• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.468-473
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• 1999

Neural networks for adaptive channel equalizers have been resorted to recently in digital communication systems. In this paper, the characteristics and the application areas and etc. for neural networks as adaptive channel equalizers are examined through simple examples.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.5
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• pp.939-951
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• 1994

In adaptive equalizers based on least mean squares (LMS) algorithms, the convergence rate is determined by the convariance matrix of an input signal. When the eigenvalue spread of the convariance matrix is close to unity, the convergence rate is quite fast. In this paper, for fast convergence of LMS-based adaptive equalizers we propose a modified reference signal pertinent to the statistical channel. From the theoretical analysis and computer simulation, it is shown that the proposed modification method is quite effective for fast convergence of LMS-based adaptive equalizers.

• Journal of the Optical Society of Korea
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• v.15 no.3
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• pp.232-236
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• 2011

We analyze the linear equalizers used in optically amplified on-off-keyed (OOK) systems to combat chromatic dispersion (CD) and polarization mode dispersion (PMD), and we derive the mathematical minimum mean squared error (MMSE) performance of these equalizers. Currently, the MMSE linear equalizer for optical OOK systems is obtained by simulations using adaptive approaches such as least mean squared (LMS) or constant modulus algorithm (CMA), but no theoretical studies on the optimal solutions for these equalizers have been performed. We model the optical OOK systems as square-law nonlinear channels and compute the MMSE equalizer coefficients directly from the estimated optical channel, signal power, and optical noise variance. The accuracy of the calculated MMSE equalizer coefficients and MMSE performance has been verified by simulations using adaptive algorithms.

• ETRI Journal
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• v.31 no.2
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• pp.162-172
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• 2009

A linear-prediction-based blind equalization algorithm for single-input single-output (SISO) finite impulse response/infinite impulse response (FIR/IIR) channels is proposed. The new algorithm is based on second-order statistics, and it does not require channel order estimation. By oversampling the channel output, the SISO channel model is converted to a special single-input multiple-output (SIMO) model. Two forward linear predictors with consecutive prediction delays are applied to the subchannel outputs of the SIMO model. It is demonstrated that the partial parameters of the SIMO model can be estimated from the difference between the prediction errors when the length of the predictors is sufficiently large. The sufficient filter length for achieving the optimal prediction is also derived. Based on the estimated parameters, both batch and adaptive minimum-mean-square-error equalizers are developed. The performance of the proposed equalizers is evaluated by computer simulations and compared with existing algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.2
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• pp.253-260
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• 2018

The acoustic communication channel in shallow underwater is typically shown as time-varying multipath fading channel characteristics. The received signal through channel transmission cause inter-symbol interference (ISI) owing to multiple components of different time delay and amplitude. To compensate for this, several techniques have been used, and one of them is acoustic equalizer. In this study, we used four equalizers - feed forward equalizer (FFE), decision directed equalizer (DDE), decision feedback equalizer (DFE) and combination DDE with DFE to compensate ISI. And we applied two adaptive algorithms to adjust coefficient of equalizers - normalized least mean square algorithm and recursive least square algorithm. As result, we found that it has a significant performance improvement over 6 dB on SNR in nonlinear equalizer. By combination of DFE and DDE has almost best performance in any case.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.6
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• pp.164-175
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• 1996

In this thesis, to cope with severe intersymbol interference and nonlinear distotions of the digital magnetic recording channel a neural decision feedback equalizer (NDFE) and an adaptive neural equalizer (NE) are applied. The digital magnetic recording channels with various recording densities and different types of the nonlinear distortions are considered. The computer simulation shows that as the nonlinear distortion is increased, the neural equalizers (NdFE, NE) have advantages of approximately 2-4 dB in signal to noise ratio (SNR) over the onventional eualizers to reach sme bit error rate and, a sthe recording density is increased, 1~5 dB of SNR improvement are also gained. Especially the NdFE gives a superior performance over the other equalizers when there is a severe nonlinear distortion in the digital magnetic channel.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8B
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• pp.1445-1452
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• 2000

In general, the communication channel can be modeled as inter-symbol interference(ISI) and additive white gaussian noise channel. Viterbi algorithm is optimum detector for transmitted data at transmitter, but it needs large computational complexity. For the sake of this problem, adaptive equalizers are employed for channel equalization which is not attractive for mixed phase channel. In this paper, we propose the effective new channel equalizer for mixed phase channel and show the better performance than previous equalizers.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.32A no.8
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• pp.11-18
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• 1995

This paper proposes a nonlinear adaptive equalizer which is based on fuzzy rules and fuzzy inference of several affine mapping for the received channel data. The proposed nolonlinear adaptive equalizers with the significantly lower computational complexity. Also it can be applied to the on-line adaptation environments owing to its fast convergence characteristics and the lower computational load. When using the decision feedback vectors, this equaalizer can be easily realized in the form of the DFE structure with out the requirement for the perfect channel knowledge as in the case of the fuzzy adaptive filter.

• Proceedings of the Acoustical Society of Korea Conference
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• 1991.06a
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• pp.38-41
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• 1991

RLS algorithms are required for applications to adaptive line enhancers, adaptive equalizers for voiceband telephone and HF modems, and wide-badn digital spectrum mobile raio in which their convergence time and tracking speed are significant. The fast QR RLS algorithm satisfies above the requirements. Its computational complexity is linearly proportional to the tap number of a filter, N and its performance remains numerically stable. From the result of simumulation, the fast QR RLS algorithm represented Cioffi is better than gradient based algorithm in its initial performance when being applied to an adaptive line enhancer for cancelling noise.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.3
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• pp.1-8
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• 1996

This paper proposes a novel transversal filter and an optimal LMS algorithm, and show how these can be realized as an adaptive equalizer. The transversal filter consists of a walsh and block pulse functions. in the LMS algorithm with equalizers, the convergence factor is an improtant design parameter because it governs stability and convergence speed. The conventional adaptation techniques use a fixed time constant convergence factor by the trial and error method. In this paper, an optimal method in the choice of the convergence factor is proposed. The proposed algorithm is obtrained that is tailored for each filter tap and is updated at each iteration. The performance of the proposed algorithm is compared iwth those of the conventional TDL and DFT equalizers by computer simulations.