• 제어로봇시스템학회논문지
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• 제7권5호
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• pp.415-419
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• 2001

This paper proposes and adaptive nonlinear equalizer based on Volterra Series along with M-band wavelet transform(M-DWT). The proposed wavelet transform-domain approach leads to diagonalization of the input vector auto-correlation matrix, which yields clustering its eigenvalue spread around one, and improving the convergence rate of the corresponding transform-domain LMS algorithm. In particular, the proposed adaptive Volterra equalizer is employed to compensate for the output distortion produced by a weakly nonlinear system. Finally, some simulation results obtained by using a TWT amplifier model are provide to demonstrated the converging performance of the proposed approach.

• 한국통신학회논문지
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• 제32권7C호
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• pp.627-634
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• 2007

고효율 변조기법 (16 QAM or 64QAM)이 비선형 고전력 증폭기 (High-Power Amplifier; HPA)를 사용하는 통신시스템에 적용되었을 때 비선형 왜곡에 의해 성능저하가 발생할 수 있다. 이런 비선형 왜곡은 수신부에서 복잡도가 낮은 LMS 알고리즘을 적용한 적응적 비선형 Volterra 등화기를 사용하여 보상할 수 있지만, 매우 느린 수렴 속도를 가지는 단점이 있다. 본 논문에서는 수렴 속도를 향상시키기 위한 병렬 M대역 이산 웨이블릿 변환영역 LMS (Parallel M-band Discrete Wavelet Transform Least Mean Square) 알고리즘을 제안한다. 모의실험을 통하여 제안된 기법이 기존의 시간 영역 LMS 알고리즘과 변환 영역 LMS 알고리즘들에 비해 수렴 속도가 우수함을 보여준다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권1호
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• pp.43-48
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• 2002

This paper discusses the application of fuzzy-ARTMAP neural network to compensate the nonlinearity of satellite communication channel. The fuzzy-ARTMAP is the class of ART(adaptive resonance theory) architectures designed fur supervised loaming. It has capabilities not fecund in other neural network approaches, that includes a small number of parameters, no requirements fur the choice of initial weights, automatic increase of hidden units, and capability of adding new data without retraining previously trained data. By a match tracking process with vigilance parameter, fuzzy-ARTMAP neural network achieves a minimax teaming rule that minimizes predictive error and maximizes generalization. Thus, the system automatically leans a minimal number of recognition categories, or hidden units, to meet accuracy criteria. As a input-converting process for implementing fuzzy-ARTMAP equalizer, the sigmoid function is chosen to convert actual channel output to the proper input values of fuzzy-ARTMAP. Simulation studies are performed over satellite nonlinear channels. QPSK signals with Gaussian noise are generated at random from Volterra model. The performance of proposed fuzzy-ARTMAP equalizer is compared with MLP equalizer.

• 한국통신학회논문지
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• 제21권9호
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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.