• 방송공학회논문지
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• 제21권3호
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• pp.391-403
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• 2016

FBMC/OQAM(Filterbank multicarrier on offset-Quadrature Amplitude Modulation) 시스템은 기존 OFDM/QAM(Orthogonal frequency division multiplexing on Quadrature Amplitude Modulation) 시스템의 보호 구간인 CP(Cyclic Prefix) 사용으로 인한 데이터 전송 효율 저하가 발생하지 않는 다중 반송파 전송 시스템이다. 하지만 주파수 선택적인 채널 상황에서 주파수 축 단일 탭 등화 방식을 사용하는 경우 OFDM/QAM 시스템에 비해 수신 성능 열화가 발생한다. 본 논문에서는 FBMC/OQAM 시스템의 반복적인 채널 추정 및 등화 기법을 제안한다. 제안된 기법을 통해 주파수 선택적인 채널 상황에서 OFDM/QAM 시스템의 주파수 축 단일 탭 등화 방식과 견주어 수신 성능이 크게 떨어지지 않음을 컴퓨터 모의실험을 통해 검증한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권11호
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• pp.5394-5409
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• 2019

The paper investigates a novel detector receiver with Kalman channel information estimator and iterative channel response equalization for MIMO (multi-input multi-output) OFDM (orthogonal frequency division multiplexing) communication systems in fast multipath fading environments. The performances of the existing linear equalizers (LE) are not good enough over most fast fading multipath channels. The existing adaptive equalizer with decision feedback structure (ADFE) can improve the performance of LE. But error-propagation effect seriously degrades the system performance of the ADFE, especially when operated in fast multipath fading environments. By considering the Kalman channel impulse response estimation for the fast fading multipath channels based on CE-BEM (complex exponential basis expansion) model, the paper proposes the iterative receiver with soft decision feedback equalization (SDFE) structure in the fast multipath fading environments. The proposed SDFE detector receiver combats the error-propagation effect for fast multipath fading channels and outperform the existing LE and ADFE. We demonstrate several simulations to confirm the ability of the proposed iterative receiver over the existing receivers.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 추계학술대회 논문집 학회본부 B
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• pp.624-626
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• 1998

Adaptive channel equalization accomplished without resorting to a training sequence is known as blind equalization. In this paper, we present a auto-switching blind, equalization for 8-VSB HDTV receiver. The scheme operate in two mode : blind equalization mode and decision-directed equalization mode. This proposed scheme changes from the blind equalization mode at high error levels to the decision-directed equalization mode at lower error levels smoothly and automatically. Manual switch from the blind equalization mode to the decision-directed mode is not necessary.

• 제어로봇시스템학회논문지
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• 제9권11호
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• pp.917-924
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• 2003

Recurrent neural networks have been successfully applied to communications channel equalization. Major disadvantages of gradient-based learning algorithms commonly employed to train recurrent neural networks are slow convergence rates and long training sequences required for satisfactory performance. In a high-speed communications system, fast convergence speed and short training symbols are essential. We propose decision feedback equalizers using a recurrent neural network trained with Kalman filtering algorithms. The main features of the proposed recurrent neural equalizers, utilizing extended Kalman filter (EKF) and unscented Kalman filter (UKF), are fast convergence rates and good performance using relatively short training symbols. Experimental results for two time-varying channels are presented to evaluate the performance of the proposed approaches over a conventional recurrent neural equalizer.

• Journal of Communications and Networks
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• 제12권5호
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• pp.459-465
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• 2010

A new blind equalization algorithm that is based on maximizing the probability that the constant modulus errors concentrate near zero is proposed. The cost function of the proposed algorithm is to maximize the probability that the equalizer output power is equal to the constant modulus of the transmitted symbols. Two blind information-theoretic learning (ITL) algorithms based on constant modulus error signals are also introduced: One for minimizing the Euclidean probability density function distance and the other for minimizing the constant modulus error entropy. The relations between the algorithms and their characteristics are investigated, and their performance is compared and analyzed through simulations in multi-path channel environments. The proposed algorithm has a lower computational complexity and a faster convergence speed than the other ITL algorithms that are based on a constant modulus error. The error samples of the proposed blind algorithm exhibit more concentrated density functions and superior error rate performance in severe multi-path channel environments when compared with the other algorithms.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2013년도 하계학술대회
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• pp.20-23
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• 2013

This paper purpose to correct color with histogram equalization, and improve image quality. Fog image is not clear enough to color information. So We need to correct each channel of fog image with histogram equalization. The algorithm offered in this paper is extracting R, G, and B channel, making histogram equalization, and adding or subtraction to brightness of each channel.

• Journal of information and communication convergence engineering
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• 제8권3호
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• pp.267-272
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• 2010

For the last decade, recurrent neural networks (RNNs) have been commonly applied to communications channel equalization. The major problems of gradient-based learning techniques, employed to train recurrent neural networks are slow convergence rates and long training sequences. In high-speed communications system, short training symbols and fast convergence speed are essentially required. In this paper, the derivative-free Kalman filter, so called the unscented Kalman filter (UKF), for training a fully connected RNN is presented in a state-space formulation of the system. The main features of the proposed recurrent neural equalizer are fast convergence speed and good performance using relatively short training symbols without the derivative computation. Through experiments of nonlinear channel equalization, the performance of the RNN with a derivative-free Kalman filter is evaluated.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.366-366
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• 2000

In this paper, an adaptive neuro-fuzzy filter using the conditional fuzzy c-means(CFCM) methods is proposed. Usualy, the number of fuzzy rules exponentially increases by applying the grid partitioning of the input space, in conventional adaptive neuro-fuzzy inference system(ANFIS) approaches. In order to solve this problem, CFCM method is adopted to render the clusters which represent the given input and output data. Parameter identification is performed by hybrid learning using back-propagation algorithm and total least square(TLS) method. Finally, we applied the proposed method to the nonlinear channel equalization problem and obtained a better performance than previous works.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 추계종합학술대회 논문집
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• pp.47-50
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• 1999

Adaptive channel equalization accomplished without resorting to a training sequence is known as blind equalization. The Constant Modulus Algorithm(CMA) and Modified CMA(MCMA) are widely referenced algorithms for blind equalization of a QAM system. This paper proposes a hybrid scheme of CMA and MCMA with Carrier Recovery that is robust for high level QAM with low steady state tracking error.

• 방송공학회논문지
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• 제13권3호
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• pp.365-379
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• 2008

본 논문에서는 단일 주파수 망(Single Frequency Network: SFN)을 통해 지상파 DMB 신호를 서비스하기 위해 필요한 동일채널 중계기(On-Channel Repeater, OCR)의 요구사항에 대하여 고찰하고, 그러한 요구사항을 만족하기 위한 등화형 OCR의 구조 및 구현방법을 제안한다. 등화형 OCR은 짧은 시스템 지연을 가질 뿐만 아니라 송/수신 안테나의 낮은 분리도로 인한 궤환신호와 송신기와 중계기 사이의 다중경로 신호를 동시에 제거하여 높은 송신출력과 우수한 출력신호 품질을 보장한다. 또한, 본 논문에서는 전산실험을 통해 등화형 OCR의 성능을 살펴보고 실험실 테스트를 통해 실제 구현된 등화형 OCR의 성능을 검증한다.