• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.13 no.2
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• pp.130-135
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• 2002

A new Sign algorithm which has improved convergence speed is presented. The data-recycling technique, whose coefficients are multiply adapted in a symbol time period by recycling the received data, is applied to Sign algorithm which has few multiplications. Sign algorithm has very few multiplications and is the most easily implemented, but it gives small rate of convergence relative to others. The proposed algorithm combines the advatage of Sign algorithm, few multiplications, and the virtue of Data-Recycling LMS algorithm, simplicity and fast convergence. The results of computer simulation show that the proposed algorithm has 2 times faster convergence rate than that of LMS algorithm. Comparing to Data-Recycling LMS algorithm, in similar convergence conditions, it requires half fewer multiplications.

• Proceedings of the Acoustical Society of Korea Conference
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• 1992.06a
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• pp.76-80
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• 1992

We consider the tapped-delay line (TDL) equalizer with the few calculation quantity and the simplity, the decision feedback equalizer (DFE) with the good property for interference, and lattice equalizer(LE) with high insensitivity to roundoff noise in mobile communication fading channel. The used adaptive algorithm is the LMS algorithm and RLS algorithm. In this paper, we have evaluated the performance of the TDL equalizer, the decision feedback equalizer, and lattice-structured equalizer, for the digital signal corrupted by the impulsive noise and the white gaussian noise under the fading channel environment. From the results of error performance analysis, it is confirmed that lattice-structured equalizer has better performance than DFE equalizer and TDL equalizer.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9C
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• pp.871-876
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• 2005

In this paper, we propose an improved partial response maximum likelihood (PRML) detection scheme that has an adaptive equalizer and can be applied in the asymmetric optical recording system with high-density. We confirmed that the proposed PRML detector improves detection performance. In addition, we implemented the detector by Verilog HDL. The adaptive equalizer is composed of tap coefficient updating unit using LMS algorithn and FIR filter. FIR filter is implemented by the transposed direct form architecture for high speed operation. Viterbi detector is implemented by the register exchange method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.11C
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• pp.1107-1119
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• 2006

Data transmission experiences multiplicative distortion in frequency nonselective fading channel. This distortion occurs in OFDM communication channel and can be compensated using an equalizer. Usually, in the case of LMS equalizer, eigenvalue distribution of training signal is enlarged. Large eigenvalue distribution causes principally the performance of a communication system to be deteriorated. This paper proposes a new algorithm that shows the same performance as the existing fast wavelet transform algorithm with less computational complexity. The proposed algorithm was applied to an adaptive equalizer of OFDM communication system. Matlab simulation results show a better performance than the existing one. The proposed algorithm was implemented in VHDL and simulated.

• The Journal of the Acoustical Society of Korea
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• v.28 no.3
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• pp.245-250
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• 2009

In communications and signal processing area, a number of least mean square adaptive algorithms have been used because of simplicity and robustness. However the LMS algorithm is known to have slow and non-uniform convergence. Various variable step size LMS adaptive algorithms have been introduced and researched to speed up the convergence rate. A variable step size LMS algorithm using the error difference for updating the step size is proposed. Compared with other algorithms, simulation results show that the proposed LMS algorithm has a fast convergence. The theoretical performance of the proposed algorithm is also analyzed for the steady state.

• Proceedings of the KIEE Conference
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• 1991.11a
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• pp.357-360
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• 1991

In this paper, we have introduced a network and showed how this can be realised as an adaptive equalizer. The walsh equlizer is built from a set of Walsh-Block pulse functions and LMS algorithms with the optimal convergence factor(C.F.). The convergence and the adaptation speed of this algorithms depends on the proper choice of a design factor $\mu$ called the C.F.. Conventional adaptation techniques use the fixed time constant C.F. by the method of trial and error. In this paper, we propose to adaptive C.F. which are optimally tailored to adapt C.F. in real time so that their values are kept optimum for a new set of input variables.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.7
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• pp.3-8
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• 2016

We propose a modified form of the decision-directed least mean square (DD LMS) algorithm that is widely used in the optimization of self-adaptive equalizers, and show the modified version greatly improves the initial convergence properties of the conventional algorithm. Existing DD LMS regards the difference between a equalizer output and a quantization value for it as an error, and achieves an optimization of the equalizer based on minimizing the mean squared error cost function for the equalizer coefficients. This error generating method is useful for binary signal or a single-level signals, however, in the case of multi-level signals, it is not effective in the initialization of the equalizer. The modified DD LMS solves this problem by modifying the error generation. We verified the usefulness and performance of the modified DD LMS through experiments with multi-level signals under distortions due to intersymbol interference and additive noise.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.1
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• pp.138-145
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• 2007

Wireless transmission system using the multipath channel is affected ISI due to the delay spread. So we use a decision feedback equalizer which consist of decision part and feedback filter for remove the ISI effectively. In this paper, we propose a improved adaptive decision feedback equalizer to mitigate ISI effectively. The proposed adaptive decision feedback equalizer is construct by using soft decision device and hierarchical feedback filter based on MMSE sub-optimal equalizer using the LMS algorithm. Soft decision device mitigate the error propagation in feedback filter by incorrectly detected decision symbol and feedback filter which is divided two step independently mitigate the ISI by using a adaptive algorithm. As a result this structure shows better performance than conventional decision feedback equalizer by mitigating the error propagation in filter cause incorrectly detecting symbol. and we get the MSE more rapidly by using larger step-size due to reduce the number of feedback filter tap. In computer simulation, we compare the bit error rate performance of proposed decision feedback equalizer with conventional one on the S-V channel model for UWB system.

• The Journal of the Acoustical Society of Korea
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• v.23 no.4E
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• pp.108-111
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• 2004

This paper has interest in the effect of a fine frequency offset, defined in ITU-T G.225, to the training performance of an adaptive equalizer. This paper uses Hilbert filter in configuring a transmission system model in order to let it get a frequency offset. Also additive white Gaussian noise and band-limited filter are considered. The signal received from the above transmission system applies to an adaptive equalizer with LMS algorithm, and its training procedures are investigated. As a result, we could find that even small fine frequency offset can severely deteriorate training performance of adaptive algorithm.

• The Journal of the Korea institute of electronic communication sciences
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• v.4 no.3
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• pp.224-229
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• 2009

In this paper, an efficient signal interference control technique to improve the convergence speed of Adaptive transversal filter with LMS algorithm is introduced. The convergence characteristics of the proposed algorithm, whose coefficients are multiply adapted in a symbol time period by recycling the received data, are analyzed to prove theoretically the improvement of convergence speed. According as the step-size parameter ${\mu}$ is increased, the rate of convergence of the algorithm is controlled. Increasing the eigenvalue spread has the effect of controlling down the rate of convergence of the adaptive equalizer and also increasing the steady-state value of the average squared error and also demonstrate the superiority of signal interference control to the filter algorithm increasing convergence speed by (B+1) times due to the data-recycling LMS Algorithms.