in impulsive noise channels as well. Gaussian kernel of MZEP-CME has a strong effect of becoming insensitive to the large differences between the power of impulse-infected outputs and the constant modulus value.