• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.16-19
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• 1996

The objective of this paper is to present a new adaptive nonlinear compensation method, which is based upon the Pth-order inverse theory and can be implemented in a systematic way, for weakly nonlinear systems that can be modeled by a Volterra series. In particular, employment of the proposed approach for the linearization of a given nonlinear system leads to the effective elimination of (up to a required order) nonlinearities in the overall system output. To demonstrate the feasibility of the proposed method, simulation results using a satellite communication system model are also provided.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.5
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• pp.450-454
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• 1997

본 논문에서는 볼테라 비선형 시스템의 선형화를 위한 새로운 적응 선행처리 기법을 제시한다. 특히, 제안된 적응 선행처리 기법은 (i) 순수 비선형 왜곡 보상을 위한 부분(pure nonlinear distortion compensator: PNDC)과, (ii) 선형 왜곡 보상을 위한 선형 역필터(linear inverse filter: LIF)의 두 부분으로 구성된다. 본 논문의 선형화 기법의 장점으로는 기존의 P차 역(Pth-order inverse) 기법에 비하여 계산량이 상당히 감소되며, 적응 알고리듬이 보다 빠르고 안정된 수렴 특성을 나타낸다. 끝으로, 모의실험을 통하여, 제안된 선행처리 기법의 성능및 실제 적용 가능성을 살펴본다.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.7
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• pp.1003-1009
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• 1995

In This Paper, Linearization problem is discussed to reduce distortion of a nonlinear system based on Schetzen's pth-orfer inverse theorem. We propose a nonlinear adaptive prefiltering algorithm which can reduse nonlinear distortion up to pth order by tandemly connecting a pth-order Volterra filter before the nonlinear system under the consideration and by adjusting the filter coefficients adaptively. The feasibility of applying the proposed algorithm to a nonlinear system is conformed via computer simulation by observing significant reduction of total nonlinear distortion for the case of random input and sinusoidal input excitation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.4C
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• pp.403-413
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• 2009

We compare three predistortion methods to prevent signal distortion and spectral re-growth due to the high PAPR (peak-to-average ratio) of OFDM signal and the non-linearity of high-power amplifiers. The three predistortion methods are pth order inverse, indirect learning architecture and look up table. The pth order inverse and indirect learning architecture methods requires less memory and has a fast convergence because these methods use a polynomial model that has a small number of coefficients. Nevertheless the convergence is fast due to the small number of coefficients and the simple computation that excludes manipulation of complex numbers by separate compensation for the magnitude and phase. The look up table method is easy to implement due to simple computation but has the disadvantage that large memory is required. Computer simulation result reveals that indirect learning architecture shows the best performance though the gain is less than 1 dB at $BER\;=\;10^{-4}$ for 64-QAM. The three predistorters are adaptive to the amplifier aging and environmental changes, and can be selected to the requirements for implementation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.9
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• pp.2456-2469
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• 1996

A digital satellite communication channel has a nonlinearity with memory due to saturation characeristis of the high poer amplifier in the satellite and transmitter/receiver linear filter used in the overall system. In this paper, we propose a complex radial basis function network(CRBFN) based adaptive equalizer for compensation of nonlinearities in digital satellite communication channels. The proposed CRBFN untilizes a complex-valued hybrid learning algorithm of k-means clustering and LMS(least mean sequare) algorithm that is an extension of Moody Darken's algorithm for real-valued data. We evaluate performance of CRBFN in terms of symbol error rates and mean squared errors nder various noise conditions for 4-PSK(phase shift keying) digital modulation schemes and compare with those of comples pth order inverse adaptive Volterra filter. The computer simulation results show that the proposed CRBFN ehibits good equalization, low computational complexity and fast learning capabilities.