• Journal of Broadcast Engineering
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• v.21 no.3
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• pp.391-403
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• 2016

FBMC/OQAM(Filterbank multicarrier on offset-Quadrature Amplitude Modulation) system is a multicarrier modulation which is not need to use cyclic prefix(CP). The CP of OFDM/QAM (orthogonal frequency division multiplexing on Quadrature Amplitude Modulation) system decreases data transmission rate. However, SER(symbol error rate) performance of FBMC/OQAM system is worse than OFDM/QAM system with frequency 1-tap equalization scheme in the frequency selective channel. In this paper, an iterative channel estimation and equalization scheme is performed in a frequency oversampling domain about each sub-channel of FBMC/OQAM system and SER performance using computer simulation is shown. Using the proposed scheme, the SER performance approaches to that of OFDM/QAM system in a frequency selective channel.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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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.

• Proceedings of the KIEE Conference
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• 1998.11b
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.11
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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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• v.12 no.5
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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.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.06a
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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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• v.8 no.3
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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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• 2000.10a
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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.

• Journal of Broadcast Engineering
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• v.13 no.3
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• pp.365-379
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• 2008

In this paper we consider technological requirements of the on-channel repeater to broadcast the terrestrial digital multimedia broadcasting (T-DMB) signals using single frequency networks (SFN) and propose the configuration and implementation method of the equalization on-channel repeater (OCR) that meet such requirements. The proposed equalization OCR not only has short time delay, but shows high output power and good quality of output signal by removing a feedback signal due to incomplete antenna isolation and multipath signal existing between the main transmitter and the OCR. In addition, computer simulations and laboratory tests results are provided to figure out performance of the proposed equalization OCR.

• Proceedings of the IEEK Conference
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• 1999.11a
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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.